From b968b1bce2649da359c6f8ef779d4467ab13c795 Mon Sep 17 00:00:00 2001 From: moguguo Date: Mon, 15 Nov 2021 16:15:03 +0800 Subject: [PATCH 01/63] =?UTF-8?q?=E5=8A=A8=E8=BD=AC=E9=9D=99=E6=A6=82?= =?UTF-8?q?=E8=BF=B0=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新动转静概述章节,补充两个Block,什么是动态图和静态图,什么场景下需要动态图转成静态图 --- docs/guides/04_dygraph_to_static/index_cn.rst | 32 +++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index b54141fa883..1a1604b52a9 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -2,9 +2,37 @@ 动态图转静态图 ############### -动态图在接口易用性,交互式调试等方面具有诸多优势,但在工业界的许多部署场景中(如大型推荐系统、移动端)Python执行开销较大,与C++有一定的差距,静态图部署更具优势。 +========================= +什么是动态图和静态图? +========================= + +从深度学习模型构建方式上看,飞桨框架支持动态图编程(Imperative programming)和静态图编程(Declarative programming)两种方式,其代码编写方式和执行方式均存在差异。 + +* **动态图编程:**采用Python风格的编程方式,解析式的执行方式。用户每写一行网络代码,即可同时获得计算结果。在 +`模型开发 <../02_paddle2.0_develop/index_cn.rst>`_章节中,介绍的都是动态图编程方式。 +* **静态图编程:**采用先编译后执行的方式。用户先预定义完整的神经网络结构,飞桨框架将神经网络描述为Program的数据结构,对Program进行编译优化,再调用执行器获得计算结果。 + +动态图编程体验更佳、更易调试,但是因为采用Python实时执行的方式,而Python执行开销较大,与C++有一定差距,性能方面不占优。静态图调试难度大,但是将前端Python编写的神经网络预定义为Program描述,转到C++端重新解析执行,脱离了Python依赖,往往执行性能更佳,并且预先拥有完整网络结构也更利于全局优化。 + +========================= +什么场景下需要动态图转静态图? +========================= + +飞桨框架在设计时,考虑同时兼顾动态图的高易用性和静态图的高性能优势,采用『动静统一』的方案: +* **在模型开发时,推荐采用动态图编程:**用户可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 +* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:**获得更好的模型运行性能。 + +方案如下图所示: +.. image:: images/dygraph_to_static.png +图1 飞桨框架动静统一方案示意图 + +> **说明:** +> 飞桨框架2.0默认的编程模式是动态图模式,包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程,可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器@to_static。 + +想了解动态图和静态图的详细对比介绍,可参见 +`动态图和静态图的差异 `_。 + -PaddlePaddle 在2.0版本之后,正式支持动态图转静态图(@to_static)的功能,对动态图代码进行智能化分析,自动转换为静态图网络结构,兼顾了动态图易用性和静态图部署性能两方面的优势。 如下将详细地介绍动静转换的各个模块内容: From 81646f45059a2307e06e02ff5712310b655ca789 Mon Sep 17 00:00:00 2001 From: moguguo Date: Mon, 15 Nov 2021 16:16:41 +0800 Subject: [PATCH 02/63] =?UTF-8?q?=E5=8A=A8=E8=BD=AC=E9=9D=99=E6=A6=82?= =?UTF-8?q?=E8=BF=B0=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 增加动转静方案示意图 --- .../images/dygraph_to_static.png | Bin 0 -> 75142 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/guides/04_dygraph_to_static/images/dygraph_to_static.png diff --git a/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png b/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png new file mode 100644 index 0000000000000000000000000000000000000000..9db5392c5b2823d30e76f28478bd5658c81e35fa GIT binary patch literal 75142 zcmeFZby!s07d9+i3ewWuAP7iz&d`lCN|#7TcSr~W4Ba(!NeW1dq||^C5(-0iH++No 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zeaapt+QK>Yzq@sKoy^62<>=p&{M{TMSH$(?oj74(a!K1>ynCf($1AS8uZ%px*Uh#0 zW)&LqdAIp&zYQgSQ=R*rRi#BTF$Oq_+8ShdroV3g$-(=gy=dBTmn5XxpA&`F_=T9c$*7)+ZJwJzgysqJKei+xovO z@qap{JpU(}tp8}UxQ4gA>&x=f7e9Be$aTK_s(8h#TY1xVIbB^o^|evYwAWHM^JXQR zJ}aH1y*PE}oMQ_ooepo`h(3tqu$R+khCyOqS^1phUG)<-KRNjDaQpi-_G`6|~IeWjXUJ}2u>}}|j9vQ85d8;D)rd1Xxm3W4RP0s^%p$ zNuAf~eJy!ZKPzUw%Js{y&DXE2Syi^ACM|W-@0g42MVpp9op7h^m45KvqeTpF7aN}U z)$>2M`g~p1{e2=^Ytp%&0g{;?A3doxAT5#%*|c*GW%WVsjY%Wau}mZ4$K_D zW_fqrlG{7wwx53aVLF?M<5V>@1@$v0ciE}GwA!nfe#(E=4(nN1#s&vwaDrBY9%P%l cMWgXQ`?L7!8{Z;0-ZKD!r>mdKI;Vst00qddqW}N^ literal 0 HcmV?d00001 From 1705fb30bb08e89a64be396b7c4961c560b232e1 Mon Sep 17 00:00:00 2001 From: Chen Long <1300851984@qq.com> Date: Tue, 16 Nov 2021 10:35:47 +0800 Subject: [PATCH 03/63] update style update style --- docs/guides/04_dygraph_to_static/index_cn.rst | 25 +++++++++++-------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 1a1604b52a9..4656fe5f01e 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -6,35 +6,38 @@ 什么是动态图和静态图? ========================= -从深度学习模型构建方式上看,飞桨框架支持动态图编程(Imperative programming)和静态图编程(Declarative programming)两种方式,其代码编写方式和执行方式均存在差异。 +从深度学习模型构建方式上看,飞桨框架支持动态图编程和静态图编程两种方式,其代码编写方式和执行方式均存在差异。 -* **动态图编程:**采用Python风格的编程方式,解析式的执行方式。用户每写一行网络代码,即可同时获得计算结果。在 -`模型开发 <../02_paddle2.0_develop/index_cn.rst>`_章节中,介绍的都是动态图编程方式。 -* **静态图编程:**采用先编译后执行的方式。用户先预定义完整的神经网络结构,飞桨框架将神经网络描述为Program的数据结构,对Program进行编译优化,再调用执行器获得计算结果。 +* **动态图编程:** 采用 Python 风格的编程方式,解析式的执行方式。每写一行网络代码,即可同时获得计算结果。在 +`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中,介绍的都是动态图编程方式。 -动态图编程体验更佳、更易调试,但是因为采用Python实时执行的方式,而Python执行开销较大,与C++有一定差距,性能方面不占优。静态图调试难度大,但是将前端Python编写的神经网络预定义为Program描述,转到C++端重新解析执行,脱离了Python依赖,往往执行性能更佳,并且预先拥有完整网络结构也更利于全局优化。 +* **静态图编程:** 采用先编译后执行的方式。先预定义完整的神经网络结构,飞桨框架将神经网络描述为 `Program` 的数据结构,对 `Program` 进行编译优化,再调用执行器获得计算结果。 + +动态图编程体验更佳、更易调试,但是因为采用 Python 实时执行的方式,而 Python 执行开销较大,与 C++ 有一定差距,性能方面不占优。静态图调试难度大,但是将前端 Python 编写的神经网络预定义为 Program描述,转到 C++ 端重新解析执行,脱离了 Python 依赖,往往执行性能更佳,并且预先拥有完整网络结构也更利于全局优化。 ========================= 什么场景下需要动态图转静态图? ========================= 飞桨框架在设计时,考虑同时兼顾动态图的高易用性和静态图的高性能优势,采用『动静统一』的方案: -* **在模型开发时,推荐采用动态图编程:**用户可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:**获得更好的模型运行性能。 + +* **在模型开发时,推荐采用动态图编程:** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 + +* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:** 获得更好的模型运行性能。 方案如下图所示: + .. image:: images/dygraph_to_static.png 图1 飞桨框架动静统一方案示意图 -> **说明:** -> 飞桨框架2.0默认的编程模式是动态图模式,包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程,可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器@to_static。 +.. note:: +飞桨框架2.0默认的编程模式是动态图模式,包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程,可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器@to_static。 想了解动态图和静态图的详细对比介绍,可参见 `动态图和静态图的差异 `_。 - -如下将详细地介绍动静转换的各个模块内容: +以下将详细地介绍动静转换的各个模块内容: - `基础接口用法 `_ : 介绍了动静转换 @to_static 的基本用法 From dc8d12431c5a2aba0456c33074cb2f56c544992d Mon Sep 17 00:00:00 2001 From: moguguo Date: Tue, 16 Nov 2021 16:57:16 +0800 Subject: [PATCH 04/63] update style --- docs/guides/04_dygraph_to_static/index_cn.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 4656fe5f01e..65626534596 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -23,15 +23,18 @@ * **在模型开发时,推荐采用动态图编程:** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:** 获得更好的模型运行性能。 +* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:** 可获得更好的模型运行性能。 方案如下图所示: .. image:: images/dygraph_to_static.png -图1 飞桨框架动静统一方案示意图 + :width: 500px + :align: center + +.. centered:: 图1 飞桨框架动静统一方案示意图 .. note:: -飞桨框架2.0默认的编程模式是动态图模式,包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程,可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器@to_static。 + 飞桨框架 2.0 及以上版本默认的编程模式是动态图模式,包括使用高层 API 编程和基础的 API 编程。如果想切换到静态图模式编程,可以在程序的开始执行 `enable_static()` 函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器 @to_static。 想了解动态图和静态图的详细对比介绍,可参见 `动态图和静态图的差异 `_。 From 3e5bc03b65b8781966714e2a5e67fa089917e47d Mon Sep 17 00:00:00 2001 From: moguguo Date: Wed, 17 Nov 2021 11:17:29 +0800 Subject: [PATCH 05/63] Update index_cn.rst --- docs/guides/04_dygraph_to_static/index_cn.rst | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 65626534596..ce77e3bc6a0 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -6,14 +6,13 @@ 什么是动态图和静态图? ========================= -从深度学习模型构建方式上看,飞桨框架支持动态图编程和静态图编程两种方式,其代码编写方式和执行方式均存在差异。 +在深度学习模型构建上,飞桨框架支持动态图编程和静态图编程两种方式,其代码编写和执行方式均存在差异。 -* **动态图编程:** 采用 Python 风格的编程方式,解析式的执行方式。每写一行网络代码,即可同时获得计算结果。在 -`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中,介绍的都是动态图编程方式。 +* **动态图编程:** 采用 Python 的编程风格,解析式地执行每一行网络代码,并同时返回计算结果。在`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中,介绍的都是动态图编程方式。 -* **静态图编程:** 采用先编译后执行的方式。先预定义完整的神经网络结构,飞桨框架将神经网络描述为 `Program` 的数据结构,对 `Program` 进行编译优化,再调用执行器获得计算结果。 +* **静态图编程:** 采用先编译后执行的方式。需先在代码中预定义完整的神经网络结构,飞桨框架会将神经网络描述为 `Program` 的数据结构,并对 `Program` 进行编译优化,再调用执行器获得计算结果。 -动态图编程体验更佳、更易调试,但是因为采用 Python 实时执行的方式,而 Python 执行开销较大,与 C++ 有一定差距,性能方面不占优。静态图调试难度大,但是将前端 Python 编写的神经网络预定义为 Program描述,转到 C++ 端重新解析执行,脱离了 Python 依赖,往往执行性能更佳,并且预先拥有完整网络结构也更利于全局优化。 +动态图编程体验更佳、更易调试,但是因为采用 Python 实时执行的方式,开销较大,在性能方面与 C++ 有一定差距;静态图调试难度大,但是将前端 Python 编写的神经网络预定义为 Program描述,转到 C++ 端重新解析执行,脱离了 Python 依赖,往往执行性能更佳,并且预先拥有完整网络结构也更利于全局优化。 ========================= 什么场景下需要动态图转静态图? @@ -21,14 +20,14 @@ 飞桨框架在设计时,考虑同时兼顾动态图的高易用性和静态图的高性能优势,采用『动静统一』的方案: -* **在模型开发时,推荐采用动态图编程:** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 +* **在模型开发时,推荐采用动态图编程。** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行:** 可获得更好的模型运行性能。 +* **在模型训练或者推理部署时,只需添加一行装饰器 @to_static,即可将动态图代码转写为静态图代码,并在底层自动使用静态图执行器运行。** 可获得更好的模型运行性能。 方案如下图所示: .. image:: images/dygraph_to_static.png - :width: 500px + :width: 800px :align: center .. centered:: 图1 飞桨框架动静统一方案示意图 @@ -40,7 +39,7 @@ `动态图和静态图的差异 `_。 -以下将详细地介绍动静转换的各个模块内容: +**以下将详细地介绍动静转换的各个模块内容:** - `基础接口用法 `_ : 介绍了动静转换 @to_static 的基本用法 From 6daef9871cd7893b9c88bad2faf575dd8e672709 Mon Sep 17 00:00:00 2001 From: moguguo Date: Wed, 17 Nov 2021 16:21:54 +0800 Subject: [PATCH 06/63] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E6=A0=BC=E5=BC=8F?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/guides/04_dygraph_to_static/index_cn.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index ce77e3bc6a0..2429f0f2938 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -8,7 +8,7 @@ 在深度学习模型构建上,飞桨框架支持动态图编程和静态图编程两种方式,其代码编写和执行方式均存在差异。 -* **动态图编程:** 采用 Python 的编程风格,解析式地执行每一行网络代码,并同时返回计算结果。在`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中,介绍的都是动态图编程方式。 +* **动态图编程:** 采用 Python 的编程风格,解析式地执行每一行网络代码,并同时返回计算结果。在 `模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中,介绍的都是动态图编程方式。 * **静态图编程:** 采用先编译后执行的方式。需先在代码中预定义完整的神经网络结构,飞桨框架会将神经网络描述为 `Program` 的数据结构,并对 `Program` 进行编译优化,再调用执行器获得计算结果。 From 9c5b702b4df7043c1a18aa374336d9dc2380e209 Mon Sep 17 00:00:00 2001 From: Chen Long <1300851984@qq.com> Date: Fri, 19 Nov 2021 17:25:48 +0800 Subject: [PATCH 07/63] update codes --- docs/guides/04_dygraph_to_static/index_cn.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 2429f0f2938..fe6a61bd9a5 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -26,12 +26,14 @@ 方案如下图所示: -.. image:: images/dygraph_to_static.png +.. figure:: images/dygraph_to_static.png :width: 800px :align: center + .. centered:: 图1 飞桨框架动静统一方案示意图 + .. note:: 飞桨框架 2.0 及以上版本默认的编程模式是动态图模式,包括使用高层 API 编程和基础的 API 编程。如果想切换到静态图模式编程,可以在程序的开始执行 `enable_static()` 函数。如果程序已经使用动态图的模式编写了,想转成静态图模式训练或者保存模型用于部署,可以使用装饰器 @to_static。 From e6dcca767432d0973f7628236a3eb623af9f9086 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 29 Nov 2021 18:08:36 +0800 Subject: [PATCH 08/63] add new quick_start --- .../01_quick_start_cn.ipynb | 414 ++++++++++++++++++ .../02_paddle2.0_develop/images/mnist.png | Bin 0 -> 260311 bytes 2 files changed, 414 insertions(+) create mode 100644 docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb create mode 100644 docs/guides/02_paddle2.0_develop/images/mnist.png diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb new file mode 100644 index 00000000000..6de39968565 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f848b574", + "metadata": {}, + "source": [ + "# 10分钟快速上手飞桨\n", + "\n", + "从完成一个简单的『手写数字识别任务』开始,可快速了解深度学习模型开发的大致流程,并初步掌握飞桨框架 API 的使用方法。\n", + "\n", + "\n", + "## 快速安装飞桨\n", + "\n", + "如果已经安装好飞桨那么可以跳过此步骤。飞桨支持很多种安装方式,这里介绍其中一种简单的安装命令:\n", + "\n", + "```bash\n", + "# 使用 pip 工具安装飞桨 CPU 版\n", + "python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple\n", + "```\n", + "\n", + "该命令用于安装CPU版本的飞桨,如果要安装其他计算平台或操作系统支持的版本,可点击 快速安装 查看安装引导。\n", + "\n", + "### 导入飞桨\n", + "\n", + "安装完成后,需要在Python解释器中使用 import 导入飞桨,即可开始实践深度学习任务。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "468426ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.2.0\n" + ] + } + ], + "source": [ + "import paddle \n", + "print(paddle.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "a3e0d5e5", + "metadata": {}, + "source": [ + "若操作成功,会输出飞桨的版本号。\n", + "\n", + "## 实践:手写数字识别任务\n", + "\n", + "手写数字识别是深度学习里的 Hello Word 任务,其目标是输入手写数字的图片,输出这个图片中的数字。本任务中用到的数据集为MNIST手写数字数据集,该手写数字数据集包含60000张训练图片和10000张测试图片,这些图片是从0~9的手写数字,分辨率为28*28。数据集中部分图像和对应的分类标签如下图所示。\n", + "![](images/mnist.png)\n", + "\n", + "开始之前,需要使用下面的代码安装opencv和numpy\n", + "\n", + "```bash\n", + "python3 -m pip install opencv-python numpy -i https://mirror.baidu.com/pypi/simple\n", + "```\n", + "\n", + "如果想直接运行代码,可以拷贝下面的完整代码到一个Python文件中进行运行。\n", + "\n", + "完整代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "93d0c8a1", + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 下载数据集并初始化DataSet\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "\n", + "# 初始化网络\n", + "lenet = paddle.vision.models.LeNet(num_classes=10)\n", + "model = paddle.Model(lenet)\n", + "\n", + "# 模型训练相关配置,准备损失计算方法,优化器和精度计算方法\n", + "model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), \n", + " paddle.nn.CrossEntropyLoss(),\n", + " paddle.metric.Accuracy())\n", + "\n", + "# 开始模型训练和测试\n", + "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)\n", + "model.evaluate(test_dataset, verbose=1)\n", + "\n", + "# 从测试集中取出一张图片并将图片shape变为1*1*28*28\n", + "img, label = test_dataset[0]\n", + "img_batch = np.expand_dims(img.astype('float32'), axis=0)\n", + "\n", + "# 执行推理并打印结果\n", + "out = model.predict_batch(img_batch)[0]\n", + "pred_label = out.argmax()\n", + "print('true label: {}, pred label: {}'.format(label[0], pred_label))" + ] + }, + { + "cell_type": "markdown", + "id": "f1be5eec", + "metadata": {}, + "source": [ + "简单的说,深度学习任务一般分为以下几个核心步骤:\n", + "\n", + "数据集的准备和加载;\n", + "\n", + "1. 模型组网;\n", + "2. 模型训练;\n", + "3. 模型评估;\n", + "4. 模型预测。\n", + "\n", + "接下来逐个步骤介绍,帮助你快速掌握使用飞桨框架API实践深度学习任务的方法。\n", + "\n", + "\n", + "### 数据集的准备和加载\n", + "\n", + "Paddle 在 paddle.vision.dataset 下提供了 CV 领域常见的数据集,如Cifar10,Cifar100,FashionMNIST,MNIST和VOC2012等,如果你想了解更多,点击 [paddle.vision.dataset文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api)。\n", + "\n", + "在本任务中,我们直接加载飞桨框架的内置的MNIST手写数字数据集。这里加载两个数据集,一个用来训练模型,一般叫做训练集;一个用来测试模型,一般叫做测试集。\n", + "\n", + "在下面的代码中,我们导入了`paddle.vision.transforms`模块,`paddle.vision.transforms` 里内置了很多和数据处理相关的api,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化,详细的api信息可以在[paddle.vision.transforms文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)查看 \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f99c914f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60000 images in train_dataset, 10000 images in test_dataset\n" + ] + } + ], + "source": [ + "import paddle\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 下载数据集并初始化DataSet\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "# 打印数据集里图片数量\n", + "print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(val_dataset)))" + ] + }, + { + "cell_type": "markdown", + "id": "6ba82de9", + "metadata": {}, + "source": [ + "### 模型组网\n", + "\n", + "飞桨的模型组网有两种方式,一种是使用飞桨内置的模型来直接进行模型的组网和初始化,一种是使用内置的 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) api来进行灵活度更高的组网。\n", + "\n", + "由于MNIST数据集比较简单,普通的神经网络就能够达到很高的精度,因此在本任务中使用飞桨内置的LeNet作为我们的模型,LeNet作为 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 的内置模型,可以很方便的调用它,只需要下面这句话即可完成LeNet的网络构建和初始化。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e8bf3841", + "metadata": {}, + "outputs": [], + "source": [ + "# 初始化网络\n", + "lenet = paddle.vision.models.LeNet(num_classes=10)" + ] + }, + { + "cell_type": "markdown", + "id": "4902817f", + "metadata": {}, + "source": [ + "### 模型训练\n", + "\n", + "在训练模型前,需要配置训练模型时的损失函数与优化方法,因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Accuracy_cn.html#accuracy) 指标来计算网络在训练集上的精度。在训练过程中可以使用飞桨框架高层API- [paddle.Model.fit()](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 来自动完成模型的训练循环,具体代码如下。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3333a7bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", + "Epoch 1/5\n", + "step 938/938 [==============================] - loss: 0.0275 - acc: 0.9408 - 15ms/step \n", + "Epoch 2/5\n", + "step 938/938 [==============================] - loss: 0.0163 - acc: 0.9778 - 16ms/step \n", + "Epoch 3/5\n", + "step 938/938 [==============================] - loss: 0.0086 - acc: 0.9832 - 16ms/step \n", + "Epoch 4/5\n", + "step 938/938 [==============================] - loss: 0.0116 - acc: 0.9867 - 16ms/step \n", + "Epoch 5/5\n", + "step 938/938 [==============================] - loss: 0.0076 - acc: 0.9885 - 16ms/step \n" + ] + } + ], + "source": [ + "# 初始化paddle.Model模型,便于进行后续的配置、训练和验证\n", + "model = paddle.Model(lenet)\n", + "\n", + "# 模型训练相关配置,准备损失计算方法,优化器和精度计算方法\n", + "model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), \n", + " paddle.nn.CrossEntropyLoss(),\n", + " paddle.metric.Accuracy())\n", + "\n", + "# 开始模型\n", + "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "684e7be6", + "metadata": {}, + "source": [ + "### 模型评估\n", + "\n", + "模型训练完成之后,可以使用预先定义的测试数据集来评估训练得到的模型的精度。评估完成会输出模型在测试集上的loss和精度。\n", + "\n", + "可以看到,初步训练得到的模型精度在98.7%附近,在逐渐了解飞桨后,您可以通过调整其中的训练参数来提升模型的精度。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b86f0289", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval begin...\n", + "step 10000/10000 [==============================] - loss: 1.1921e-07 - acc: 0.9886 - 2ms/step \n", + "Eval samples: 10000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'loss': [1.1920929e-07], 'acc': 0.9886}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 进行模型评估\n", + "model.evaluate(test_dataset, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "39ee250a", + "metadata": {}, + "source": [ + "### 模型保存\n", + "\n", + "模型训练完成之后,可以通过如下命令进行保存,在下面的命令中,output为模型保存的文件夹名称,minst为模型保存的文件名称" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "07bff4b4", + "metadata": {}, + "outputs": [], + "source": [ + "# 保存模型,文件夹会自动创建\n", + "model.save('./output/mnist')" + ] + }, + { + "cell_type": "markdown", + "id": "0daaf2e3", + "metadata": {}, + "source": [ + "模型保存会在`output`目录下保存两个文件,`mnist.pdopt`为优化器的参数,`mnist.pdparams`为网络的参数\n", + "```bash\n", + "output\n", + "├── mnist.pdopt # 优化器的参数\n", + "└── mnist.pdparams # 网络的参数\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e991757c", + "metadata": {}, + "source": [ + "### 模型加载推理\n", + "\n", + "模型训练和保存完成后,可以直接用于推理预测。本次的推理过程如下:\n", + "\n", + "1. 加载模型\n", + "2. 从测试集中选择一张图片作为输入\n", + "3. 进行推理并输出结果\n", + "\n", + "具体代码:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bb8328ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "true label: 7, pred label: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 加载模型\n", + "model.load('output/mnist')\n", + "\n", + "# 从测试集中取出一张图片并将图片shape变为1*1*28*28\n", + "img, label = test_dataset[0]\n", + "img_batch = np.expand_dims(img.astype('float32'), axis=0)\n", + "\n", + "# 执行推理并打印结果\n", + "out = model.predict_batch(img_batch)[0]\n", + "pred_label = out.argmax()\n", + "print('true label: {}, pred label: {}'.format(label[0], pred_label))\n", + "# 可视化图片\n", + "from matplotlib import pyplot as plt\n", + "plt.imshow(img[0])" + ] + }, + { + "cell_type": "markdown", + "id": "54a041fd", + "metadata": {}, + "source": [ + "至此你就通过飞桨几个简单的API完成了一个深度学习任务,你也可以针对自己的需求来更换其中的代码,比如对数据集使用更多的数据增强、使用更大的 CNN 模型和自定义的神经网络等,飞桨官网提供了丰富的教程与案例可供参考" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26dc3069", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", 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+0800 Subject: [PATCH 09/63] =?UTF-8?q?rm=20=E6=82=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../02_paddle2.0_develop/01_quick_start_cn.ipynb | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 6de39968565..36c773336f3 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -23,7 +23,9 @@ "\n", "### 导入飞桨\n", "\n", - "安装完成后,需要在Python解释器中使用 import 导入飞桨,即可开始实践深度学习任务。" + "安装完成后,需要在Python解释器中使用 import 导入飞桨,即可开始实践深度学习任务。\n", + "\n", + "若操作成功,会输出飞桨的版本号。" ] }, { @@ -50,7 +52,7 @@ "id": "a3e0d5e5", "metadata": {}, "source": [ - "若操作成功,会输出飞桨的版本号。\n", + "\n", "\n", "## 实践:手写数字识别任务\n", "\n", @@ -127,9 +129,9 @@ "\n", "Paddle 在 paddle.vision.dataset 下提供了 CV 领域常见的数据集,如Cifar10,Cifar100,FashionMNIST,MNIST和VOC2012等,如果你想了解更多,点击 [paddle.vision.dataset文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api)。\n", "\n", - "在本任务中,我们直接加载飞桨框架的内置的MNIST手写数字数据集。这里加载两个数据集,一个用来训练模型,一般叫做训练集;一个用来测试模型,一般叫做测试集。\n", + "在本任务中,直接加载飞桨框架的内置的MNIST手写数字数据集。这里加载两个数据集,一个用来训练模型,一般叫做训练集;一个用来测试模型,一般叫做测试集。\n", "\n", - "在下面的代码中,我们导入了`paddle.vision.transforms`模块,`paddle.vision.transforms` 里内置了很多和数据处理相关的api,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化,详细的api信息可以在[paddle.vision.transforms文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)查看 \n" + "在下面的代码中,导入了`paddle.vision.transforms`模块,`paddle.vision.transforms` 里内置了很多和数据处理相关的api,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化,详细的api信息可以在[paddle.vision.transforms文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)查看 \n" ] }, { @@ -166,7 +168,7 @@ "\n", "飞桨的模型组网有两种方式,一种是使用飞桨内置的模型来直接进行模型的组网和初始化,一种是使用内置的 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) api来进行灵活度更高的组网。\n", "\n", - "由于MNIST数据集比较简单,普通的神经网络就能够达到很高的精度,因此在本任务中使用飞桨内置的LeNet作为我们的模型,LeNet作为 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 的内置模型,可以很方便的调用它,只需要下面这句话即可完成LeNet的网络构建和初始化。" + "由于MNIST数据集比较简单,普通的神经网络就能够达到很高的精度,因此在本任务中使用飞桨内置的LeNet作为模型,LeNet作为 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 的内置模型,可以很方便的调用它,只需要下面这句话即可完成LeNet的网络构建和初始化。" ] }, { @@ -236,7 +238,7 @@ "\n", "模型训练完成之后,可以使用预先定义的测试数据集来评估训练得到的模型的精度。评估完成会输出模型在测试集上的loss和精度。\n", "\n", - "可以看到,初步训练得到的模型精度在98.7%附近,在逐渐了解飞桨后,您可以通过调整其中的训练参数来提升模型的精度。\n" + "可以看到,初步训练得到的模型精度在98.7%附近,在逐渐了解飞桨后,可以通过调整其中的训练参数来提升模型的精度。\n" ] }, { From f89a8ca81e3dd90a069384027f9d0705d0613e0b Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 29 Nov 2021 18:25:23 +0800 Subject: [PATCH 10/63] fix bug --- .../01_quick_start_cn.ipynb | 38 +++++++++---------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 36c773336f3..df902e1c4bc 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "93d0c8a1", "metadata": {}, "outputs": [], @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "f99c914f", "metadata": {}, "outputs": [ @@ -156,7 +156,7 @@ "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", "# 打印数据集里图片数量\n", - "print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(val_dataset)))" + "print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(test_dataset)))" ] }, { @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "e8bf3841", "metadata": {}, "outputs": [], @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "id": "3333a7bb", "metadata": {}, "outputs": [ @@ -204,15 +204,15 @@ "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", "Epoch 1/5\n", - "step 938/938 [==============================] - loss: 0.0275 - acc: 0.9408 - 15ms/step \n", + "step 938/938 [==============================] - loss: 0.0312 - acc: 0.9869 - 16ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0163 - acc: 0.9778 - 16ms/step \n", + "step 938/938 [==============================] - loss: 0.0414 - acc: 0.9889 - 16ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 0.0086 - acc: 0.9832 - 16ms/step \n", + "step 938/938 [==============================] - loss: 2.4796e-04 - acc: 0.9904 - 16ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 0.0116 - acc: 0.9867 - 16ms/step \n", + "step 938/938 [==============================] - loss: 9.6802e-04 - acc: 0.9919 - 16ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.0076 - acc: 0.9885 - 16ms/step \n" + "step 938/938 [==============================] - loss: 0.0047 - acc: 0.9922 - 16ms/step \n" ] } ], @@ -238,12 +238,12 @@ "\n", "模型训练完成之后,可以使用预先定义的测试数据集来评估训练得到的模型的精度。评估完成会输出模型在测试集上的loss和精度。\n", "\n", - "可以看到,初步训练得到的模型精度在98.7%附近,在逐渐了解飞桨后,可以通过调整其中的训练参数来提升模型的精度。\n" + "可以看到,初步训练得到的模型精度在98%附近,在逐渐了解飞桨后,可以通过调整其中的训练参数来提升模型的精度。\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "b86f0289", "metadata": {}, "outputs": [ @@ -252,17 +252,17 @@ "output_type": "stream", "text": [ "Eval begin...\n", - "step 10000/10000 [==============================] - loss: 1.1921e-07 - acc: 0.9886 - 2ms/step \n", + "step 10000/10000 [==============================] - loss: 1.1921e-07 - acc: 0.9800 - 2ms/step - loss: 0\n", "Eval samples: 10000\n" ] }, { "data": { "text/plain": [ - "{'loss': [1.1920929e-07], 'acc': 0.9886}" + "{'loss': [1.1920929e-07], 'acc': 0.98}" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "id": "07bff4b4", "metadata": {}, "outputs": [], @@ -324,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "bb8328ef", "metadata": {}, "outputs": [ @@ -338,10 +338,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, From d18bba4f898555c6afd09b54e1dcdb4d5742f2a8 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 29 Nov 2021 18:29:44 +0800 Subject: [PATCH 11/63] del latest block --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index df902e1c4bc..4a6060250bc 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -382,14 +382,6 @@ "source": [ "至此你就通过飞桨几个简单的API完成了一个深度学习任务,你也可以针对自己的需求来更换其中的代码,比如对数据集使用更多的数据增强、使用更大的 CNN 模型和自定义的神经网络等,飞桨官网提供了丰富的教程与案例可供参考" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26dc3069", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From f678916e330a401d36b6c2bdb6adbb9f579d2c59 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 29 Nov 2021 18:33:27 +0800 Subject: [PATCH 12/63] del opencv --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 4a6060250bc..d70c4d03d82 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -16,10 +16,10 @@ "\n", "```bash\n", "# 使用 pip 工具安装飞桨 CPU 版\n", - "python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple\n", + "python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple --trusted-host\n", "```\n", "\n", - "该命令用于安装CPU版本的飞桨,如果要安装其他计算平台或操作系统支持的版本,可点击 快速安装 查看安装引导。\n", + "该命令用于安装CPU版本的飞桨,如果要安装其他计算平台或操作系统支持的版本,可点击 [ 快速安装]( ) 查看安装引导。\n", "\n", "### 导入飞桨\n", "\n", From 068d777aaa0ec8bdce966b659c0bf83800906faa Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 29 Nov 2021 18:34:46 +0800 Subject: [PATCH 13/63] del opencv --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index d70c4d03d82..17756512443 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -59,10 +59,10 @@ "手写数字识别是深度学习里的 Hello Word 任务,其目标是输入手写数字的图片,输出这个图片中的数字。本任务中用到的数据集为MNIST手写数字数据集,该手写数字数据集包含60000张训练图片和10000张测试图片,这些图片是从0~9的手写数字,分辨率为28*28。数据集中部分图像和对应的分类标签如下图所示。\n", "![](images/mnist.png)\n", "\n", - "开始之前,需要使用下面的代码安装opencv和numpy\n", + "开始之前,需要使用下面的代码安装matplotlib和numpy\n", "\n", "```bash\n", - "python3 -m pip install opencv-python numpy -i https://mirror.baidu.com/pypi/simple\n", + "python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple\n", "```\n", "\n", "如果想直接运行代码,可以拷贝下面的完整代码到一个Python文件中进行运行。\n", From 2fcd472148ec513a79e082fd169491b717c3d815 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 30 Nov 2021 17:28:02 +0800 Subject: [PATCH 14/63] opt doc --- .../01_quick_start_cn.ipynb | 61 ++++++++++--------- 1 file changed, 33 insertions(+), 28 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 17756512443..98a4213759e 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -94,10 +94,16 @@ " paddle.nn.CrossEntropyLoss(),\n", " paddle.metric.Accuracy())\n", "\n", - "# 开始模型训练和测试\n", + "# 模型训练\n", "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)\n", + "# 模型评估\n", "model.evaluate(test_dataset, verbose=1)\n", "\n", + "# 保存模型\n", + "model.save('./output/mnist')\n", + "# 加载模型\n", + "model.load('output/mnist')\n", + "\n", "# 从测试集中取出一张图片并将图片shape变为1*1*28*28\n", "img, label = test_dataset[0]\n", "img_batch = np.expand_dims(img.astype('float32'), axis=0)\n", @@ -115,17 +121,16 @@ "source": [ "简单的说,深度学习任务一般分为以下几个核心步骤:\n", "\n", - "数据集的准备和加载;\n", "\n", - "1. 模型组网;\n", - "2. 模型训练;\n", - "3. 模型评估;\n", - "4. 模型预测。\n", + "1. 数据集定义与加载;\n", + "2. 模型组网;\n", + "3. 模型训练和评估;\n", + "5. 模型预测。\n", "\n", "接下来逐个步骤介绍,帮助你快速掌握使用飞桨框架API实践深度学习任务的方法。\n", "\n", "\n", - "### 数据集的准备和加载\n", + "### 数据集定义与加载\n", "\n", "Paddle 在 paddle.vision.dataset 下提供了 CV 领域常见的数据集,如Cifar10,Cifar100,FashionMNIST,MNIST和VOC2012等,如果你想了解更多,点击 [paddle.vision.dataset文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api)。\n", "\n", @@ -187,7 +192,9 @@ "id": "4902817f", "metadata": {}, "source": [ - "### 模型训练\n", + "### 模型训练评估\n", + "\n", + "#### 模型训练\n", "\n", "在训练模型前,需要配置训练模型时的损失函数与优化方法,因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Accuracy_cn.html#accuracy) 指标来计算网络在训练集上的精度。在训练过程中可以使用飞桨框架高层API- [paddle.Model.fit()](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 来自动完成模型的训练循环,具体代码如下。" ] @@ -234,7 +241,7 @@ "id": "684e7be6", "metadata": {}, "source": [ - "### 模型评估\n", + "#### 模型评估\n", "\n", "模型训练完成之后,可以使用预先定义的测试数据集来评估训练得到的模型的精度。评估完成会输出模型在测试集上的loss和精度。\n", "\n", @@ -274,11 +281,24 @@ }, { "cell_type": "markdown", - "id": "39ee250a", + "id": "e991757c", "metadata": {}, "source": [ - "### 模型保存\n", + "### 模型预测\n", "\n", + "在普遍的离线预测场景下,完成训练完成后,需要将模型进行保存,然后再进行模型加载之后进行预测。因此,本次的推理过程如下:\n", + "\n", + "1. 保存模型\n", + "1. 加载模型\n", + "2. 从测试集中选择一张图片作为输入\n", + "3. 进行推理并输出结果\n" + ] + }, + { + "cell_type": "markdown", + "id": "39ee250a", + "metadata": {}, + "source": [ "模型训练完成之后,可以通过如下命令进行保存,在下面的命令中,output为模型保存的文件夹名称,minst为模型保存的文件名称" ] }, @@ -303,23 +323,8 @@ "output\n", "├── mnist.pdopt # 优化器的参数\n", "└── mnist.pdparams # 网络的参数\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e991757c", - "metadata": {}, - "source": [ - "### 模型加载推理\n", - "\n", - "模型训练和保存完成后,可以直接用于推理预测。本次的推理过程如下:\n", - "\n", - "1. 加载模型\n", - "2. 从测试集中选择一张图片作为输入\n", - "3. 进行推理并输出结果\n", - "\n", - "具体代码:" + "```\n", + "模型保存完成后,通过下面的命令进行模型加载和预测" ] }, { From 21f81deb53a0d129395fe2cfd4b1e571b4007b91 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 30 Nov 2021 17:28:48 +0800 Subject: [PATCH 15/63] add 01_quick_start_cn.ipynb to idx --- docs/guides/02_paddle2.0_develop/index_cn.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/02_paddle2.0_develop/index_cn.rst b/docs/guides/02_paddle2.0_develop/index_cn.rst index b627c73f872..d93742de15e 100644 --- a/docs/guides/02_paddle2.0_develop/index_cn.rst +++ b/docs/guides/02_paddle2.0_develop/index_cn.rst @@ -20,7 +20,7 @@ .. toctree:: :hidden: - 01_quick_start_cn.rst + 01_quick_start_cn.ipynb 02_data_load_cn.rst 03_data_preprocessing_cn.rst 04_model_cn.rst From c39309c9789a384e1ca24e216ad8ce68109db731 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 30 Nov 2021 17:50:07 +0800 Subject: [PATCH 16/63] add learn more --- .../01_quick_start_cn.ipynb | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 98a4213759e..dfa6ebe152e 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -164,6 +164,14 @@ "print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(test_dataset)))" ] }, + { + "cell_type": "markdown", + "id": "2d89cb67", + "metadata": {}, + "source": [ + "如果希望查看更多关于自定义数据集与加载的内容,可以参考xxx,如果希望查看更多关于自定义数据预处理的内容,可以参考xxx。" + ] + }, { "cell_type": "markdown", "id": "6ba82de9", @@ -187,6 +195,14 @@ "lenet = paddle.vision.models.LeNet(num_classes=10)" ] }, + { + "cell_type": "markdown", + "id": "67dfcc50", + "metadata": {}, + "source": [ + "如果希望查看更多关于模型组网的内容,可以参考xxx。" + ] + }, { "cell_type": "markdown", "id": "4902817f", @@ -279,6 +295,14 @@ "model.evaluate(test_dataset, verbose=1)" ] }, + { + "cell_type": "markdown", + "id": "94c4a7af", + "metadata": {}, + "source": [ + "如果希望查看更多模型训练与评估的内容,可以参考xxx。" + ] + }, { "cell_type": "markdown", "id": "e991757c", @@ -385,6 +409,10 @@ "id": "54a041fd", "metadata": {}, "source": [ + "如果希望查看更多模型保存与加载的内容,可以参考xxx;\n", + "\n", + "如果希望查看更多模型预测的内容,可以参考xxxx。\n", + "\n", "至此你就通过飞桨几个简单的API完成了一个深度学习任务,你也可以针对自己的需求来更换其中的代码,比如对数据集使用更多的数据增强、使用更大的 CNN 模型和自定义的神经网络等,飞桨官网提供了丰富的教程与案例可供参考" ] } From dc80a56335ab139433870df464c7eb963731a1f2 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 30 Nov 2021 12:05:22 +0000 Subject: [PATCH 17/63] rm 01_quick_start_cn.rst --- .../01_quick_start_cn.rst | 116 ------------------ 1 file changed, 116 deletions(-) delete mode 100644 docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst deleted file mode 100644 index b354c0c995d..00000000000 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst +++ /dev/null @@ -1,116 +0,0 @@ -.. _cn_doc_quick_start: - -10分钟快速上手飞桨(PaddlePaddle) -================================= - -本示例通过一个基础案例,带你快速了解如何使用飞桨框架。 - -一、安装飞桨 ------------ - -如果你已经安装好飞桨那么可以跳过此步骤。飞桨提供了一个方便易用的安装引导页面,你可以通过选择自己的系统和软件版本来获取对应的安装命令,具体可以点击\ `快速安装 `__\ 查看。 - -二、导入飞桨 ------------ - -安装好飞桨后你就可以在Python程序中导入飞桨。 - -.. code:: ipython3 - - import paddle - print(paddle.__version__) - -.. parsed-literal:: - - 2.2.0 - - -三、实践:手写数字识别任务 ---------------------------- - -简单的说,深度学习任务一般分为几个核心步骤:1.数据集的准备和加载;2.模型构建;3.模型训练;4.模型评估。接下来你可以使用飞桨框架API,一步步实现上述步骤。 - -3.1 加载内置数据集 -~~~~~~~~~~~~~~~~~ - -飞桨框架内置了一些常见的数据集,在这个示例中,你可以加载飞桨框架的内置数据集:手写数字体数据集。这里加载两个数据集,一个用来训练模型,一个用来评估模型。 - -.. code:: ipython3 - - import paddle.vision.transforms as T - transform = T.Normalize(mean=[127.5], std=[127.5], data_format='CHW') - - # 下载数据集 - train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) - val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) - -3.2 模型搭建 -~~~~~~~~~~~~ - -通过 ``Sequential`` 将一层一层的网络结构组建起来。注意,需要先对数据进行 ``Flatten`` 操作,将[1, 28, 28]形状的图片数据改变形状为[1, 784]。 - -.. code:: ipython3 - - mnist = paddle.nn.Sequential( - paddle.nn.Flatten(), - paddle.nn.Linear(784, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.2), - paddle.nn.Linear(512, 10) - ) - -3.3 模型训练 -~~~~~~~~~~~~ - -在训练模型前,需要配置训练模型时损失的计算方法与优化方法,你可以使用飞桨框架提供的 ``prepare`` 完成,之后使用 ``fit`` 接口来开始训练模型。 - -.. code:: ipython3 - - # 预计模型结构生成模型对象,便于进行后续的配置、训练和验证 - model = paddle.Model(mnist) - - # 模型训练相关配置,准备损失计算方法,优化器和精度计算方法 - model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), - paddle.nn.CrossEntropyLoss(), - paddle.metric.Accuracy()) - - # 开始模型训练 - model.fit(train_dataset, - epochs=5, - batch_size=64, - verbose=1) - - -.. parsed-literal:: - - - The loss value printed in the log is the current step, and the metric is the average value of previous steps. - Epoch 1/5 - step 938/938 [==============================] - loss: 0.1801 - acc: 0.9032 - 8ms/step - Epoch 2/5 - step 938/938 [==============================] - loss: 0.0544 - acc: 0.9502 - 8ms/step - Epoch 3/5 - step 938/938 [==============================] - loss: 0.0069 - acc: 0.9595 - 7ms/step - Epoch 4/5 - step 938/938 [==============================] - loss: 0.0094 - acc: 0.9638 - 7ms/step - Epoch 5/5 - step 938/938 [==============================] - loss: 0.1414 - acc: 0.9670 - 8ms/step - -3.4 模型评估 -~~~~~~~~~~~~ - -你可以使用预先定义的验证数据集来评估前一步训练得到的模型的精度。 - -.. code:: ipython3 - - model.evaluate(val_dataset, verbose=0) - - -.. parsed-literal:: - - {'loss': [2.145765e-06], 'acc': 0.9751} - - -可以看出,初步训练得到的模型效果在97.5%附近,在逐渐了解飞桨后,你可以通过调整其中的训练参数来提升模型的精度。 - -至此你就通过飞桨几个简单的API完成了一个深度学习任务,你也可以针对自己的需求来更换其中的代码,比如对数据集进行增强、使用 ``CNN`` 模型等,飞桨官网提供了丰富的教程与案例可供参考。 From ccc5167b5857ff7c5a3e5f8fd35da922c39a16ae Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 14 Dec 2021 21:17:13 +0800 Subject: [PATCH 18/63] add 02 and 03 --- .../02_data_load_cn.ipynb | 308 ++++++++++++++++++ .../02_paddle2.0_develop/02_data_load_cn.rst | 125 ------- .../03_data_preprocessing_cn.ipynb | 223 +++++++++++++ .../03_data_preprocessing_cn.rst | 88 ----- docs/guides/02_paddle2.0_develop/index_cn.rst | 4 +- 5 files changed, 533 insertions(+), 215 deletions(-) create mode 100644 docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb delete mode 100644 docs/guides/02_paddle2.0_develop/02_data_load_cn.rst create mode 100644 docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb delete mode 100644 docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.rst diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb new file mode 100644 index 00000000000..50640a84b71 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6baddc28", + "metadata": {}, + "source": [ + "# 数据集定义与加载\n", + "\n", + "深度学习模型在训练时需要大量的数据来完成模型调优,这个过程均是数字的计算,无法直接使用原始图片和文本等来完成计算。因此与需要对原始的各种数据文件进行处理,转换成深度学习模型可以使用的数据类型。\n", + "\n", + "飞桨内的数据集加载过程由 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 两个api完成。Dataset主要完成单张图像或样本的解析与标签的制作,DataLoader主要完成单张图像或样本的组batch工作和对数据集的多进程读取加速作用。\n", + "\n", + "飞桨内置了深度学习任务中常用的数据集,对应API所在目录为 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 与 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#paddle-text) ,你可以通过以下代码查看飞桨框架中的内置数据集。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ace5670c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "视觉相关数据集: ['DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']\n", + "自然语言相关数据集: ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'UCIHousing', 'WMT14', 'WMT16', 'ViterbiDecoder', 'viterbi_decode']\n" + ] + } + ], + "source": [ + "import paddle\n", + "print('视觉相关数据集:', paddle.vision.datasets.__all__)\n", + "print('自然语言相关数据集:', paddle.text.__all__)" + ] + }, + { + "cell_type": "markdown", + "id": "c9049cc8", + "metadata": {}, + "source": [ + "## 加载数据集\n", + "\n", + "通过飞桨框架,可以很方便的加载深度学习里的常用数据集。下面演示如何快速加载MNIST数据集。\n", + "\n", + "在加载过程中,会通过`transform`字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等,这里在初始化MNIST数据集时传入了 `ToTensor` 变换来将图像转换为飞桨的内置数据类型, `mode`字段用于区分训练集和测试集。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "56a6ede2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train images: 60000 , test images: 10000\n" + ] + } + ], + "source": [ + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 下载数据集并初始化DataSet\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "print('train images: ',len(train_dataset),', test images: ',len(test_dataset))" + ] + }, + { + "cell_type": "markdown", + "id": "162eed4d", + "metadata": {}, + "source": [ + "## 迭代数据集&可视化\n", + "\n", + "完成数据集初始化之后,可以使用下面的代码直接对数据集进行迭代" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "914637b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of image: [1, 28, 28]\n" + ] + }, + { + "data": { + "image/png": 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0M8OYHhGba7dfkjS9k80MY8RpvNtpn2nGu+a5a2T682bxBt17zY+IT0n6rKSLaqerXSkGX4N109jpqKbxbpdhphn/rU4+d41Of96sToR9k6SZQ+5/vLasK0TEptrvrZLuVvdNRb1l7wy6td9bO9zPb3XTNN7DTTOuLnjuOjn9eSfC/qCkObZn2z5A0hckrexAH+9he1LtjRPZniTpDHXfVNQrJS2s3V4o6Z4O9vIu3TKNd71pxtXh567j059HRNt/JJ2lwXfkn5P0V53ooU5fn5D0SO3n8U73JulWDZ7W7dLgexsXSvqIpFWSnpH0n5KmdlFv/ybpMUmPajBYMzrU23wNnqI/Kmlt7eesTj93hb7a8rxxuSyQBG/QAUkQdiAJwg4kQdiBJAg7kARhB5Ig7EAS/w9pgMSoTFggTAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "for data in train_dataset:\n", + " image, label = data\n", + " print('shape of image: ',image.shape)\n", + " plt.title(str(label))\n", + " plt.imshow(image[0]) \n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "ab2f3fb4", + "metadata": {}, + "source": [ + "## 自定义数据集\n", + "\n", + "\n", + "在实际的场景中,需要使用已有的数据来定义数据集。这时可以使用飞桨提供的`paddle.io.Dataset`基类来快速实现自定义数据集。\n", + "\n", + "自定义数据集需要集成自 `paddle.io.Dataset` 并且实现下面的三个方法\n", + "\n", + "1. `__init__`: 完成一些数据集初始化操作,定义数据集大小\n", + "2. `__getitem__`: 定义给定index时如何获取数据,在此函数中需要完成数据的预处理工作,如读取图像,对图像进行数据增强和制作标签等操作,最终返回处理好的单条数据(训练数据,对应的标签)\n", + "3. `__len__`: 返回数据集总数目" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1d26950f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from paddle.io import Dataset\n", + "\n", + "class MyDataset(Dataset):\n", + " \"\"\"\n", + " 步骤一:继承paddle.io.Dataset类\n", + " \"\"\"\n", + " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", + " \"\"\"\n", + " 步骤二:实现构造函数,定义数据集大小\n", + " \"\"\"\n", + " super(MyDataset, self).__init__()\n", + " self.num_samples = num_samples\n", + " self.image_size = image_size\n", + " self.class_num = class_num\n", + "\n", + " def __getitem__(self, index):\n", + " \"\"\"\n", + " 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)\n", + " \"\"\"\n", + " image = np.random.rand(*self.image_size)\n", + " image = np.expand_dims(image, axis=0)\n", + " label = np.random.randint(0, self.class_num - 1)\n", + "\n", + " return image, label\n", + "\n", + " def __len__(self):\n", + " \"\"\"\n", + " 步骤四:实现__len__方法,返回数据集总数目\n", + " \"\"\"\n", + " return self.num_samples" + ] + }, + { + "cell_type": "markdown", + "id": "0e705d33", + "metadata": {}, + "source": [ + "和内置数据集一样,可以直接对自定义数据集进行迭代" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9d1570a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of image: (1, 28, 28)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM)\n", + "\n", + "for data in custom_dataset:\n", + " image, label = data\n", + " print('shape of image: ',image.shape)\n", + " plt.title(str(label))\n", + " plt.imshow(image[0]) \n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "de3fd19b", + "metadata": {}, + "source": [ + "## 使用DataLoader 读取训练数据集\n", + "\n", + "通过直接迭代DataSet的方式虽然可以对数据集进行访问,但是这种访问方式只能单线程进行并且还需要手动进行Batch的组合。在飞桨中,推荐使用 `paddle.io.DataLoader`来对数据集进行多进程的读取并且自动完成组batch的工作,开发者只需要进行数据处理部分逻辑的编写。\n", + "\n", + "[飞桨高层API](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/high_level_api.html#api) 自动完成DataLoader的过程, 对于非高层API的使用情况,可以通过如下代码,可以快速的使用`paddle.io.DataLoader`完成数据的加载。" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c3ad4116", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of image: [64, 1, 28, 28] shape of label: [64, 1]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=1)\n", + "for batch_id, data in enumerate(train_loader()):\n", + " images, labels = data\n", + " print('shape of image: ',images.shape, 'shape of label: ', labels.shape)\n", + " plt.title(str(labels[0].numpy()))\n", + " plt.imshow(images[0][0]) \n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "ae21b353", + "metadata": {}, + "source": [ + "通过上述的方法,可以初始化一个数据迭代器train_loader, 用于加载训练数据。通过batch_size=64设置了数据集的批大小为64,通过设置shuffle=True可以在取数据前会打乱数据集顺序。此外,还可以通过设置num_workers来开启多进程数据加载,提升加载速度。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d12b2a5a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.rst b/docs/guides/02_paddle2.0_develop/02_data_load_cn.rst deleted file mode 100644 index dcf970e84be..00000000000 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.rst +++ /dev/null @@ -1,125 +0,0 @@ -.. _cn_doc_data_load: - -数据集定义与加载 -================ - -深度学习模型在训练时需要大量的数据来完成模型调优,这个过程均是数字的计算,无法直接使用原始图片和文本等来完成计算。因此与需要对原始的各种数据文件进行处理,转换成深度学习模型可以使用的数据类型。 - -一、框架自带数据集 ---------------------- - -飞桨框架将深度学习任务中常用到的数据集作为领域API开放,对应API所在目录为\ ``paddle.vision.datasets``\ 与\ ``paddle.text``\,你可以通过以下代码飞桨框架中提供了哪些数据集。 - -.. code:: ipython3 - - import paddle - print('视觉相关数据集:', paddle.vision.datasets.__all__) - print('自然语言相关数据集:', paddle.text.__all__) - - -.. parsed-literal:: - - 视觉相关数据集: ['DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012'] - 自然语言相关数据集: ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'UCIHousing', 'WMT14', 'WMT16'] - -.. warning:: - 除\ ``paddle.vision.dataset``\ 与\ ``paddle.text``\ 外,飞桨框架还内置了另一套数据集,路径为\ ``paddle.dataset.*``\ ,但是该数据集的使用方式较老,会在未来的版本废弃,请尽量不要使用该目录下数据集的API。 - -这里你可以定义手写数字体的数据集,其他数据集的使用方式也都类似。用\ ``mode``\ 来标识训练集与测试集。数据集接口会自动从远端下载数据集到本机缓存目录\ ``~/.cache/paddle/dataset``\ 。 - -.. code:: ipython3 - - from paddle.vision.transforms import ToTensor - # 训练数据集 用ToTensor将数据格式转为Tensor - train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) - - # 验证数据集 - val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) - - -二、自定义数据集 -------------------- - -在实际的场景中,更多需要使用你已有的相关数据来定义数据集。你可以使用飞桨提供的\ ``paddle.io.Dataset``\ 基类,来快速实现自定义数据集。 - -.. code:: ipython3 - - import paddle - from paddle.io import Dataset - - BATCH_SIZE = 64 - BATCH_NUM = 20 - - IMAGE_SIZE = (28, 28) - CLASS_NUM = 10 - - - class MyDataset(Dataset): - """ - 步骤一:继承paddle.io.Dataset类 - """ - def __init__(self, num_samples): - """ - 步骤二:实现构造函数,定义数据集大小 - """ - super(MyDataset, self).__init__() - self.num_samples = num_samples - - def __getitem__(self, index): - """ - 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签) - """ - data = paddle.uniform(IMAGE_SIZE, dtype='float32') - label = paddle.randint(0, CLASS_NUM-1, dtype='int64') - - return data, label - - def __len__(self): - """ - 步骤四:实现__len__方法,返回数据集总数目 - """ - return self.num_samples - - # 测试定义的数据集 - custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM) - - print('=============custom dataset=============') - for data, label in custom_dataset: - print(data.shape, label.shape) - break - - -.. parsed-literal:: - - =============custom dataset============= - [28, 28] [1] - -通过以上的方式,你就可以根据实际场景,构造自己的数据集。 - - -三、数据加载 ------------- - -飞桨推荐使用\ ``paddle.io.DataLoader``\ 完成数据的加载。简单的示例如下: - -.. code:: ipython3 - - train_loader = paddle.io.DataLoader(custom_dataset, batch_size=BATCH_SIZE, shuffle=True) - # 如果要加载内置数据集,将 custom_dataset 换为 train_dataset 即可 - for batch_id, data in enumerate(train_loader()): - x_data = data[0] - y_data = data[1] - - print(x_data.shape) - print(y_data.shape) - break - -.. parsed-literal:: - - [64, 28, 28] - [64, 1] - -通过上述的方法,你就定义了一个数据迭代器\ ``train_loader``\ , 用于加载训练数据。通过\ ``batch_size=64``\ 设置了数据集的批大小为64,通过\ ``shuffle=True``\ ,在取数据前会打乱数据。此外,你还可以通过设置\ ``num_workers``\ 来开启多进程数据加载,提升加载速度。 - -.. note:: - DataLoader 默认用异步加载数据的方式来读取数据,一方面可以提升数据加载的速度,另一方面也会占据更少的内存。如果你需要同时加载全部数据到内存中,请设置\ ``use_buffer_reader=False``\ 。 diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb new file mode 100644 index 00000000000..ce8afa67b72 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "159b5b2a", + "metadata": {}, + "source": [ + "# 数据预处理\n", + "\n", + "数据预处理包含对图像进行数据增强和对标签进行处理等操作,这里主要介绍图像处理部分。\n", + "\n", + "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。\n", + "\n", + "## 飞桨框架内置数据处理API\n", + "\n", + "飞桨框架在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理操作,可以通过以下方式查看" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "93904999", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "数据处理方法: ['BaseTransform', 'Compose', 'Resize', 'RandomResizedCrop', 'CenterCrop', 'RandomHorizontalFlip', 'RandomVerticalFlip', 'Transpose', 'Normalize', 'BrightnessTransform', 'SaturationTransform', 'ContrastTransform', 'HueTransform', 'ColorJitter', 'RandomCrop', 'Pad', 'RandomRotation', 'Grayscale', 'ToTensor', 'to_tensor', 'hflip', 'vflip', 'resize', 'pad', 'rotate', 'to_grayscale', 'crop', 'center_crop', 'adjust_brightness', 'adjust_contrast', 'adjust_hue', 'normalize']\n" + ] + } + ], + "source": [ + "import paddle\n", + "print('数据处理方法:', paddle.vision.transforms.__all__)" + ] + }, + { + "cell_type": "markdown", + "id": "a4b999ee", + "metadata": {}, + "source": [ + "对于飞桨框架内置的数据处理,可以单个初始化调用,也可以将多个数据处理进行组合使用,具体使用方式如下\n", + "\n", + "* 单个使用" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "69b80bc1", + "metadata": {}, + "outputs": [], + "source": [ + "from paddle.vision.transforms import Resize\n", + "\n", + "# 定义想要使用的数据处理方式,这里初始化一个改变图片大小的变换\n", + "transform = Resize(size=28)" + ] + }, + { + "cell_type": "markdown", + "id": "533d6372", + "metadata": {}, + "source": [ + "* 多个组合使用\n", + "\n", + "这种使用模式下,需要先定义好每个数据处理操作,然后用`Compose`进行组合" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4a1a5cb3", + "metadata": {}, + "outputs": [], + "source": [ + "from paddle.vision.transforms import Compose, RandomRotation\n", + "\n", + "# 定义想要使用的数据处理方式,这里包括随机旋转,改变图片大小\n", + "transform = Compose([RandomRotation(10), Resize(size=32)])" + ] + }, + { + "cell_type": "markdown", + "id": "0a76dd29", + "metadata": {}, + "source": [ + "定义好数据预处理操作后,可以直接在DataSet中进行使用,下面介绍介绍两种数据增强使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集\n", + "\n", + "## 基于框架内置数据集\n", + "\n", + "在框架内置数据集中使用内置的数据处理操作时,只需要将数据处理操作传递给`transform`字段即可" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a7970f84", + "metadata": {}, + "outputs": [], + "source": [ + "# 通过transform参数传递定义好的数据增强方法即可完成对自带数据集的增强\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)" + ] + }, + { + "cell_type": "markdown", + "id": "74013246", + "metadata": {}, + "source": [ + "## 基于自定义的数据集\n", + "\n", + "对于自定义的数据集,可以在数据集的构造函数中进行数据处理方法的定义,之后在 `__getitem__` 方法中对返回的数据进行应用, 如下述代码所示" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "45ea330a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from paddle.io import Dataset\n", + "\n", + "class MyDataset(Dataset):\n", + " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", + " super(MyDataset, self).__init__()\n", + " self.num_samples = num_samples\n", + " self.image_size = image_size\n", + " self.class_num = class_num\n", + " \n", + " # 在 `__init__` 中定义数据处理方法,此处为随机旋转\n", + " self.transform = RandomRotation(10)\n", + "\n", + " def __getitem__(self, index):\n", + " \"\"\"\n", + " 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)\n", + " \"\"\"\n", + " image = np.random.rand(*self.image_size)\n", + " \n", + " # 在 `__getitem__` 中对数据集使用数据处理方法\n", + " data = self.transform(data)\n", + " \n", + " \n", + " image = np.expand_dims(image, axis=0)\n", + " label = np.random.randint(0, self.class_num - 1)\n", + "\n", + " return image, label\n", + "\n", + " def __len__(self):\n", + " \"\"\"\n", + " 步骤四:实现__len__方法,返回数据集总数目\n", + " \"\"\"\n", + " return self.num_samples" + ] + }, + { + "cell_type": "markdown", + "id": "3278dbe9", + "metadata": {}, + "source": [ + "下面通过框架内置数据集对比处理前后的图像" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b4f7532b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28, 28)\n", + "(32, 32)\n" + ] + } + ], + "source": [ + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=None)\n", + "train_dataset_with_Resize = paddle.vision.datasets.MNIST(mode='train', transform=Resize(32))\n", + "\n", + "image, label = train_dataset[0]\n", + "image_with_Resize, label_with_Resize = train_dataset_with_Resize[0]\n", + "\n", + "print(image.size)\n", + "print(image_with_Resize.size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87428d26", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.rst b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.rst deleted file mode 100644 index 23e0d0c290c..00000000000 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.rst +++ /dev/null @@ -1,88 +0,0 @@ -.. _cn_doc_data_preprocessing: - -数据预处理 -================ - -训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做增强,对数据进行处理得到不同的图像,从而泛化数据集。数据增强API是定义在领域目录的transofrms下,这里介绍两种使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集。 - - -一、飞桨框架内置数据集 ------------------------ - -针对飞桨框架内置图像数据集的预处理,飞桨框架将这部分API整合到\ ``paddle.vision.transforms``\ 下,你可以通过以下方式查看: - -.. code:: ipython3 - - import paddle - print('数据处理方法:', paddle.vision.transforms.__all__) - - -.. parsed-literal:: - - 数据处理方法: ['BaseTransform', 'Compose', 'Resize', 'RandomResizedCrop', 'CenterCrop', 'RandomHorizontalFlip', 'RandomVerticalFlip', 'Transpose', 'Normalize', 'BrightnessTransform', 'SaturationTransform', 'ContrastTransform', 'HueTransform', 'ColorJitter', 'RandomCrop', 'Pad', 'RandomRotation', 'Grayscale', 'ToTensor', 'to_tensor', 'hflip', 'vflip', 'resize', 'pad', 'rotate', 'to_grayscale', 'crop', 'center_crop', 'adjust_brightness', 'adjust_contrast', 'adjust_hue', 'normalize'] - -你可以同构以下方式随机调整图像的亮度、对比度、饱和度,并调整图像的大小,对图像的其他调整,可以参考相关的API文档。 - -.. code:: ipython3 - - from paddle.vision.transforms import Compose, Resize, ColorJitter - - # 定义想要使用的数据增强方式,这里包括随机调整亮度、对比度和饱和度,改变图片大小 - transform = Compose([ColorJitter(), Resize(size=32)]) - - # 通过transform参数传递定义好的数据增强方法即可完成对自带数据集的增强 - train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) - - -二、自定义数据集 ------------------------ - -对于自定义的数据集,你可以在数据集的构造函数中进行数据增强方法的定义,之后对 ``__getitem__`` 中返回的数据进行应用,就可以完成自定义数据增强。 - -.. code:: ipython3 - - import paddle - from paddle.io import Dataset - from paddle.vision.transforms import Compose, Resize - - BATCH_SIZE = 64 - BATCH_NUM = 20 - - IMAGE_SIZE = (28, 28) - CLASS_NUM = 10 - - class MyDataset(Dataset): - def __init__(self, num_samples): - super(MyDataset, self).__init__() - self.num_samples = num_samples - # 在 `__init__` 中定义数据增强方法,此处为调整图像大小 - self.transform = Compose([Resize(size=32)]) - - def __getitem__(self, index): - data = paddle.uniform(IMAGE_SIZE, dtype='float32') - # 在 `__getitem__` 中对数据集使用数据增强方法 - data = self.transform(data.numpy()) - - label = paddle.randint(0, CLASS_NUM-1, dtype='int64') - - return data, label - - def __len__(self): - return self.num_samples - - # 测试定义的数据集 - custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM) - - print('=============custom dataset=============') - for data, label in custom_dataset: - print(data.shape, label.shape) - break - - -.. parsed-literal:: - - =============custom dataset============= - [32, 32] [1] - - -可以看出,输出的形状从 ``[28, 28, 1]`` 变为了 ``[32, 32, 1]``,证明完成了图像的大小调整。 \ No newline at end of file diff --git a/docs/guides/02_paddle2.0_develop/index_cn.rst b/docs/guides/02_paddle2.0_develop/index_cn.rst index d93742de15e..171b95a642f 100644 --- a/docs/guides/02_paddle2.0_develop/index_cn.rst +++ b/docs/guides/02_paddle2.0_develop/index_cn.rst @@ -21,8 +21,8 @@ :hidden: 01_quick_start_cn.ipynb - 02_data_load_cn.rst - 03_data_preprocessing_cn.rst + 02_data_load_cn.ipynb + 03_data_preprocessing_cn.ipynb 04_model_cn.rst 05_train_eval_predict_cn.rst 06_device_cn.rst From ac831e2f6afd0df77da9e361536a32bf2af2dc70 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 14 Dec 2021 21:52:28 +0800 Subject: [PATCH 19/63] add 04 --- .../02_data_load_cn.ipynb | 8 - .../03_data_preprocessing_cn.ipynb | 8 - .../02_paddle2.0_develop/04_model_cn.ipynb | 277 ++++++++++++++++++ docs/guides/02_paddle2.0_develop/index_cn.rst | 2 +- 4 files changed, 278 insertions(+), 17 deletions(-) create mode 100644 docs/guides/02_paddle2.0_develop/04_model_cn.ipynb diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 50640a84b71..13775f93729 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -274,14 +274,6 @@ "source": [ "通过上述的方法,可以初始化一个数据迭代器train_loader, 用于加载训练数据。通过batch_size=64设置了数据集的批大小为64,通过设置shuffle=True可以在取数据前会打乱数据集顺序。此外,还可以通过设置num_workers来开启多进程数据加载,提升加载速度。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d12b2a5a", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index ce8afa67b72..bbbd8ecc895 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -189,14 +189,6 @@ "print(image.size)\n", "print(image_with_Resize.size)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87428d26", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb new file mode 100644 index 00000000000..b6cb64b3386 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "72d769ce", + "metadata": {}, + "source": [ + "# 模型组网\n", + "\n", + "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的LeNet网络分别介绍这三种形式。\n", + "\n", + "## 通过内置模型组网\n", + "\n", + "飞桨在 [paddle.vision.models.LeNet](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了常用的分类模型,可以进行很方便的调用,通过下面的命令可以直接初始化一个LeNet模型" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9c9d3513", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LeNet(\n", + " (features): Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "import paddle\n", + "\n", + "lenet = paddle.vision.models.LeNet(num_classes=10)\n", + "print(lenet)" + ] + }, + { + "cell_type": "markdown", + "id": "c48f8ac6", + "metadata": {}, + "source": [ + "可以看到LeNet包含`features`和`fc`两个子网络,总共包含2个卷积层,2个ReLU激活层,2个MaxPool2D层,三个全链接层。\n", + "\n", + "## 通过 Sequential 组网\n", + "\n", + "针对顺序的线性网络结构,可以直接使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 来快速完成组网,这种方式可以减少类的定义等代码编写。具体代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a86cc3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (6): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (7): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (8): Linear(in_features=84, out_features=10, dtype=float32)\n", + ")\n" + ] + } + ], + "source": [ + "from paddle import nn\n", + "\n", + "lenet_Sequential = nn.Sequential(\n", + " nn.Conv2D(1, 6, 3, stride=1, padding=1),\n", + " nn.ReLU(),\n", + " nn.MaxPool2D(2, 2),\n", + " nn.Conv2D(6, 16, 5, stride=1, padding=0),\n", + " nn.ReLU(),\n", + " nn.MaxPool2D(2, 2),\n", + " nn.Linear(400, 120),\n", + " nn.Linear(120, 84), \n", + " nn.Linear(84, 10)\n", + ")\n", + "print(lenet_Sequential)" + ] + }, + { + "cell_type": "markdown", + "id": "b9524d1c", + "metadata": {}, + "source": [ + "## 通过 SubClass 组网\n", + "\n", + "针对一些比较复杂的网络结构,就可以使用 SubClass 组网的方式来进行模型代码编写。通过 SubClass 组网进行组网需要完成下列三个步骤:\n", + "1. 创建一个继承自[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)\n", + "2. 在类的构造函数`__init__`中进行子Layer的定义,完成网络的构建\n", + "3. 在类的`forward`函数中使用定义的子Layer进行前向计算。\n", + "\n", + "子Layer可以通过 基础API(卷积,池化或全连接),Sequential 或 SubClass 的形式进行定义,子Layer在构造函数中一次定义后可在forward中多次调用。使用SubClass 组网形式实现LeNet的代码如下" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cf89df53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LeNet(\n", + " (features): Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class LeNet(nn.Layer):\n", + " def __init__(self, num_classes=10):\n", + " super(LeNet, self).__init__()\n", + " self.num_classes = num_classes\n", + " self.features = nn.Sequential(\n", + " nn.Conv2D(\n", + " 1, 6, 3, stride=1, padding=1),\n", + " nn.ReLU(),\n", + " nn.MaxPool2D(2, 2),\n", + " nn.Conv2D(\n", + " 6, 16, 5, stride=1, padding=0),\n", + " nn.ReLU(),\n", + " nn.MaxPool2D(2, 2))\n", + "\n", + " if num_classes > 0:\n", + " self.fc = nn.Sequential(\n", + " nn.Linear(400, 120),\n", + " nn.Linear(120, 84), nn.Linear(84, num_classes))\n", + "\n", + " def forward(self, inputs):\n", + " x = self.features(inputs)\n", + "\n", + " if self.num_classes > 0:\n", + " x = paddle.flatten(x, 1)\n", + " x = self.fc(x)\n", + " return x\n", + "lenet_SubClass = LeNet()\n", + "print(lenet_SubClass)" + ] + }, + { + "cell_type": "markdown", + "id": "8843cf9c", + "metadata": {}, + "source": [ + "## 飞桨内置基础API\n", + "\n", + "飞桨内置了大量基础的组网API,组网相关的API都在[paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#paddle-nn)目录下。组网相关的API类别与具体的API列表如下表:\n", + "\n", + "| 功能 | API名称 |\n", + "| --- | ---|\n", + "| Conv | Conv1D、Conv2D、Conv3D、Conv1DTranspose、Conv2DTranspose、Conv3DTranspose |\n", + "| Pool | AdaptiveAvgPool1D、AdaptiveAvgPool2D、AdaptiveAvgPool3D、 AdaptiveMaxPool1D、AdaptiveMaxPool2D、AdaptiveMaxPool3D、 AvgPool1D、AvgPool2D、AvgPool3D、MaxPool1D、MaxPool2D、MaxPool3D |\n", + "| Padding | Pad1D、Pad2D、Pad3D |\n", + "| Activation | ELU、GELU、Hardshrink、Hardtanh、HSigmoid、LeakyReLU、LogSigmoid、 LogSoftmax、PReLU、ReLU、ReLU6、SELU、Sigmoid、Softmax、Softplus、 Softshrink、Softsign、Tanh、Tanhshrink |\n", + "| Normlization | BatchNorm、BatchNorm1D、BatchNorm2D、BatchNorm3D、GroupNorm、 InstanceNorm1D、InstanceNorm2D、InstanceNorm3D、LayerNorm、SpectralNorm、 SyncBatchNorm |\n", + "| Recurrent NN | BiRNN、GRU、GRUCell、LSTM、LSTMCell、RNN、RNNCellBase、SimpleRNN、SimpleRNNCell | \n", + "| Transformer | Transformer、TransformerDecoder、TransformerDecoderLayer、| TransformerEncoder、TransformerEncoderLayer |\n", + "| Dropout | AlphaDropout、Dropout、Dropout2d、Dropout3d |\n", + "| Loss | BCELoss、BCEWithLogitsLoss、CrossEntropyLoss、CTCLoss、KLDivLoss、L1Loss、 MarginRankingLoss、MSELoss、NLLLoss、SmoothL1Loss |\n", + "\n", + "## 模型的参数\n", + "\n", + "飞桨内置的 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 方法可以很方便的查看网络的基础结构,每层的输入输出shape和参数信息。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4617d646", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-5 [[64, 1, 28, 28]] [64, 6, 28, 28] 60 \n", + " ReLU-5 [[64, 6, 28, 28]] [64, 6, 28, 28] 0 \n", + " MaxPool2D-5 [[64, 6, 28, 28]] [64, 6, 14, 14] 0 \n", + " Conv2D-6 [[64, 6, 14, 14]] [64, 16, 10, 10] 2,416 \n", + " ReLU-6 [[64, 16, 10, 10]] [64, 16, 10, 10] 0 \n", + " MaxPool2D-6 [[64, 16, 10, 10]] [64, 16, 5, 5] 0 \n", + " Linear-7 [[64, 400]] [64, 120] 48,120 \n", + " Linear-8 [[64, 120]] [64, 84] 10,164 \n", + " Linear-9 [[64, 84]] [64, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.19\n", + "Forward/backward pass size (MB): 7.03\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 7.46\n", + "---------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'total_params': 61610, 'trainable_params': 61610}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paddle.summary(lenet, (64, 1, 28, 28))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/index_cn.rst b/docs/guides/02_paddle2.0_develop/index_cn.rst index 171b95a642f..a85c778bf83 100644 --- a/docs/guides/02_paddle2.0_develop/index_cn.rst +++ b/docs/guides/02_paddle2.0_develop/index_cn.rst @@ -23,7 +23,7 @@ 01_quick_start_cn.ipynb 02_data_load_cn.ipynb 03_data_preprocessing_cn.ipynb - 04_model_cn.rst + 04_model_cn.ipynb 05_train_eval_predict_cn.rst 06_device_cn.rst 07_customize_cn.rst From 4299e7493acc36918b3c506de6a639b041a862a1 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 14 Dec 2021 21:59:01 +0800 Subject: [PATCH 20/63] add 04 --- .../02_paddle2.0_develop/04_model_cn.rst | 142 ------------------ 1 file changed, 142 deletions(-) delete mode 100644 docs/guides/02_paddle2.0_develop/04_model_cn.rst diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.rst b/docs/guides/02_paddle2.0_develop/04_model_cn.rst deleted file mode 100644 index 47f86a710bc..00000000000 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.rst +++ /dev/null @@ -1,142 +0,0 @@ -.. _cn_doc_model: - -模型组网 -============ - -完成数据集的构建后,需要构建网络模型。首先介绍飞桨组网相关的API,主要是\ ``paddle.nn``\ 下的API介绍,然后介绍动态图下飞桨框架支持的两种组网方式,分别为 ``Sequential`` 组网与 ``SubClass`` 组网,最后,介绍飞桨框架内置的算法模型。 - -一、paddle.nn 简介 -------------------------- - -飞桨框架2.0中,组网相关的API都在\ ``paddle.nn``\ 目录下,你可以通过 ``Sequential`` 或 ``SubClass`` 的方式构建具体的模型。组网相关的API类别与具体的API列表如下表: - -+---------------+---------------------------------------------------------------------------+ -| 功能 | API名称 | -+===============+===========================================================================+ -| Conv | Conv1D、Conv2D、Conv3D、Conv1DTranspose、Conv2DTranspose、Conv3DTranspose | -+---------------+---------------------------------------------------------------------------+ -| Pool | AdaptiveAvgPool1D、AdaptiveAvgPool2D、AdaptiveAvgPool3D、 | -| | AdaptiveMaxPool1D、AdaptiveMaxPool2D、AdaptiveMaxPool3D、 | -| | AvgPool1D、AvgPool2D、AvgPool3D、MaxPool1D、MaxPool2D、MaxPool3D | -+---------------+---------------------------------------------------------------------------+ -| Padding | Pad1D、Pad2D、Pad3D | -+---------------+---------------------------------------------------------------------------+ -| Activation | ELU、GELU、Hardshrink、Hardtanh、HSigmoid、LeakyReLU、LogSigmoid、 | -| | LogSoftmax、PReLU、ReLU、ReLU6、SELU、Sigmoid、Softmax、Softplus、 | -| | Softshrink、Softsign、Tanh、Tanhshrink | -+---------------+---------------------------------------------------------------------------+ -| Normlization | BatchNorm、BatchNorm1D、BatchNorm2D、BatchNorm3D、GroupNorm、 | -| | InstanceNorm1D、InstanceNorm2D、InstanceNorm3D、LayerNorm、SpectralNorm、 | -| | SyncBatchNorm | -+---------------+---------------------------------------------------------------------------+ -| Recurrent NN | BiRNN、GRU、GRUCell、LSTM、LSTMCell、RNN、RNNCellBase、SimpleRNN、 | -| | SimpleRNNCell | -+---------------+---------------------------------------------------------------------------+ -| Transformer | Transformer、TransformerDecoder、TransformerDecoderLayer、 | -| | TransformerEncoder、TransformerEncoderLayer | -+---------------+---------------------------------------------------------------------------+ -| Dropout | AlphaDropout、Dropout、Dropout2d、Dropout3d | -+---------------+---------------------------------------------------------------------------+ -| Loss | BCELoss、BCEWithLogitsLoss、CrossEntropyLoss、CTCLoss、KLDivLoss、L1Loss | -| | MarginRankingLoss、MSELoss、NLLLoss、SmoothL1Loss | -+---------------+---------------------------------------------------------------------------+ - - -二、Sequential 组网 -------------------------- - -针对顺序的线性网络结构你可以直接使用Sequential来快速完成组网,可以减少类的定义等代码编写。具体代码如下: - -.. code:: ipython3 - - import paddle - # Sequential形式组网 - mnist = paddle.nn.Sequential( - paddle.nn.Flatten(), - paddle.nn.Linear(784, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.2), - paddle.nn.Linear(512, 10) - ) - -三、SubClass 组网 -------------------------- - -针对一些比较复杂的网络结构,就可以使用Layer子类定义的方式来进行模型代码编写,在\ ``__init__``\ 构造函数中进行组网Layer的声明,在\ ``forward``\ 中使用声明的Layer变量进行前向计算。子类组网方式也可以实现sublayer的复用,针对相同的layer可以在构造函数中一次性定义,在forward中多次调用。 - -.. code:: ipython3 - - # Layer类继承方式组网 - class Mnist(paddle.nn.Layer): - def __init__(self): - super(Mnist, self).__init__() - - self.flatten = paddle.nn.Flatten() - self.linear_1 = paddle.nn.Linear(784, 512) - self.linear_2 = paddle.nn.Linear(512, 10) - self.relu = paddle.nn.ReLU() - self.dropout = paddle.nn.Dropout(0.2) - - def forward(self, inputs): - y = self.flatten(inputs) - y = self.linear_1(y) - y = self.relu(y) - y = self.dropout(y) - y = self.linear_2(y) - - return y - - mnist_2 = Mnist() - -四、飞桨框架内置模型 --------------------------------- - -你除了可以通过上述方式组建模型外,还可以使用飞桨框架内置的模型,路径为 ``paddle.vision.models`` ,具体列表如下: - -.. code:: ipython3 - - print('飞桨框架内置模型:', paddle.vision.models.__all__) - - -.. parsed-literal:: - - 飞桨框架内置模型: ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'VGG', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'MobileNetV1', 'mobilenet_v1', 'MobileNetV2', 'mobilenet_v2', 'LeNet'] - -使用方式如下: - -.. code:: ipython3 - - lenet = paddle.vision.models.LeNet() - - -你可以通过\ ``paddle.summary()``\ 方法查看模型的结构与每一层输入输出形状,具体如下: - -.. code:: ipython3 - - paddle.summary(lenet, (64, 1, 28, 28)) - - -.. parsed-literal:: - - --------------------------------------------------------------------------- - Layer (type) Input Shape Output Shape Param # - =========================================================================== - Conv2D-1 [[64, 1, 28, 28]] [64, 6, 28, 28] 60 - ReLU-1 [[64, 6, 28, 28]] [64, 6, 28, 28] 0 - MaxPool2D-1 [[64, 6, 28, 28]] [64, 6, 14, 14] 0 - Conv2D-2 [[64, 6, 14, 14]] [64, 16, 10, 10] 2,416 - ReLU-2 [[64, 16, 10, 10]] [64, 16, 10, 10] 0 - MaxPool2D-2 [[64, 16, 10, 10]] [64, 16, 5, 5] 0 - Linear-1 [[64, 400]] [64, 120] 48,120 - Linear-2 [[64, 120]] [64, 84] 10,164 - Linear-3 [[64, 84]] [64, 10] 850 - =========================================================================== - Total params: 61,610 - Trainable params: 61,610 - Non-trainable params: 0 - --------------------------------------------------------------------------- - Input size (MB): 0.19 - Forward/backward pass size (MB): 7.03 - Params size (MB): 0.24 - Estimated Total Size (MB): 7.46 - --------------------------------------------------------------------------- From 78d9881e02d1237caa86fc361d0fd828052f3d62 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 20 Dec 2021 16:05:27 +0800 Subject: [PATCH 21/63] update 01 --- .../01_quick_start_cn.ipynb | 545 +++++++++++++++--- .../images/model_develop_flow.png | Bin 0 -> 190803 bytes 2 files changed, 452 insertions(+), 93 deletions(-) create mode 100644 docs/guides/02_paddle2.0_develop/images/model_develop_flow.png diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index dfa6ebe152e..06d45fb7b71 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -5,23 +5,63 @@ "id": "f848b574", "metadata": {}, "source": [ + "\n", "# 10分钟快速上手飞桨\n", "\n", "从完成一个简单的『手写数字识别任务』开始,可快速了解深度学习模型开发的大致流程,并初步掌握飞桨框架 API 的使用方法。\n", "\n", + "## 一、快速安装飞桨\n", "\n", - "## 快速安装飞桨\n", - "\n", - "如果已经安装好飞桨那么可以跳过此步骤。飞桨支持很多种安装方式,这里介绍其中一种简单的安装命令:\n", - "\n", - "```bash\n", + "如果已经安装好飞桨那么可以跳过此步骤。飞桨支持很多种安装方式,这里介绍其中一种简单的安装命令:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e7d9b5cf-fffe-4086-b84d-3f58eee2f602", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-20T07:51:58.100025Z", + "iopub.status.busy": "2021-12-20T07:51:58.099277Z", + "iopub.status.idle": "2021-12-20T07:51:59.862320Z", + "shell.execute_reply": "2021-12-20T07:51:59.861499Z", + "shell.execute_reply.started": "2021-12-20T07:51:58.099995Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", + "Requirement already satisfied: paddlepaddle in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.1)\n", + "Requirement already satisfied: requests>=2.20.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (2.22.0)\n", + "Requirement already satisfied: astor in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (0.8.1)\n", + "Requirement already satisfied: Pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (7.1.2)\n", + "Requirement already satisfied: numpy>=1.13; python_version >= \"3.5\" and platform_system != \"Windows\" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (1.16.4)\n", + "Requirement already satisfied: protobuf>=3.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (3.14.0)\n", + "Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (1.15.0)\n", + "Requirement already satisfied: decorator in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (4.4.2)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (1.25.6)\n", + "Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (2.8)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (2019.9.11)\n", + "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (3.0.4)\n" + ] + } + ], + "source": [ "# 使用 pip 工具安装飞桨 CPU 版\n", - "python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple --trusted-host\n", - "```\n", - "\n", - "该命令用于安装CPU版本的飞桨,如果要安装其他计算平台或操作系统支持的版本,可点击 [ 快速安装]( ) 查看安装引导。\n", + "! python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple" + ] + }, + { + "cell_type": "markdown", + "id": "54e32a09-1e8f-4bc6-a3bd-1eebd621fa66", + "metadata": {}, + "source": [ + "该命令用于安装 CPU 版本的飞桨,如果要安装其他计算平台或操作系统支持的版本,可点击 [ 快速安装]( ) 查看安装引导。\n", "\n", - "### 导入飞桨\n", + "### 二、导入飞桨\n", "\n", "安装完成后,需要在Python解释器中使用 import 导入飞桨,即可开始实践深度学习任务。\n", "\n", @@ -30,15 +70,23 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "468426ec", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-16T04:18:43.298378Z", + "iopub.status.busy": "2021-12-16T04:18:43.297973Z", + "iopub.status.idle": "2021-12-16T04:18:43.302570Z", + "shell.execute_reply": "2021-12-16T04:18:43.301792Z", + "shell.execute_reply.started": "2021-12-16T04:18:43.298346Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], @@ -54,42 +102,134 @@ "source": [ "\n", "\n", - "## 实践:手写数字识别任务\n", + "## 三、实践:手写数字识别任务\n", "\n", - "手写数字识别是深度学习里的 Hello Word 任务,其目标是输入手写数字的图片,输出这个图片中的数字。本任务中用到的数据集为MNIST手写数字数据集,该手写数字数据集包含60000张训练图片和10000张测试图片,这些图片是从0~9的手写数字,分辨率为28*28。数据集中部分图像和对应的分类标签如下图所示。\n", - "![](images/mnist.png)\n", + "『手写数字识别』是深度学习里的 Hello World 任务,用于对 0 ~ 9 的十类数字进行分类,即输入手写数字的图片,可识别出这个图片中的数字。\n", "\n", - "开始之前,需要使用下面的代码安装matplotlib和numpy\n", - "\n", - "```bash\n", - "python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple\n", - "```\n", + "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "如果想直接运行代码,可以拷贝下面的完整代码到一个Python文件中进行运行。\n", + "
\n", + "

图1:MNIST数据集样例
\n", "\n", - "完整代码如下:" + "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" ] }, { "cell_type": "code", "execution_count": 2, - "id": "93d0c8a1", + "id": "0b50ba8c-93dd-42d9-8b52-35025ecb122b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-16T06:31:04.356444Z", + "iopub.status.busy": "2021-12-16T06:31:04.355435Z", + "iopub.status.idle": "2021-12-16T06:31:06.274730Z", + "shell.execute_reply": "2021-12-16T06:31:06.273827Z", + "shell.execute_reply.started": "2021-12-16T06:31:04.356403Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", + "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.3)\n", + "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.16.4)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.8.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.4.2)\n", + "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.15.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n", + "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2019.3)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n", + "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (41.4.0)\n" + ] + } + ], + "source": [ + "# 使用 pip 工具安装 matplotlib 和 numpy\n", + "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple" + ] + }, + { + "cell_type": "markdown", + "id": "7ba0ec0a-bc6c-43cc-8478-488cc231bca8", "metadata": {}, - "outputs": [], + "source": [ + "下面是手写数字识别任务的完整代码,如果想直接运行代码,可以拷贝下面的完整代码到一个Python文件中运行。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93d0c8a1", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-16T06:31:11.340019Z", + "iopub.status.busy": "2021-12-16T06:31:11.338995Z", + "iopub.status.idle": "2021-12-16T06:33:13.307650Z", + "shell.execute_reply": "2021-12-16T06:33:13.306938Z", + "shell.execute_reply.started": "2021-12-16T06:31:11.339980Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", + "Epoch 1/5\n", + "step 938/938 [==============================] - loss: 0.0414 - acc: 0.9518 - 20ms/step \n", + "Epoch 2/5\n", + "step 938/938 [==============================] - loss: 0.0219 - acc: 0.9801 - 20ms/step \n", + "Epoch 3/5\n", + "step 938/938 [==============================] - loss: 0.0156 - acc: 0.9834 - 19ms/step \n", + "Epoch 4/5\n", + "step 938/938 [==============================] - loss: 0.0021 - acc: 0.9866 - 20ms/step \n", + "Epoch 5/5\n", + "step 938/938 [==============================] - loss: 0.0165 - acc: 0.9884 - 19ms/step \n", + "Eval begin...\n", + "step 10000/10000 [==============================] - loss: 1.0729e-06 - acc: 0.9850 - 2ms/step \n", + "Eval samples: 10000\n", + "true label: 7, pred label: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import paddle\n", "import numpy as np\n", "from paddle.vision.transforms import ToTensor\n", "\n", - "# 下载数据集并初始化DataSet\n", + "# 下载数据集并初始化 DataSet\n", "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", "\n", - "# 初始化网络\n", + "# 模型组网并初始化网络\n", "lenet = paddle.vision.models.LeNet(num_classes=10)\n", "model = paddle.Model(lenet)\n", "\n", - "# 模型训练相关配置,准备损失计算方法,优化器和精度计算方法\n", + "# 模型训练的配置准备,准备损失函数,优化器和评价指标\n", "model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), \n", " paddle.nn.CrossEntropyLoss(),\n", " paddle.metric.Accuracy())\n", @@ -111,7 +251,10 @@ "# 执行推理并打印结果\n", "out = model.predict_batch(img_batch)[0]\n", "pred_label = out.argmax()\n", - "print('true label: {}, pred label: {}'.format(label[0], pred_label))" + "print('true label: {}, pred label: {}'.format(label[0], pred_label))\n", + "# 可视化图片\n", + "from matplotlib import pyplot as plt\n", + "plt.imshow(img[0])" ] }, { @@ -119,32 +262,113 @@ "id": "f1be5eec", "metadata": {}, "source": [ - "简单的说,深度学习任务一般分为以下几个核心步骤:\n", - "\n", + "以上代码使用 MNIST 数据集训练并测试了 LeNet 模型,并最终成功推理出了一张手写数字图片的标签,该图片推理结果是 7 ( pred label: 7),真实标签也是7 (true label: 7)。\n", "\n", - "1. 数据集定义与加载;\n", - "2. 模型组网;\n", - "3. 模型训练和评估;\n", - "5. 模型预测。\n", + "简单地说,深度学习任务一般分为以下几个核心步骤:\n", "\n", - "接下来逐个步骤介绍,帮助你快速掌握使用飞桨框架API实践深度学习任务的方法。\n", "\n", + "1. 数据集定义与加载\n", + "2. 模型组网\n", + "3. 模型训练和评估\n", + "4. 模型推理\n", "\n", - "### 数据集定义与加载\n", - "\n", - "Paddle 在 paddle.vision.dataset 下提供了 CV 领域常见的数据集,如Cifar10,Cifar100,FashionMNIST,MNIST和VOC2012等,如果你想了解更多,点击 [paddle.vision.dataset文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api)。\n", + "接下来逐个步骤介绍,帮助你快速掌握使用飞桨框架实践深度学习任务的方法。\n" + ] + }, + { + "cell_type": "markdown", + "id": "9fdc68f2-fe82-45f0-8022-e180a5960bf7", + "metadata": {}, + "source": [ + "### 3.1 数据集定义与加载\n", "\n", - "在本任务中,直接加载飞桨框架的内置的MNIST手写数字数据集。这里加载两个数据集,一个用来训练模型,一般叫做训练集;一个用来测试模型,一般叫做测试集。\n", + "飞桨在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", "\n", - "在下面的代码中,导入了`paddle.vision.transforms`模块,`paddle.vision.transforms` 里内置了很多和数据处理相关的api,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化,详细的api信息可以在[paddle.vision.transforms文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)查看 \n" + "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化 MNIST 数据集时传入了 `ToTensor` 变换来将图像转换为飞桨支持的 Tensor 数据类型。\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "f99c914f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-17T06:55:43.185059Z", + "iopub.status.busy": "2021-12-17T06:55:43.184142Z", + "iopub.status.idle": "2021-12-17T06:55:53.018229Z", + "shell.execute_reply": "2021-12-17T06:55:53.017346Z", + "shell.execute_reply.started": "2021-12-17T06:55:43.185027Z" + } + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 48/2421 [..............................] - ETA: 5s - 2ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz \n", + "Begin to download\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 8/8 [============================>.] - ETA: 0s - 2ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Download finished\n", + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz \n", + "Begin to download\n", + "\n", + "Download finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 39/403 [=>............................] - ETA: 1s - 3ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz \n", + "Begin to download\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 2/2 [===========================>..] - ETA: 0s - 2ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Download finished\n", + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz \n", + "Begin to download\n", + "\n", + "Download finished\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -169,7 +393,13 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "如果希望查看更多关于自定义数据集与加载的内容,可以参考xxx,如果希望查看更多关于自定义数据预处理的内容,可以参考xxx。" + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "\n", + "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", + "\n", + "更多参考:\n", + "* [数据集定义与加载](02_data_load_cn.html)\n", + "* [数据预处理](03_data_preprocessing_cn.html)\n" ] }, { @@ -177,22 +407,68 @@ "id": "6ba82de9", "metadata": {}, "source": [ - "### 模型组网\n", + "### 3.2 模型组网\n", + "\n", + "飞桨的模型组网有多种方式,既可以直接使用飞桨内置的模型,也可以自定义组网。\n", "\n", - "飞桨的模型组网有两种方式,一种是使用飞桨内置的模型来直接进行模型的组网和初始化,一种是使用内置的 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) api来进行灵活度更高的组网。\n", "\n", - "由于MNIST数据集比较简单,普通的神经网络就能够达到很高的精度,因此在本任务中使用飞桨内置的LeNet作为模型,LeNet作为 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 的内置模型,可以很方便的调用它,只需要下面这句话即可完成LeNet的网络构建和初始化。" + "『手写数字识别任务』比较简单,普通的神经网络就能达到很高的精度,在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为10。\n", + "\n", + "另外通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 可方便地打印网络的基础结构和参数信息。" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "e8bf3841", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-17T06:59:47.978121Z", + "iopub.status.busy": "2021-12-17T06:59:47.977323Z", + "iopub.status.idle": "2021-12-17T06:59:47.990123Z", + "shell.execute_reply": "2021-12-17T06:59:47.989596Z", + "shell.execute_reply.started": "2021-12-17T06:59:47.978088Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-13 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-13 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-13 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-14 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-14 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-14 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-19 [[1, 400]] [1, 120] 48,120 \n", + " Linear-20 [[1, 120]] [1, 84] 10,164 \n", + " Linear-21 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n", + "{'total_params': 61610, 'trainable_params': 61610}\n" + ] + } + ], "source": [ - "# 初始化网络\n", - "lenet = paddle.vision.models.LeNet(num_classes=10)" + "# 模型组网并初始化网络\n", + "lenet = paddle.vision.models.LeNet(num_classes=10)\n", + "\n", + "# 可视化模型组网结构和参数\n", + "params_info = paddle.summary(lenet,(1, 1, 28, 28))\n", + "print(params_info)" ] }, { @@ -200,7 +476,10 @@ "id": "67dfcc50", "metadata": {}, "source": [ - "如果希望查看更多关于模型组网的内容,可以参考xxx。" + "通过飞桨的 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html) 和 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html) API 可以更灵活方便的组建自定义的神经网络,详细使用方法可参考『模型组网』章节。\n", + "\n", + "更多参考:\n", + "* [模型组网](04_model_cn.html)" ] }, { @@ -208,60 +487,95 @@ "id": "4902817f", "metadata": {}, "source": [ - "### 模型训练评估\n", + "### 3.3 模型训练评估\n", + "\n", + "#### 3.3.1 模型训练\n", + "\n", + "模型训练需完成如下步骤:\n", + "\n", + "1. **使用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用[飞桨高层 API ](http://)进行训练、评估、推理的实例,方便后续操作。\n", + "2. **使用 [paddle.Model.prepare](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", + "3. **使用 [paddle.Model.fit](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", "\n", - "#### 模型训练\n", "\n", - "在训练模型前,需要配置训练模型时的损失函数与优化方法,因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Accuracy_cn.html#accuracy) 指标来计算网络在训练集上的精度。在训练过程中可以使用飞桨框架高层API- [paddle.Model.fit()](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 来自动完成模型的训练循环,具体代码如下。" + "因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Accuracy_cn.html#accuracy) (精度)指标来计算模型在训练集上的精度。" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "3333a7bb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-17T04:01:43.380962Z", + "iopub.status.busy": "2021-12-17T04:01:43.380575Z", + "iopub.status.idle": "2021-12-17T04:03:17.852495Z", + "shell.execute_reply": "2021-12-17T04:03:17.851918Z", + "shell.execute_reply.started": "2021-12-17T04:01:43.380928Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", - "Epoch 1/5\n", - "step 938/938 [==============================] - loss: 0.0312 - acc: 0.9869 - 16ms/step \n", + "Epoch 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " return (isinstance(seq, collections.Sequence) and\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 938/938 [==============================] - loss: 0.1031 - acc: 0.9476 - 20ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0414 - acc: 0.9889 - 16ms/step \n", + "step 938/938 [==============================] - loss: 0.0154 - acc: 0.9794 - 20ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 2.4796e-04 - acc: 0.9904 - 16ms/step \n", + "step 938/938 [==============================] - loss: 0.0077 - acc: 0.9837 - 20ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 9.6802e-04 - acc: 0.9919 - 16ms/step \n", + "step 938/938 [==============================] - loss: 0.0113 - acc: 0.9860 - 21ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.0047 - acc: 0.9922 - 16ms/step \n" + "step 938/938 [==============================] - loss: 0.0537 - acc: 0.9874 - 21ms/step \n" ] } ], "source": [ - "# 初始化paddle.Model模型,便于进行后续的配置、训练和验证\n", + "# 封装模型,便于进行后续的训练、评估和推理\n", "model = paddle.Model(lenet)\n", "\n", - "# 模型训练相关配置,准备损失计算方法,优化器和精度计算方法\n", + "# 模型训练的配置准备,准备损失函数,优化器和评价指标\n", "model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), \n", " paddle.nn.CrossEntropyLoss(),\n", " paddle.metric.Accuracy())\n", "\n", - "# 开始模型\n", + "# 开始训练\n", "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)" ] }, + { + "cell_type": "markdown", + "id": "351c2504-9392-4480-b522-f3435c4d58dc", + "metadata": {}, + "source": [ + "从训练过程的打印日志中,可观察到损失函数值 loss 逐渐变小,精度 acc 逐渐上升的趋势,反映出不错的训练效果。" + ] + }, { "cell_type": "markdown", "id": "684e7be6", "metadata": {}, "source": [ - "#### 模型评估\n", - "\n", - "模型训练完成之后,可以使用预先定义的测试数据集来评估训练得到的模型的精度。评估完成会输出模型在测试集上的loss和精度。\n", + "#### 3.3.2 模型评估\n", "\n", - "可以看到,初步训练得到的模型精度在98%附近,在逐渐了解飞桨后,可以通过调整其中的训练参数来提升模型的精度。\n" + "模型训练完成之后,调用 [paddle.Model.evaluate](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) ,使用预先定义的测试数据集,来评估训练好的模型效果,评估完成后将输出模型在测试集上的损失函数值 loss 和精度 acc。\n" ] }, { @@ -300,7 +614,10 @@ "id": "94c4a7af", "metadata": {}, "source": [ - "如果希望查看更多模型训练与评估的内容,可以参考xxx。" + "从结果可以看到,初步训练得到的模型精度在98%附近,在逐渐熟悉深度学习模型开发和训练技巧后,可以通过调整其中的训练参数来进一步提升模型的精度。\n", + "\n", + "更多参考:\n", + "* [模型训练与评估](http://)xxxxxxx" ] }, { @@ -308,14 +625,8 @@ "id": "e991757c", "metadata": {}, "source": [ - "### 模型预测\n", - "\n", - "在普遍的离线预测场景下,完成训练完成后,需要将模型进行保存,然后再进行模型加载之后进行预测。因此,本次的推理过程如下:\n", - "\n", - "1. 保存模型\n", - "1. 加载模型\n", - "2. 从测试集中选择一张图片作为输入\n", - "3. 进行推理并输出结果\n" + "### 3.4 模型推理\n", + "\n" ] }, { @@ -323,14 +634,26 @@ "id": "39ee250a", "metadata": {}, "source": [ - "模型训练完成之后,可以通过如下命令进行保存,在下面的命令中,output为模型保存的文件夹名称,minst为模型保存的文件名称" + "#### 3.4.1 模型保存\n", + "\n", + "模型训练完成后,通常需要将训练好的模型参数和优化器等信息,持久化保存到参数文件中,便于后续执行推理验证。\n", + "\n", + "在飞桨中可通过调用 [paddle.Model.save](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#save-path-training-true) 保存模型。代码示例如下,其中 output 为模型保存的文件夹名称,minst 为保存的模型文件名称。" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "07bff4b4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-17T04:11:42.623618Z", + "iopub.status.busy": "2021-12-17T04:11:42.622719Z", + "iopub.status.idle": "2021-12-17T04:11:42.637039Z", + "shell.execute_reply": "2021-12-17T04:11:42.636284Z", + "shell.execute_reply.started": "2021-12-17T04:11:42.623579Z" + } + }, "outputs": [], "source": [ "# 保存模型,文件夹会自动创建\n", @@ -342,13 +665,24 @@ "id": "0daaf2e3", "metadata": {}, "source": [ - "模型保存会在`output`目录下保存两个文件,`mnist.pdopt`为优化器的参数,`mnist.pdparams`为网络的参数\n", + "以上代码执行后会在`output`目录下保存两个文件,`mnist.pdopt`为优化器的参数,`mnist.pdparams`为模型的参数\n", "```bash\n", "output\n", "├── mnist.pdopt # 优化器的参数\n", - "└── mnist.pdparams # 网络的参数\n", - "```\n", - "模型保存完成后,通过下面的命令进行模型加载和预测" + "└── mnist.pdparams # 模型的参数\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2d87664-6b04-4969-8af4-02f57356e1d4", + "metadata": {}, + "source": [ + "#### 3.4.2 模型加载并执行推理\n", + "\n", + "执行模型推理时,可调用 [paddle.Model.load](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可通过调用 [paddle.Model.predict_batch](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", + "\n", + "如下示例中,针对前面创建的 `model` 实例加载保存的参数文件 `output/mnist`,并选择测试集中的一张图片 `test_dataset[0]` 作为输入,执行推理并打印结果,可以看到推理的结果与可视化图片一致。\n" ] }, { @@ -409,17 +743,38 @@ "id": "54a041fd", "metadata": {}, "source": [ - "如果希望查看更多模型保存与加载的内容,可以参考xxx;\n", + "更多参考:\n", + "* [模型保存与加载](http://)xxxxxxx\n", + "* [模型推理](http://)xxxxxxx" + ] + }, + { + "cell_type": "markdown", + "id": "07dc4cc1-4f8e-439c-a7e5-ce9a8c8d014f", + "metadata": {}, + "source": [ + "## 四、总结\n", + "\n", + "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "如果希望查看更多模型预测的内容,可以参考xxxx。\n", + "
\n", + "

图1:模型开发流程
\n", "\n", - "至此你就通过飞桨几个简单的API完成了一个深度学习任务,你也可以针对自己的需求来更换其中的代码,比如对数据集使用更多的数据增强、使用更大的 CNN 模型和自定义的神经网络等,飞桨官网提供了丰富的教程与案例可供参考" + "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "852da582", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -433,8 +788,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" - } + "version": "3.8.8" + }, + "toc-autonumbering": false, + "toc-showcode": false, + "toc-showmarkdowntxt": false, + "toc-showtags": false }, "nbformat": 4, "nbformat_minor": 5 diff --git a/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png b/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png new file mode 100644 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z8XStM&|3yjmcpI?l;%KF{-Dl)wS~|#bm;v~&&Nv>;0N+a=DO_fi|4fPSB0R*WX9T@|d6 z_d@^zU~m^g+p?kVWkyqzh3Q^7>^1I-77;$w^l5e|g<$zl-Se4Krh67yZ$@upo$lZI zKEBcvzgDuZz?{6Tw>Fr*)!%9n?qY&ohBv~-%jkOkwakC8pKBMhYaWVqwhY$2k_$cd zsJGz%l=@3Y)M^o`tQW(^9~l>X`Zn3k5vkX#btNXp4BrT_h$l{)0?N%jPp92Z|HVsC zrgEGFqvyr3)MM_o@F{76Xs-R}#`~J|Wb_4U#zUptEXjLWZAx+lbOJDf_~NsyJKI2?XhSyT1HZF!%HNRp*`#7R)%vXqHZ1BzHninJUnw;D zzG^FJ=O(rli8qFK8}`!(OCHW_Z)Ut{zyDmHQ}qU9svvF+9G?4j`IKJwr>|XS0*wo0 zfu>APsOS<+rB>}JAm(pyqUBYt*79!fG&H;wurZ=D$wv9xk zK65Qp8I|p)kr_j6Iv_SY?xLdn_{~x=axi{>vD^ zSbf}g{T-)<%*^NzOfYKiZogOnBk?#Hh0^pN9R_U0{#t~|A=KFZV45tkng7ZEEbwo% dK#2`(_MB9w@nHcx`u-}gIoLQ^vkv&i{{i-H#svTX literal 0 HcmV?d00001 From bdecb29aca45fca27095e67c1eda119862a0d285 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 20 Dec 2021 17:08:18 +0800 Subject: [PATCH 22/63] update 01 --- .../01_quick_start_cn.ipynb | 165 +++--------------- 1 file changed, 23 insertions(+), 142 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 06d45fb7b71..5ce0aa86e36 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "e7d9b5cf-fffe-4086-b84d-3f58eee2f602", "metadata": { "execution": { @@ -28,27 +28,7 @@ "shell.execute_reply.started": "2021-12-20T07:51:58.099995Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", - "Requirement already satisfied: paddlepaddle in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.1)\n", - "Requirement already satisfied: requests>=2.20.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (2.22.0)\n", - "Requirement already satisfied: astor in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (0.8.1)\n", - "Requirement already satisfied: Pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (7.1.2)\n", - "Requirement already satisfied: numpy>=1.13; python_version >= \"3.5\" and platform_system != \"Windows\" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (1.16.4)\n", - "Requirement already satisfied: protobuf>=3.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (3.14.0)\n", - "Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (1.15.0)\n", - "Requirement already satisfied: decorator in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle) (4.4.2)\n", - "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (1.25.6)\n", - "Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (2.8)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (2019.9.11)\n", - "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle) (3.0.4)\n" - ] - } - ], + "outputs": [], "source": [ "# 使用 pip 工具安装飞桨 CPU 版\n", "! python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple" @@ -116,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "0b50ba8c-93dd-42d9-8b52-35025ecb122b", "metadata": { "execution": { @@ -127,24 +107,7 @@ "shell.execute_reply.started": "2021-12-16T06:31:04.356403Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", - "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.3)\n", - "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.16.4)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.8.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.4.2)\n", - "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.15.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n", - "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2019.3)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n", - "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (41.4.0)\n" - ] - } - ], + "outputs": [], "source": [ "# 使用 pip 工具安装 matplotlib 和 numpy\n", "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple" @@ -289,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "f99c914f", "metadata": { "execution": { @@ -301,74 +264,6 @@ } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 48/2421 [..............................] - ETA: 5s - 2ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz \n", - "Begin to download\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 8/8 [============================>.] - ETA: 0s - 2ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Download finished\n", - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz \n", - "Begin to download\n", - "\n", - "Download finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 39/403 [=>............................] - ETA: 1s - 3ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz \n", - "Begin to download\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 2/2 [===========================>..] - ETA: 0s - 2ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Download finished\n", - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz \n", - "Begin to download\n", - "\n", - "Download finished\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -419,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "e8bf3841", "metadata": { "execution": { @@ -438,15 +333,15 @@ "---------------------------------------------------------------------------\n", " Layer (type) Input Shape Output Shape Param # \n", "===========================================================================\n", - " Conv2D-13 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", - " ReLU-13 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", - " MaxPool2D-13 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", - " Conv2D-14 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", - " ReLU-14 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", - " MaxPool2D-14 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Linear-19 [[1, 400]] [1, 120] 48,120 \n", - " Linear-20 [[1, 120]] [1, 84] 10,164 \n", - " Linear-21 [[1, 84]] [1, 10] 850 \n", + " Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-1 [[1, 400]] [1, 120] 48,120 \n", + " Linear-2 [[1, 120]] [1, 84] 10,164 \n", + " Linear-3 [[1, 84]] [1, 10] 850 \n", "===========================================================================\n", "Total params: 61,610\n", "Trainable params: 61,610\n", @@ -503,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "3333a7bb", "metadata": { "execution": { @@ -520,30 +415,16 @@ "output_type": "stream", "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", - "Epoch 1/5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", - " return (isinstance(seq, collections.Sequence) and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 938/938 [==============================] - loss: 0.1031 - acc: 0.9476 - 20ms/step \n", + "Epoch 1/5\n", + "step 938/938 [==============================] - loss: 0.0021 - acc: 0.9887 - 14ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0154 - acc: 0.9794 - 20ms/step \n", + "step 938/938 [==============================] - loss: 0.0044 - acc: 0.9904 - 14ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 0.0077 - acc: 0.9837 - 20ms/step \n", + "step 938/938 [==============================] - loss: 0.0011 - acc: 0.9913 - 14ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 0.0113 - acc: 0.9860 - 21ms/step \n", + "step 938/938 [==============================] - loss: 0.0193 - acc: 0.9919 - 14ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.0537 - acc: 0.9874 - 21ms/step \n" + "step 938/938 [==============================] - loss: 0.0173 - acc: 0.9934 - 14ms/step \n" ] } ], @@ -766,7 +647,7 @@ { "cell_type": "code", "execution_count": null, - "id": "852da582", + "id": "6f4fffdc", "metadata": {}, "outputs": [], "source": [] From 158d8e7eebaa2fbe4b48cee22a574c4201d53558 Mon Sep 17 00:00:00 2001 From: WangChen0902 <827913668@qq.com> Date: Thu, 23 Dec 2021 08:11:03 +0000 Subject: [PATCH 23/63] modify 05 06 07 --- .../05_train_eval_predict_cn.ipynb | 505 +++++++++++++++ .../05_train_eval_predict_cn.rst | 276 -------- .../02_paddle2.0_develop/06_device_cn.ipynb | 286 ++++++++ .../02_paddle2.0_develop/06_device_cn.rst | 187 ------ .../07_customize_cn.ipynb | 611 ++++++++++++++++++ .../02_paddle2.0_develop/07_customize_cn.rst | 241 ------- 6 files changed, 1402 insertions(+), 704 deletions(-) create mode 100644 docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb delete mode 100644 docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst create mode 100644 docs/guides/02_paddle2.0_develop/06_device_cn.ipynb delete mode 100644 docs/guides/02_paddle2.0_develop/06_device_cn.rst create mode 100644 docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb delete mode 100644 docs/guides/02_paddle2.0_develop/07_customize_cn.rst diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb new file mode 100644 index 00000000000..51454ad2826 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f97a8a96", + "metadata": {}, + "source": [ + "# 训练与预测验证\n", + "\n", + "在完成数据预处理,数据加载与模型的组建后,你就可以进行模型的训练与预测了。飞桨主框架提供了两种训练与预测的方法,一种是用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 对模型进行封装,通过高层API如 [Model.fit](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 、 [Model.evaluate](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 、 [Model.predict](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 等完成模型的训练与预测;另一种就是基于基础API常规的训练方式。\n", + "\n", + "高层API实现的模型训练与预测如 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都可以通过基础API实现,本文先介绍高层API的训练方式,然后会将高层API拆解为基础API的方式,方便对比学习。\n", + "\n", + "## 一、训练前准备\n", + "\n", + "在封装模型前,需要先完成数据的加载,由于这一部分高层API与基础API通用,所以都可用下面的代码实现:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "86a3f26d", + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 加载数据集\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())" + ] + }, + { + "cell_type": "markdown", + "id": "d9d0367d", + "metadata": {}, + "source": [ + "通过上述的代码,你就完成了训练集与测试集的构建,下面分别用两种方式完成模型的训练与预测。\n", + "\n", + "## 二、通过 `paddle.Model` 训练与预测\n", + "\n", + "在这里你可以采用Sequential组网或者SubClass组网的方式来创建一个mnist网络模型,你可使用 `paddle.Model` 完成模型的封装,将网络结构组合成一个可快速使用高层API进行训练和预测的对象。代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a7705595", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W1222 09:27:56.863570 7417 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n", + "W1222 09:27:56.870544 7417 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n" + ] + } + ], + "source": [ + "mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1), \n", + " paddle.nn.Linear(784, 512), \n", + " paddle.nn.ReLU(), \n", + " paddle.nn.Dropout(0.2), \n", + " paddle.nn.Linear(512, 10)\n", + ")\n", + "\n", + "model = paddle.Model(mnist)" + ] + }, + { + "cell_type": "markdown", + "id": "4500460a", + "metadata": {}, + "source": [ + "### 2.1 用 `Model.prepare` 配置模型\n", + "\n", + "用 `paddle.Model` 完成模型的封装后,在训练前,需要对模型进行配置,通过 [Model.prepare](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 接口来对训练进行提前的配置准备工作,包括设置模型优化器,Loss计算方法,精度计算方法等。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ccefe291", + "metadata": {}, + "outputs": [], + "source": [ + "# 为模型训练做准备,设置优化器,损失函数和精度计算方式\n", + "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + " loss=paddle.nn.CrossEntropyLoss(), \n", + " metrics=paddle.metric.Accuracy())" + ] + }, + { + "cell_type": "markdown", + "id": "5da3322e", + "metadata": {}, + "source": [ + "### 2.2 用 `Model.fit` 训练模型\n", + "\n", + "做好模型训练的前期准备工作后,调用 `fit` 接口来启动训练过程,需要指定至少3个关键参数:训练数据集,训练轮次和单次训练数据批次大小。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "51021638", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", + "Epoch 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/python3.7.0/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " return (isinstance(seq, collections.Sequence) and\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 938/938 [==============================] - loss: 0.1663 - acc: 0.9299 - 32ms/step \n", + "Epoch 2/5\n", + "step 938/938 [==============================] - loss: 0.0393 - acc: 0.9689 - 32ms/step \n", + "Epoch 3/5\n", + "step 938/938 [==============================] - loss: 0.0341 - acc: 0.9774 - 32ms/step \n", + "Epoch 4/5\n", + "step 938/938 [==============================] - loss: 0.0118 - acc: 0.9827 - 32ms/step \n", + "Epoch 5/5\n", + "step 938/938 [==============================] - loss: 0.1354 - acc: 0.9865 - 33ms/step \n" + ] + } + ], + "source": [ + "# 启动模型训练,指定训练数据集,设置训练轮次,设置每次数据集计算的批次大小,设置日志格式\n", + "model.fit(train_dataset, \n", + " epochs=5, \n", + " batch_size=64,\n", + " verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "eeff5f00", + "metadata": {}, + "source": [ + "### 2.3 用 `Model.evaluate` 评估模型\n", + "\n", + "对于训练好的模型进行评估可以使用 `evaluate` 接口,事先定义好用于评估使用的数据集后,直接调用 `evaluate` 接口即可完成模型评估操作,结束后根据在 `prepare` 中 `loss` 和 `metric` 的定义来进行相关评估结果计算返回。\n", + "\n", + "返回格式是一个字典:\n", + "* 只包含loss, `{'loss': xxx}` \n", + "* 包含loss和一个评估指标, `{'loss': xxx, 'metric name': xxx}` \n", + "* 包含loss和多个评估指标, `{'loss': xxx, 'metric name1': xxx, 'metric name2': xxx}` " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "70f670ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval begin...\n", + "step 10000/10000 [==============================] - loss: 4.7684e-07 - acc: 0.9816 - 3ms/step \n", + "Eval samples: 10000\n", + "{'loss': [4.7683704e-07], 'acc': 0.9816}\n" + ] + } + ], + "source": [ + "# 用 evaluate 在测试集上对模型进行验证\n", + "eval_result = model.evaluate(test_dataset, verbose=1)\n", + "print(eval_result)" + ] + }, + { + "cell_type": "markdown", + "id": "109ac763", + "metadata": {}, + "source": [ + "### 2.4 用 `Model.predict` 预测模型\n", + "\n", + "高层API中提供了 `predict` 接口来方便用户对训练好的模型进行预测验证,只需要基于训练好的模型将需要进行预测测试的数据放到接口中进行计算即可,接口会将经过模型计算得到的预测结果进行返回。\n", + "\n", + "返回格式是一个list,元素数目对应模型的输出数目:\n", + "* 模型是单一输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)]`\n", + "* 模型是多输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), …]`\n", + "\n", + "numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d6318f18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predict begin...\n", + "step 10000/10000 [==============================] - 2ms/step \n", + "Predict samples: 10000\n", + "true label: 7, pred label: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 用 predict 在测试集上对模型进行测试\n", + "test_result = model.predict(test_dataset)\n", + "\n", + "# 从测试集中取出一张图片\n", + "img, label = test_dataset[0]\n", + "\n", + "# 执行推理并打印结果\n", + "pred_label = test_result[0][0].argmax()\n", + "print('true label: {}, pred label: {}'.format(label[0], pred_label))\n", + "# 可视化图片\n", + "from matplotlib import pyplot as plt\n", + "plt.imshow(img[0])" + ] + }, + { + "cell_type": "markdown", + "id": "9508034b", + "metadata": {}, + "source": [ + "## 三、通过基础API实现模型的训练与预测\n", + "\n", + "除了通过第一部分的高层API实现模型的训练与预测,飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础API封装而来。下面通过拆解高层API到基础API的方式,来了解如何用基础API完成模型的训练与预测。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "da17af7e", + "metadata": {}, + "outputs": [], + "source": [ + "# 定义网络结构( 采用SubClass 组网 )\n", + "class Mnist(paddle.nn.Layer):\n", + " def __init__(self):\n", + " super(Mnist, self).__init__()\n", + " self.flatten = paddle.nn.Flatten()\n", + " self.linear_1 = paddle.nn.Linear(784, 512)\n", + " self.linear_2 = paddle.nn.Linear(512, 10)\n", + " self.relu = paddle.nn.ReLU()\n", + " self.dropout = paddle.nn.Dropout(0.2)\n", + " \n", + " def forward(self, inputs):\n", + " y = self.flatten(inputs)\n", + " y = self.linear_1(y)\n", + " y = self.relu(y)\n", + " y = self.dropout(y)\n", + " y = self.linear_2(y)\n", + " return y" + ] + }, + { + "cell_type": "markdown", + "id": "c02524fd", + "metadata": {}, + "source": [ + "### 3.1 拆解 `Model.prepare` 、 `Model.fit` -- 用基础API训练模型\n", + "\n", + "飞桨框架通过基础API对模型进行训练与预测,对应第一部分的 `Model.prepare` 与 `Model.fit` :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8419b510", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0, batch_id: 900, loss is: [0.03877499], acc is: [1.]\n", + "epoch: 1, batch_id: 900, loss is: [0.04977579], acc is: [0.984375]\n", + "epoch: 2, batch_id: 900, loss is: [0.01578258], acc is: [1.]\n", + "epoch: 3, batch_id: 900, loss is: [0.10209924], acc is: [0.96875]\n", + "epoch: 4, batch_id: 900, loss is: [0.04281481], acc is: [1.]\n" + ] + } + ], + "source": [ + "# dataset与mnist的定义与第一部分内容一致\n", + "# 用 DataLoader 实现数据加载\n", + "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)\n", + "\n", + "mnist=Mnist()\n", + "mnist.train()\n", + "\n", + "# 设置迭代次数\n", + "epochs = 5\n", + "\n", + "# 设置优化器\n", + "optim = paddle.optimizer.Adam(parameters=mnist.parameters())\n", + "# 设置损失函数\n", + "loss_fn = paddle.nn.CrossEntropyLoss()\n", + "for epoch in range(epochs):\n", + " for batch_id, data in enumerate(train_loader()):\n", + " \n", + " x_data = data[0] # 训练数据\n", + " y_data = data[1] # 训练数据标签\n", + " predicts = mnist(x_data) # 预测结果 \n", + " \n", + " # 计算损失 等价于 prepare 中loss的设置\n", + " loss = loss_fn(predicts, y_data)\n", + " \n", + " # 计算准确率 等价于 prepare 中metrics的设置\n", + " acc = paddle.metric.accuracy(predicts, y_data)\n", + " \n", + " # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中\n", + " # 反向传播 \n", + " loss.backward()\n", + " \n", + " if (batch_id+1) % 900 == 0:\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id+1, loss.numpy(), acc.numpy()))\n", + " # 更新参数 \n", + " optim.step()\n", + " # 梯度清零\n", + " optim.clear_grad()" + ] + }, + { + "cell_type": "markdown", + "id": "00a077d3", + "metadata": {}, + "source": [ + "### 3.2 拆解 `Model.evaluate` -- 用基础API验证模型\n", + "\n", + "飞桨框架通过基础API对模型进行验证,对应第一部分的 `Model.evaluate` :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d27f6ec2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "batch_id: 30, loss is: [0.12935154], acc is: [0.953125]\n", + "batch_id: 60, loss is: [0.19010888], acc is: [0.921875]\n", + "batch_id: 90, loss is: [0.07307276], acc is: [0.984375]\n", + "batch_id: 120, loss is: [0.01087341], acc is: [1.]\n", + "batch_id: 150, loss is: [0.11148524], acc is: [0.984375]\n" + ] + } + ], + "source": [ + "# 加载测试数据集\n", + "test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True)\n", + "loss_fn = paddle.nn.CrossEntropyLoss()\n", + "# 将该模型及其所有子层设置为预测模式。这只会影响某些模块,如Dropout和BatchNorm\n", + "mnist.eval()\n", + "# 禁用动态图梯度计算\n", + "for batch_id, data in enumerate(test_loader()):\n", + " \n", + " x_data = data[0] # 测试数据\n", + " y_data = data[1] # 测试数据标签\n", + " predicts = mnist(x_data) # 预测结果\n", + " \n", + " # 计算损失与精度\n", + " loss = loss_fn(predicts, y_data)\n", + " acc = paddle.metric.accuracy(predicts, y_data)\n", + " \n", + " # 打印信息\n", + " if (batch_id+1) % 30 == 0:\n", + " print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id+1, loss.numpy(), acc.numpy()))" + ] + }, + { + "cell_type": "markdown", + "id": "214cc6de", + "metadata": {}, + "source": [ + "### 3.3 拆解 `Model.predict` -- 用基础API测试模型\n", + "\n", + "飞桨框架通过基础API对模型进行测试,对应第一部分的 `Model.predict` :" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1d79305f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predict finished\n", + "true label: 7, pred label: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 加载测试数据集\n", + "test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True)\n", + "mnist.eval()\n", + "for batch_id, data in enumerate(test_loader()):\n", + " x_data = data[0] \n", + " predicts = mnist(x_data)\n", + " # 获取预测结果\n", + "print(\"predict finished\")\n", + "\n", + "# 从测试集中取出一组数据\n", + "img, label = test_loader().next()\n", + "\n", + "# 执行推理并打印结果\n", + "pred_label = mnist(img)[0].argmax()\n", + "print('true label: {}, pred label: {}'.format(label[0].item(), pred_label[0].item()))\n", + "# 可视化图片\n", + "from matplotlib import pyplot as plt\n", + "plt.imshow(img[0][0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst deleted file mode 100644 index 3c2182c9b33..00000000000 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst +++ /dev/null @@ -1,276 +0,0 @@ -.. _cn_doc_train_eval_predict: - -训练与预测验证 -===================== - -在完成数据预处理,数据加载与模型的组建后,你就可以进行模型的训练与预测了。飞桨主框架提供了两种训练与预测的方法,一种是用\ ``paddle.Model``\ 对模型进行封装,通过高层API如\ ``Model.fit()、Model.evaluate()、Model.predict()``\ 等完成模型的训练与预测;另一种就是基于基础API常规的训练方式。 - -.. note:: - - 高层API实现的模型训练与预测如\ ``Model.fit()、Model.evaluate()、Model.predict()``\ 都可以通过基础API实现,本文先介绍高层API的训练方式,然后会将高层API拆解为基础API的方式,方便对比学习。 - -一、训练前准备 ---------------------- - -在封装模型前,需要先完成数据的加载,由于这一部分高层API与基础API通用,所以都可用下面的代码实现: - -.. code:: ipython3 - - import paddle - from paddle.vision.transforms import ToTensor - - # 加载数据集 - train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) - test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) - - -通过上述的代码,你就完成了训练集与测试集的构建,下面分别用两种方式完成模型的训练与预测。 - -二、通过\ ``paddle.Model``\ 训练与预测 ------------------------------------- - -在这里你可以采用Sequential组网或者SubClass 组网的方式来创建一个mnist网络模型,你可使用\ ``paddle.Model``\ 完成模型的封装,将网络结构组合成一个可快速使用高层API进行训练和预测的对象。代码如下: - -.. code:: ipython3 - - # 定义网络结构(采用 Sequential组网方式 ) - mnist = paddle.nn.Sequential( - paddle.nn.Flatten(1, -1), - paddle.nn.Linear(784, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.2), - paddle.nn.Linear(512, 10) - ) - - - model = paddle.Model(mnist) - -2.1 用\ ``Model.prepare()``\ 配置模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -用\ ``paddle.Model``\ 完成模型的封装后,在训练前,需要对模型进行配置,通过\ ``Model.prepare``\ 接口来对训练进行提前的配置准备工作,包括设置模型优化器,Loss计算方法,精度计算方法等。 - -.. code:: ipython3 - - # 为模型训练做准备,设置优化器,损失函数和精度计算方式 - model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), - loss=paddle.nn.CrossEntropyLoss(), - metrics=paddle.metric.Accuracy()) - -2.2 用\ ``Model.fit()``\ 训练模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -做好模型训练的前期准备工作后,调用\ ``fit()``\ 接口来启动训练过程,需要指定至少3个关键参数:训练数据集,训练轮次和单次训练数据批次大小。 - -.. code:: ipython3 - - # 启动模型训练,指定训练数据集,设置训练轮次,设置每次数据集计算的批次大小,设置日志格式 - model.fit(train_dataset, - epochs=5, - batch_size=64, - verbose=1) - - -.. parsed-literal:: - - The loss value printed in the log is the current step, and the metric is the average value of previous step. - Epoch 1/5 - step 938/938 [==============================] - loss: 0.1785 - acc: 0.9281 - 19ms/step - Epoch 2/5 - step 938/938 [==============================] - loss: 0.0365 - acc: 0.9688 - 19ms/step - Epoch 3/5 - step 938/938 [==============================] - loss: 0.0757 - acc: 0.9781 - 19ms/step - Epoch 4/5 - step 938/938 [==============================] - loss: 0.0054 - acc: 0.9824 - 19ms/step - Epoch 5/5 - step 938/938 [==============================] - loss: 0.0640 - acc: 0.9858 - 19ms/step - -2.3 用\ ``Model.evaluate()``\ 评估模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -对于训练好的模型进行评估可以使用\ ``evaluate``\ 接口,事先定义好用于评估使用的数据集后,直接调用\ ``evaluate``\ 接口即可完成模型评估操作,结束后根据在\ ``prepare``\ 中\ ``loss``\ 和\ ``metric``\ 的定义来进行相关评估结果计算返回。 - -返回格式是一个字典: \* 只包含loss,\ ``{'loss': xxx}`` \* -包含loss和一个评估指标,\ ``{'loss': xxx, 'metric name': xxx}`` \* -包含loss和多个评估指标,\ ``{'loss': xxx, 'metric name1': xxx, 'metric name2': xxx}`` - -.. code:: ipython3 - - # 用 evaluate 在测试集上对模型进行验证 - eval_result = model.evaluate(test_dataset, verbose=1) - - -.. parsed-literal:: - - Eval begin... - The loss value printed in the log is the current batch, and the metric is the average value of previous step. - step 10000/10000 [==============================] - loss: 3.5763e-07 - acc: 0.9809 - 2ms/step - Eval samples: 10000 - -2.4 用\ ``Model.predict()``\ 预测模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -高层API中提供了\ ``predict``\ 接口来方便用户对训练好的模型进行预测验证,只需要基于训练好的模型将需要进行预测测试的数据放到接口中进行计算即可,接口会将经过模型计算得到的预测结果进行返回。 - -返回格式是一个list,元素数目对应模型的输出数目: \* -模型是单一输出:[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)] -\* 模型是多输出:[(numpy_ndarray_1, numpy_ndarray_2, …, -numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, -numpy_ndarray_n), …] - -numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。 - -.. code:: ipython3 - - # 用 predict 在测试集上对模型进行测试 - test_result = model.predict(test_dataset) - -.. parsed-literal:: - - Predict begin... - step 10000/10000 [==============================] - 2ms/step - Predict samples: 10000 - - -三、通过基础API实现模型的训练与预测 ------------------------------------------ - -除了通过第一部分的高层API实现模型的训练与预测,飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说,\ ``Model.prepare()、Model.fit()、Model.evaluate()、Model.predict()``\ 都是由基础API封装而来。下面通过拆解高层API到基础API的方式,来了解如何用基础API完成模型的训练与预测。 - -.. code:: ipython3 - - # 定义网络结构( 采用SubClass 组网 ) - class Mnist(paddle.nn.Layer): - def __init__(self): - super(Mnist, self).__init__() - self.flatten = paddle.nn.Flatten() - self.linear_1 = paddle.nn.Linear(784, 512) - self.linear_2 = paddle.nn.Linear(512, 10) - self.relu = paddle.nn.ReLU() - self.dropout = paddle.nn.Dropout(0.2) - - def forward(self, inputs): - y = self.flatten(inputs) - y = self.linear_1(y) - y = self.relu(y) - y = self.dropout(y) - y = self.linear_2(y) - return y - - -3.1 拆解\ ``Model.prepare()、Model.fit()``\ -- 用基础API训练模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -飞桨框架通过基础API对模型进行训练与预测,对应第一部分的\ ``Model.prepare()``\ 与\ ``Model.fit()``\ : - -.. code:: ipython3 - - # dataset与mnist的定义与第一部分内容一致 - - # 用 DataLoader 实现数据加载 - train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) - - mnist=Mnist() - mnist.train() - - # 设置迭代次数 - epochs = 5 - - # 设置优化器 - optim = paddle.optimizer.Adam(parameters=mnist.parameters()) - # 设置损失函数 - loss_fn = paddle.nn.CrossEntropyLoss() - - for epoch in range(epochs): - for batch_id, data in enumerate(train_loader()): - - x_data = data[0] # 训练数据 - y_data = data[1] # 训练数据标签 - predicts = mnist(x_data) # 预测结果 - - # 计算损失 等价于 prepare 中loss的设置 - loss = loss_fn(predicts, y_data) - - # 计算准确率 等价于 prepare 中metrics的设置 - acc = paddle.metric.accuracy(predicts, y_data) - - # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中 - - # 反向传播 - loss.backward() - - if (batch_id+1) % 900 == 0: - print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id+1, loss.numpy(), acc.numpy())) - - # 更新参数 - optim.step() - - # 梯度清零 - optim.clear_grad() - - -.. parsed-literal:: - - epoch: 0, batch_id: 900, loss is: [0.29550618], acc is: [0.90625] - epoch: 1, batch_id: 900, loss is: [0.05875912], acc is: [0.984375] - epoch: 2, batch_id: 900, loss is: [0.05824642], acc is: [0.96875] - epoch: 3, batch_id: 900, loss is: [0.02940615], acc is: [1.] - epoch: 4, batch_id: 900, loss is: [0.05713747], acc is: [0.984375] - -3.2 拆解\ ``Model.evaluate()``\ -- 用基础API验证模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -飞桨框架通过基础API对模型进行验证,对应第一部分的\ ``Model.evaluate()``\ : - -.. code:: ipython3 - - # 加载测试数据集 - test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True) - loss_fn = paddle.nn.CrossEntropyLoss() - - mnist.eval() - - for batch_id, data in enumerate(test_loader()): - - x_data = data[0] # 测试数据 - y_data = data[1] # 测试数据标签 - predicts = mnist(x_data) # 预测结果 - - # 计算损失与精度 - loss = loss_fn(predicts, y_data) - acc = paddle.metric.accuracy(predicts, y_data) - - # 打印信息 - if (batch_id+1) % 30 == 0: - print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id+1, loss.numpy(), acc.numpy())) - -.. parsed-literal:: - - batch_id: 30, loss is: [0.15860887], acc is: [0.953125] - batch_id: 60, loss is: [0.21005578], acc is: [0.921875] - batch_id: 90, loss is: [0.0889321], acc is: [0.953125] - batch_id: 120, loss is: [0.00115552], acc is: [1.] - batch_id: 150, loss is: [0.12016675], acc is: [0.984375] - - -3.3 拆解\ ``Model.predict()``\ -- 用基础API测试模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -飞桨框架通过基础API对模型进行测试,对应第一部分的\ ``Model.predict()``\ : - -.. code:: ipython3 - - # 加载测试数据集 - test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True) - - mnist.eval() - for batch_id, data in enumerate(test_loader()): - x_data = data[0] - predicts = mnist(x_data) - # 获取预测结果 - print("predict finished") - - -.. parsed-literal:: - - predict finished diff --git a/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb b/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb new file mode 100644 index 00000000000..200c953d629 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cd536a25", + "metadata": {}, + "source": [ + "# 单机多卡训练\n", + "\n", + "飞桨框架2.0增加 [paddle.distributed.spawn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/distributed/spawn_cn.html) 函数来启动单机多卡训练,同时原有的 [paddle.distributed.launch](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/distributed/launch_cn.html) 的方式依然保留。\n", + "\n", + "## 一、launch启动\n", + "\n", + "### 1.1 高层API场景\n", + "\n", + "当调用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 高层API来实现训练时,想要启动单机多卡训练非常简单,代码不需要做任何修改,只需要在启动时增加一下参数 `-m paddle.distributed.launch` 。\n", + "使用高层API的训练代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a2702a8", + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 加载数据集\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "\n", + "mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1), \n", + " paddle.nn.Linear(784, 512), \n", + " paddle.nn.ReLU(), \n", + " paddle.nn.Dropout(0.2), \n", + " paddle.nn.Linear(512, 10)\n", + ")\n", + "\n", + "model = paddle.Model(mnist)\n", + "\n", + "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + " loss=paddle.nn.CrossEntropyLoss(), \n", + " metrics=paddle.metric.Accuracy())\n", + "\n", + "model.fit(train_dataset, \n", + " epochs=5, \n", + " batch_size=64,\n", + " verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "847c28b9", + "metadata": {}, + "source": [ + "将上述代码保存为train.py,使用高层API启动多卡训练的命令如下:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3db288b6", + "metadata": {}, + "outputs": [], + "source": [ + "# 单机单卡启动,默认使用第0号卡\n", + "! python train.py\n", + "# 单机多卡启动,默认使用当前可见的所有卡\n", + "! python -m paddle.distributed.launch train.py\n", + "# 单机多卡启动,设置当前使用的第0号和第1号卡\n", + "! python -m paddle.distributed.launch --gpus='0,1' train.py\n", + "# 单机多卡启动,设置当前使用第0号和第1号卡\n", + "! export CUDA_VISIBLE_DEVICES=0,1\n", + "! python -m paddle.distributed.launch train.py" + ] + }, + { + "cell_type": "markdown", + "id": "17552b14", + "metadata": {}, + "source": [ + "### 1.2 基础API场景\n", + "\n", + "如果使用基础API实现现训练,想要启动单机多卡训练,需要对单机单卡的代码进行3处修改,具体如下:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4ebc36a", + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "from paddle.vision.transforms import ToTensor\n", + "# 第1处改动 导入分布式训练所需的包\n", + "import paddle.distributed as dist\n", + "# 加载数据集\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "# 第2处改动,初始化并行环境\n", + "dist.init_parallel_env()\n", + "\n", + "# 定义网络结构\n", + "mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1),\n", + " paddle.nn.Linear(784, 512),\n", + " paddle.nn.ReLU(),\n", + " paddle.nn.Dropout(0.2),\n", + " paddle.nn.Linear(512, 10)\n", + ")\n", + "# 用 DataLoader 实现数据加载\n", + "train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)\n", + "\n", + "# 第3处改动,增加paddle.DataParallel封装\n", + "mnist = paddle.DataParallel(mnist)\n", + "mnist.train()\n", + "# 设置迭代次数\n", + "epochs = 5\n", + "# 设置优化器\n", + "optim = paddle.optimizer.Adam(parameters=mnist.parameters())\n", + "for epoch in range(epochs):\n", + " for batch_id, data in enumerate(train_loader()):\n", + " x_data = data[0] # 训练数据\n", + " y_data = data[1] # 训练数据标签\n", + " predicts = mnist(x_data) # 预测结果\n", + " # 计算损失 等价于 prepare 中loss的设置\n", + " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", + " # 计算准确率 等价于 prepare 中metrics的设置\n", + " acc = paddle.metric.accuracy(predicts, y_data)\n", + " # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中\n", + " # 反向传播\n", + " loss.backward()\n", + " if (batch_id+1) % 1800 == 0:\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, loss.numpy(), acc.numpy()))\n", + " # 更新参数\n", + " optim.step()\n", + " # 梯度清零\n", + " optim.clear_grad()" + ] + }, + { + "cell_type": "markdown", + "id": "abaa5a5e", + "metadata": {}, + "source": [ + "修改完后保存文件为train.py,然后使用跟高层API相同的启动方式即可。\n", + "**注意:** 单卡训练不支持调用 `init_parallel_env` ,请使用以下几种方式进行分布式训练。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56786ff8", + "metadata": {}, + "outputs": [], + "source": [ + "# 单机多卡启动,默认使用当前可见的所有卡\n", + "! python -m paddle.distributed.launch train.py\n", + "# 单机多卡启动,设置当前使用的第0号和第1号卡\n", + "! python -m paddle.distributed.launch --gpus '0,1' train.py\n", + "# 单机多卡启动,设置当前使用第0号和第1号卡\n", + "! export CUDA_VISIBLE_DEVICES=0,1\n", + "! python -m paddle.distributed.launch train.py" + ] + }, + { + "cell_type": "markdown", + "id": "f5045368", + "metadata": {}, + "source": [ + "## 二、spawn启动\n", + "\n", + " `launch` 方式启动训练,以文件为单位启动多进程,需要用户在启动时调用 `paddle.distributed.launch` ,对于进程的管理要求较高。飞桨框架2.0版本增加了 `spawn` 启动方式,可以更好地控制进程,在日志打印、训练退出时更友好。使用示例如下:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbcdcdb", + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import paddle\n", + "import paddle.nn as nn\n", + "import paddle.optimizer as opt\n", + "import paddle.distributed as dist\n", + "\n", + "class LinearNet(nn.Layer):\n", + " def __init__(self):\n", + " super(LinearNet, self).__init__()\n", + " self._linear1 = nn.Linear(10, 10)\n", + " self._linear2 = nn.Linear(10, 1)\n", + "\n", + " def forward(self, x):\n", + " return self._linear2(self._linear1(x))\n", + "\n", + "def train(print_result=False):\n", + "\n", + " # 1. 初始化并行训练环境\n", + " dist.init_parallel_env()\n", + "\n", + " # 2. 创建并行训练 Layer 和 Optimizer\n", + " layer = LinearNet()\n", + " dp_layer = paddle.DataParallel(layer)\n", + "\n", + " loss_fn = nn.MSELoss()\n", + " adam = opt.Adam(\n", + " learning_rate=0.001, parameters=dp_layer.parameters())\n", + "\n", + " # 3. 运行网络\n", + " inputs = paddle.randn([10, 10], 'float32')\n", + " outputs = dp_layer(inputs)\n", + " labels = paddle.randn([10, 1], 'float32')\n", + " loss = loss_fn(outputs, labels)\n", + "\n", + " if print_result is True:\n", + " print(\"loss:\", loss.numpy())\n", + "\n", + " loss.backward()\n", + "\n", + " adam.step()\n", + " adam.clear_grad()\n", + "\n", + "# 传入训练函数、参数、指定进程数并指定当前使用的卡号\n", + "if __name__ == '__main__':\n", + " dist.spawn(train, args=(True,), nprocs=2, gpus='4,5')" + ] + }, + { + "cell_type": "raw", + "id": "ea540e08", + "metadata": {}, + "source": [ + "上述代码在本地运行结果如下:\n", + "init nccl context nranks: 2 local rank: 0 gpu id: 4 ring id: 0\n", + "init nccl context nranks: 2 local rank: 1 gpu id: 5 ring id: 0\n", + "Please NOTE: device: 5, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n", + "Please NOTE: device: 4, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n", + "device: 4, cuDNN Version: 7.6.\n", + "device: 5, cuDNN Version: 7.6.\n", + "loss: [2.041318]\n", + "loss: [4.749344]" + ] + }, + { + "cell_type": "markdown", + "id": "1e47c613", + "metadata": {}, + "source": [ + "调用 [paddle.distributed.spawn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/distributed/spawn_cn.html) 来启动多卡训练时,可根据需要设置参数:\n", + "* func:由 spawn 方法启动的进程所调用的目标函数。\n", + "* args:传入目标函数 func 的参数。\n", + "* nprocs:启动进程的数目。当仅需要使用部分可见的GPU设备进行训练时,可设置该参数指定GPU数。例如:当前机器有8张GPU卡 {0,1,2,3,4,5,6,7},此时会使用前两张卡 {0,1};或者当前机器通过配置环境变量 CUDA_VISIBLE_DEVICES=4,5,6,7,仅使4张GPU卡可见,此时会使用可见的前两张卡 {4,5}。若不设置该参数,默认使用所有可见的GPU设备训练。\n", + "* gpus:指定训练使用的GPU ID。例如 gpus='4,5' 可指定使用第4号卡和第5号卡。若不设置该参数,默认使用GPU ID序号较小的GPU。" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/06_device_cn.rst b/docs/guides/02_paddle2.0_develop/06_device_cn.rst deleted file mode 100644 index a86d9a43769..00000000000 --- a/docs/guides/02_paddle2.0_develop/06_device_cn.rst +++ /dev/null @@ -1,187 +0,0 @@ -.. _cn_doc_device: - -单机多卡训练 -================== - -飞桨框架2.0增加\ ``paddle.distributed.spawn``\ 函数来启动单机多卡训练,同时原有的\ ``paddle.distributed.launch``\ 的方式依然保留。 - -一、launch启动 ---------------------- - -1.1 高层API场景 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -当调用\ ``paddle.Model``\高层API来实现训练时,想要启动单机多卡训练非常简单,代码不需要做任何修改,只需要在启动时增加一下参数\ ``-m paddle.distributed.launch``\ 。 - -.. code:: bash - - # 单机单卡启动,默认使用第0号卡 - $ python train.py - - # 单机多卡启动,默认使用当前可见的所有卡 - $ python -m paddle.distributed.launch train.py - - # 单机多卡启动,设置当前使用的第0号和第1号卡 - $ python -m paddle.distributed.launch --gpus='0,1' train.py - - # 单机多卡启动,设置当前使用第0号和第1号卡 - $ export CUDA_VISIBLE_DEVICES=0,1 - $ python -m paddle.distributed.launch train.py - -1.2 基础API场景 -~~~~~~~~~~~~~~~~~~ - -如果使用基础API实现训练,想要启动单机多卡训练,需要对单机单卡的代码进行3处修改,具体如下: - -.. code:: python3 - - import paddle - # 第1处改动 导入分布式训练所需的包 - import paddle.distributed as dist - - # 加载数据集 - train_dataset = paddle.vision.datasets.MNIST(mode='train') - test_dataset = paddle.vision.datasets.MNIST(mode='test') - - # 第2处改动,初始化并行环境 - dist.init_parallel_env() - - # 定义网络结构 - mnist = paddle.nn.Sequential( - paddle.nn.Flatten(1, -1), - paddle.nn.Linear(784, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.2), - paddle.nn.Linear(512, 10) - ) - - # 用 DataLoader 实现数据加载 - train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True) - - # 第3处改动,增加paddle.DataParallel封装 - mnist = paddle.DataParallel(mnist) - mnist.train() - - # 设置迭代次数 - epochs = 5 - - # 设置优化器 - optim = paddle.optimizer.Adam(parameters=mnist.parameters()) - - for epoch in range(epochs): - for batch_id, data in enumerate(train_loader()): - - x_data = data[0] # 训练数据 - y_data = data[1] # 训练数据标签 - predicts = mnist(x_data) # 预测结果 - - # 计算损失 等价于 prepare 中loss的设置 - loss = paddle.nn.functional.cross_entropy(predicts, y_data) - - # 计算准确率 等价于 prepare 中metrics的设置 - acc = paddle.metric.accuracy(predicts, y_data) - - # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中 - - # 反向传播 - loss.backward() - - if (batch_id+1) % 1800 == 0: - print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy())) - - # 更新参数 - optim.step() - - # 梯度清零 - optim.clear_grad() - -修改完后保存文件,然后使用跟高层API相同的启动方式即可。 -**注意:** 单卡训练不支持调用\ ``init_parallel_env``\ ,请使用以下几种方式进行分布式训练。 - -.. code:: bash - - # 单机多卡启动,默认使用当前可见的所有卡 - $ python -m paddle.distributed.launch train.py - - # 单机多卡启动,设置当前使用的第0号和第1号卡 - $ python -m paddle.distributed.launch --gpus '0,1' train.py - - # 单机多卡启动,设置当前使用第0号和第1号卡 - $ export CUDA_VISIBLE_DEVICES=0,1 - $ python -m paddle.distributed.launch train.py - -二、spawn启动 -------------------------------- -launch方式启动训练,以文件为单位启动多进程,需要用户在启动时调用\ ``paddle.distributed.launch``\,对于进程的管理要求较高。飞桨框架2.0版本增加了\ ``spawn``\ 启动方式,可以更好地控制进程,在日志打印、训练退出时更友好。使用示例如下: - -.. code:: python3 - - from __future__ import print_function - - import paddle - import paddle.nn as nn - import paddle.optimizer as opt - import paddle.distributed as dist - - class LinearNet(nn.Layer): - def __init__(self): - super(LinearNet, self).__init__() - self._linear1 = nn.Linear(10, 10) - self._linear2 = nn.Linear(10, 1) - - def forward(self, x): - return self._linear2(self._linear1(x)) - - def train(print_result=False): - - # 1. 初始化并行训练环境 - dist.init_parallel_env() - - # 2. 创建并行训练 Layer 和 Optimizer - layer = LinearNet() - dp_layer = paddle.DataParallel(layer) - - loss_fn = nn.MSELoss() - adam = opt.Adam( - learning_rate=0.001, parameters=dp_layer.parameters()) - - # 3. 运行网络 - inputs = paddle.randn([10, 10], 'float32') - outputs = dp_layer(inputs) - labels = paddle.randn([10, 1], 'float32') - loss = loss_fn(outputs, labels) - - if print_result is True: - print("loss:", loss.numpy()) - - loss.backward() - - adam.step() - adam.clear_grad() - - # 使用方式1:仅传入训练函数 - # 适用场景:训练函数不需要任何参数,并且需要使用所有当前可见的GPU设备并行训练 - if __name__ == '__main__': - dist.spawn(train) - - # 使用方式2:传入训练函数和参数 - # 适用场景:训练函数需要一些参数,并且需要使用所有当前可见的GPU设备并行训练 - if __name__ == '__main__': - dist.spawn(train, args=(True,)) - - # 使用方式3:传入训练函数、参数并指定并行进程数 - # 适用场景:训练函数需要一些参数,并且仅需要使用部分可见的GPU设备并行训练,例如: - # 当前机器有8张GPU卡 {0,1,2,3,4,5,6,7},此时会使用前两张卡 {0,1}; - # 或者当前机器通过配置环境变量 CUDA_VISIBLE_DEVICES=4,5,6,7,仅使4张 - # GPU卡可见,此时会使用可见的前两张卡 {4,5} - if __name__ == '__main__': - dist.spawn(train, args=(True,), nprocs=2) - - # 使用方式4:传入训练函数、参数、指定进程数并指定当前使用的卡号 - # 使用场景:训练函数需要一些参数,并且仅需要使用部分可见的GPU设备并行训练,但是 - # 可能由于权限问题,无权配置当前机器的环境变量,例如:当前机器有8张GPU卡 - # {0,1,2,3,4,5,6,7},但你无权配置CUDA_VISIBLE_DEVICES,此时可以通过 - # 指定参数 gpus 选择希望使用的卡,例如 gpus='4,5', - # 可以指定使用第4号卡和第5号卡 - if __name__ == '__main__': - dist.spawn(train, nprocs=2, gpus='4,5') diff --git a/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb new file mode 100644 index 00000000000..78ec043c702 --- /dev/null +++ b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb @@ -0,0 +1,611 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "83e96f0c", + "metadata": {}, + "source": [ + "# 自定义指标\n", + "\n", + "除了使用飞桨框架内置的指标外,飞桨框架还支持用户根据自己的实际场景,完成指标的自定义。\n", + "\n", + "## 一、自定义Loss\n", + "\n", + "有时你会遇到特定任务的Loss计算方式在框架既有的Loss接口中不存在,或算法不符合自己的需求,那么期望能够自己来进行Loss的自定义。这里介绍如何进行Loss的自定义操作,首先来看下面的代码:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9958927b", + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "\n", + "class SelfDefineLoss(paddle.nn.Layer):\n", + " \"\"\"\n", + " 1. 继承paddle.nn.Layer\n", + " \"\"\"\n", + " def __init__(self):\n", + " \"\"\"\n", + " 2. 构造函数根据自己的实际算法需求和使用需求进行参数定义即可\n", + " \"\"\"\n", + " super().__init__()\n", + " \n", + " def forward(self, x, label):\n", + " \"\"\"\n", + " 3. 实现forward函数,forward在调用时会传递两个参数:x和label\n", + " - x:单个或批次训练数据经过模型前向计算输出结果\n", + " - label:单个或批次训练数据对应的标签数据\n", + " 接口返回值是一个Tensor,根据自定义的逻辑加和或计算均值后的损失\n", + " \"\"\"\n", + " # 使用Paddle中相关API自定义的计算逻辑\n", + " # output = xxxxx\n", + " # return output" + ] + }, + { + "cell_type": "markdown", + "id": "80390e85", + "metadata": {}, + "source": [ + "接下来以交叉熵损失为例说明如何自定义损失函数:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6abeac3a", + "metadata": {}, + "outputs": [], + "source": [ + "class CrossEntropy(paddle.nn.Layer):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x, label):\n", + " loss = paddle.nn.functional.cross_entropy(\n", + " x,\n", + " label)\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "id": "2a431650", + "metadata": {}, + "source": [ + "## 二、自定义Metric\n", + "\n", + "和Loss一样,你也可以来通过框架实现自定义的评估方法,具体的实现如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0d27802a", + "metadata": {}, + "outputs": [], + "source": [ + "class SelfDefineMetric(paddle.metric.Metric):\n", + " \"\"\"\n", + " 1. 继承paddle.metric.Metric\n", + " \"\"\"\n", + " def __init__(self):\n", + " \"\"\"\n", + " 2. 构造函数实现,自定义参数即可\n", + " \"\"\"\n", + " super(SelfDefineMetric, self).__init__()\n", + " \n", + " def name(self):\n", + " \"\"\"\n", + " 3. 实现name方法,返回定义的评估指标名字\n", + " \"\"\"\n", + " # return '自定义评价指标的名字'\n", + " \n", + " def compute(self, **args):\n", + " \"\"\"\n", + " 4. 本步骤可以省略,实现compute方法,这个方法主要用于`update`的加速,可以在这个方法中调用一些paddle实现好的Tensor计算API,编译到模型网络中一起使用低层C++ OP计算。\n", + " \"\"\"\n", + " # return '自己想要返回的数据,会做为update的参数传入。'\n", + " \n", + " def update(self, **args):\n", + " \"\"\"\n", + " 5. 实现update方法,用于单个batch训练时进行评估指标计算。\n", + " - 当`compute`类函数未实现时,会将模型的计算输出和标签数据的展平作为`update`的参数传入。\n", + " - 当`compute`类函数做了实现时,会将compute的返回结果作为`update`的参数传入。\n", + " \"\"\"\n", + " # return acc_value\n", + " \n", + " def accumulate(self):\n", + " \"\"\"\n", + " 6. 实现accumulate方法,返回历史batch训练积累后计算得到的评价指标值。\n", + " 每次`update`调用时进行数据积累,`accumulate`计算时对积累的所有数据进行计算并返回。\n", + " 结算结果会在`fit`接口的训练日志中呈现。\n", + " \"\"\"\n", + " # 利用update中积累的成员变量数据进行计算后返回\n", + " # return accumulated_acc_value\n", + " \n", + " def reset(self):\n", + " \"\"\"\n", + " 7. 实现reset方法,每个Epoch结束后进行评估指标的重置,这样下个Epoch可以重新进行计算。\n", + " \"\"\"\n", + " # do reset action" + ] + }, + { + "cell_type": "markdown", + "id": "926454a0", + "metadata": {}, + "source": [ + "接下来看一个框架中的具体例子,是框架中已提供的Accuracy计算接口,这里就是按照上述说明中的方法完成了实现。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "318b1aac", + "metadata": {}, + "outputs": [], + "source": [ + "class Accuracy(paddle.metric.Metric):\n", + " def __init__(self, topk=(1, ), name=None, *args, **kwargs):\n", + " super(Accuracy, self).__init__(*args, **kwargs)\n", + " self.topk = topk\n", + " self.maxk = max(topk)\n", + " self._init_name(name)\n", + " self.reset()\n", + "\n", + " def compute(self, pred, label, *args):\n", + " pred = paddle.argsort(pred, descending=True)\n", + " pred = paddle.slice(\n", + " pred, axes=[len(pred.shape) - 1], starts=[0], ends=[self.maxk])\n", + " if (len(label.shape) == 1) or \\\n", + " (len(label.shape) == 2 and label.shape[-1] == 1):\n", + " # In static mode, the real label data shape may be different\n", + " # from shape defined by paddle.static.InputSpec in model\n", + " # building, reshape to the right shape.\n", + " label = paddle.reshape(label, (-1, 1))\n", + " elif label.shape[-1] != 1:\n", + " # one-hot label\n", + " label = paddle.argmax(label, axis=-1, keepdim=True)\n", + " correct = pred == label\n", + " return paddle.cast(correct, dtype='float32')\n", + "\n", + " def update(self, correct, *args):\n", + " if isinstance(correct, paddle.Tensor):\n", + " correct = correct.numpy()\n", + " num_samples = np.prod(np.array(correct.shape[:-1]))\n", + " accs = []\n", + " for i, k in enumerate(self.topk):\n", + " num_corrects = correct[..., :k].sum()\n", + " accs.append(float(num_corrects) / num_samples)\n", + " self.total[i] += num_corrects\n", + " self.count[i] += num_samples\n", + " accs = accs[0] if len(self.topk) == 1 else accs\n", + " return accs\n", + "\n", + " def reset(self):\n", + " self.total = [0.] * len(self.topk)\n", + " self.count = [0] * len(self.topk)\n", + "\n", + " def accumulate(self):\n", + " res = []\n", + " for t, c in zip(self.total, self.count):\n", + " r = float(t) / c if c > 0 else 0.\n", + " res.append(r)\n", + " res = res[0] if len(self.topk) == 1 else res\n", + " return res\n", + "\n", + " def _init_name(self, name):\n", + " name = name or 'acc'\n", + " if self.maxk != 1:\n", + " self._name = ['{}_top{}'.format(name, k) for k in self.topk]\n", + " else:\n", + " self._name = [name]\n", + "\n", + " def name(self):\n", + " return self._name" + ] + }, + { + "cell_type": "markdown", + "id": "66ad9fa9", + "metadata": {}, + "source": [ + "## 三、自定义Callback\n", + "\n", + " `fit` 接口的callback参数支持传入一个 ` Callback` 类实例,用来在每轮训练和每个 ` batch` 训练前后进行调用,可以通过 ` callback` 收集到训练过程中的一些数据和参数,或者实现一些自定义操作。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a18a7d5", + "metadata": {}, + "outputs": [], + "source": [ + "class SelfDefineCallback(paddle.callbacks.Callback):\n", + " \"\"\"\n", + " 1. 继承paddle.callbacks.Callback\n", + " 2. 按照自己的需求实现以下类成员方法:\n", + " def on_train_begin(self, logs=None) 训练开始前,`Model.fit`接口中调用\n", + " def on_train_end(self, logs=None) 训练结束后,`Model.fit`接口中调用\n", + " def on_eval_begin(self, logs=None) 评估开始前,`Model.evaluate`接口调用\n", + " def on_eval_end(self, logs=None) 评估结束后,`Model.evaluate`接口调用\n", + " def on_predict_begin(self, logs=None) 预测测试开始前,`Model.predict`接口中调用\n", + " def on_predict_end(self, logs=None) 预测测试结束后,`Model.predict`接口中调用\n", + " def on_epoch_begin(self, epoch, logs=None) 每轮训练开始前,`Model.fit`接口中调用\n", + " def on_epoch_end(self, epoch, logs=None) 每轮训练结束后,`Model.fit`接口中调用\n", + " def on_train_batch_begin(self, step, logs=None) 单个Batch训练开始前,`Model.fit`和`Model.train_batch`接口中调用\n", + " def on_train_batch_end(self, step, logs=None) 单个Batch训练结束后,`Model.fit`和`Model.train_batch`接口中调用\n", + " def on_eval_batch_begin(self, step, logs=None) 单个Batch评估开始前,`Model.evalute`和`Model.eval_batch`接口中调用\n", + " def on_eval_batch_end(self, step, logs=None) 单个Batch评估结束后,`Model.evalute`和`Model.eval_batch`接口中调用\n", + " def on_predict_batch_begin(self, step, logs=None) 单个Batch预测测试开始前,`Model.predict`和`Model.test_batch`接口中调用\n", + " def on_predict_batch_end(self, step, logs=None) 单个Batch预测测试结束后,`Model.predict`和`Model.test_batch`接口中调用\n", + " \"\"\"\n", + " \n", + " def __init__(self):\n", + " super().__init__()\n", + " # 按照需求定义自己的类成员方法" + ] + }, + { + "cell_type": "markdown", + "id": "b23a392c", + "metadata": {}, + "source": [ + "看两个框架中的实际例子。其中第一个例子时框架自带的 `ModelCheckpoint` 回调函数,可以在 `fit` 训练模型时自动存储每轮训练得到的模型;第二个例子是框架自带的 `ProgBarLogger` 回调函数,用于在 `fit` 训练时打印损失函数和评估指标。这两个回调函数会在 `fit` 执行时默认被调用。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c6e92b5", + "metadata": {}, + "outputs": [], + "source": [ + "class ModelCheckpoint(paddle.callbacks.Callback):\n", + " def __init__(self, save_freq=1, save_dir=None):\n", + " self.save_freq = save_freq\n", + " self.save_dir = save_dir\n", + " \n", + " def on_epoch_begin(self, epoch=None, logs=None):\n", + " self.epoch = epoch\n", + " \n", + " def _is_save(self):\n", + " return self.model and self.save_dir and ParallelEnv().local_rank == 0\n", + " \n", + " def on_epoch_end(self, epoch, logs=None):\n", + " if self._is_save() and self.epoch % self.save_freq == 0:\n", + " path = '{}/{}'.format(self.save_dir, epoch)\n", + " print('save checkpoint at {}'.format(os.path.abspath(path)))\n", + " self.model.save(path)\n", + " \n", + " def on_train_end(self, logs=None):\n", + " if self._is_save():\n", + " path = '{}/final'.format(self.save_dir)\n", + " print('save checkpoint at {}'.format(os.path.abspath(path)))\n", + " self.model.save(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0384287c", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from paddle.distributed import ParallelEnv\n", + "from paddle.utils import try_import\n", + "from paddle.hapi.progressbar import ProgressBar\n", + "\n", + "class ProgBarLogger(paddle.callbacks.Callback):\n", + " def __init__(self, log_freq=1, verbose=2):\n", + " self.epochs = None\n", + " self.steps = None\n", + " self.progbar = None\n", + " self.verbose = verbose\n", + " self.log_freq = log_freq\n", + "\n", + " def _is_print(self):\n", + " return self.verbose and ParallelEnv().local_rank == 0\n", + "\n", + " def on_train_begin(self, logs=None):\n", + " self.epochs = self.params['epochs']\n", + " assert self.epochs\n", + " self.train_metrics = self.params['metrics']\n", + " assert self.train_metrics\n", + "\n", + " self._train_timer = {\n", + " 'data_time': 0,\n", + " 'batch_time': 0,\n", + " 'count': 0,\n", + " 'samples': 0,\n", + " }\n", + " if self._is_print():\n", + " print(\n", + " \"The loss value printed in the log is the current step, and the metric is the average value of previous steps.\"\n", + " )\n", + "\n", + " def on_epoch_begin(self, epoch=None, logs=None):\n", + " self.steps = self.params['steps']\n", + " self.epoch = epoch\n", + " self.train_step = 0\n", + " if self.epochs and self._is_print():\n", + " print('Epoch %d/%d' % (epoch + 1, self.epochs))\n", + " self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose)\n", + "\n", + " self._train_timer['batch_start_time'] = time.time()\n", + "\n", + " def _updates(self, logs, mode):\n", + " values = []\n", + " metrics = getattr(self, '%s_metrics' % (mode))\n", + " progbar = getattr(self, '%s_progbar' % (mode))\n", + " steps = getattr(self, '%s_step' % (mode))\n", + "\n", + " for k in metrics:\n", + " if k in logs:\n", + " values.append((k, logs[k]))\n", + "\n", + " if self.verbose == 3 and hasattr(self, '_%s_timer' % (mode)):\n", + " timer = getattr(self, '_%s_timer' % (mode))\n", + " cnt = timer['count'] if timer['count'] > 0 else 1.0\n", + " samples = timer['samples'] if timer['samples'] > 0 else 1.0\n", + " values.append(\n", + " ('avg_reader_cost', \"%.5f sec\" % (timer['data_time'] / cnt)))\n", + " values.append(\n", + " ('avg_batch_cost', \"%.5f sec\" % (timer['batch_time'] / cnt)))\n", + " values.append(\n", + " ('ips', \"%.5f samples/sec\" %\n", + " (samples / (timer['data_time'] + timer['batch_time']))))\n", + " timer['count'] = 0\n", + " timer['samples'] = 0\n", + " timer['data_time'] = 0.\n", + " timer['batch_time'] = 0.\n", + "\n", + " progbar.update(steps, values)\n", + "\n", + " def on_train_batch_begin(self, step, logs=None):\n", + " self._train_timer['batch_data_end_time'] = time.time()\n", + " self._train_timer['data_time'] += (\n", + " self._train_timer['batch_data_end_time'] -\n", + " self._train_timer['batch_start_time'])\n", + "\n", + " def on_train_batch_end(self, step, logs=None):\n", + " logs = logs or {}\n", + " self.train_step += 1\n", + "\n", + " self._train_timer['batch_time'] += (\n", + " time.time() - self._train_timer['batch_data_end_time'])\n", + " self._train_timer['count'] += 1\n", + " samples = logs.get('batch_size', 1)\n", + " self._train_timer['samples'] += samples\n", + " if self._is_print() and self.train_step % self.log_freq == 0:\n", + " if self.steps is None or self.train_step < self.steps:\n", + " self._updates(logs, 'train')\n", + " self._train_timer['batch_start_time'] = time.time()\n", + "\n", + " def on_epoch_end(self, epoch, logs=None):\n", + " logs = logs or {}\n", + " if self._is_print() and (self.steps is not None):\n", + " self._updates(logs, 'train')\n", + "\n", + " def on_eval_begin(self, logs=None):\n", + " self.eval_steps = logs.get('steps', None)\n", + " self.eval_metrics = logs.get('metrics', [])\n", + " self.eval_step = 0\n", + " self.evaled_samples = 0\n", + "\n", + " self._eval_timer = {\n", + " 'data_time': 0,\n", + " 'batch_time': 0,\n", + " 'count': 0,\n", + " 'samples': 0,\n", + " }\n", + "\n", + " self.eval_progbar = ProgressBar(\n", + " num=self.eval_steps, verbose=self.verbose)\n", + " if self._is_print():\n", + " print('Eval begin...')\n", + "\n", + " self._eval_timer['batch_start_time'] = time.time()\n", + "\n", + " def on_eval_batch_begin(self, step, logs=None):\n", + " self._eval_timer['batch_data_end_time'] = time.time()\n", + " self._eval_timer['data_time'] += (\n", + " self._eval_timer['batch_data_end_time'] -\n", + " self._eval_timer['batch_start_time'])\n", + "\n", + " def on_eval_batch_end(self, step, logs=None):\n", + " logs = logs or {}\n", + " self.eval_step += 1\n", + " samples = logs.get('batch_size', 1)\n", + " self.evaled_samples += samples\n", + "\n", + " self._eval_timer['batch_time'] += (\n", + " time.time() - self._eval_timer['batch_data_end_time'])\n", + " self._eval_timer['count'] += 1\n", + " samples = logs.get('batch_size', 1)\n", + " self._eval_timer['samples'] += samples\n", + "\n", + " if self._is_print() and self.eval_step % self.log_freq == 0:\n", + " if self.eval_steps is None or self.eval_step < self.eval_steps:\n", + " self._updates(logs, 'eval')\n", + "\n", + " self._eval_timer['batch_start_time'] = time.time()\n", + "\n", + " def on_predict_begin(self, logs=None):\n", + " self.test_steps = logs.get('steps', None)\n", + " self.test_metrics = logs.get('metrics', [])\n", + " self.test_step = 0\n", + " self.tested_samples = 0\n", + "\n", + " self._test_timer = {\n", + " 'data_time': 0,\n", + " 'batch_time': 0,\n", + " 'count': 0,\n", + " 'samples': 0,\n", + " }\n", + "\n", + " self.test_progbar = ProgressBar(\n", + " num=self.test_steps, verbose=self.verbose)\n", + " if self._is_print():\n", + " print('Predict begin...')\n", + "\n", + " self._test_timer['batch_start_time'] = time.time()\n", + "\n", + " def on_predict_batch_begin(self, step, logs=None):\n", + " self._test_timer['batch_data_end_time'] = time.time()\n", + " self._test_timer['data_time'] += (\n", + " self._test_timer['batch_data_end_time'] -\n", + " self._test_timer['batch_start_time'])\n", + "\n", + " def on_predict_batch_end(self, step, logs=None):\n", + " logs = logs or {}\n", + " self.test_step += 1\n", + " samples = logs.get('batch_size', 1)\n", + " self.tested_samples += samples\n", + "\n", + " self._test_timer['batch_time'] += (\n", + " time.time() - self._test_timer['batch_data_end_time'])\n", + " self._test_timer['count'] += 1\n", + " samples = logs.get('batch_size', 1)\n", + " self._test_timer['samples'] += samples\n", + "\n", + " if self.test_step % self.log_freq == 0 and self._is_print():\n", + " if self.test_steps is None or self.test_step < self.test_steps:\n", + " self._updates(logs, 'test')\n", + "\n", + " self._test_timer['batch_start_time'] = time.time()\n", + "\n", + " def on_eval_end(self, logs=None):\n", + " logs = logs or {}\n", + " if self._is_print() and (self.eval_steps is not None):\n", + " self._updates(logs, 'eval')\n", + " print('Eval samples: %d' % (self.evaled_samples))\n", + "\n", + " def on_predict_end(self, logs=None):\n", + " logs = logs or {}\n", + " if self._is_print():\n", + " if self.test_step % self.log_freq != 0 or self.verbose == 1:\n", + " self._updates(logs, 'test')\n", + " print('Predict samples: %d' % (self.tested_samples))" + ] + }, + { + "cell_type": "markdown", + "id": "0a7d7f04", + "metadata": {}, + "source": [ + "## 四、自定义指标的使用\n", + "\n", + "接下来以mnist为例,使用自定义的指标替换框架中的指标,代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d2a66f19", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W1223 04:29:17.810079 9910 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n", + "W1223 04:29:17.815956 9910 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", + "Epoch 1/5\n", + "step 2/938 [..............................] - loss: 2.1375 - acc: 0.1562 - ETA: 7:27 - 478ms/step " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/python3.7.0/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " return (isinstance(seq, collections.Sequence) and\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 938/938 [==============================] - loss: 0.1039 - acc: 0.9303 - 138ms/step \n", + "Epoch 2/5\n", + "step 938/938 [==============================] - loss: 0.0887 - acc: 0.9696 - 123ms/step \n", + "Epoch 3/5\n", + "step 938/938 [==============================] - loss: 0.0285 - acc: 0.9782 - 158ms/step \n", + "Epoch 4/5\n", + "step 938/938 [==============================] - loss: 0.0049 - acc: 0.9833 - 158ms/step \n", + "Epoch 5/5\n", + "step 938/938 [==============================] - loss: 0.1041 - acc: 0.9863 - 145ms/step \n" + ] + } + ], + "source": [ + "import paddle\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 加载数据集\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "\n", + "mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1), \n", + " paddle.nn.Linear(784, 512), \n", + " paddle.nn.ReLU(), \n", + " paddle.nn.Dropout(0.2), \n", + " paddle.nn.Linear(512, 10)\n", + ")\n", + "\n", + "model = paddle.Model(mnist)\n", + "\n", + "# 将paddle.nn.CrossEntropyLoss替换为CrossEntropy,\n", + "# 将paddle.metric.Accuracy替换为Accuracy\n", + "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + " loss=CrossEntropy(), \n", + " metrics=Accuracy())\n", + "\n", + "# 启动模型训练,加入自定义的两个Callbacks\n", + "model.fit(train_dataset, \n", + " epochs=5, \n", + " batch_size=64, \n", + " verbose=0, \n", + " callbacks=[ProgBarLogger(verbose=1), ModelCheckpoint()]\n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guides/02_paddle2.0_develop/07_customize_cn.rst b/docs/guides/02_paddle2.0_develop/07_customize_cn.rst deleted file mode 100644 index 3e5626fc3be..00000000000 --- a/docs/guides/02_paddle2.0_develop/07_customize_cn.rst +++ /dev/null @@ -1,241 +0,0 @@ -.. _cn_doc_customize: - -自定义指标 -=================== - -除了使用飞桨框架内置的指标外,飞桨框架还支持用户根据自己的实际场景,完成指标的自定义。 - - -一、自定义Loss ------------------------ - -有时你会遇到特定任务的Loss计算方式在框架既有的Loss接口中不存在,或算法不符合自己的需求,那么期望能够自己来进行Loss的自定义。这里介绍如何进行Loss的自定义操作,首先来看下面的代码: - -.. code:: ipython3 - - class SelfDefineLoss(paddle.nn.Layer): - """ - 1. 继承paddle.nn.Layer - """ - def __init__(self): - """ - 2. 构造函数根据自己的实际算法需求和使用需求进行参数定义即可 - """ - super(SelfDefineLoss, self).__init__() - - def forward(self, input, label): - """ - 3. 实现forward函数,forward在调用时会传递两个参数:input和label - - input:单个或批次训练数据经过模型前向计算输出结果 - - label:单个或批次训练数据对应的标签数据 - 接口返回值是一个Tensor,根据自定义的逻辑加和或计算均值后的损失 - """ - # 使用Paddle中相关API自定义的计算逻辑 - # output = xxxxx - # return output - -接下来是一个具体的例子,在图像分割示例代码中写的一个自定义Loss,当时主要是使用自定义的softmax计算维度。 - -.. code:: python - - class SoftmaxWithCrossEntropy(paddle.nn.Layer): - def __init__(self): - super(SoftmaxWithCrossEntropy, self).__init__() - - def forward(self, input, label): - loss = F.softmax_with_cross_entropy(input, - label, - return_softmax=False, - axis=1) - return paddle.mean(loss) - - -二、自定义Metric ----------------------------- - -和Loss一样,你也可以来通过框架实现自定义的评估方法,具体的实现如下: - -.. code:: ipython3 - - class SelfDefineMetric(paddle.metric.Metric): - """ - 1. 继承paddle.metric.Metric - """ - def __init__(self): - """ - 2. 构造函数实现,自定义参数即可 - """ - super(SelfDefineMetric, self).__init__() - - def name(self): - """ - 3. 实现name方法,返回定义的评估指标名字 - """ - return '自定义评价指标的名字' - - def compute(self, ...) - """ - 4. 本步骤可以省略,实现compute方法,这个方法主要用于`update`的加速,可以在这个方法中调用一些paddle实现好的Tensor计算API,编译到模型网络中一起使用低层C++ OP计算。 - """ - return 自己想要返回的数据,会做为update的参数传入。 - - def update(self, ...): - """ - 5. 实现update方法,用于单个batch训练时进行评估指标计算。 - - 当`compute`类函数未实现时,会将模型的计算输出和标签数据的展平作为`update`的参数传入。 - - 当`compute`类函数做了实现时,会将compute的返回结果作为`update`的参数传入。 - """ - return acc value - - def accumulate(self): - """ - 6. 实现accumulate方法,返回历史batch训练积累后计算得到的评价指标值。 - 每次`update`调用时进行数据积累,`accumulate`计算时对积累的所有数据进行计算并返回。 - 结算结果会在`fit`接口的训练日志中呈现。 - """ - # 利用update中积累的成员变量数据进行计算后返回 - return accumulated acc value - - def reset(self): - """ - 7. 实现reset方法,每个Epoch结束后进行评估指标的重置,这样下个Epoch可以重新进行计算。 - """ - # do reset action - -接下来看一个框架中的具体例子,是框架中已提供的一个评估指标计算接口,这里就是按照上述说明中的方法完成了实现。 - -.. code:: ipython3 - - from paddle.metric import Metric - - class Precision(Metric): - """ - Precision (also called positive predictive value) is the fraction of - relevant instances among the retrieved instances. Refer to - https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers - Noted that this class manages the precision score only for binary - classification task. - - ...... - """ - - def __init__(self, name='precision', *args, **kwargs): - super(Precision, self).__init__(*args, **kwargs) - self.tp = 0 # true positive - self.fp = 0 # false positive - self._name = name - - def update(self, preds, labels): - """ - Update the states based on the current mini-batch prediction results. - Args: - preds (numpy.ndarray): The prediction result, usually the output - of two-class sigmoid function. It should be a vector (column - vector or row vector) with data type: 'float64' or 'float32'. - labels (numpy.ndarray): The ground truth (labels), - the shape should keep the same as preds. - The data type is 'int32' or 'int64'. - """ - if isinstance(preds, paddle.Tensor): - preds = preds.numpy() - elif not _is_numpy_(preds): - raise ValueError("The 'preds' must be a numpy ndarray or Tensor.") - if isinstance(labels, paddle.Tensor): - labels = labels.numpy() - elif not _is_numpy_(labels): - raise ValueError("The 'labels' must be a numpy ndarray or Tensor.") - - sample_num = labels.shape[0] - preds = np.floor(preds + 0.5).astype("int32") - - for i in range(sample_num): - pred = preds[i] - label = labels[i] - if pred == 1: - if pred == label: - self.tp += 1 - else: - self.fp += 1 - - def reset(self): - """ - Resets all of the metric state. - """ - self.tp = 0 - self.fp = 0 - - def accumulate(self): - """ - Calculate the final precision. - - Returns: - A scaler float: results of the calculated precision. - """ - ap = self.tp + self.fp - return float(self.tp) / ap if ap != 0 else .0 - - def name(self): - """ - Returns metric name - """ - return self._name - - -三、自定义Callback -------------------------------- - -``fit``\ 接口的callback参数支持传入一个\`` Callback``\ 类实例,用来在每轮训练和每个\`` batch``\ 训练前后进行调用,可以通过\`` callback``\ 收集到训练过程中的一些数据和参数,或者实现一些自定义操作。 - -.. code:: ipython3 - - class SelfDefineCallback(paddle.callbacks.Callback): - """ - 1. 继承paddle.callbacks.Callback - 2. 按照自己的需求实现以下类成员方法: - def on_train_begin(self, logs=None) 训练开始前,`Model.fit`接口中调用 - def on_train_end(self, logs=None) 训练结束后,`Model.fit`接口中调用 - def on_eval_begin(self, logs=None) 评估开始前,`Model.evaluate`接口调用 - def on_eval_end(self, logs=None) 评估结束后,`Model.evaluate`接口调用 - def on_predict_begin(self, logs=None) 预测测试开始前,`Model.predict`接口中调用 - def on_predict_end(self, logs=None) 预测测试结束后,`Model.predict`接口中调用 - def on_epoch_begin(self, epoch, logs=None) 每轮训练开始前,`Model.fit`接口中调用 - def on_epoch_end(self, epoch, logs=None) 每轮训练结束后,`Model.fit`接口中调用 - def on_train_batch_begin(self, step, logs=None) 单个Batch训练开始前,`Model.fit`和`Model.train_batch`接口中调用 - def on_train_batch_end(self, step, logs=None) 单个Batch训练结束后,`Model.fit`和`Model.train_batch`接口中调用 - def on_eval_batch_begin(self, step, logs=None) 单个Batch评估开始前,`Model.evalute`和`Model.eval_batch`接口中调用 - def on_eval_batch_end(self, step, logs=None) 单个Batch评估结束后,`Model.evalute`和`Model.eval_batch`接口中调用 - def on_predict_batch_begin(self, step, logs=None) 单个Batch预测测试开始前,`Model.predict`和`Model.test_batch`接口中调用 - def on_predict_batch_end(self, step, logs=None) 单个Batch预测测试结束后,`Model.predict`和`Model.test_batch`接口中调用 - """ - - def __init__(self): - super(SelfDefineCallback, self).__init__() - # 按照需求定义自己的类成员方法 - - -看一个框架中的实际例子,这是框架自带的\`` ModelCheckpoint``\ 回调函数,可以在\`` fit``\ 训练模型时自动存储每轮训练得到的模型。 - -.. code:: python - - class ModelCheckpoint(Callback): - def __init__(self, save_freq=1, save_dir=None): - self.save_freq = save_freq - self.save_dir = save_dir - - def on_epoch_begin(self, epoch=None, logs=None): - self.epoch = epoch - - def _is_save(self): - return self.model and self.save_dir and ParallelEnv().local_rank == 0 - - def on_epoch_end(self, epoch, logs=None): - if self._is_save() and self.epoch % self.save_freq == 0: - path = '{}/{}'.format(self.save_dir, epoch) - print('save checkpoint at {}'.format(os.path.abspath(path))) - self.model.save(path) - - def on_train_end(self, logs=None): - if self._is_save(): - path = '{}/final'.format(self.save_dir) - print('save checkpoint at {}'.format(os.path.abspath(path))) - self.model.save(path) From df4a59176714cd8cb47a1db61ababf995f3c356b Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 29 Dec 2021 16:52:46 +0800 Subject: [PATCH 24/63] update 02,03,04 --- .../02_data_load_cn.ipynb | 55 +++++++++++-------- .../03_data_preprocessing_cn.ipynb | 30 +++++----- .../02_paddle2.0_develop/04_model_cn.ipynb | 54 ++++++++++++++++-- 3 files changed, 94 insertions(+), 45 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 13775f93729..c587df14eac 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -7,16 +7,17 @@ "source": [ "# 数据集定义与加载\n", "\n", - "深度学习模型在训练时需要大量的数据来完成模型调优,这个过程均是数字的计算,无法直接使用原始图片和文本等来完成计算。因此与需要对原始的各种数据文件进行处理,转换成深度学习模型可以使用的数据类型。\n", + "深度学习模型在训练时需要大量的数据来完成模型调优,这个过程均是数字的计算,无法直接使用原始数据如图片、文本等来完成计算。因此需要对原始的各种数据文件进行处理,转换成深度学习模型可以使用的数据类型。\n", "\n", - "飞桨内的数据集加载过程由 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 两个api完成。Dataset主要完成单张图像或样本的解析与标签的制作,DataLoader主要完成单张图像或样本的组batch工作和对数据集的多进程读取加速作用。\n", "\n", - "飞桨内置了深度学习任务中常用的数据集,对应API所在目录为 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 与 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#paddle-text) ,你可以通过以下代码查看飞桨框架中的内置数据集。" + "训练模型时,往往不是采用单个数据输入模型进行训练,这样训练耗时比较大;而常常使用minibatch的方式,每次输入部分数据训练模型。飞桨内的数据集加载过程由 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 两个 api 完成。Dataset 主要完成单张图像或样本的解析与标签的制作,DataLoader 主要完成单张图像或样本的组 batch 工作和对数据集的多进程读取加速的作用。\n", + "\n", + "飞桨内置了深度学习任务中常用的数据集,对应 API 所在目录为 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 与 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#paddle-text) ,你可以通过以下代码查看飞桨框架中的内置数据集。" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "ace5670c", "metadata": {}, "outputs": [ @@ -42,14 +43,16 @@ "source": [ "## 加载数据集\n", "\n", - "通过飞桨框架,可以很方便的加载深度学习里的常用数据集。下面演示如何快速加载MNIST数据集。\n", + "通过飞桨框架,可以很方便的加载深度学习里的常用数据集。下面演示如何快速加载 MNIST 数据集。\n", + "\n", + "MNIST 数据集用于对 0 ~ 9 的十类数字进行分类,即输入28 * 28分辨率的手写数字图片,识别出这个图片中的数字。\n", "\n", "在加载过程中,会通过`transform`字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等,这里在初始化MNIST数据集时传入了 `ToTensor` 变换来将图像转换为飞桨的内置数据类型, `mode`字段用于区分训练集和测试集。" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "56a6ede2", "metadata": {}, "outputs": [ @@ -77,12 +80,12 @@ "source": [ "## 迭代数据集&可视化\n", "\n", - "完成数据集初始化之后,可以使用下面的代码直接对数据集进行迭代" + "完成数据集初始化之后,初始化之后的 dataset 的一个可迭代的对象,可以使用下面的代码直接对数据集进行迭代" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "914637b6", "metadata": {}, "outputs": [ @@ -95,7 +98,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -112,7 +115,7 @@ "for data in train_dataset:\n", " image, label = data\n", " print('shape of image: ',image.shape)\n", - " plt.title(str(label))\n", + " plt.title(\"label: {}\".format(str(label[0])))\n", " plt.imshow(image[0]) \n", " break" ] @@ -129,14 +132,14 @@ "\n", "自定义数据集需要集成自 `paddle.io.Dataset` 并且实现下面的三个方法\n", "\n", - "1. `__init__`: 完成一些数据集初始化操作,定义数据集大小\n", - "2. `__getitem__`: 定义给定index时如何获取数据,在此函数中需要完成数据的预处理工作,如读取图像,对图像进行数据增强和制作标签等操作,最终返回处理好的单条数据(训练数据,对应的标签)\n", + "1. `__init__`: 完成一些数据集初始化操作,包含根据传入的标签文件进行数据集的读取,对数据预处理方法进行定义;\n", + "2. `__getitem__`: 定义给定 index 时如何从 dataset 中获取数据。即根据 index ,指定从硬盘中读取的数据与标签,如果定义了数据预处理方法,则对该数据使用 transform,最终返回处理好的单条数据(训练数据,对应的标签)。关于 transform 更多的内容,可以参考 [数据预处理](03_data_preprocessing_cn.html)。\n", "3. `__len__`: 返回数据集总数目" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 7, "id": "1d26950f", "metadata": {}, "outputs": [], @@ -148,20 +151,23 @@ " \"\"\"\n", " 步骤一:继承paddle.io.Dataset类\n", " \"\"\"\n", - " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", - " \"\"\"\n", + " def __init__(self, num_samples, transform=None, image_size=(28,28), class_num=10):\n", + " \"\"\" \n", " 步骤二:实现构造函数,定义数据集大小\n", " \"\"\"\n", " super(MyDataset, self).__init__()\n", " self.num_samples = num_samples\n", " self.image_size = image_size\n", " self.class_num = class_num\n", + " self.transform = transform\n", "\n", " def __getitem__(self, index):\n", " \"\"\"\n", " 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)\n", " \"\"\"\n", " image = np.random.rand(*self.image_size)\n", + " if self.transform is not None:\n", + " image = self.transform(image)\n", " image = np.expand_dims(image, axis=0)\n", " label = np.random.randint(0, self.class_num - 1)\n", "\n", @@ -184,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "id": "9d1570a3", "metadata": {}, "outputs": [ @@ -197,7 +203,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] @@ -209,7 +215,7 @@ } ], "source": [ - "custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM)\n", + "custom_dataset = MyDataset(100)\n", "\n", "for data in custom_dataset:\n", " image, label = data\n", @@ -226,14 +232,15 @@ "source": [ "## 使用DataLoader 读取训练数据集\n", "\n", - "通过直接迭代DataSet的方式虽然可以对数据集进行访问,但是这种访问方式只能单线程进行并且还需要手动进行Batch的组合。在飞桨中,推荐使用 `paddle.io.DataLoader`来对数据集进行多进程的读取并且自动完成组batch的工作,开发者只需要进行数据处理部分逻辑的编写。\n", + "通过直接迭代 Dataset 的方式虽然可以对数据集进行访问,但是这种访问方式只能单线程进行,在进行模型训练时,还需要进行 batch 组合,对数据随机打乱避免过拟合,以及采用多线程的方式加载数据来加速等工作。在飞桨中,推荐使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 来完成这部分工作,开发者只需要进行数据处理部分逻辑的编写。\n", "\n", - "[飞桨高层API](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/high_level_api.html#api) 自动完成DataLoader的过程, 对于非高层API的使用情况,可以通过如下代码,可以快速的使用`paddle.io.DataLoader`完成数据的加载。" + "飞桨高层API [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/high_level_api.html#api) \n", + "进行训练、评估、预测时,会自动完成对数据组batch的操作。对于其他场景,都可以通过如下代码,可以快速的使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 完成数据的加载" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "id": "c3ad4116", "metadata": {}, "outputs": [ @@ -246,7 +253,7 @@ }, { "data": { - "image/png": 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" ] @@ -278,7 +285,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -292,7 +299,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index bbbd8ecc895..9db5553e8c5 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "93904999", "metadata": {}, "outputs": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "id": "69b80bc1", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 3, "id": "4a1a5cb3", "metadata": {}, "outputs": [], @@ -86,16 +86,16 @@ "id": "0a76dd29", "metadata": {}, "source": [ - "定义好数据预处理操作后,可以直接在DataSet中进行使用,下面介绍介绍两种数据增强使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集\n", + "定义好数据预处理操作后,可以直接在Dataset中进行使用,下面介绍介绍两种数据增强使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集\n", "\n", "## 基于框架内置数据集\n", "\n", - "在框架内置数据集中使用内置的数据处理操作时,只需要将数据处理操作传递给`transform`字段即可" + "飞桨框架中内置的数据集 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-datasets), 每个都包含`transform`参数,可以传入定义好的数据处理操作。" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "a7970f84", "metadata": {}, "outputs": [], @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "45ea330a", "metadata": {}, "outputs": [], @@ -161,12 +161,12 @@ "id": "3278dbe9", "metadata": {}, "source": [ - "下面通过框架内置数据集对比处理前后的图像" + "下面通过框架内置数据集对比处理前后的图像,这里使用的数据处理操作为[Resize](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/transforms/resize_cn.html#cn-api-vision-transforms-resize),用于对改变输入图像的大小" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 6, "id": "b4f7532b", "metadata": {}, "outputs": [ @@ -174,8 +174,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(28, 28)\n", - "(32, 32)\n" + "image size before resize: (28, 28)\n", + "image size before resize: (32, 32)\n" ] } ], @@ -186,14 +186,14 @@ "image, label = train_dataset[0]\n", "image_with_Resize, label_with_Resize = train_dataset_with_Resize[0]\n", "\n", - "print(image.size)\n", - "print(image_with_Resize.size)" + "print('image size before resize: {}'.format(image.size))\n", + "print('image size before resize: {}'.format(image_with_Resize.size))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -207,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index b6cb64b3386..020aa252238 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -7,7 +7,10 @@ "source": [ "# 模型组网\n", "\n", - "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的LeNet网络分别介绍这三种形式。\n", + "\n", + "深度学习中的神经网络可以视为是输入到输出的映射函数,如图像到语义(0-9)的映射,足够深的神经网络理论上可以拟合任何复杂的函数。飞桨框架中,各种各样的神经网络层都在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下,每个神经网络层都是 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的子类。每个神经网络由各种各样的神经网络层组成,并可以构建各种复杂的结构。\n", + "\n", + "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的 LeNet 网络分别介绍这三种形式。\n", "\n", "## 通过内置模型组网\n", "\n", @@ -16,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "9c9d3513", "metadata": {}, "outputs": [ @@ -63,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "9a86cc3e", "metadata": {}, "outputs": [ @@ -119,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "cf89df53", "metadata": {}, "outputs": [ @@ -176,6 +179,45 @@ "print(lenet_SubClass)" ] }, + { + "cell_type": "markdown", + "id": "38716a23", + "metadata": {}, + "source": [ + "## 模型前向\n", + "\n", + "完成模型组网之后,可以通过传递数据到模型来完成一次模型的前向过程。\n", + "\n", + "在下面的代码中,传入一个 shape 为 [1, 1, 28, 28] 的图像给网络,这样会执行模型的 forward 函数以得到模型的输出。注意,这里不可以直接调用 model.forward。完成计算年后,模型会返回一个10维的Tensor,表示输入是每个类别的分数,可以通过调用 [nn.Softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Softmax_cn.html#softmax) 获取每个类别的概率。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "68e0f6be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output shape: [1, 10]\n", + "probs: Tensor(shape=[1, 10], dtype=float32, place=CPUPlace, stop_gradient=False,\n", + " [[0.09509374, 0.12776302, 0.08948905, 0.09307785, 0.09075093, 0.13374873,\n", + " 0.08719291, 0.07862016, 0.11715320, 0.08711041]])\n" + ] + } + ], + "source": [ + "x = paddle.ones([1, 1, 28, 28])\n", + "y = lenet(x)\n", + "print('output shape: {}'.format(y.shape))\n", + "\n", + "soft_max_op = paddle.nn.Softmax()\n", + "probs = soft_max_op(y)\n", + "print('probs: {}'.format(probs))" + ] + }, { "cell_type": "markdown", "id": "8843cf9c", @@ -255,7 +297,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -269,7 +311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.10" } }, "nbformat": 4, From eb3c0f65f4bba941c09f146cc5b929b3a3f06f22 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Thu, 6 Jan 2022 10:31:24 +0800 Subject: [PATCH 25/63] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E7=BB=84=E7=BD=91=E5=92=8C=E5=BF=AB=E9=80=9F=E5=85=A5=E9=97=A8?= =?UTF-8?q?=E7=AB=A0=E8=8A=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1、更新模型组网章节 2、更新快速入门章节paddle.text(目录不含.dataset) --- .../01_quick_start_cn.ipynb | 8 +- .../02_paddle2.0_develop/04_model_cn.ipynb | 408 +++++++++++------- .../images/model_develop_flow.png | Bin 190803 -> 190267 bytes 3 files changed, 254 insertions(+), 162 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 5ce0aa86e36..c18eab57c37 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -168,7 +168,7 @@ }, { "data": { - "image/png": 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" ] @@ -288,7 +288,7 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", "\n", @@ -591,7 +591,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -638,7 +638,7 @@ "\n", "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", + "
\n", "

图1:模型开发流程
\n", "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 020aa252238..332482f366c 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -2,54 +2,131 @@ "cells": [ { "cell_type": "markdown", - "id": "72d769ce", + "id": "ca3e772f-5f57-450f-9370-bdc80f6ef241", "metadata": {}, "source": [ "# 模型组网\n", "\n", "\n", - "深度学习中的神经网络可以视为是输入到输出的映射函数,如图像到语义(0-9)的映射,足够深的神经网络理论上可以拟合任何复杂的函数。飞桨框架中,各种各样的神经网络层都在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下,每个神经网络层都是 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的子类。每个神经网络由各种各样的神经网络层组成,并可以构建各种复杂的结构。\n", + "模型组网是深度学习任务中的重要一环,该环节定义了神经网络的层次结构、数据从输入到输出的计算过程(即前向计算)等。\n", "\n", - "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的 LeNet 网络分别介绍这三种形式。\n", + "飞桨框架提供了多种模型组网方式,本文介绍如下几种常见用法:\n", + "* **直接使用内置模型**\n", + "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**\n", + "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网**\n", + "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**\n", "\n", - "## 通过内置模型组网\n", + "另外飞桨框架提供了 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 函数方便查看网络结构、每层的输入输出 shape 和参数信息。" + ] + }, + { + "cell_type": "markdown", + "id": "3615327e-4ac0-4616-a091-077046fb40f8", + "metadata": {}, + "source": [ + "## 一、直接使用内置模型\n", "\n", - "飞桨在 [paddle.vision.models.LeNet](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了常用的分类模型,可以进行很方便的调用,通过下面的命令可以直接初始化一个LeNet模型" + "飞桨框架目前在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了计算机视觉领域的一些经典模型,只需一行代码即可完成网络构建和初始化,适合完成一些简单的深度学习任务,满足深度学习初阶用户感受模型的输入和输出形式、了解模型的性能。" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "9c9d3513", - "metadata": {}, + "execution_count": 1, + "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T07:21:41.979509Z", + "iopub.status.busy": "2022-01-05T07:21:41.978819Z", + "iopub.status.idle": "2022-01-05T07:21:44.967951Z", + "shell.execute_reply": "2022-01-05T07:21:44.966827Z", + "shell.execute_reply.started": "2022-01-05T07:21:41.979443Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "LeNet(\n", - " (features): Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " )\n", - " (fc): Sequential(\n", - " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", - " )\n", - ")\n" + "飞桨框架内置模型: ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'VGG', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'MobileNetV1', 'mobilenet_v1', 'MobileNetV2', 'mobilenet_v2', 'LeNet']\n" ] } ], "source": [ "import paddle\n", "\n", + "print('飞桨框架内置模型:', paddle.vision.models.__all__)" + ] + }, + { + "cell_type": "markdown", + "id": "79693587-4896-463d-be11-36cc939748d6", + "metadata": {}, + "source": [ + "以 LeNet 模型为例,可通过如下代码组网:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "883f5395-3b1c-4d58-a70e-1dd7886a7d36", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T08:23:47.069913Z", + "iopub.status.busy": "2022-01-05T08:23:47.068838Z", + "iopub.status.idle": "2022-01-05T08:23:47.101420Z", + "shell.execute_reply": "2022-01-05T08:23:47.100556Z", + "shell.execute_reply.started": "2022-01-05T08:23:47.069844Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-4 [[1, 400]] [1, 120] 48,120 \n", + " Linear-5 [[1, 120]] [1, 84] 10,164 \n", + " Linear-6 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'total_params': 61610, 'trainable_params': 61610}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 模型组网并初始化网络\n", "lenet = paddle.vision.models.LeNet(num_classes=10)\n", - "print(lenet)" + "\n", + "# 可视化模型组网结构和参数\n", + "paddle.summary(lenet,(1, 1, 28, 28))" ] }, { @@ -57,40 +134,86 @@ "id": "c48f8ac6", "metadata": {}, "source": [ - "可以看到LeNet包含`features`和`fc`两个子网络,总共包含2个卷积层,2个ReLU激活层,2个MaxPool2D层,三个全链接层。\n", + "通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 `Conv2D` 卷积层、`ReLU` 激活层、`MaxPool2D` 池化层以及 `Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", + "
\n", + "

图1:LeNet网络结构示意图
\n", + "\n", + "另外在 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c5fda8b-b153-49ee-b168-30f5293730ae", + "metadata": {}, + "source": [ + "## 二、Paddle.nn 介绍\n", + "\n", + "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 中查看。\n", + "\n", + "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential)、 \n", + "[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", + " \n", + "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、xxx)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", + "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", + "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**:xxxxxx\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7ab2efd1-2623-4678-809e-9264f14d9c7c", + "metadata": {}, + "source": [ + "\n", + "## 三、使用 paddle.nn.Sequential 组网\n", + "\n", "\n", - "## 通过 Sequential 组网\n", + "构建顺序的线性网络结构时,可以选择该方式,只需要按模型的结构顺序,一层一层加到 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 子类中即可。\n", "\n", - "针对顺序的线性网络结构,可以直接使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 来快速完成组网,这种方式可以减少类的定义等代码编写。具体代码如下:" + "参照前面图 1 所示的 LeNet 模型结构,构建该网络结构的代码如下:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "9a86cc3e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T08:40:20.974280Z", + "iopub.status.busy": "2022-01-05T08:40:20.973687Z", + "iopub.status.idle": "2022-01-05T08:40:21.032406Z", + "shell.execute_reply": "2022-01-05T08:40:21.030756Z", + "shell.execute_reply.started": "2022-01-05T08:40:20.974221Z" + }, + "scrolled": true + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (6): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (7): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (8): Linear(in_features=84, out_features=10, dtype=float32)\n", - ")\n" + "ename": "ValueError", + "evalue": "(InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_215/4050339869.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# 可视化模型组网结构和参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpaddle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlenet_Sequential\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(net, input_size, dtypes, input)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0m_input_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_input_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummary_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_input_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36msummary_string\u001b[0;34m(model, input_size, dtypes, input)\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/base.py\u001b[0m in \u001b[0;36m_decorate_function\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_decorate_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/common.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m out = F.linear(\n\u001b[0;32m--> 172\u001b[0;31m x=input, weight=self.weight, bias=self.bias, name=self.name)\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/common.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(x, weight, bias, name)\u001b[0m\n\u001b[1;32m 1478\u001b[0m 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"lenet_Sequential = nn.Sequential(\n", " nn.Conv2D(1, 6, 3, stride=1, padding=1),\n", " nn.ReLU(),\n", @@ -102,7 +225,21 @@ " nn.Linear(120, 84), \n", " nn.Linear(84, 10)\n", ")\n", - "print(lenet_Sequential)" + "# 可视化模型组网结构和参数\n", + "paddle.summary(lenet_Sequential,(1, 1, 28, 28))" + ] + }, + { + "cell_type": "markdown", + "id": "19fd4c4c-9ff4-434e-b06c-32daf8e1ae43", + "metadata": {}, + "source": [ + "以上代码实现的组网与 paddle.vision.models.LeNet 完全一样。\n", + "\n", + "Sequential组网中框架做了什么:\n", + "\n", + "使用Sequential组网方式的条件和限制:\n", + "\n" ] }, { @@ -110,45 +247,66 @@ "id": "b9524d1c", "metadata": {}, "source": [ - "## 通过 SubClass 组网\n", + "## 四、使用 paddle.nn.Layer 组网\n", + "\n", + "构建一些比较复杂的网络结构时,可以选择该方式,组网包括三个步骤:\n", + "1. 创建一个继承自 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的类;\n", + "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(sublayer);\n", + "1. 在类的前向计算函数 `forward` 中使用定义好的 sublayer 进行前向计算。\n", "\n", - "针对一些比较复杂的网络结构,就可以使用 SubClass 组网的方式来进行模型代码编写。通过 SubClass 组网进行组网需要完成下列三个步骤:\n", - "1. 创建一个继承自[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)\n", - "2. 在类的构造函数`__init__`中进行子Layer的定义,完成网络的构建\n", - "3. 在类的`forward`函数中使用定义的子Layer进行前向计算。\n", + "并且 sublayer 既可以通过基础的神经网络层 API(如卷积层、池化层、全连接层等)定义,也可以通过 nn.Sequential 或 nn.Layer 定义。由此可见,paddle.nn.Layer 的组网用法非常灵活,便于构建各种复杂网络。\n", "\n", - "子Layer可以通过 基础API(卷积,池化或全连接),Sequential 或 SubClass 的形式进行定义,子Layer在构造函数中一次定义后可在forward中多次调用。使用SubClass 组网形式实现LeNet的代码如下" + "仍然以 LeNet 模型为例,使用 paddle.nn.Layer 组网的代码如下:\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "cf89df53", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-03T12:04:22.365846Z", + "iopub.status.busy": "2022-01-03T12:04:22.365241Z", + "iopub.status.idle": "2022-01-03T12:04:22.386477Z", + "shell.execute_reply": "2022-01-03T12:04:22.385631Z", + "shell.execute_reply.started": "2022-01-03T12:04:22.365800Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "LeNet(\n", - " (features): Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " )\n", - " (fc): Sequential(\n", - " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", - " )\n", - ")\n" + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-5 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-5 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-5 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-6 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-6 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-6 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-7 [[1, 400]] [1, 120] 48,120 \n", + " Linear-8 [[1, 120]] [1, 84] 10,164 \n", + " Linear-9 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n", + "{'total_params': 61610, 'trainable_params': 61610}\n" ] } ], "source": [ + "# 使用 Subclass 方式构建 LeNet 模型\n", "class LeNet(nn.Layer):\n", " def __init__(self, num_classes=10):\n", " super(LeNet, self).__init__()\n", @@ -176,130 +334,64 @@ " x = self.fc(x)\n", " return x\n", "lenet_SubClass = LeNet()\n", - "print(lenet_SubClass)" + "\n", + "# 可视化模型组网结构和参数\n", + "params_info = paddle.summary(lenet_SubClass,(1, 1, 28, 28))\n", + "print(params_info)" ] }, { "cell_type": "markdown", - "id": "38716a23", + "id": "541d133d", "metadata": {}, "source": [ - "## 模型前向\n", - "\n", - "完成模型组网之后,可以通过传递数据到模型来完成一次模型的前向过程。\n", + "## 五、使用 paddle.nn.LayerList 组网\n", "\n", - "在下面的代码中,传入一个 shape 为 [1, 1, 28, 28] 的图像给网络,这样会执行模型的 forward 函数以得到模型的输出。注意,这里不可以直接调用 model.forward。完成计算年后,模型会返回一个10维的Tensor,表示输入是每个类别的分数,可以通过调用 [nn.Softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Softmax_cn.html#softmax) 获取每个类别的概率。" + "待补充" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "68e0f6be", + "cell_type": "markdown", + "id": "c55565c4-cc47-4654-98cf-bccc8bff1331", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output shape: [1, 10]\n", - "probs: Tensor(shape=[1, 10], dtype=float32, place=CPUPlace, stop_gradient=False,\n", - " [[0.09509374, 0.12776302, 0.08948905, 0.09307785, 0.09075093, 0.13374873,\n", - " 0.08719291, 0.07862016, 0.11715320, 0.08711041]])\n" - ] - } - ], "source": [ - "x = paddle.ones([1, 1, 28, 28])\n", - "y = lenet(x)\n", - "print('output shape: {}'.format(y.shape))\n", + "# 六、总结\n", "\n", - "soft_max_op = paddle.nn.Softmax()\n", - "probs = soft_max_op(y)\n", - "print('probs: {}'.format(probs))" + "待补充" ] }, { "cell_type": "markdown", - "id": "8843cf9c", + "id": "490f2617-b7c2-4b3c-9ee6-1dae6c04bd59", "metadata": {}, "source": [ - "## 飞桨内置基础API\n", + "# 扩展阅读:模型的层(Layer)\n", + "\n", + "选几个经典的层解读一下\n", + "\n", "\n", - "飞桨内置了大量基础的组网API,组网相关的API都在[paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#paddle-nn)目录下。组网相关的API类别与具体的API列表如下表:\n", "\n", - "| 功能 | API名称 |\n", - "| --- | ---|\n", - "| Conv | Conv1D、Conv2D、Conv3D、Conv1DTranspose、Conv2DTranspose、Conv3DTranspose |\n", - "| Pool | AdaptiveAvgPool1D、AdaptiveAvgPool2D、AdaptiveAvgPool3D、 AdaptiveMaxPool1D、AdaptiveMaxPool2D、AdaptiveMaxPool3D、 AvgPool1D、AvgPool2D、AvgPool3D、MaxPool1D、MaxPool2D、MaxPool3D |\n", - "| Padding | Pad1D、Pad2D、Pad3D |\n", - "| Activation | ELU、GELU、Hardshrink、Hardtanh、HSigmoid、LeakyReLU、LogSigmoid、 LogSoftmax、PReLU、ReLU、ReLU6、SELU、Sigmoid、Softmax、Softplus、 Softshrink、Softsign、Tanh、Tanhshrink |\n", - "| Normlization | BatchNorm、BatchNorm1D、BatchNorm2D、BatchNorm3D、GroupNorm、 InstanceNorm1D、InstanceNorm2D、InstanceNorm3D、LayerNorm、SpectralNorm、 SyncBatchNorm |\n", - "| Recurrent NN | BiRNN、GRU、GRUCell、LSTM、LSTMCell、RNN、RNNCellBase、SimpleRNN、SimpleRNNCell | \n", - "| Transformer | Transformer、TransformerDecoder、TransformerDecoderLayer、| TransformerEncoder、TransformerEncoderLayer |\n", - "| Dropout | AlphaDropout、Dropout、Dropout2d、Dropout3d |\n", - "| Loss | BCELoss、BCEWithLogitsLoss、CrossEntropyLoss、CTCLoss、KLDivLoss、L1Loss、 MarginRankingLoss、MSELoss、NLLLoss、SmoothL1Loss |\n", "\n", - "## 模型的参数\n", "\n", - "飞桨内置的 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 方法可以很方便的查看网络的基础结构,每层的输入输出shape和参数信息。" + "\n" ] }, { - "cell_type": "code", - "execution_count": 14, - "id": "4617d646", + "cell_type": "markdown", + "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------------\n", - " Layer (type) Input Shape Output Shape Param # \n", - "===========================================================================\n", - " Conv2D-5 [[64, 1, 28, 28]] [64, 6, 28, 28] 60 \n", - " ReLU-5 [[64, 6, 28, 28]] [64, 6, 28, 28] 0 \n", - " MaxPool2D-5 [[64, 6, 28, 28]] [64, 6, 14, 14] 0 \n", - " Conv2D-6 [[64, 6, 14, 14]] [64, 16, 10, 10] 2,416 \n", - " ReLU-6 [[64, 16, 10, 10]] [64, 16, 10, 10] 0 \n", - " MaxPool2D-6 [[64, 16, 10, 10]] [64, 16, 5, 5] 0 \n", - " Linear-7 [[64, 400]] [64, 120] 48,120 \n", - " Linear-8 [[64, 120]] [64, 84] 10,164 \n", - " Linear-9 [[64, 84]] [64, 10] 850 \n", - "===========================================================================\n", - "Total params: 61,610\n", - "Trainable params: 61,610\n", - "Non-trainable params: 0\n", - "---------------------------------------------------------------------------\n", - "Input size (MB): 0.19\n", - "Forward/backward pass size (MB): 7.03\n", - "Params size (MB): 0.24\n", - "Estimated Total Size (MB): 7.46\n", - "---------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "{'total_params': 61610, 'trainable_params': 61610}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "paddle.summary(lenet, (64, 1, 28, 28))" + "# 扩展阅读:模型的参数(Parameter)\n", + "\n", + "补充parameters的介绍\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -311,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png b/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png index ab5ebe1533900b65d7339a563734edb0510ff435..1a0f6196bebb10dcbbe3e8c7ee6ad3cb20aaf151 100644 GIT binary patch literal 190267 zcma&ObyStx{yj{0D$=PS-6`GO-6<{I(%mVIbc=Lt8tDcB$xXLNOV{t&-h1vn=f2-R zUdPyDhfl4wJ~`)HL@Fyvp`#F@KtVyF%Sel>LP5cYK|#U7At3<2pmv^R18>kSs#2m* zRTIPqz!y(*Z5ay%1t>b;GZGXWG$9l`?O+I&O&JA6 zs4xsA7J1)>G!1fx2kbx~6oNc=c44{&i}44u?gjtH0iP@#ouwZ(9w+wKt0!K^MJX1N 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charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit eb3c0f65f4bba941c09f146cc5b929b3a3f06f22. --- .../01_quick_start_cn.ipynb | 8 +- .../02_paddle2.0_develop/04_model_cn.ipynb | 408 +++++++----------- .../images/model_develop_flow.png | Bin 190267 -> 190803 bytes 3 files changed, 162 insertions(+), 254 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index c18eab57c37..5ce0aa86e36 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -168,7 +168,7 @@ }, { "data": { - "image/png": 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" ] @@ -288,7 +288,7 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", "\n", @@ -591,7 +591,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -638,7 +638,7 @@ "\n", "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", + "
\n", "

图1:模型开发流程
\n", "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 332482f366c..020aa252238 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -2,131 +2,54 @@ "cells": [ { "cell_type": "markdown", - "id": "ca3e772f-5f57-450f-9370-bdc80f6ef241", + "id": "72d769ce", "metadata": {}, "source": [ "# 模型组网\n", "\n", "\n", - "模型组网是深度学习任务中的重要一环,该环节定义了神经网络的层次结构、数据从输入到输出的计算过程(即前向计算)等。\n", + "深度学习中的神经网络可以视为是输入到输出的映射函数,如图像到语义(0-9)的映射,足够深的神经网络理论上可以拟合任何复杂的函数。飞桨框架中,各种各样的神经网络层都在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下,每个神经网络层都是 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的子类。每个神经网络由各种各样的神经网络层组成,并可以构建各种复杂的结构。\n", "\n", - "飞桨框架提供了多种模型组网方式,本文介绍如下几种常见用法:\n", - "* **直接使用内置模型**\n", - "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**\n", - "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网**\n", - "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**\n", + "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的 LeNet 网络分别介绍这三种形式。\n", "\n", - "另外飞桨框架提供了 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 函数方便查看网络结构、每层的输入输出 shape 和参数信息。" - ] - }, - { - "cell_type": "markdown", - "id": "3615327e-4ac0-4616-a091-077046fb40f8", - "metadata": {}, - "source": [ - "## 一、直接使用内置模型\n", + "## 通过内置模型组网\n", "\n", - "飞桨框架目前在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了计算机视觉领域的一些经典模型,只需一行代码即可完成网络构建和初始化,适合完成一些简单的深度学习任务,满足深度学习初阶用户感受模型的输入和输出形式、了解模型的性能。" + "飞桨在 [paddle.vision.models.LeNet](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了常用的分类模型,可以进行很方便的调用,通过下面的命令可以直接初始化一个LeNet模型" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", - "metadata": { - "execution": { - "iopub.execute_input": "2022-01-05T07:21:41.979509Z", - "iopub.status.busy": "2022-01-05T07:21:41.978819Z", - "iopub.status.idle": "2022-01-05T07:21:44.967951Z", - "shell.execute_reply": "2022-01-05T07:21:44.966827Z", - "shell.execute_reply.started": "2022-01-05T07:21:41.979443Z" - }, - "scrolled": true - }, + "execution_count": 2, + "id": "9c9d3513", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "飞桨框架内置模型: ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'VGG', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'MobileNetV1', 'mobilenet_v1', 'MobileNetV2', 'mobilenet_v2', 'LeNet']\n" + "LeNet(\n", + " (features): Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", + " )\n", + ")\n" ] } ], "source": [ "import paddle\n", "\n", - "print('飞桨框架内置模型:', paddle.vision.models.__all__)" - ] - }, - { - "cell_type": "markdown", - "id": "79693587-4896-463d-be11-36cc939748d6", - "metadata": {}, - "source": [ - "以 LeNet 模型为例,可通过如下代码组网:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "883f5395-3b1c-4d58-a70e-1dd7886a7d36", - "metadata": { - "execution": { - "iopub.execute_input": "2022-01-05T08:23:47.069913Z", - "iopub.status.busy": "2022-01-05T08:23:47.068838Z", - "iopub.status.idle": "2022-01-05T08:23:47.101420Z", - "shell.execute_reply": "2022-01-05T08:23:47.100556Z", - "shell.execute_reply.started": "2022-01-05T08:23:47.069844Z" - }, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------------\n", - " Layer (type) Input Shape Output Shape Param # \n", - "===========================================================================\n", - " Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", - " ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", - " MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", - " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", - " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", - " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Linear-4 [[1, 400]] [1, 120] 48,120 \n", - " Linear-5 [[1, 120]] [1, 84] 10,164 \n", - " Linear-6 [[1, 84]] [1, 10] 850 \n", - "===========================================================================\n", - "Total params: 61,610\n", - "Trainable params: 61,610\n", - "Non-trainable params: 0\n", - "---------------------------------------------------------------------------\n", - "Input size (MB): 0.00\n", - "Forward/backward pass size (MB): 0.11\n", - "Params size (MB): 0.24\n", - "Estimated Total Size (MB): 0.35\n", - "---------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "{'total_params': 61610, 'trainable_params': 61610}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 模型组网并初始化网络\n", "lenet = paddle.vision.models.LeNet(num_classes=10)\n", - "\n", - "# 可视化模型组网结构和参数\n", - "paddle.summary(lenet,(1, 1, 28, 28))" + "print(lenet)" ] }, { @@ -134,86 +57,40 @@ "id": "c48f8ac6", "metadata": {}, "source": [ - "通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 `Conv2D` 卷积层、`ReLU` 激活层、`MaxPool2D` 池化层以及 `Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", - "
\n", - "

图1:LeNet网络结构示意图
\n", - "\n", - "另外在 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "4c5fda8b-b153-49ee-b168-30f5293730ae", - "metadata": {}, - "source": [ - "## 二、Paddle.nn 介绍\n", - "\n", - "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 中查看。\n", - "\n", - "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential)、 \n", - "[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", - " \n", - "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、xxx)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", - "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", - "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**:xxxxxx\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "7ab2efd1-2623-4678-809e-9264f14d9c7c", - "metadata": {}, - "source": [ - "\n", - "## 三、使用 paddle.nn.Sequential 组网\n", - "\n", + "可以看到LeNet包含`features`和`fc`两个子网络,总共包含2个卷积层,2个ReLU激活层,2个MaxPool2D层,三个全链接层。\n", "\n", - "构建顺序的线性网络结构时,可以选择该方式,只需要按模型的结构顺序,一层一层加到 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 子类中即可。\n", + "## 通过 Sequential 组网\n", "\n", - "参照前面图 1 所示的 LeNet 模型结构,构建该网络结构的代码如下:" + "针对顺序的线性网络结构,可以直接使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 来快速完成组网,这种方式可以减少类的定义等代码编写。具体代码如下:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "9a86cc3e", - "metadata": { - "execution": { - "iopub.execute_input": "2022-01-05T08:40:20.974280Z", - "iopub.status.busy": "2022-01-05T08:40:20.973687Z", - "iopub.status.idle": "2022-01-05T08:40:21.032406Z", - "shell.execute_reply": "2022-01-05T08:40:21.030756Z", - "shell.execute_reply.started": "2022-01-05T08:40:20.974221Z" - }, - "scrolled": true - }, + "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "(InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_215/4050339869.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# 可视化模型组网结构和参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpaddle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlenet_Sequential\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(net, input_size, dtypes, input)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0m_input_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_input_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummary_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_input_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36msummary_string\u001b[0;34m(model, input_size, dtypes, input)\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/base.py\u001b[0m in \u001b[0;36m_decorate_function\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0;34m@\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py\u001b[0m in \u001b[0;36msummary_string\u001b[0;34m(model, input_size, dtypes, input)\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;31m# make a forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m 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in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_built\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mforward_post_hook\u001b[0m \u001b[0;32min\u001b[0m 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\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/common.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(x, weight, bias, name)\u001b[0m\n\u001b[1;32m 1478\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0min_dygraph_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1479\u001b[0m pre_bias = _C_ops.matmul_v2(x, weight, 'trans_x', False, 'trans_y',\n\u001b[0;32m-> 1480\u001b[0;31m False)\n\u001b[0m\u001b[1;32m 1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1482\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: (InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]" + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (6): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (7): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (8): Linear(in_features=84, out_features=10, dtype=float32)\n", + ")\n" ] } ], "source": [ "from paddle import nn\n", "\n", - "# 使用 paddle.nn.Sequential 构建 LeNet 模型\n", "lenet_Sequential = nn.Sequential(\n", " nn.Conv2D(1, 6, 3, stride=1, padding=1),\n", " nn.ReLU(),\n", @@ -225,21 +102,7 @@ " nn.Linear(120, 84), \n", " nn.Linear(84, 10)\n", ")\n", - "# 可视化模型组网结构和参数\n", - "paddle.summary(lenet_Sequential,(1, 1, 28, 28))" - ] - }, - { - "cell_type": "markdown", - "id": "19fd4c4c-9ff4-434e-b06c-32daf8e1ae43", - "metadata": {}, - "source": [ - "以上代码实现的组网与 paddle.vision.models.LeNet 完全一样。\n", - "\n", - "Sequential组网中框架做了什么:\n", - "\n", - "使用Sequential组网方式的条件和限制:\n", - "\n" + "print(lenet_Sequential)" ] }, { @@ -247,66 +110,45 @@ "id": "b9524d1c", "metadata": {}, "source": [ - "## 四、使用 paddle.nn.Layer 组网\n", - "\n", - "构建一些比较复杂的网络结构时,可以选择该方式,组网包括三个步骤:\n", - "1. 创建一个继承自 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的类;\n", - "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(sublayer);\n", - "1. 在类的前向计算函数 `forward` 中使用定义好的 sublayer 进行前向计算。\n", + "## 通过 SubClass 组网\n", "\n", - "并且 sublayer 既可以通过基础的神经网络层 API(如卷积层、池化层、全连接层等)定义,也可以通过 nn.Sequential 或 nn.Layer 定义。由此可见,paddle.nn.Layer 的组网用法非常灵活,便于构建各种复杂网络。\n", + "针对一些比较复杂的网络结构,就可以使用 SubClass 组网的方式来进行模型代码编写。通过 SubClass 组网进行组网需要完成下列三个步骤:\n", + "1. 创建一个继承自[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)\n", + "2. 在类的构造函数`__init__`中进行子Layer的定义,完成网络的构建\n", + "3. 在类的`forward`函数中使用定义的子Layer进行前向计算。\n", "\n", - "仍然以 LeNet 模型为例,使用 paddle.nn.Layer 组网的代码如下:\n" + "子Layer可以通过 基础API(卷积,池化或全连接),Sequential 或 SubClass 的形式进行定义,子Layer在构造函数中一次定义后可在forward中多次调用。使用SubClass 组网形式实现LeNet的代码如下" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "cf89df53", - "metadata": { - "execution": { - "iopub.execute_input": "2022-01-03T12:04:22.365846Z", - "iopub.status.busy": "2022-01-03T12:04:22.365241Z", - "iopub.status.idle": "2022-01-03T12:04:22.386477Z", - "shell.execute_reply": "2022-01-03T12:04:22.385631Z", - "shell.execute_reply.started": "2022-01-03T12:04:22.365800Z" - }, - "scrolled": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "---------------------------------------------------------------------------\n", - " Layer (type) Input Shape Output Shape Param # \n", - "===========================================================================\n", - " Conv2D-5 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", - " ReLU-5 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", - " MaxPool2D-5 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", - " Conv2D-6 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", - " ReLU-6 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", - " MaxPool2D-6 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Linear-7 [[1, 400]] [1, 120] 48,120 \n", - " Linear-8 [[1, 120]] [1, 84] 10,164 \n", - " Linear-9 [[1, 84]] [1, 10] 850 \n", - "===========================================================================\n", - "Total params: 61,610\n", - "Trainable params: 61,610\n", - "Non-trainable params: 0\n", - "---------------------------------------------------------------------------\n", - "Input size (MB): 0.00\n", - "Forward/backward pass size (MB): 0.11\n", - "Params size (MB): 0.24\n", - "Estimated Total Size (MB): 0.35\n", - "---------------------------------------------------------------------------\n", - "\n", - "{'total_params': 61610, 'trainable_params': 61610}\n" + "LeNet(\n", + " (features): Sequential(\n", + " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", + " (1): ReLU()\n", + " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", + " (4): ReLU()\n", + " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", + " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", + " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", + " )\n", + ")\n" ] } ], "source": [ - "# 使用 Subclass 方式构建 LeNet 模型\n", "class LeNet(nn.Layer):\n", " def __init__(self, num_classes=10):\n", " super(LeNet, self).__init__()\n", @@ -334,64 +176,130 @@ " x = self.fc(x)\n", " return x\n", "lenet_SubClass = LeNet()\n", - "\n", - "# 可视化模型组网结构和参数\n", - "params_info = paddle.summary(lenet_SubClass,(1, 1, 28, 28))\n", - "print(params_info)" + "print(lenet_SubClass)" ] }, { "cell_type": "markdown", - "id": "541d133d", + "id": "38716a23", "metadata": {}, "source": [ - "## 五、使用 paddle.nn.LayerList 组网\n", + "## 模型前向\n", + "\n", + "完成模型组网之后,可以通过传递数据到模型来完成一次模型的前向过程。\n", "\n", - "待补充" + "在下面的代码中,传入一个 shape 为 [1, 1, 28, 28] 的图像给网络,这样会执行模型的 forward 函数以得到模型的输出。注意,这里不可以直接调用 model.forward。完成计算年后,模型会返回一个10维的Tensor,表示输入是每个类别的分数,可以通过调用 [nn.Softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Softmax_cn.html#softmax) 获取每个类别的概率。" ] }, { - "cell_type": "markdown", - "id": "c55565c4-cc47-4654-98cf-bccc8bff1331", + "cell_type": "code", + "execution_count": 9, + "id": "68e0f6be", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output shape: [1, 10]\n", + "probs: Tensor(shape=[1, 10], dtype=float32, place=CPUPlace, stop_gradient=False,\n", + " [[0.09509374, 0.12776302, 0.08948905, 0.09307785, 0.09075093, 0.13374873,\n", + " 0.08719291, 0.07862016, 0.11715320, 0.08711041]])\n" + ] + } + ], "source": [ - "# 六、总结\n", + "x = paddle.ones([1, 1, 28, 28])\n", + "y = lenet(x)\n", + "print('output shape: {}'.format(y.shape))\n", "\n", - "待补充" + "soft_max_op = paddle.nn.Softmax()\n", + "probs = soft_max_op(y)\n", + "print('probs: {}'.format(probs))" ] }, { "cell_type": "markdown", - "id": "490f2617-b7c2-4b3c-9ee6-1dae6c04bd59", + "id": "8843cf9c", "metadata": {}, "source": [ - "# 扩展阅读:模型的层(Layer)\n", - "\n", - "选几个经典的层解读一下\n", - "\n", + "## 飞桨内置基础API\n", "\n", + "飞桨内置了大量基础的组网API,组网相关的API都在[paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#paddle-nn)目录下。组网相关的API类别与具体的API列表如下表:\n", "\n", + "| 功能 | API名称 |\n", + "| --- | ---|\n", + "| Conv | Conv1D、Conv2D、Conv3D、Conv1DTranspose、Conv2DTranspose、Conv3DTranspose |\n", + "| Pool | AdaptiveAvgPool1D、AdaptiveAvgPool2D、AdaptiveAvgPool3D、 AdaptiveMaxPool1D、AdaptiveMaxPool2D、AdaptiveMaxPool3D、 AvgPool1D、AvgPool2D、AvgPool3D、MaxPool1D、MaxPool2D、MaxPool3D |\n", + "| Padding | Pad1D、Pad2D、Pad3D |\n", + "| Activation | ELU、GELU、Hardshrink、Hardtanh、HSigmoid、LeakyReLU、LogSigmoid、 LogSoftmax、PReLU、ReLU、ReLU6、SELU、Sigmoid、Softmax、Softplus、 Softshrink、Softsign、Tanh、Tanhshrink |\n", + "| Normlization | BatchNorm、BatchNorm1D、BatchNorm2D、BatchNorm3D、GroupNorm、 InstanceNorm1D、InstanceNorm2D、InstanceNorm3D、LayerNorm、SpectralNorm、 SyncBatchNorm |\n", + "| Recurrent NN | BiRNN、GRU、GRUCell、LSTM、LSTMCell、RNN、RNNCellBase、SimpleRNN、SimpleRNNCell | \n", + "| Transformer | Transformer、TransformerDecoder、TransformerDecoderLayer、| TransformerEncoder、TransformerEncoderLayer |\n", + "| Dropout | AlphaDropout、Dropout、Dropout2d、Dropout3d |\n", + "| Loss | BCELoss、BCEWithLogitsLoss、CrossEntropyLoss、CTCLoss、KLDivLoss、L1Loss、 MarginRankingLoss、MSELoss、NLLLoss、SmoothL1Loss |\n", "\n", + "## 模型的参数\n", "\n", - "\n" + "飞桨内置的 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 方法可以很方便的查看网络的基础结构,每层的输入输出shape和参数信息。" ] }, { - "cell_type": "markdown", - "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", + "cell_type": "code", + "execution_count": 14, + "id": "4617d646", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-5 [[64, 1, 28, 28]] [64, 6, 28, 28] 60 \n", + " ReLU-5 [[64, 6, 28, 28]] [64, 6, 28, 28] 0 \n", + " MaxPool2D-5 [[64, 6, 28, 28]] [64, 6, 14, 14] 0 \n", + " Conv2D-6 [[64, 6, 14, 14]] [64, 16, 10, 10] 2,416 \n", + " ReLU-6 [[64, 16, 10, 10]] [64, 16, 10, 10] 0 \n", + " MaxPool2D-6 [[64, 16, 10, 10]] [64, 16, 5, 5] 0 \n", + " Linear-7 [[64, 400]] [64, 120] 48,120 \n", + " Linear-8 [[64, 120]] [64, 84] 10,164 \n", + " Linear-9 [[64, 84]] [64, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.19\n", + "Forward/backward pass size (MB): 7.03\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 7.46\n", + "---------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'total_params': 61610, 'trainable_params': 61610}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 扩展阅读:模型的参数(Parameter)\n", - "\n", - "补充parameters的介绍\n" + "paddle.summary(lenet, (64, 1, 28, 28))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + 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" ] @@ -288,7 +288,7 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", "\n", @@ -362,8 +362,7 @@ "lenet = paddle.vision.models.LeNet(num_classes=10)\n", "\n", "# 可视化模型组网结构和参数\n", - "params_info = paddle.summary(lenet,(1, 1, 28, 28))\n", - "print(params_info)" + "paddle.summary(lenet,(1, 1, 28, 28))" ] }, { @@ -591,7 +590,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -638,7 +637,7 @@ "\n", "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", + "
\n", "

图1:模型开发流程
\n", "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 020aa252238..332482f366c 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -2,54 +2,131 @@ "cells": [ { "cell_type": "markdown", - "id": "72d769ce", + "id": "ca3e772f-5f57-450f-9370-bdc80f6ef241", "metadata": {}, "source": [ "# 模型组网\n", "\n", "\n", - "深度学习中的神经网络可以视为是输入到输出的映射函数,如图像到语义(0-9)的映射,足够深的神经网络理论上可以拟合任何复杂的函数。飞桨框架中,各种各样的神经网络层都在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下,每个神经网络层都是 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的子类。每个神经网络由各种各样的神经网络层组成,并可以构建各种复杂的结构。\n", + "模型组网是深度学习任务中的重要一环,该环节定义了神经网络的层次结构、数据从输入到输出的计算过程(即前向计算)等。\n", "\n", - "飞桨的模型组网分为通过内置模型组网,通过 Sequential 组网和通过 SubClass 组网三种形式,下面通过前面使用的 LeNet 网络分别介绍这三种形式。\n", + "飞桨框架提供了多种模型组网方式,本文介绍如下几种常见用法:\n", + "* **直接使用内置模型**\n", + "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**\n", + "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网**\n", + "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**\n", "\n", - "## 通过内置模型组网\n", + "另外飞桨框架提供了 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 函数方便查看网络结构、每层的输入输出 shape 和参数信息。" + ] + }, + { + "cell_type": "markdown", + "id": "3615327e-4ac0-4616-a091-077046fb40f8", + "metadata": {}, + "source": [ + "## 一、直接使用内置模型\n", "\n", - "飞桨在 [paddle.vision.models.LeNet](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了常用的分类模型,可以进行很方便的调用,通过下面的命令可以直接初始化一个LeNet模型" + "飞桨框架目前在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了计算机视觉领域的一些经典模型,只需一行代码即可完成网络构建和初始化,适合完成一些简单的深度学习任务,满足深度学习初阶用户感受模型的输入和输出形式、了解模型的性能。" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "9c9d3513", - "metadata": {}, + "execution_count": 1, + "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T07:21:41.979509Z", + "iopub.status.busy": "2022-01-05T07:21:41.978819Z", + "iopub.status.idle": "2022-01-05T07:21:44.967951Z", + "shell.execute_reply": "2022-01-05T07:21:44.966827Z", + "shell.execute_reply.started": "2022-01-05T07:21:41.979443Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "LeNet(\n", - " (features): Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " )\n", - " (fc): Sequential(\n", - " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", - " )\n", - ")\n" + "飞桨框架内置模型: ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'VGG', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'MobileNetV1', 'mobilenet_v1', 'MobileNetV2', 'mobilenet_v2', 'LeNet']\n" ] } ], "source": [ "import paddle\n", "\n", + "print('飞桨框架内置模型:', paddle.vision.models.__all__)" + ] + }, + { + "cell_type": "markdown", + "id": "79693587-4896-463d-be11-36cc939748d6", + "metadata": {}, + "source": [ + "以 LeNet 模型为例,可通过如下代码组网:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "883f5395-3b1c-4d58-a70e-1dd7886a7d36", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T08:23:47.069913Z", + "iopub.status.busy": "2022-01-05T08:23:47.068838Z", + "iopub.status.idle": "2022-01-05T08:23:47.101420Z", + "shell.execute_reply": "2022-01-05T08:23:47.100556Z", + "shell.execute_reply.started": "2022-01-05T08:23:47.069844Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-4 [[1, 400]] [1, 120] 48,120 \n", + " Linear-5 [[1, 120]] [1, 84] 10,164 \n", + " Linear-6 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'total_params': 61610, 'trainable_params': 61610}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 模型组网并初始化网络\n", "lenet = paddle.vision.models.LeNet(num_classes=10)\n", - "print(lenet)" + "\n", + "# 可视化模型组网结构和参数\n", + "paddle.summary(lenet,(1, 1, 28, 28))" ] }, { @@ -57,40 +134,86 @@ "id": "c48f8ac6", "metadata": {}, "source": [ - "可以看到LeNet包含`features`和`fc`两个子网络,总共包含2个卷积层,2个ReLU激活层,2个MaxPool2D层,三个全链接层。\n", + "通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 `Conv2D` 卷积层、`ReLU` 激活层、`MaxPool2D` 池化层以及 `Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", + "
\n", + "

图1:LeNet网络结构示意图
\n", + "\n", + "另外在 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c5fda8b-b153-49ee-b168-30f5293730ae", + "metadata": {}, + "source": [ + "## 二、Paddle.nn 介绍\n", + "\n", + "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 中查看。\n", + "\n", + "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential)、 \n", + "[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", + " \n", + "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、xxx)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", + "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", + "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**:xxxxxx\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7ab2efd1-2623-4678-809e-9264f14d9c7c", + "metadata": {}, + "source": [ + "\n", + "## 三、使用 paddle.nn.Sequential 组网\n", + "\n", "\n", - "## 通过 Sequential 组网\n", + "构建顺序的线性网络结构时,可以选择该方式,只需要按模型的结构顺序,一层一层加到 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 子类中即可。\n", "\n", - "针对顺序的线性网络结构,可以直接使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 来快速完成组网,这种方式可以减少类的定义等代码编写。具体代码如下:" + "参照前面图 1 所示的 LeNet 模型结构,构建该网络结构的代码如下:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "9a86cc3e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-05T08:40:20.974280Z", + "iopub.status.busy": "2022-01-05T08:40:20.973687Z", + "iopub.status.idle": "2022-01-05T08:40:21.032406Z", + "shell.execute_reply": "2022-01-05T08:40:21.030756Z", + "shell.execute_reply.started": "2022-01-05T08:40:20.974221Z" + }, + "scrolled": true + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (6): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (7): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (8): Linear(in_features=84, out_features=10, dtype=float32)\n", - ")\n" + "ename": "ValueError", + "evalue": "(InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_215/4050339869.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# 可视化模型组网结构和参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpaddle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlenet_Sequential\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(net, input_size, dtypes, input)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0m_input_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_input_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummary_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_input_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36msummary_string\u001b[0;34m(model, input_size, dtypes, input)\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/base.py\u001b[0m in \u001b[0;36m_decorate_function\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_decorate_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/common.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m out = F.linear(\n\u001b[0;32m--> 172\u001b[0;31m x=input, weight=self.weight, bias=self.bias, name=self.name)\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/common.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(x, weight, bias, name)\u001b[0m\n\u001b[1;32m 1478\u001b[0m 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"lenet_Sequential = nn.Sequential(\n", " nn.Conv2D(1, 6, 3, stride=1, padding=1),\n", " nn.ReLU(),\n", @@ -102,7 +225,21 @@ " nn.Linear(120, 84), \n", " nn.Linear(84, 10)\n", ")\n", - "print(lenet_Sequential)" + "# 可视化模型组网结构和参数\n", + "paddle.summary(lenet_Sequential,(1, 1, 28, 28))" + ] + }, + { + "cell_type": "markdown", + "id": "19fd4c4c-9ff4-434e-b06c-32daf8e1ae43", + "metadata": {}, + "source": [ + "以上代码实现的组网与 paddle.vision.models.LeNet 完全一样。\n", + "\n", + "Sequential组网中框架做了什么:\n", + "\n", + "使用Sequential组网方式的条件和限制:\n", + "\n" ] }, { @@ -110,45 +247,66 @@ "id": "b9524d1c", "metadata": {}, "source": [ - "## 通过 SubClass 组网\n", + "## 四、使用 paddle.nn.Layer 组网\n", + "\n", + "构建一些比较复杂的网络结构时,可以选择该方式,组网包括三个步骤:\n", + "1. 创建一个继承自 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的类;\n", + "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(sublayer);\n", + "1. 在类的前向计算函数 `forward` 中使用定义好的 sublayer 进行前向计算。\n", "\n", - "针对一些比较复杂的网络结构,就可以使用 SubClass 组网的方式来进行模型代码编写。通过 SubClass 组网进行组网需要完成下列三个步骤:\n", - "1. 创建一个继承自[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)\n", - "2. 在类的构造函数`__init__`中进行子Layer的定义,完成网络的构建\n", - "3. 在类的`forward`函数中使用定义的子Layer进行前向计算。\n", + "并且 sublayer 既可以通过基础的神经网络层 API(如卷积层、池化层、全连接层等)定义,也可以通过 nn.Sequential 或 nn.Layer 定义。由此可见,paddle.nn.Layer 的组网用法非常灵活,便于构建各种复杂网络。\n", "\n", - "子Layer可以通过 基础API(卷积,池化或全连接),Sequential 或 SubClass 的形式进行定义,子Layer在构造函数中一次定义后可在forward中多次调用。使用SubClass 组网形式实现LeNet的代码如下" + "仍然以 LeNet 模型为例,使用 paddle.nn.Layer 组网的代码如下:\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "cf89df53", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-03T12:04:22.365846Z", + "iopub.status.busy": "2022-01-03T12:04:22.365241Z", + "iopub.status.idle": "2022-01-03T12:04:22.386477Z", + "shell.execute_reply": "2022-01-03T12:04:22.385631Z", + "shell.execute_reply.started": "2022-01-03T12:04:22.365800Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "LeNet(\n", - " (features): Sequential(\n", - " (0): Conv2D(1, 6, kernel_size=[3, 3], padding=1, data_format=NCHW)\n", - " (1): ReLU()\n", - " (2): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " (3): Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW)\n", - " (4): ReLU()\n", - " (5): MaxPool2D(kernel_size=2, stride=2, padding=0)\n", - " )\n", - " (fc): Sequential(\n", - " (0): Linear(in_features=400, out_features=120, dtype=float32)\n", - " (1): Linear(in_features=120, out_features=84, dtype=float32)\n", - " (2): Linear(in_features=84, out_features=10, dtype=float32)\n", - " )\n", - ")\n" + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-5 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-5 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-5 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-6 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-6 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-6 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-7 [[1, 400]] [1, 120] 48,120 \n", + " Linear-8 [[1, 120]] [1, 84] 10,164 \n", + " Linear-9 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n", + "{'total_params': 61610, 'trainable_params': 61610}\n" ] } ], "source": [ + "# 使用 Subclass 方式构建 LeNet 模型\n", "class LeNet(nn.Layer):\n", " def __init__(self, num_classes=10):\n", " super(LeNet, self).__init__()\n", @@ -176,130 +334,64 @@ " x = self.fc(x)\n", " return x\n", "lenet_SubClass = LeNet()\n", - "print(lenet_SubClass)" + "\n", + "# 可视化模型组网结构和参数\n", + "params_info = paddle.summary(lenet_SubClass,(1, 1, 28, 28))\n", + "print(params_info)" ] }, { "cell_type": "markdown", - "id": "38716a23", + "id": "541d133d", "metadata": {}, "source": [ - "## 模型前向\n", - "\n", - "完成模型组网之后,可以通过传递数据到模型来完成一次模型的前向过程。\n", + "## 五、使用 paddle.nn.LayerList 组网\n", "\n", - "在下面的代码中,传入一个 shape 为 [1, 1, 28, 28] 的图像给网络,这样会执行模型的 forward 函数以得到模型的输出。注意,这里不可以直接调用 model.forward。完成计算年后,模型会返回一个10维的Tensor,表示输入是每个类别的分数,可以通过调用 [nn.Softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Softmax_cn.html#softmax) 获取每个类别的概率。" + "待补充" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "68e0f6be", + "cell_type": "markdown", + "id": "c55565c4-cc47-4654-98cf-bccc8bff1331", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output shape: [1, 10]\n", - "probs: Tensor(shape=[1, 10], dtype=float32, place=CPUPlace, stop_gradient=False,\n", - " [[0.09509374, 0.12776302, 0.08948905, 0.09307785, 0.09075093, 0.13374873,\n", - " 0.08719291, 0.07862016, 0.11715320, 0.08711041]])\n" - ] - } - ], "source": [ - "x = paddle.ones([1, 1, 28, 28])\n", - "y = lenet(x)\n", - "print('output shape: {}'.format(y.shape))\n", + "# 六、总结\n", "\n", - "soft_max_op = paddle.nn.Softmax()\n", - "probs = soft_max_op(y)\n", - "print('probs: {}'.format(probs))" + "待补充" ] }, { "cell_type": "markdown", - "id": "8843cf9c", + "id": "490f2617-b7c2-4b3c-9ee6-1dae6c04bd59", "metadata": {}, "source": [ - "## 飞桨内置基础API\n", + "# 扩展阅读:模型的层(Layer)\n", + "\n", + "选几个经典的层解读一下\n", + "\n", "\n", - "飞桨内置了大量基础的组网API,组网相关的API都在[paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#paddle-nn)目录下。组网相关的API类别与具体的API列表如下表:\n", "\n", - "| 功能 | API名称 |\n", - "| --- | ---|\n", - "| Conv | Conv1D、Conv2D、Conv3D、Conv1DTranspose、Conv2DTranspose、Conv3DTranspose |\n", - "| Pool | AdaptiveAvgPool1D、AdaptiveAvgPool2D、AdaptiveAvgPool3D、 AdaptiveMaxPool1D、AdaptiveMaxPool2D、AdaptiveMaxPool3D、 AvgPool1D、AvgPool2D、AvgPool3D、MaxPool1D、MaxPool2D、MaxPool3D |\n", - "| Padding | Pad1D、Pad2D、Pad3D |\n", - "| Activation | ELU、GELU、Hardshrink、Hardtanh、HSigmoid、LeakyReLU、LogSigmoid、 LogSoftmax、PReLU、ReLU、ReLU6、SELU、Sigmoid、Softmax、Softplus、 Softshrink、Softsign、Tanh、Tanhshrink |\n", - "| Normlization | BatchNorm、BatchNorm1D、BatchNorm2D、BatchNorm3D、GroupNorm、 InstanceNorm1D、InstanceNorm2D、InstanceNorm3D、LayerNorm、SpectralNorm、 SyncBatchNorm |\n", - "| Recurrent NN | BiRNN、GRU、GRUCell、LSTM、LSTMCell、RNN、RNNCellBase、SimpleRNN、SimpleRNNCell | \n", - "| Transformer | Transformer、TransformerDecoder、TransformerDecoderLayer、| TransformerEncoder、TransformerEncoderLayer |\n", - "| Dropout | AlphaDropout、Dropout、Dropout2d、Dropout3d |\n", - "| Loss | BCELoss、BCEWithLogitsLoss、CrossEntropyLoss、CTCLoss、KLDivLoss、L1Loss、 MarginRankingLoss、MSELoss、NLLLoss、SmoothL1Loss |\n", "\n", - "## 模型的参数\n", "\n", - "飞桨内置的 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 方法可以很方便的查看网络的基础结构,每层的输入输出shape和参数信息。" + "\n" ] }, { - "cell_type": "code", - "execution_count": 14, - "id": "4617d646", + "cell_type": "markdown", + "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------------\n", - " Layer (type) Input Shape Output Shape Param # \n", - "===========================================================================\n", - " Conv2D-5 [[64, 1, 28, 28]] [64, 6, 28, 28] 60 \n", - " ReLU-5 [[64, 6, 28, 28]] [64, 6, 28, 28] 0 \n", - " MaxPool2D-5 [[64, 6, 28, 28]] [64, 6, 14, 14] 0 \n", - " Conv2D-6 [[64, 6, 14, 14]] [64, 16, 10, 10] 2,416 \n", - " ReLU-6 [[64, 16, 10, 10]] [64, 16, 10, 10] 0 \n", - " MaxPool2D-6 [[64, 16, 10, 10]] [64, 16, 5, 5] 0 \n", - " Linear-7 [[64, 400]] [64, 120] 48,120 \n", - " Linear-8 [[64, 120]] [64, 84] 10,164 \n", - " Linear-9 [[64, 84]] [64, 10] 850 \n", - "===========================================================================\n", - "Total params: 61,610\n", - "Trainable params: 61,610\n", - "Non-trainable params: 0\n", - "---------------------------------------------------------------------------\n", - "Input size (MB): 0.19\n", - "Forward/backward pass size (MB): 7.03\n", - "Params size (MB): 0.24\n", - "Estimated Total Size (MB): 7.46\n", - "---------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "{'total_params': 61610, 'trainable_params': 61610}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "paddle.summary(lenet, (64, 1, 28, 28))" + "# 扩展阅读:模型的参数(Parameter)\n", + "\n", + "补充parameters的介绍\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -311,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png b/docs/guides/02_paddle2.0_develop/images/model_develop_flow.png index ab5ebe1533900b65d7339a563734edb0510ff435..1a0f6196bebb10dcbbe3e8c7ee6ad3cb20aaf151 100644 GIT binary patch literal 190267 zcma&ObyStx{yj{0D$=PS-6`GO-6<{I(%mVIbc=Lt8tDcB$xXLNOV{t&-h1vn=f2-R zUdPyDhfl4wJ~`)HL@Fyvp`#F@KtVyF%Sel>LP5cYK|#U7At3<2pmv^R18>kSs#2m* zRTIPqz!y(*Z5ay%1t>b;GZGXWG$9l`?O+I&O&JA6 zs4xsA7J1)>G!1fx2kbx~6oNc=c44{&i}44u?gjtH0iP@#ouwZ(9w+wKt0!K^MJX1N 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z8XStM&|3yjmcpI?l;%KF{-Dl)wS~|#bm;v~&&Nv>;0N+a=DO_fi|4fPSB0R*WX9T@|d6 z_d@^zU~m^g+p?kVWkyqzh3Q^7>^1I-77;$w^l5e|g<$zl-Se4Krh67yZ$@upo$lZI zKEBcvzgDuZz?{6Tw>Fr*)!%9n?qY&ohBv~-%jkOkwakC8pKBMhYaWVqwhY$2k_$cd zsJGz%l=@3Y)M^o`tQW(^9~l>X`Zn3k5vkX#btNXp4BrT_h$l{)0?N%jPp92Z|HVsC zrgEGFqvyr3)MM_o@F{76Xs-R}#`~J|Wb_4U#zUptEXjLWZAx+lbOJDf_~NsyJKI2?XhSyT1HZF!%HNRp*`#7R)%vXqHZ1BzHninJUnw;D zzG^FJ=O(rli8qFK8}`!(OCHW_Z)Ut{zyDmHQ}qU9svvF+9G?4j`IKJwr>|XS0*wo0 zfu>APsOS<+rB>}JAm(pyqUBYt*79!fG&H;wurZ=D$wv9xk zK65Qp8I|p)kr_j6Iv_SY?xLdn_{~x=axi{>vD^ zSbf}g{T-)<%*^NzOfYKiZogOnBk?#Hh0^pN9R_U0{#t~|A=KFZV45tkng7ZEEbwo% dK#2`(_MB9w@nHcx`u-}gIoLQ^vkv&i{{i-H#svTX From 0f2a74831993046898af7ffacef8bde5887c3241 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Mon, 10 Jan 2022 16:29:36 +0800 Subject: [PATCH 28/63] update data load and data preprocessing topics update data load and data preprocessing topics --- .../02_data_load_cn.ipynb | 301 ++++++++++++++---- .../03_data_preprocessing_cn.ipynb | 111 +++---- 2 files changed, 287 insertions(+), 125 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index c587df14eac..0c7dd41534d 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -7,55 +7,154 @@ "source": [ "# 数据集定义与加载\n", "\n", - "深度学习模型在训练时需要大量的数据来完成模型调优,这个过程均是数字的计算,无法直接使用原始数据如图片、文本等来完成计算。因此需要对原始的各种数据文件进行处理,转换成深度学习模型可以使用的数据类型。\n", "\n", + "深度学习模型需要大量的数据来完成训练和评估,这些数据样本可能是图片(image)、文本(txt)、语音(audio)等多种类型,而模型训练过程实际是数学计算过程,因此数据样本在送入模型前需要经过一系列处理,如划分数据集、变换数据形状(shape)、制作数据迭代读取器以备分批训练等。\n", "\n", - "训练模型时,往往不是采用单个数据输入模型进行训练,这样训练耗时比较大;而常常使用minibatch的方式,每次输入部分数据训练模型。飞桨内的数据集加载过程由 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 两个 api 完成。Dataset 主要完成单张图像或样本的解析与标签的制作,DataLoader 主要完成单张图像或样本的组 batch 工作和对数据集的多进程读取加速的作用。\n", + "这些处理动作可封装到函数中xxxxx,后续训练时通过xxx方式调用。归纳起来主要需定义如下几个函数:\n", "\n", - "飞桨内置了深度学习任务中常用的数据集,对应 API 所在目录为 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 与 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#paddle-text) ,你可以通过以下代码查看飞桨框架中的内置数据集。" + "* **定义数据集**:将磁盘中保存的原始图片、文字等样本和对应的标签映射到 Dataset,方便后续通过索引(index)读取数据,在 Dataset 中还可以进行一些数据变换、数据增广等预处理操作。在飞桨框架中推荐使用 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 自定义数据集,另外在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 目录下飞桨内置了一些经典数据集方便直接调用。\n", + "\n", + "\n", + "* **定义数据读取器**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", + "\n", + "* **(可选)定义数据采样器**:xxxxx是什么,做了什么,目的是什么。用什么接口来做。\n", + "\n", + "本文以图像数据集为例介绍。\n" ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "ace5670c", + "cell_type": "markdown", + "id": "0e6cbad6-1346-47d3-8bd1-e28cbb55c8ad", "metadata": {}, + "source": [ + "## 一、定义数据集\n", + "\n", + "### 1.1 直接加载内置数据集\n", + "\n", + "飞桨框架在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 目录下内置了一些经典数据集可直接调用,如 CV 领域的 MNIST、Cifar10、VOC2012,NLP 领域的 Movielens、Imdb 等,通过以下代码可查看飞桨框架中的内置数据集。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4cc7b788-00c6-4e1b-a6ab-49317c861aa5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-10T02:42:53.325579Z", + "iopub.status.busy": "2022-01-10T02:42:53.325030Z", + "iopub.status.idle": "2022-01-10T02:42:54.698658Z", + "shell.execute_reply": "2022-01-10T02:42:54.697869Z", + "shell.execute_reply.started": "2022-01-10T02:42:53.325539Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "视觉相关数据集: ['DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']\n", - "自然语言相关数据集: ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'UCIHousing', 'WMT14', 'WMT16', 'ViterbiDecoder', 'viterbi_decode']\n" + "计算机视觉(CV)相关数据集: ['DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']\n", + "自然语言处理(NLP)相关数据集: ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'UCIHousing', 'WMT14', 'WMT16', 'ViterbiDecoder', 'viterbi_decode']\n" ] } ], "source": [ "import paddle\n", - "print('视觉相关数据集:', paddle.vision.datasets.__all__)\n", - "print('自然语言相关数据集:', paddle.text.__all__)" + "print('计算机视觉(CV)相关数据集:', paddle.vision.datasets.__all__)\n", + "print('自然语言处理(NLP)相关数据集:', paddle.text.__all__)" ] }, { "cell_type": "markdown", - "id": "c9049cc8", + "id": "9235d4f3-9e6f-4926-b003-da4eed882631", "metadata": {}, "source": [ - "## 加载数据集\n", - "\n", - "通过飞桨框架,可以很方便的加载深度学习里的常用数据集。下面演示如何快速加载 MNIST 数据集。\n", - "\n", - "MNIST 数据集用于对 0 ~ 9 的十类数字进行分类,即输入28 * 28分辨率的手写数字图片,识别出这个图片中的数字。\n", - "\n", - "在加载过程中,会通过`transform`字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms)里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等,这里在初始化MNIST数据集时传入了 `ToTensor` 变换来将图像转换为飞桨的内置数据类型, `mode`字段用于区分训练集和测试集。" + "以 [MNIST](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/datasets/MNIST_cn.html) 数据集为例,加载内置数据集的代码示例如下所示。" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "56a6ede2", - "metadata": {}, + "execution_count": 2, + "id": "5ddfd1f0-b188-4407-a331-7f7f622b805c", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-10T02:42:58.403305Z", + "iopub.status.busy": "2022-01-10T02:42:58.402126Z", + "iopub.status.idle": "2022-01-10T02:43:07.498070Z", + "shell.execute_reply": "2022-01-10T02:43:07.497331Z", + "shell.execute_reply.started": "2022-01-10T02:42:58.403262Z" + }, + "scrolled": true + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 105/2421 [>.............................] - ETA: 2s - 1ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz \n", + "Begin to download\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 8/8 [============================>.] - ETA: 0s - 2ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Download finished\n", + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz \n", + "Begin to download\n", + "\n", + "Download finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 116/403 [=======>......................] - ETA: 0s - 1ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz \n", + "Begin to download\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item 2/2 [===========================>..] - ETA: 0s - 2ms/item" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Download finished\n", + "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz \n", + "Begin to download\n", + "\n", + "Download finished\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -75,19 +174,34 @@ }, { "cell_type": "markdown", - "id": "162eed4d", + "id": "29fbad59-3234-41c6-82e6-da194972666a", "metadata": {}, "source": [ - "## 迭代数据集&可视化\n", - "\n", - "完成数据集初始化之后,初始化之后的 dataset 的一个可迭代的对象,可以使用下面的代码直接对数据集进行迭代" + "内置的 [MNIST](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/datasets/MNIST_cn.html) 数据集已经划分好了训练集和测试集,通过 `mode` 字段传入 `'train'` 或 `'test'` 来区分。另外可通过 `transform` 字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等,这里在初始化MNIST数据集时传入了 `ToTensor` 变换来将图像转换为飞桨的内置数据类型。\n" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "914637b6", + "cell_type": "markdown", + "id": "79102100-e52e-42d6-9b17-48cdb9b59991", "metadata": {}, + "source": [ + "完成数据集初始化之后,可以使用下面的代码直接对数据集进行迭代。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d1bbf911-41a1-452a-80f8-b19c4aec2939", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-10T02:50:30.296150Z", + "iopub.status.busy": "2022-01-10T02:50:30.294929Z", + "iopub.status.idle": "2022-01-10T02:50:30.465409Z", + "shell.execute_reply": "2022-01-10T02:50:30.464593Z", + "shell.execute_reply.started": "2022-01-10T02:50:30.296089Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -98,7 +212,7 @@ }, { "data": { - "image/png": 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" ] @@ -115,33 +229,41 @@ "for data in train_dataset:\n", " image, label = data\n", " print('shape of image: ',image.shape)\n", - " plt.title(\"label: {}\".format(str(label[0])))\n", + " plt.title(str(label))\n", " plt.imshow(image[0]) \n", " break" ] }, + { + "cell_type": "markdown", + "id": "95b8738b-35a3-4748-8aa6-624e17bed362", + "metadata": {}, + "source": [ + "### 1.2 使用 paddle.io.Dataset 自定义数据集" + ] + }, { "cell_type": "markdown", "id": "ab2f3fb4", "metadata": {}, "source": [ - "## 自定义数据集\n", - "\n", + "在实际的场景中,需要使用自有的数据来定义数据集,这时可以通过飞桨提供的 `paddle.io.Dataset` 基类来实现自定义数据集。\n", "\n", - "在实际的场景中,需要使用已有的数据来定义数据集。这时可以使用飞桨提供的`paddle.io.Dataset`基类来快速实现自定义数据集。\n", + "可构建一个子类继承自 `paddle.io.Dataset` ,并且实现下面的三个函数:\n", "\n", - "自定义数据集需要集成自 `paddle.io.Dataset` 并且实现下面的三个方法\n", - "\n", - "1. `__init__`: 完成一些数据集初始化操作,包含根据传入的标签文件进行数据集的读取,对数据预处理方法进行定义;\n", - "2. `__getitem__`: 定义给定 index 时如何从 dataset 中获取数据。即根据 index ,指定从硬盘中读取的数据与标签,如果定义了数据预处理方法,则对该数据使用 transform,最终返回处理好的单条数据(训练数据,对应的标签)。关于 transform 更多的内容,可以参考 [数据预处理](03_data_preprocessing_cn.html)。\n", - "3. `__len__`: 返回数据集总数目" + "1. `__init__`:完成一些数据集初始化操作,如指定数据和标签文件的存储路径、定义数据集大小等。\n", + "2. `__getitem__`:定义指定索引(index)时如何获取数据,并且在此函数中可定义一些数据预处理工作,如读取图像、对图像进行数据增强、制作标签等操作,最终返回处理好的单条数据(训练数据、对应的标签)。\n", + "3. `__len__`:返回数据集的样本总数。\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "id": "1d26950f", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -149,25 +271,22 @@ "\n", "class MyDataset(Dataset):\n", " \"\"\"\n", - " 步骤一:继承paddle.io.Dataset类\n", + " 步骤一:继承 paddle.io.Dataset 类\n", " \"\"\"\n", - " def __init__(self, num_samples, transform=None, image_size=(28,28), class_num=10):\n", - " \"\"\" \n", - " 步骤二:实现构造函数,定义数据集大小\n", + " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", + " \"\"\"\n", + " 步骤二:实现 __init__ 函数,定义数据集大小\n", " \"\"\"\n", " super(MyDataset, self).__init__()\n", " self.num_samples = num_samples\n", " self.image_size = image_size\n", " self.class_num = class_num\n", - " self.transform = transform\n", "\n", " def __getitem__(self, index):\n", " \"\"\"\n", - " 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)\n", + " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", " \"\"\"\n", " image = np.random.rand(*self.image_size)\n", - " if self.transform is not None:\n", - " image = self.transform(image)\n", " image = np.expand_dims(image, axis=0)\n", " label = np.random.randint(0, self.class_num - 1)\n", "\n", @@ -175,7 +294,7 @@ "\n", " def __len__(self):\n", " \"\"\"\n", - " 步骤四:实现__len__方法,返回数据集总数目\n", + " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", " \"\"\"\n", " return self.num_samples" ] @@ -185,14 +304,17 @@ "id": "0e705d33", "metadata": {}, "source": [ - "和内置数据集一样,可以直接对自定义数据集进行迭代" + "\n", + "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代。" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 42, "id": "9d1570a3", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -203,7 +325,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -215,7 +337,7 @@ } ], "source": [ - "custom_dataset = MyDataset(100)\n", + "custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM)\n", "\n", "for data in custom_dataset:\n", " image, label = data\n", @@ -225,24 +347,45 @@ " break" ] }, + { + "cell_type": "markdown", + "id": "33efb8b2-cf16-40b3-9016-e72126ad3910", + "metadata": {}, + "source": [ + "### 1.3 使用 paddle.io.IterableDataset 自定义数据集\n", + "\n", + "待补充" + ] + }, + { + "cell_type": "markdown", + "id": "d1419e2d-4b4c-4ddf-b457-0bd536ca6e3c", + "metadata": {}, + "source": [ + "### 1.4 使用 paddle.io.TensorDataset 自定义数据集\n", + "\n", + "\n", + "待补充" + ] + }, { "cell_type": "markdown", "id": "de3fd19b", "metadata": {}, "source": [ - "## 使用DataLoader 读取训练数据集\n", + "## 二、使用 paddle.io.DataLoader 定义数据读取器\n", "\n", - "通过直接迭代 Dataset 的方式虽然可以对数据集进行访问,但是这种访问方式只能单线程进行,在进行模型训练时,还需要进行 batch 组合,对数据随机打乱避免过拟合,以及采用多线程的方式加载数据来加速等工作。在飞桨中,推荐使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 来完成这部分工作,开发者只需要进行数据处理部分逻辑的编写。\n", "\n", - "飞桨高层API [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/high_level_api.html#api) \n", - "进行训练、评估、预测时,会自动完成对数据组batch的操作。对于其他场景,都可以通过如下代码,可以快速的使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 完成数据的加载" + "通过前面介绍的直接迭代读取 DataSet 的方式虽然可实现对数据集的访问,但是这种访问方式只能单线程进行并且还需要手动分批次(batch)。在飞桨框架中,推荐使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API 对数据集进行多进程的读取,并且可自动完成划分 batch 的工作,开发者只需要进行数据处理模块的编写。" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 47, "id": "c3ad4116", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -253,7 +396,7 @@ }, { "data": { - "image/png": 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" ] @@ -265,7 +408,10 @@ } ], "source": [ + "# 定义并初始化数据读取器\n", "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=1)\n", + "\n", + "# 调用 DataLoader 迭代读取数据\n", "for batch_id, data in enumerate(train_loader()):\n", " images, labels = data\n", " print('shape of image: ',images.shape, 'shape of label: ', labels.shape)\n", @@ -279,15 +425,40 @@ "id": "ae21b353", "metadata": {}, "source": [ - "通过上述的方法,可以初始化一个数据迭代器train_loader, 用于加载训练数据。通过batch_size=64设置了数据集的批大小为64,通过设置shuffle=True可以在取数据前会打乱数据集顺序。此外,还可以通过设置num_workers来开启多进程数据加载,提升加载速度。" + "通过上述方法,初始化了一个数据读取器 `train_loader`,用于加载训练数据集 `train_dataset`。在数据读取器中常见的几个配置如下:\n", + "\n", + "* **训练样本乱序**:通过设置 `shuffle=True` ,可以在取数据前打乱样本顺序。\n", + "* **生成批次数据**:通过 `batch_size` 设置生成批次数据的批大小,示例中设置为 64。\n", + "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。\n" + ] + }, + { + "cell_type": "markdown", + "id": "5077293d-68fe-4bdd-a877-798f6d7680a6", + "metadata": {}, + "source": [ + "## 三、定义数据采样器\n", + "\n", + "\n", + "待补充" + ] + }, + { + "cell_type": "markdown", + "id": "d3a256d5-33f0-4018-bd5a-3ee6d9bff372", + "metadata": {}, + "source": [ + "## 四、总结\n", + "\n", + "待补充" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -299,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 9db5553e8c5..321846e6cde 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -11,16 +11,27 @@ "\n", "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。\n", "\n", - "## 飞桨框架内置数据处理API\n", "\n", - "飞桨框架在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理操作,可以通过以下方式查看" + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b1d901d6-ceac-4c05-acdd-191e34317c62", + "metadata": {}, + "source": [ + "## paddle.vision.transforms 介绍\n", + "\n", + "飞桨框架在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理方法,可以通过以下代码查看:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "93904999", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -40,16 +51,18 @@ "id": "a4b999ee", "metadata": {}, "source": [ - "对于飞桨框架内置的数据处理,可以单个初始化调用,也可以将多个数据处理进行组合使用,具体使用方式如下\n", + "对于飞桨框架内置的数据处理方法,可以单个初始化调用,也可以将多个数据处理方法进行组合使用,具体使用方式如下:\n", "\n", "* 单个使用" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "69b80bc1", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "from paddle.vision.transforms import Resize\n", @@ -65,14 +78,16 @@ "source": [ "* 多个组合使用\n", "\n", - "这种使用模式下,需要先定义好每个数据处理操作,然后用`Compose`进行组合" + "这种使用模式下,需要先定义好每个数据处理方法,然后用`Compose` 进行组合" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 35, "id": "4a1a5cb3", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "from paddle.vision.transforms import Compose, RandomRotation\n", @@ -83,21 +98,25 @@ }, { "cell_type": "markdown", - "id": "0a76dd29", + "id": "b1bd06c0-b4f2-46a4-b446-cde3a1fe8afe", "metadata": {}, "source": [ - "定义好数据预处理操作后,可以直接在Dataset中进行使用,下面介绍介绍两种数据增强使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集\n", + "## 在数据集中定义数据预处理操作\n", "\n", - "## 基于框架内置数据集\n", + "定义好数据预处理方法后,可以直接在 DataSet 中使用,下面介绍介绍两种数据处理使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集。\n", "\n", - "飞桨框架中内置的数据集 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-datasets), 每个都包含`transform`参数,可以传入定义好的数据处理操作。" + "### 基于框架内置数据集\n", + "\n", + "在框架内置数据集中使用内置的数据处理方法时,只需要将数据处理操方法传递给 `transform` 字段即可。" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "a7970f84", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# 通过transform参数传递定义好的数据增强方法即可完成对自带数据集的增强\n", @@ -109,24 +128,26 @@ "id": "74013246", "metadata": {}, "source": [ - "## 基于自定义的数据集\n", + "### 基于自定义的数据集\n", "\n", - "对于自定义的数据集,可以在数据集的构造函数中进行数据处理方法的定义,之后在 `__getitem__` 方法中对返回的数据进行应用, 如下述代码所示" + "对于自定义的数据集,可以在数据集的 `__init__` 函数中定义数据处理方法,之后在 `__getitem__` 方法中对返回的数据进行应用, 如下述代码所示:\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "id": "45ea330a", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import numpy as np\n", "from paddle.io import Dataset\n", "\n", - "class MyDataset(Dataset):\n", + "class MyDataset(Dataset): \n", " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", - " super(MyDataset, self).__init__()\n", + " super(MyDataset, self).__init__()\n", " self.num_samples = num_samples\n", " self.image_size = image_size\n", " self.class_num = class_num\n", @@ -134,10 +155,7 @@ " # 在 `__init__` 中定义数据处理方法,此处为随机旋转\n", " self.transform = RandomRotation(10)\n", "\n", - " def __getitem__(self, index):\n", - " \"\"\"\n", - " 步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)\n", - " \"\"\"\n", + " def __getitem__(self, index): \n", " image = np.random.rand(*self.image_size)\n", " \n", " # 在 `__getitem__` 中对数据集使用数据处理方法\n", @@ -149,53 +167,26 @@ "\n", " return image, label\n", "\n", - " def __len__(self):\n", - " \"\"\"\n", - " 步骤四:实现__len__方法,返回数据集总数目\n", - " \"\"\"\n", + " def __len__(self): \n", " return self.num_samples" ] }, { "cell_type": "markdown", - "id": "3278dbe9", + "id": "2b21d528-1ab3-4eb2-be73-d6871df4ba65", "metadata": {}, "source": [ - "下面通过框架内置数据集对比处理前后的图像,这里使用的数据处理操作为[Resize](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/transforms/resize_cn.html#cn-api-vision-transforms-resize),用于对改变输入图像的大小" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b4f7532b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "image size before resize: (28, 28)\n", - "image size before resize: (32, 32)\n" - ] - } - ], - "source": [ - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=None)\n", - "train_dataset_with_Resize = paddle.vision.datasets.MNIST(mode='train', transform=Resize(32))\n", - "\n", - "image, label = train_dataset[0]\n", - "image_with_Resize, label_with_Resize = train_dataset_with_Resize[0]\n", + "## 数据处理的几种方法介绍\n", "\n", - "print('image size before resize: {}'.format(image.size))\n", - "print('image size before resize: {}'.format(image_with_Resize.size))" + "待补充几个典型方法和转换前后对比示例" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -207,7 +198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, From a5b4f2a59f4ad63a95bf88fe5520e65b3307d9bd Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 12 Jan 2022 18:23:33 +0800 Subject: [PATCH 29/63] update 01-04 --- .../01_quick_start_cn.ipynb | 83 +++---- .../02_data_load_cn.ipynb | 217 ++++++---------- .../03_data_preprocessing_cn.ipynb | 234 +++++++++++++++--- .../02_paddle2.0_develop/04_model_cn.ipynb | 221 ++++++++++++----- 4 files changed, 471 insertions(+), 284 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index b77f952d255..64ae4df96f3 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "468426ec", "metadata": { "execution": { @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "93d0c8a1", "metadata": { "execution": { @@ -141,17 +141,17 @@ "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", "Epoch 1/5\n", - "step 938/938 [==============================] - loss: 0.0414 - acc: 0.9518 - 20ms/step \n", + "step 938/938 [==============================] - loss: 0.0217 - acc: 0.9439 - 14ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0219 - acc: 0.9801 - 20ms/step \n", + "step 938/938 [==============================] - loss: 0.0288 - acc: 0.9785 - 14ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 0.0156 - acc: 0.9834 - 19ms/step \n", + "step 938/938 [==============================] - loss: 0.0050 - acc: 0.9834 - 14ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 0.0021 - acc: 0.9866 - 20ms/step \n", + "step 938/938 [==============================] - loss: 0.0036 - acc: 0.9852 - 14ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.0165 - acc: 0.9884 - 19ms/step \n", + "step 938/938 [==============================] - loss: 0.0079 - acc: 0.9866 - 14ms/step \n", "Eval begin...\n", - "step 10000/10000 [==============================] - loss: 1.0729e-06 - acc: 0.9850 - 2ms/step \n", + "step 10000/10000 [==============================] - loss: 0.0015 - acc: 0.9847 - 1ms/step \n", "Eval samples: 10000\n", "true label: 7, pred label: 7\n" ] @@ -159,16 +159,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -182,11 +182,12 @@ "source": [ "import paddle\n", "import numpy as np\n", - "from paddle.vision.transforms import ToTensor\n", + "from paddle.vision.transforms import Normalize\n", "\n", + "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", "# 下载数据集并初始化 DataSet\n", - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", - "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)\n", "\n", "# 模型组网并初始化网络\n", "lenet = paddle.vision.models.LeNet(num_classes=10)\n", @@ -245,9 +246,9 @@ "source": [ "### 3.1 数据集定义与加载\n", "\n", - "飞桨在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", + "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", "\n", - "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化 MNIST 数据集时传入了 `ToTensor` 变换来将图像转换为飞桨支持的 Tensor 数据类型。\n" + "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化 MNIST 数据集时传入了 `ToTensor` 变换来将图像转换为飞桨支持的 Tensor 数据类型。\n" ] }, { @@ -274,11 +275,13 @@ ], "source": [ "import paddle\n", - "from paddle.vision.transforms import ToTensor\n", + "from paddle.vision.transforms import Normalize\n", + "\n", + "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", + "# 下载数据集并初始化 DataSet\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)\n", "\n", - "# 下载数据集并初始化DataSet\n", - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", - "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", "# 打印数据集里图片数量\n", "print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(test_dataset)))" ] @@ -288,7 +291,7 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", "\n", @@ -307,9 +310,9 @@ "飞桨的模型组网有多种方式,既可以直接使用飞桨内置的模型,也可以自定义组网。\n", "\n", "\n", - "『手写数字识别任务』比较简单,普通的神经网络就能达到很高的精度,在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为10。\n", + "『手写数字识别任务』比较简单,普通的神经网络就能达到很高的精度,在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](../../api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为10。\n", "\n", - "另外通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 可方便地打印网络的基础结构和参数信息。" + "另外通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 可方便地打印网络的基础结构和参数信息。" ] }, { @@ -370,7 +373,7 @@ "id": "67dfcc50", "metadata": {}, "source": [ - "通过飞桨的 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html) 和 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html) API 可以更灵活方便的组建自定义的神经网络,详细使用方法可参考『模型组网』章节。\n", + "通过飞桨的 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html) 和 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html) API 可以更灵活方便的组建自定义的神经网络,详细使用方法可参考『模型组网』章节。\n", "\n", "更多参考:\n", "* [模型组网](04_model_cn.html)" @@ -387,12 +390,12 @@ "\n", "模型训练需完成如下步骤:\n", "\n", - "1. **使用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用[飞桨高层 API ](http://)进行训练、评估、推理的实例,方便后续操作。\n", - "2. **使用 [paddle.Model.prepare](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", - "3. **使用 [paddle.Model.fit](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", + "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用飞桨高层 API进行训练、评估、推理的实例,方便后续操作。\n", + "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](../../api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", + "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", "\n", "\n", - "因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/metric/Accuracy_cn.html#accuracy) (精度)指标来计算模型在训练集上的精度。" + "因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) (精度)指标来计算模型在训练集上的精度。" ] }, { @@ -455,7 +458,7 @@ "source": [ "#### 3.3.2 模型评估\n", "\n", - "模型训练完成之后,调用 [paddle.Model.evaluate](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) ,使用预先定义的测试数据集,来评估训练好的模型效果,评估完成后将输出模型在测试集上的损失函数值 loss 和精度 acc。\n" + "模型训练完成之后,调用 [paddle.Model.evaluate](../../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) ,使用预先定义的测试数据集,来评估训练好的模型效果,评估完成后将输出模型在测试集上的损失函数值 loss 和精度 acc。\n" ] }, { @@ -497,7 +500,7 @@ "从结果可以看到,初步训练得到的模型精度在98%附近,在逐渐熟悉深度学习模型开发和训练技巧后,可以通过调整其中的训练参数来进一步提升模型的精度。\n", "\n", "更多参考:\n", - "* [模型训练与评估](http://)xxxxxxx" + "* [模型训练与评估](05_train_eval_predict_cn.html)" ] }, { @@ -518,7 +521,7 @@ "\n", "模型训练完成后,通常需要将训练好的模型参数和优化器等信息,持久化保存到参数文件中,便于后续执行推理验证。\n", "\n", - "在飞桨中可通过调用 [paddle.Model.save](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#save-path-training-true) 保存模型。代码示例如下,其中 output 为模型保存的文件夹名称,minst 为保存的模型文件名称。" + "在飞桨中可通过调用 [paddle.Model.save](../../api/paddle/Model_cn.html#save-path-training-true) 保存模型。代码示例如下,其中 output 为模型保存的文件夹名称,minst 为保存的模型文件名称。" ] }, { @@ -560,7 +563,7 @@ "source": [ "#### 3.4.2 模型加载并执行推理\n", "\n", - "执行模型推理时,可调用 [paddle.Model.load](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可通过调用 [paddle.Model.predict_batch](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", + "执行模型推理时,可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可通过调用 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", "\n", "如下示例中,针对前面创建的 `model` 实例加载保存的参数文件 `output/mnist`,并选择测试集中的一张图片 `test_dataset[0]` 作为输入,执行推理并打印结果,可以看到推理的结果与可视化图片一致。\n" ] @@ -624,8 +627,8 @@ "metadata": {}, "source": [ "更多参考:\n", - "* [模型保存与加载](http://)xxxxxxx\n", - "* [模型推理](http://)xxxxxxx" + "* [模型保存与加载](08_model_save_load_cn.html)\n", + "* [模型推理](09_model_to_onnx_cn.html)" ] }, { @@ -642,19 +645,11 @@ "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f4fffdc", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -668,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.10" }, "toc-autonumbering": false, "toc-showcode": false, diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 0c7dd41534d..b92c146c825 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -10,16 +10,14 @@ "\n", "深度学习模型需要大量的数据来完成训练和评估,这些数据样本可能是图片(image)、文本(txt)、语音(audio)等多种类型,而模型训练过程实际是数学计算过程,因此数据样本在送入模型前需要经过一系列处理,如划分数据集、变换数据形状(shape)、制作数据迭代读取器以备分批训练等。\n", "\n", - "这些处理动作可封装到函数中xxxxx,后续训练时通过xxx方式调用。归纳起来主要需定义如下几个函数:\n", + "归纳起来主要需定义如下几个类:\n", "\n", - "* **定义数据集**:将磁盘中保存的原始图片、文字等样本和对应的标签映射到 Dataset,方便后续通过索引(index)读取数据,在 Dataset 中还可以进行一些数据变换、数据增广等预处理操作。在飞桨框架中推荐使用 [paddle.io.Dataset](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/Dataset_cn.html#dataset) 自定义数据集,另外在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 目录下飞桨内置了一些经典数据集方便直接调用。\n", + "* **定义数据集类**:将磁盘中保存的原始图片、文字等样本和对应的标签映射到 Dataset,方便后续通过索引(index)读取数据,在 Dataset 中还可以进行一些数据变换、数据增广等预处理操作。在飞桨框架中推荐使用 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 自定义数据集,另外在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 目录下飞桨内置了一些经典数据集方便直接调用。\n", "\n", "\n", - "* **定义数据读取器**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", + "* **定义数据读取器类**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", "\n", - "* **(可选)定义数据采样器**:xxxxx是什么,做了什么,目的是什么。用什么接口来做。\n", - "\n", - "本文以图像数据集为例介绍。\n" + "本文以图像数据集为例介绍,文本数据集可参考 [NLP实践](../../practices/nlp/index_cn.html)。" ] }, { @@ -31,7 +29,7 @@ "\n", "### 1.1 直接加载内置数据集\n", "\n", - "飞桨框架在 [paddle.vision.datasets](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#api) 目录下内置了一些经典数据集可直接调用,如 CV 领域的 MNIST、Cifar10、VOC2012,NLP 领域的 Movielens、Imdb 等,通过以下代码可查看飞桨框架中的内置数据集。" + "飞桨框架在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../..//api/paddle/text/Overview_cn.html#api) 目录下内置了一些经典数据集可直接调用,如 CV 领域的 MNIST、Cifar10、VOC2012,NLP 领域的 Movielens、Imdb 等,通过以下代码可查看飞桨框架中的内置数据集。" ] }, { @@ -87,74 +85,6 @@ "scrolled": true }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 105/2421 [>.............................] - ETA: 2s - 1ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz \n", - "Begin to download\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 8/8 [============================>.] - ETA: 0s - 2ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Download finished\n", - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz \n", - "Begin to download\n", - "\n", - "Download finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 116/403 [=======>......................] - ETA: 0s - 1ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz \n", - "Begin to download\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item 2/2 [===========================>..] - ETA: 0s - 2ms/item" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Download finished\n", - "Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz \n", - "Begin to download\n", - "\n", - "Download finished\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -164,11 +94,12 @@ } ], "source": [ - "from paddle.vision.transforms import ToTensor\n", + "from paddle.vision.transforms import Normalize\n", "\n", + "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", "# 下载数据集并初始化DataSet\n", - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", - "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)\n", "print('train images: ',len(train_dataset),', test images: ',len(test_dataset))" ] }, @@ -177,7 +108,7 @@ "id": "29fbad59-3234-41c6-82e6-da194972666a", "metadata": {}, "source": [ - "内置的 [MNIST](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/datasets/MNIST_cn.html) 数据集已经划分好了训练集和测试集,通过 `mode` 字段传入 `'train'` 或 `'test'` 来区分。另外可通过 `transform` 字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等,这里在初始化MNIST数据集时传入了 `ToTensor` 变换来将图像转换为飞桨的内置数据类型。\n" + "内置的 [MNIST](../../api/paddle/vision/datasets/MNIST_cn.html) 数据集已经划分好了训练集和测试集,通过 `mode` 字段传入 `'train'` 或 `'test'` 来区分。另外可通过 `transform` 字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等。这里在初始化MNIST数据集时传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型的收敛速度。" ] }, { @@ -190,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "d1bbf911-41a1-452a-80f8-b19c4aec2939", "metadata": { "execution": { @@ -207,12 +138,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "shape of image: [1, 28, 28]\n" + "shape of image: (1, 28, 28)\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -259,13 +190,45 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, + "id": "f9487da0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-01-12 14:38:05-- https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", + "正在解析主机 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)... 111.206.210.93, 111.206.210.81\n", + "正在连接 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)|111.206.210.93|:443... 已连接。\n", + "已发出 HTTP 请求,正在等待回应... 200 OK\n", + "长度:104252416 (99M) [application/x-tar]\n", + "正在保存至: “mnist.tar”\n", + "\n", + "mnist.tar 100%[===================>] 99.42M 2.29MB/s 用时 45s \n", + "\n", + "2022-01-12 14:38:51 (2.21 MB/s) - 已保存 “mnist.tar” [104252416/104252416])\n", + "\n" + ] + } + ], + "source": [ + "# 下载 MNIST 数据集并解压\n", + "! wget https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", + "! tar -xf mnist.tar" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "id": "1d26950f", "metadata": { "scrolled": true }, "outputs": [], "source": [ + "import os\n", + "import cv2\n", "import numpy as np\n", "from paddle.io import Dataset\n", "\n", @@ -273,30 +236,39 @@ " \"\"\"\n", " 步骤一:继承 paddle.io.Dataset 类\n", " \"\"\"\n", - " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", + " def __init__(self, data_dir, label_path, transform=None):\n", " \"\"\"\n", " 步骤二:实现 __init__ 函数,定义数据集大小\n", " \"\"\"\n", " super(MyDataset, self).__init__()\n", - " self.num_samples = num_samples\n", - " self.image_size = image_size\n", - " self.class_num = class_num\n", + " self.data_list = []\n", + " with open(label_path,encoding='utf-8') as f:\n", + " for line in f.readlines():\n", + " image_path, label = line.strip().split('\\t')\n", + " image_path = os.path.join(data_dir, image_path)\n", + " self.data_list.append([image_path, label])\n", + " self.transform = transform\n", "\n", " def __getitem__(self, index):\n", " \"\"\"\n", " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", " \"\"\"\n", - " image = np.random.rand(*self.image_size)\n", - " image = np.expand_dims(image, axis=0)\n", - " label = np.random.randint(0, self.class_num - 1)\n", - "\n", + " # 根据索引,从列表中取出一个\n", + " image_path, label = self.data_list[index]\n", + " # 读取灰度图\n", + " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", + " # 图像数据格式转换为 float32,此步骤为必须\n", + " image = image.astype('float32')\n", + " if self.transform is not None:\n", + " image = self.transform(image)\n", + " label = int(label)\n", " return image, label\n", "\n", " def __len__(self):\n", " \"\"\"\n", " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", " \"\"\"\n", - " return self.num_samples" + " return len(self.data_list)" ] }, { @@ -304,13 +276,14 @@ "id": "0e705d33", "metadata": {}, "source": [ + "在上面的代码中,定义了一个自定义的数据集类MyDataset,MyDataset继承自Dataset,并且实现了`__init__`,`__getitem__`和`__len__`三个方法。在`__init__`方法中完成了对标注文件的读取和解析,并且将所有的图像路径和对应的Label存放到一个列表中,在`__getitem__`方法中完成了图像的读取和预处理以及Label格式的转换,在`__len__`方法中返回`__init__`方法中初始化好的列表长度。\n", "\n", "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代。" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 33, "id": "9d1570a3", "metadata": { "scrolled": true @@ -325,7 +298,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -337,7 +310,7 @@ } ], "source": [ - "custom_dataset = MyDataset(BATCH_SIZE * BATCH_NUM)\n", + "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)\n", "\n", "for data in custom_dataset:\n", " image, label = data\n", @@ -347,27 +320,6 @@ " break" ] }, - { - "cell_type": "markdown", - "id": "33efb8b2-cf16-40b3-9016-e72126ad3910", - "metadata": {}, - "source": [ - "### 1.3 使用 paddle.io.IterableDataset 自定义数据集\n", - "\n", - "待补充" - ] - }, - { - "cell_type": "markdown", - "id": "d1419e2d-4b4c-4ddf-b457-0bd536ca6e3c", - "metadata": {}, - "source": [ - "### 1.4 使用 paddle.io.TensorDataset 自定义数据集\n", - "\n", - "\n", - "待补充" - ] - }, { "cell_type": "markdown", "id": "de3fd19b", @@ -376,12 +328,12 @@ "## 二、使用 paddle.io.DataLoader 定义数据读取器\n", "\n", "\n", - "通过前面介绍的直接迭代读取 DataSet 的方式虽然可实现对数据集的访问,但是这种访问方式只能单线程进行并且还需要手动分批次(batch)。在飞桨框架中,推荐使用 [paddle.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/io/DataLoader_cn.html#dataloader) API 对数据集进行多进程的读取,并且可自动完成划分 batch 的工作,开发者只需要进行数据处理模块的编写。" + "通过前面介绍的直接迭代读取 DataSet 的方式虽然可实现对数据集的访问,但是这种访问方式只能单线程进行并且还需要手动分批次(batch)。在飞桨框架中,推荐使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API 对数据集进行多进程的读取,并且可自动完成划分 batch 的工作,开发者只需要进行数据处理模块的编写。" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 34, "id": "c3ad4116", "metadata": { "scrolled": true @@ -391,12 +343,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "shape of image: [64, 1, 28, 28] shape of label: [64, 1]\n" + "shape of image: [64, 1, 28, 28] shape of label: [64]\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -409,7 +361,7 @@ ], "source": [ "# 定义并初始化数据读取器\n", - "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=1)\n", + "train_loader = paddle.io.DataLoader(custom_dataset, batch_size=64, shuffle=True, num_workers=1)\n", "\n", "# 调用 DataLoader 迭代读取数据\n", "for batch_id, data in enumerate(train_loader()):\n", @@ -429,18 +381,7 @@ "\n", "* **训练样本乱序**:通过设置 `shuffle=True` ,可以在取数据前打乱样本顺序。\n", "* **生成批次数据**:通过 `batch_size` 设置生成批次数据的批大小,示例中设置为 64。\n", - "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。\n" - ] - }, - { - "cell_type": "markdown", - "id": "5077293d-68fe-4bdd-a877-798f6d7680a6", - "metadata": {}, - "source": [ - "## 三、定义数据采样器\n", - "\n", - "\n", - "待补充" + "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。" ] }, { @@ -448,17 +389,17 @@ "id": "d3a256d5-33f0-4018-bd5a-3ee6d9bff372", "metadata": {}, "source": [ - "## 四、总结\n", + "## 三、总结\n", "\n", - "待补充" + "本节中介绍了Paddle中的数据送入模型之前的处理流程:数据集+数据读取器。进一步介绍了如何使用内置数据集和自定义数据集,在数据集中,本节仅对数据集进行了归一化,如需了解更多数据增强或数据处理操作,可以参考[数据预处理](03_data_preprocessing_cn.html)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -470,7 +411,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 321846e6cde..e60ad61baba 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -9,10 +9,7 @@ "\n", "数据预处理包含对图像进行数据增强和对标签进行处理等操作,这里主要介绍图像处理部分。\n", "\n", - "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。\n", - "\n", - "\n", - "\n" + "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。" ] }, { @@ -22,12 +19,12 @@ "source": [ "## paddle.vision.transforms 介绍\n", "\n", - "飞桨框架在 [paddle.vision.transforms](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理方法,可以通过以下代码查看:" + "飞桨框架在 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理方法,可以通过以下代码查看:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "93904999", "metadata": { "scrolled": true @@ -58,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "69b80bc1", "metadata": { "scrolled": true @@ -83,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "id": "4a1a5cb3", "metadata": { "scrolled": true @@ -103,7 +100,7 @@ "source": [ "## 在数据集中定义数据预处理操作\n", "\n", - "定义好数据预处理方法后,可以直接在 DataSet 中使用,下面介绍介绍两种数据处理使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集。\n", + "定义好数据预处理方法后,可以直接在 DataSet 中使用,下面介绍两种数据处理使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集。\n", "\n", "### 基于框架内置数据集\n", "\n", @@ -112,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "a7970f84", "metadata": { "scrolled": true @@ -130,45 +127,82 @@ "source": [ "### 基于自定义的数据集\n", "\n", - "对于自定义的数据集,可以在数据集的 `__init__` 函数中定义数据处理方法,之后在 `__getitem__` 方法中对返回的数据进行应用, 如下述代码所示:\n" + "对于自定义的数据集,可以在数据集的 `__init__` 函数中定义数据处理方法,之后在 `__getitem__` 方法中对返回的数据应用数据预处理, 如下述代码所示:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7cabb3c", + "metadata": {}, + "outputs": [], + "source": [ + "# 下载 MNIST 数据集并解压\n", + "! wget https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", + "! tar -xf mnist.tar" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "45ea330a", "metadata": { "scrolled": true }, "outputs": [], "source": [ + "import os\n", + "import cv2\n", "import numpy as np\n", "from paddle.io import Dataset\n", "\n", - "class MyDataset(Dataset): \n", - " def __init__(self, num_samples, image_size=(28,28), class_num=10):\n", - " super(MyDataset, self).__init__()\n", - " self.num_samples = num_samples\n", - " self.image_size = image_size\n", - " self.class_num = class_num\n", - " \n", - " # 在 `__init__` 中定义数据处理方法,此处为随机旋转\n", - " self.transform = RandomRotation(10)\n", - "\n", - " def __getitem__(self, index): \n", - " image = np.random.rand(*self.image_size)\n", - " \n", - " # 在 `__getitem__` 中对数据集使用数据处理方法\n", - " data = self.transform(data)\n", - " \n", - " \n", - " image = np.expand_dims(image, axis=0)\n", - " label = np.random.randint(0, self.class_num - 1)\n", + "class MyDataset(Dataset):\n", + " \"\"\"\n", + " 步骤一:继承 paddle.io.Dataset 类\n", + " \"\"\"\n", + " def __init__(self, data_dir, label_path, transform=None):\n", + " \"\"\"\n", + " 步骤二:实现 __init__ 函数,定义数据集大小\n", + " \"\"\"\n", + " super(MyDataset, self).__init__()\n", + " self.data_list = []\n", + " with open(label_path,encoding='utf-8') as f:\n", + " for line in f.readlines():\n", + " image_path, label = line.strip().split('\\t')\n", + " image_path = os.path.join(data_dir, image_path)\n", + " self.data_list.append([image_path, label])\n", + " self.transform = transform\n", "\n", + " def __getitem__(self, index):\n", + " \"\"\"\n", + " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", + " \"\"\"\n", + " # 根据索引,从列表中取出一个\n", + " image_path, label = self.data_list[index]\n", + " # 读取灰度图\n", + " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", + " # 图像数据格式转换为 float32,此步骤为必须\n", + " image = image.astype('float32')\n", + " if self.transform is not None:\n", + " image = self.transform(image)\n", + " label = int(label)\n", " return image, label\n", "\n", - " def __len__(self): \n", - " return self.num_samples" + " def __len__(self):\n", + " \"\"\"\n", + " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", + " \"\"\"\n", + " return len(self.data_list)\n", + " \n", + "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)" + ] + }, + { + "cell_type": "markdown", + "id": "e8990b3f-3a00-4d03-a99a-88185da6964c", + "metadata": {}, + "source": [ + "在自定义数据集中,直接将定义好的数据预处理传入`__init__`方法,将其定义为自定义数据集类的一个属性,然后在`__getitem__`中将数据预处理应用到图像上。" ] }, { @@ -178,15 +212,141 @@ "source": [ "## 数据处理的几种方法介绍\n", "\n", - "待补充几个典型方法和转换前后对比示例" + "### RandomRotation:\n", + "\n", + "依据degrees参数指定的角度范围,按照均匀分布随机产生一个角度对图像进行旋转。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "76edf274", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cdaFTsI489UUh75G+2aBYh0KdL3JWdjOHfm/ia+PPZ5Rvv/LMP2o+bsv4rUJ527zXaj5Gkn+8IPOBRuBCCFFSZMCFEKKkyIALIURJkQEXQoiSIgMuhBAlpc9XodSVGL7A8900ND1GUThsd6tmt6+qPVS3iKZhaWgy49Ow+d1vij3f0YqT5e1xv2a2jUxk/3rJKWHbQcvS0OLWlXG48Unn357IjhuxIGy7wruXUqC0FOirtQY6WFTgop5VHVnkaMfd6yiwUNiH4Lh1VXSvcdUVQNPmacGUotUx7cuD1TwF97xlwvhEdvBv4tQPV/x2z0Q26rn3h22X7JLeh53OeCRsG/ZrmwmhvG32nI6CgtutEbgQQpQUGXAhhCgpMuBCCFFSZMCFEKKk9K4T0yyptO7rancgUuQMWxfnUg67EDhEvL12h0yhUylwnkQOGYDV79spkV199RWJbGRT4DwC3gi68NHvnxm23XZaWlV+3OI/hW1DCpxCF777XxPZXkdfHrZ9e0tBiPYAwsxoGtzxeRflpvY13XNMFjoQ63As1sPM/71vItth6l/Ctos/NzmRrRmVjhM3WxJ/ls+fek0iu+jLx4ZtB89Oc3EXOf7P3TJ1ul+zZOew7ZNH/GciW1OQF3+vX52eyP5xVVL7A4Bd/v3Pobw7aAQuhBAlRQZcCCFKigy4EEKUFBlwIYQoKRs14GZ2rZktMLOnqmRjzOz3ZvZ8/r//VqgVogDptig7VlSZ+Z8NzD4MLAducPfdctl3gYXufpGZnQ2Mdvf/2NjJRtgY38cO6HJnO3v511NPJeqeCAuGuHjDkqNT7z3EnvaPD0lX0hRVmv/weaclsrfd9mzYtrJoUSiPaJm4XSJrm/lq2LbpPWml+R/ccXXYNiogcdDWe9Tcr1p51O9lqS+Ml+4E9Lhu11OhvWB1U/OoNBXCrvfFldDvuiMN937kxEvDtiuD842vo0jHvLa0UAjAMk9v/2pP78OOBevf/rR6eCKLPhsAb1TSUPqL39gvbPudcY/HJ+wmiyppqoKi1BFXL9onkT3yntaazlOk2xsdgbv7A8DCTuIpwPX56+uBw2rqhRD9COm2KDtdnQMf5+7z8tevAeMa1B8h+hrptigN3XZiejYHUzgHYWYnm9l0M5u+joJsZ0L0Q6Tbor/TVQM+38zGA+T/4zyigLtPc/fJ7j65lbhIrxD9COm2KA1dDaW/AzgWuCj//8uG9SgnzNvdgFDhKP94YY7vwNFTVGl+wVfSKut//PplYduVQaX3RZXU+fP5j8chxFvOfiqRVQryn4fXW5AjOnJYFjmO20al8qLQ/1cLHF79lC7ptjU10bR5x3tddJ+bhqb54H/wzG/Dtgf+5muJ7O6tYmfxxSf/LZG92lZ72H6RY3JMc/rldM7cT4Rt/zwndYTf/N7UaX/j0jSdBMDnhj+fyFa2x2ZqiKUOwDPHPhS2Xd6eOlIfXRPk5QcmtixJZJEjHmBIU9qH0UG/AM7bMk1r8ckdpoRtK7M65QPvalV6M7sZeBh4h5nNNrMTyJT7Y2b2PHBg/l6IUiHdFmVnoyNwdz+qYFPX1wMK0Q+Qbouyo0hMIYQoKTLgQghRUmTAhRCipPRqQYco6X1hRXhLv1sKq2YHNA1PQ3ILKQh9jVbCRAnrAX521sWJ7KW2OJT6Tyt3SWR3HJaG3VeefyHcv57Q//ZVqxJZtDIF4krhRWkK5p0WpA4oKIxR5MEfSLh7zcVJfv18tFIivkcvH5quOLlp2RZh258eEOjQDXEf5v5q+0Q2YVpcpd1Xp5+78X+MjzvxtMWJ7OxVh6QNt4hTzNxBkH6iOR5n+uzXEln0mQWYdVKa+iEqdgIw9wu7JrLHv/6jsO26YEXZ6oLiD9MW75bIZk+Jq9KPv7LTtbXFK7w0AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUXnViOuCVjg7DQidmHUROOV8bh7zX4wh9eer7Etn9x6TOSohzKe/68OfDttufPC+RVRa+lDYscFbaZmloc+F1BccoCvGuJ3/1qe+8P5EVOSt3uPOkRLYLcVXz0uKOt3XKWx3lni+gUuBI/8myrRLZz973jrBtU5o6HD9gbth2vM9JZDY2do7aZmkO/HkfilM3+Lr0uCELC/LUNyBdRsTW3/1TIitKMjDu+2nbvZd9KWx73wWXJ7KRTZuHbU8cmTqJb+SgsG3TsI5h/tYWj7U1AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUXnVi4l6YT7s7RNFXhY66gIXHp8VgAZ774pWJbI3HifuPf/VDiSyKSgNoe7NzGcaYlglbx/vPSR1TRZGn7UGe8KJIzMqeaYTouqmxs2nKsAcDaezEfOfX0oi32GVXcjo54G6ZlTrDMtL7P7Mt1tcPbZ46t19/OH7W9+yWPuuWHSeGbdvnplGMlTfeDNtGNI8YEcorNX6+W7YeH+8f9KGuCOwC3Y7sQVTEG+K8+KNv+HPY9raz02McNyKuATK6Oe3biSf9Kmx71xUdI1U9iPgEjcCFEKK0yIALIURJkQEXQoiSIgMuhBAlpZaamNea2QIze6pKNtXM5pjZ4/lfkC9SiP6NdFuUnVpWoVwH/ADonFn4cne/pK6zmWGtHcNyk/Djf7YNvluCKvFQXJE9YtmRab7hO86Lw+PnBVH+B/7llLDttkem1bTh9Zr7FRGtNoGCVTcF96Blq3GJrDJ+bNj2i9emBdiPHF4Q8hysONnlhjjceMfKYwXH6HOuo1G6HfCZk04L5avGpikLHvnuVWHble3pqo6jRvw9PuFT70lE9386brp6/90T2eA/PhO2jVZw1POZi6jMj1dqNCK1RkS0OiVabVK4/+7pCi2AC2/bO5EdF6xeA5jdtjyR3bVrnBe95n5trIG7PwDUtu5NiBIh3RZlpztz4Kea2RP5z9DufY0I0b+QbotS0FUDfiWwE7AHMA+4tKihmZ1sZtPNbPo6j8tzCdGP6JpuU3uwiRCNoksG3N3nu3vF3duBq4F0IuitttPcfbK7T261wUXNhOgXdFm3iSN0hehJuhRKb2bj3X19UuvDgac21L5qP2xQawdZUWh98xajEllhqG+QQ/jVqR8Imz5yYjqgWlIQ1/2p752VyLYrKPxqI9Pw5qL+RmHIlaVL02MGeb+LaCpou2KvNNT3uz+MC7TuOSj6Po9zhO/7+GcT2Q5nPxy2taFDE1k94dG9SVd1O2LQb+Kc56uOTh3pe50fO4Af+1bqEFsYODYBzhzzYiL7yL3Phm3POT51xj9/XurYBHj711MndOfP8XraV6xI29ahx9ThxIwck0UpNOrpQ/tH9kzPtTiePbj7C+kCiJXtaf50gBO2+2DNfaiVjRpwM7sZ2B8Ya2azgXOB/c1sD7IaDTOBeGmGEP0Y6bYoOxs14O5+VCC+pgf6IkSvIt0WZUeRmEIIUVJkwIUQoqTIgAshREkx76Eq0BEjbIzvYwd07ECBdzhcpVBQ5bt5ZLqqY9f70lUdABdv9bdEdvCznwzb2qfSMPJ6CkUUElxH6FEPPPpFrDvwvaH8vhvSKd0X16UhvRBXld/vic+EbUd8Ji0IYBPSCuoAlefTogQ9waN+L0t9Ye2l4BtIpNv18PwP9qm57Z2fuiKU79CSrhga0hSviFhUSfU4KjgAcPTM/RPZq9+JQ8sjNv9lWgyhKViZBLDqI5MS2dDH4pD39qVpOH9UlATAHno8kc25fdewbcT975sWysc2p9dx0NZ71HzcWinSbY3AhRCipMiACyFESZEBF0KIkiIDLoQQJaV3q9IDNHV0tPi6OvL/FjhcZwQOlbu3ujpse9OyLRJZ8zHx6dpWrUpkTYPjfC6RMzjK2w3ga4Mc6E3pd+mqKXEajlH/M3XqfG3CjWHbiG1aYsfxjr87IZG98/QXwrbtwXNrL3BWRo7q/hpK31fsfOqjNbc98xsHh/IZF9fuWLz+gPTzMdniEP0bJ96fyJb88Ndh2y+8eHgi+/KlMxLZwUPi57//iakT89yH7wrbvrT2bYnsx69uGbaF7RPJqRPuD1t+bOhziWxsc+rgB3g1yPHdm2gELoQQJUUGXAghSooMuBBClBQZcCGEKCky4EIIUVJ6NZR+ZNMWvu/gQzrI2lcXlFlrCgoJFFSl/9ErDyayKCwc4MNPpl7yoZ99I2wbVXpv2XFi2HblLqn3u3l13N+Zn0rDm/f9QJp4/4fbxZ7+kU2bJ7Ll7fF9/Mb8NIn8I5dPDtuOuSOtSh4VmoB4NU7Rs6wn8X53KHMofU+x9qD4WQ95el4iu/7hW8O2Ubj4n9cEK6mASa2pzj+4emQiK1qFEqV52K4l1XeAVouLjXSXHe48qea2u5wSF+1oNAqlF0KIAYYMuBBClBQZcCGEKCky4EIIUVJqKWq8LXADMI6s0Os0d/+emY0BfgZMJCv+eoS7pwm0q3D3YqdlZwKHZVEO4chhWfG41PwXt38okb3+cFpRHmBcy5JENr8tdWwCfGRo6oTcuSV29ER5l1cGlcaHBM5KiB1In7vnq2HbSRekebtHzZketq3UURE8oijNQE84LBtBI3W7vzLot/GzZpsJiejz2+4XNl1+xL6JbPgv07z6AM/94N2JbMpeadupU2Pn6oIDU91+6eNxmdIoV32lPR6TLnyiKMQ+5Z3n/z2R9VcdrmUE3gac4e6TgH2Br5jZJOBs4F533xm4N38vRJmQbotSs1ED7u7z3P2x/PUyYAYwAZgCXJ83ux44rIf6KESPIN0WZaeubIRmNhHYE3gUGOfu6xeTvkb2MzTa52TgZIDBxCWbhOhrpNuijNTsxDSzYcDPgdPdvUN0h2fRQGFEkLtPc/fJ7j65lTiNqRB9iXRblJWaDLiZtZIp+E3ufnsunm9m4/Pt44EFPdNFIXoO6bYoMxsNpTczI5sHXOjup1fJLwbedPeLzOxsYIy7n7WhY41sGevvHzalg8wLVj7U4/X97dzHE9kaj1eAzG5LQ3jHN8eVuxcGK0O2aYlD9ONVJPFxHwgW4kxqTSvQH/WPI+Nz/ThdQVC0KqC7hRNaxseV5tvmpatbiuivofSN1O3+GkpfD9FzgvhZRUU6INa3qLBJ0bnijsWPNCqMYhNifa0ExUbquYa+pki3a5kD3w/4AvCkmT2ey84BLgJuNbMTgFeAIxrUVyF6C+m2KDUbNeDu/iBQNKop95BDbNJIt0XZUSSmEEKUFBlwIYQoKb1ald4r7YX5pTtjrakD0Ntix+SiSupkicLVAXZqbU1kswsqS0cOy6fXppXqAY7464mJbOXCuA8Tb0sdx1F+5qbZs8L9h5HK68nq3jQ8Th1Ae5p+oC5nZcFx21f0zzBk0XXqcfRFCxVqtQNQnKLBK6m+1pMMvj86K+tFI3AhhCgpMuBCCFFSZMCFEKKkyIALIURJkQEXQoiS0qurUCKatxgTyiuL0mIKFIT9f/7AYxLZqh1Gh21XjU0rWa8dFvuuF++WermHvxhXwt7+/z2dyCqLg2sgLkzRtiINpS+intD05rFbpP16482az1VXH5bFxS5EOWiEDkUrkSK9KErR0L5o8QZ62BFfl6avaH91Ts37DwQ0AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUPndiVt5c2P1jPPdCIhv0XNw2ztAdU3sda6jU0ba9DodluH8dubQb4bDsbh9EualHh2p1ZNeVoqEglD5iIITH14NG4EIIUVJkwIUQoqTIgAshREmRARdCiJKyUQNuZtua2R/M7Bkze9rMTsvlU81sjpk9nv8d0vPdFaJxSLdF2allFUobcIa7P2Zmw4G/mtnv822Xu/slPdc9IXoU6XY/IywKUqlnjdemRS1FjecB8/LXy8xsBjChpzsmRE8j3RZlp645cDObCOwJPJqLTjWzJ8zsWjMLs0eZ2clmNt3Mpq9j01qjKcqDdFuUkZoNuJkNA34OnO7uS4ErgZ2APchGMZdG+7n7NHef7O6TW9ms+z0WosFIt0VZqcmAm1krmYLf5O63A7j7fHevuHs7cDWwd891U4ieQbotysxG58DNzIBrgBnuflmVfHw+hwhwOPBUz3RRiJ5But3/aF+hFA31UMsqlP2ALwBPmtnjuewc4Cgz2wNwYCZwSg/0T4ieRLotSk0tq1AeBKKSNXc3vjtC9B7SbVF2FIkphBAlRQZcCCFKigy4EEKUlD4v6CCEEP+kXWHz9aARuBBClBQZcCGEKCky4EIIUVJkwIUQoqSYu/feycxeB17J344F3ui1k/ceuq6+Y3t337IvTlyl22W4T11loF5bGa4r1O1eNeAdTmw23d0n98nJexBd16bNQL5PA/XaynxdmkIRQoiSIgMuhBAlpS8N+LQ+PHdPouvatBnI92mgXltpr6vP5sCFEEJ0D02hCCFESel1A25mB5vZc2b2gpmd3dvnbyR5wdsFZvZUlWyMmf3ezJ7P/4cFcfszZratmf3BzJ4xs6fN7LRcXvpr60kGim5Lr8tzbb1qwM2sGfgh8AlgElnlk0m92YcGcx1wcCfZ2cC97r4zcG/+vmy0AWe4+yRgX+Ar+XMaCNfWIwww3b4O6XUp6O0R+N7AC+7+kruvBW4BpvRyHxqGuz8ALOwkngJcn7++HjisN/vUCNx9nrs/lr9eBswAJjAArq0HGTC6Lb0uz7X1tgGfAMyqej87lw0kxlUVxH0NGNeXnekuZjYR2BN4lAF2bQ1moOv2gHr2A0Wv5cTsQTxb4lPaZT5mNgz4OXC6uy+t3lb2axNdp+zPfiDpdW8b8DnAtlXvt8llA4n5ZjYeIP+/oI/70yXMrJVMyW9y99tz8YC4th5ioOv2gHj2A02ve9uA/wXY2cx2MLNBwJHAHb3ch57mDuDY/PWxwC/7sC9dwswMuAaY4e6XVW0q/bX1IANdt0v/7AeiXvd6II+ZHQJcATQD17r7t3u1Aw3EzG4G9ifLZjYfOBf4L+BWYDuy7HRHuHtnh1C/xsw+CPwReBJoz8XnkM0XlvraepKBotvS6/JcmyIxhRCipMiJKYQQJUUGXAghSooMuBBClBQZcCGEKCky4EIIUVJkwIUQoqTIgAshREmRARdCiJLy/wGsr+QQ3cUyvQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "from matplotlib import pyplot as plt\n", + "from paddle.vision.transforms import RandomRotation\n", + "\n", + "transform = RandomRotation(90)\n", + "\n", + "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "RandomRotation_image = transform(image)\n", + "plt.subplot(1,2,1)\n", + "plt.title('origin image')\n", + "plt.imshow(image)\n", + "plt.subplot(1,2,2)\n", + "plt.title('RandomRotation image')\n", + "plt.imshow(RandomRotation_image)" + ] + }, + { + "cell_type": "markdown", + "id": "f3cac20c", + "metadata": {}, + "source": [ + "### RandomHorizontalFlip\n", + "\n", + "基于概率来执行图片的水平翻转。" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f6adefc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gQggRKcoHXiwthQdUsvIVs75bqOusNG49/ZENp/R/L5D1qgmPCQAfNK0IZNvXh9Oos2gyP4/yiLrCj3FCv1mB7CHsU/D+mzLevcuaxu6FhVe0+MG77gyDfd1r/QBgMTbk2eYjPZ0sBN4q8fBzxxeX97tjAwdiQ0/gQggRKXLgQggRKXLgQggRKXLgQggRKXLgQggRKZ0+CqWmlx9Rb1nlTPfOiFzX9A6PkZUwnnXhJWet8u2Wa3XHp+R6SfoxNJw2v9td4WregD/iJGu0wZzm/oHsqCtPd3W7NYYrhdev8lcPP+3S+wPZSf0WubornSneojC8e5eVhGDy8i0C2aTf/MDVbeoVriq/rq+/0vxV59wcyIbUrnR1+9SECyrsdk+YDuCN43d090fDp4Eot6TwUSilaJ/F+Iiavn3DMqzMWESlJRzRVZT/885fkJYQQoiqQw5cCCEiRQ5cCCEiRQ5cCCEipbJBTDJYad2aCg8gIisY1uQsT51VhG7ONPYWP1Dn4gQiALgB1pqe/grZa762fSCbNOnaQNa/xg8qfeYU4Zt/PN/VHT4xXFV+yNIXXF2XjMDx5bsfFci+csI1ru6X6vxp/mLjDHIC1q+v8wN1l98b1snIe//pHzjLjh2uumPfQDZ3/C6u7lM/+0MgO3/w84Fs2SPPufufdto5gaz782E6ASAjYFlE+8zC9RFZAx0831NE0D7L/7X1k1jr+wI9gQshRKTIgQshRKTIgQshRKTIgQshRKRs1IGTvJXkIpJv5skGkXyc5Hvp/+pdoVaIDGTbInYKGYUyGcD/BnB7nuxCANPM7AqSF6bf/3OjRzKDrV3bjmIm1LSNzKa0FLHidMvq1aHQCh+F4i3GAPhJ55ccGa7GDQCXTrglkHkJ8rNW/t7/krMD2fB7/Uh9bukyV+5RN3JEIGue87Gru93/WRrIev/Yj6j3qil88YcKMxmlsu0y4aVN6M1wgQXAr5OWjFEZxdS1Z0PDJ/n2dsiSXwSyZy6+LpANzmhHF95wWyD7zYRTXN3+d04PZMW0zyxcH5Gl6/iemh7+6DNPt+ByZfiojT6Bm9kzABa3EY8F0HqnbwNwRGGlEKJ6kG2L2GlvH/gQM2tIPy8AMKRE5RGis5Fti2jocBDTzAxAZh8EyfEkZ5Cc0YT2d58IUWlk26Laaa8DX0hyKACk//08ogDMbKKZjTaz0fXw+7CFqCJk2yIa2juV/kEAJwK4Iv3/l5KVKMXNyVtEsDELL/9uZg5hJwCUFXRYdGa4yvqzv7za1V3lrPS+JBdOlT3+2ye6+28+781AlsvIf+5eb0auYS+IlRWQaR4QyrOm/n/c7AfdqpSy23YxePcu6z57dVKXVX8ZAUsPz4ZyS5a4uptPCW3z6OeOD2R3PRYGKwFgj+5hG3/iimtd3f36/zyQbXF9x9NEeCkwWlb6+c89svxUR9ci8ChkGOHdAF4EsBPJeSRPQWLcB5N8D8BB6XchokK2LWJno0/gZnZcxqYDS1wWISqKbFvEjmZiCiFEpMiBCyFEpMiBCyFEpFR0QQeSwaiGzCgsw9+WYqbhe6tFZ5KRgN2LGi89ZrSre88FYSL7D5v9KPcLq8IVuR88Ykwgy733vrt/MVP/vWnBWSthWy4cHZOVpqDh7HA0zrKMhTG8NAGiMEbUhffugyZ/VI9XJ8N+4NdfsGAAANb69lrU1HJvNNTbYXmPP/DH7v7f/3M4PX6fXuGq9oDf5sYtPc/VHXDPjEDm2TsAoCb0PVn+xLveLD/lTfP3fIwn5xot6CCEEF0KOXAhhIgUOXAhhIgUOXAhhIiUigYxDYDl1g8YdnQqKeAH5WydP+W9mEDo7AlfC2RPjwsDJwAw1Ak27fJiOIUYALYZ3xDIcos/DBWzpuQ6AajM63KOkTWVvpiVu8/a+elAlhWs3PavpwWyHfFywefalPHu3ezDJ7m6Xp08gM1dXW819I7k6m+lUNvMvefYO4AH99spkP1x4gGu7syv3xXIHrj8Slf3gC+fH8hGXvSiq+sFJr3rAopLVVFMTvK2gziyhi3oCVwIISJFDlwIISJFDlwIISJFDlwIISKlokFMmBXXkV8g3mymzECdw+KTv+7K3/nJjYFsrfnBjJM/3i+QjTx7qavb/HnbZRh96oZt5e8//5NAVsxMsayZmLk9wxmiTRP8vM9j+zznSP0g5s7nzgzL5WqKtnj3Dof7umP7vBPI7nncnzlcP2FgIKt99V1X12tLxdibZ8eeDQNAzmkbI8/u6eqefG/Y5m4a/qSr67Xlr33wU1d30H+Hwc2sAG9Nv36uvKMEfrK9ixoLIYSoTuTAhRAiUuTAhRAiUuTAhRAiUgpZE/NWkotIvpknm0ByPsnX0r/DyltMIUqPbFvEDje20jvJ/QGsAHC7me2ayiYAWGFm/rzVDPrVbGZj6g9ZT2bNTRkndn5bnFXi00KGsozrajw2zLt93+8Lv4yDXj7dlQ8/9r2Cj9HRkTjFrG5dt+WQQJYbOtjVPWnKw4Hs2L7+KBSPHW/3o/rbXfxKICvFtO22vGTTsNwW+4mTHUpq2xxke7P0S2l6U7g/vOQrru6748KRFllMaQxHoUw+1v+tqm34LJA1L1jo6pZj5XUvj3YWc6fs4Mqf+NrNBR/jqAt+Ecj6TgnzlAMoyve4qSoy1yKoX+/79KZHsbzl8+BkG30CN7NnABQ27k2IiJBti9jpSB/4WSRfT19Dw59zIeJFti2ioL0O/EYA2wPYA0ADgKuyFEmOJzmD5Iwm85d3EqKKaJ9to/RdQkJsjHY5cDNbaGY5M2sBMAnAXhvQnWhmo81sdD17ZKkJURW027bhz9AVopy0ayo9yaFm1prU+kgAb25IP28/sNv6nfNZAb3azQYEstxnn/sHdoIGH0/Yx1Wdfmr4QLUsY1739667IJCNmPiGq8v+4dTirPLWOtNvc8uXh8fMyEHsUZOhu/IrIwLZ76+/wdXds5v3e+7nCB/z2tGBbNsL/fzK7N07kJUjiFkK2mvbZSuPExTMus9jdg/rZPoe97q6R/UJA5Pb3T/Z1b3gzDMCWc9/+AsrewFLb7HkrPovtG0AQO3gzQLZiJM+dnW/Mz5syw+d/XtX95ErrwlkY3b2F0seMeEFV+5RO2hAIMvyEW39JJv92PxGHTjJuwEcAGAwyXkALgZwAMk9kOQZnwPAH5ohRBUj2xaxs1EHbmbHOeJbylAWISqKbFvEjmZiCiFEpMiBCyFEpMiBCyFEpGx0Kn0p8aYbZ420cKPU3rRVALX9w8j1Lk/6kes/bPlqIDvk7e+6uvxeOI28mIUiMnGuw13deuXKgg/ZdNBXXfmTt4dduh80+SMIvFXlv/H6D1zdfj9YEMg4bEtXN2sF8lJT7FT6UlKuqfQetTts58ptflgny+/36+T53e8PZMXYxbfGneLq1j/xL1felhpnZBKQ0b5K4KO89mUP+XO0Ht35b4Hs/AV7urozv+WMmlnm+x7vOgr1f1m2rSdwIYSIFDlwIYSIFDlwIYSIFDlwIYSIlMquSg8EOXGtqYhcwRnBjFm/C1dTf3jLSa7uXY3h9Nvacf7pmlevDmQ1Pfx8Ll4w2JsGDQC2zsmBXhP+lq4e66fhGPDzcLrwucPudHU9tq7zAyfbPRYGpnY+531Xt8Wpt5aMYKUXqKnWqfTVhnfvsoLCXt7s/sf6U7W3uzas61kH31RwuS64ybe3G+Z/M5AtvTpM59D7yVnu/m3zYAPhtPJW3Gn7GQMdWpy2XDfOD2LeNS30Ed7gBwDY9nenBbIdT3/Z1fXI9H9tc4dnLIWgJ3AhhIgUOXAhhIgUOXAhhIgUOXAhhIgUOXAhhIiUik6l71+zmY3psf7K1y1rMpZZ81ZwzliV/oaPngtk3vRfANj/jSMDWe+jw+T2ANDS2BjI6rYb6equ2nHzQFa7xi/vnO+FowXG7PN2ILt+xCPu/v1regayFS3+fbxo4b6BbPo1o13dQQ++Fciykul7o3Gy6tJNE1CKlARt6IpT6Yu5d8XUibdwwuLvj3J1x5w7I5BdNiRscwDQpyYsw7KWcATImR8f6u4//YWdA9nIh/xFX3I9Qh/R691PXd3mD+cEspq+4SIsALDy3sGB7JndHnB1vfQDZ2wTtrnkhIX7tLZ1OX3Nw1jWnlXphRBCVCdy4EIIESly4EIIESly4EIIESmFLGo8HMDtAIYgWeh1opldR3IQgHsAjESy+OsPzSxMoJ2HmWUHLdvidO5n5RD2ApY585ea/8k2zweyT1/0gxlD6pYFsoXNYWATAP69dxiE3KHOmTIPYGBtGJha1RIGano5wUoA+Ofa8LjHPPEzV3fUZWGO6AHzw6AUAOScqcnFkJVmoBwBy1JQStsuF969y7rPxeAFpwdM8e3ivWeHBrLdLvLt7Z6Dbghke3UP7XjiiMfc/XuNfDqQLTnGt5/3msMp9v9YGQZBgay23N/V3bzunUCW5U8835OZ67yI/P5t/WTWYJNCnsCbAZxnZqMAjAFwJslRAC4EMM3MdgAwLf0uREzItkXUbNSBm1mDmb2Sfm4EMAvAMABjAdyWqt0G4IgylVGIsiDbFrFTVDZCkiMB7AngJQBDzKwh3bQAyWuot894AOMBoAfCrgMhqgHZtoiRgoOYJPsAuA/AOWa2XgeaJR00bieNmU00s9FmNroefhpTIToT2baIlYIcOMl6JAZ+l5m1roa6kOTQdPtQAIvKU0QhyodsW8RMIaNQCOAWALPM7Oq8TQ8COBHAFen/v2z0WLU1qO2z/hReLyk74Effi4niNmdkQN+vZ5gMf2htOLUdABY7I0O2rvOn6K9qCR/SetX4r9XPOANxRtWHI0vGzjrKP9fNwwLZTn/xE843d3DhhLqh/qrmzQ3h6JYsKjWVvlhKadvloqP3rpj6y2qLzR/NDWQ7neX/pp07Nhyd0uv0+YHs7h2nuPvPWBO2xf17+O1oV4btc69BH7i685rDKe+Davx235ALj9uc8YZV6zwDF+OnvPoFwsVguMJ/1i6kD/wbAH4M4A2Sr6WyXyEx7qkkTwHwEYAfFlJgIaoI2baImo06cDN7DkBWgqDSZ+8RokLItkXsaCamEEJEihy4EEJESkVXpbdcS2Z+6bZ4K2xbsz81fUkuDOp409UBYPv6cPqtF+AA/IDlzHVhbmMA+OG/Tg1kqxb7ZRh5rxPwnNkQyGrmhcEjAOiDUF5MVvesPMhoCacLFxWszDhuy8rOD1jGSsuaMAideZ+d/PVZ9edO967xn+e841pGcLzP1OmhcGooOnFrP6ywapdw2v6co/1erl6DQrua+tX/cnV36Ra25ax2n7WWgIfne0C/vKwLfU+hAWnLmMqvJ3AhhIgUOXAhhIgUOXAhhIgUOXAhhIgUOXAhhIiUio5C8ajdbJArzy0JE7AjI6n58QeNC2Srtx3o6q4eHK4Mva6PHzVeumsY+e37gbOyNIBt/ntmIMstda4B/giA5g5Ov82KZtcO3iws12efF3yuosrgjFYQHcRZ2CTrPhdjF8VM9y7GhgotQ/O8cHo9APRYsjSQ7fh3v6y1A8IFGX508nmubuP24X0c8Kb//NptRehnen7mp+boOdtZ58Ped3UtFx4j0/99vtiVt0VP4EIIESly4EIIESly4EIIESly4EIIESnMWu24HPTjINubSvImysNLNg3LbXFWdsGyItsW5STLtvUELoQQkSIHLoQQkSIHLoQQkSIHLoQQkbJRB05yOMmnSL5FcibJs1P5BJLzSb6W/h1W/uIKUTpk2yJ2CplK3wzgPDN7hWRfAP8i+Xi67Rozu7J8xROirMi2RdQUsqhxA4CG9HMjyVkAhpW7YEKUG9m2iJ2i+sBJjgSwJ4CXUtFZJF8neStJN3sUyfEkZ5Cc0QR/GSYhOhvZtoiRgh04yT4A7gNwjpktB3AjgO0B7IHkKeYqbz8zm2hmo81sdD26d7zEQpQY2baIlYIcOMl6JAZ+l5ndDwBmttDMcpastjkJwF7lK6YQ5UG2LWKmkFEoBHALgFlmdnWePH/56CMBvFn64glRPmTbInYKGYXyDQA/BvAGyddS2a8AHEdyDwAGYA6A08tQPiHKiWxbRE0ho1CeA+AlCHq49MURonLItkXsaCamEEJEihy4EEJEihy4EEJEihy4EEJEihy4EEJEihy4EEJEihy4EEJEihy4EEJESkVXpSf5KYCP0q+DAXxWsZNXDl1X57GNmW3eGSfOs+0Y7lN76arXFsN1ubZdUQe+3onJGWY2ulNOXkZ0XZs2Xfk+ddVri/m61IUihBCRIgcuhBCR0pkOfGInnruc6Lo2bbryfeqq1xbtdXVaH7gQQoiOoS4UIYSIlIo7cJKHkHyH5PskL6z0+UtJuuDtIpJv5skGkXyc5Hvpf3dB3GqG5HCST5F8i+RMkmen8uivrZx0FduWXcdzbRV14CRrAVwP4FAAo5CsfDKqkmUoMZMBHNJGdiGAaWa2A4Bp6ffYaAZwnpmNAjAGwJlpPXWFaysLXcy2J0N2HQWVfgLfC8D7Zvahma0DMAXA2AqXoWSY2TMAFrcRjwVwW/r5NgBHVLJMpcDMGszslfRzI4BZAIahC1xbGekyti27jufaKu3AhwGYm/d9XirrSgwxs4b08wIAQzqzMB2F5EgAewJ4CV3s2kpMV7ftLlX3XcWuFcQsI5YM8Yl2mA/JPgDuA3COmS3P3xb7tYn2E3vddyW7rrQDnw9geN73rVNZV2IhyaEAkP5f1MnlaRck65EY+V1mdn8q7hLXVia6um13ibrvanZdaQf+MoAdSG5LshuAYwE8WOEylJsHAZyYfj4RwF86sSztgiQB3AJglpldnbcp+msrI13dtqOv+65o1xWfyEPyMADXAqgFcKuZ/baiBSghJO8GcACSbGYLAVwM4M8ApgIYgSQ73Q/NrG1AqKohuS+AZwG8AaAlFf8KSX9h1NdWTrqKbcuu47k2zcQUQohIURBTCCEiRQ5cCCEiRQ5cCCEiRQ5cCCEiRQ5cCCEiRQ5cCCEiRQ5cCCEiRQ5cCCEi5f8BJPj6ffHyqJgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "from matplotlib import pyplot as plt\n", + "from paddle.vision.transforms import RandomHorizontalFlip\n", + "\n", + "transform = RandomHorizontalFlip(0.5)\n", + "\n", + "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "RandomRotation_image = transform(image)\n", + "plt.subplot(1,2,1)\n", + "plt.title('origin image')\n", + "plt.imshow(image)\n", + "plt.subplot(1,2,2)\n", + "plt.title('RandomRotation image')\n", + "plt.imshow(RandomRotation_image)" + ] + }, + { + "cell_type": "markdown", + "id": "94cdd766", + "metadata": {}, + "source": [ + "更多数据预处理可以参考 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) " + ] + }, + { + "cell_type": "markdown", + "id": "5927ccca", + "metadata": {}, + "source": [ + "## 总结\n", + "\n", + "本节介绍了数据预处理在数据集中的使用方式并介绍了常用的数据预处理操作。" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -198,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 332482f366c..5592aa0980a 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -12,11 +12,10 @@ "\n", "飞桨框架提供了多种模型组网方式,本文介绍如下几种常见用法:\n", "* **直接使用内置模型**\n", - "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**\n", - "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网**\n", - "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**\n", + "* **使用 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential) 组网**\n", + "* **使用 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 组网**\n", "\n", - "另外飞桨框架提供了 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 函数方便查看网络结构、每层的输入输出 shape 和参数信息。" + "另外飞桨框架提供了 [paddle.summary](../../api/paddle/summary_cn.html#summary) 函数方便查看网络结构、每层的输入输出 shape 和参数信息。" ] }, { @@ -26,12 +25,12 @@ "source": [ "## 一、直接使用内置模型\n", "\n", - "飞桨框架目前在 [paddle.vision.models](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#about-models) 下内置了计算机视觉领域的一些经典模型,只需一行代码即可完成网络构建和初始化,适合完成一些简单的深度学习任务,满足深度学习初阶用户感受模型的输入和输出形式、了解模型的性能。" + "飞桨框架目前在 [paddle.vision.models](../../api/paddle/vision/Overview_cn.html#about-models) 下内置了计算机视觉领域的一些经典模型,只需一行代码即可完成网络构建和初始化,适合完成一些简单的深度学习任务,满足深度学习初阶用户感受模型的输入和输出形式、了解模型的性能。" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", "metadata": { "execution": { @@ -88,15 +87,15 @@ "---------------------------------------------------------------------------\n", " Layer (type) Input Shape Output Shape Param # \n", "===========================================================================\n", - " Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", - " ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", - " MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", - " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", - " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", - " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Linear-4 [[1, 400]] [1, 120] 48,120 \n", - " Linear-5 [[1, 120]] [1, 84] 10,164 \n", - " Linear-6 [[1, 84]] [1, 10] 850 \n", + " Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Linear-1 [[1, 400]] [1, 120] 48,120 \n", + " Linear-2 [[1, 120]] [1, 84] 10,164 \n", + " Linear-3 [[1, 84]] [1, 10] 850 \n", "===========================================================================\n", "Total params: 61,610\n", "Trainable params: 61,610\n", @@ -134,11 +133,11 @@ "id": "c48f8ac6", "metadata": {}, "source": [ - "通过 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 `Conv2D` 卷积层、`ReLU` 激活层、`MaxPool2D` 池化层以及 `Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", + "通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 2个`Conv2D` 卷积层、2个`ReLU` 激活层、2个`MaxPool2D` 池化层以及3个`Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", "
\n", "

图1:LeNet网络结构示意图
\n", "\n", - "另外在 [paddle.summary](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", + "另外在 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", "\n" ] }, @@ -149,14 +148,13 @@ "source": [ "## 二、Paddle.nn 介绍\n", "\n", - "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html) 中查看。\n", + "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](../../api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](../../api/paddle/nn/Overview_cn.html) 中查看。\n", "\n", - "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential)、 \n", - "[paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", + "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential)、 \n", + "[paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](../../api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", " \n", - "* **使用 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、xxx)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", - "* **使用 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", - "* **使用 [paddle.nn.LayerList](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist) 组网**:xxxxxx\n", + "* **使用 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、AlexNet 和 VGG)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", + "* **使用 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", "\n" ] }, @@ -169,7 +167,7 @@ "## 三、使用 paddle.nn.Sequential 组网\n", "\n", "\n", - "构建顺序的线性网络结构时,可以选择该方式,只需要按模型的结构顺序,一层一层加到 [paddle.nn.Sequential](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Sequential_cn.html#sequential) 子类中即可。\n", + "构建顺序的线性网络结构时,可以选择该方式,只需要按模型的结构顺序,一层一层加到 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential) 子类中即可。\n", "\n", "参照前面图 1 所示的 LeNet 模型结构,构建该网络结构的代码如下:" ] @@ -190,24 +188,44 @@ }, "outputs": [ { - "ename": "ValueError", - "evalue": "(InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_215/4050339869.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# 可视化模型组网结构和参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpaddle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlenet_Sequential\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(net, input_size, dtypes, input)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0m_input_size\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/base.py\u001b[0m in \u001b[0;36m_decorate_function\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_decorate_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 331\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_built\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mforward_post_hook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_post_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/common.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m out = F.linear(\n\u001b[0;32m--> 172\u001b[0;31m x=input, weight=self.weight, bias=self.bias, name=self.name)\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/common.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(x, weight, bias, name)\u001b[0m\n\u001b[1;32m 1478\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0min_dygraph_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1479\u001b[0m pre_bias = _C_ops.matmul_v2(x, weight, 'trans_x', False, 'trans_y',\n\u001b[0;32m-> 1480\u001b[0;31m False)\n\u001b[0m\u001b[1;32m 1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1482\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: (InvalidArgument) Input(Y) has error dim.Y'dims[0] must be equal to 5But received Y'dims[0] is 400\n [Hint: Expected y_dims[y_ndim - 2] == K, but received y_dims[y_ndim - 2]:400 != K:5.] (at /paddle/paddle/fluid/operators/matmul_v2_op.h:268)\n [operator < matmul_v2 > error]" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------\n", + " Layer (type) Input Shape Output Shape Param # \n", + "===========================================================================\n", + " Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 \n", + " ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 \n", + " MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 \n", + " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", + " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", + " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", + " Flatten-1 [[1, 16, 5, 5]] [1, 400] 0 \n", + " Linear-4 [[1, 400]] [1, 120] 48,120 \n", + " Linear-5 [[1, 120]] [1, 84] 10,164 \n", + " Linear-6 [[1, 84]] [1, 10] 850 \n", + "===========================================================================\n", + "Total params: 61,610\n", + "Trainable params: 61,610\n", + "Non-trainable params: 0\n", + "---------------------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.11\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.35\n", + "---------------------------------------------------------------------------\n", + "\n" ] + }, + { + "data": { + "text/plain": [ + "{'total_params': 61610, 'trainable_params': 61610}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -221,6 +239,7 @@ " nn.Conv2D(6, 16, 5, stride=1, padding=0),\n", " nn.ReLU(),\n", " nn.MaxPool2D(2, 2),\n", + " nn.Flatten(),\n", " nn.Linear(400, 120),\n", " nn.Linear(120, 84), \n", " nn.Linear(84, 10)\n", @@ -234,12 +253,7 @@ "id": "19fd4c4c-9ff4-434e-b06c-32daf8e1ae43", "metadata": {}, "source": [ - "以上代码实现的组网与 paddle.vision.models.LeNet 完全一样。\n", - "\n", - "Sequential组网中框架做了什么:\n", - "\n", - "使用Sequential组网方式的条件和限制:\n", - "\n" + "使用Sequential组网时,Sequential会自动完成网络的前向计算过程,但是Sequential组网只能完成简单的线性模型,对于需要进行分支判断的模型需要使用paddle.nn.Layer 组网的方式进行实现。" ] }, { @@ -250,18 +264,16 @@ "## 四、使用 paddle.nn.Layer 组网\n", "\n", "构建一些比较复杂的网络结构时,可以选择该方式,组网包括三个步骤:\n", - "1. 创建一个继承自 [paddle.nn.Layer](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Layer_cn.html#layer) 的类;\n", + "1. 创建一个继承自 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 的类;\n", "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(sublayer);\n", "1. 在类的前向计算函数 `forward` 中使用定义好的 sublayer 进行前向计算。\n", "\n", - "并且 sublayer 既可以通过基础的神经网络层 API(如卷积层、池化层、全连接层等)定义,也可以通过 nn.Sequential 或 nn.Layer 定义。由此可见,paddle.nn.Layer 的组网用法非常灵活,便于构建各种复杂网络。\n", - "\n", "仍然以 LeNet 模型为例,使用 paddle.nn.Layer 组网的代码如下:\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "cf89df53", "metadata": { "execution": { @@ -324,7 +336,9 @@ " if num_classes > 0:\n", " self.fc = nn.Sequential(\n", " nn.Linear(400, 120),\n", - " nn.Linear(120, 84), nn.Linear(84, num_classes))\n", + " nn.Linear(120, 84), \n", + " nn.Linear(84, num_classes)\n", + " )\n", "\n", " def forward(self, inputs):\n", " x = self.features(inputs)\n", @@ -342,12 +356,10 @@ }, { "cell_type": "markdown", - "id": "541d133d", + "id": "59606321", "metadata": {}, "source": [ - "## 五、使用 paddle.nn.LayerList 组网\n", - "\n", - "待补充" + "在上面的代码中,将Lenet分为了`features`和`fc`两个子模块,`features`用于对输入图像进行特征提取,`fc`用于输出十个数字的分类。" ] }, { @@ -357,7 +369,7 @@ "source": [ "# 六、总结\n", "\n", - "待补充" + "本节中,介绍了飞桨中模型组网的三种方式,并且以LeNet为例介绍了如何使用这三种组网方式实现LeNet。" ] }, { @@ -367,13 +379,64 @@ "source": [ "# 扩展阅读:模型的层(Layer)\n", "\n", - "选几个经典的层解读一下\n", - "\n", - "\n", + "## Conv2D\n", + "[Conv2D](../../api/paddle/nn/Conv2D_cn.html#conv2d)主要用于对输入的特征图进行卷积操作,广泛用于深度学习网络中。Conv2D 根据输入、卷积核、步长(stride)、填充(padding)、空洞大小(dilations)等参数计算输出特征层大小。输入和输出是NCHW或NHWC格式,其中N是批尺寸,C是通道数,H是特征高度,W是特征宽度。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8a076b51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 6, 3, 3)\n" + ] + } + ], + "source": [ + "x = paddle.uniform((2, 3, 8, 8), dtype='float32', min=-1., max=1.)\n", "\n", + "conv = nn.Conv2D(3, 6, (3, 3), stride=2) #卷积层输入通道数为3,输出通道数为6,卷积核尺寸为3*3,步长为2\n", + "y = conv(x) #输入数据x\n", + "y = y.numpy()\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "63121da0", + "metadata": {}, + "source": [ + "## MaxPool2D\n", "\n", + "[MaxPool2D](../../api/paddle/nn/MaxPool2D_cn.html#maxpool2d)主要用于缩小特征图大小,根据`kernel_size`参数这都的窗口大小,对指定窗口内特征图进行取最大值的操作。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ac0cacd8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3, 3, 3)\n" + ] + } + ], + "source": [ + "x = paddle.uniform((2, 3, 8, 8), dtype='float32', min=-1., max=1.)\n", "\n", - "\n" + "pool = nn.MaxPool2D(3, stride=2) # 池化核尺寸为3*3,步长为2\n", + "y = pool(x) #输入数据x\n", + "y = y.numpy()\n", + "print(y.shape)" ] }, { @@ -383,15 +446,43 @@ "source": [ "# 扩展阅读:模型的参数(Parameter)\n", "\n", - "补充parameters的介绍\n" + "在飞桨中,可通过下面的代码获取网络中在训练期间优化的所有参数" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "29bd4185", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Layer: features.0.weight | Size: [6, 1, 3, 3]\n", + "Layer: features.0.bias | Size: [6]\n", + "Layer: features.3.weight | Size: [16, 6, 5, 5]\n", + "Layer: features.3.bias | Size: [16]\n", + "Layer: fc.0.weight | Size: [400, 120]\n", + "Layer: fc.0.bias | Size: [120]\n", + "Layer: fc.1.weight | Size: [120, 84]\n", + "Layer: fc.1.bias | Size: [84]\n", + "Layer: fc.2.weight | Size: [84, 10]\n", + "Layer: fc.2.bias | Size: [10]\n" + ] + } + ], + "source": [ + " for name, param in lenet.named_parameters():\n", + " print(f\"Layer: {name} | Size: {param.shape}\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -403,7 +494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, From 3a91c1677ad89ebe67917f0562cce1ed40df5896 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 19 Jan 2022 12:08:14 +0800 Subject: [PATCH 30/63] update 01-04 --- .../01_quick_start_cn.ipynb | 4 +- .../02_data_load_cn.ipynb | 108 +++++++++++++----- .../03_data_preprocessing_cn.ipynb | 91 ++++++++++++--- .../02_paddle2.0_develop/04_model_cn.ipynb | 48 ++++++-- 4 files changed, 199 insertions(+), 52 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 64ae4df96f3..6d70ef7b368 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -248,7 +248,7 @@ "\n", "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", "\n", - "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化 MNIST 数据集时传入了 `ToTensor` 变换来将图像转换为飞桨支持的 Tensor 数据类型。\n" + "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化MNIST数据集时传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型的收敛速度型。\n" ] }, { @@ -390,7 +390,7 @@ "\n", "模型训练需完成如下步骤:\n", "\n", - "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用飞桨高层 API进行训练、评估、推理的实例,方便后续操作。\n", + "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用[飞桨高层 API](../../practices/quick_start/high_level_api.html)进行训练、评估、推理的实例,方便后续操作。\n", "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](../../api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", "\n", diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index b92c146c825..24e5fa4ebd1 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -17,6 +17,9 @@ "\n", "* **定义数据读取器类**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", "\n", + "\n", + "* **定义数据采样器类(可选)**:定义从数据集中的采样行为,如乱序采样、批次采样和分布式批次采样。\n", + "\n", "本文以图像数据集为例介绍,文本数据集可参考 [NLP实践](../../practices/nlp/index_cn.html)。" ] }, @@ -67,6 +70,8 @@ "id": "9235d4f3-9e6f-4926-b003-da4eed882631", "metadata": {}, "source": [ + "打印出的自然语言处理(NLP)相关数据集中只有前七个是数据集。\n", + "\n", "以 [MNIST](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/datasets/MNIST_cn.html) 数据集为例,加载内置数据集的代码示例如下所示。" ] }, @@ -121,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "d1bbf911-41a1-452a-80f8-b19c4aec2939", "metadata": { "execution": { @@ -183,9 +188,8 @@ "可构建一个子类继承自 `paddle.io.Dataset` ,并且实现下面的三个函数:\n", "\n", "1. `__init__`:完成一些数据集初始化操作,如指定数据和标签文件的存储路径、定义数据集大小等。\n", - "2. `__getitem__`:定义指定索引(index)时如何获取数据,并且在此函数中可定义一些数据预处理工作,如读取图像、对图像进行数据增强、制作标签等操作,最终返回处理好的单条数据(训练数据、对应的标签)。\n", - "3. `__len__`:返回数据集的样本总数。\n", - "\n" + "2. `__getitem__`:定义指定索引(index)时如何获取数据,并且在此函数中可定义一些数据预处理工作,如读取图像、对图像进行数据增强、制作标签等操作,最终返回处理好的单条数据(训练数据、对应的标签)。详细数据预处理介绍可参考 [数据预处理](03_data_preprocessing_cn.html)。\n", + "3. `__len__`:返回数据集的样本总数。\n" ] }, { @@ -220,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "id": "1d26950f", "metadata": { "scrolled": true @@ -283,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "id": "9d1570a3", "metadata": { "scrolled": true @@ -320,6 +324,14 @@ " break" ] }, + { + "cell_type": "markdown", + "id": "fb7ad7bd", + "metadata": {}, + "source": [ + "数据集在模型训练时的使用方式可参考 [模型训练与评估](05_train_eval_predict_cn.html)" + ] + }, { "cell_type": "markdown", "id": "de3fd19b", @@ -333,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 7, "id": "c3ad4116", "metadata": { "scrolled": true @@ -343,20 +355,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "shape of image: [64, 1, 28, 28] shape of label: [64]\n" + "batch_id: 0, 训练数据shape: [64, 1, 28, 28], 标签数据shape: [64]\n" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ @@ -366,9 +366,7 @@ "# 调用 DataLoader 迭代读取数据\n", "for batch_id, data in enumerate(train_loader()):\n", " images, labels = data\n", - " print('shape of image: ',images.shape, 'shape of label: ', labels.shape)\n", - " plt.title(str(labels[0].numpy()))\n", - " plt.imshow(images[0][0]) \n", + " print(\"batch_id: {}, 训练数据shape: {}, 标签数据shape: {}\".format(batch_id, images.shape, labels.shape))\n", " break" ] }, @@ -381,7 +379,63 @@ "\n", "* **训练样本乱序**:通过设置 `shuffle=True` ,可以在取数据前打乱样本顺序。\n", "* **生成批次数据**:通过 `batch_size` 设置生成批次数据的批大小,示例中设置为 64。\n", - "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。" + "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。\n", + "\n", + "数据读取器在模型训练时的使用方式可参考 [模型训练与评估](05_train_eval_predict_cn.html)" + ] + }, + { + "cell_type": "markdown", + "id": "4c387f37", + "metadata": {}, + "source": [ + "## 三、定义数据采样器\n", + "\n", + "数据采样器定义了数据读取器从数据集中读取数据时的采样行为,主要用于从数据读取器中迭代式获取的样本下标数组,然后数据读取器根据下标数组从数据集中取出对应的数据。常用的主要有用于单机单卡训练[BatchSampler](../../api/paddle/io/BatchSampler_cn.html)和用于分布式训练的[DistributedBatchSampler](../../api/paddle/io/DistributedBatchSampler_cn.html)。下面以BatchSampler为例,介绍如何使用数据采样器。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "477c89ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "单独迭代 BatchSampler\n", + "[4695, 55786, 11346, 24091, 44102, 40683, 47319, 23923]\n", + "在DataLoader中使用 BatchSampler\n", + "batch_id: 0, 训练数据shape: [8, 1, 28, 28], 标签数据shape: [8]\n" + ] + } + ], + "source": [ + "from paddle.io import BatchSampler\n", + "\n", + "bs = BatchSampler(custom_dataset, batch_size=8, shuffle=True)\n", + "\n", + "print(\"单独迭代 BatchSampler\")\n", + "for batch_indices in bs:\n", + " print(batch_indices)\n", + " break\n", + " \n", + "train_loader = paddle.io.DataLoader(custom_dataset, batch_sampler=bs, num_workers=1)\n", + "\n", + "print(\"在DataLoader中使用 BatchSampler\")\n", + "for batch_id, data in enumerate(train_loader()):\n", + " images, labels = data\n", + " print(\"batch_id: {}, 训练数据shape: {}, 标签数据shape: {}\".format(batch_id, images.shape, labels.shape))\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "a503bc9a", + "metadata": {}, + "source": [ + "这里定义的BatchSampler在每个迭代时会返回一个batch_size大小的样本下标数组,数据读取器根据这个下标数组就能从数据集中拿到对应的数据。在DataLoader中使用 BatchSampler时,DataLoader中的batch_size,shuffle和drop_last等参数只需在BatchSampler中设定即可。" ] }, { @@ -389,9 +443,11 @@ "id": "d3a256d5-33f0-4018-bd5a-3ee6d9bff372", "metadata": {}, "source": [ - "## 三、总结\n", + "## 四、总结\n", + "\n", + "本节中介绍了Paddle中的数据送入模型之前的处理流程:数据集+数据读取器。进一步介绍了如何使用内置数据集和自定义数据集,在数据集中,本节仅对数据集进行了归一化,如需了解更多数据增强或数据处理操作,可以参考[数据预处理](03_data_preprocessing_cn.html)。 \n", "\n", - "本节中介绍了Paddle中的数据送入模型之前的处理流程:数据集+数据读取器。进一步介绍了如何使用内置数据集和自定义数据集,在数据集中,本节仅对数据集进行了归一化,如需了解更多数据增强或数据处理操作,可以参考[数据预处理](03_data_preprocessing_cn.html)" + "本节内容中如果遇到了问题,可以参考[FQA](faq.html)。 " ] } ], diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index e60ad61baba..3c48da49785 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -9,7 +9,8 @@ "\n", "数据预处理包含对图像进行数据增强和对标签进行处理等操作,这里主要介绍图像处理部分。\n", "\n", - "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。" + "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。\n", + "\n" ] }, { @@ -24,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "93904999", "metadata": { "scrolled": true @@ -55,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "69b80bc1", "metadata": { "scrolled": true @@ -80,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "4a1a5cb3", "metadata": { "scrolled": true @@ -109,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "a7970f84", "metadata": { "scrolled": true @@ -144,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "45ea330a", "metadata": { "scrolled": true @@ -219,23 +220,23 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "76edf274", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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cdaFTsI489UUh75G+2aBYh0KdL3JWdjOHfm/ia+PPZ5Rvv/LMP2o+bsv4rUJ527zXaj5Gkn+8IPOBRuBCCFFSZMCFEKKkyIALIURJkQEXQoiSIgMuhBAlpc9XodSVGL7A8900ND1GUThsd6tmt6+qPVS3iKZhaWgy49Ow+d1vij3f0YqT5e1xv2a2jUxk/3rJKWHbQcvS0OLWlXG48Unn357IjhuxIGy7wruXUqC0FOirtQY6WFTgop5VHVnkaMfd6yiwUNiH4Lh1VXSvcdUVQNPmacGUotUx7cuD1TwF97xlwvhEdvBv4tQPV/x2z0Q26rn3h22X7JLeh53OeCRsG/ZrmwmhvG32nI6CgtutEbgQQpQUGXAhhCgpMuBCCFFSZMCFEKKk9K4T0yyptO7rancgUuQMWxfnUg67EDhEvL12h0yhUylwnkQOGYDV79spkV199RWJbGRT4DwC3gi68NHvnxm23XZaWlV+3OI/hW1DCpxCF777XxPZXkdfHrZ9e0tBiPYAwsxoGtzxeRflpvY13XNMFjoQ63As1sPM/71vItth6l/Ctos/NzmRrRmVjhM3WxJ/ls+fek0iu+jLx4ZtB89Oc3EXOf7P3TJ1ul+zZOew7ZNH/GciW1OQF3+vX52eyP5xVVL7A4Bd/v3Pobw7aAQuhBAlRQZcCCFKigy4EEKUFBlwIYQoKRs14GZ2rZktMLOnqmRjzOz3ZvZ8/r//VqgVogDptig7VlSZ+Z8NzD4MLAducPfdctl3gYXufpGZnQ2Mdvf/2NjJRtgY38cO6HJnO3v511NPJeqeCAuGuHjDkqNT7z3EnvaPD0lX0hRVmv/weaclsrfd9mzYtrJoUSiPaJm4XSJrm/lq2LbpPWml+R/ccXXYNiogcdDWe9Tcr1p51O9lqS+Ml+4E9Lhu11OhvWB1U/OoNBXCrvfFldDvuiMN937kxEvDtiuD842vo0jHvLa0UAjAMk9v/2pP78OOBevf/rR6eCKLPhsAb1TSUPqL39gvbPudcY/HJ+wmiyppqoKi1BFXL9onkT3yntaazlOk2xsdgbv7A8DCTuIpwPX56+uBw2rqhRD9COm2KDtdnQMf5+7z8tevAeMa1B8h+hrptigN3XZiejYHUzgHYWYnm9l0M5u+joJsZ0L0Q6Tbor/TVQM+38zGA+T/4zyigLtPc/fJ7j65lbhIrxD9COm2KA1dDaW/AzgWuCj//8uG9SgnzNvdgFDhKP94YY7vwNFTVGl+wVfSKut//PplYduVQaX3RZXU+fP5j8chxFvOfiqRVQryn4fXW5AjOnJYFjmO20al8qLQ/1cLHF79lC7ptjU10bR5x3tddJ+bhqb54H/wzG/Dtgf+5muJ7O6tYmfxxSf/LZG92lZ72H6RY3JMc/rldM7cT4Rt/zwndYTf/N7UaX/j0jSdBMDnhj+fyFa2x2ZqiKUOwDPHPhS2Xd6eOlIfXRPk5QcmtixJZJEjHmBIU9qH0UG/AM7bMk1r8ckdpoRtK7M65QPvalV6M7sZeBh4h5nNNrMTyJT7Y2b2PHBg/l6IUiHdFmVnoyNwdz+qYFPX1wMK0Q+Qbouyo0hMIYQoKTLgQghRUmTAhRCipPRqQYco6X1hRXhLv1sKq2YHNA1PQ3ILKQh9jVbCRAnrAX521sWJ7KW2OJT6Tyt3SWR3HJaG3VeefyHcv57Q//ZVqxJZtDIF4krhRWkK5p0WpA4oKIxR5MEfSLh7zcVJfv18tFIivkcvH5quOLlp2RZh258eEOjQDXEf5v5q+0Q2YVpcpd1Xp5+78X+MjzvxtMWJ7OxVh6QNt4hTzNxBkH6iOR5n+uzXEln0mQWYdVKa+iEqdgIw9wu7JrLHv/6jsO26YEXZ6oLiD9MW75bIZk+Jq9KPv7LTtbXFK7w0AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUXnViOuCVjg7DQidmHUROOV8bh7zX4wh9eer7Etn9x6TOSohzKe/68OfDttufPC+RVRa+lDYscFbaZmloc+F1BccoCvGuJ3/1qe+8P5EVOSt3uPOkRLYLcVXz0uKOt3XKWx3lni+gUuBI/8myrRLZz973jrBtU5o6HD9gbth2vM9JZDY2do7aZmkO/HkfilM3+Lr0uCELC/LUNyBdRsTW3/1TIitKMjDu+2nbvZd9KWx73wWXJ7KRTZuHbU8cmTqJb+SgsG3TsI5h/tYWj7U1AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUXnVi4l6YT7s7RNFXhY66gIXHp8VgAZ774pWJbI3HifuPf/VDiSyKSgNoe7NzGcaYlglbx/vPSR1TRZGn7UGe8KJIzMqeaYTouqmxs2nKsAcDaezEfOfX0oi32GVXcjo54G6ZlTrDMtL7P7Mt1tcPbZ46t19/OH7W9+yWPuuWHSeGbdvnplGMlTfeDNtGNI8YEcorNX6+W7YeH+8f9KGuCOwC3Y7sQVTEG+K8+KNv+HPY9raz02McNyKuATK6Oe3biSf9Kmx71xUdI1U9iPgEjcCFEKK0yIALIURJkQEXQoiSIgMuhBAlpZaamNea2QIze6pKNtXM5pjZ4/lfkC9SiP6NdFuUnVpWoVwH/ADonFn4cne/pK6zmWGtHcNyk/Djf7YNvluCKvFQXJE9YtmRab7hO86Lw+PnBVH+B/7llLDttkem1bTh9Zr7FRGtNoGCVTcF96Blq3GJrDJ+bNj2i9emBdiPHF4Q8hysONnlhjjceMfKYwXH6HOuo1G6HfCZk04L5avGpikLHvnuVWHble3pqo6jRvw9PuFT70lE9386brp6/90T2eA/PhO2jVZw1POZi6jMj1dqNCK1RkS0OiVabVK4/+7pCi2AC2/bO5EdF6xeA5jdtjyR3bVrnBe95n5trIG7PwDUtu5NiBIh3RZlpztz4Kea2RP5z9DufY0I0b+QbotS0FUDfiWwE7AHMA+4tKihmZ1sZtPNbPo6j8tzCdGP6JpuU3uwiRCNoksG3N3nu3vF3duBq4F0IuitttPcfbK7T261wUXNhOgXdFm3iSN0hehJuhRKb2bj3X19UuvDgac21L5qP2xQawdZUWh98xajEllhqG+QQ/jVqR8Imz5yYjqgWlIQ1/2p752VyLYrKPxqI9Pw5qL+RmHIlaVL02MGeb+LaCpou2KvNNT3uz+MC7TuOSj6Po9zhO/7+GcT2Q5nPxy2taFDE1k94dG9SVd1O2LQb+Kc56uOTh3pe50fO4Af+1bqEFsYODYBzhzzYiL7yL3Phm3POT51xj9/XurYBHj711MndOfP8XraV6xI29ahx9ThxIwck0UpNOrpQ/tH9kzPtTiePbj7C+kCiJXtaf50gBO2+2DNfaiVjRpwM7sZ2B8Ya2azgXOB/c1sD7IaDTOBeGmGEP0Y6bYoOxs14O5+VCC+pgf6IkSvIt0WZUeRmEIIUVJkwIUQoqTIgAshREkx76Eq0BEjbIzvYwd07ECBdzhcpVBQ5bt5ZLqqY9f70lUdABdv9bdEdvCznwzb2qfSMPJ6CkUUElxH6FEPPPpFrDvwvaH8vhvSKd0X16UhvRBXld/vic+EbUd8Ji0IYBPSCuoAlefTogQ9waN+L0t9Ye2l4BtIpNv18PwP9qm57Z2fuiKU79CSrhga0hSviFhUSfU4KjgAcPTM/RPZq9+JQ8sjNv9lWgyhKViZBLDqI5MS2dDH4pD39qVpOH9UlATAHno8kc25fdewbcT975sWysc2p9dx0NZ71HzcWinSbY3AhRCipMiACyFESZEBF0KIkiIDLoQQJaV3q9IDNHV0tPi6OvL/FjhcZwQOlbu3ujpse9OyLRJZ8zHx6dpWrUpkTYPjfC6RMzjK2w3ga4Mc6E3pd+mqKXEajlH/M3XqfG3CjWHbiG1aYsfxjr87IZG98/QXwrbtwXNrL3BWRo7q/hpK31fsfOqjNbc98xsHh/IZF9fuWLz+gPTzMdniEP0bJ96fyJb88Ndh2y+8eHgi+/KlMxLZwUPi57//iakT89yH7wrbvrT2bYnsx69uGbaF7RPJqRPuD1t+bOhziWxsc+rgB3g1yPHdm2gELoQQJUUGXAghSooMuBBClBQZcCGEKCky4EIIUVJ6NZR+ZNMWvu/gQzrI2lcXlFlrCgoJFFSl/9ErDyayKCwc4MNPpl7yoZ99I2wbVXpv2XFi2HblLqn3u3l13N+Zn0rDm/f9QJp4/4fbxZ7+kU2bJ7Ll7fF9/Mb8NIn8I5dPDtuOuSOtSh4VmoB4NU7Rs6wn8X53KHMofU+x9qD4WQ95el4iu/7hW8O2Ubj4n9cEK6mASa2pzj+4emQiK1qFEqV52K4l1XeAVouLjXSXHe48qea2u5wSF+1oNAqlF0KIAYYMuBBClBQZcCGEKCky4EIIUVJqKWq8LXADMI6s0Os0d/+emY0BfgZMJCv+eoS7pwm0q3D3YqdlZwKHZVEO4chhWfG41PwXt38okb3+cFpRHmBcy5JENr8tdWwCfGRo6oTcuSV29ER5l1cGlcaHBM5KiB1In7vnq2HbSRekebtHzZketq3UURE8oijNQE84LBtBI3W7vzLot/GzZpsJiejz2+4XNl1+xL6JbPgv07z6AM/94N2JbMpeadupU2Pn6oIDU91+6eNxmdIoV32lPR6TLnyiKMQ+5Z3n/z2R9VcdrmUE3gac4e6TgH2Br5jZJOBs4F533xm4N38vRJmQbotSs1ED7u7z3P2x/PUyYAYwAZgCXJ83ux44rIf6KESPIN0WZaeubIRmNhHYE3gUGOfu6xeTvkb2MzTa52TgZIDBxCWbhOhrpNuijNTsxDSzYcDPgdPdvUN0h2fRQGFEkLtPc/fJ7j65lTiNqRB9iXRblJWaDLiZtZIp+E3ufnsunm9m4/Pt44EFPdNFIXoO6bYoMxsNpTczI5sHXOjup1fJLwbedPeLzOxsYIy7n7WhY41sGevvHzalg8wLVj7U4/X97dzHE9kaj1eAzG5LQ3jHN8eVuxcGK0O2aYlD9ONVJPFxHwgW4kxqTSvQH/WPI+Nz/ThdQVC0KqC7hRNaxseV5tvmpatbiuivofSN1O3+GkpfD9FzgvhZRUU6INa3qLBJ0bnijsWPNCqMYhNifa0ExUbquYa+pki3a5kD3w/4AvCkmT2ey84BLgJuNbMTgFeAIxrUVyF6C+m2KDUbNeDu/iBQNKop95BDbNJIt0XZUSSmEEKUFBlwIYQoKb1ald4r7YX5pTtjrakD0Ntix+SiSupkicLVAXZqbU1kswsqS0cOy6fXppXqAY7464mJbOXCuA8Tb0sdx1F+5qbZs8L9h5HK68nq3jQ8Th1Ae5p+oC5nZcFx21f0zzBk0XXqcfRFCxVqtQNQnKLBK6m+1pMMvj86K+tFI3AhhCgpMuBCCFFSZMCFEKKkyIALIURJkQEXQoiS0qurUCKatxgTyiuL0mIKFIT9f/7AYxLZqh1Gh21XjU0rWa8dFvuuF++WermHvxhXwt7+/z2dyCqLg2sgLkzRtiINpS+intD05rFbpP16482az1VXH5bFxS5EOWiEDkUrkSK9KErR0L5o8QZ62BFfl6avaH91Ts37DwQ0AhdCiJIiAy6EECVFBlwIIUqKDLgQQpSUPndiVt5c2P1jPPdCIhv0XNw2ztAdU3sda6jU0ba9DodluH8dubQb4bDsbh9EualHh2p1ZNeVoqEglD5iIITH14NG4EIIUVJkwIUQoqTIgAshREmRARdCiJKyUQNuZtua2R/M7Bkze9rMTsvlU81sjpk9nv8d0vPdFaJxSLdF2allFUobcIa7P2Zmw4G/mtnv822Xu/slPdc9IXoU6XY/IywKUqlnjdemRS1FjecB8/LXy8xsBjChpzsmRE8j3RZlp645cDObCOwJPJqLTjWzJ8zsWjMLs0eZ2clmNt3Mpq9j01qjKcqDdFuUkZoNuJkNA34OnO7uS4ErgZ2APchGMZdG+7n7NHef7O6TW9ms+z0WosFIt0VZqcmAm1krmYLf5O63A7j7fHevuHs7cDWwd891U4ieQbotysxG58DNzIBrgBnuflmVfHw+hwhwOPBUz3RRiJ5But3/aF+hFA31UMsqlP2ALwBPmtnjuewc4Cgz2wNwYCZwSg/0T4ieRLotSk0tq1AeBKKSNXc3vjtC9B7SbVF2FIkphBAlRQZcCCFKigy4EEKUlD4v6CCEEP+kXWHz9aARuBBClBQZcCGEKCky4EIIUVJkwIUQoqSYu/feycxeB17J344F3ui1k/ceuq6+Y3t337IvTlyl22W4T11loF5bGa4r1O1eNeAdTmw23d0n98nJexBd16bNQL5PA/XaynxdmkIRQoiSIgMuhBAlpS8N+LQ+PHdPouvatBnI92mgXltpr6vP5sCFEEJ0D02hCCFESel1A25mB5vZc2b2gpmd3dvnbyR5wdsFZvZUlWyMmf3ezJ7P/4cFcfszZratmf3BzJ4xs6fN7LRcXvpr60kGim5Lr8tzbb1qwM2sGfgh8AlgElnlk0m92YcGcx1wcCfZ2cC97r4zcG/+vmy0AWe4+yRgX+Ar+XMaCNfWIwww3b4O6XUp6O0R+N7AC+7+kruvBW4BpvRyHxqGuz8ALOwkngJcn7++HjisN/vUCNx9nrs/lr9eBswAJjAArq0HGTC6Lb0uz7X1tgGfAMyqej87lw0kxlUVxH0NGNeXnekuZjYR2BN4lAF2bQ1moOv2gHr2A0Wv5cTsQTxb4lPaZT5mNgz4OXC6uy+t3lb2axNdp+zPfiDpdW8b8DnAtlXvt8llA4n5ZjYeIP+/oI/70yXMrJVMyW9y99tz8YC4th5ioOv2gHj2A02ve9uA/wXY2cx2MLNBwJHAHb3ch57mDuDY/PWxwC/7sC9dwswMuAaY4e6XVW0q/bX1IANdt0v/7AeiXvd6II+ZHQJcATQD17r7t3u1Aw3EzG4G9ifLZjYfOBf4L+BWYDuy7HRHuHtnh1C/xsw+CPwReBJoz8XnkM0XlvraepKBotvS6/JcmyIxhRCipMiJKYQQJUUGXAghSooMuBBClBQZcCGEKCky4EIIUVJkwIUQoqTIgAshREmRARdCiJLy/wGsr+QQ3cUyvQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -314,13 +315,71 @@ "\n", "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", "\n", - "RandomRotation_image = transform(image)\n", + "RandomHorizontalFlip_image = transform(image)\n", "plt.subplot(1,2,1)\n", "plt.title('origin image')\n", "plt.imshow(image)\n", "plt.subplot(1,2,2)\n", - "plt.title('RandomRotation image')\n", - "plt.imshow(RandomRotation_image)" + "plt.title('RandomHorizontalFlip image')\n", + "plt.imshow(RandomHorizontalFlip_image)" + ] + }, + { + "cell_type": "markdown", + "id": "c8272853", + "metadata": {}, + "source": [ + "### RandomVerticalFlip\n", + "\n", + "基于概率来执行图片的垂直翻转。" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "470047b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "from matplotlib import pyplot as plt\n", + "from paddle.vision.transforms import RandomVerticalFlip\n", + "\n", + "transform = RandomVerticalFlip(0.5)\n", + "\n", + "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "RandomVerticalFlip_image = transform(image)\n", + "plt.subplot(1,2,1)\n", + "plt.title('origin image')\n", + "plt.imshow(image)\n", + "plt.subplot(1,2,2)\n", + "plt.title('RandomVerticalFlip image')\n", + "plt.imshow(RandomVerticalFlip_image)" ] }, { @@ -338,7 +397,7 @@ "source": [ "## 总结\n", "\n", - "本节介绍了数据预处理在数据集中的使用方式并介绍了常用的数据预处理操作。" + "本节介绍了数据预处理在数据集中的使用方式并介绍了常用的数据预处理操作。关于文本的数据预处理可以参考[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data_prepare/overview.rst),语音的数据预处理可以参考 [PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs)。" ] } ], diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 5592aa0980a..ba0f9087099 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", "metadata": { "execution": { @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "883f5395-3b1c-4d58-a70e-1dd7886a7d36", "metadata": { "execution": { @@ -115,7 +115,7 @@ "{'total_params': 61610, 'trainable_params': 61610}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "9a86cc3e", "metadata": { "execution": { @@ -223,7 +223,7 @@ "{'total_params': 61610, 'trainable_params': 61610}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "cf89df53", "metadata": { "execution": { @@ -334,7 +334,7 @@ " nn.MaxPool2D(2, 2))\n", "\n", " if num_classes > 0:\n", - " self.fc = nn.Sequential(\n", + " self.linear = nn.Sequential(\n", " nn.Linear(400, 120),\n", " nn.Linear(120, 84), \n", " nn.Linear(84, num_classes)\n", @@ -345,7 +345,7 @@ "\n", " if self.num_classes > 0:\n", " x = paddle.flatten(x, 1)\n", - " x = self.fc(x)\n", + " x = self.linear(x)\n", " return x\n", "lenet_SubClass = LeNet()\n", "\n", @@ -439,6 +439,38 @@ "print(y.shape)" ] }, + { + "cell_type": "markdown", + "id": "c40d6243", + "metadata": {}, + "source": [ + "## ReLU\n", + "\n", + "[ReLU](../../api/paddle/nn/ReLU_cn.html#relu)是深度学习中常用的激活层,主要用于对输入进行非线性变换。ReLU将输入中小于0的部分变为0,大于0的部分保持不变。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7261f42c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(shape=[3], dtype=float32, place=CPUPlace, stop_gradient=True,\n", + " [0., 0., 1.])\n" + ] + } + ], + "source": [ + "x = paddle.to_tensor([-2., 0., 1.])\n", + "relu = paddle.nn.ReLU()\n", + "y = relu(x)\n", + "print(y)" + ] + }, { "cell_type": "markdown", "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", From fda10b8e9615602ade869e66603e6a990ffa8427 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 19 Jan 2022 13:26:14 +0800 Subject: [PATCH 31/63] update 01-04 --- .../02_paddle2.0_develop/03_data_preprocessing_cn.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 3c48da49785..a3b47efae70 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -7,10 +7,9 @@ "source": [ "# 数据预处理\n", "\n", - "数据预处理包含对图像进行数据增强和对标签进行处理等操作,这里主要介绍图像处理部分。\n", + "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行特定的处理得到不同的图像,从而增强模型的泛化性。\n", "\n", - "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行处理得到不同的图像,从而增强模型的泛化性。\n", - "\n" + "在本节中主要介绍图像中的数据预处理。" ] }, { From 9453523b01df731ee794c12512f75f1125b73712 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 19 Jan 2022 13:38:48 +0800 Subject: [PATCH 32/63] update 01-04 --- .../02_paddle2.0_develop/04_model_cn.ipynb | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index ba0f9087099..ba8b8cf6d8c 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -439,6 +439,37 @@ "print(y.shape)" ] }, + { + "cell_type": "markdown", + "id": "15082b1f", + "metadata": {}, + "source": [ + "## Linear\n", + "\n", + "[Linear](../../api/paddle/nn/Linear_cn.html#linear)中每个神经元与上一层的所有神经元相连,实现对前一层的线性组合和线性变换。在卷积神经网络分类任务中,输出分类结果之前,通常采用全连接层对特征进行处理。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b3bacc6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4]\n" + ] + } + ], + "source": [ + "x = paddle.uniform((2, 6), dtype='float32', min=-1., max=1.)\n", + "linear = paddle.nn.Linear(6, 4)\n", + "y = linear(x)\n", + "print(y.shape)" + ] + }, { "cell_type": "markdown", "id": "c40d6243", From de42b98c1884d4065cb0c702d8aadb91f7126b2e Mon Sep 17 00:00:00 2001 From: WangChen0902 <827913668@qq.com> Date: Thu, 20 Jan 2022 19:49:20 +0800 Subject: [PATCH 33/63] update train_eval_predict, device and customize --- .../05_train_eval_predict_cn.ipynb | 404 ++++++++++++++---- .../02_paddle2.0_develop/06_device_cn.ipynb | 211 +++++++-- .../07_customize_cn.ipynb | 236 ++++++++-- 3 files changed, 701 insertions(+), 150 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb index 51454ad2826..439cee17231 100644 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -5,90 +5,237 @@ "id": "f97a8a96", "metadata": {}, "source": [ - "# 训练与预测验证\n", + "-----------------\n", + "1. 训练前准备\n", + " 1. 指定训练的设备\n", + " 1. 准备训练用的数据集和模型\n", + "1. 通过 paddle.Model 高层 API 训练与评估验证\n", + " 1. 使用 paddle.Model 封装模型\n", + " 1. 使用 Model.prepare 配置训练准备参数\n", + " 1. 损失函数\n", + " 1. 优化器\n", + " 1. 评价指标\n", + " 1. 使用 Model.fit 训练模型\n", + " 1. 使用 Model.evaluate 评估模型\n", + " 1. 使用 Model.predict 执行推理\n", + " \n", + "1. 通过基础 API 训练与评估验证\n", + " 1. 模型训练(拆解 Model.prepare、Model.fit)\n", + " 1. 模型评估(拆解 Model.evaluate)\n", + " 1. 模型推理(拆解 Model.predict)\n", + "\n", + "1. 扩展阅读:恢复训练---补FAQ,并在本文加引用\n", + "1. 扩展阅读:欠拟合和过拟合---补FAQ,并在本文加引用\n", + "1. 扩展阅读:自定义LOSS、Metric、Callback,优化器不能自定义?自定义哪些东西的界限怎么划的?\n", + "1. 扩展阅读:训练过程可视化分析---独立一篇并在本文加引用\n", + "\n", "\n", - "在完成数据预处理,数据加载与模型的组建后,你就可以进行模型的训练与预测了。飞桨主框架提供了两种训练与预测的方法,一种是用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 对模型进行封装,通过高层API如 [Model.fit](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 、 [Model.evaluate](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 、 [Model.predict](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 等完成模型的训练与预测;另一种就是基于基础API常规的训练方式。\n", + "# 模型训练、评估与推理\n", "\n", - "高层API实现的模型训练与预测如 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都可以通过基础API实现,本文先介绍高层API的训练方式,然后会将高层API拆解为基础API的方式,方便对比学习。\n", + "在完成数据预处理,数据加载与模型的组建后,你就可以进行模型的训练与推理了。训练时通过很多个循环(epoch)训练模型,每轮会将输入数据传入定义好的模型,得到预测值。预测值将与label做loss,然后loss进行反向传播,之后再通过优化器优化网络的参数。推理时只需要将数据输入训练好的网络,得到预测值。飞桨主框架提供了两种训练与推理的方法,一种是用 [paddle.Model](../api/paddle/Model_cn.html) 对模型进行封装,通过高层API如 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 、 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 、 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 等完成模型的训练、评估与推理;另一种就是基于基础API常规的训练方式。通过高层API完成模型的训练、评估与推理,可以省略很多复杂的步骤,适合新手上手,但是各个参数必须符合API的格式;另一种就是基于基础API常规的训练方式,这种方法比较灵活,但是需要实现训练中所有的步骤设定所有的流程。\n", "\n", + "高层API实现的模型训练、评估与推理如 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都可以通过基础API实现,本文先介绍高层API的训练方式,然后会将高层API拆解为基础API的方式,方便对比学习。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1659a965-891d-420e-bc6e-7b8346e5d641", + "metadata": {}, + "source": [ "## 一、训练前准备\n", "\n", - "在封装模型前,需要先完成数据的加载,由于这一部分高层API与基础API通用,所以都可用下面的代码实现:" + "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "86a3f26d", - "metadata": {}, + "execution_count": null, + "id": "a88a8752-bbab-4ecf-b384-4199626210df", + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "import paddle\n", - "import numpy as np\n", - "from paddle.vision.transforms import ToTensor\n", - "\n", - "# 加载数据集\n", - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", - "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())" + "# 使用 pip 工具安装 matplotlib 和 numpy\n", + "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple" ] }, { "cell_type": "markdown", - "id": "d9d0367d", + "id": "bc9cf191-82e7-47e0-b6c5-bf9c55802b3f", "metadata": {}, "source": [ - "通过上述的代码,你就完成了训练集与测试集的构建,下面分别用两种方式完成模型的训练与预测。\n", + "### 1.1 指定训练的硬件\n", "\n", - "## 二、通过 `paddle.Model` 训练与预测\n", + "模型训练时,需要用到 CPU、 GPU 等计算处理器资源,由于飞桨框架的安装包是区分处理器类型的,默认情况下飞桨框架会根据所安装的版本自动选择对应硬件,比如安装的 GPU 版本的飞桨,则自动使用 GPU 训练模型,无需手动指定。\n", "\n", - "在这里你可以采用Sequential组网或者SubClass组网的方式来创建一个mnist网络模型,你可使用 `paddle.Model` 完成模型的封装,将网络结构组合成一个可快速使用高层API进行训练和预测的对象。代码如下:" + "但是如果安装的 GPU 版本的飞桨框架,想切换到 CPU 上训练,则可通过 [paddle.device.set_device](../api/paddle/device/set_device_cn.html#set-device) API 修改,如果本机有多个 GPU 卡,也可以通过该 API 选择指定的卡进行训练。" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "a7705595", - "metadata": {}, + "execution_count": 3, + "id": "0da73e3c-adb6-49bb-9da2-637ab920f558", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:07:59.984715Z", + "iopub.status.busy": "2022-01-19T13:07:59.983585Z", + "iopub.status.idle": "2022-01-19T13:07:59.991700Z", + "shell.execute_reply": "2022-01-19T13:07:59.990882Z", + "shell.execute_reply.started": "2022-01-19T13:07:59.984656Z" + }, + "scrolled": true + }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "W1222 09:27:56.863570 7417 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n", - "W1222 09:27:56.870544 7417 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n" - ] + "data": { + "text/plain": [ + "CPUPlace" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ + "import paddle\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "# 指定在 CPU 上训练\n", + "paddle.device.set_device('cpu')\n", + "\n", + "# 指定在 GPU 第 0 号卡上训练\n", + "# paddle.device.set_device('gpu:0')" + ] + }, + { + "cell_type": "markdown", + "id": "28d8eeac-909d-4152-b223-89a3da9e4fc6", + "metadata": {}, + "source": [ + "> * 本文仅以单机单卡场景为例,介绍模型训练的方法,如果需要使用单机多卡、多机多卡训练,请参考如下章节:[单机多卡训练](06_device_cn.html)、[分布式训练](./06_distributed_training/distributed_introduction.html)。\n", + "> * 飞桨框架除了支持在 CPU、GPU 上训练,还支持在百度昆仑 XPU、华为昇腾 NPU 等 AI 计算处理器上训练,对应的训练指导请参考 [硬件支持](./09_hardware_support/index_cn.html) 章节。\n", + "> * 注意使用 `paddle.device.set_device` 时,只能指定CUDA_VISIBLE_DEVICES可见的显卡,例如同时设置 `CUDA_VISIBLE_DEVICES=1` 和 `paddle.device.set_device('gpu:0')` 时会冲突报错" + ] + }, + { + "cell_type": "markdown", + "id": "ddc1ef31-4128-445d-9ff7-18d43f5b90b8", + "metadata": {}, + "source": [ + "### 1.2 准备训练用的数据集和模型\n", + "\n", + "模型训练前,需要先完成数据集的加载和模型组网,以 MNIST 手写数字识别任务为例,代码示例如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c20487eb-ae05-4a35-a459-a00484f02f1b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:08:15.735730Z", + "iopub.status.busy": "2022-01-19T13:08:15.735285Z", + "iopub.status.idle": "2022-01-19T13:08:20.338671Z", + "shell.execute_reply": "2022-01-19T13:08:20.337632Z", + "shell.execute_reply.started": "2022-01-19T13:08:15.735677Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "\n", + "# 加载 MNIST 训练集和测试集\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "\n", + "# 使用 Sequential 进行模型组网\n", "mnist = paddle.nn.Sequential(\n", " paddle.nn.Flatten(1, -1), \n", " paddle.nn.Linear(784, 512), \n", " paddle.nn.ReLU(), \n", " paddle.nn.Dropout(0.2), \n", " paddle.nn.Linear(512, 10)\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d9d0367d", + "metadata": {}, + "source": [ "\n", + "\n", + "## 二、通过 paddle.Model 高层 API 训练、评估与推理\n", + "\n", + "\n", + "以手写数字识别任务为例,使用高层 API 进行模型训练、评估与推理的步骤如下:\n" + ] + }, + { + "cell_type": "markdown", + "id": "94022bff-6b80-40aa-a366-e41365099794", + "metadata": {}, + "source": [ + "### 2.1 使用 paddle.Model 封装模型\n", + "\n", + "使用高层 API 训练模型前,可使用 [paddle.Model](../api/paddle/Model_cn.html) 将模型封装为一个实例,方便后续进行训练、评估与推理。代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a7705595", + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:08:27.651130Z", + "iopub.status.busy": "2022-01-19T13:08:27.649829Z", + "iopub.status.idle": "2022-01-19T13:08:27.660944Z", + "shell.execute_reply": "2022-01-19T13:08:27.659530Z", + "shell.execute_reply.started": "2022-01-19T13:08:27.651041Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "# 封装模型,便于进行后续的训练、评估和推理\n", "model = paddle.Model(mnist)" ] }, { "cell_type": "markdown", - "id": "4500460a", + "id": "ebab1c33-2e92-4632-b902-f8c2dea85e9d", "metadata": {}, "source": [ - "### 2.1 用 `Model.prepare` 配置模型\n", + "### 2.2 使用 Model.prepare 配置训练准备参数\n", "\n", - "用 `paddle.Model` 完成模型的封装后,在训练前,需要对模型进行配置,通过 [Model.prepare](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 接口来对训练进行提前的配置准备工作,包括设置模型优化器,Loss计算方法,精度计算方法等。" + "用 `paddle.Model` 完成模型的封装后,在训练前,需要对模型进行配置,通过 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 接口来对训练进行提前的配置准备工作,包括设置模型优化器,Loss计算方法,精度计算方法等。\n", + "\n", + "* 优化器(optimizer)用于计算和更新梯度,优化器能够保存参数状态并根据梯度更新传入优化器的参数,这里我们使用常用的Adam优化器 `paddle.optimizer.Adam` ,并传入封装好的模型全部参数 `model.parameters` 。更多优化器API详见 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer);\n", + "* 损失函数(loss)用来评价模型的预测值和真实值不一样的程度,在这里我们使用交叉熵损失函数 `paddle.nn.CrossEntropyLoss` 。更多Loss API详见 [Loss层](../api/paddle/nn/Overview_cn.html#loss-layers);\n", + "* 评价指标(metrics)用于评估模型的好坏,不同的任务通常有不同的评价指标,本任务中我们使用分类任务常用的准确率指标 `paddle.metric.Accuracy` 。更多评估器API详见 [paddle.metric](../api/paddle/metric/Overview_cn.html)。\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "ccefe291", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:08:30.072813Z", + "iopub.status.busy": "2022-01-19T13:08:30.071887Z", + "iopub.status.idle": "2022-01-19T13:08:30.079309Z", + "shell.execute_reply": "2022-01-19T13:08:30.078609Z", + "shell.execute_reply.started": "2022-01-19T13:08:30.072752Z" + }, + "scrolled": true + }, "outputs": [], "source": [ - "# 为模型训练做准备,设置优化器,损失函数和精度计算方式\n", + "# 为模型训练做准备,设置优化器并将网络的参数传入优化器,设置损失函数和精度计算方式\n", "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", " loss=paddle.nn.CrossEntropyLoss(), \n", " metrics=paddle.metric.Accuracy())" @@ -99,16 +246,31 @@ "id": "5da3322e", "metadata": {}, "source": [ - "### 2.2 用 `Model.fit` 训练模型\n", + "### 2.3 使用 Model.fit 训练模型\n", + "\n", + "做好模型训练的前期准备工作后,调用 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 接口来启动训练过程,需要指定至少3个关键参数:训练数据集,训练轮次和单次训练数据批次大小:\n", "\n", - "做好模型训练的前期准备工作后,调用 `fit` 接口来启动训练过程,需要指定至少3个关键参数:训练数据集,训练轮次和单次训练数据批次大小。" + "* 训练数据集:传入之前定义好的训练数据集;\n", + "* 训练轮次(epoch):训练时遍历数据集的次数;\n", + "* 批次大小(batch_size):通常情况下,数据集需要分批读取训练,设定每个批次数据的大小。\n", + "\n", + "除此之外,还可以传入 `Callback` 参数,这个参数可以在模型训练的各个阶段进行一些自定义操作,详见第7章。\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "51021638", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:08:46.924974Z", + "iopub.status.busy": "2022-01-19T13:08:46.924271Z", + "iopub.status.idle": "2022-01-19T13:11:05.819413Z", + "shell.execute_reply": "2022-01-19T13:11:05.818324Z", + "shell.execute_reply.started": "2022-01-19T13:08:46.924912Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -122,7 +284,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/python3.7.0/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", " return (isinstance(seq, collections.Sequence) and\n" ] }, @@ -130,15 +292,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 938/938 [==============================] - loss: 0.1663 - acc: 0.9299 - 32ms/step \n", + "step 938/938 [==============================] - loss: 0.1962 - acc: 0.9300 - 29ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0393 - acc: 0.9689 - 32ms/step \n", + "step 938/938 [==============================] - loss: 0.0445 - acc: 0.9689 - 29ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 0.0341 - acc: 0.9774 - 32ms/step \n", + "step 938/938 [==============================] - loss: 0.0638 - acc: 0.9780 - 29ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 0.0118 - acc: 0.9827 - 32ms/step \n", + "step 938/938 [==============================] - loss: 0.0035 - acc: 0.9825 - 29ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.1354 - acc: 0.9865 - 33ms/step \n" + "step 938/938 [==============================] - loss: 0.0786 - acc: 0.9860 - 33ms/step \n" ] } ], @@ -155,9 +317,9 @@ "id": "eeff5f00", "metadata": {}, "source": [ - "### 2.3 用 `Model.evaluate` 评估模型\n", + "### 2.4 使用 Model.evaluate 评估模型\n", "\n", - "对于训练好的模型进行评估可以使用 `evaluate` 接口,事先定义好用于评估使用的数据集后,直接调用 `evaluate` 接口即可完成模型评估操作,结束后根据在 `prepare` 中 `loss` 和 `metric` 的定义来进行相关评估结果计算返回。\n", + "对于训练好的模型进行评估可以使用 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 接口,事先定义好用于评估使用的数据集后,直接调用 `evaluate` 接口即可完成模型评估操作,结束后根据在 `prepare` 中 `loss` 和 `metric` 的定义来进行相关评估结果计算返回。\n", "\n", "返回格式是一个字典:\n", "* 只包含loss, `{'loss': xxx}` \n", @@ -167,18 +329,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "70f670ec", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:13:03.190834Z", + "iopub.status.busy": "2022-01-19T13:13:03.190285Z", + "iopub.status.idle": "2022-01-19T13:13:33.046705Z", + "shell.execute_reply": "2022-01-19T13:13:33.045836Z", + "shell.execute_reply.started": "2022-01-19T13:13:03.190759Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Eval begin...\n", - "step 10000/10000 [==============================] - loss: 4.7684e-07 - acc: 0.9816 - 3ms/step \n", + "step 10000/10000 [==============================] - loss: 3.5763e-07 - acc: 0.9810 - 3ms/step \n", "Eval samples: 10000\n", - "{'loss': [4.7683704e-07], 'acc': 0.9816}\n" + "{'loss': [3.5762793e-07], 'acc': 0.981}\n" ] } ], @@ -193,22 +364,33 @@ "id": "109ac763", "metadata": {}, "source": [ - "### 2.4 用 `Model.predict` 预测模型\n", + "### 2.5 使用 Model.predict 执行推理\n", "\n", - "高层API中提供了 `predict` 接口来方便用户对训练好的模型进行预测验证,只需要基于训练好的模型将需要进行预测测试的数据放到接口中进行计算即可,接口会将经过模型计算得到的预测结果进行返回。\n", + "高层API中提供了 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 接口来方便用户对训练好的模型进行推理验证,只需要基于训练好的模型将需要进行推理验证的数据放到接口中进行计算即可,接口会将经过模型计算得到的预测结果进行返回。\n", "\n", "返回格式是一个list,元素数目对应模型的输出数目:\n", "* 模型是单一输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)]`\n", "* 模型是多输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), …]`\n", "\n", - "numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。" + "numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "id": "d6318f18", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-19T13:28:57.526737Z", + "iopub.status.busy": "2022-01-19T13:28:57.525675Z", + "iopub.status.idle": "2022-01-19T13:29:18.834313Z", + "shell.execute_reply": "2022-01-19T13:29:18.833570Z", + "shell.execute_reply.started": "2022-01-19T13:28:57.526676Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -217,22 +399,24 @@ "Predict begin...\n", "step 10000/10000 [==============================] - 2ms/step \n", "Predict samples: 10000\n", + "[[ -6.5593615 -6.4680595 -1.4708003 2.1043894 -11.743436 -4.4516582\n", + " -14.733968 12.036645 -6.582403 -1.8672216]]\n", "true label: 7, pred label: 7\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -244,38 +428,60 @@ } ], "source": [ - "# 用 predict 在测试集上对模型进行测试\n", + "# 用 predict 在测试集上对模型进行推理\n", "test_result = model.predict(test_dataset)\n", + "# 由于模型是单一输出,test_result的形状为[1, 10000],10000是测试数据集的数据量。这里打印第一个数据的结果,这个数组表示每个数字的预测概率\n", + "print(test_result[0][0])\n", "\n", "# 从测试集中取出一张图片\n", "img, label = test_dataset[0]\n", "\n", - "# 执行推理并打印结果\n", + "# 执行推理并打印结果,这里的argmax函数用于取出预测值中概率最高的一个的下标,作为预测标签\n", "pred_label = test_result[0][0].argmax()\n", "print('true label: {}, pred label: {}'.format(label[0], pred_label))\n", - "# 可视化图片\n", + "# 使用matplotlib库,可视化图片\n", "from matplotlib import pyplot as plt\n", "plt.imshow(img[0])" ] }, + { + "cell_type": "markdown", + "id": "a3110884-fc0f-41e5-901e-40d136eaab6c", + "metadata": {}, + "source": [ + "### 2.6 其他高层API\n", + "\n", + "除了上面介绍的三个API之外, `paddle.Model` 类也提供了其他与训练、评估与推理相关的API:\n", + "\n", + "* [Model.train_batch](../api/paddle/Model_cn.html#train-batch-inputs-labels-none):在一个批次的数据集上进行训练;\n", + "* [Model.eval_batch](../api/paddle/Model_cn.html#eval-batch-inputs-labels-none):在一个批次的数据集上进行评估;\n", + "* [Model.predict_batch](../api/paddle/Model_cn.html#predict-batch-inputs):在一个批次的数据集上进行推理。\n", + "\n", + "这三个API与上面介绍的三个API的输入数据的维度有所不同,需要用户在实际应用场景进行判断使用。" + ] + }, { "cell_type": "markdown", "id": "9508034b", "metadata": {}, "source": [ - "## 三、通过基础API实现模型的训练与预测\n", + "## 三、通过基础 API 训练与评估验证\n", "\n", - "除了通过第一部分的高层API实现模型的训练与预测,飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础API封装而来。下面通过拆解高层API到基础API的方式,来了解如何用基础API完成模型的训练与预测。" + "除了通过第一部分的高层API实现模型的训练与预测,飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础API封装而来。下面通过拆解高层API到基础API的方式,来了解如何用基础API完成模型的训练与预测。\n", + "\n", + "(待补充:高层API实现不了的功能)" ] }, { "cell_type": "code", "execution_count": 7, "id": "da17af7e", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "# 定义网络结构( 采用SubClass 组网 )\n", + "# 定义网络结构(采用 SubClass 组网)\n", "class Mnist(paddle.nn.Layer):\n", " def __init__(self):\n", " super(Mnist, self).__init__()\n", @@ -299,16 +505,30 @@ "id": "c02524fd", "metadata": {}, "source": [ - "### 3.1 拆解 `Model.prepare` 、 `Model.fit` -- 用基础API训练模型\n", + "### 3.1 模型训练(拆解 Model.prepare、Model.fit)\n", + "\n", + "飞桨框架通过基础API对模型进行训练,对应第一部分的 `Model.prepare` 与 `Model.fit` 。模型训练一般包括如下几个步骤:\n", "\n", - "飞桨框架通过基础API对模型进行训练与预测,对应第一部分的 `Model.prepare` 与 `Model.fit` :" + "1. 加载训练数据集、声明模型、设置模型为 `train` 模式\n", + "1. 设置优化器、损失函数与各个超参数\n", + "1. 从DataLoader获取一批次训练数据\n", + "1. 执行一次预测,即从模型获得输入数据的预测值\n", + "1. 计算预测值与数据集标签的损失\n", + "1. 计算预测值与数据集标签的准确率\n", + "1. 将损失进行反向传播\n", + "1. 打印模型的轮数、批次、损失值、准确率等信息\n", + "1. 执行一次优化器步骤,也就是根据我们选择的优化算法,根据当前批次数据的梯度更新传入优化器的参数\n", + "1. 将优化器的梯度进行清零\n", + " \n" ] }, { "cell_type": "code", "execution_count": 8, "id": "8419b510", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -323,11 +543,13 @@ } ], "source": [ - "# dataset与mnist的定义与第一部分内容一致\n", + "# dataset与mnist的定义与使用高层API的内容一致\n", "# 用 DataLoader 实现数据加载\n", "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)\n", "\n", + "# 声明Mnist类的一个实例\n", "mnist=Mnist()\n", + "# 将此层及其所有子层设置为训练模式。这只会影响某些模块,如Dropout和BatchNorm。\n", "mnist.train()\n", "\n", "# 设置迭代次数\n", @@ -367,16 +589,23 @@ "id": "00a077d3", "metadata": {}, "source": [ - "### 3.2 拆解 `Model.evaluate` -- 用基础API验证模型\n", + "### 3.2 模型评估(拆解 Model.evaluate)\n", + "\n", + "飞桨框架通过基础API对模型进行验证,对应第一部分的 `Model.evaluate` 。与模型训练相比,模型评估的流程有如下几点不同之处:\n", "\n", - "飞桨框架通过基础API对模型进行验证,对应第一部分的 `Model.evaluate` :" + "* 加载的数据从训练数据集改为测试数据集\n", + "* 模型实例从 `train` 模式改为 `eval` 模式\n", + "* 不需要反向传播、优化器参数更新和优化器梯度清零\n", + "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "d27f6ec2", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -393,6 +622,7 @@ "source": [ "# 加载测试数据集\n", "test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True)\n", + "# 设置损失函数\n", "loss_fn = paddle.nn.CrossEntropyLoss()\n", "# 将该模型及其所有子层设置为预测模式。这只会影响某些模块,如Dropout和BatchNorm\n", "mnist.eval()\n", @@ -417,16 +647,22 @@ "id": "214cc6de", "metadata": {}, "source": [ - "### 3.3 拆解 `Model.predict` -- 用基础API测试模型\n", + "### 3.3 模型推理(拆解 Model.predict)\n", + "\n", + "飞桨框架通过基础API对模型进行推理,对应第一部分的 `Model.predict` 。模型的推理过程相对独立,是在模型训练与评估之后单独进行的步骤。只需要执行如下步骤:\n", "\n", - "飞桨框架通过基础API对模型进行测试,对应第一部分的 `Model.predict` :" + "* 加载测试数据集,并将模型设置为 `eval` 模式\n", + "* 读取测试数据并获得预测结果\n", + "* 对预测结果进行后处理" ] }, { "cell_type": "code", "execution_count": 10, "id": "1d79305f", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -462,11 +698,13 @@ "source": [ "# 加载测试数据集\n", "test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True)\n", + "# 将该模型及其所有子层设置为预测模式\n", "mnist.eval()\n", "for batch_id, data in enumerate(test_loader()):\n", + " # 取出测试数据\n", " x_data = data[0] \n", - " predicts = mnist(x_data)\n", " # 获取预测结果\n", + " predicts = mnist(x_data)\n", "print(\"predict finished\")\n", "\n", "# 从测试集中取出一组数据\n", @@ -479,13 +717,23 @@ "from matplotlib import pyplot as plt\n", "plt.imshow(img[0][0])" ] + }, + { + "cell_type": "markdown", + "id": "924b3c12-90e7-41e9-9aef-205866c02ee5", + "metadata": {}, + "source": [ + "# 四、总结\n", + "\n", + "待补充" + ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -497,7 +745,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb b/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb index 200c953d629..569173abb51 100644 --- a/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/06_device_cn.ipynb @@ -7,31 +7,34 @@ "source": [ "# 单机多卡训练\n", "\n", - "飞桨框架2.0增加 [paddle.distributed.spawn](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/distributed/spawn_cn.html) 函数来启动单机多卡训练,同时原有的 [paddle.distributed.launch](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/distributed/launch_cn.html) 的方式依然保留。\n", + "随着深度学习的发展,模型和数据集越来越大,有时单张显卡无法满足训练任务的显存要求,或者单卡训练用时太久,影响训练速度,这些情况下需要用到多卡训练的方式。飞桨框架 2.0 增加 [paddle.distributed.spawn](../api/paddle/distributed/spawn_cn.html) 函数来启动单机多卡训练,同时原有的 [paddle.distributed.launch](../api/paddle/distributed/launch_cn.html) 的方式依然保留。\n", "\n", "## 一、launch启动\n", "\n", "### 1.1 高层API场景\n", "\n", - "当调用 [paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html) 高层API来实现训练时,想要启动单机多卡训练非常简单,代码不需要做任何修改,只需要在启动时增加一下参数 `-m paddle.distributed.launch` 。\n", - "使用高层API的训练代码如下:" + "当调用 [paddle.Model](../api/paddle/Model_cn.html) 高层API来实现训练时,想要启动单机多卡训练非常简单,代码不需要做任何修改,只需要在启动时增加一下参数 `-m paddle.distributed.launch` 。\n", + "以MNIST为例,使用高层API的训练代码如下:" ] }, { "cell_type": "code", "execution_count": null, "id": "5a2702a8", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import paddle\n", "import numpy as np\n", "from paddle.vision.transforms import ToTensor\n", "\n", - "# 加载数据集\n", + "# 加载训练数据集和测试数据集\n", "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", "\n", + "# 使用 Sequential 模型组网\n", "mnist = paddle.nn.Sequential(\n", " paddle.nn.Flatten(1, -1), \n", " paddle.nn.Linear(784, 512), \n", @@ -40,12 +43,15 @@ " paddle.nn.Linear(512, 10)\n", ")\n", "\n", + "# 使用 paddle.Model 封装模型\n", "model = paddle.Model(mnist)\n", "\n", + "# 使用 Model.prepare 配置训练准备参数\n", "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", " loss=paddle.nn.CrossEntropyLoss(), \n", " metrics=paddle.metric.Accuracy())\n", "\n", + "# 使用 Model.fit 训练模型\n", "model.fit(train_dataset, \n", " epochs=5, \n", " batch_size=64,\n", @@ -64,7 +70,9 @@ "cell_type": "code", "execution_count": null, "id": "3db288b6", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# 单机单卡启动,默认使用第0号卡\n", @@ -78,6 +86,17 @@ "! python -m paddle.distributed.launch train.py" ] }, + { + "cell_type": "markdown", + "id": "3fcc2fdc-795d-400b-8f40-a69d88595558", + "metadata": {}, + "source": [ + "这里补充一段介绍这个方式启动后发生了什么?任务怎么分配到不同卡上的\n", + "另外针对这里应该会有常见的问题定位流程,补充一下介绍,有FAQ可以补一下到FAQ的链接。\n", + "\n", + "(待补充)" + ] + }, { "cell_type": "markdown", "id": "17552b14", @@ -85,19 +104,21 @@ "source": [ "### 1.2 基础API场景\n", "\n", - "如果使用基础API实现现训练,想要启动单机多卡训练,需要对单机单卡的代码进行3处修改,具体如下:" + "如果使用基础API实现现训练,想要启动单机多卡训练,需要对单机单卡的代码进行3处修改,具体如下:\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d4ebc36a", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import paddle\n", "from paddle.vision.transforms import ToTensor\n", - "# 第1处改动 导入分布式训练所需的包\n", + "# 第1处改动,导入分布式训练所需的包\n", "import paddle.distributed as dist\n", "# 加载数据集\n", "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", @@ -149,14 +170,19 @@ "metadata": {}, "source": [ "修改完后保存文件为train.py,然后使用跟高层API相同的启动方式即可。\n", - "**注意:** 单卡训练不支持调用 `init_parallel_env` ,请使用以下几种方式进行分布式训练。" + "\n", + "补充:\n", + "\n", + "这里基础API实现的效果和高层一模一样吗?完全没有差异?有没有基础API可以更灵活应用的场景?为什么高层不用补额外的配置代码?\n" ] }, { "cell_type": "code", "execution_count": null, "id": "56786ff8", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# 单机多卡启动,默认使用当前可见的所有卡\n", @@ -175,58 +201,141 @@ "source": [ "## 二、spawn启动\n", "\n", - " `launch` 方式启动训练,以文件为单位启动多进程,需要用户在启动时调用 `paddle.distributed.launch` ,对于进程的管理要求较高。飞桨框架2.0版本增加了 `spawn` 启动方式,可以更好地控制进程,在日志打印、训练退出时更友好。使用示例如下:" + " `launch` 方式启动训练,以文件为单位启动多进程,需要用户在启动时调用 `paddle.distributed.launch` ,对于进程的管理要求较高。飞桨框架2.0版本增加了 `spawn` 启动方式,可以更好地控制进程,在日志打印、训练退出时更友好。\n", + " \n", + "(补充“对进程的管理要求较高”、“可以更好地控制进程,在日志打印、训练退出时更友好”这几句话的理解)" + ] + }, + { + "cell_type": "markdown", + "id": "199558fc-9c58-4ff5-8e9e-1bbddab9662d", + "metadata": {}, + "source": [ + "### 2.1 高层API场景\n", + "\n", + "使用 `spawn` 方式启动多卡训练时,需要先将训练的过程封装成一个函数,将超参数设为该函数的参数传入训练流程中。代码如下所示:" ] }, { "cell_type": "code", "execution_count": null, - "id": "4cbcdcdb", - "metadata": {}, + "id": "0ed54b0d-dcbb-4c52-b2aa-63bae432d79a", + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "from __future__ import print_function\n", - "\n", "import paddle\n", - "import paddle.nn as nn\n", - "import paddle.optimizer as opt\n", + "import numpy as np\n", + "from paddle.vision.transforms import ToTensor\n", + "# 高层API场景使用spwan方式时,需要导入paddle.distributed包\n", "import paddle.distributed as dist\n", "\n", - "class LinearNet(nn.Layer):\n", - " def __init__(self):\n", - " super(LinearNet, self).__init__()\n", - " self._linear1 = nn.Linear(10, 10)\n", - " self._linear2 = nn.Linear(10, 1)\n", + "def train():\n", + " # 加载训练数据集和测试数据集\n", + " train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + " test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", "\n", - " def forward(self, x):\n", - " return self._linear2(self._linear1(x))\n", + " # 使用 Sequential 模型组网\n", + " mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1), \n", + " paddle.nn.Linear(784, 512), \n", + " paddle.nn.ReLU(), \n", + " paddle.nn.Dropout(0.2), \n", + " paddle.nn.Linear(512, 10)\n", + " )\n", "\n", - "def train(print_result=False):\n", + " # 使用 paddle.Model 封装模型\n", + " model = paddle.Model(mnist)\n", "\n", - " # 1. 初始化并行训练环境\n", - " dist.init_parallel_env()\n", + " # 使用 Model.prepare 配置训练准备参数\n", + " model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + " loss=paddle.nn.CrossEntropyLoss(), \n", + " metrics=paddle.metric.Accuracy())\n", + "\n", + " # 使用 Model.fit 训练模型\n", + " model.fit(train_dataset, \n", + " epochs=5, \n", + " batch_size=64,\n", + " verbose=1)\n", "\n", - " # 2. 创建并行训练 Layer 和 Optimizer\n", - " layer = LinearNet()\n", - " dp_layer = paddle.DataParallel(layer)\n", "\n", - " loss_fn = nn.MSELoss()\n", - " adam = opt.Adam(\n", - " learning_rate=0.001, parameters=dp_layer.parameters())\n", + "# 传入训练函数,指定进程数并指定当前使用的卡号\n", + "# (这里我测试使用多卡会报错,只能单卡跑)\n", + "if __name__ == '__main__':\n", + " dist.spawn(train, nprocs=1, gpus='0')" + ] + }, + { + "cell_type": "markdown", + "id": "c2ae28fc-5084-4393-a622-cf60d5e9df00", + "metadata": {}, + "source": [ + "### 2.2 基础API场景\n", + "\n", + "与高层API场景类似,使用 `spawn` 方式启动多卡训练时,需要先将训练的过程封装成一个函数,将超参数设为该函数的参数传入训练流程中。同时,也需要与 `paddle.distributed.launch` 过程类似,进行三处改动:导入分布式包、初始化并行环境和将模型封装。具体代码如下:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbcdcdb", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", "\n", - " # 3. 运行网络\n", - " inputs = paddle.randn([10, 10], 'float32')\n", - " outputs = dp_layer(inputs)\n", - " labels = paddle.randn([10, 1], 'float32')\n", - " loss = loss_fn(outputs, labels)\n", + "import paddle\n", + "import paddle.nn as nn\n", + "import paddle.optimizer as opt\n", + "# 第1处改动,导入分布式训练所需的包\n", + "import paddle.distributed as dist\n", "\n", - " if print_result is True:\n", - " print(\"loss:\", loss.numpy())\n", + "def train(print_result=False):\n", + " # 加载数据集\n", + " train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", + " test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + " # 第2处改动,初始化并行环境\n", + " dist.init_parallel_env()\n", "\n", - " loss.backward()\n", + " # 定义网络结构\n", + " mnist = paddle.nn.Sequential(\n", + " paddle.nn.Flatten(1, -1),\n", + " paddle.nn.Linear(784, 512),\n", + " paddle.nn.ReLU(),\n", + " paddle.nn.Dropout(0.2),\n", + " paddle.nn.Linear(512, 10)\n", + " )\n", + " # 用 DataLoader 实现数据加载\n", + " train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)\n", "\n", - " adam.step()\n", - " adam.clear_grad()\n", + " # 第3处改动,增加paddle.DataParallel封装\n", + " mnist = paddle.DataParallel(mnist)\n", + " mnist.train()\n", + " # 设置迭代次数\n", + " epochs = 5\n", + " # 设置优化器\n", + " optim = paddle.optimizer.Adam(parameters=mnist.parameters())\n", + " for epoch in range(epochs):\n", + " for batch_id, data in enumerate(train_loader()):\n", + " x_data = data[0] # 训练数据\n", + " y_data = data[1] # 训练数据标签\n", + " predicts = mnist(x_data) # 预测结果\n", + " # 计算损失 等价于 prepare 中loss的设置\n", + " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", + " # 计算准确率 等价于 prepare 中metrics的设置\n", + " acc = paddle.metric.accuracy(predicts, y_data)\n", + " # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中\n", + " # 反向传播\n", + " loss.backward()\n", + " if (batch_id+1) % 1800 == 0 and print_reslut:\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, loss.numpy(), acc.numpy()))\n", + " # 更新参数\n", + " optim.step()\n", + " # 梯度清零\n", + " optim.clear_grad()\n", "\n", "# 传入训练函数、参数、指定进程数并指定当前使用的卡号\n", "if __name__ == '__main__':\n", @@ -260,13 +369,23 @@ "* nprocs:启动进程的数目。当仅需要使用部分可见的GPU设备进行训练时,可设置该参数指定GPU数。例如:当前机器有8张GPU卡 {0,1,2,3,4,5,6,7},此时会使用前两张卡 {0,1};或者当前机器通过配置环境变量 CUDA_VISIBLE_DEVICES=4,5,6,7,仅使4张GPU卡可见,此时会使用可见的前两张卡 {4,5}。若不设置该参数,默认使用所有可见的GPU设备训练。\n", "* gpus:指定训练使用的GPU ID。例如 gpus='4,5' 可指定使用第4号卡和第5号卡。若不设置该参数,默认使用GPU ID序号较小的GPU。" ] + }, + { + "cell_type": "markdown", + "id": "a132576e-7021-4fcc-a56d-d5ecb4bccebd", + "metadata": {}, + "source": [ + "# 三、总结\n", + "\n", + "待补充" + ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -278,7 +397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb index 78ec043c702..82bc45fe8fa 100644 --- a/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb @@ -5,20 +5,36 @@ "id": "83e96f0c", "metadata": {}, "source": [ - "# 自定义指标\n", + "# 自定义Loss、Metric、Optimizer及Callback\n", "\n", - "除了使用飞桨框架内置的指标外,飞桨框架还支持用户根据自己的实际场景,完成指标的自定义。\n", + "除了使用飞桨框架内置的API,飞桨框架还支持用户根据自己的实际场景,完成Loss、Metric、Optimizer及Callback的自定义。\n", "\n", - "## 一、自定义Loss\n", + "## 一、自定义损失函数 Loss\n", "\n", - "有时你会遇到特定任务的Loss计算方式在框架既有的Loss接口中不存在,或算法不符合自己的需求,那么期望能够自己来进行Loss的自定义。这里介绍如何进行Loss的自定义操作,首先来看下面的代码:" + "### 1.1 损失函数介绍\n", + "\n", + "在深度学习中,损失函数是用来评估模型的预测结果与真实结果之间的差距,一般用L表示,损失函数越小,模型的鲁棒性就越好。\n", + "\n", + "模型训练的过程其实是对损失函数的函数图形采用梯度下降的方法来使得损失函数不断减小到局部最优值,来得到对任务来说比较合理的模型参数。\n", + "\n", + "一般在深度学习框架中,有许多常用的损失函数,例如在图像分类任务中,我们常常使用交叉熵损失,在目标检测任务中,常常使用Focal loss、L1/L2损失函数等,在图像识别任务中,我们经常会使用到Triplet Loss以及Center Loss等。然而,这些损失函数API有的时候可能不太适合我们试图解决的业务问题,因此自定义损失函数应运而生。\n", + "\n", + "### 1.2 自定义Loss\n", + "飞桨中实现自定义Loss的方法和使用 `paddle.nn.Layer` 组网的方法类似,包括三个步骤:\n", + "\n", + "1. 创建一个继承自 `paddle.nn.Layer` 的类;\n", + "1. 在类的构造函数 `__init__` 中定义需要的参数;\n", + "1. 在类的前向计算函数 `forward` 中进行损失函数计算。\n", + "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "9958927b", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import paddle\n", @@ -57,7 +73,9 @@ "cell_type": "code", "execution_count": 2, "id": "6abeac3a", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "class CrossEntropy(paddle.nn.Layer):\n", @@ -65,6 +83,7 @@ " super().__init__()\n", "\n", " def forward(self, x, label):\n", + " # 使用paddle内置的cross_entropy算子实现算法\n", " loss = paddle.nn.functional.cross_entropy(\n", " x,\n", " label)\n", @@ -76,16 +95,36 @@ "id": "2a431650", "metadata": {}, "source": [ - "## 二、自定义Metric\n", + "## 二、自定义评估指标 Metric\n", + "\n", + "### 2.1 评估指标介绍\n", + "\n", + "评估指标用英文表示是Metrics,有的时候也成为性能指标,用来衡量反馈一个模型的实际效果好坏,一般是通过计算模型的预测结果和真实结果之间的某种【距离】得出。\n", + "\n", + "和损失函数类型,我们一般会在不同的任务场景中选择不同的评估指标来做模型评估,例如在分类任务中,比较常见的评估指标包括了Accuracy、Recall、Precision和AUC等,在回归中有MAE和MSE等等。\n", + "\n", + "这些常见的评估指标在飞桨框架中都会有对应的API实现,直接使用即可。那么如果我们遇到一些想要做个性化实现的操作时,该怎么办呢?那么这里就涉及到了如何自定义评估指标。\n", + "\n", + "### 2.2 自定义评估指标\n", + "\n", + "和Loss一样,你也可以来通过框架实现自定义的评估方法,包括如下几个步骤:\n", "\n", - "和Loss一样,你也可以来通过框架实现自定义的评估方法,具体的实现如下:" + "1. 创建一个继承自 `paddle.metric.Metric` 的类;\n", + "1. 在类的构造函数 `__init__` 中定义需要的参数;\n", + "1. 实现 `name` 方法,返回定义的评估指标名字\n", + "1. 实现 `compute` 方法,这个方法主要用于 `update` 的加速,可省略\n", + "1. 实现 `update` 方法,用于单个batch训练时进行评估指标计算\n", + "1. 实现 `accumulate` 方法,返回历史batch训练积累后计算得到的评价指标值\n", + "1. 实现 `reset` 方法,每个epoch结束后进行评估指标的重置\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "0d27802a", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "class SelfDefineMetric(paddle.metric.Metric):\n", @@ -146,11 +185,19 @@ "cell_type": "code", "execution_count": 5, "id": "318b1aac", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "class Accuracy(paddle.metric.Metric):\n", + " \"\"\"\n", + " 继承paddle.metric.Metric\n", + " \"\"\"\n", " def __init__(self, topk=(1, ), name=None, *args, **kwargs):\n", + " \"\"\"\n", + " 构造函数实现\n", + " \"\"\"\n", " super(Accuracy, self).__init__(*args, **kwargs)\n", " self.topk = topk\n", " self.maxk = max(topk)\n", @@ -158,6 +205,9 @@ " self.reset()\n", "\n", " def compute(self, pred, label, *args):\n", + " \"\"\"\n", + " 实现compute方法\n", + " \"\"\"\n", " pred = paddle.argsort(pred, descending=True)\n", " pred = paddle.slice(\n", " pred, axes=[len(pred.shape) - 1], starts=[0], ends=[self.maxk])\n", @@ -174,6 +224,11 @@ " return paddle.cast(correct, dtype='float32')\n", "\n", " def update(self, correct, *args):\n", + " \"\"\"\n", + " 实现update方法,用于单个batch训练时进行评估指标计算。\n", + " - 当`compute`类函数未实现时,会将模型的计算输出和标签数据的展平作为`update`的参数传入。\n", + " - 当`compute`类函数做了实现时,会将compute的返回结果作为`update`的参数传入。\n", + " \"\"\"\n", " if isinstance(correct, paddle.Tensor):\n", " correct = correct.numpy()\n", " num_samples = np.prod(np.array(correct.shape[:-1]))\n", @@ -187,10 +242,18 @@ " return accs\n", "\n", " def reset(self):\n", + " \"\"\"\n", + " 实现reset方法,每个Epoch结束后进行评估指标的重置,这样下个Epoch可以重新进行计算。\n", + " \"\"\"\n", " self.total = [0.] * len(self.topk)\n", " self.count = [0] * len(self.topk)\n", "\n", " def accumulate(self):\n", + " \"\"\"\n", + " 实现accumulate方法,返回历史batch训练积累后计算得到的评价指标值。\n", + " 每次`update`调用时进行数据积累,`accumulate`计算时对积累的所有数据进行计算并返回。\n", + " 结算结果会在`fit`接口的训练日志中呈现。\n", + " \"\"\"\n", " res = []\n", " for t, c in zip(self.total, self.count):\n", " r = float(t) / c if c > 0 else 0.\n", @@ -206,6 +269,9 @@ " self._name = [name]\n", "\n", " def name(self):\n", + " \"\"\"\n", + " 实现name方法,返回定义的评估指标名字\n", + " \"\"\"\n", " return self._name" ] }, @@ -214,16 +280,24 @@ "id": "66ad9fa9", "metadata": {}, "source": [ - "## 三、自定义Callback\n", + "## 三、自定义回调函数 Callback\n", + "\n", + "### 3.1 回调函数介绍\n", + "\n", + "`fit` 接口的callback参数支持传入一个 ` Callback` 类实例,用来在每轮训练和每个 ` batch` 训练前后进行调用,可以通过 ` callback` 收集到训练过程中的一些数据和参数,或者实现一些自定义操作。\n", + "\n", + "### 3.2 自定义回调函数\n", "\n", - " `fit` 接口的callback参数支持传入一个 ` Callback` 类实例,用来在每轮训练和每个 ` batch` 训练前后进行调用,可以通过 ` callback` 收集到训练过程中的一些数据和参数,或者实现一些自定义操作。" + "自定义回调函数的模板如下所示:" ] }, { "cell_type": "code", "execution_count": 6, "id": "9a18a7d5", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "class SelfDefineCallback(paddle.callbacks.Callback):\n", @@ -234,16 +308,16 @@ " def on_train_end(self, logs=None) 训练结束后,`Model.fit`接口中调用\n", " def on_eval_begin(self, logs=None) 评估开始前,`Model.evaluate`接口调用\n", " def on_eval_end(self, logs=None) 评估结束后,`Model.evaluate`接口调用\n", - " def on_predict_begin(self, logs=None) 预测测试开始前,`Model.predict`接口中调用\n", - " def on_predict_end(self, logs=None) 预测测试结束后,`Model.predict`接口中调用\n", + " def on_predict_begin(self, logs=None) 推理开始前,`Model.predict`接口中调用\n", + " def on_predict_end(self, logs=None) 推理结束后,`Model.predict`接口中调用\n", " def on_epoch_begin(self, epoch, logs=None) 每轮训练开始前,`Model.fit`接口中调用\n", " def on_epoch_end(self, epoch, logs=None) 每轮训练结束后,`Model.fit`接口中调用\n", " def on_train_batch_begin(self, step, logs=None) 单个Batch训练开始前,`Model.fit`和`Model.train_batch`接口中调用\n", " def on_train_batch_end(self, step, logs=None) 单个Batch训练结束后,`Model.fit`和`Model.train_batch`接口中调用\n", " def on_eval_batch_begin(self, step, logs=None) 单个Batch评估开始前,`Model.evalute`和`Model.eval_batch`接口中调用\n", " def on_eval_batch_end(self, step, logs=None) 单个Batch评估结束后,`Model.evalute`和`Model.eval_batch`接口中调用\n", - " def on_predict_batch_begin(self, step, logs=None) 单个Batch预测测试开始前,`Model.predict`和`Model.test_batch`接口中调用\n", - " def on_predict_batch_end(self, step, logs=None) 单个Batch预测测试结束后,`Model.predict`和`Model.test_batch`接口中调用\n", + " def on_predict_batch_begin(self, step, logs=None) 单个Batch推理开始前,`Model.predict`和`Model.test_batch`接口中调用\n", + " def on_predict_batch_end(self, step, logs=None) 单个Batch推理结束后,`Model.predict`和`Model.test_batch`接口中调用\n", " \"\"\"\n", " \n", " def __init__(self):\n", @@ -256,34 +330,52 @@ "id": "b23a392c", "metadata": {}, "source": [ - "看两个框架中的实际例子。其中第一个例子时框架自带的 `ModelCheckpoint` 回调函数,可以在 `fit` 训练模型时自动存储每轮训练得到的模型;第二个例子是框架自带的 `ProgBarLogger` 回调函数,用于在 `fit` 训练时打印损失函数和评估指标。这两个回调函数会在 `fit` 执行时默认被调用。" + "看两个框架中的实际例子。其中第一个例子时框架自带的 `ModelCheckpoint` 回调函数,可以在 `fit` 训练模型时自动存储每轮训练得到的模型;第二个例子是框架自带的 `ProgBarLogger` 回调函数,用于在 `fit` 训练时打印损失函数和评估指标。这两个回调函数会在 `fit` 执行时默认被调用。\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "6c6e92b5", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "class ModelCheckpoint(paddle.callbacks.Callback):\n", + " \"\"\"\n", + " 继承paddle.callbacks.Callback,该类的功能是\n", + " 训练模型时自动存储每轮训练得到的模型\n", + " \"\"\"\n", " def __init__(self, save_freq=1, save_dir=None):\n", + " \"\"\"\n", + " 构造函数实现\n", + " \"\"\"\n", " self.save_freq = save_freq\n", " self.save_dir = save_dir\n", " \n", " def on_epoch_begin(self, epoch=None, logs=None):\n", + " \"\"\"\n", + " 每轮训练开始前,获取当前轮数\n", + " \"\"\"\n", " self.epoch = epoch\n", " \n", " def _is_save(self):\n", " return self.model and self.save_dir and ParallelEnv().local_rank == 0\n", " \n", " def on_epoch_end(self, epoch, logs=None):\n", + " \"\"\"\n", + " 每轮训练结束后,保存每轮的checkpoint\n", + " \"\"\"\n", " if self._is_save() and self.epoch % self.save_freq == 0:\n", " path = '{}/{}'.format(self.save_dir, epoch)\n", " print('save checkpoint at {}'.format(os.path.abspath(path)))\n", " self.model.save(path)\n", " \n", " def on_train_end(self, logs=None):\n", + " \"\"\"\n", + " 训练结束后,保存最后一轮的checkpoint\n", + " \"\"\"\n", " if self._is_save():\n", " path = '{}/final'.format(self.save_dir)\n", " print('save checkpoint at {}'.format(os.path.abspath(path)))\n", @@ -292,9 +384,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "0384287c", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import time\n", @@ -303,7 +397,14 @@ "from paddle.hapi.progressbar import ProgressBar\n", "\n", "class ProgBarLogger(paddle.callbacks.Callback):\n", + " \"\"\"\n", + " 继承paddle.callbacks.Callback,该类的功能是\n", + " 训练模型时打印损失函数和评估指标\n", + " \"\"\"\n", " def __init__(self, log_freq=1, verbose=2):\n", + " \"\"\"\n", + " 构造函数实现\n", + " \"\"\"\n", " self.epochs = None\n", " self.steps = None\n", " self.progbar = None\n", @@ -314,6 +415,9 @@ " return self.verbose and ParallelEnv().local_rank == 0\n", "\n", " def on_train_begin(self, logs=None):\n", + " \"\"\"\n", + " 训练开始前,获取总epoch、metric等信息\n", + " \"\"\"\n", " self.epochs = self.params['epochs']\n", " assert self.epochs\n", " self.train_metrics = self.params['metrics']\n", @@ -331,6 +435,9 @@ " )\n", "\n", " def on_epoch_begin(self, epoch=None, logs=None):\n", + " \"\"\"\n", + " 每轮训练开始前,获取当前轮数、步数,声明进度条与计时器等\n", + " \"\"\"\n", " self.steps = self.params['steps']\n", " self.epoch = epoch\n", " self.train_step = 0\n", @@ -369,12 +476,18 @@ " progbar.update(steps, values)\n", "\n", " def on_train_batch_begin(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch训练开始前,进行计时\n", + " \"\"\"\n", " self._train_timer['batch_data_end_time'] = time.time()\n", " self._train_timer['data_time'] += (\n", " self._train_timer['batch_data_end_time'] -\n", " self._train_timer['batch_start_time'])\n", "\n", " def on_train_batch_end(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch训练结束后,更新参数\n", + " \"\"\"\n", " logs = logs or {}\n", " self.train_step += 1\n", "\n", @@ -389,11 +502,17 @@ " self._train_timer['batch_start_time'] = time.time()\n", "\n", " def on_epoch_end(self, epoch, logs=None):\n", + " \"\"\"\n", + " 每轮训练结束后,更新参数\n", + " \"\"\"\n", " logs = logs or {}\n", " if self._is_print() and (self.steps is not None):\n", " self._updates(logs, 'train')\n", "\n", " def on_eval_begin(self, logs=None):\n", + " \"\"\"\n", + " 评估开始前,获取当前步数,声明进度条与计时器等\n", + " \"\"\"\n", " self.eval_steps = logs.get('steps', None)\n", " self.eval_metrics = logs.get('metrics', [])\n", " self.eval_step = 0\n", @@ -414,12 +533,18 @@ " self._eval_timer['batch_start_time'] = time.time()\n", "\n", " def on_eval_batch_begin(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch评估开始前,进行计时\n", + " \"\"\"\n", " self._eval_timer['batch_data_end_time'] = time.time()\n", " self._eval_timer['data_time'] += (\n", " self._eval_timer['batch_data_end_time'] -\n", " self._eval_timer['batch_start_time'])\n", "\n", " def on_eval_batch_end(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch评估结束后,更新参数\n", + " \"\"\"\n", " logs = logs or {}\n", " self.eval_step += 1\n", " samples = logs.get('batch_size', 1)\n", @@ -438,6 +563,9 @@ " self._eval_timer['batch_start_time'] = time.time()\n", "\n", " def on_predict_begin(self, logs=None):\n", + " \"\"\"\n", + " 推理开始前,获取当前步数,声明进度条与计时器等\n", + " \"\"\"\n", " self.test_steps = logs.get('steps', None)\n", " self.test_metrics = logs.get('metrics', [])\n", " self.test_step = 0\n", @@ -458,12 +586,18 @@ " self._test_timer['batch_start_time'] = time.time()\n", "\n", " def on_predict_batch_begin(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch推理开始前,进行计时\n", + " \"\"\"\n", " self._test_timer['batch_data_end_time'] = time.time()\n", " self._test_timer['data_time'] += (\n", " self._test_timer['batch_data_end_time'] -\n", " self._test_timer['batch_start_time'])\n", "\n", " def on_predict_batch_end(self, step, logs=None):\n", + " \"\"\"\n", + " 单个Batch推理结束后,更新参数\n", + " \"\"\"\n", " logs = logs or {}\n", " self.test_step += 1\n", " samples = logs.get('batch_size', 1)\n", @@ -482,12 +616,18 @@ " self._test_timer['batch_start_time'] = time.time()\n", "\n", " def on_eval_end(self, logs=None):\n", + " \"\"\"\n", + " 评估结束后,更新参数,打印信息\n", + " \"\"\"\n", " logs = logs or {}\n", " if self._is_print() and (self.eval_steps is not None):\n", " self._updates(logs, 'eval')\n", " print('Eval samples: %d' % (self.evaled_samples))\n", "\n", " def on_predict_end(self, logs=None):\n", + " \"\"\"\n", + " 推理结束后,更新参数,打印信息\n", + " \"\"\"\n", " logs = logs or {}\n", " if self._is_print():\n", " if self.test_step % self.log_freq != 0 or self.verbose == 1:\n", @@ -495,12 +635,43 @@ " print('Predict samples: %d' % (self.tested_samples))" ] }, + { + "cell_type": "markdown", + "id": "b8330408-b9f7-427b-91b4-94229f59da06", + "metadata": {}, + "source": [ + "## 四、自定义优化器 Optimizer\n", + "\n", + "### 4.1 优化器介绍\n", + "\n", + "优化器在模型训练过程中,用于计算和更新网络参数,合适的优化器可以有效减少训练时间,提高最终模型性能。除了之前例子中用到的 Adam 优化器外,还有很多其他的优化器,以实现目标函数能更快速更有效地收敛到全局最优点。我们需要根据任务选择合适的优化器,并设置合理的参数。\n", + "\n", + "### 4.2 自定义优化器\n", + "\n", + "通常我们需要为实际的任务选择合适的优化器,为优化器设置合理的学习率下降策略:\n", + "\n", + "* 优化器:飞桨中提供了很多可选择的优化器,详见 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer);\n", + "* 学习率:除了指定学习率固定值外,在很多任务中学习率会随着训练进程而变化。飞桨中可以使用 [paddle.optimizer.lr](../api/paddle/optimizer/Overview_cn.html#about-lr)下的各个API来实现丰富的学习率下降策略。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9be7d431-7ff2-406f-9c7b-4b98bee9bf66", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "Adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=model.parameters())" + ] + }, { "cell_type": "markdown", "id": "0a7d7f04", "metadata": {}, "source": [ - "## 四、自定义指标的使用\n", + "## 五、自定义Loss、Metric、Optimizer及Callback的使用\n", "\n", "接下来以mnist为例,使用自定义的指标替换框架中的指标,代码如下:" ] @@ -509,7 +680,9 @@ "cell_type": "code", "execution_count": 9, "id": "d2a66f19", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", @@ -571,9 +744,10 @@ "\n", "model = paddle.Model(mnist)\n", "\n", + "# 将paddle.optimizer.Adam替换为Adam,\n", "# 将paddle.nn.CrossEntropyLoss替换为CrossEntropy,\n", "# 将paddle.metric.Accuracy替换为Accuracy\n", - "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + "model.prepare(optimizer=Adam()), \n", " loss=CrossEntropy(), \n", " metrics=Accuracy())\n", "\n", @@ -585,13 +759,23 @@ " callbacks=[ProgBarLogger(verbose=1), ModelCheckpoint()]\n", " )" ] + }, + { + "cell_type": "markdown", + "id": "df90a96d-8801-4c17-9709-63afcf4f7756", + "metadata": {}, + "source": [ + "# 五、总结\n", + "\n", + "待补充" + ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -603,7 +787,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" } }, "nbformat": 4, From 94b74d246022a31f2ad8222b152fc36cb5739ae5 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Wed, 9 Feb 2022 15:57:46 +0800 Subject: [PATCH 34/63] update model_develop 01-04 topics update model_develop 01-04 topics --- .../01_quick_start_cn.ipynb | 66 +++--- .../02_data_load_cn.ipynb | 215 +++++++++++++----- .../03_data_preprocessing_cn.ipynb | 100 +++++--- .../02_paddle2.0_develop/04_model_cn.ipynb | 148 +++++++----- .../images/data_pipeline.png | Bin 0 -> 147904 bytes .../images/data_preprocessing.png | Bin 0 -> 150760 bytes .../02_paddle2.0_develop/images/lenet.png | Bin 0 -> 150249 bytes .../02_paddle2.0_develop/images/model.png | Bin 0 -> 104897 bytes 8 files changed, 346 insertions(+), 183 deletions(-) create mode 100644 docs/guides/02_paddle2.0_develop/images/data_pipeline.png create mode 100644 docs/guides/02_paddle2.0_develop/images/data_preprocessing.png create mode 100644 docs/guides/02_paddle2.0_develop/images/lenet.png create mode 100644 docs/guides/02_paddle2.0_develop/images/model.png diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 6d70ef7b368..c3cca372ad3 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -26,7 +26,8 @@ "iopub.status.idle": "2021-12-20T07:51:59.862320Z", "shell.execute_reply": "2021-12-20T07:51:59.861499Z", "shell.execute_reply.started": "2021-12-20T07:51:58.099995Z" - } + }, + "scrolled": true }, "outputs": [], "source": [ @@ -59,7 +60,8 @@ "iopub.status.idle": "2021-12-16T04:18:43.302570Z", "shell.execute_reply": "2021-12-16T04:18:43.301792Z", "shell.execute_reply.started": "2021-12-16T04:18:43.298346Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -105,7 +107,8 @@ "iopub.status.idle": "2021-12-16T06:31:06.274730Z", "shell.execute_reply": "2021-12-16T06:31:06.273827Z", "shell.execute_reply.started": "2021-12-16T06:31:04.356403Z" - } + }, + "scrolled": true }, "outputs": [], "source": [ @@ -132,7 +135,8 @@ "iopub.status.idle": "2021-12-16T06:33:13.307650Z", "shell.execute_reply": "2021-12-16T06:33:13.306938Z", "shell.execute_reply.started": "2021-12-16T06:31:11.339980Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -233,7 +237,7 @@ "\n", "1. 数据集定义与加载\n", "2. 模型组网\n", - "3. 模型训练和评估\n", + "3. 模型训练与评估\n", "4. 模型推理\n", "\n", "接下来逐个步骤介绍,帮助你快速掌握使用飞桨框架实践深度学习任务的方法。\n" @@ -248,7 +252,7 @@ "\n", "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", "\n", - "MNIST 数据集是图像格式文件,而深度学习模型通常不能直接用图像格式的数据进行训练,需要转换为模型支持的数据格式,因此本任务中还导入了 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 模块,在初始化MNIST数据集时传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型的收敛速度型。\n" + "飞桨在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转和归一化等处理。本任务在初始化 MNIST 数据集时通过 `transform` 字段传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型训练的收敛速度。" ] }, { @@ -262,7 +266,8 @@ "iopub.status.idle": "2021-12-17T06:55:53.018229Z", "shell.execute_reply": "2021-12-17T06:55:53.017346Z", "shell.execute_reply.started": "2021-12-17T06:55:43.185027Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -291,9 +296,9 @@ "id": "2d89cb67", "metadata": {}, "source": [ - "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载的 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", + "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载功能的 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", - "在 `paddle.vision.transforms` 模块中还内置了很多数据增广的 API,如对图像进行中心裁剪、水平翻转和图像归一化等操作,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", + "在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下内置了很多图像变换操作的 API,如对图像的翻转、裁剪、调整亮度等,可实现数据增强,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", "\n", "更多参考:\n", "* [数据集定义与加载](02_data_load_cn.html)\n", @@ -310,7 +315,7 @@ "飞桨的模型组网有多种方式,既可以直接使用飞桨内置的模型,也可以自定义组网。\n", "\n", "\n", - "『手写数字识别任务』比较简单,普通的神经网络就能达到很高的精度,在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](../../api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为10。\n", + "『手写数字识别任务』比较简单,普通的神经网络就能达到很高的精度,在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](../../api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为 10。\n", "\n", "另外通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 可方便地打印网络的基础结构和参数信息。" ] @@ -326,7 +331,8 @@ "iopub.status.idle": "2021-12-17T06:59:47.990123Z", "shell.execute_reply": "2021-12-17T06:59:47.989596Z", "shell.execute_reply.started": "2021-12-17T06:59:47.978088Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -384,18 +390,18 @@ "id": "4902817f", "metadata": {}, "source": [ - "### 3.3 模型训练评估\n", + "### 3.3 模型训练与评估\n", "\n", "#### 3.3.1 模型训练\n", "\n", "模型训练需完成如下步骤:\n", "\n", - "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用[飞桨高层 API](../../practices/quick_start/high_level_api.html)进行训练、评估、推理的实例,方便后续操作。\n", + "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用 [飞桨高层 API](../../practices/quick_start/high_level_api.html) 进行训练、评估、推理的实例,方便后续操作。\n", "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](../../api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", "\n", "\n", - "因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用常见的 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam) 优化器,使用 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) (精度)指标来计算模型在训练集上的精度。" + "因为是分类任务,这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) (交叉熵损失函数),优化器使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam),评价指标使用 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) 来计算模型在训练集上的精度。" ] }, { @@ -409,7 +415,8 @@ "iopub.status.idle": "2021-12-17T04:03:17.852495Z", "shell.execute_reply": "2021-12-17T04:03:17.851918Z", "shell.execute_reply.started": "2021-12-17T04:01:43.380928Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -465,7 +472,9 @@ "cell_type": "code", "execution_count": 8, "id": "b86f0289", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -500,7 +509,7 @@ "从结果可以看到,初步训练得到的模型精度在98%附近,在逐渐熟悉深度学习模型开发和训练技巧后,可以通过调整其中的训练参数来进一步提升模型的精度。\n", "\n", "更多参考:\n", - "* [模型训练与评估](05_train_eval_predict_cn.html)" + "* [模型训练、评估与推理](05_train_eval_predict_cn.html)" ] }, { @@ -535,7 +544,8 @@ "iopub.status.idle": "2021-12-17T04:11:42.637039Z", "shell.execute_reply": "2021-12-17T04:11:42.636284Z", "shell.execute_reply.started": "2021-12-17T04:11:42.623579Z" - } + }, + "scrolled": true }, "outputs": [], "source": [ @@ -548,7 +558,7 @@ "id": "0daaf2e3", "metadata": {}, "source": [ - "以上代码执行后会在`output`目录下保存两个文件,`mnist.pdopt`为优化器的参数,`mnist.pdparams`为模型的参数\n", + "以上代码执行后会在`output`目录下保存两个文件,`mnist.pdopt`为优化器的参数,`mnist.pdparams`为模型的参数。\n", "```bash\n", "output\n", "├── mnist.pdopt # 优化器的参数\n", @@ -563,16 +573,18 @@ "source": [ "#### 3.4.2 模型加载并执行推理\n", "\n", - "执行模型推理时,可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可通过调用 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", + "执行模型推理时,可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可调用 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", "\n", - "如下示例中,针对前面创建的 `model` 实例加载保存的参数文件 `output/mnist`,并选择测试集中的一张图片 `test_dataset[0]` 作为输入,执行推理并打印结果,可以看到推理的结果与可视化图片一致。\n" + "如下示例中,针对前面创建的 `model` 网络加载保存的参数文件 `output/mnist`,并选择测试集中的一张图片 `test_dataset[0]` 作为输入,执行推理并打印结果,可以看到推理的结果与可视化图片一致。\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "bb8328ef", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -628,7 +640,7 @@ "source": [ "更多参考:\n", "* [模型保存与加载](08_model_save_load_cn.html)\n", - "* [模型推理](09_model_to_onnx_cn.html)" + "* [模型训练、评估与推理](05_train_eval_predict_cn.html)\n" ] }, { @@ -643,15 +655,15 @@ "
\n", "

图1:模型开发流程
\n", "\n", - "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增广、使用更大的 CNN 模型、自定义神经网络、调优性能等,同时飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" + "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增强、使用更大的 CNN 模型、调优性能等。飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -663,7 +675,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" }, "toc-autonumbering": false, "toc-showcode": false, diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 24e5fa4ebd1..70ce6af8580 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -8,19 +8,17 @@ "# 数据集定义与加载\n", "\n", "\n", - "深度学习模型需要大量的数据来完成训练和评估,这些数据样本可能是图片(image)、文本(txt)、语音(audio)等多种类型,而模型训练过程实际是数学计算过程,因此数据样本在送入模型前需要经过一系列处理,如划分数据集、变换数据形状(shape)、制作数据迭代读取器以备分批训练等。\n", + "深度学习模型需要大量的数据来完成训练和评估,这些数据样本可能是图片(image)、文本(txt)、语音(audio)等多种类型,而模型训练过程实际是数学计算过程,因此数据样本在送入模型前需要经过一系列处理,如转换数据格式、划分数据集、变换数据形状(shape)、制作数据迭代读取器以备分批训练等。\n", "\n", - "归纳起来主要需定义如下几个类:\n", + "在飞桨框架中,可通过如下两个核心步骤完成数据集的定义与加载:\n", "\n", - "* **定义数据集类**:将磁盘中保存的原始图片、文字等样本和对应的标签映射到 Dataset,方便后续通过索引(index)读取数据,在 Dataset 中还可以进行一些数据变换、数据增广等预处理操作。在飞桨框架中推荐使用 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 自定义数据集,另外在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 目录下飞桨内置了一些经典数据集方便直接调用。\n", + "1. **定义数据集**:将磁盘中保存的原始图片、文字等样本和对应的标签映射到 Dataset,方便后续通过索引(index)读取数据,在 Dataset 中还可以进行一些数据变换、数据增广等预处理操作。在飞桨框架中推荐使用 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 自定义数据集,另外在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 目录下飞桨内置了一些经典数据集方便直接调用。\n", "\n", "\n", - "* **定义数据读取器类**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", + "2. **迭代读取数据集**:自动将数据集的样本进行分批(batch)、乱序(shuffle)等操作,方便训练时迭代读取,同时还支持多进程异步读取功能可加快数据读取速度。在飞桨框架中可使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) 迭代读取数据集。\n", "\n", "\n", - "* **定义数据采样器类(可选)**:定义从数据集中的采样行为,如乱序采样、批次采样和分布式批次采样。\n", - "\n", - "本文以图像数据集为例介绍,文本数据集可参考 [NLP实践](../../practices/nlp/index_cn.html)。" + "本文以图像数据集为例介绍,文本数据集可参考 [NLP 应用实践](../../practices/nlp/index_cn.html)。" ] }, { @@ -32,7 +30,7 @@ "\n", "### 1.1 直接加载内置数据集\n", "\n", - "飞桨框架在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../..//api/paddle/text/Overview_cn.html#api) 目录下内置了一些经典数据集可直接调用,如 CV 领域的 MNIST、Cifar10、VOC2012,NLP 领域的 Movielens、Imdb 等,通过以下代码可查看飞桨框架中的内置数据集。" + "飞桨框架在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 和 [paddle.text](../..//api/paddle/text/Overview_cn.html#api) 目录下内置了一些经典数据集可直接调用,通过以下代码可查看飞桨框架中的内置数据集。" ] }, { @@ -70,9 +68,10 @@ "id": "9235d4f3-9e6f-4926-b003-da4eed882631", "metadata": {}, "source": [ - "打印出的自然语言处理(NLP)相关数据集中只有前七个是数据集。\n", + "从打印结果可以看到飞桨内置了 CV 领域的 MNIST、FashionMNIST、Flowers、Cifar10、Cifar100、VOC2012 数据集,以及 NLP 领域的 Conll05st、Imdb、Imikolov、Movielens、UCIHousing、WMT14、WMT16 数据集。\n", + "\n", "\n", - "以 [MNIST](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/datasets/MNIST_cn.html) 数据集为例,加载内置数据集的代码示例如下所示。" + "以 [MNIST](../../api/paddle/vision/datasets/MNIST_cn.html) 数据集为例,加载内置数据集的代码示例如下所示。" ] }, { @@ -102,7 +101,7 @@ "from paddle.vision.transforms import Normalize\n", "\n", "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", - "# 下载数据集并初始化DataSet\n", + "# 下载数据集并初始化 DataSet\n", "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)\n", "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)\n", "print('train images: ',len(train_dataset),', test images: ',len(test_dataset))" @@ -113,7 +112,9 @@ "id": "29fbad59-3234-41c6-82e6-da194972666a", "metadata": {}, "source": [ - "内置的 [MNIST](../../api/paddle/vision/datasets/MNIST_cn.html) 数据集已经划分好了训练集和测试集,通过 `mode` 字段传入 `'train'` 或 `'test'` 来区分。另外可通过 `transform` 字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 里提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等。这里在初始化MNIST数据集时传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型的收敛速度。" + "内置的 [MNIST](../../api/paddle/vision/datasets/MNIST_cn.html) 数据集已经划分好了训练集和测试集,通过 `mode` 字段传入 `'train'` 或 `'test'` 来区分。\n", + "\n", + "另外可通过 `transform` 字段传入一些对图像进行变换的操作,飞桨在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转图像和对图像进行归一化等。这里在初始化 MNIST 数据集时传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型训练的收敛速度。" ] }, { @@ -121,7 +122,7 @@ "id": "79102100-e52e-42d6-9b17-48cdb9b59991", "metadata": {}, "source": [ - "完成数据集初始化之后,可以使用下面的代码直接对数据集进行迭代。" + "完成数据集初始化之后,可以使用下面的代码直接对数据集进行迭代读取。" ] }, { @@ -183,53 +184,84 @@ "id": "ab2f3fb4", "metadata": {}, "source": [ - "在实际的场景中,需要使用自有的数据来定义数据集,这时可以通过飞桨提供的 `paddle.io.Dataset` 基类来实现自定义数据集。\n", + "在实际的场景中,一般需要使用自有的数据来定义数据集,这时可以通过 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 基类来实现自定义数据集。\n", "\n", "可构建一个子类继承自 `paddle.io.Dataset` ,并且实现下面的三个函数:\n", "\n", - "1. `__init__`:完成一些数据集初始化操作,如指定数据和标签文件的存储路径、定义数据集大小等。\n", - "2. `__getitem__`:定义指定索引(index)时如何获取数据,并且在此函数中可定义一些数据预处理工作,如读取图像、对图像进行数据增强、制作标签等操作,最终返回处理好的单条数据(训练数据、对应的标签)。详细数据预处理介绍可参考 [数据预处理](03_data_preprocessing_cn.html)。\n", - "3. `__len__`:返回数据集的样本总数。\n" + "1. `__init__`:完成数据集初始化操作,将磁盘中的样本文件路径和对应标签映射到一个列表中。\n", + "2. `__getitem__`:定义指定索引(index)时如何获取样本数据,最终返回对应 index 的单条数据(样本数据、对应的标签)。\n", + "3. `__len__`:返回数据集的样本总数。\n", + "\n", + "下面介绍下载 MNIST 原始数据集文件后,用 `paddle.io.Dataset` 定义数据集的代码示例。\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "f9487da0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-01-28T05:34:50.747072Z", + "iopub.status.busy": "2022-01-28T05:34:50.746471Z", + "iopub.status.idle": "2022-01-28T05:34:56.882604Z", + "shell.execute_reply": "2022-01-28T05:34:56.881341Z", + "shell.execute_reply.started": "2022-01-28T05:34:50.747033Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2022-01-12 14:38:05-- https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", - "正在解析主机 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)... 111.206.210.93, 111.206.210.81\n", - "正在连接 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)|111.206.210.93|:443... 已连接。\n", + "--2022-01-28 13:34:50-- https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", + "正在解析主机 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)... 182.61.200.195, 182.61.200.229, 2409:8c04:1001:1002:0:ff:b001:368a\n", + "正在连接 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)|182.61.200.195|:443... 已连接。\n", "已发出 HTTP 请求,正在等待回应... 200 OK\n", - "长度:104252416 (99M) [application/x-tar]\n", + "长度: 104252416 (99M) [application/x-tar]\n", "正在保存至: “mnist.tar”\n", "\n", - "mnist.tar 100%[===================>] 99.42M 2.29MB/s 用时 45s \n", + "mnist.tar 100%[===================>] 99.42M 27.1MB/s in 3.8s \n", "\n", - "2022-01-12 14:38:51 (2.21 MB/s) - 已保存 “mnist.tar” [104252416/104252416])\n", + "2022-01-28 13:34:54 (26.3 MB/s) - 已保存 “mnist.tar” [104252416/104252416])\n", "\n" ] } ], "source": [ - "# 下载 MNIST 数据集并解压\n", + "# 下载原始的 MNIST 数据集并解压\n", "! wget https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar\n", "! tar -xf mnist.tar" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "1d26950f", "metadata": { + "execution": { + "iopub.execute_input": "2022-01-28T05:37:13.849337Z", + "iopub.status.busy": "2022-01-28T05:37:13.848816Z", + "iopub.status.idle": "2022-01-28T05:37:13.868808Z", + "shell.execute_reply": "2022-01-28T05:37:13.867867Z", + "shell.execute_reply.started": "2022-01-28T05:37:13.849276Z" + }, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'transform' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_187/1554865552.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;31m# 打印数据集样本数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mcustom_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMyDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mnist/train'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mnist/train/label.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MyDataset images: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustom_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'transform' is not defined" + ] + } + ], "source": [ "import os\n", "import cv2\n", @@ -242,7 +274,7 @@ " \"\"\"\n", " def __init__(self, data_dir, label_path, transform=None):\n", " \"\"\"\n", - " 步骤二:实现 __init__ 函数,定义数据集大小\n", + " 步骤二:实现 __init__ 函数,初始化数据集,将样本和标签映射到列表中\n", " \"\"\"\n", " super(MyDataset, self).__init__()\n", " self.data_list = []\n", @@ -255,9 +287,9 @@ "\n", " def __getitem__(self, index):\n", " \"\"\"\n", - " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", + " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(样本数据、对应的标签)\n", " \"\"\"\n", - " # 根据索引,从列表中取出一个\n", + " # 根据索引,从列表中取出一个图像\n", " image_path, label = self.data_list[index]\n", " # 读取灰度图\n", " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", @@ -265,14 +297,21 @@ " image = image.astype('float32')\n", " if self.transform is not None:\n", " image = self.transform(image)\n", + " # Label格式转换为 int\n", " label = int(label)\n", + " # 返回图像和对应标签\n", " return image, label\n", "\n", " def __len__(self):\n", " \"\"\"\n", " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", " \"\"\"\n", - " return len(self.data_list)" + " return len(self.data_list)\n", + "\n", + "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", + "# 打印数据集样本数 \n", + "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)\n", + "print('MyDataset images: ',len(custom_dataset))" ] }, { @@ -280,9 +319,23 @@ "id": "0e705d33", "metadata": {}, "source": [ - "在上面的代码中,定义了一个自定义的数据集类MyDataset,MyDataset继承自Dataset,并且实现了`__init__`,`__getitem__`和`__len__`三个方法。在`__init__`方法中完成了对标注文件的读取和解析,并且将所有的图像路径和对应的Label存放到一个列表中,在`__getitem__`方法中完成了图像的读取和预处理以及Label格式的转换,在`__len__`方法中返回`__init__`方法中初始化好的列表长度。\n", + "在上面的代码中,自定义了一个数据集类 `MyDataset`,`MyDataset` 继承自 `paddle.io.Dataset` 基类 ,并且实现了 `__init__`,`__getitem__` 和 `__len__` 三个函数。\n", + "* 在 `__init__` 函数中完成了对标签文件的读取和解析,并将所有的图像路径 `image_path` 和对应的标签 `label` 存放到一个列表 `data_list` 中。\n", + "* 在 `__getitem__` 函数中定义了指定 index 获取对应图像数据的方法,完成了图像的读取、预处理和图像标签格式的转换,最终返回图像和对应标签 `image, label`。\n", + "* 在 `__len__` 函数中返回 `__init__` 函数中初始化好的数据集列表 `data_list` 长度。\n", "\n", - "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代。" + "\n", + "\n", + "\n", + "另外,在 `__init__` 函数和 `__getitem__` 函数中还可实现一些数据预处理操作,如对图像的翻转、裁剪、归一化等操作,最终返回处理好的单条数据(样本数据、对应的标签),该操作可增加图像数据多样性,对增强模型的泛化能力带来帮助。飞桨框架在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下内置了几十种图像数据处理方法,详细使用方法可参考 [数据预处理](03_data_preprocessing_cn.html) 章节。\n", + "\n", + "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代读取。\n", + "\n", + "# 备注\n", + "\n", + "1. 代码打印一下数据集训练集、测试集的样本数,并更新一下代码回显。\n", + "2. float32必须的原因解释一下。label转成int的原因解释一下。\n", + "\n" ] }, { @@ -324,23 +377,16 @@ " break" ] }, - { - "cell_type": "markdown", - "id": "fb7ad7bd", - "metadata": {}, - "source": [ - "数据集在模型训练时的使用方式可参考 [模型训练与评估](05_train_eval_predict_cn.html)" - ] - }, { "cell_type": "markdown", "id": "de3fd19b", "metadata": {}, "source": [ - "## 二、使用 paddle.io.DataLoader 定义数据读取器\n", + "## 二、迭代读取数据集\n", "\n", + "### 2.1 使用 paddle.io.DataLoader 定义数据读取器\n", "\n", - "通过前面介绍的直接迭代读取 DataSet 的方式虽然可实现对数据集的访问,但是这种访问方式只能单线程进行并且还需要手动分批次(batch)。在飞桨框架中,推荐使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API 对数据集进行多进程的读取,并且可自动完成划分 batch 的工作,开发者只需要进行数据处理模块的编写。" + "通过前面介绍的直接迭代读取 Dataset 的方式虽然可实现对数据集的访问,但是这种访问方式只能单线程进行并且还需要手动分批次(batch)。在飞桨框架中,推荐使用 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API 对数据集进行多进程的读取,并且可自动完成划分 batch 的工作。" ] }, { @@ -361,7 +407,7 @@ ], "source": [ "# 定义并初始化数据读取器\n", - "train_loader = paddle.io.DataLoader(custom_dataset, batch_size=64, shuffle=True, num_workers=1)\n", + "train_loader = paddle.io.DataLoader(custom_dataset, batch_size=64, shuffle=True, num_workers=1, drop_last=True)\n", "\n", "# 调用 DataLoader 迭代读取数据\n", "for batch_id, data in enumerate(train_loader()):\n", @@ -375,13 +421,19 @@ "id": "ae21b353", "metadata": {}, "source": [ - "通过上述方法,初始化了一个数据读取器 `train_loader`,用于加载训练数据集 `train_dataset`。在数据读取器中常见的几个配置如下:\n", + "通过上述方法,初始化了一个数据读取器 `train_loader`,用于加载训练数据集 `custom_dataset`。在数据读取器中几个常用的字段如下:\n", + "\n", + "* `batch_size`:**每批次读取样本数**,示例中 `batch_size=64` 表示每批次读取 64 个样本。\n", + "* `shuffle`:**样本乱序**,示例中 `shuffle=True` 表示在取数据时打乱样本顺序,以减少过拟合发生的可能。\n", + "* `drop_last`:**丢弃不完整的批次样本**,示例中 `drop_last=True` 表示丢弃因数据集样本数不能被 batch_size 整除而产生的最后一个不完整的 batch 样本。\n", + "* `num_workers`:**同步/异步读取数据**,通过 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。\n", + "\n", + "\n", + "定义好数据读取器之后,便可用 for 循环方便地迭代读取批次数据,用于模型训练了。值得注意的是,如果使用高层 API 的 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 读取数据集进行训练,则只需定义数据集 Dataset 即可,不需要再单独定义 Dataloader,因为 paddle.Model.fit 中实际已经封装了一部分 Dataloader 的功能,详细可参考 [模型训练、评估与推理](05_train_eval_predict_cn.html) 章节。\n", + "\n", "\n", - "* **训练样本乱序**:通过设置 `shuffle=True` ,可以在取数据前打乱样本顺序。\n", - "* **生成批次数据**:通过 `batch_size` 设置生成批次数据的批大小,示例中设置为 64。\n", - "* **同步/异步读取数据**:通过设置 `num_workers` 来设置加载数据的子进程个数,num_workers的值设为大于0时,即开启多进程方式异步加载数据,可提升数据读取速度。\n", "\n", - "数据读取器在模型训练时的使用方式可参考 [模型训练与评估](05_train_eval_predict_cn.html)" + "> Dataloader 实际上是通过批采样器 BatchSampler 产生的批次索引列表,并根据索引取得 DataSet 中的对应样本数据,以实现批次数据的加载。Dataloader 中定义了采样的批次大小、顺序等信息,对应字段包括 `batch_size`、`shuffle`、`drop_last`。这三个字段也可以用一个 `batch_sampler` 字段代替,并在 `batch_sampler` 中传入自定义的批采样器实例。以上两种方式二选一即可,可实现相同的效果。下面小节中介绍后一种自定义采样器的使用方法,该用法可以更灵活地定义采样规则。\n" ] }, { @@ -389,16 +441,25 @@ "id": "4c387f37", "metadata": {}, "source": [ - "## 三、定义数据采样器\n", + "### 2.2 (可选)自定义采样器\n", "\n", - "数据采样器定义了数据读取器从数据集中读取数据时的采样行为,主要用于从数据读取器中迭代式获取的样本下标数组,然后数据读取器根据下标数组从数据集中取出对应的数据。常用的主要有用于单机单卡训练[BatchSampler](../../api/paddle/io/BatchSampler_cn.html)和用于分布式训练的[DistributedBatchSampler](../../api/paddle/io/DistributedBatchSampler_cn.html)。下面以BatchSampler为例,介绍如何使用数据采样器。" + "采样器定义了从数据集中的采样行为,如顺序采样、批次采样、随机采样、分布式采样等。采样器会根据设定的采样规则,返回数据集中的索引列表,然后数据读取器 Dataloader 即可根据索引列表从数据集中取出对应的样本。\n", + "\n", + "飞桨框架在 [paddle.io](../../api/paddle/io/Overview_cn.html) 目录下提供了多种采样器,如批采样器 [BatchSampler](../../api/paddle/io/BatchSampler_cn.html)、分布式批采样器 [DistributedBatchSampler](../../api/paddle/io/DistributedBatchSampler_cn.html)、顺序采样器 [SequenceSampler](../../api/paddle/io/SequenceSampler_cn.html)、随机采样器 [RandomSampler](../../api/paddle/io/RandomSampler_cn.html) 等。\n", + "\n", + "\n", + "下面通过两段示例代码,介绍采样器的用法。\n", + "\n", + "首先,以 BatchSampler 为例,介绍在 DataLoader 中使用 BatchSampler 获取采样数据的方法。\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "477c89ef", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -414,16 +475,18 @@ "source": [ "from paddle.io import BatchSampler\n", "\n", - "bs = BatchSampler(custom_dataset, batch_size=8, shuffle=True)\n", + "# 定义一个批采样器,并设置采样的数据集源、采样批大小、是否乱序等\n", + "bs = BatchSampler(custom_dataset, batch_size=8, shuffle=True, drop_last=True)\n", "\n", - "print(\"单独迭代 BatchSampler\")\n", + "print(\"BatchSampler 每轮迭代返回一个索引列表\")\n", "for batch_indices in bs:\n", " print(batch_indices)\n", " break\n", - " \n", + "\n", + "# 在 DataLoader 中使用 BatchSampler 获取采样数据 \n", "train_loader = paddle.io.DataLoader(custom_dataset, batch_sampler=bs, num_workers=1)\n", "\n", - "print(\"在DataLoader中使用 BatchSampler\")\n", + "print(\"在 DataLoader 中使用 BatchSampler,返回索引对应的一组样本和标签数据 \")\n", "for batch_id, data in enumerate(train_loader()):\n", " images, labels = data\n", " print(\"batch_id: {}, 训练数据shape: {}, 标签数据shape: {}\".format(batch_id, images.shape, labels.shape))\n", @@ -435,7 +498,28 @@ "id": "a503bc9a", "metadata": {}, "source": [ - "这里定义的BatchSampler在每个迭代时会返回一个batch_size大小的样本下标数组,数据读取器根据这个下标数组就能从数据集中拿到对应的数据。在DataLoader中使用 BatchSampler时,DataLoader中的batch_size,shuffle和drop_last等参数只需在BatchSampler中设定即可。" + "以上示例代码中,定义了一个批采样器实例 `bs`,每轮迭代会返回一个 `batch_size` 大小的索引列表(示例中一轮迭代返回 8 个索引值),数据读取器 `train_loader` 通过 `batch_sampler=bs` 字段传入批采样器,即可根据这些索引获取对应的一组样本数据。另外可以看到,`batch_size`、`shuffle`、`drop_last`这三个参数只在 BatchSampler 中设定。\n", + "\n", + "\n", + "下面再通过一段代码示例,对比几个不同采样器的采样行为。\n", + "\n", + "# 备注\n", + "1. 补充多种sampler的用法代码和代码解释:\n", + "SequenceSampler, RandomSampler, BatchSampler, DistributedBatchSampler\n", + "参考:https://aistudio.baidu.com/aistudio/projectdetail/1299687?channelType=0&channel=0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d4c4622-b19a-47ad-a603-fc53c44650fa", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from paddle.io import SequenceSampler, RandomSampler, BatchSampler, DistributedBatchSampler\n", + "\n" ] }, { @@ -445,17 +529,22 @@ "source": [ "## 四、总结\n", "\n", - "本节中介绍了Paddle中的数据送入模型之前的处理流程:数据集+数据读取器。进一步介绍了如何使用内置数据集和自定义数据集,在数据集中,本节仅对数据集进行了归一化,如需了解更多数据增强或数据处理操作,可以参考[数据预处理](03_data_preprocessing_cn.html)。 \n", + "本节中介绍了在飞桨框架中将数据送入模型训练之前的处理流程,总结整个流程和用到的关键 API 如下图所示。\n", + "\n", + "
\n", + "

图1:数据集定义和加载流程
\n", + "\n", + "主要包括定义数据集和定义数据读取器两个步骤,另外在数据读取器中可调用采样器实现更灵活地采样。其中,在定义数据集时,本节仅对数据集进行了归一化处理,如需了解更多数据增强相关操作,可以参考 [数据预处理](03_data_preprocessing_cn.html)。 \n", "\n", - "本节内容中如果遇到了问题,可以参考[FQA](faq.html)。 " + "以上所有数据处理工作完成后,即可进入下一个任务:[模型训练、评估与推理](05_train_eval_predict_cn.html)。" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -467,7 +556,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index a3b47efae70..4635df641f8 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -7,9 +7,10 @@ "source": [ "# 数据预处理\n", "\n", - "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据处理或增强。数据处理通过对数据进行特定的处理得到不同的图像,从而增强模型的泛化性。\n", "\n", - "在本节中主要介绍图像中的数据预处理。" + "在模型训练过程中有时会遇到过拟合的问题,其中一个解决方法就是对训练数据做数据增强处理。通过对数据进行特定的处理,如图像的裁剪、翻转、调整亮度等处理,以增加样本的多样性,从而增强模型的泛化能力。\n", + "\n", + "本节以图像数据为例,介绍数据预处理的方法。\n" ] }, { @@ -17,16 +18,23 @@ "id": "b1d901d6-ceac-4c05-acdd-191e34317c62", "metadata": {}, "source": [ - "## paddle.vision.transforms 介绍\n", + "## 一、paddle.vision.transforms 介绍\n", "\n", - "飞桨框架在 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种数据处理方法,可以通过以下代码查看:" + "飞桨框架在 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) 下内置了数十种图像数据处理方法,可以通过以下代码查看:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "93904999", "metadata": { + "execution": { + "iopub.execute_input": "2022-02-08T02:09:43.023836Z", + "iopub.status.busy": "2022-02-08T02:09:43.023261Z", + "iopub.status.idle": "2022-02-08T02:09:44.351088Z", + "shell.execute_reply": "2022-02-08T02:09:44.350240Z", + "shell.execute_reply.started": "2022-02-08T02:09:43.023799Z" + }, "scrolled": true }, "outputs": [ @@ -34,13 +42,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "数据处理方法: ['BaseTransform', 'Compose', 'Resize', 'RandomResizedCrop', 'CenterCrop', 'RandomHorizontalFlip', 'RandomVerticalFlip', 'Transpose', 'Normalize', 'BrightnessTransform', 'SaturationTransform', 'ContrastTransform', 'HueTransform', 'ColorJitter', 'RandomCrop', 'Pad', 'RandomRotation', 'Grayscale', 'ToTensor', 'to_tensor', 'hflip', 'vflip', 'resize', 'pad', 'rotate', 'to_grayscale', 'crop', 'center_crop', 'adjust_brightness', 'adjust_contrast', 'adjust_hue', 'normalize']\n" + "图像数据处理方法: ['BaseTransform', 'Compose', 'Resize', 'RandomResizedCrop', 'CenterCrop', 'RandomHorizontalFlip', 'RandomVerticalFlip', 'Transpose', 'Normalize', 'BrightnessTransform', 'SaturationTransform', 'ContrastTransform', 'HueTransform', 'ColorJitter', 'RandomCrop', 'Pad', 'RandomRotation', 'Grayscale', 'ToTensor', 'to_tensor', 'hflip', 'vflip', 'resize', 'pad', 'rotate', 'to_grayscale', 'crop', 'center_crop', 'adjust_brightness', 'adjust_contrast', 'adjust_hue', 'normalize']\n" ] } ], "source": [ "import paddle\n", - "print('数据处理方法:', paddle.vision.transforms.__all__)" + "print('图像数据处理方法:', paddle.vision.transforms.__all__)" ] }, { @@ -48,7 +56,11 @@ "id": "a4b999ee", "metadata": {}, "source": [ - "对于飞桨框架内置的数据处理方法,可以单个初始化调用,也可以将多个数据处理方法进行组合使用,具体使用方式如下:\n", + "包括图像随机裁剪、图像旋转变换、改变图像亮度、改变图像对比度等常见操作,各个操作方法的简介可参考 [API 文档](../../api/paddle/vision/Overview_cn.html#about-transforms)。\n", + "\n", + "\n", + "\n", + "对于飞桨框架内置的数据预处理方法,可以单个调用,也可以将多个数据预处理方法进行组合使用,具体使用方式如下:\n", "\n", "* 单个使用" ] @@ -64,7 +76,7 @@ "source": [ "from paddle.vision.transforms import Resize\n", "\n", - "# 定义想要使用的数据处理方式,这里初始化一个改变图片大小的变换\n", + "# 定义一个待使用的数据处理方法,这里定义了一个调整图像大小的方法\n", "transform = Resize(size=28)" ] }, @@ -75,7 +87,7 @@ "source": [ "* 多个组合使用\n", "\n", - "这种使用模式下,需要先定义好每个数据处理方法,然后用`Compose` 进行组合" + "这种使用模式下,需要先定义好每个数据处理方法,然后用`Compose` 进行组合。" ] }, { @@ -89,7 +101,7 @@ "source": [ "from paddle.vision.transforms import Compose, RandomRotation\n", "\n", - "# 定义想要使用的数据处理方式,这里包括随机旋转,改变图片大小\n", + "# 定义待使用的数据处理方法,这里包括随机旋转、改变图片大小两个组合处理\n", "transform = Compose([RandomRotation(10), Resize(size=32)])" ] }, @@ -98,13 +110,13 @@ "id": "b1bd06c0-b4f2-46a4-b446-cde3a1fe8afe", "metadata": {}, "source": [ - "## 在数据集中定义数据预处理操作\n", + "## 二、在数据集中应用数据预处理操作\n", "\n", - "定义好数据预处理方法后,可以直接在 DataSet 中使用,下面介绍两种数据处理使用方式,一种是基于框架内置数据集,一种是基于自定义的数据集。\n", + "定义好数据处理方法后,可以直接在数据集 Dataset 中应用,下面介绍两种数据预处理应用方式:一种是在框架内置数据集中应用,一种是在自定义的数据集中应用。\n", "\n", - "### 基于框架内置数据集\n", + "### 2.1 在框架内置数据集中应用\n", "\n", - "在框架内置数据集中使用内置的数据处理方法时,只需要将数据处理操方法传递给 `transform` 字段即可。" + "前面已定义好数据处理的方法,在加载内置数据集时,将其传递给 `transform` 字段即可。\n" ] }, { @@ -116,7 +128,7 @@ }, "outputs": [], "source": [ - "# 通过transform参数传递定义好的数据增强方法即可完成对自带数据集的增强\n", + "# 通过 transform 字段传递定义好的数据处理方法,即可完成对框架内置数据集的增强\n", "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)" ] }, @@ -125,16 +137,18 @@ "id": "74013246", "metadata": {}, "source": [ - "### 基于自定义的数据集\n", + "### 2.2 在自定义的数据集中应用\n", "\n", - "对于自定义的数据集,可以在数据集的 `__init__` 函数中定义数据处理方法,之后在 `__getitem__` 方法中对返回的数据应用数据预处理, 如下述代码所示:" + "对于自定义的数据集,可以在数据集中将定义好的数据处理方法传入 `__init__` 函数,将其定义为自定义数据集类的一个属性,然后在 `__getitem__` 中将其应用到图像上,如下述代码所示:" ] }, { "cell_type": "code", "execution_count": null, "id": "d7cabb3c", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# 下载 MNIST 数据集并解压\n", @@ -171,18 +185,17 @@ " image_path, label = line.strip().split('\\t')\n", " image_path = os.path.join(data_dir, image_path)\n", " self.data_list.append([image_path, label])\n", + " # 传入定义好的数据处理方法,作为自定义数据集类的一个属性\n", " self.transform = transform\n", "\n", " def __getitem__(self, index):\n", " \"\"\"\n", " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", " \"\"\"\n", - " # 根据索引,从列表中取出一个\n", " image_path, label = self.data_list[index]\n", - " # 读取灰度图\n", " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", - " # 图像数据格式转换为 float32,此步骤为必须\n", " image = image.astype('float32')\n", + " # 应用数据处理方法到图像上\n", " if self.transform is not None:\n", " image = self.transform(image)\n", " label = int(label)\n", @@ -202,7 +215,10 @@ "id": "e8990b3f-3a00-4d03-a99a-88185da6964c", "metadata": {}, "source": [ - "在自定义数据集中,直接将定义好的数据预处理传入`__init__`方法,将其定义为自定义数据集类的一个属性,然后在`__getitem__`中将数据预处理应用到图像上。" + "在以上示例代码中,定义了xxxx数据处理方法,并传入 `__init__` 函数,然后在 `__getitem__` 中将其应用到图像上。\n", + "\n", + "# 备注\n", + "1. 代码中补充数据处理方法的定义,比如里面包含图像旋转、归一化,再补充文字说明一下。" ] }, { @@ -210,18 +226,25 @@ "id": "2b21d528-1ab3-4eb2-be73-d6871df4ba65", "metadata": {}, "source": [ - "## 数据处理的几种方法介绍\n", + "## 三、数据预处理的几种方法介绍\n", + "\n", + "通过可视化的方法,可方便地对比飞桨内置数据处理方法的效果,下面介绍其中几个方法的对比示例。\n", "\n", "### RandomRotation:\n", "\n", - "依据degrees参数指定的角度范围,按照均匀分布随机产生一个角度对图像进行旋转。" + "依据degrees参数指定的角度范围,按照均匀分布随机产生一个角度对图像进行旋转。\n", + "\n", + "# 备注\n", + "1. 换一个花朵的公开数据集图片,翻转一个、裁剪一个、变换颜色一个。" ] }, { "cell_type": "code", "execution_count": 8, "id": "76edf274", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -279,7 +302,9 @@ "cell_type": "code", "execution_count": 15, "id": "f6adefc6", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -337,7 +362,9 @@ "cell_type": "code", "execution_count": 17, "id": "470047b1", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -386,7 +413,7 @@ "id": "94cdd766", "metadata": {}, "source": [ - "更多数据预处理可以参考 [paddle.vision.transforms](../../api/paddle/vision/Overview_cn.html#about-transforms) " + "更多数据处理方法介绍可以参考 [paddle.vision.transforms API 文档](../../api/paddle/vision/Overview_cn.html#about-transforms)。" ] }, { @@ -394,17 +421,22 @@ "id": "5927ccca", "metadata": {}, "source": [ - "## 总结\n", + "## 四、总结\n", + "\n", + "本节介绍了数据预处理方法在数据集中的使用方式,可先将一个或多个方法组合定义到一个实例中,再在数据集中应用,总结整个流程和用到的关键 API 如下图所示。\n", + "\n", + "
\n", + "

图1:数据预处理流程
\n", "\n", - "本节介绍了数据预处理在数据集中的使用方式并介绍了常用的数据预处理操作。关于文本的数据预处理可以参考[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data_prepare/overview.rst),语音的数据预处理可以参考 [PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs)。" + "图像、文本等不同类型的数据预处理方法不同,关于文本的数据预处理可以参考 [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data_prepare/overview.rst)。" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -416,7 +448,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index ba8b8cf6d8c..8eb07a66d8f 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -30,15 +30,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", "metadata": { "execution": { - "iopub.execute_input": "2022-01-05T07:21:41.979509Z", - "iopub.status.busy": "2022-01-05T07:21:41.978819Z", - "iopub.status.idle": "2022-01-05T07:21:44.967951Z", - "shell.execute_reply": "2022-01-05T07:21:44.966827Z", - "shell.execute_reply.started": "2022-01-05T07:21:41.979443Z" + "iopub.execute_input": "2022-02-09T07:06:35.736296Z", + "iopub.status.busy": "2022-02-09T07:06:35.735856Z", + "iopub.status.idle": "2022-02-09T07:06:35.743307Z", + "shell.execute_reply": "2022-02-09T07:06:35.742125Z", + "shell.execute_reply.started": "2022-02-09T07:06:35.736251Z" }, "scrolled": true }, @@ -134,7 +134,7 @@ "metadata": {}, "source": [ "通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 2个`Conv2D` 卷积层、2个`ReLU` 激活层、2个`MaxPool2D` 池化层以及3个`Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", - "
\n", + "
\n", "

图1:LeNet网络结构示意图
\n", "\n", "另外在 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", @@ -151,10 +151,10 @@ "经典模型可以满足一些简单深度学习任务的需求,然后更多情况下,需要使用深度学习框架构建一个自己的神经网络,这时可以使用飞桨框架 [paddle.nn](../../api/paddle/nn/Overview_cn.html) 下的 API 构建网络,该目录下定义了丰富的神经网络层和相关函数 API,如卷积网络相关的 Conv1D、Conv2D、Conv3D,循环神经网络相关的 RNN、LSTM、GRU 等,方便组网调用,详细清单可在 [API 文档](../../api/paddle/nn/Overview_cn.html) 中查看。\n", "\n", "飞桨提供继承类(class)的方式构建网络,并提供了几个基类,如:[paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential)、 \n", - "[paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer)、[paddle.nn.LayerList](../../api/paddle/nn/LayerList_cn.html#cn-api-fluid-dygraph-layerlist),构建一个继承基类的子类,并在子类中添加子层(sublayers,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式:\n", + "[paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 等,构建一个继承基类的子类,并在子类中添加层(layer,如卷积层、全连接层等)可实现网络的构建,不同基类对应不同的组网方式,本节介绍如下两种常用方法:\n", " \n", "* **使用 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html#sequential) 组网**:构建顺序的线性网络结构(如 LeNet、AlexNet 和 VGG)时,可以选择该方式。相比于 Layer 方式 ,Sequential 方式可以用更少的代码完成线性网络的构建。\n", - "* **使用 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。\n", + "* **使用 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 组网(推荐)**:构建一些比较复杂的网络结构时,可以选择该方式。相比于 Sequential 方式,Layer 方式可以更灵活地组建各种网络结构。Sequential 方式搭建的网络也可以作为子网加入 Layer 方式的组网中。\n", "\n" ] }, @@ -178,11 +178,11 @@ "id": "9a86cc3e", "metadata": { "execution": { - "iopub.execute_input": "2022-01-05T08:40:20.974280Z", - "iopub.status.busy": "2022-01-05T08:40:20.973687Z", - "iopub.status.idle": "2022-01-05T08:40:21.032406Z", - "shell.execute_reply": "2022-01-05T08:40:21.030756Z", - "shell.execute_reply.started": "2022-01-05T08:40:20.974221Z" + "iopub.execute_input": "2022-02-09T07:06:41.002891Z", + "iopub.status.busy": "2022-02-09T07:06:41.002485Z", + "iopub.status.idle": "2022-02-09T07:06:41.045284Z", + "shell.execute_reply": "2022-02-09T07:06:41.044712Z", + "shell.execute_reply.started": "2022-02-09T07:06:41.002860Z" }, "scrolled": true }, @@ -200,7 +200,7 @@ " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Flatten-1 [[1, 16, 5, 5]] [1, 400] 0 \n", + " Flatten-2 [[1, 16, 5, 5]] [1, 400] 0 \n", " Linear-4 [[1, 400]] [1, 120] 48,120 \n", " Linear-5 [[1, 120]] [1, 84] 10,164 \n", " Linear-6 [[1, 84]] [1, 10] 850 \n", @@ -253,7 +253,7 @@ "id": "19fd4c4c-9ff4-434e-b06c-32daf8e1ae43", "metadata": {}, "source": [ - "使用Sequential组网时,Sequential会自动完成网络的前向计算过程,但是Sequential组网只能完成简单的线性模型,对于需要进行分支判断的模型需要使用paddle.nn.Layer 组网的方式进行实现。" + "使用 Sequential 组网时,会自动完成网络的前向计算过程,但是 Sequential 组网只能完成简单的线性结构模型,对于需要进行分支判断的模型需要使用 paddle.nn.Layer 组网方式实现。" ] }, { @@ -265,8 +265,8 @@ "\n", "构建一些比较复杂的网络结构时,可以选择该方式,组网包括三个步骤:\n", "1. 创建一个继承自 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html#layer) 的类;\n", - "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(sublayer);\n", - "1. 在类的前向计算函数 `forward` 中使用定义好的 sublayer 进行前向计算。\n", + "1. 在类的构造函数 `__init__` 中定义组网用到的神经网络层(layer);\n", + "1. 在类的前向计算函数 `forward` 中使用定义好的 layer 进行前向计算。\n", "\n", "仍然以 LeNet 模型为例,使用 paddle.nn.Layer 组网的代码如下:\n" ] @@ -277,11 +277,11 @@ "id": "cf89df53", "metadata": { "execution": { - "iopub.execute_input": "2022-01-03T12:04:22.365846Z", - "iopub.status.busy": "2022-01-03T12:04:22.365241Z", - "iopub.status.idle": "2022-01-03T12:04:22.386477Z", - "shell.execute_reply": "2022-01-03T12:04:22.385631Z", - "shell.execute_reply.started": "2022-01-03T12:04:22.365800Z" + "iopub.execute_input": "2022-02-09T07:06:44.908285Z", + "iopub.status.busy": "2022-02-09T07:06:44.907738Z", + "iopub.status.idle": "2022-02-09T07:06:44.926711Z", + "shell.execute_reply": "2022-02-09T07:06:44.925815Z", + "shell.execute_reply.started": "2022-02-09T07:06:44.908250Z" }, "scrolled": true }, @@ -323,6 +323,7 @@ " def __init__(self, num_classes=10):\n", " super(LeNet, self).__init__()\n", " self.num_classes = num_classes\n", + " # 构建 features 子网,用于对输入图像进行特征提取\n", " self.features = nn.Sequential(\n", " nn.Conv2D(\n", " 1, 6, 3, stride=1, padding=1),\n", @@ -332,14 +333,14 @@ " 6, 16, 5, stride=1, padding=0),\n", " nn.ReLU(),\n", " nn.MaxPool2D(2, 2))\n", - "\n", + " # 构建 linear 子网,用于分类\n", " if num_classes > 0:\n", " self.linear = nn.Sequential(\n", " nn.Linear(400, 120),\n", " nn.Linear(120, 84), \n", " nn.Linear(84, num_classes)\n", " )\n", - "\n", + " # 执行前向计算\n", " def forward(self, inputs):\n", " x = self.features(inputs)\n", "\n", @@ -359,7 +360,7 @@ "id": "59606321", "metadata": {}, "source": [ - "在上面的代码中,将Lenet分为了`features`和`fc`两个子模块,`features`用于对输入图像进行特征提取,`fc`用于输出十个数字的分类。" + "在上面的代码中,将 Lenet 分为了 `features` 和 `linear` 两个子网,`features` 用于对输入图像进行特征提取,`linear` 用于输出十个数字的分类。" ] }, { @@ -367,9 +368,12 @@ "id": "c55565c4-cc47-4654-98cf-bccc8bff1331", "metadata": {}, "source": [ - "# 六、总结\n", + "# 五、总结\n", "\n", - "本节中,介绍了飞桨中模型组网的三种方式,并且以LeNet为例介绍了如何使用这三种组网方式实现LeNet。" + "本节介绍了飞桨框架中模型组网的几种方式,并且以 LeNet 为例介绍了如何使用这几种组网方式实现,总结模型组网的方法和用到的关键 API 如下图所示。\n", + "\n", + "
\n", + "

图2:模型组网方法
" ] }, { @@ -377,17 +381,27 @@ "id": "490f2617-b7c2-4b3c-9ee6-1dae6c04bd59", "metadata": {}, "source": [ - "# 扩展阅读:模型的层(Layer)\n", + "# 扩展:模型的层(Layer)\n", + "\n", + "模型组网中一个关键组成就是神经网络层,不同的神经网络层组合在一起,从输入的数据样本中习得数据内在规律,最终实现输出预测结果。每个层从前一层获得输入数据,然后输出结果作为下一层的输入,并且大多数层包含可调的参数,在反向传播梯度时更新参数。\n", + "\n", + "在飞桨框架中内置了丰富的神经网络层,用类(class)的方式表示,构建模型时可直接作为实例添加到子类中,只需设置一些必要的参数,并定义前向计算函数即可,反向传播和参数保存由框架自动完成。\n", + "\n", + "下面展开介绍几个常用的神经网络层。\n", + "\n", + "\n", "\n", "## Conv2D\n", - "[Conv2D](../../api/paddle/nn/Conv2D_cn.html#conv2d)主要用于对输入的特征图进行卷积操作,广泛用于深度学习网络中。Conv2D 根据输入、卷积核、步长(stride)、填充(padding)、空洞大小(dilations)等参数计算输出特征层大小。输入和输出是NCHW或NHWC格式,其中N是批尺寸,C是通道数,H是特征高度,W是特征宽度。" + "[Conv2D](../../api/paddle/nn/Conv2D_cn.html#conv2d) (二维卷积层)主要用于对输入的特征图进行卷积操作,广泛用于深度学习网络中。Conv2D 根据输入、卷积核、步长(stride)、填充(padding)、空洞大小(dilations)等参数计算输出特征层大小。输入和输出是 NCHW 或 NHWC 格式,其中 N 是 batchsize 大小,C 是通道数,H 是特征高度,W 是特征宽度。" ] }, { "cell_type": "code", "execution_count": 7, "id": "8a076b51", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -400,8 +414,8 @@ "source": [ "x = paddle.uniform((2, 3, 8, 8), dtype='float32', min=-1., max=1.)\n", "\n", - "conv = nn.Conv2D(3, 6, (3, 3), stride=2) #卷积层输入通道数为3,输出通道数为6,卷积核尺寸为3*3,步长为2\n", - "y = conv(x) #输入数据x\n", + "conv = nn.Conv2D(3, 6, (3, 3), stride=2) # 卷积层输入通道数为3,输出通道数为6,卷积核尺寸为3*3,步长为2\n", + "y = conv(x) # 输入数据x\n", "y = y.numpy()\n", "print(y.shape)" ] @@ -413,14 +427,16 @@ "source": [ "## MaxPool2D\n", "\n", - "[MaxPool2D](../../api/paddle/nn/MaxPool2D_cn.html#maxpool2d)主要用于缩小特征图大小,根据`kernel_size`参数这都的窗口大小,对指定窗口内特征图进行取最大值的操作。" + "[MaxPool2D](../../api/paddle/nn/MaxPool2D_cn.html#maxpool2d) (二维最大池化层)主要用于缩小特征图大小,根据 `kernel_size` 参数指定的窗口大小,对窗口内特征图进行取最大值的操作。" ] }, { "cell_type": "code", "execution_count": 9, "id": "ac0cacd8", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -446,14 +462,16 @@ "source": [ "## Linear\n", "\n", - "[Linear](../../api/paddle/nn/Linear_cn.html#linear)中每个神经元与上一层的所有神经元相连,实现对前一层的线性组合和线性变换。在卷积神经网络分类任务中,输出分类结果之前,通常采用全连接层对特征进行处理。" + "[Linear](../../api/paddle/nn/Linear_cn.html#linear) (全连接层)中每个神经元与上一层的所有神经元相连,实现对前一层的线性组合和线性变换。在卷积神经网络分类任务中,输出分类结果之前,通常采用全连接层对特征进行处理。" ] }, { "cell_type": "code", "execution_count": 10, "id": "b3bacc6f", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -477,14 +495,16 @@ "source": [ "## ReLU\n", "\n", - "[ReLU](../../api/paddle/nn/ReLU_cn.html#relu)是深度学习中常用的激活层,主要用于对输入进行非线性变换。ReLU将输入中小于0的部分变为0,大于0的部分保持不变。" + "[ReLU](../../api/paddle/nn/ReLU_cn.html#relu) 是深度学习任务中常用的激活层,主要用于对输入进行非线性变换。ReLU 将输入中小于 0 的部分变为 0,大于 0 的部分保持不变。" ] }, { "cell_type": "code", "execution_count": 8, "id": "7261f42c", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -507,31 +527,41 @@ "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", "metadata": {}, "source": [ - "# 扩展阅读:模型的参数(Parameter)\n", + "# 扩展:模型的参数(Parameter)\n", + "\n", + "在飞桨中,可通过下面的代码获取网络中在训练期间优化的所有参数\n", "\n", - "在飞桨中,可通过下面的代码获取网络中在训练期间优化的所有参数" + "\n", + "# 备注\n", + "1. 上面多补充一下参数的使用场景\n", + "2. 代码再跑一下结果\n", + "3. 下面的代码补充一小段代码解释" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "29bd4185", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-02-09T07:06:53.239755Z", + "iopub.status.busy": "2022-02-09T07:06:53.239227Z", + "iopub.status.idle": "2022-02-09T07:06:53.254632Z", + "shell.execute_reply": "2022-02-09T07:06:53.253562Z", + "shell.execute_reply.started": "2022-02-09T07:06:53.239722Z" + }, + "scrolled": true + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Layer: features.0.weight | Size: [6, 1, 3, 3]\n", - "Layer: features.0.bias | Size: [6]\n", - "Layer: features.3.weight | Size: [16, 6, 5, 5]\n", - "Layer: features.3.bias | Size: [16]\n", - "Layer: fc.0.weight | Size: [400, 120]\n", - "Layer: fc.0.bias | Size: [120]\n", - "Layer: fc.1.weight | Size: [120, 84]\n", - "Layer: fc.1.bias | Size: [84]\n", - "Layer: fc.2.weight | Size: [84, 10]\n", - "Layer: fc.2.bias | Size: [10]\n" + "ename": "NameError", + "evalue": "name 'lenet' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_173/2336593892.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlenet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Layer: {name} | Size: {param.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'lenet' is not defined" ] } ], @@ -543,9 +573,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -557,7 +587,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/images/data_pipeline.png b/docs/guides/02_paddle2.0_develop/images/data_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..8094d30c36a636981ead3ea079697d560ed8aa26 GIT binary patch literal 147904 zcmeFZbySr9w=a%}!k~f-Wl$$mQ`f1E!KYt5SAdEU9-d%yN;@BIY4krBUx`v4aM1LKawYcY8Yj2nU& z7}t$(ZUVnNjbOzD-mclni;H69^^h$CUz`n9C5)g@32N*ZdmjExJYY+eV 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line.strip().split('\\t')\n", " image_path = os.path.join(data_dir, image_path)\n", " self.data_list.append([image_path, label])\n", + " # 传入定义好的数据处理方法,作为自定义数据集类的一个属性\n", " self.transform = transform\n", "\n", " def __getitem__(self, index):\n", @@ -293,11 +290,12 @@ " image_path, label = self.data_list[index]\n", " # 读取灰度图\n", " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", - " # 图像数据格式转换为 float32,此步骤为必须\n", + " # 飞桨训练时内部数据格式默认为float32,将图像数据格式转换为 float32\n", " image = image.astype('float32')\n", + " # 应用数据处理方法到图像上\n", " if self.transform is not None:\n", " image = self.transform(image)\n", - " # Label格式转换为 int\n", + " # CrossEntropyLoss要求label格式为int,将Label格式转换为 int\n", " label = int(label)\n", " # 返回图像和对应标签\n", " return image, label\n", @@ -310,8 +308,9 @@ "\n", "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", "# 打印数据集样本数 \n", - "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)\n", - "print('MyDataset images: ',len(custom_dataset))" + "train_custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)\n", + "test_custom_dataset = MyDataset('mnist/val','mnist/val/label.txt', transform)\n", + "print('train_custom_dataset images: ',len(train_custom_dataset), 'test_custom_dataset images: ',len(test_custom_dataset))" ] }, { @@ -329,18 +328,12 @@ "\n", "另外,在 `__init__` 函数和 `__getitem__` 函数中还可实现一些数据预处理操作,如对图像的翻转、裁剪、归一化等操作,最终返回处理好的单条数据(样本数据、对应的标签),该操作可增加图像数据多样性,对增强模型的泛化能力带来帮助。飞桨框架在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下内置了几十种图像数据处理方法,详细使用方法可参考 [数据预处理](03_data_preprocessing_cn.html) 章节。\n", "\n", - "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代读取。\n", - "\n", - "# 备注\n", - "\n", - "1. 代码打印一下数据集训练集、测试集的样本数,并更新一下代码回显。\n", - "2. float32必须的原因解释一下。label转成int的原因解释一下。\n", - "\n" + "和内置数据集类似,可以使用下面的代码直接对自定义数据集进行迭代读取。" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "9d1570a3", "metadata": { "scrolled": true @@ -367,9 +360,7 @@ } ], "source": [ - "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)\n", - "\n", - "for data in custom_dataset:\n", + "for data in train_custom_dataset:\n", " image, label = data\n", " print('shape of image: ',image.shape)\n", " plt.title(str(label))\n", @@ -501,25 +492,96 @@ "以上示例代码中,定义了一个批采样器实例 `bs`,每轮迭代会返回一个 `batch_size` 大小的索引列表(示例中一轮迭代返回 8 个索引值),数据读取器 `train_loader` 通过 `batch_sampler=bs` 字段传入批采样器,即可根据这些索引获取对应的一组样本数据。另外可以看到,`batch_size`、`shuffle`、`drop_last`这三个参数只在 BatchSampler 中设定。\n", "\n", "\n", - "下面再通过一段代码示例,对比几个不同采样器的采样行为。\n", - "\n", - "# 备注\n", - "1. 补充多种sampler的用法代码和代码解释:\n", - "SequenceSampler, RandomSampler, BatchSampler, DistributedBatchSampler\n", - "参考:https://aistudio.baidu.com/aistudio/projectdetail/1299687?channelType=0&channel=0" + "下面再通过一段代码示例,对比几个不同采样器的采样行为。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "7d4c4622-b19a-47ad-a603-fc53c44650fa", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------顺序采样----------------\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", + "[20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n", + "[30, 31, 32, 33, 34, 35, 36, 37, 38, 39]\n", + "[40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\n", + "[50, 51, 52, 53, 54, 55, 56, 57, 58, 59]\n", + "[60, 61, 62, 63, 64, 65, 66, 67, 68, 69]\n", + "[70, 71, 72, 73, 74, 75, 76, 77, 78, 79]\n", + "[80, 81, 82, 83, 84, 85, 86, 87, 88, 89]\n", + "[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n", + "-----------------随机采样----------------\n", + "[44, 29, 37, 11, 21, 53, 65, 3, 26, 23]\n", + "[17, 4, 48, 84, 86, 90, 92, 76, 97, 69]\n", + "[35, 51, 71, 45, 25, 38, 32, 83, 22, 57]\n", + "[47, 55, 39, 46, 78, 61, 68, 66, 18, 41]\n", + "[77, 81, 15, 63, 91, 54, 24, 75, 59, 99]\n", + "[73, 88, 20, 43, 93, 56, 95, 60, 87, 72]\n", + "[70, 98, 1, 64, 0, 16, 33, 14, 80, 89]\n", + "[36, 40, 62, 50, 9, 34, 8, 19, 82, 6]\n", + "[74, 27, 30, 58, 31, 28, 12, 13, 7, 49]\n", + "[10, 52, 2, 94, 67, 96, 79, 42, 5, 85]\n", + "-----------------分布式采样----------------\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n", + "[40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\n", + "[60, 61, 62, 63, 64, 65, 66, 67, 68, 69]\n", + "[80, 81, 82, 83, 84, 85, 86, 87, 88, 89]\n" + ] + } + ], "source": [ "from paddle.io import SequenceSampler, RandomSampler, BatchSampler, DistributedBatchSampler\n", - "\n" + "\n", + "class RandomDataset(paddle.io.Dataset):\n", + " def __init__(self, num_samples):\n", + " self.num_samples = num_samples\n", + "\n", + " def __getitem__(self, idx):\n", + " image = np.random.random([784]).astype('float32')\n", + " label = np.random.randint(0, 9, (1, )).astype('int64')\n", + " return image, label\n", + "\n", + " def __len__(self):\n", + " return self.num_samples\n", + " \n", + "train_dataset = RandomDataset(100)\n", + "\n", + "print('-----------------顺序采样----------------')\n", + "sampler = SequenceSampler(train_dataset)\n", + "batch_sampler = BatchSampler(sampler=sampler, batch_size=10)\n", + "\n", + "for index in batch_sampler:\n", + " print(index)\n", + " \n", + "print('-----------------随机采样----------------')\n", + "sampler = RandomSampler(train_dataset)\n", + "batch_sampler = BatchSampler(sampler=sampler, batch_size=10)\n", + "\n", + "for index in batch_sampler:\n", + " print(index)\n", + "\n", + "print('-----------------分布式采样----------------')\n", + "batch_sampler = DistributedBatchSampler(train_dataset, num_replicas=2, batch_size=10)\n", + "\n", + "for index in batch_sampler:\n", + " print(index)" + ] + }, + { + "cell_type": "markdown", + "id": "970dad59", + "metadata": {}, + "source": [ + "可以看到顺序采样按照顺序输出各个样本的索引。随机采样先将样本顺序打乱,输出乱序后的下标。在分布式采样中,设置了`num_replicas=2`,样本会被划分到两张卡上,所以这里只输出一半样本的索引。" ] }, { @@ -542,9 +604,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -556,7 +618,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 4635df641f8..f3f8f6a0c79 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "93904999", "metadata": { "execution": { @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "69b80bc1", "metadata": { "scrolled": true @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "4a1a5cb3", "metadata": { "scrolled": true @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "45ea330a", "metadata": { "scrolled": true @@ -176,7 +176,7 @@ " \"\"\"\n", " def __init__(self, data_dir, label_path, transform=None):\n", " \"\"\"\n", - " 步骤二:实现 __init__ 函数,定义数据集大小\n", + " 步骤二:实现 __init__ 函数,初始化数据集,将样本和标签映射到列表中\n", " \"\"\"\n", " super(MyDataset, self).__init__()\n", " self.data_list = []\n", @@ -190,15 +190,20 @@ "\n", " def __getitem__(self, index):\n", " \"\"\"\n", - " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(训练数据、对应的标签)\n", + " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(样本数据、对应的标签)\n", " \"\"\"\n", + " # 根据索引,从列表中取出一个图像\n", " image_path, label = self.data_list[index]\n", + " # 读取灰度图\n", " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", + " # 飞桨训练时内部数据格式默认为float32,将图像数据格式转换为 float32\n", " image = image.astype('float32')\n", " # 应用数据处理方法到图像上\n", " if self.transform is not None:\n", " image = self.transform(image)\n", + " # CrossEntropyLoss要求label格式为int,将Label格式转换为 int\n", " label = int(label)\n", + " # 返回图像和对应标签\n", " return image, label\n", "\n", " def __len__(self):\n", @@ -206,7 +211,8 @@ " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", " \"\"\"\n", " return len(self.data_list)\n", - " \n", + "\n", + "transform = Compose([RandomRotation(10), Resize(size=32)])\n", "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)" ] }, @@ -215,10 +221,7 @@ "id": "e8990b3f-3a00-4d03-a99a-88185da6964c", "metadata": {}, "source": [ - "在以上示例代码中,定义了xxxx数据处理方法,并传入 `__init__` 函数,然后在 `__getitem__` 中将其应用到图像上。\n", - "\n", - "# 备注\n", - "1. 代码中补充数据处理方法的定义,比如里面包含图像旋转、归一化,再补充文字说明一下。" + "在以上示例代码中,定义了随机旋转和改变图片大小数据处理方法,并传入 `__init__` 函数,然后在 `__getitem__` 中将其应用到图像上。" ] }, { @@ -230,17 +233,14 @@ "\n", "通过可视化的方法,可方便地对比飞桨内置数据处理方法的效果,下面介绍其中几个方法的对比示例。\n", "\n", - "### RandomRotation:\n", - "\n", - "依据degrees参数指定的角度范围,按照均匀分布随机产生一个角度对图像进行旋转。\n", + "### CenterCrop:\n", "\n", - "# 备注\n", - "1. 换一个花朵的公开数据集图片,翻转一个、裁剪一个、变换颜色一个。" + "对输入图像进行裁剪,保持图片中心点不变。" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "76edf274", "metadata": { "scrolled": true @@ -249,16 +249,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -270,22 +270,23 @@ } ], "source": [ + "import cv2\n", "import numpy as np\n", "from PIL import Image\n", "from matplotlib import pyplot as plt\n", - "from paddle.vision.transforms import RandomRotation\n", + "from paddle.vision.transforms import CenterCrop\n", "\n", - "transform = RandomRotation(90)\n", + "transform = CenterCrop(224)\n", "\n", - "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "image = cv2.imread('images/flower.jpg')\n", "\n", - "RandomRotation_image = transform(image)\n", + "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", "plt.title('origin image')\n", "plt.imshow(image)\n", "plt.subplot(1,2,2)\n", - "plt.title('RandomRotation image')\n", - "plt.imshow(RandomRotation_image)" + "plt.title('CenterCrop image')\n", + "plt.imshow(image_after_transform)" ] }, { @@ -300,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "f6adefc6", "metadata": { "scrolled": true @@ -309,16 +310,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -337,15 +338,15 @@ "\n", "transform = RandomHorizontalFlip(0.5)\n", "\n", - "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "image = cv2.imread('images/flower.jpg')\n", "\n", - "RandomHorizontalFlip_image = transform(image)\n", + "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", "plt.title('origin image')\n", "plt.imshow(image)\n", "plt.subplot(1,2,2)\n", "plt.title('RandomHorizontalFlip image')\n", - "plt.imshow(RandomHorizontalFlip_image)" + "plt.imshow(image_after_transform)" ] }, { @@ -353,14 +354,14 @@ "id": "c8272853", "metadata": {}, "source": [ - "### RandomVerticalFlip\n", + "### ColorJitter\n", "\n", - "基于概率来执行图片的垂直翻转。" + "随机调整图像的亮度,对比度,饱和度和色调。" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "id": "470047b1", "metadata": { "scrolled": true @@ -369,16 +370,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -393,19 +394,19 @@ "import numpy as np\n", "from PIL import Image\n", "from matplotlib import pyplot as plt\n", - "from paddle.vision.transforms import RandomVerticalFlip\n", + "from paddle.vision.transforms import ColorJitter\n", "\n", - "transform = RandomVerticalFlip(0.5)\n", + "transform = ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n", "\n", - "image = cv2.imread('mnist/train/imgs/0/0.jpg', cv2.IMREAD_GRAYSCALE)\n", + "image = cv2.imread('images/flower.jpg')\n", "\n", - "RandomVerticalFlip_image = transform(image)\n", + "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", "plt.title('origin image')\n", "plt.imshow(image)\n", "plt.subplot(1,2,2)\n", - "plt.title('RandomVerticalFlip image')\n", - "plt.imshow(RandomVerticalFlip_image)" + "plt.title('ColorJitter image')\n", + "plt.imshow(image_after_transform)" ] }, { @@ -434,9 +435,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -448,7 +449,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 8eb07a66d8f..416081ee59a 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "a90c961d-6bd8-4d07-9a0f-44c25da7c023", "metadata": { "execution": { @@ -200,7 +200,7 @@ " Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 \n", " ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 \n", " MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 \n", - " Flatten-2 [[1, 16, 5, 5]] [1, 400] 0 \n", + " Flatten-1 [[1, 16, 5, 5]] [1, 400] 0 \n", " Linear-4 [[1, 400]] [1, 120] 48,120 \n", " Linear-5 [[1, 120]] [1, 84] 10,164 \n", " Linear-6 [[1, 84]] [1, 10] 850 \n", @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "8a076b51", "metadata": { "scrolled": true @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "ac0cacd8", "metadata": { "scrolled": true @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "b3bacc6f", "metadata": { "scrolled": true @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "7261f42c", "metadata": { "scrolled": true @@ -529,18 +529,14 @@ "source": [ "# 扩展:模型的参数(Parameter)\n", "\n", - "在飞桨中,可通过下面的代码获取网络中在训练期间优化的所有参数\n", + "在飞桨中,可通过网络的`parameters()`和`named_parameters()`方法获取网络中在训练期间优化的所有参数,更加这些方法可以对网络进行更加精细化的控制,如设置某些层的参数不更新。\n", "\n", - "\n", - "# 备注\n", - "1. 上面多补充一下参数的使用场景\n", - "2. 代码再跑一下结果\n", - "3. 下面的代码补充一小段代码解释" + "下面通过一段代码来演示`named_parameters()`的使用,在代码中,获取了lenet所有参数的名字和值,并且打印出了参数的名字和shape。" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "29bd4185", "metadata": { "execution": { @@ -554,14 +550,19 @@ }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'lenet' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_173/2336593892.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlenet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Layer: {name} | Size: {param.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'lenet' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Layer: features.0.weight | Size: [6, 1, 3, 3]\n", + "Layer: features.0.bias | Size: [6]\n", + "Layer: features.3.weight | Size: [16, 6, 5, 5]\n", + "Layer: features.3.bias | Size: [16]\n", + "Layer: fc.0.weight | Size: [400, 120]\n", + "Layer: fc.0.bias | Size: [120]\n", + "Layer: fc.1.weight | Size: [120, 84]\n", + "Layer: fc.1.bias | Size: [84]\n", + "Layer: fc.2.weight | Size: [84, 10]\n", + "Layer: fc.2.bias | Size: [10]\n" ] } ], @@ -573,9 +574,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -587,7 +588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, From e6f16a0117ad63c003409281b6b2d3c16ea23a94 Mon Sep 17 00:00:00 2001 From: moguguo Date: Thu, 10 Feb 2022 12:07:13 +0800 Subject: [PATCH 36/63] Update 01_quick_start_cn.ipynb MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 625195665dd..79428317cc8 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -406,7 +406,7 @@ "模型训练需完成如下步骤:\n", "\n", "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。** 将网络结构组合成可快速使用 [飞桨高层 API](../../practices/quick_start/high_level_api.html) 进行训练、评估、推理的实例,方便后续操作。\n", - "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn](../../api/paddle/nn/Overview_cn.html#loss) 下提供了损失函数相关 API,在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", + "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。** 包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API,在 [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) 提供了损失函数相关 API,在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等,执行后将自动完成模型的训练循环。\n", "\n", "\n", From fc02f2309f822ce1b33ba5830fead44f82787846 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Mon, 14 Feb 2022 15:06:16 +0800 Subject: [PATCH 37/63] update model_develop 05 and 07 --- .../05_train_eval_predict_cn.ipynb | 209 +++++++++++------- .../07_customize_cn.ipynb | 91 +++----- 2 files changed, 163 insertions(+), 137 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb index 439cee17231..1807d86d7cf 100644 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -5,37 +5,22 @@ "id": "f97a8a96", "metadata": {}, "source": [ - "-----------------\n", - "1. 训练前准备\n", - " 1. 指定训练的设备\n", - " 1. 准备训练用的数据集和模型\n", - "1. 通过 paddle.Model 高层 API 训练与评估验证\n", - " 1. 使用 paddle.Model 封装模型\n", - " 1. 使用 Model.prepare 配置训练准备参数\n", - " 1. 损失函数\n", - " 1. 优化器\n", - " 1. 评价指标\n", - " 1. 使用 Model.fit 训练模型\n", - " 1. 使用 Model.evaluate 评估模型\n", - " 1. 使用 Model.predict 执行推理\n", - " \n", - "1. 通过基础 API 训练与评估验证\n", - " 1. 模型训练(拆解 Model.prepare、Model.fit)\n", - " 1. 模型评估(拆解 Model.evaluate)\n", - " 1. 模型推理(拆解 Model.predict)\n", + "# 模型训练、评估与推理\n", "\n", - "1. 扩展阅读:恢复训练---补FAQ,并在本文加引用\n", - "1. 扩展阅读:欠拟合和过拟合---补FAQ,并在本文加引用\n", - "1. 扩展阅读:自定义LOSS、Metric、Callback,优化器不能自定义?自定义哪些东西的界限怎么划的?\n", - "1. 扩展阅读:训练过程可视化分析---独立一篇并在本文加引用\n", + "在准备好数据集和模型后,就可以将数据送入模型中启动训练评估了,概括地讲包括如下几步:\n", + "1. **模型训练**:训练包括多轮迭代(epoch),每轮迭代遍历一次训练数据集,并且每次从中获取一小批(mini-batch)样本,送入模型执行前向计算得到预测值,并计算预测值(predict_label)与真实值(true_label)之间的损失函数值(loss)。执行梯度反向传播,并根据设置的优化算法(optimizer)更新模型的参数。观察每轮迭代的 loss 值减小趋势,可判断模型训练效果。\n", + "2. **模型评估**:将测试数据集送入训练好的模型进行评估,得到预测值,计算预测值与真实值之间的损失函数值(loss),并计算评价指标值(metric),便于评估模型效果。\n", + "3. **模型推理**:将待验证的数据(样本)送入训练好的模型执行推理,观察并验证推理结果(标签)是否符合预期。\n", "\n", "\n", - "# 模型训练、评估与推理\n", + "飞桨框架提供了两种训练、评估与推理的方法:\n", "\n", - "在完成数据预处理,数据加载与模型的组建后,你就可以进行模型的训练与推理了。训练时通过很多个循环(epoch)训练模型,每轮会将输入数据传入定义好的模型,得到预测值。预测值将与label做loss,然后loss进行反向传播,之后再通过优化器优化网络的参数。推理时只需要将数据输入训练好的网络,得到预测值。飞桨主框架提供了两种训练与推理的方法,一种是用 [paddle.Model](../api/paddle/Model_cn.html) 对模型进行封装,通过高层API如 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 、 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 、 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 等完成模型的训练、评估与推理;另一种就是基于基础API常规的训练方式。通过高层API完成模型的训练、评估与推理,可以省略很多复杂的步骤,适合新手上手,但是各个参数必须符合API的格式;另一种就是基于基础API常规的训练方式,这种方法比较灵活,但是需要实现训练中所有的步骤设定所有的流程。\n", + "- **使用飞桨高层 API**:先用 [paddle.Model](../api/paddle/Model_cn.html) 对模型进行封装,然后通过 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 、 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 、 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 等完成模型的训练、评估与推理。该方式代码量少,适合快速上手。\n", "\n", - "高层API实现的模型训练、评估与推理如 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都可以通过基础API实现,本文先介绍高层API的训练方式,然后会将高层API拆解为基础API的方式,方便对比学习。\n", - "\n" + "- **使用飞桨基础 API**:提供了损失函数、优化器、评价指标、更新参数、反向传播等基础组件的实现,可以更灵活地应用到模型训练、评估与推理任务中,当然也可以很方便地自定义一些组件用于相关任务中。\n", + "\n", + "\n", + "高层 API 如 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 等都可以通过基础 API 实现,本文先介绍高层 API 的使用方式,然后将高层 API 拆解为基础 API 介绍,方便对比学习。\n" ] }, { @@ -66,11 +51,11 @@ "id": "bc9cf191-82e7-47e0-b6c5-bf9c55802b3f", "metadata": {}, "source": [ - "### 1.1 指定训练的硬件\n", + "### 1.1 (可选)指定训练的硬件\n", "\n", - "模型训练时,需要用到 CPU、 GPU 等计算处理器资源,由于飞桨框架的安装包是区分处理器类型的,默认情况下飞桨框架会根据所安装的版本自动选择对应硬件,比如安装的 GPU 版本的飞桨,则自动使用 GPU 训练模型,无需手动指定。\n", + "模型训练时,需要用到 CPU、 GPU 等计算处理器资源,由于飞桨框架的安装包是区分处理器类型的,默认情况下飞桨框架会根据所安装的版本自动选择对应硬件,比如安装的 GPU 版本的飞桨,则自动使用 GPU 训练模型,无需手动指定。因此一般情况下,无需执行此步骤。\n", "\n", - "但是如果安装的 GPU 版本的飞桨框架,想切换到 CPU 上训练,则可通过 [paddle.device.set_device](../api/paddle/device/set_device_cn.html#set-device) API 修改,如果本机有多个 GPU 卡,也可以通过该 API 选择指定的卡进行训练。" + "但是如果安装的 GPU 版本的飞桨框架,想切换到 CPU 上训练,则可通过 [paddle.device.set_device](../api/paddle/device/set_device_cn.html#set-device) 修改,如果本机有多个 GPU 卡,也可以通过该 API 选择指定的卡进行训练。" ] }, { @@ -101,8 +86,6 @@ ], "source": [ "import paddle\n", - "import numpy as np\n", - "from paddle.vision.transforms import ToTensor\n", "\n", "# 指定在 CPU 上训练\n", "paddle.device.set_device('cpu')\n", @@ -116,9 +99,23 @@ "id": "28d8eeac-909d-4152-b223-89a3da9e4fc6", "metadata": {}, "source": [ + "需要注意的是,使用 `paddle.device.set_device` 时,只能使用 `CUDA_VISIBLE_DEVICES` 设置范围内的显卡,例如可以设置`export CUDA_VISIBLE_DEVICES=0,1,2` 和 `paddle.device.set_device('gpu:0')`,但是设置 `export CUDA_VISIBLE_DEVICES=1` 和 `paddle.device.set_device('gpu:0')` 时会冲突报错。\n", + "\n", + "\n", + "> 注:\n", "> * 本文仅以单机单卡场景为例,介绍模型训练的方法,如果需要使用单机多卡、多机多卡训练,请参考如下章节:[单机多卡训练](06_device_cn.html)、[分布式训练](./06_distributed_training/distributed_introduction.html)。\n", "> * 飞桨框架除了支持在 CPU、GPU 上训练,还支持在百度昆仑 XPU、华为昇腾 NPU 等 AI 计算处理器上训练,对应的训练指导请参考 [硬件支持](./09_hardware_support/index_cn.html) 章节。\n", - "> * 注意使用 `paddle.device.set_device` 时,只能指定CUDA_VISIBLE_DEVICES可见的显卡,例如同时设置 `CUDA_VISIBLE_DEVICES=1` 和 `paddle.device.set_device('gpu:0')` 时会冲突报错" + "\n", + "\n", + "# 备注\n", + "\n", + "2. 这俩改放到对应需要的位置,另外totensor改为normalize\n", + "\n", + "import numpy as np\n", + "\n", + "from paddle.vision.transforms import ToTensor\n", + "\n", + "___确认下哪里需要np,补充代码。\n" ] }, { @@ -128,7 +125,7 @@ "source": [ "### 1.2 准备训练用的数据集和模型\n", "\n", - "模型训练前,需要先完成数据集的加载和模型组网,以 MNIST 手写数字识别任务为例,代码示例如下:" + "模型训练前,需要先完成数据集的加载和模型组网,以 MNIST 手写数字识别任务为例,代码示例如下:\n" ] }, { @@ -147,12 +144,14 @@ }, "outputs": [], "source": [ + "from paddle.vision.transforms import Normalize\n", "\n", + "transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')\n", "# 加载 MNIST 训练集和测试集\n", - "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())\n", - "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)\n", "\n", - "# 使用 Sequential 进行模型组网\n", + "# 模型组网,构建并初始化一个模型 mnist\n", "mnist = paddle.nn.Sequential(\n", " paddle.nn.Flatten(1, -1), \n", " paddle.nn.Linear(784, 512), \n", @@ -169,7 +168,7 @@ "source": [ "\n", "\n", - "## 二、通过 paddle.Model 高层 API 训练、评估与推理\n", + "## 二、使用 paddle.Model 高层 API 训练、评估与推理\n", "\n", "\n", "以手写数字识别任务为例,使用高层 API 进行模型训练、评估与推理的步骤如下:\n" @@ -201,7 +200,7 @@ }, "outputs": [], "source": [ - "# 封装模型,便于进行后续的训练、评估和推理\n", + "# 封装模型为一个 model 实例,便于进行后续的训练、评估和推理\n", "model = paddle.Model(mnist)" ] }, @@ -212,11 +211,17 @@ "source": [ "### 2.2 使用 Model.prepare 配置训练准备参数\n", "\n", - "用 `paddle.Model` 完成模型的封装后,在训练前,需要对模型进行配置,通过 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 接口来对训练进行提前的配置准备工作,包括设置模型优化器,Loss计算方法,精度计算方法等。\n", + "用 `paddle.Model` 完成模型的封装后,需通过 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 进行训练前的配置准备工作,包括设置优化算法、Loss 计算方法、评价指标计算方法:\n", + "\n", + "- **优化器(optimizer)**:即寻找最优解的方法,可计算和更新梯度,并根据梯度更新模型参数。飞桨框架在 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer) 下提供了优化器相关 API。\n", + "- **损失函数(loss)**:用于评估模型的预测值和真实值的差距,模型训练过程即取得尽可能小的 loss 的过程。飞桨框架在 [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) 提供了适用不同深度学习任务的损失函数相关 API。\n", + "- **评价指标(metrics)**:用于评估模型的好坏,不同的任务通常有不同的评价指标。飞桨框架在 [paddle.metric](../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", + "\n", + "\n", + "# 备注\n", + "1. 学习率这个重要的超参,补充代码和介绍\n", "\n", - "* 优化器(optimizer)用于计算和更新梯度,优化器能够保存参数状态并根据梯度更新传入优化器的参数,这里我们使用常用的Adam优化器 `paddle.optimizer.Adam` ,并传入封装好的模型全部参数 `model.parameters` 。更多优化器API详见 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer);\n", - "* 损失函数(loss)用来评价模型的预测值和真实值不一样的程度,在这里我们使用交叉熵损失函数 `paddle.nn.CrossEntropyLoss` 。更多Loss API详见 [Loss层](../api/paddle/nn/Overview_cn.html#loss-layers);\n", - "* 评价指标(metrics)用于评估模型的好坏,不同的任务通常有不同的评价指标,本任务中我们使用分类任务常用的准确率指标 `paddle.metric.Accuracy` 。更多评估器API详见 [paddle.metric](../api/paddle/metric/Overview_cn.html)。\n" + "————补充下学习率的介绍,代码也补充一个" ] }, { @@ -241,6 +246,14 @@ " metrics=paddle.metric.Accuracy())" ] }, + { + "cell_type": "markdown", + "id": "6a73ec3f-8e7a-40ab-bf92-01ba7247c507", + "metadata": {}, + "source": [ + "示例中使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam) 优化器,并传入封装好的全部模型参数 `model.parameters` 用于后续更新;使用交叉熵损失函数 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) 用于分类任务评估;使用分类任务常用的准确率指标 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) 计算模型在训练集上的精度。" + ] + }, { "cell_type": "markdown", "id": "5da3322e", @@ -248,13 +261,14 @@ "source": [ "### 2.3 使用 Model.fit 训练模型\n", "\n", - "做好模型训练的前期准备工作后,调用 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 接口来启动训练过程,需要指定至少3个关键参数:训练数据集,训练轮次和单次训练数据批次大小:\n", + "做好模型训练的前期准备工作后,调用 [Model.fit](../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 接口来启动训练。\n", + "训练过程采用二层循环嵌套方式:内层循环完成整个数据集的一次遍历,采用分批次方式;外层循环根据设置的训练轮次完成数据集的多次遍历。因此需要指定至少三个关键参数:训练数据集,训练轮次和每批次大小:\n", "\n", - "* 训练数据集:传入之前定义好的训练数据集;\n", - "* 训练轮次(epoch):训练时遍历数据集的次数;\n", - "* 批次大小(batch_size):通常情况下,数据集需要分批读取训练,设定每个批次数据的大小。\n", + "- **训练数据集**:传入之前定义好的训练数据集。\n", + "- **训练轮次(epoch)**:训练时遍历数据集的次数,即外循环轮次。\n", + "- **批次大小(batch_size)**:内循环中每个批次的训练样本数。\n", "\n", - "除此之外,还可以传入 `Callback` 参数,这个参数可以在模型训练的各个阶段进行一些自定义操作,详见第7章。\n" + "除此之外,还可以设置样本乱序(`shuffle`)、丢弃不完整的批次样本(`drop_last`)、同步/异步读取数据(`num_workers`) 等参数,另外可通过 `Callback` 参数传入回调函数,在模型训练的各个阶段进行一些自定义操作,比如收集训练过程中的一些数据和参数,详细介绍可参见 [自定义 Callback](07_customize_cn.html) 章节。" ] }, { @@ -312,6 +326,15 @@ " verbose=1)" ] }, + { + "cell_type": "markdown", + "id": "74aa5dcd-70f7-45de-b2d4-3e5527bb8458", + "metadata": {}, + "source": [ + "示例中传入数据集 `train_dataset` 进行迭代训练,共遍历 5 轮(`epochs=5`),每轮迭代中分批次取数据训练,每批次 64 个样本(`batch_size=64`),并打印训练过程中的日志(`verbose=1`)。\n", + "从打印日志中可观察到损失函数 loss 值减小,精度指标 acc 值提高的趋势,说明模型训练取得了成效。" + ] + }, { "cell_type": "markdown", "id": "eeff5f00", @@ -319,12 +342,12 @@ "source": [ "### 2.4 使用 Model.evaluate 评估模型\n", "\n", - "对于训练好的模型进行评估可以使用 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 接口,事先定义好用于评估使用的数据集后,直接调用 `evaluate` 接口即可完成模型评估操作,结束后根据在 `prepare` 中 `loss` 和 `metric` 的定义来进行相关评估结果计算返回。\n", + "训练好模型后,可在事先定义好的测试数据集上,使用 [Model.evaluate](../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) 接口完成模型评估操作,结束后根据在 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 中定义的 `loss` 和 `metric` 计算并返回相关评估结果。\n", "\n", "返回格式是一个字典:\n", "* 只包含loss, `{'loss': xxx}` \n", "* 包含loss和一个评估指标, `{'loss': xxx, 'metric name': xxx}` \n", - "* 包含loss和多个评估指标, `{'loss': xxx, 'metric name1': xxx, 'metric name2': xxx}` " + "* 包含loss和多个评估指标, `{'loss': xxx, 'metric name1': xxx, 'metric name2': xxx}`\n" ] }, { @@ -359,6 +382,14 @@ "print(eval_result)" ] }, + { + "cell_type": "markdown", + "id": "62c8053a-bdee-4539-8df7-646919d22805", + "metadata": {}, + "source": [ + "示例中返回一个 loss 和 一个 acc 准确率指标的结果。在模型之前未\"见过\"的测试集上,评估出仍然有 98.1% 的准确率,验证了模型在该任务上取得不错的效果。" + ] + }, { "cell_type": "markdown", "id": "109ac763", @@ -366,14 +397,27 @@ "source": [ "### 2.5 使用 Model.predict 执行推理\n", "\n", - "高层API中提供了 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 接口来方便用户对训练好的模型进行推理验证,只需要基于训练好的模型将需要进行推理验证的数据放到接口中进行计算即可,接口会将经过模型计算得到的预测结果进行返回。\n", + "高层 API 中提供了 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 接口,可对训练好的模型进行推理验证。只需传入待执行推理验证的样本数据,即可计算并返回推理结果。\n", "\n", - "返回格式是一个list,元素数目对应模型的输出数目:\n", + "返回格式是一个列表,元素数目对应模型的输出数目:\n", "* 模型是单一输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)]`\n", "* 模型是多输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), …]`\n", "\n", "numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。\n", "\n", + "\n", + "# 备注\n", + "1. 这里有两个数目,待确认是什么区别?\n", + "\n", + "返回格式是一个列表,元素数目对应模型的输出数目:\n", + "\n", + "数目对应预测数据集的数目\n", + "\n", + "——————更新一下描述使更清晰\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, @@ -428,6 +472,7 @@ } ], "source": [ + "\n", "# 用 predict 在测试集上对模型进行推理\n", "test_result = model.predict(test_dataset)\n", "# 由于模型是单一输出,test_result的形状为[1, 10000],10000是测试数据集的数据量。这里打印第一个数据的结果,这个数组表示每个数字的预测概率\n", @@ -449,15 +494,24 @@ "id": "a3110884-fc0f-41e5-901e-40d136eaab6c", "metadata": {}, "source": [ - "### 2.6 其他高层API\n", "\n", - "除了上面介绍的三个API之外, `paddle.Model` 类也提供了其他与训练、评估与推理相关的API:\n", + "示例中对测试集 `test_dataset` 中每一个样本执行预测,测试数据集中包含 10000 个数据,因此将取得 10000 个预测输出。\n", + "\n", + "打印第一个样本数据的预测输出,可以看到,在手写数字识别任务中,经过模型的计算得到一个数组 [[ -6.5593615 -6.4680595 -1.4708003 2.1043894 -11.743436 -4.4516582\n", + " -14.733968 12.036645 -6.582403 -1.8672216]],数组中的数字表示对应分类(0~9)的预测概率,取概率最高的值(12.036645)的下标(对应 label 7),即得到该样本数据的预测结果(pred label: 7),可视化该样本图像(true label: 7),与预测结果一致,说明模型准确预测了样本图像上的数字。\n", + "\n", + "\n", + "\n", + "除了上面介绍的三个 API 之外, `paddle.Model` 类也提供了其他与训练、评估与推理相关的 API:\n", "\n", "* [Model.train_batch](../api/paddle/Model_cn.html#train-batch-inputs-labels-none):在一个批次的数据集上进行训练;\n", "* [Model.eval_batch](../api/paddle/Model_cn.html#eval-batch-inputs-labels-none):在一个批次的数据集上进行评估;\n", "* [Model.predict_batch](../api/paddle/Model_cn.html#predict-batch-inputs):在一个批次的数据集上进行推理。\n", "\n", - "这三个API与上面介绍的三个API的输入数据的维度有所不同,需要用户在实际应用场景进行判断使用。" + "这三个 API 与上面介绍的三个 API 的输入数据的维度有所不同,详细介绍可参考对应 API 文档。\n", + "\n", + "# 备注\n", + "2. 为啥概率是负的,为啥概率会超过1?12.多?----- 改一下示例结果的解读" ] }, { @@ -465,11 +519,12 @@ "id": "9508034b", "metadata": {}, "source": [ - "## 三、通过基础 API 训练与评估验证\n", + "## 三、使用基础 API 训练、评估与推理\n", "\n", - "除了通过第一部分的高层API实现模型的训练与预测,飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础API封装而来。下面通过拆解高层API到基础API的方式,来了解如何用基础API完成模型的训练与预测。\n", + "除了通过高层 API 实现模型的训练、评估与推理,飞桨框架也同样支持通过基础 API。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础 API 封装而来。下面通过拆解高层 API 到基础 API 的方式,来了解如何用基础 API 完成模型训练、评估与推理。\n", "\n", - "(待补充:高层API实现不了的功能)" + "# 备注\n", + "1. 都用前面那一个网络,同步更新下面基础API的代码" ] }, { @@ -507,18 +562,19 @@ "source": [ "### 3.1 模型训练(拆解 Model.prepare、Model.fit)\n", "\n", - "飞桨框架通过基础API对模型进行训练,对应第一部分的 `Model.prepare` 与 `Model.fit` 。模型训练一般包括如下几个步骤:\n", + "飞桨框架通过基础 API 对模型进行训练,对应高层 API 的 `Model.prepare` 与 `Model.fit` ,一般包括如下几个步骤:\n", "\n", - "1. 加载训练数据集、声明模型、设置模型为 `train` 模式\n", + "1. 加载训练数据集、声明模型、设置模型实例为 `train` 模式\n", "1. 设置优化器、损失函数与各个超参数\n", - "1. 从DataLoader获取一批次训练数据\n", - "1. 执行一次预测,即从模型获得输入数据的预测值\n", - "1. 计算预测值与数据集标签的损失\n", - "1. 计算预测值与数据集标签的准确率\n", - "1. 将损失进行反向传播\n", - "1. 打印模型的轮数、批次、损失值、准确率等信息\n", - "1. 执行一次优化器步骤,也就是根据我们选择的优化算法,根据当前批次数据的梯度更新传入优化器的参数\n", - "1. 将优化器的梯度进行清零\n", + "1. 设置模型训练的二层循环嵌套,并在内层循环嵌套中设置如下内容\n", + " - 3.1 从 DataLoader 获取一批次训练数据\n", + " - 3.2 执行一次预测,即从模型获得输入数据的预测值\n", + " - 3.3 计算预测值与数据集标签的损失\n", + " - 3.4 计算预测值与数据集标签的准确率\n", + " - 3.5 将损失进行反向传播\n", + " - 3.6 打印模型的轮数、批次、损失值、准确率等信息\n", + " - 3.7 执行一次优化器步骤,即按照选择的优化算法,根据当前批次数据的梯度更新传入优化器的参数\n", + " - 3.8 将优化器的梯度进行清零\n", " \n" ] }, @@ -591,11 +647,11 @@ "source": [ "### 3.2 模型评估(拆解 Model.evaluate)\n", "\n", - "飞桨框架通过基础API对模型进行验证,对应第一部分的 `Model.evaluate` 。与模型训练相比,模型评估的流程有如下几点不同之处:\n", + "飞桨框架通过基础 API 对训练好的模型进行评估,对应高层 API 的 `Model.evaluate` 。与模型训练相比,模型评估的流程有如下几点不同之处:\n", "\n", - "* 加载的数据从训练数据集改为测试数据集\n", - "* 模型实例从 `train` 模式改为 `eval` 模式\n", - "* 不需要反向传播、优化器参数更新和优化器梯度清零\n", + "1. 加载的数据从训练数据集改为测试数据集\n", + "1. 模型实例从 `train` 模式改为 `eval` 模式\n", + "1. 不需要反向传播、优化器参数更新和优化器梯度清零\n", "\n" ] }, @@ -649,11 +705,11 @@ "source": [ "### 3.3 模型推理(拆解 Model.predict)\n", "\n", - "飞桨框架通过基础API对模型进行推理,对应第一部分的 `Model.predict` 。模型的推理过程相对独立,是在模型训练与评估之后单独进行的步骤。只需要执行如下步骤:\n", + "飞桨框架通过基础 API 对训练好的模型执行推理,对应高层 API 的 `Model.predict` 。模型的推理过程相对独立,是在模型训练与评估之后单独进行的步骤。只需要执行如下步骤:\n", "\n", - "* 加载测试数据集,并将模型设置为 `eval` 模式\n", - "* 读取测试数据并获得预测结果\n", - "* 对预测结果进行后处理" + "1. 加载待执行推理的测试数据,并将模型设置为 `eval` 模式\n", + "1. 读取测试数据并获得预测结果\n", + "1. 对预测结果进行后处理" ] }, { @@ -725,7 +781,8 @@ "source": [ "# 四、总结\n", "\n", - "待补充" + "本节中介绍了在飞桨框架中使用高层 API 进行模型训练、评估和推理的方法,并拆解出对应的基础 API 实现方法。实际应用中,飞桨的高层 API 和基础 API 可以组合使用,并不是完全割裂开的,这样有助于开发者更便捷地完成算法迭代。\n", + "\n" ] } ], diff --git a/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb index 82bc45fe8fa..40bedf93810 100644 --- a/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/07_customize_cn.ipynb @@ -5,26 +5,25 @@ "id": "83e96f0c", "metadata": {}, "source": [ - "# 自定义Loss、Metric、Optimizer及Callback\n", + "# 自定义Loss、Metric 及 Callback\n", "\n", - "除了使用飞桨框架内置的API,飞桨框架还支持用户根据自己的实际场景,完成Loss、Metric、Optimizer及Callback的自定义。\n", + "除了使用飞桨框架内置的 API,有时会需要根据实际场景,自定义 Loss、Metric 及 Callback 来使用,本节介绍在飞桨框架中自定义的方法。\n", "\n", "## 一、自定义损失函数 Loss\n", "\n", "### 1.1 损失函数介绍\n", "\n", - "在深度学习中,损失函数是用来评估模型的预测结果与真实结果之间的差距,一般用L表示,损失函数越小,模型的鲁棒性就越好。\n", + "损失函数用来评估模型的预测结果与真实结果之间的差距,损失函数越小,模型的鲁棒性就越好。模型训练的过程其实是对损失函数采用梯度下降的方法,使得损失函数不断减小到局部最优值,而得到对任务来说比较合理的模型参数。\n", "\n", - "模型训练的过程其实是对损失函数的函数图形采用梯度下降的方法来使得损失函数不断减小到局部最优值,来得到对任务来说比较合理的模型参数。\n", + "一般在深度学习任务中,有许多常用的损失函数,例如在图像分类任务中常用的交叉熵损失函数,在目标检测任务中常用的 Focal loss、L1/L2 损失函数等,在图像识别任务中常用的 Triplet Loss 以及 Center Loss 等。如果框架中提供的损失函数不能满足试图解决的业务问题,可按照框架的 API 结构要求自定义损失函数。\n", "\n", - "一般在深度学习框架中,有许多常用的损失函数,例如在图像分类任务中,我们常常使用交叉熵损失,在目标检测任务中,常常使用Focal loss、L1/L2损失函数等,在图像识别任务中,我们经常会使用到Triplet Loss以及Center Loss等。然而,这些损失函数API有的时候可能不太适合我们试图解决的业务问题,因此自定义损失函数应运而生。\n", - "\n", - "### 1.2 自定义Loss\n", - "飞桨中实现自定义Loss的方法和使用 `paddle.nn.Layer` 组网的方法类似,包括三个步骤:\n", + "### 1.2 自定义 Loss 步骤\n", + "飞桨框架中实现自定义 Loss 的方法和使用 [paddle.nn.Layer](../api/paddle/nn/Layer_cn.html) 进行模型组网的方法类似,包括三个步骤:\n", "\n", "1. 创建一个继承自 `paddle.nn.Layer` 的类;\n", "1. 在类的构造函数 `__init__` 中定义需要的参数;\n", "1. 在类的前向计算函数 `forward` 中进行损失函数计算。\n", + "\n", "\n" ] }, @@ -54,9 +53,9 @@ " 3. 实现forward函数,forward在调用时会传递两个参数:x和label\n", " - x:单个或批次训练数据经过模型前向计算输出结果\n", " - label:单个或批次训练数据对应的标签数据\n", - " 接口返回值是一个Tensor,根据自定义的逻辑加和或计算均值后的损失\n", + " 接口返回值是一个Tensor,根据需要将所有x和label计算得到的loss值求和或取均值\n", " \"\"\"\n", - " # 使用Paddle中相关API自定义的计算逻辑\n", + " # 返回forword中计算的结果\n", " # output = xxxxx\n", " # return output" ] @@ -66,7 +65,7 @@ "id": "80390e85", "metadata": {}, "source": [ - "接下来以交叉熵损失为例说明如何自定义损失函数:" + "下面是定义交叉熵损失函数 Loss 的示例:" ] }, { @@ -99,23 +98,21 @@ "\n", "### 2.1 评估指标介绍\n", "\n", - "评估指标用英文表示是Metrics,有的时候也成为性能指标,用来衡量反馈一个模型的实际效果好坏,一般是通过计算模型的预测结果和真实结果之间的某种【距离】得出。\n", + "评估指标用来衡量一个模型的实际效果好坏,一般是通过计算模型的预测结果和真实结果之间的某种\"距离\"得出。\n", "\n", - "和损失函数类型,我们一般会在不同的任务场景中选择不同的评估指标来做模型评估,例如在分类任务中,比较常见的评估指标包括了Accuracy、Recall、Precision和AUC等,在回归中有MAE和MSE等等。\n", - "\n", - "这些常见的评估指标在飞桨框架中都会有对应的API实现,直接使用即可。那么如果我们遇到一些想要做个性化实现的操作时,该怎么办呢?那么这里就涉及到了如何自定义评估指标。\n", + "和损失函数类似,一般会在不同的任务场景中选择不同的评估指标来做模型评估,例如在分类任务中常见的评估指标包括了 Accuracy、Recall、Precision 和 AUC 等,在回归任务中常用的有 MAE 和 MSE 等等。这些常见的评估指标在飞桨框架中都有对应的 API 实现,可直接使用。如果不能满足需求,则可按照框架的 API 结构要求自定义评估指标。\n", "\n", "### 2.2 自定义评估指标\n", "\n", - "和Loss一样,你也可以来通过框架实现自定义的评估方法,包括如下几个步骤:\n", + "通过框架实现自定义评估指标的方法,包括如下几个步骤:\n", "\n", - "1. 创建一个继承自 `paddle.metric.Metric` 的类;\n", + "1. 创建一个继承自 [paddle.metric.Metric](../api/paddle/metric/Metric_cn.html) 的类;\n", "1. 在类的构造函数 `__init__` 中定义需要的参数;\n", - "1. 实现 `name` 方法,返回定义的评估指标名字\n", - "1. 实现 `compute` 方法,这个方法主要用于 `update` 的加速,可省略\n", - "1. 实现 `update` 方法,用于单个batch训练时进行评估指标计算\n", - "1. 实现 `accumulate` 方法,返回历史batch训练积累后计算得到的评价指标值\n", - "1. 实现 `reset` 方法,每个epoch结束后进行评估指标的重置\n" + "1. 实现 `name` 方法,返回定义的评估指标名字;\n", + "1. 实现 `compute` 方法,这个方法主要用于 `update` 的加速,可省略;\n", + "1. 实现 `update` 方法,用于单个 batch 训练时进行评估指标计算;\n", + "1. 实现 `accumulate` 方法,返回历史 batch 训练积累后计算得到的评价指标值;\n", + "1. 实现 `reset` 方法,每个 epoch 结束后进行评估指标的重置。\n" ] }, { @@ -145,7 +142,7 @@ " \n", " def compute(self, **args):\n", " \"\"\"\n", - " 4. 本步骤可以省略,实现compute方法,这个方法主要用于`update`的加速,可以在这个方法中调用一些paddle实现好的Tensor计算API,编译到模型网络中一起使用低层C++ OP计算。\n", + " 4. 本步骤可以省略,实现compute方法,这个方法主要用于`update`的加速,可以在这个方法中调用一些飞桨框架实现好的Tensor计算API,编译到模型网络中一起使用低层C++ OP计算。\n", " \"\"\"\n", " # return '自己想要返回的数据,会做为update的参数传入。'\n", " \n", @@ -178,7 +175,7 @@ "id": "926454a0", "metadata": {}, "source": [ - "接下来看一个框架中的具体例子,是框架中已提供的Accuracy计算接口,这里就是按照上述说明中的方法完成了实现。" + "接下来看一个框架中的具体例子,Accuracy 评价指标的示例,这里就是按照上述说明中的方法完成了实现。" ] }, { @@ -284,11 +281,14 @@ "\n", "### 3.1 回调函数介绍\n", "\n", - "`fit` 接口的callback参数支持传入一个 ` Callback` 类实例,用来在每轮训练和每个 ` batch` 训练前后进行调用,可以通过 ` callback` 收集到训练过程中的一些数据和参数,或者实现一些自定义操作。\n", + "Callback 回调函数常用于对模型训练、评估、推理过程状态和参数的观察,如训练进度、loss 值等信息;也可用于实现一些自定义操作,如设置当 loss 值达到一定阈值时停止训练、按照设定规则定期保存模型等。可方便地掌握模型训练状态,及时做出灵活调整,获得预期训练效果。\n", + "\n", + "Callback 一般用在 `Model.fit` 、`Model.evaluate`、`Model.predict` 等飞桨高层 API 中,先定义一个继承自 [paddle.callbacks.Callback](../api/paddle/callbacks/Callback_cn.html) 的类,然后通过高层 API 接口的 callback 参数传入类的实例,用于模型训练、评估或推理过程中调用。\n", "\n", "### 3.2 自定义回调函数\n", "\n", - "自定义回调函数的模板如下所示:" + "自定义回调函数的实现模板如下所示:\n", + "\n" ] }, { @@ -330,7 +330,7 @@ "id": "b23a392c", "metadata": {}, "source": [ - "看两个框架中的实际例子。其中第一个例子时框架自带的 `ModelCheckpoint` 回调函数,可以在 `fit` 训练模型时自动存储每轮训练得到的模型;第二个例子是框架自带的 `ProgBarLogger` 回调函数,用于在 `fit` 训练时打印损失函数和评估指标。这两个回调函数会在 `fit` 执行时默认被调用。\n" + "飞桨框架在 [paddle.callbacks](../api/paddle/callbacks/Overview_cn.html) 下内置了一些常用的回调函数相关 API,接下来看两个框架中的实际例子。其中第一个例子时框架自带的 `ModelCheckpoint` 回调函数,可以在 `Model.fit` 训练模型时自动存储每轮训练得到的模型;第二个例子是框架自带的 `ProgBarLogger` 回调函数,用于在 `Model.fit` 训练时打印损失函数和评估指标。这两个回调函数会在 `Model.fit` 执行时默认被调用。\n" ] }, { @@ -635,45 +635,14 @@ " print('Predict samples: %d' % (self.tested_samples))" ] }, - { - "cell_type": "markdown", - "id": "b8330408-b9f7-427b-91b4-94229f59da06", - "metadata": {}, - "source": [ - "## 四、自定义优化器 Optimizer\n", - "\n", - "### 4.1 优化器介绍\n", - "\n", - "优化器在模型训练过程中,用于计算和更新网络参数,合适的优化器可以有效减少训练时间,提高最终模型性能。除了之前例子中用到的 Adam 优化器外,还有很多其他的优化器,以实现目标函数能更快速更有效地收敛到全局最优点。我们需要根据任务选择合适的优化器,并设置合理的参数。\n", - "\n", - "### 4.2 自定义优化器\n", - "\n", - "通常我们需要为实际的任务选择合适的优化器,为优化器设置合理的学习率下降策略:\n", - "\n", - "* 优化器:飞桨中提供了很多可选择的优化器,详见 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer);\n", - "* 学习率:除了指定学习率固定值外,在很多任务中学习率会随着训练进程而变化。飞桨中可以使用 [paddle.optimizer.lr](../api/paddle/optimizer/Overview_cn.html#about-lr)下的各个API来实现丰富的学习率下降策略。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9be7d431-7ff2-406f-9c7b-4b98bee9bf66", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "Adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=model.parameters())" - ] - }, { "cell_type": "markdown", "id": "0a7d7f04", "metadata": {}, "source": [ - "## 五、自定义Loss、Metric、Optimizer及Callback的使用\n", + "## 五、自定义Loss、Metric 及 Callback 的使用\n", "\n", - "接下来以mnist为例,使用自定义的指标替换框架中的指标,代码如下:" + "以下代码示例中,介绍了自定义 Loss、Metric 及 Callback 后,如何在模型训练中使用:" ] }, { @@ -767,7 +736,7 @@ "source": [ "# 五、总结\n", "\n", - "待补充" + "本节中介绍了飞桨框架中一些高阶自定义用法,包括自定义 Loss、Metric 及 Callback。飞桨框架既内置了丰富的组件,方便用户直接使用提升模型开发效率,也提供开放的接口方便用户根据任务需求自定义组件来使用,以便更灵活地进行模型开发。" ] } ], From 0efcbed664096abd91ec503f67b2a8e02de22174 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Mon, 14 Feb 2022 16:17:09 +0800 Subject: [PATCH 38/63] update model_develop 01-04 --- .../01_quick_start_cn.ipynb | 16 ++++++------- .../02_data_load_cn.ipynb | 13 ++++++---- .../03_data_preprocessing_cn.ipynb | 22 +++++++---------- .../02_paddle2.0_develop/04_model_cn.ipynb | 24 +++++++++---------- 4 files changed, 37 insertions(+), 38 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 79428317cc8..93a2a0ff33d 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -250,9 +250,7 @@ "source": [ "### 3.1 数据集定义与加载\n", "\n", - "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。\n", - "\n", - "飞桨在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下提供了一些常用的图像变换操作,如对图像进行中心裁剪、水平翻转和归一化等处理。本任务在初始化 MNIST 数据集时通过 `transform` 字段传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型训练的收敛速度。" + "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉(Computer Vision,CV)领域常见的数据集,如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中,先后加载了 MNIST 训练集(`mode='train'`)和测试集(`mode='test'`),训练集用于训练模型,测试集用于评估模型效果。" ] }, { @@ -298,7 +296,9 @@ "source": [ "飞桨除了内置了 CV 领域常见的数据集,还在 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理(Natural Language Processing,NLP)领域常见的数据集,并提供了自定义数据集与加载功能的 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API,详细使用方法可参考『数据集定义与加载』 章节。\n", "\n", - "在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下内置了很多图像变换操作的 API,如对图像的翻转、裁剪、调整亮度等,可实现数据增强,以增加训练样本的多样性,提升模型的泛化能力,详细使用方法可参考『数据预处理』 章节。\n", + "\n", + "另外在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下提供了一些常用的图像变换操作,如对图像的翻转、裁剪、调整亮度等处理,可实现数据增强,以增加训练样本的多样性,提升模型的泛化能力。本任务在初始化 MNIST 数据集时通过 `transform` 字段传入了 `Normalize` 变换对图像进行归一化,对图像进行归一化可以加快模型训练的收敛速度。该功能的具体使用方法可参考『数据预处理』 章节。\n", + "\n", "\n", "更多参考:\n", "* [数据集定义与加载](02_data_load_cn.html)\n", @@ -582,7 +582,7 @@ "source": [ "#### 3.4.2 模型加载并执行推理\n", "\n", - "执行模型推理时,可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后可调用 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", + "执行模型推理时,可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型,然后即可通过 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n", "\n", "如下示例中,针对前面创建的 `model` 网络加载保存的参数文件 `output/mnist`,并选择测试集中的一张图片 `test_dataset[0]` 作为输入,执行推理并打印结果,可以看到推理的结果与可视化图片一致。\n" ] @@ -670,9 +670,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -684,7 +684,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" }, "toc-autonumbering": false, "toc-showcode": false, diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index b436e84227b..3e359e19490 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -581,7 +581,10 @@ "id": "970dad59", "metadata": {}, "source": [ - "可以看到顺序采样按照顺序输出各个样本的索引。随机采样先将样本顺序打乱,输出乱序后的下标。在分布式采样中,设置了`num_replicas=2`,样本会被划分到两张卡上,所以这里只输出一半样本的索引。" + "从代码输出结果可以看出:\n", + "* 顺序采样:按照顺序的方式输出各个样本的索引。\n", + "* 随机采样:先将样本顺序打乱,再输出乱序后的样本索引。\n", + "* 分布式采样:常用于分布式训练场景,将样本数据切分成多份,分别放到不同卡上训练。示例中设置了 `num_replicas=2`,样本会被划分到两张卡上,所以这里只输出一半样本的索引。" ] }, { @@ -589,7 +592,7 @@ "id": "d3a256d5-33f0-4018-bd5a-3ee6d9bff372", "metadata": {}, "source": [ - "## 四、总结\n", + "## 三、总结\n", "\n", "本节中介绍了在飞桨框架中将数据送入模型训练之前的处理流程,总结整个流程和用到的关键 API 如下图所示。\n", "\n", @@ -604,9 +607,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -618,7 +621,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index f3f8f6a0c79..19358e31f6e 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -192,18 +192,13 @@ " \"\"\"\n", " 步骤三:实现 __getitem__ 函数,定义指定 index 时如何获取数据,并返回单条数据(样本数据、对应的标签)\n", " \"\"\"\n", - " # 根据索引,从列表中取出一个图像\n", " image_path, label = self.data_list[index]\n", - " # 读取灰度图\n", " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", - " # 飞桨训练时内部数据格式默认为float32,将图像数据格式转换为 float32\n", " image = image.astype('float32')\n", " # 应用数据处理方法到图像上\n", " if self.transform is not None:\n", " image = self.transform(image)\n", - " # CrossEntropyLoss要求label格式为int,将Label格式转换为 int\n", " label = int(label)\n", - " # 返回图像和对应标签\n", " return image, label\n", "\n", " def __len__(self):\n", @@ -211,8 +206,9 @@ " 步骤四:实现 __len__ 函数,返回数据集的样本总数\n", " \"\"\"\n", " return len(self.data_list)\n", - "\n", + "# 定义随机旋转和改变图片大小的数据处理方法\n", "transform = Compose([RandomRotation(10), Resize(size=32)])\n", + "\n", "custom_dataset = MyDataset('mnist/train','mnist/train/label.txt', transform)" ] }, @@ -233,7 +229,7 @@ "\n", "通过可视化的方法,可方便地对比飞桨内置数据处理方法的效果,下面介绍其中几个方法的对比示例。\n", "\n", - "### CenterCrop:\n", + "### 3.1 CenterCrop:\n", "\n", "对输入图像进行裁剪,保持图片中心点不变。" ] @@ -294,7 +290,7 @@ "id": "f3cac20c", "metadata": {}, "source": [ - "### RandomHorizontalFlip\n", + "### 3.2 RandomHorizontalFlip\n", "\n", "基于概率来执行图片的水平翻转。" ] @@ -354,9 +350,9 @@ "id": "c8272853", "metadata": {}, "source": [ - "### ColorJitter\n", + "### 3.3 ColorJitter\n", "\n", - "随机调整图像的亮度,对比度,饱和度和色调。" + "随机调整图像的亮度、对比度、饱和度和色调。" ] }, { @@ -435,9 +431,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -449,7 +445,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 416081ee59a..58263ecfaad 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -368,7 +368,7 @@ "id": "c55565c4-cc47-4654-98cf-bccc8bff1331", "metadata": {}, "source": [ - "# 五、总结\n", + "## 五、总结\n", "\n", "本节介绍了飞桨框架中模型组网的几种方式,并且以 LeNet 为例介绍了如何使用这几种组网方式实现,总结模型组网的方法和用到的关键 API 如下图所示。\n", "\n", @@ -381,7 +381,7 @@ "id": "490f2617-b7c2-4b3c-9ee6-1dae6c04bd59", "metadata": {}, "source": [ - "# 扩展:模型的层(Layer)\n", + "## 扩展:模型的层(Layer)\n", "\n", "模型组网中一个关键组成就是神经网络层,不同的神经网络层组合在一起,从输入的数据样本中习得数据内在规律,最终实现输出预测结果。每个层从前一层获得输入数据,然后输出结果作为下一层的输入,并且大多数层包含可调的参数,在反向传播梯度时更新参数。\n", "\n", @@ -391,7 +391,7 @@ "\n", "\n", "\n", - "## Conv2D\n", + "### Conv2D\n", "[Conv2D](../../api/paddle/nn/Conv2D_cn.html#conv2d) (二维卷积层)主要用于对输入的特征图进行卷积操作,广泛用于深度学习网络中。Conv2D 根据输入、卷积核、步长(stride)、填充(padding)、空洞大小(dilations)等参数计算输出特征层大小。输入和输出是 NCHW 或 NHWC 格式,其中 N 是 batchsize 大小,C 是通道数,H 是特征高度,W 是特征宽度。" ] }, @@ -425,7 +425,7 @@ "id": "63121da0", "metadata": {}, "source": [ - "## MaxPool2D\n", + "### MaxPool2D\n", "\n", "[MaxPool2D](../../api/paddle/nn/MaxPool2D_cn.html#maxpool2d) (二维最大池化层)主要用于缩小特征图大小,根据 `kernel_size` 参数指定的窗口大小,对窗口内特征图进行取最大值的操作。" ] @@ -460,7 +460,7 @@ "id": "15082b1f", "metadata": {}, "source": [ - "## Linear\n", + "### Linear\n", "\n", "[Linear](../../api/paddle/nn/Linear_cn.html#linear) (全连接层)中每个神经元与上一层的所有神经元相连,实现对前一层的线性组合和线性变换。在卷积神经网络分类任务中,输出分类结果之前,通常采用全连接层对特征进行处理。" ] @@ -493,7 +493,7 @@ "id": "c40d6243", "metadata": {}, "source": [ - "## ReLU\n", + "### ReLU\n", "\n", "[ReLU](../../api/paddle/nn/ReLU_cn.html#relu) 是深度学习任务中常用的激活层,主要用于对输入进行非线性变换。ReLU 将输入中小于 0 的部分变为 0,大于 0 的部分保持不变。" ] @@ -527,11 +527,11 @@ "id": "f5b24ab6-802e-4b72-a6cf-6d06b459fd93", "metadata": {}, "source": [ - "# 扩展:模型的参数(Parameter)\n", + "## 扩展:模型的参数(Parameter)\n", "\n", - "在飞桨中,可通过网络的`parameters()`和`named_parameters()`方法获取网络中在训练期间优化的所有参数,更加这些方法可以对网络进行更加精细化的控制,如设置某些层的参数不更新。\n", + "在飞桨框架中,可通过网络的 `parameters()` 和 `named_parameters()` 方法获取网络在训练期间优化的所有参数(权重 weight 和偏置 bias),通过这些方法可以实现对网络更加精细化的控制,如设置某些层的参数不更新。\n", "\n", - "下面通过一段代码来演示`named_parameters()`的使用,在代码中,获取了lenet所有参数的名字和值,并且打印出了参数的名字和shape。" + "下面这段示例代码,通过 `named_parameters()` 获取了 LeNet 网络所有参数的名字和值,并且打印出了参数的名字(name)和形状(shape)。" ] }, { @@ -574,9 +574,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", - "name": "python3" + "name": "py35-paddle1.2.0" }, "language_info": { "codemirror_mode": { @@ -588,7 +588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.4" } }, "nbformat": 4, From a54f6cb877ab47003c5e9b1ab34375d926945b21 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 14 Feb 2022 16:41:45 +0800 Subject: [PATCH 39/63] update 03 --- .../03_data_preprocessing_cn.ipynb | 77 ++++++++++++++----- 1 file changed, 57 insertions(+), 20 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 19358e31f6e..7ae83e8bd22 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "93904999", "metadata": { "execution": { @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "69b80bc1", "metadata": { "scrolled": true @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "4a1a5cb3", "metadata": { "scrolled": true @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "a7970f84", "metadata": { "scrolled": true @@ -229,6 +229,43 @@ "\n", "通过可视化的方法,可方便地对比飞桨内置数据处理方法的效果,下面介绍其中几个方法的对比示例。\n", "\n", + "首先下载示例图片" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cd1d0519", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-02-14 16:40:43-- https://paddle-imagenet-models-name.bj.bcebos.com/data/demo_images/flower_demo.png\n", + "正在解析主机 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)... 111.206.210.93, 111.206.210.81\n", + "正在连接 paddle-imagenet-models-name.bj.bcebos.com (paddle-imagenet-models-name.bj.bcebos.com)|111.206.210.93|:443... 已连接。\n", + "已发出 HTTP 请求,正在等待回应... 200 OK\n", + "长度:68587 (67K) [image/png]\n", + "正在保存至: “flower_demo.png”\n", + "\n", + "flower_demo.png 100%[===================>] 66.98K 228KB/s 用时 0.3s \n", + "\n", + "2022-02-14 16:40:43 (228 KB/s) - 已保存 “flower_demo.png” [68587/68587])\n", + "\n" + ] + } + ], + "source": [ + "# 下载示例图片\n", + "! wget https://paddle-imagenet-models-name.bj.bcebos.com/data/demo_images/flower_demo.png" + ] + }, + { + "cell_type": "markdown", + "id": "0fb6c1e2", + "metadata": {}, + "source": [ "### 3.1 CenterCrop:\n", "\n", "对输入图像进行裁剪,保持图片中心点不变。" @@ -236,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "76edf274", "metadata": { "scrolled": true @@ -245,10 +282,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -274,7 +311,7 @@ "\n", "transform = CenterCrop(224)\n", "\n", - "image = cv2.imread('images/flower.jpg')\n", + "image = cv2.imread('flower_demo.png')\n", "\n", "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", @@ -297,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "f6adefc6", "metadata": { "scrolled": true @@ -306,10 +343,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -334,7 +371,7 @@ "\n", "transform = RandomHorizontalFlip(0.5)\n", "\n", - "image = cv2.imread('images/flower.jpg')\n", + "image = cv2.imread('flower_demo.png')\n", "\n", "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", @@ -357,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "470047b1", "metadata": { "scrolled": true @@ -366,16 +403,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -394,7 +431,7 @@ "\n", "transform = ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n", "\n", - "image = cv2.imread('images/flower.jpg')\n", + "image = cv2.imread('flower_demo.png')\n", "\n", "image_after_transform = transform(image)\n", "plt.subplot(1,2,1)\n", @@ -431,9 +468,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py35-paddle1.2.0" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -445,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.10" } }, "nbformat": 4, From 54d7b244ec57c14d6ee6cf82f8fc5e91f9b8d846 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 10:40:44 +0800 Subject: [PATCH 40/63] update model_develop images-link --- .../02_paddle2.0_develop/01_quick_start_cn.ipynb | 10 +++++----- docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb | 8 ++++---- .../03_data_preprocessing_cn.ipynb | 10 +++++----- docs/guides/02_paddle2.0_develop/04_model_cn.ipynb | 8 ++++---- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 93a2a0ff33d..cd959a834ab 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -90,8 +90,8 @@ "\n", "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "
\n", - "

图1:MNIST数据集样例
\n", + "
\n", + "

图 1:MNIST 数据集样例
\n", "\n", "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" ] @@ -172,7 +172,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -661,8 +661,8 @@ "\n", "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图1:模型开发流程
\n", + "
\n", + "

图 2:模型开发流程
\n", "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增强、使用更大的 CNN 模型、调优性能等。飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" ] diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 3e359e19490..524be7e7706 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -149,7 +149,7 @@ }, { "data": { - "image/png": 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0M8OYHhGba7dfkjS9k80MY8RpvNtpn2nGu+a5a2T682bxBt17zY+IT0n6rKSLaqerXSkGX4N109jpqKbxbpdhphn/rU4+d41Of96sToR9k6SZQ+5/vLasK0TEptrvrZLuVvdNRb1l7wy6td9bO9zPb3XTNN7DTTOuLnjuOjn9eSfC/qCkObZn2z5A0hckrexAH+9he1LtjRPZniTpDHXfVNQrJS2s3V4o6Z4O9vIu3TKNd71pxtXh567j059HRNt/JJ2lwXfkn5P0V53ooU5fn5D0SO3n8U73JulWDZ7W7dLgexsXSvqIpFWSnpH0n5KmdlFv/ybpMUmPajBYMzrU23wNnqI/Kmlt7eesTj93hb7a8rxxuSyQBG/QAUkQdiAJwg4kQdiBJAg7kARhB5Ig7EAS/w9pgMSoTFggTAAAAABJRU5ErkJggg==\n", 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" ] @@ -348,7 +348,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -596,8 +596,8 @@ "\n", "本节中介绍了在飞桨框架中将数据送入模型训练之前的处理流程,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图1:数据集定义和加载流程
\n", + "
\n", + "

图 1:数据集定义和加载流程
\n", "\n", "主要包括定义数据集和定义数据读取器两个步骤,另外在数据读取器中可调用采样器实现更灵活地采样。其中,在定义数据集时,本节仅对数据集进行了归一化处理,如需了解更多数据增强相关操作,可以参考 [数据预处理](03_data_preprocessing_cn.html)。 \n", "\n", diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index 7ae83e8bd22..deaa723197d 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -291,7 +291,7 @@ }, { "data": { - "image/png": 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\n", 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Opyyn0xe87s5i7p9BiIhOKYXP9zzO8NlDSunfA//+Ux0vAjEliAEVEyhFiIEYAyFBIBthbTVKgw+RJBDXiVMBQvCkENBakTOWCVtaRMgJ1ARaVL4hxEgM+XwCOfSSEhFBGcOk7dBVjQfarqP1gS4lIhAVzFZLfJuYzzynbUenFLXJIRvBU4nCao10ntY3zMsEvZpF09IWkRAiDQu0b/DLlr5uuVhpFsGzjBUmGvq+YNG0jDZ6HK9mxBTxKqCMok2RlgDBs7m5CT4w7PXpomJy4yZbvQ0unz8Hk5Zrjz7D5fEe4+GYwc6Y2fEJtQSKFEDnlRIJCELnAydHM7Y3RlSlRYLhZQ+9jOvtITeunrBqp+z2+sxXK6y1qFiQUgHG0pqWRydH/Nv3/izlIyMeHF6gSB1VFKJ3RK0w4zEPvOpV/PjPvZVb3YpXvfSVOBGsdwzqGh8aiqT43b/tO3n/Rz6A1gN+9N/9R4bjHX737/gOpqvAhq0gQVh1LGZzBmpIPa6w/R7WFpA8ISk2RpucHk64ef0mrnPPe+2dee6fBCLyiIi8VUROReQDIvLr79r2D0Tkb4jIvxeRBfCV6/e+764xf3jdIPiGiPweEUki8tBd+3/f+vlXiMg1Efl/icj+ep/veoF5vVVEfs/6+XeKyE+JyF9ez/MJEXnD+v2r6+N9x137fqOIvEtEpuvtf+oTjv07ReRpETkSkT8hIk+JyNestykR+SMi8vh6+w9L7pd5hueEIFohRqGMQoygjAIDPnl8dOskZGZIIAml1LMNF5QotAjWGMrCUpaWsrKUPZuNovOkEFGZL4KKKd9EYkApQa+NnI8RFxLLpiWJ4CJ0MeFiBKVQRlMUBa5zHB5NOTg4wftIr6xQMeHnc1gs6YXAMCa2UFwYjqh3xxzGhicnB5xqj9uqmI9gtaM4rBuutcec0jJvHbt6l5dsvIhL5gL39a9wob7AQAaoaAg+4nF47XDK4YhcvnKF+65cYVBWyKrFHZ/y9Ac/yOnBbcpewTQs+cjNpzhYndDbHmDGNQu/IlhACykJMYJRllKVrOaO6zeOaBoFvqCMNW961Rt5eHQRc9iw2RUUU89IehhfkKJl3iVmCiYDzS+cPMGPXf0Frvfm3FITYl/jvAMxBFsy3rvAm77263jv4x/hHR95D4duQls4Zm5OjJ6e7fO6L3493/FbfzuTkxME4Qf/6Q/xj/7pDzNrPNNFx3zh6NcbWG+Y7U85vnGEnzQQFBIVEoXRYMzmeIOu6Yjx+X3JM+P+AhARC/wbstzpOXLrsn8sIi++a9hvI7cVG5I7sty9/9eTlfu+htye7Cs+ySnPk3tVXiKrzv01Edn8FKf7enI/yG3gn5A52K9bn/e3A39VRO40Y1iQVf42gG8Efp+IfMt6zi8lN1r+drLq35353MGn0r7tDHchSURyHAO0IHrNVpRIlEhMgSSJKAlRghghESAljFJYbahKi7WGojKZhVEJibuod9pQaoNRGkWmQRqlcoJWBKM0kgStDNYWNE2LX9PrNEJpNBvDHsN+n4imixBEEZQiasH2Smy/JGpwydMmR1SJD73//cyOTtkajtnc2ET1KxZF4LHZLT58eoPbqWFqNJgBDw8e5N50kXu4yCPDF3FR9rhYXGBTbVIES3SJmCJosHXJsl2xXC7olxWvfeSlvOEVr2SvN2C73weJ2HHN0yc32V+dEPqK0flNFqmhpSPoRFKQUsQoS6/oUxUDlsvIrVunEEtoNDtmk9/8Vd/KI5v3MZxr7u3vkY4aemLoFZakPK32zCvHas/wcycf5qduv5ebds4pK0xVs1o0nBwcY5Jisz/iy770Dbz3A+/hiZtPsNItrgiYYc18tSSlyG/8jm/nta95JYqAcy3/7F/8c370rW9mf3pKuzbWVhksiuVkyemtI5qTGamLKAy0gV7Zo7LFC153Z8b9hfGryN1Z/kJKqUsp/WeyvOdvvWvMj6SUfiqlFFNKzSfs/23A308pfSCltGTdh/EF4IA/k1Jy6+X/nKxj8angyZTS31+HhX6IzOr4MymlNqX0Y+Smxw8BpJTemlJ633rO7yXraN/R5v5NwL9JKb1tXajzvXx8Q4XvITdTvpZSatef6TfJWff654WPni50hOSJ0eFiR8CTVFyT0BPoRFL5obQ8yyVfU6XRSGbRSKQoFaJl7dVrlGisUlhlMKKQGFHrmDspkIiU1mKVZtCrUQhNs8J1LVoSVaEZ1gWDUhj1Sqq6RpUVqSjwVuNKg+8b/Khk0VNMamE+MHR9wwN7l9hVFeXMMb96mziZkdolMawgdqjOMUolr7rwCBfcFupqoLxt6B2V9E5rtrtNdmWHURqjGkURC4wYUoxMZ1OKouDxjzzKZP+IWx/4KOfKASNlcW3L7oULyKDk0dtPcegmqA1LsVlBBaqEKB5RQvQBI5pSVygqrl875OrTB4ivmN1cMHYDfvtX/gYeLPdQNxsumW3qNmH8klIcpIZJe8zEzLmujnjb9XfxaHOdA5a4ytB1HWrVEk8W6HnHiy9e4ZUvfgnvfM87+NDTH0bGBSsTKHZGHBwf0KsMv+8PfA91oRn0LMvVjL/5/X+D//DW/0CqhcPpIcomBsMe480tvAtMTye0yxWh84gyGFOwsbmNvIDAwJlxf2FcBK6mj2/y+zQf78l+Ynux/2r/T3EswNEntE17odZnn4hPbBlGSun52oi9XkTeIiIHIjIhG+yd55rz+qZ0dNdx7rRvOxWRU3Ji8RPbt53hDiQXvSSJRCKegEuOXA+TQIEyGjEqjzNC0mldSLQuXAo5KaoUiEoUlcUUOseVReVwTBJUunOfiEgMkDLNUSQnGK0x1FWFC12O2xeGsrTUdUFVGXzncc0K6RxFhFpr+mVBXa+57GFFpyOxZ3C1ZkFge2eH2HqKpNjuDbmwsYkNjjp6+t6z7RWv33mA+8OI0dKyHYds+xH1vGTTjdiRbYZ+QO0qBgzRzuCXHiFQ9gpmizlf+oY3sjyZEFcdu/0N3vLvfoxh0WfQGyJFwZNHN7i9PKGxkd7ukEVYEnRA2YQtBKUiKXgICaNKrO2zfzDl9s0JdCVxCpeq8/z61389L999mI22ZtMX9BrHKMAoChtlRVloUh9uhxPe/sz7eGq1zwELzNBSGWC+gNMF41jwq17xGna3tviZX/gZ3v34+5mkFZNuiTMwPzrkVa94Od/9u78Lt5xhlWe1mvIPf/j/5uc/+Av0zvWZ+gmTZkrVr9k6t0PZqzk4OmQ2nRKdR1xgNNzMRWDPgzPj/sK4AdwjH98q7Aofz21+IXGem2Qu9B3c83wDP8f4J2QK3z0ppTG5a8/dbcSenbOI1ORQzx38t7Rv+5UHAVNoRINPDh8dIQWCRMTkEI0ygrIqG3ot63h7RK156iJrB18L2gi6AFPmitMUE8R1lWuMqBQxkkMy1miMNSgtLLqGlsAqdCx9B6XG9EuiFSiEpCGqRHQt46bjXlWw5wPbXcdu8GwlR6+d0/dLqnaBXkxp5hPee3Sd60VgOio5kMAHbjzFkyc3mPoZVaU53x/xYL3L4DSSZg0jW1NLgW0VpjH0GSIrQ1woNnvbmFBQ6grnWlKKFFXFsnWsushXf+038t63v4vXvew1+GnLuByys3OOWCo+fP1xrp/eItZQjAuC8RQ9TRJHVRpSaHN9QFIUpkdMBTf3p0wnDllZ1ER42e6L+KbXfR33Fxe5JJvca7a56IacW9T0j8GcOlSIdLrjg/uP8pbH38EHF9eZ9TypiogkTCekU8cg1bzhtV+KLS1veft/4d1PfIAjP0UNeiAFi4NTvvO3/U6++Wu+jp7SlIUghfB9f+nP8u4n3sf4vh2O2lNOmwlmWGB6BS4F5qsFy8WcFBM6JIx+/gXzmXF/Ybyd7D3/YcltzL4C+CZyPPtTwQ+T24Y9IiI9cpeZLwQMgeOUUiMiX0LOG9zBPwe+aZ2QLchhl7vLm/9b2rf9ioMIqEKDkkxxJMfXUelZbx6VPfUoCbTgU1yX5gtyJ8+qeLYgSRuVY/QxU+UkZc9dEhAiEiOFMVRlSWENPnqa5HGlQkYVZrNHGpbMcMxo8bUi1gpfJBocwbWUKVHEiPYdyreobokNDfdsj3n40h4Xxn0kNtyKU/aLlmfcKVe7E05qT7NpmNSOiekoNocEpSmqCorM71c2sVjNmEymuNYjUdGuHNELIpauc1ijCdHjBeZNw4tf9nJu3thnWA15+sNPcv3Rq6hW2NrYRvVLbpzsc7w8hVIoxyVNWJC0R3Qk4Uipoy4MRdYOIOmCeRc5mTS4RiErjZoJ9/cv8Y1f8jU8UN/LpXCRK91FHoxXuM9fYM9tMvAlooS2Cnx48iS/cPghrqkTFkNBjfsUvSGxU4Rl4tz2Hl/6xjeCFX7ufe/gmePrOASjh4grcYdLvvt3/l7uP3eJUllEK0IhfO9f/D6ePLzO7v0XWcYVwUTsoGD38nlccEwmp4Sug9Yh6sxz/0VhHXP+JuAbgENyovF3ppQ+/Cnu/6PA/wW8hdxu62fXm9rP/Gw/Lfx+4M+sW599L/kmBMCat/0HyTewm+S4/z4fm/N/S/u2X5EQLYjOXHUxKtMeVfbUk8oGXWlZM2XA+0AIaxbEHbL7muZoC01ZGUL0pBQRBCWZKbO+Z6AAqzVlkbnvnXe0OvH0yT7/5d0/z3/+hbfzgatPsN9OWdqOhZ5ze3XCjekRt5YTnnJLHvULbtjIfpU4KCLNZg3nxvitionpOJWOEz/HWo9JK4gregMNhWOeZpwy5ThNmUjDnAD9CoaJmUxpzIp5nJMKqIZ9goKoBNOvGe9sg1LE4ElEnA8s2paTyZyDg2OGvQ3uv3Af0kZYBSQpLj9wH51K3J4ccTg9xCuHFIlAS1EK2gTqnsFoMGtthCiGgOVosuTGjSNWJx1FZzFz4UXb9/NlD72Rl9av5P7wMA/Hh3ko3s/FbpdhWyM+EivYTye8/ca7edu19/JEOOVWbJgh6GKAUTXtyvNFr3glX/SaL6Kl4R3v+Tmu3d5nuVAUaUjhKh7avY//9X/6oyin6NUDhlvbqEGP/+V7/yhP7j+DHZXM/RKHpx5W7F2+wHw55/jwkMVk+oJxg7Mk2CfB2tg9ZyPglNJ3frL3Ukp/HvjzkGmVQCQbzY8bm1J6Kx8fwiGldN8LzOsr7nr+D4B/cNfrx/h4b5uU0uW7nv9zsof+fMd+9nhrhs2fJItksc4//KX14wyfBElykjSuY+9KaYwCLxGtIOmUQy1WEJNvACF6QgiotWRAjEJMEVGeyhSYyhBim0MxkuXBRBRKAkrIfHjSWkgs4IlgCkxV4YLj5v5NZosJ09GY2WDIpO7jW4cxBd4WxMtbLDtFz1RISLR+RTQOM7S4SvGRj34IfGR3d5vLVQ8rmunJjJNmQqdPsUVgspxx/4MPc377IkcHS1RdUW0IYgxLEgfHU/abY86ZgrSpGW7usCoS0+gYbG/i5h2+y4lnW5aElJDSkDow/ZqdrRHJJBKBsipQFpasOPEzRmVF0S9oTzoKZbA65w+a+SSHM4yhMAZTDIhOMZkt6RcaczhnsF1BCy86/xC9tMXjj11j0qzQcUwIBl9EEM8yrYh9ze3lIT/9zPsY11uojZqqGDAuhrjQYGmITeB1r34dp7MpTz35FI9+5KPsvmgbY0pKW7A4WfCql76a/+F3/37+9g/+34wHQ3SlaJsVf/lv/FX++J/8Y8QSjBeMGVIUBZtbuxw/dZuT5SnRPz8V8sy4f5YhIt9KLnrpAf8fMhPFv/Ben1+IyDeRmxkL8P8F3gc89fmc0y9VJAFfZlaMCQU6FBACWiJWORrl6A0KTJ+1EqDDpSw9oCVhrdBEjycSjUN6BVIkpA1oJbkoCgjJEVNAa8FoizIQXMM0NCRrkKjZsUO+4mWvwbwqoVRcJ1oLtCpZzAM3b5xw2gRaHyF4hiVUdFAr2nHBxAbe8pM/Q+kjb3zopcRl5HopNKGh0R0nZctR6khVj8F8TH+quT2/wUpKnjALLpQDVqsJSUVO7+9YmsC8fJLDOEUqw2mzZOpbrBZ2zCaxO8ZYT+uWLKpA2RsyuHePThW0KTBT+wjCiD6j7T5Xb17DlILpVWzQow6Wcpmw0dGxoF8nhIgTSxMtXShIZkTrNI/fnjEJkXvqir1Rn2Z1wrlNhb18H9evtdBsUdtNigDWzTgoF5zohmZY8djpEW9++oP0yxFDUWyYSE8bRsGSXAVieNMrvowbH3ia2e2bPF2+j/vuf5BgR9T9MYLld/zGb+fieIe//vf+Bht7m7Q7fZ453eeP/+U/xx/7Q3+Yc70xq5WjFyzbvV286bi5f5Ou6Z732vusGPezbjUfh99L9oID8F/IIZEvdHwz8A/Jxv3ngd+Szrq6/KKQYE1xhBQFiUDIhUaGRGEVVWVRVoi52BSlNFbrTH8UMFYRVUTVGtvTiIXYRnyXkKiIKsfgk85awlYLRV3QRA8IPsQs+RsCrl0SpcMasLZAjBCiop17wipReM0FVQGOYeew4hhsDCi3NpjqyPAlX4RyEXcwgbIgbVfMFiuW8ykMoBKDSomX7t3PvUWfk9vH9GpNYxv0eIwZ1dw+3qdNLcYYXOiotWK1avCzOalp6Y03ELEUxZjTyTU2NvrM3AqnDb5dkEJD07WUPc3WaAPbCTubmywmU64e32SkNtGqZDAeE2aBZrWgHhTs7AzpW8ts3rJ/vKJbtYhWGG3w0XBwOEObfJkPBgklnt3RBtWFPkYnimZJG7eJ6hIxwtTt0xYRqRL77SE//dGf49IrvozzUiOxRIni9PYp/YtbXLjvJbzp9b+aL37lKxj0h9y4cZvTxQxT1tRK0x1O+fJXfgn+tzb8wI/8EKJLdjZ2efzxp/kn//iH+Z7f8V1MmwXGDCi9Yvv8HodPH3wsfPcc+Iwb93W3mr/GXd1qRORf/0rtVpNSen5Nzi9QpJR+D/B7Pt/z+OUAUeSqVJ+ZL6zpjFnLMaKswtQ2a6H7lHVhgqDFAiqHY0xCWSiHBbaniSrSek8ICpMyddJLQLRHS0C0JmlwzqNVSbHWZi9KhS1Kgk9oBVVZU5Z95vOA67JKZN8WbHkhRcGWhqpfM+jXSBsR71jMWqarhqKskY0+fnZMWi4YlSVeBRZtYlxU3FuM+VWXHqB/qeT26YxbneMwJkYbuwyrASjho489ymJywgMvegAXHJdkTBgFgjbcXiypeiX9vme5nFEWFV2XIDaI1ygX6amSusjc/osXLrBarHjy8Amm7ZwTM6df1ixXHZoFYvr0okOcoLSmrizet8TY0kbPaDzidOa4feuUxaLlpQ9fZmgtKSzY2SoYbvQYn4wJt7bxLrJyBbOgOJEJVEJwc64uVvz8rW0s8MDgAkNqkg+46ZyDsOJL3/ilTA4OcM5x4Z7LHO2fIjHRrVpsEnzjeOPr3kA5GvAD/+qH8CvP/dtX+Kk3v40vfskr+ZJXvhqLIZmSelhz34vvRZ5TyT3js+G5P9utBkBE7nSr+RVp3M/wKxtKcvFRFEFEcnLVCGLJsgFG0IUCSYTkc5+JkJB114sQI0EFKCLluEJKaL2n9R5FBUpn1o32GJ0orcEqjW87nPNEZdC2QFlPSi3edyQiyhQUZYHWmrZdsVouCQFU8tQhEQmUvT7Dcz3ECKtmiV8tqUTjegNOSBzNTkl6wU5VMdzuc3NxiCwSySWCS5ycLomSSCu48dR1nnYrdi5cYndnj/l0yW7/Hu7Zfoh2taLAc3nnHEcntzltW0ZmzOn8FJGCwg5YtUvqosAHjw2JSgxh2nI022djb5eiV2BLw8q3XJvcotwa0lM9qiJRVnAaO+R0wtwLG/WAqrToFHGtp9JCio7alCCa09OGJx474crOgI1NwdqGwgYeKIcEfy/poMdsmpinBUo6VmVLZ6bErQ1+/vYHMbrAVD3O+ZadekA7XzIabhFXAVvVHB0fELVhc2+b1cGS1XTO8dGUreEIJYo3vOr11Jtj/vLf/aus2oZh2eOHfviHefDBB2jLjlb3sIsJF8/toY1+/mvvs3A9P1e3mkvPM/YMZ/hljSwClmBt2JURTKExpUJXCl0qVKFIKhLXyVAJWSLYGINoISoPZaAcaSgSTdcSkpBECICXQLIJOzQUmxWpiKx8k7cHSEFRlxatcicnYwxlUaGUwfvEatVCivR7FVVliZUQ+5rY08RKaHRiGj0H7YoT4IDADd/QlJpzwyH37+2hlNDFiJQl1H2emS942jvefuMm3/+jP87tJrI53sUvA4994GkmBy1hWfGhd1/lnT/9ERbHkVptMC53MJ0mnCw5efoWfVVRqIIYI8qCqhLFyFAOC9CJWTensy2hCmxd3uZFr3oJ5x48T9v3HMmcVZlotWLRJEIsCckwW67o2iV1AVujgp1RifItpdYUqqa0Y/aPPU/cXHG8BIdGtKGyBZfHu7xi70Fed+ERXjG+j/v1DjsYqqLDlw2zXsf7T5/iQ5OrzHTHMjRsbW3i246maeltDLGDmrf97E9zeLBPURg2tzbZ3t0hxoRCoZzw8isv4Q//j/9PLo4H7I77xNjxI//+X7GIDfvzE6bditnpFGvs8157n7eE6t2tyFC8llKvCb0C6zZguXRIgARKcguwXLb3LANIchuCTA3J3cg+RhO5MygJRqocc3QrxCjQghZDVY3xXUOznKyXzApRufxb5GOnB0GJzq2wRKG0Xo/VGDEQc3u0KLkBQUyBGD0hrfnKorIg1J1WZ+vZJ+FZSltuqPCx7+jO57oz9k6btpRyS7U8KO+TW3qtFQBT1hvRaMQLEtI64eZBCVpbtLa51VsMH3/OEIhJ0EUNCVyzILkWQSh7Q7S1uLYheEdR1mhr8V1Lu1yQUnj2kz37CdKz/2+QHE+OKbdBE6XyV7v+THmMPPt/vFOZybPHUOuxaX19qLV7cud6SNma8rF9AIiQUnqBBexnE3c47WQeuhaUFXQSTFQkCxR52smAtYZGQr5KlCIpRzCeamAoBlnf3cfMoEmAF4+XQFFqbL8Eq+gWjlXXoFIfJQZJQvAd3jUUVlPVfbQyhCCslg3OObTW2HVbPF8KVV1T1AbvI4uu4aRZcJwcswQnXUdnFOOqYqOX/2VH01NmzYKkhOl8ztLDe554hjKVXHnFFzEs+5TO49pAmCfCbIWomntGl9m0G6Q53H78gCv37+GqjtQFPnz1Jpde9iD7yw6KPm3bgLZ0ElE6ofqKqKAtOlrdoHsK1Vcc3jrC+h4+Jgya6KBsNF0SOq3wriG6hnLYo19XJC0MewU2GlZTR2V7mLrg5uKU2ZMTpl3Fg1e2KCPEdslmpXlRbxs1vwS3Don+lEbNmMcldT3gYD7lPdc/wjlTs7F5D5NmwmBzzHS1RLeWrZ1t7r//Xv7dv/63fOkrv4R7L1xBaUUQAaWxpqIcVrx696V893d/J//nX/8r7OwMOV0c85EnP8I9m/eAE66fHPJC/vlnw7h/St1q7m5Fpvo2qRcNgERSGtXvYQYFqjZITMTkUFYjWBSJNjiUKMQFjDFEnVBEolYkEWQtlZr7HQjet/TSJXbMvVx9/OdoywnF7galrrnn8hsZln3e+dYfxM0nlIM+sTSYXoEpKpS21OWAXtlnNBpTVRWmB2ZgcFWkDR692KKfdrEbHSs9pVk4aDyxa5i0MzqmJA1FUaOsAVNQikEnhVcBT5d/6FERg0NShBQIKaCNgZTQYlGSpVdDCIg4tNKEkOiajsVqSeM9wXt0SIgIle0xZMTpRw85ePoxemNNGijK3oiN4WVissyOT1gcn6BioqwrTGFJWKIeE1XN7NrjrG48iSwbelvbPPDqN2LqMfiG6JZc/ch7OXzmSYy1JGXzfIHgulz6DhhroTD4BHV/CCI0ixkQCCqRrCCFRpclVikkBpplS1p26DYSfb5JGF0QgkeiQ2lBakssAIlok8MeoYPkAncalaYEcflZuMo/RYiA1jksk+84EVREdEQs6EKDzfcdndS6D2mW4PVEOjwUif5miaqhW3hSTKRoiAKRjqQTtt9DlYa2WTFbrXIhkI6IzseOPnc+Usaue4Uqgk8s5itiDBijadsFRVFQjwaMRjUqRGazCdPFjHnoWEliGTylNWz1e+wMRxi94KidkoxCRBhpw6CsoI24RaDWiq2ixs4d95QDTman9BqFj4omOFYuMAQubJ3j+PZNrh09zste+SAPnzuPXSy5+t6nGe4N2Dm3x0l7ytwt8LTEqgCriB5u79/AtSvuuXAFpRLYxNHsiEDi4s4F2v0l02lga1BR9jWDuqZQgbJf0etXdG1HX0qKVBCkYN4GVoUQvOX2PDB/9JTkKh68Z5eNvQFdmDMoNduXHmFcC80zE1ZuxaDf43TRYMqKG7NbvPPaB9jb3SSqPtAx2ByzaBZYFPdcvox6beDn3/5zFK8z7G7ukUqDk8R0/zbnZJcUZrz2ZY/w+77nv+fv/dA/JISG93/wfey9/gJdGzg5cXTd55YK+Wy3GrJR/y18fAXkf42UkBDz75GIKkDQBKWxRjDkBJFShiKBUVXmCyfBB0+IjuQ68Jnba7WFFIgIjkTU0Lo5KMN48wIHR4ewCMR+4Ghyi637Xs54+xInJytkodC6pm9G1L0BojVlaRgMC+p+R9UXfNmj05GT+Qnz1YKyS5j+JiFMicWSpALeObSP1DFrOTdxQRs8stSYssRpS6UNniwmhRh81FmqlYgSUElI6448XjzWaHr1kNlsQpTcDLjzHlUY+qaPaVti0+U+kNYSbGJlPaP7tji+XeEmDbUuaKLjoDli0BtTmpJZF5ncuom1mmJjg8qUuPltfCf4tkU5n7vSrKZE1zE8v83Bjad48mffQnu8T2FKvO0RTQANIgllLRKz/omtSygKCImgFUoblKkIoUMVFgqVb3pJgwMdPWWhCGJJtsPGRPQREZ07+HiFSMwl8zGgbW70IEogJkISJJqPFf/I55d5KpKeXZWkGPHJkyQgNqGrLP8rkoXAvGRpgiDgCUSTKAeWwVYFJhKTW98fNOgAOmJri60NbfCcns5YzRoICa3B6kTA47oWow1GGYJPxBhwLrJYNYTgc8cm8WyMRww2SoxOzE9nTGdHLNqGToEtCjbLgsqWnBuOKbXidtcS1jLGPaO5OBjw0O55yqCZnKxQneZ8f5dxKlGnHbeTw9eWNmomK8e0WZGU5UIsuDC+xIc/9AFOuM65+3f4mhe/kfd85MP4CJPbc7bGfW61t5i5BXSBKFCr3C+21xm29RC3fYH3vedDeDQ3Dm5Tes2rt17MSCqauUe3AWMsEiPHywU+ttmZioIpDMOB4KNjFVtsEYldTTOHj370CNcm7rnSZ7CZKEvFsLfB4KFXYUeWH3/8J3ns4BZSGFzqiDbxoZMnqR6v+KpXvYGoGkapoCgKIBC948rlS8yPJ7z5LT/ON3zjN7O5ucP8dM5gc0jrGuzAYLTiy7/8K5i2C/7pv/mXKCm4+vQzlBsPY73G++e/tj/jxj2l5EXkTrcaDXz/J+tWk1ICt464aIX3MXvsIeCToCSRJBEkEk2WPvViCEoj0WIpMHFI8g7XeYJPBHKCKiiFpkQbx5IJvY17MLeeJB4u8Kpgpo7oGs+le1/O/OYhoiKUBapnkaHClkK/X2IrTVDQJkXnWubzFSeLJau2Y0MJnQ3QdRgLWiKxiCSJlGhCHCOhZOUWaJ01IUJsWQSfwxYhEsSTcOtSc4UWBSGsO90rlEmEEJFk0Kpk1bRrxkVu+OBj1uFQZYVbOrzzhJhY+iV2Z8zeyx7g2s++H3V7SRj2WJoF3YljPNxhtHMPISjSYkbV72GLEd4tCKsJkkCbEsoNdh9+CWZji5PZCdeuPs50McOgECvQA1GaqAK2yGwP0RpKjaoMCU2JwsdESBH6hsIWIAWxdcTOU+g+1WCDqqgpywrvHadHByyPrpJSgy0tWhfQCt1qRWo9qtRoa3K4ioTYhFGWFDQxZE1z5vGFLr/PMvINRtYhuMha4peYFSDtnWRqXsV4AlFHklHECNpqqpFGVRDoEBIqanyXSNphSqHoGZKG5dwzm7ckD4UqcthKUq72DIHSGrRovI90PrFYtjmWLQnwbO+O2N7uISZxMp9xy51yYDoWAj5BrzIMqx6VMtQSaRdLxATG4yE3bx3RF+HB8QZXbJX153vQxIAJDpywWDQYW9G5QCAQYstqOeXypXswXaTUBVeG5zErw+SxKfPFAeEIds/vsaE3WE6m1D3NJE1Zug5V16QkHN2+xfXDp3ik/xCX+7vct3eJJ45u0BYrnpk/w70759g9dw+L5QK67GeoQqOCR5ynVxqsURQV9PqWqkzUbUvdRILuQX/E5PSUq9cOcMy5z24xNIaUAr1qzGvvex39wZi3vvttvP/2Y7hSsyg8K+P42evvY/OePb748itIzZytwYDKlrhFi2sa7r3nEqenp/z4297Mr3rjm9je2KbtItEFQqOpVgbTq/mKX/21TJuOt/7E2zg+2CcMrmBNDx8+h8YdPv1uNTl4otYxZcAHcB5xiqgTojVKGVLInr2nIylHSoKsm9l2SeUPYwRjFSoUpE5hY17SR4m04Zgr515Cc/wSbu9/gNJDIYnVYsLmuSvIxhDnjtClw1cJV+aLwK1j9ErBqmtZxZbFsqVtXPYQTWLZnBDqFXUnKLJYVDQQLUiKVI1BdTXedDkmXmSvM/hIpilool7HWX1uwKDXuQURQTwk6XAJyqrPqm3X2tcKfDYELS0Jj0WjtEJLRICZtNQXNxhdvMjq0RugWnrbFc1izsncs7l3gb3LV1jMT/CSsOMBXgkr35Iaz3D3IfauPMjGxQvsHx1xcusWvp1hyirzpItI1N26TZggKXdwFqsR0SQPhICLHbEymEGFS5rYtchySTpuiIuQOcMjjYwttlDUvRGhi8hsyaI9oJ13aB2IXUtyHkLuOuQjmEqh1gJYGAGV1p144ifU6n7uEWMipizbm0ggeW4igi4MkL35mASHJ+mEWIVGY2pNb2SI4ulcAxiMWGKCqFbUw4piUNAGaNqAD4JKZt1i7k4OqKXQCqMtKSliEFznaZqGGCNlYej1Ks6d20DrxLSZc2N1zONxye0y4NAMxVIVlkGvQoXEtF3QJc/OeMzKCqlpGZYFm2WFDR5RilBoVv2cJ5l1LZ21NB5aDUkpJm7B09N9Ni6dZzTcgiaipES7AnEKe1JwvtvFXi2wYUFVK/qXd2h7Kw79hOUssHQNG+kyYeM859oxvtTsDbd4x2PvJg0KmqLhFvtcqDcwdUfdlnQpUWlNK5HkI0k8lQbpAr3C0isSVkWGJJbJ47Sm2hhyuoKj44Y27nPpni12dwZ45zBW8XD1EONX9Rl+sORtN95J3IS2B6Gy/Mf3/BSV9PjSyy9ntVpS6h51WeI7z7yZ8OCLH+SD//ktvOVnf4Jf89Vfx2bVp+xVtEtHnCj8csb40ohv/Opfh1UlP/GffoJ7x5d5aONFvFAm6QuiQjUvSQ0+BHIMwoPzqGhykjMmks9hGQi40GRZVNY/GJ0gaWLQwFriVEq0KhDnSSIkMWgNw62SncsvYtbdJqkWrGPaHLBpLjI8t8Htq9fzebX7GEUtBnQUQox0LrJsA95D9B69/najNLjUUXiLFgg4uuTAGvQQql4NR6C7yCp0+ccsQnABqw1KFGFdMi6sGRZr1T+lVG6/hadpJpzf2KRxuSIxOnJM3q9bpYnQRoeSrAUek8cxY2YrRg/twrQl+RbdMxR1TXPimS5O2BxusnNhm9liydKdIj3F6MIGqiu5994XMxiOuXbtMa4/8ZEc0+8cKgaSKLQU0Dmi60iickWkSQQckhSCJsVEMIm0AaZnECcsj05J0wa9kLzMXjm61ZLm+BYzk/VXUkik1qN9gCSEVUuKXU5OJvJxo1+fxxBrQSSBSYhOEBIvKHr9OUBMkZDIifiUb7hJKaKGZHUuQEpkDfaY/29oISLoWmPrXKQUvENiTsybQqP6Bb2tCimF+ZGjWbY55xDyvjEm1DoEpK1FjCEGEIlI9BAdCY+qe4zOjwiVcLpccH12yNVuyS08ByIUtmRoalifOwTPPHbYXoW1hv3jfaRp6fc30RhWjQOtcKKYJcdTx/scXT2mTmM0BmMti+UCkcSt1RGXuhN2y11OD05ISpGc4GYR3fVQjUV7A76kkhJ3dYr0YWgrrATGto+2hps3r3O9eYb68pCX7j3AB4Yf4iDOCN2KJ2ePs1v0uLK1zeKZBSpGKltgi4qUHMvlklQktDJUVlHYkkJFRtbS1HAya7GlphoOmKyE+WzKrZsnLJcdw0HFzngDI4a9/h5f/sgbKDdL3vrMO0k+choci27OW3/+J9jrD7l/tIfMAnvDTbzx9IdbpFXLa17+at7/kY/yn9/8Zn7t134duhR64z5tE1i1Hjtpqfolv+aNX8nk2iEnB7dp6gsY+7lNqH76kOxp5aB7QnxCe4XRNcGul7Ap5OV8TJmZknIxiBaNdIFAwgVH8mEtoOQQLNYW1IM+SRlc0/GRp99BX9+DthXzboK4FfPmEO+XXLzyEg72n8y2wOVzJUmkCGltzEPb0jUh84V9S+5QH3IsVBzOBaLSJCWIsShtMIVCRcG0gp7niz8tNZIcyiRalT9bilk5MCUwIiQfiTE+y5FONuJTRLQlJkXrMqtCxwDBEQV80mslQU/ourwKiIlQN6zKTeS+LdSkYWtvi6KsWGw0tL7F6yW+8FTa0bUdZTlic+8COo3olnMeff/7OLp5g9T69fcRSU1DUpmPrZIirCIudCRjnhXHSjESjcL0ND2rcG1CpgEdDDJPlI0mRogqs2VUjNAFcDnUlGIg+g5SRBcVmTAj61BHDquThOgVoZHMZrKQtF93ufg8u+0JOu8IZHaMpIQINCnitEVEiD5io6KMCnEe0CyjJymFqnLiNPmAiCGmxLSdI1SUfSFVQnSJ1HUU669OouAlUBcVITmK0lBvjgghElcNpSTm7TFVEdjaGlOf32Yijplqefr0NrfaKV400Snu642pvGEzlPR8RFTuHlQoS79fs4wr2m7FVtVn0/YppY8KLVUq2HSZ1RPqIemCwk86RqZiq7dFdFtUFVwYW97/gQ/wipdcwaclVkqSE2IHoXUYnR0lJLDqIHSawvcZmBrrVzgCHuGl2y9ltpxw+oETRtMBX21fybuuvp+VdVw9vsbV7THbG33qUYWfwcnhksHWDnVVU5QFzk/x0RLUiFUoQXnK0mGHwqgfWazmWSmz5xi6krZLHN6cMO9FYlcxHBp6YrhU38s3PbTLi0cP8R/f/Rb204x2qHjq+Bl++D3/lt/4q34t98RdUtMRQ0GhKkxYcY+K1Hvwnic+yE/+9E/ysje8hh3bsb3RZ3G85PRgwWbYoGdLfsOv+Sb+3b/49yymkxe89L4gjHtKiUDIXjq5OUFsA8GDUx6IKJ29Vwl5uZp8Itdy56q+ED2CwlAiSlNUBi0CxuBUonELUvTooCjVNkpXhCUk53BuyfHpARfOX6QebtAuTmiWLXWAZBSLpmXZdZB8Zh34juQD3uclv601xpAZLgghBJIyJKXRokgxsVwuacOcjhVOhL4ZkLzJEqvJE4lZ6S5EtFIYa4i5HQ/Be4REZcb4DprpnL6tcMuGGCIxQJCCRJPFpoInpgBKqIdDBI03ASShd3vU45KiV6MRCidIp0ESq9mEzrVIWVPbIZUtOTm8ydUPfRA/mSBBIKrsnWcXEEmJ4FwutgkBSZIfUeHLCl8aCm3AhHxjnDWkVaS3cxlTDomr6frnCTFGgvdAppqS8qqJFEHlpF1MAYmZebJmqK6bNyt8G5AQsbVCV3lF9wK9DD5HkHV7NI1ZU2hTCkgIKKWz1K9LpLAmc4ZIcDlvRGmwpaBMovH+WYanCx1GF5T9kgR0rssrvDs/CRKFtTjf0euXDDf6RKvwLmLFErsGpz290YCNC9ukkaVbdHz06jMcL1c0ylKpis0QGTuFdUKlYLwx5ub+AaYQdvc2MVoxjR7nOnpFSa0LDIbSGvysoUSxVQ2YtTOqzfOghMmNKTeeup1/37HB1ppts8fJ00vUrMA0NYvDjq7pUORq3BQT1hqM1tR2gJII0efkMJEgBnfiGfU2GA9HzE+mXNZ7yHbi2vI2qUrc+PA1XvnFL8b0LSf7UzbsBsezJaUd4/PPjnbRMG+P6fe3GI9rvA4oDYONEb1RxWo5R+YJYxQpeJwkumXDzVv7eDYo601i2yGx5GWXX8Hm9g7/6Z3/hSeWN7lv6xK3bx3zUz/xNr75dd+A0Su0txSqR+oCfVsRygH37l7g6eNDHv3oRxi94hV02tAb9FBK4TqHUQVVVfPlX/7l/Oybfy4n6p8HXxDGPTMaBDGK6EMOUcRI6FrQObMffeaCG60JOKy1eL/muEeoqx66sAiWIAopMv0rSsSlSNQCMbNrurTCVlvI6W1C02B7PZbtCSKX2N7e5ebyhM51LJYLyqoipUhS2SuMop+lfZWmoNJ9rE4oyauG1mejKimSdDZGxIRvWxq3IhXQH/SpYkEzTUjrKJIixpSpcDERQ6bwBZXj1ypEIgHfRLYGu7BynN/aZXV6DALlYIuYLK0/xqiAoiCmSJciqMzvlRjxcQXAYrUgLOZ0J3OWJwuqosfuhYv064t0uiEoQzftuLH/BMvJIRJa8BA7l3txao0YgcLinYMQiM5BDKAK0DW6rDGbI0zfwmLO6nhOXK5IPlDpHkYrtClpY6YHRudzYj3FdWY95NVKXNMayd+5NuausQklAjHz/lMMJBfpnFCmCl1pkoq52vPzdm2DigqS5LqIlBtelzonzU2ErgvPdmUKIeJcR0hCVSiKNdUzupz4jMkTk6esFL1eifcd3arLN/kY1yn2tJYQDtSjClUrll1DMoLDcXhyTOxbyr1N0sBwMF/w2FNXOZ3OMk3X1gxUTT96hp1gRGH7PSbOMRHP7nCEMZoieVyzpFs1jIaZZaV8QlyiwBAxtMuIzBy2qPCnmr3iPEuzwLUtVW3ojWriMLI4aLFzRZoZ3HGL14m6thit6YKDqPA+h6Z8aDBGMFZoG0dV1eikcV2HKhP93pCmc7zk/EsYFpvUYcATN5/m/T/5Hi6+9k2UWxXtMjCPjqPVilHPoGwfn1oWrmO5mOCkYVgl+j1LWVUUtiQFhXcGqSyF6Rj0IrPFisPplNWNliCJnd0RSlmiU/TLHb7q9V/H+NH38OT+M9Sp5Pix27xTv4Mve/2bqLTFL1dUhaEIgh4MOJxU7MUhNz/6DM8UfUYvfSnD4RilNF27Ikboli07m1u87nVfQvjbX+CqkLkASdbhh1yQk+PcCY3mTuxZch03xmqssRhjctjEOcQWxEITY4QY0SIkuy5GUkLyWUUvRU/SjuF4l+Vsh9XqKYrQ0bQzlssFGxvbHNzUNM6zmM+BzH5JgBODsiXRVBSFUJIv5JSytnb0ATqI0RGblqizlGuuswkMqj7K1khhcV2L6gt9VRI9dFEQ7XNpuhKSzu3BbBJ0mZkyPjlCOOFF934R/WGPm9ceR1nLhfOXmJ6ccDxLJK1BqbxK8T7zzcViJWKUQqKnPVziT1va2xPcfImr+mwPLjPcuIjXSw4nRyyWx7TtKTaW1NWAoD1tu8yetNaIMVluVoTkPTrmm28wBdXOOcrhkOBa9OmC2eQI5i0mptx0OeT/iU2G4DykiL9DWwTkjpEnh1+4U9y0xp2uh7JeOaQQyA7rOs/gEu2kQbUGU9mPK9L6fECRw1aEOwVYksOJKOgSoXFQ5pBL/mzZO6wqjdE5JJiL2RShaygrTa9fgIKu8zgfcqgs5n1FAJUo64KitrjYITaiSsNq4VkVgc2dXey5PtePZnzkiWs0zjOohiSxdKmidia3mAsabSxRaT544xkGe5tU4x4xdkhKtKtMu+yZkp62pDawOm0YVyPAwGrFWHr4TuPFYGNCGU0qLXVZUKoSHyOrxYq0FNIyUKsKXRjqqofRGtIqUy6UwvsW70BpTYyC9wHXrJUwRSA4govsjc8zO5yzKRvYXsGVh67wvg+9i+PTQ3Y2zjNrckjnaDZDmTG9ukKMRhnH0nWsjicsK2Hc9XA+Mqh7GCnZ3hrhvUfJHKManE/UpWLpHYdHx9jKMNzqozEUWrM3HPOGl/bZsBs88fTjHA4Oeeappzl64IDeTg1B0EqjJVHqknPDHUQ0p0dTrn3kGfr1kEuXYGO8Qa8/yGqeKXH76Zvs7ezm7+d58AVh3AF0TOtuNesfb3CwaDFFn1AVYKAAJBnECE6yvjUhkMTl+DyQVMztx8SglCUANgoDlQtkbBnQtoEU6VV7LI9vkRYtqV6w6I7Y2rlC2fsw7vQEVrmkWBkL5LZoxIjVa03uHOomqZboY+5YL/JswjGVlihQKI0pCqLKy3IfHd53ucikhugUJmgkuJxf0AmMepazrYohySdUWKCCMFQ9BqMR2hgW/QJvPE2c42mIQSEpzzfp7MeJ97gAQRKlFXRpWB5NaE8dqou0qzk3HnsM3aswvRFKaQZVhUp9FvMGs+rQ3iMxU7TwERMTYizESFqvNFAGu7nJ8MIFknjmT10jnZxi1hXGyui80koJHQ0gqJieQ9lOsrFal5mKSE6mi8rditbJxySyNuo8W91852kKibjI2iE5vvX5QZRElwJlVOvKayGGRCRhjKFbBVLbYaoyVwynvDrRylLXBgG6psOWBvB0XUd/MKI3MnTe07XroqZ1TYSkiNYaZRLloCStVQ6rvmXerViyotodY3YGvP/abW7vH+NDwpiaWpcUumB22lLFyICSfmHwwNI19Kxmd3OISEAVCh8dLgRKa+nbgqGpUIuYyQ9Rs1p54jJgAji/QqmOTjoGY4MZQGUStkjc3p+i6PIqM3qMLfEofOcJEnFdXsWJSC5M1AVdF2i7gNE1bl3IXJQlRVEhKhBWDr9MGGOpqbi4u8XopZaP3nw//cs9TGFxqw4jPSaTJUKPqmcpSoMpbHYidWTZOhJLUtTURcF0doI1mvF4yEhqbAGF1ZzMG+arBSeHB+haoOwzHFSQ4Pz4POOXDRhJjycOnubq8S1+/qffSXok8fJ7HyEsWoIXVjNHT4ZsacXFouHa4T5PfPhJdCwpdIUg9AaW3nDMzq5nfvV0zZt/bnxBGPeUEsn5XNCyTpRJSvjFMosrbfRJA01nEqXKcW5RQiSQ+ZExF4VguFNlHkL2BAtjqYuSezfPc87usOpO2V8esn+0QGyJxIpuuUS7lulqyvkLD9Ab7rA8OIZFA1aThgZTFOj1ysF3Dc4r8AUqFDnJt/aalNJoa4nkHx1K45XgQiAFj3QdXdc9KyPgG4/RNRhLCgbXLEmdJ6pEKhSCRUqLVAXLeYvtFdxcHfOwHyDFJno4YD5f0oaA2Bobs5ef1jIDxlpSyPG6EMJaiySxms6IrcNILn2fn55w7bHH2Lx0BdszuQxaF5S6I/iQY8bG4kNOfFpjEGPwdz4LUAwGDDdGxNAyPT6gWy2yESbbNTF31mH5nLYsaSWv0u5WB3jWqK85jFoU2hiU1qh0V0FQHnSXRMPHIEkQIrj47PE+HwgkQq3wK0G0giB0XYdBY0WIncu1SCnnZmIApQ3aWpTOjk7wHltqwtrLs6VBGegal2UIUk5cf0zGQWEKRTW0JBNARaJyrNyMpW8Y7uzy1K2bPHPriNL2qeqSbt5R6AI/7ajnnp7RiJXcfksLxMClzRE9Sblo0BomzYoYAj1bUYmhQkMCU9S4kJgvG1wXUUkIXcciNdSFxpY1YdGiSkXbBSazUzovVHaAqjWxi5AMXeczUwzo9Xp470jk3Jv3AURRViXOJxwQvKcLkYRHm0TohOQiRVGwPJxTVIZzo23iqmVga7plIAZP54RFagidoj8yFD2FNgWiOkKKLNqWtnVUZcGgrhESbbeiLDTbm0Mqq6lUYtLkwsbb1xu2OIeutugPa0JK9AcDXvKil+AM6LLP9HDC049eZRxGXBjvkZIihILl6QxLxZbaYsKK0/0ZB/1Dxv1NJscTNjb6bG8MGfTHrJjnle/z4AvCuLNeWie3DsvobASi9zBfErxHdTV62INK55LqdVyWlFAUEFXuIJ/WCVqf0Cb/aIyHnjb0q5r9yW1mqcP3oR/PsTrZYz5/GtM0zGenzKcN587dx9EzT4Fr6JYNYoqc8LIWo0vEK7TRaCyCJvqQ54onKUBpxBpS8LSLJctnBaEi3vscO1YaT+7KbrVQDAaogSJNTukWp3ktHgJeJaJeMDw/Zt5Z6uEmN5YzroSSl195LU+6fVaTJ5Cg6I/2mJwcYJTKYaKQk2wpZi8RwMWAsnbdNV1QVmOLAlHC8uiYajDADs4hUrBaHNHO5kgXqcoBthjggyOE3Cko+i5TN9chEyOCm8+YHRzg2xUSI1JY7oTVAoDKglcuhjWjSJM6+Jj7/fF4Vk8IiCGuIzSfPIaeZH2TkI/dJD4diMhTQNZIAJ9S+mIR2QJ+CLiP3Lzk21JKJy90nGW34oSGni6JvkMnjTEKs6a3dvPlmt0la80YjSRBi0Ktcw4SE3jBdQFrSqzNPPeQIjGmfONuAzFkZpm2wnCzT1EbWmnBJNrQ4aJnY3uDd3/wQzgMV+55kOnpksntOZvVCGmgjpYyJUrRRIGFhFy5bIRhVaEl4Y2ik0CLR6GpVIFxCZFAYQoiwnzhWQaPS+B9yoyhQqF7FRHB1JaEZrHydE6zWoELidAkksuyxcZoirKgbZvsmMSA1hoHoC1aKSIaT7euG8hFYCl49Do/U9U9QvTMl3OUUpwf7dGsGirRKBNQXaS0PUITWHUdte1R93poqzKDTeek/sKtaJsVyXg2qgEdHt909Hs1460tBoWlPj3B2siMjqPb1yh6AjZS9Cxdl+hvD3mReQQ++iSbZoPVyYLT4ylDMyboHqY/Ik0cy+mSgRqyW59jtrjO7SduU1Jz8coet2f7WB8pxbB9/gIvVMTxBWHcn/W8Ul5eZmZMys0KyJS74DyqBbfVx1aG6BOBuC7YKVHarGOzipjuGFBDColVt+KJZ55isevYb2ecSJsLoSgYDvdYLo4JswWhXLJczKjqTShqpOtwwWOdp5AsctUbj/GrFVU/L6PbRaYn5pVDjveHtaiTJFAh4lMgOJAQCQjWWkLwuBSzHjeRNjb0h1tsDi4x2dcsJ6fELpF0oG0W7PUM88Iy3jrH6mSCqXZ5ePcyt3/hKqJha7zJk6dHLLsWS8q1ARIQlfBJMDrr9LiYwDtMAtGGZHTWg1mX+M+Oj+id36LXH7DUJafTKXHmqMsxZjCm7I+zTGzX0p7eJjiXPfeYaOcL2uV8zXiBhKALS9HroZCcDxEy2yYpTFGiyxLdrjLz6BM89jtIMeI6h1aSb6paE9eiYnJH3U3gOfuJVAbcL9pz/8qU0uFdr/8I8OaU0l8QkT+yfv2/vtABlm3DT7zvHbziykvp90ekzjG0NSYEbNvRzVeURYkEIfnM9/I+e57ETCaQJCQn+FVEG4MtNDE5Ykx4F3Gdx7drGrCCulfRG1Z0saVTbVaWJGv8vOc976U/2uT8xft46slDDm6dstXbRrcJt2ixUlDZnM/qrIDNWi22UJRWIDp853DeE7oWHaFQQpEUpWgKU3DaNsxDR6gLZtM5rnPUgxGt90ynHVESW9WAGAqWs4BbVsTO4nyBjope3xBiR1Z8i8QU8cHhY0BpQ0gJ7yOehIsNZNIRMXmUgrKqiMGxWnU0XYuxisJWaJWYTxpKW1MogxgILlIYjQvQrVpmp0sKqxhtWXrDIS62oAIhWhKORTsjLBoGdZ++7VEkQfssmLexMaLe6LO/POHW5JCnn3yUXbfHzqVz6NKiEHq9Hhd3z3Oajpi1QmUr5u2CziR6dgC9kuZ4gpGC7cEms9BwcHLC8fVjNocbaJu4+vh1pofHvOKhVzy7snkufEEY9ztqhrJuAhzJAmBRVK5ChdxxZuVQkwhtRPcUpiowpcLZSCcrVNSkpEBFlDa44Ndai4EYEpVfsqDJNDQPrbpFYa8w2rzEZPVhzGrOyeQW9155BZs7Fzm6cUL0nnbZUEtBXdUMNjdoiyyx2jUdrfdoKTBK5xxgyh6MrD9TQtDr96No7JqrHWPCRIPRBmsLQlqxnN1C7JB6Z5fz997P5NYxA21wXceAPs14l2I4YAyczE7ZKx7kGy59BT9/+jbe/cx7aOIcIRBTpipGPKISohUxWlQyWcBKAsnkm+gdjZoYEtiCpCoWXWJzVDIcjDnVhs4tWLSHiG8YsIu1ZfbS6jqLl7kO17R412IjuWoVjY6CVZayrFF1neUkuo60rkbWZYWU1Trc03J39OROjPVOyCcGT1xFxNqsuaOyPIOsaZNi9Dr2I4TOEZ0Hq7AbA/zx4jN1pX4z8BXr5z8AvJVPYtxNUTCj460ffAeD4ZhHHniEouphlwG1aomdx9gK2kBOvWYGktaBdtXgyfH22EF0+eYWQqDzLhu4kH8vrGmzvapgY2NIIrLqVoQqEH2i6bK+x4P3PUC/v8nR0YJ3v+3t3HvpQTZHNc20oydVbh5hEskErMlFPabUqAI6FQjJ4WIHzlE4h+sCCujbgn5ZsVo52uAJlWbVRRoDxaimvzni5PYp09MJ2+d36fd7+Kng20hsFSrWRKfo2oDVgraKdi1tHJNHrGE1WSJKg+SEvtEWhaIqLTF4mi7incMnMptGDFoJdVESoqeZN4jpg440bSClQNKaaTOjlD5iSprGMZu1mAqKQUXVr4mqJeqItprlwrGar4htwntoWxhXFZUt1v8Hz9bGDpvntrl2cJ3VZMKJhcH2EKX7qFQy7NXIqI+fTUEcc+8oK6EDbA/sRsnqeIEshcuj88QVLI8bbj15k8sPXKAsa3rlgNODU4z6Ak+oJnIhYYq5QjV7eAZjS0QMSkrKckhdjylUxWp+wmoxR29alC4wKtKqTHuUOxQ61tWuspYjEJi1J5lTHrp8Vi2ofsPe5kUWT13FLRbURDbrARfO38/J7SeQbk5hszdUjCtUX+Nah29WuGXAOaEoarRLNHSgE3odhgAheEGJRZTknpeS46hWWVShKXSBijkJi4XZ7ITJ0Qm9F72Y+t5twsmCoTGcO3eBdnpKVfdpW0e7nNKfFzwwf4AnFo+xu3tAe3SVGFxuvGw0IWR5XRfAdIFWewqtoTYsak1aNkhUa1phbjcm1SZe1XQh5JBNNaSVea5qTA6/nOREcmhIoinKkqrfp+r16BpHSh5bWMQaXOsgRGxRoMcjEmC6jug9xiRsodGFpVsnQ59lxMj6GkhrptOaYUJKeOdR8HEMmKyUqtG2IIoGGkgJM6wohjX+aP6LvSx/TEQS8LfWKqZ7KaWb6+23gL3n2vFuOevdyxfYuXKJ6WLBzev7TD74Lr74/pfzcG8b4x0xeLQ2uRAr5P9Dirm6dLVaEQnUdS9781IgUWWZ3ujxIXPXY8jerQB1XVMUFuc6ooTsnUrm1A96Y4xacf2Zm7zlzT/F/efu597dC7TTFdKCNoAoGhtJJlGrxEAr0NDqRKsTnWSWU79N9HWBLmFU9alMQegci9WSlQ9MY+TWdI5ShtHWJg7Hqp1Q9WD3XJ86Jg5un+LaJSmUaMmJYK8TpEDbNsQYsIUlIlnczCrKsiJEctVzUnnFEjP1uMRQGYNSeTxB4UMg6JCvwaoglpY2TZgvj9AmUtcl2pZYalQb8K5hsQrEkxnRNvTFIqXDFB5ls5a8KTSp0ZweL6DrWA6GDAYV25sjKpOVOnUBL3vZJrf2r3G0PGESVlh1jtGoIujEztYQugVPXXuGRQgMhobSgOoV9HZ7tK5ldbJgc7DBvTuXefLWNZppg195RvWQamy4/ug12s91D9VPF7miskB0jSl6WFOhTIWxPXQ0aFVgbIkpSkpToBykqadQBThFKCKhFtSweDYeb60hBwYEq3KMU7TGJoWK5LixgtSfIcU2G1v3c3z4btrpLWYn++xu30PV20CnQFEUxELR0ZLiii4G3KqDVkhR0TOJc7pPEzSncUUr/lkv1Nhcss06cZiXmgqJQllUFNUI5xJGgakti8V1KjynN68yunCBNCzgXI99N2G8u0lpehTKUZd9BscjRostrmw+zC/c+GmKQojYrCgpWUOEKGzKANMueVzNUQRshL1qg4NwwDxFtIqoTLKm0GBUpOlWJCWU/TGNPcZ3DUopoo+EVZclfwWapkWPhow2t6k3ajqJ2HX8s1utcMtlllWIAW0spq6odC6aSpKyBMOazSIqx5lFa4LPXPYkuS2apFzclqtSUzb+ZMPvQ0C6LieSEaIK6JHBjgvwOVzxi8CXpZSui8g54D+JyIfv3phSSmvD/1/hbjnrl7z2lenC+QvI/JiFj1x9+jo//eF3Ud3/Sh5O28QuooyGQpMWHoVgRFAp4VuXGVoiazEwm8OMK0dUiaCgi13W2ek8FYZCWZpmRSAgZW6vh8CgN8jkhKB4/MNP8eDlh7hy4UFCk1dazgeMVXTRkUpFMokkOTEP4FOkSRG/lgqpjaCTYmd7xKAeEqJiMm9oY5YJ6RtNyZLbBwecHp+QnOf85gbnxlvUpRDnWY2xshZfWUKT8DhiWtEFhdZCTDGHf2JCq0wU6JwDVCY3rGmfRnSWb8gxP1zX4ZyjqitERUJosqSJElJqqcpMxzSlpioU2mp82xIlYgsBpWgaz/TI4VNksKPpbfagaCElOtegKk25WbGaOI6aKdO4ZBEb+mXBaFDQq0rEJXZH21SFYf90n8X+MabTlLpHVfXohkO2NreY3rxFO19Sjio8jlBq9FZFGYX9G/tcvOcKF8bnuH56k9RAQlP1KjZ3zhHi84vifUEYd0Szu/sQvcEeSIle/5M8QuyyLIFzDsTTr3vQ6+NWc+pU0h4taab7pM2S3ksu48qIFEJlCxQJbRJaQIvFp5QlBLwmxYCXgIsLrDtmc/Mi89lVbh8+QWH3uPzgI/R7G8zdlGSgDY4iWtrlimFvi6OTljZ5RjHwhu2H+a6v+g6u7V/j7//cP+bxeJsUhKQCcd31OCVPUglRJUabTOdDQ9L0NrY5d+48t64/mnMOBMJywljO8/i1p5iUA3YfeJAuOcQFxr0tNso+xTyxb+ZsNtvYpcebjqQVjRd6JLaLASMpeXDzMq+2D/C3r/0oN5lRusT/8pt/P3/nb/0d3jt7hhgiWiV8l6i6BUXaoEtgVcFgvIEfDpmdulxIFrPsQpScE0kxe1kuOHqbOxSFzXx31+XvYJ0bkS7mKly1XlVJwCDYspdlGu6wXtbhuTtyvWpdrStITqyv4/txzXVPiSwTET3Jh8w2HGjUoCSqgDvJPOxPFyml6+u/+yLyL8ntI2+LyIWU0k0RuQDsf7LjKAGtI4O6Yjza4OnNI64tZrzz+uNcHG+gGp116iXkalsSJgbEJZplw9b5bdy0w8eI1uvmJktQVtMWLS616ADGQWUKbFI08yWictgyuUClLaUq8B6WkyWPPPgKlrPA9LijKnv5u7SQSo9RCVM4qjozo+ZasvFLYKMitpkpo0YjtECNASX8wns/jNUVG+UI2yWqzvFIb5uXnr+Xx/dvcXBywMXNHtuDPmEV6BaClU36hcIRaGPWz1FK4YOn7g3WfVcyAy24wMZom9VqlY00Ede1VEXJoluilMY7T1lWmUEkiqpXs2hOkMJz/p4telpgNSMFjw9mfc00tO2MIIZiPMCjWbUOQo2fGCbLhq4JdD5QbkXqsUbVCmvBbhn0JrgVSNQs/ZJVM2EVDQNX0istZWEYFX3KzQvMFwvC4RI9LIk+omOPWm2xVwlpBUUK6FLT2ITfKLEYltePuXn1BvdcvEL0kZvP3MKXkfP3nGfj4m6u5H6+a+/Tvuo/CzCmZHt8L4Nqm0Iq8EJoszKi0uv2ZCRMSpgA2lh64xFFYWhOjphe36dyNbu9i/RlgEahNFhjMFqIEsFoTJHDI1ElAvkCEC208RRdO3Z2H8Z5zenkJt47zu3ei62H+NRBcCifcHPPxsY5jBHOxQ2+Yvw1/Jrd72Ly3ks8bN/Er7/v11G7AqcSHp1Fo2I2bOhs6JQx1P0RyUKb5vg0Y8USuznk/P0voj+4SK+3y+mi5aWPvJyNfh+TAhiFKgyrbgU9zWxvxuH2kh29y732IQgqMyZCwseGvq749pf/Jh598ikGO1t867kvZ9gIk8Upx/M53/kbv52e0uSQB4gyKD2kKsbYakTSGlVbys0RZtQnWSHQkWTtCStZqz8aklFAQKWURauUIRlLNBYvCvGJ1DpC09Isl7jGY6Sg6m9gqx7KmtwPUklmHoUs+0wIRJ9DbUVZZp14u6ZF3ulLeieckxJUBeVoiCkKumlLmLWf9vUoIn0RGd55Dnwt8H7gXwPfsR72HcCPfApHo4yJAZqRLbFWaKVjzoonb17ldDUnGcXSOZzLlbox+KwWGkF5MOTVZnKRZrakW3brVnxC9IHkAiYqYudZLpaEmDC2IJELxpRYcIJbeVazFbPTBaFLGFMSQiIIYIUuOYxODEJgMyQ2lTBIkX4I1K1j5COjENgrS871CjYKS1wuSM2Kxz/6YX7kX/4LegUo17Fb9zlf1rB/RHk6477RBuOqykJ7QVDkanLBQFL4LmQHDrDGrsNNuUGL1QVGl0QX0UqTQsI5j3Oe2XSGC1n8q3MOEUVRVvR6fVarBu8D/d6AulezMR5yfnvM5d0truzucnl3m4tbG1za3WBrYChUg2FJoTqMtBQ2UhWG+cmS608ccHqrQ/kByVdoXYKOmF5guGsohgE7cJhBoJUFR/ND9k8PmTUrugCJkl5/i7I3oE2OlW8RqxgMBox7fVTnYdnhli2rxYouBFKp2bi4xbXbNziZnnLhwkV6RY/D2wdMTk+xVYEtnt8//6TGXUS+X0T2ReT9d723JSL/SUQeXf/dXL8vIvJ/ichjIvJeEXnNJ7/4QYnJbfB8IAZHiA7nW0L0JBNRhVAWlsoWCGDLgno4xIeW5ewAyoKdex7CpJJ07LK8boBCchxZaUPnHV3X5RLtlIgoVAIlHq9WNPGAne3zDHr3MmsmnBwdc+Gehzh/6X6S6Hzh+UBYNVRlSb8Y8N+99Fv5dVd+F6vHznF6OKCb9HnjhTdxv1ym8rnZcFRp/SDTJCUSQ2BjtIEuC6wRNvoF7fKE208+weTgmHP3v4gLL3sNutxg7nK5/+RgH4uAEoabW1hvMg3tuKKY1NwbX0FqhabtSKEj0HH96DZjvcOLN+7ln73/R/nKF38Vr+29BNNGPnzzCV71qlexYwZUYlF1Qbk3wp4zpHqO04c09hA3nmPvN4weGTJ8YIA6VxIHJhckqdxkQ1lDFlDzuNDhQ0dIIQuIFQVJq/8/c/8dpFl2nneCv+Ou+1z6rCzfXe27ATQc0QBBgIQhCZIgQSsKIjWUKClGoqTdGW2EqInZkSZmJ5ahGEmj0ExoVho5LilSlLg0EL0DQMKzYRrtu9Fdviqz0n3uuuP2j5PggisCokhKwKnIyKybmZWV8Z177jnv+zy/B2s7fL3EtzV1vaRtOiISUw2Sakap31ukk40+/t7Hru+xbUuw9kQjL05i4TRSybTDkymZiZUCWWTY2hGXPpnD/jCT8PePbeB3hBCfAT4O/EKM8ZeBHwHeKYR4AXjHyd+/5BBAEQUDCwMkEk/ja2rVc72+w24/ozeJOBq6gG8tobe0izrpvZ1AOEG/bPG9pWs7JKSyRO8JjSN2CZYXnKfr+hPFWWITpdaPIlrJ8qhhfrRMDJ4oyUyG1Im8ihZJz65lIjMKidAKLyI9DlVppt0cNTTkK4ZeOprYsmxnHE4PeO0bXo0sBB96/BNMTq3hVeDO/i52PuPsaMzFyRrCge1dOikKmV7nEJKW/6Tfluc5g8EgBdG0PfWiwfYOJTVda4keiAKjMsq8+L2H+3A4YH1tjTzPaOqapmnwLpKbksW85aXnb3Ht8m0OD6Y0TY+UikE1ZGUyZmtzjXNntrnr7Cnuu3ia++/a4fROhdTH1PUeRgQKMWB2u+P680eYMIZe45ylKCUybzGjnmzsqFYlg7WcfJLRip65a5g7S68UFCVqdYBaL7ClpxUdKousrY3ZnIyRzhM7Ry4SoydIQRxo1s9v8elnn2C+nHP+7DmM0Fz53Mss6znK/PEaqv8S+N+AH/2Ca19MEvYu4N6TtzcA//jk/ZccWkkGVU7Xe5xzOAJCpJqfyASFzhB9IJcZmTF4Felsx/F0Rt/1jM6fx48rXnrxeXToGMlVopaoTKFzTfCWZd0kBskJ4CoQkcInLbmH2s2ZTDq2ti5w805N3RwTtQajKQYrJ5PcI0WDsj33nn0157OH2X/RE2zPOJMsuoiWm3z3fX+OT1x7P59qPs01dUwQifbofUAKhReB1i4pi4yxyQiLmvHaKtfpmO8f4RbHrJ+/i1MX7qVzntXBgCtXLqO8RuocZYZs5mPunZ1G7VUIGs7wAMqegJacJasdfYT3X38/3/vou/gbv/q/8IE7n+S//cYf4sVPPcXN3ducuesC3/KWr+eZ25d53t5mvqHpBktsV+Pp6W1DtBEtBWZckI8NarMiHDnibsNyf4kLAZyj6ztCs0TnBegMKRO4TRuDD5auXSZpgQgEI1ES6n5BVDpFKLbiRPBxQsdC/J4wks8vXD7FKooTZ6rKMzQQbY8QgnI8xA0Vfd3gpw20gRjkf7LOPcb4EvCqP+D6AfD2/6R/jM9n2fZpJ6UFSzrmqmeReWrr6URyNksbsHWPt5F22VBUJRqF6zy+T8wlQjyZ0wEZoYgGFSOu7vE+8Y6S+iglkEmdEUKqIS+OW1wT0CJPrJK+JwpQuSLqVH4JGhYqo9YgdcTqBPUL0vLhZx9n/+iARx59mAcefogXn3+O5bVbjMYrnLl0L2/5rm/mZ//NzzE+vcO5ySZSWPJcszIakOc5SqikggsCHwXeJoettcnhLKVBqXSS9C6g5MnyFERSprUdymhMbpBSEYViUEm0FFjb0XU9Whu0NigtaPukktJZjus7jo9bRO6wXY4dQlWCycD5E528khSZYTKu2NiacOb8OotlTxCKedNxuJjS1w23r+6xspUx2dBIPEI6onZEkUrAMtOo3KAKQwyKTnliJghGQubIRpoyZojjFh9b6COFUUx7h2sdMSoYVKhcUm4O6XvHht3m5t4tHjmzzqm1ba7tX2X/YA8X/hgmphjjB4UQF///Ln8xSdi3AT8ak7zho0KIlc/XKL/Uz5BKIPM8lT5EgyQiT0IgRCEQJnEaspAhBfi2pl0s6aynXNkmv3CRRTuH6ZJibQ1nNboFMUnUuPlsQT1rMTopQ7z3abcQI+KERdPT0HSHrG1sc7BcZb7YYzE9RMSKaDXjtSErG6Nk7OgtD65dQh4NCD2oVEmibaDRikvm9Vy89wHuPvoIP33zx9hTe/Rooks6fCkcy/kh9559kPtXHmDU7zB3h0y7G/hY4Lxn78qz3L52nfsfeR2DC+dYzzO8jBQIJqMVBlGznRXsC+idZCzHrIWSg34PYyPf8/A389HnP8HHX/4If+Wx7+aVo0v8/DO/wTc/9LW8503v5sd+7Sd49s4NvvGNb+edmeZ/ed8/5cl+F7yjUxLRN8TpAf0igi5xpSJkAVnBYKukGA+QEqa3p/iFp3Ue13iqiSAfZiepWgIhNAU5/bymq7vEa8gkMkaW8xmFGSYDGqQFDoFUJ/LMz+/eI6n8o5IzNviY+icESp0hdEAUGlllqKanO6qJbcIOeOH5fRrL/8IjEmlCYOocnVLEImMee/b7mlNiwnaZ0XqP6TxZJ5jPl4gg8L1nsFKhMSxnU4ILyYhkLWVVEoWALlLGHKKnrlskkpEZY0xGax1BCfKixDuYHyzo5hYZDaCJJ6Cxums42jtCF4oLl87jVaQ2gqVP4StBeGxMD4FLb309zTNP8fjuVQ4Hms2zG2SV5qXrNzle7rN2doudt76OX778DG96qGS1MKwLzWiYIaUiVzkiCLq2x/rEejrRymGyLJ02SP01YzKkUPR9BwGc9xiVrPZaaKx1NG0K/G5tw2g4Ih8U2D6Va2TU5FmBkEl9o4TA+Z5lm7weS9uTLXq0jhSFYjiuEDFimy65vRUgI+PhgGJYsiHGnBMbzLs5tZ3S2GPmBw3lQCHzgNDh94yCPjiE1OjSpKzY0NH5SFlGZAExk1RZySgbQlZgxZLcCkw03Nw74ubeIcOdHDXM8bFn4/wWRmXs39zjpZdf4qFXPkAxMjjT4bz9onPvj9pQ/WKSsDPAtS/4uusn177k4h4jBG/RWUAFkVC90RJ1QMQcGT7PGklvzjqC82TDMWZzi2wwoZs3XDz3SlRVcOSO8Is5duiZ14ccHO1BD159/tcVoNJCLyPJKCEdjT9mPTvN1uoZ7tx8jtnBbYaTEQrPwa0rLOcVm9tn0WPFfeYMw2bIsUypQc73KCXBKNqFpGCD106+jtYe81N3fowus2iXkQ1H+NixZnNeKx7l7vI1KEYMipqv+5ZXkeWSjzz/Ud73yV+iGQQW80NuPjHj9KW76HuH6y2qihSZIjMakYN0A1ZMx73mXq71NygU7B07/vJb/mv+8S/9H3zq+mXe9eq38dHf+Af8vz/x7/iz3/BdfPy5T/DBJz/E97zmG5jf2OfVOw/w9CeuYMcZW4MR59SEJ6dL7KwG4Qiyp3VL5FBS3KUR26uMuk3mBwvc0hJ8Teh8KnVJRTkeoYxOzHqgKiIxc8TgUZVktJ6xdiZjbXVId/4sfb914imo6Wdz2llNd1zTL3s6D0IapCkweZYooW2XvKNGIQclDBRt1xKPFtBaMlJoiNMR7B9JLfMnMiLgVMFMNcw6TxMhSINDJm7JUBFcwDU9sVX41uF8KifIqAg2Us8bUJHUeZKgJD54hBfoaKiXLe2iSzJInSFQCEIKOvEC1znqeYO3CU1ATPeT0opxOaZaLQkyYQqkhCqhzhLXxjtMdDg8Sge+6sEH8DJyeDxlcWeftu9Zv3ieo6bn9rWblKdPcc/OOZwe0ntJ08MCh4qKPAhkBCUURkJhJMYEtAEtc5CazlmiTwE26vOyaKDI85PYQJJPJCpykxGjZ1BVAFjX0/U91WCQguK1QZuTexON1jltL7EOnNbM+x4pA3LRUiwdRaEoCk1VacrCpEATQGcCnYGTgXxSgsqom5KAx9ue6eIIL3u8gTzPUoawlNjgEn5CRJCWNno0Gc4pooChKjC5pJgMsHVAWMGwqhg0lttXb3C2OEM5UkQZqdYqxnbCtZeucPau04w3R7g8+70A9D9o/LHVMl9KEvalxhdqgbNyQNfOcMLSeEuQPUo5jDBkIUPYRNFz3iXnZUx2+sHKCnJ1RLucM5FD1rYv0NMz31+i/ZDmTsN+e0gXG4zU1FiKvEAblfZyJ1pqSYL/yNLRugWbq6dY7F9lOd9ntLXOaLRC189xnWV6eMiKn7A2WaVoNJkOGKURWMqipBhYlkuPbwuyLuONG2/l6cXj/G7/HAwrsrxk2A+5pC9y/+A1nM3PQJCMzQpmGOiN5bWv+VriuXN85HNP8NkXHqc5us2kKsg2TpEhUNZS5SvEkCPKBpY5JmoeKF7Dp8VnaLKOfXfEqWKbxx56A7/1wu/wN972F3ngQzu8//H3895XfRvf8di38jMf/hW+8y3fxf70af7UO97Nhz75cW4Kw1Ct8Off+k38/Z/+VyzEDWJQGNfjO4Xb62jVnHJ9Hb0xJhtWxHpJCJFoe/q2RvUDTByRZRlFkZPlQ+QDE8bjhygHJdmkIhtk6YhtNFKG5C6MpNc3aJQVsLAc3dzn8jNX+dxzV6kXFu8siIBQSTHjC5DDnN53+NkS2fZoIdHaQKZgpHCH9R93mv+RR4iRq4tDllFwvGyIVjCWI+LMM1ZjxnqCXVrKqBHW4zqHDaQGczT4JoBPi9pyUVOtDZIUN3gG5ZjZ7ozD3WNEFykmJQKFtQ6nAlGAbx3NrKFrLS6cOHqVIMTPb5UCxkhkljjz0keGUbAqJME5hDGMVlcYrQ1RuWK2rDmeTrlna0w2HLAQkpsHR+jdA3yes7TgbYrxs0KjTIZWydCGA2IKvA9SEIxgOCjpB56mSYZFrVOoum893qccg0jEWp8a7iLiOofODIOqxLoerSLOeaRM+cbGarRWRDzWuRPSZqCzEaEr8rLARc+yD1jrKKuC5dzBvKUqNbm2FJlgbVKB8nhTE0yHKiI6lwQb0GhKM0neDTeg9TWu6fGNTQEppUQYUiaztDgfiH2Hr4tUr9eWoAJ5r6ikIuQpJ3e0MmHHlCyvXGfv6k3ufehukJHh2gChYFYf8bFPfpSvffvXoIY6eSS+yPijLu5fTBJ2Azj3BV939uTafzC+UAtcrWzEtq/plMVLgzE5BohhgHSff2KHZG5oO4RLNbJyWCXGxdEBZmVIT0/bzxPHvYkpTzJUxFDTxR6RJ4iXUBJxEl8nTpo7VTYgyMisTrD+ja1z7M1u4UJgfXiatlsSc4UPPaf1GJaK0bCkLHuWvcFFQPRUOqc9qaESNSv5Bm8//1YuP3OL41ywunS89xXvpLuzjem3kG0GdWAaIvdmBdXKEGM1ajVHPaCRheHG1ZexwZCFwOJwn1OTLYqQI0WGMPOTOqbhrskjbPcXeKm+xivPX2K0NHz3o9/Of/++v8PCWr7xnnfw93/hn/Lilef5xte+ncc/+Qwf/OjjvOHe82Sd5N1f805+/KmPUGvBYLTKmx95I9PPfZJBVnIhL/nUM59m396B4x6/XyPXR6hMY6TChkgUDoulGsDGpSHbZ1YZjg1VpTBlMiTZ6LFEgrAQHQnVLZPlLIISgkxa9NCQTQznz53l0uvu5S3HPS9+8iWe+MRnODg4xBOJmYaRJOiAXHqUFXh5wv0OjiITlOsV82nzR5zmf/wRQmBpO1qnkD1MXM6k1pxRG0wWBUZKilgQXEfdtDRdhwsS20c21xSLeUPXenQp8RFa6xionBA9zbJjdrikaxylyMlMAUhsZ+mxJ/Jfx2I6p+t7QKWwEJnkphJSWIj3BAdaKTKlUc6iY6T3LaPViu3hkEIblssaDheMo2RQVEifkblAYVZRsWX/YJFs/VmFiJKBVAy0oRQSZZOTNUFxOPEoC6SQxOhwtgehESe5CUKRZJAqcZJa22Nth8kMRZklw5O39LZNqhsh6LoOqSQm13jvEkxNRLROGcwuerIiB6Vomo6ocpQyye0sA961WK+IMTCbzplPG4bzgmwIqvJUI0M5SH2/XBuCVQgnqcIK42ICMgHFZvUBbtnilUcPJCrPsNESfEQ0NqXGGUF/0juwSpKNBwQcpqsZorl47hzPPPsUey/fYufiaXrZU67m7Nx1ivKO4TNPfoZXf/Wr/7PgBz4vCfsRfr8k7OeBvyqE+ElSI3X6H6u3A3hnafsasb6GaCSh73De44hEb5HSM6gyBpkhtn16kmsIuaA+PMDeOWIRBgmv2s+ZT4+JMkNWhrIa08WaIKYYrZA6SSM/P9GiD8iqQOYZvQvU3YzGHLO+fomjeoqbLfi2V7ydX//EkuNJwIwMO2abLGiUEeycGXD1msN2EkVESYWWEZlFRBCMcsP9+SM8uPYwH++fY2uwzgOrD3CjXSGzWQoFzgKz2nK5lqwoT7ZqOCUGvG70EPNLE4rts9yZ70N0bK7u4GLkqD3iODuFGOjktI0Fkzjm0vgRdifH/MaVT/F9b/kuNqpVHlp7gN/81K/zHY98M//qZ36Mx59+nG957dv5gfd8N3/v3/wz3vHYf8PuZ5/nPV/3bXzkxotczyLPz3d54yOv4eP7N1jbWufvfMuf5m//g/+Z31wcgu+xuzNee+5ensn32dVLiA69OeD0w+fZeeAMK5tjBoVJUXsC+g6aztLMatrjJfW8xbskf+u7xEkRSiG1xlSK0UrF2saY8cqIahQoVhX3v+1+7nvjvVx//hrPP/UStw4OWLgOu+zQFlAZQgRC8EThkeMMM1Jfiq30n32EGMmVpD5aIPc7Jo3n3LxgvZUU+5Z8S2CcwUbLwnUsuoR/LU2JtYHlsqFetggHemToQ8AtG6IIZL6gnre4PpKPCqTQqVfhQmK4S0noU1qQCx6lBagTLmeIJ6IFUEogZMIoO+8AgZURXRaU6yvEIhEgm6bBFDnDaoxSirYO5HPP4fU91P6MDaWZWkuna6qVVVbLIXlM3gWjTco66MG7gO893qawDXcidfUhnHychJJFnqV73Vt0ViRInYTedjRtTZYZiiyj7y2rqxOOjo8ZjQZkmWK5bECcmOdElnb9ISLpsH2LFJFiUCWEg+vRuiDTBikCSkSKSmMyQdN6psvuJOqvocgV41HF1pqGYUArgVYC10f6riHiWClWiNoTdE/vOxazRerzKcgLmQJIWs+8qWlMYBEUY1GiZVImhRDIleThC/dx5crLXLMvce6Ru1jGlpUzq+SDnBeff4Fnn3/+D+YpnYz/6OIuhPgJUvN0QwhxHfjbpEX9p4QQPwhcAb7n5Mt/Efgm4EWgBv7cH+YGcH3D7Po1VsqKftoQ+hkCR93OCJTkwxFRr6HUCKmgdRavAviexd4t2r3r7B0coMoRIfRYZykn65Qbp8i0YpAVdDrpSoX8fAJOhADkBjPI6KQnBIsUkkW/z/pkh621HZpuAW3kTz3yHv7Rh/5XwuY2Gw9tYjpDW7dUg3WqgaONEq09UndI05GrktB7ZO7Jllu8Zvurub2/z87KGZQZURY5uZOsZJHxUOAzxSxaJCphgUvPudGQexhzfZGxXo046I45WjYEO+SamDH2R4wzmTT8QVK4nG22kKMJC1Xzi/NP82f11/EdD38X/+gD/4B3v/Lb+c43vptf+fSHOfreOQ/cexevv/ch3v/rH+Cdj72JShm+/qE38BMvPcGz+3u8402v5u4nNjiSIKuCUZ4RZcR2gcnM85ff/B387y/OOFwes3n/DjuvuJvx+ghVKoTzdPOOaAtcKzk+arh++WUOX75Kd2eGD5HRZEIUAtd2aCHwMRKloBepTCNkxBjJ2vYqZ+8/y9kHzrG+PWbrVWfYeeUFmukxV566ypOPP8u8a/FSYumJMiKHErlWYHN+P6vgv/AIwTM/OOTgxVsMDgTnNjZ4/d2v4c6nb1Ecw+rmALv0hErSEGi8I3SRUTWmsx5rA31vmS+WjNSI0cqExnUMV0Y8+7HnqG8v2RqtoVRG03UMVEXwHmt7oiLV6V0AGYkqZdWGSNq9C4GUJ3gOJRJl0jqW3lOUGZOVCaYqaK3FWYfSGVVWIqKimTtwkt2re7RHS8aiROqC5eKY1nmMzsiqitBbGuvJlUoLe+vxzhFsxNr0MEmluZxlbel7i9EKHwRSAiKe1LAFru8BgTY69XNEklYak1Q2VVlSVjmLxSzBwnJD33eJIYOD2IP3SARZVhBcS9/05HmeMBlGI6I7kdwG5o3DmBJCSZWVZFrTNQva3jH3INoGYwKZEWSZoTQ50hQoFWm7BTJk5EXOaLxKHy3z5Yxu3uDomLsOm2kG6xphDIfLBSNyzKBABMf8+JCt4Tpx/QyX6+u8+OLz3PXovQQN+bjgwl0XePZzz9L3fwz8QIzxT3+RT/0HkrATlcwP/eGm/Rd8n/PUx8cM2jlFGZk3M1xtqfdbote4yYRoG/xgTKUNVlu8jvTzGe3+EbGpaXxDnM1RIiAzTSxKpJ9j54FY9YiJwQufMLRAxBK1oCrHjNQQb1uCE1hg3k9Z1HfYWtvhxu4LPL33FH/5td/Fw5++xNNXX2RyvmRiBjQusVvGq4pm3pFlGdUQWMsJC4hSYopIPIZLo4vcd3CGM9kGOQOqImfQC7KBQA0kQ2tQBw0TQOc5R23DLPZs2ZJHh3fz+OxlvKyRkyGTnVUOXr7BMu85t7FFfbUj2ozACvfKBzjdPc7nxh3vX36Sb9t+E6/YuZ/NYpNffebXefc3fjcfevqzfPLZ53jbw6/lvV//Hv75j/0o3WOCtcmYt7/mrXzo2lV2Z1NElfPer3snv/Sh9yN9oHASbSUtglxrNtd3+Kr7H2XjdefZPdsSpcTXgtlLDfvX9uiXFiMrcjPAaslkbZUVqbi+fJHZfEYMLrHuY6IM2hDQmcHoHK2HIKCeL7i5e50bT97k08PHWbl7mzOvfwUbFzY4NdZc+Kr72Hn4LqY3j3npyRe4+tRLTOcN+eYghUu7L29Yh28D/VNH3GfWeP1jj1Dpgum1IxZ3HCNXYpwjE4LjeUuzbIltxNYOt6ZY9oGmheXScVzPqNYrbOdZv7hJebpk9fgUNw4+y3S+5JHtEflIUwdH51OWgKtt0qzHVIqMgpQ5SkgeBSWRUhBUStRKVNZEeVwfD9haG4LwWNcxLA1KaLwV+D4yP1wyPa6Zt5Eg8hS60nREIsNhxWBYYgoFwdF2jrltKG2Oa/vkzowZwUe8T+XRSHKoIhOqN8aQ4vVOQk0QEZ0ZnLN4bynKCnlCAs3zgmVdI2VytkuZejla61QBsJYYQSmDkuKkJBVp+6S2MTqyXE4hFokQ6Xqkhjyv0KqgqwPNwuGURJLRdJYYl7R9JC8iYNFSMB6PqMqc4aikqFaIIpWbpJKMqiHDfMxifsz+8T52NuU4dvTB4UdjCpnTxkihM1SpEZnkzuGUqlrl7FDzzJ1nefGzz/OKVzxENSwQ3vLIKx6iKMovOve+IvADMUactdh2RrU2whhJHzxZ6InOojpDd+cW3f5NwvoGcZS0h0c3rsNsjgwQCMhokYjETo89tjnEEuiWAqoSOdBoqdPxU0rKzHChPMtXXXw90Xcspnvc8Uc8u3uZpj5k49TdjP0m15s7fObO83zfO76X//Un/j4TRoQuJSzNZx3bpwuW0zllVTAYZtAFFnOHzhTGCIR2lBR81anXUlWeLGSMsxEDIVHaE7RCR5FqpHVHvjfk9KbCOigXiqPoMaZCBYiiR0VH4y2fPr7BXeMd5EASpgIfDBt+i0c4x4t+j1v+Op/yn+Mb1aN865u+lf/95/8J73joa/jed343v/Zrv8JXP/hKJtWY93z7t/P+j3+Y7/7m97C9eZY3n7uf9918lpuHB7zh/ofo9++wU414zSOv4mee/h1KoVg/t0leDfimN76FDy4/zS8dPsnurRnNXsAvNV2T0XowoWZ9Q/H1P/jtvP71D3NxvMYzH/gUf/dv/o/s3z4k6BNXrfVJFGc9WgWEhqgUaljgfYfsHF0Li7bi6jXHZ5/6JPniNmvbq1y49yI757d5/f1v4nVf9ypuvHiN3cMj7syOmR0epvTjL9fcrgP33j7Fm17/EF3f4+qewVIzanOUD5TDDBk9Q5Fz66glHvRoY1gGj/Ga/TsdzgY8nqrICZ1DFoY7ZWTzqy/ypodPcePyLW7tL2iVZGR7Qt0wGQyx1uM1OBLfyKdyd1rwlcQLiCJiCCd8lgQtWxnknN6YoAh0tiHPBUZHBJG+DRwezNi9fUzTQhNzIoJCRpp2ijaR1bWKaqDIVECYhADJfCDS44MjOIlSJi3yQqCrjOnMpmhJo2i9QwaQIiEnTJEzGOQsFjU+pPvOqNTWyjNBxJKfxClKISmyAdbapPkJOUaZZPIKgc4pIBBOeO95YSgHBUG0ONsiyIhGEgJIJ2jrOcZoOt+BSaHkUmtMnqNMSjfr+5pBUXDYeKZ9Q95aslwwXilStrMmRUF6i5aG9ZUtxuvrLOKSO/M71Md7UA0YTjaJMtBZz9rpTdpccuWF2wzGGfdsPciN3ZfYf/4W5+8+w2hYIazA2q90cJiUlGWGPTpiESw4iY4GXQwQucYMJtS9pasX9PkSYcA1Pc3eHWTfkimFihEhZIoYU2nH5uoWT4BeIrYLhEwvTPQRGQWbZpV33vVO7h09ymq1zui0xespv/PCb/PE0WUG4xK9fR9XXnqW37nxLD/8ju/l1WdfxyBbo68VWkQWs5aL944YrSUnpFJQVppFEkGh1Inhos3YNhdYX9V4m9yFuUm1RemB4DA6EqPm5ecXnAoDzMgxCSPutxl7wwWz7iq3n3mKV53aYb8suT6bsj+p2Zxo3LRBi4BejrhoXsOkeIq+XPILtz7KG87fz2u2X8Wrdh7k1z75m3z7V38rn3jqQzx5+Wkeueshzj9whvv83fzu04/zuntezVve/EY++bOX+ezNqzy4eYavftNbiL3n7d/4DfyLD/0MG/du8jWveCVrxSpuRfLUh57liaceJ3aKx974dh586JX83C/9Cm42R3RzTj/0APLCkH5d01WK137Tm/kLt3+IH/lb/xPxJPgcJRPymRTK0fcWTEzB4TI5Jc3qJuXGGWIL/saMeu+Y9rkFtz9+jWwjZ/2+U5x76CwXX3mOTXkJt2i587mX+a3nPvrlm9w2oI8C/XFHXBdoldG7nmbRkXGyo0bQdy7hb/tIlJFyUDFfLPA2uVLzlRyBIPjA9KjBnirwKhBHks2HziO6SJh6wkHNtF2ioicEh7UeMk2UJwYxIVKQiUhZvVKIhCDoezKpGFQlW2dWIVM0rkEomYxpCObzlju3pxztL1k0PZKcQgSc7zAnvKCdjVXGqyOsCBgS60cqSaYE3mjKUU7oJKEVWOvpe0eIkq7rUdJglMIFT8o9DwkzoVQKuZGCsiiIIfVqxMnaQfq1cNaDMif3t8T3gd5ZtFEnSpokiXXeQe+ZrI6oqiyVpqQmyDQXldKYTCdDUgAXOqKwZFVOPpAE4RmuVCAD8/kMJyIyz1KQuFF413NwPON4MWcwyBiOMkaTEoi0vUMaqIYV49GIdbFCFzpu3rrF/vFtSjlgXE1opgtCVjJYr7hx/Rrn79rhzM4FFvUhl6/eZPPiJtmkPIlu/IPHV8TiLrXElIbQ97jGEqMBCwKD1AUqG2Ckw2QZuixQpiRzmi4f46qIcg4lJCYfIHWOrgqE1iA6aDuM0LhOYK2g14l/kgvJPeO7uWQeQs/WOJ4bnM+59/QW79pZ5b7hjOv5Ha6sH3DYrHP96jFP3LzG2171DuxxRohJ4dE2kbrzDCc59dICBp0JTC5PtPsKLQ3agRQV42zEQWNR2pMZiVGKvg30bUfrO5pWcRQg7FpiU9OutYTVQGNbBuWArQunmdFw1C4x4y2ebG7ylo1LiDstKyNLpaE92OFSvcOTxQs8MX2BDzef4V3x9XzfW/40P/aRn2FWT/lz3/ln+Kc/+2Oc/wsXkbctb77v9Tz9wvMwhHN338VbX7yfj1+7iUYxEIZry12EivzX/9V7ma7CY+MzZE7w1Isv8pu/8kGO+5qvesvX8Df/H3+bvl/y4Y98gPrwkLN3XeT+t7yRWW+5eXiERhIGGW/89nfx5t/+EL/1vt+EkJQ0SilOoq9TXda5RAFWEjkcUqxtIpxlfvUKbneXjEgwEaUEfb3k2tMvcPPqyzx/do2dc2e4eNcFzrzmfka//MyXb24HQeY1Umb0PjXLeg82JGVQ6yJZlPQuslg04BwyKKqiYHlnlgJW+o610fD3astHd44Z33caWWmO6jrpqPMMX0T0SkG/LJl6j3QprEWIQBASf4J3iCeuX8LJ2nDSfxqUBWurY0QuabFw4ga1XjKfLznYnzI9WtD3nqgESkKlEzdJx8jOpXMMVkccdz2IgMajZLq/pReIQqfSnYU+gPOJuySJZEqnEBcR0EbT+3RPxxhom5ZeiISd1glTkWmTKNEIeu8RAQZlxXJZJ/NVTCqZ3ORonSiTKaHNk2cFSpFyj51gPl/Q9za5W2XK9jVaJcmtl5RFjsoEeaERJpEoHS3z2TT1DLRhtkxYaaMVmTFkZcVyOSWQIHuLumZlbcRwdYwwYEaGmEWkkRSmZPX0JovpnMPreyzmR+jC4PrIcCPnlNrk6eee5oEH72Nr5xy3p7c5mtUYbb8kQOYrAxxmNF6BMxqfGbq+ZTmfslzOaH2HxZMPSzbObLN6Zofts+e4756HeO2rH+PsxbuoJmsUozH5yinGp+9hdOoeyo3zxGyAR6GFwc8tugUVIyZAbnJW8w1W2cA2Erew2N4wm0W2JztcUHfz8PxRXts9wiu3H6A6vc5TB1d4+MGHGegCeRJoIKRisewphxpzkgijNCgDJkuNKmMUWgaqXJLnAlQgN5LcgIgBfLrRq9GAY2+ptzxHg8hClRy7wFP7h0xDjQ1z5EAyc0soJfmK4tnuGstJAxPJdGFRWeCRiys8Gh6i6gxedvzstV/lprzD6eoU733De9hSJWdWzvKqex7mx3/6J+gM2Lbh3vvvRq5IXG554zvejD/a50Z3zEGoGT18mpdml3n7u7+O5vZtNk/fjTKeTzzzcWpfU2Y53/Dt30KxPkbg0O0MnQe+8c9/P01WMbs549aVPZ56+TovTA+Zryn+2v/0f+Ouh+7CeXeS8epQUqaFXkhwDt9aIhl5OUAHT713gzC9TaEdYahgolAjSVYoMiUxwdAcdTz/8Wf59X/7q3zgtz5C/x8EcP+XGzKCWzpsDxGDD4rOBTof8Uicj8Qg8C4yn9c4l5yOIkK7rFnO57R1g1IJa6tR7F67SXu0oO46muiwuaQfCpYjxbQQNCPDXHsWwib5r/DYmNRnXiTXbPQR31n6ugPnKbRmkOeUxtCHDqciKs/wQTCdduzdnnF0UOO9RBlzEuAR6MKMwVrGpVdcYPPiOqLShAyc8HR4rIr0RtAZQRsdy76ntj1BgCmyE14/ZFqnkA4RyLOEVwCRFD8+IOVJeMtJVqw8CZmx1qKEoO96nO2ZjCqIYLseJSWZ0cQQ6PsuAeiAYB0iQt92BO8p84JBOaTMCpRQaKlw3jObzymKgigiTdckrr3rWNZzls0i8eUrAwp0kTjvNngWTc2iaUFqZnXLorEsWs/e/pz9oyVN77Au4kPCQ0hl8NYyGg84e+k8Z+85Q7FiyMYRMwisbQ05e26H5559DiUyNla3cY6TrOYv3k/6iljcpdaoYoTMC6QWZMakgL2uQdRLjIOV0Qorq2PKjYxTO6vcc/ES99//Sh559eu48MDDbJ67h8n2DnIyIQ5GeAz93NJNF9SLI8TegnDckdcV33D2TWwzQfqAymaMjGAkM8ajnGgkaqBZW5eM2wlnr53j1f3DPHLmfg5kjRgWrK6tYoxHG4fOFW3bUw0VgxEYIzCVIhtFxus68XFkWuTHwxIpNVVRUmmBkoHO+zTZDPQ24o4lGQqKHlP1eNvTZw2361uIzuOCoDloCc2Co73nKVYlt/QeofJIX3BlL9LKkq+790HO2nWicFz2u7xv+gH6YLlYbTM9PqQXnnd/zbs5OLjFv/vdX4StnCgiw/UJeSGZrI54+9e+mReuPUsfLYXP2F45TaFHrOoxy9hRrA04vbZFqSvuffWjfO17vh2lDE9+7An2XrrJ17/rG9FnthGqpF/27F894PnnrvHiMy+xtzgmv7DGX/tbf50qz5EBVHBIKU4IkRotEmNeRo/wPXZ6iJvNkEojBwVZZShygdASLzVRZAQJfVB4b3CLyM3PXmN5PPvyzW2haFuPsxIw2AC9j3ghcALQmoCkbjrqumPZtugsKUFc39I3NUWRneQBBEQQ3L58g8ObewyygnJY0UTLYd+x1IFlEYmrFWplQCf8STkmYKPDn/yJIbHxo/VgPblQjMuCYZEhcCid8suaZcfezWNuX9tncdQSO4EKMtWPXYuQPSunR+w8eBqxrjmWlmUWaDNopKfBs5SBpYaFDvQq0MSOPjqiigiZePNtW2P7Bq0gMwIpI1KCcz3BOyAltAnAGI3RGu89QgSqKtXjizwx/7smyRzXVidUZY4kkBnJsCoYDkqGVUFZ5FRlztrKmOg8tutQArJMIU/478FbQKCzRNfsraO3FqMNw9Eo1d0zw8rKCuWgTORZBXmZk1cFQklklpEVFbO6o/Mwbx0vfu46L750gxvX77C/N2M5tXTLgLUC5wQxCoqqYPvus2zfvcHmZoWm4+z2JnedPs8zjz/J9mSdC9vn8K3lS9lHvyIWd5RhcuZuJpunKQdDBqsrVCsb6GoVHyNawWg8IRYFyqSjVlHkrK6vcP8DD/D6NzzGw4+8gnsvXeT81jqrhaFQgsF4xHBlTDUZMB6MmUhFJjz3DM7wfQ9/K6VtKKXm1WcGnF/L2BpnbK4MUVGwsW7Y2u7YzBWnrm9zafcM8xdvcev4OqO1CiEgMwYpoWsBoSmGGnQkmkCvOkQeiTJp6oeDgmqUI2PKmzQiRfC1HhrnUEZz+WBKkIrcOUoV2XNLdssFu3GXuznDG9Rr2GCbTljyQc6mkIx8z7PLa9h1ic0CwRtu3eg4P7ybb916I2eXQxwl//rwE/y8/SSdhmJrm0/ceBJfRf7iD/x5/t3P/DT/7ld/g24mWN5eUlBivOQ1l17BaT1hUCqmn7jOucvbHPzuIW9+9M1ML+/hW3jdO95IVZWcu+cCYjihFZpzm2f4v3zzn+HR0w8ys55qWPK6ex7k/Oo2G6sbDEdjDvePmPeW17z1Tbzqda+kygxGZ+nYXRbE3OCVTEEWMdB3Dc0y6YVFXoEZEL0CByIKhFTpa0/gWghQJsle45evn0oUAh8kXW1PdughLRTepwhGbUCJBLZD4IF8UKUwE+9p2xpl9EmYuKHvPUe7RxzfuoPwnuAsy+UClEDlgsN6SicsepCjc43zqTbtQ+LFe++JISCjQIYUQTcZDKnynGAduZEMs4y+7pgeHLM4mmMbi4wSIxWKiIiWslCcP3+Ki5dOkw8ScIwcXrp6Ga0lpTH45iTSUkSUluhcoPP0XijobE+IFqnAY8kKyDKB9x1CpnKbEAFBQCkoCpOyUesF8WQjoJQgeJcwAtLR2xaiPVmge8DhXEOMfWrUq57hSKN1SJ/zDcNhzmBoyHPJ+tqA4dDQuwatFMvFgjLPGVQDDvYPuXL5Kgf7h/RNR9/11MsWYxTVMMMUJycaLYgyooxmOBlTjYcEmR4UaxtblMWY2dzy8ku3efbJlzncq2mOPe0sEJwhBI2tLTmSKodRaRgaw4XNHTarNT74S79NGTRnVreRXyIs/iui5u6Do9xco5DbuGaO63raJifqAX0zIxsOqAYFtakRWqFVnupuhSLPR2idoaTBeU/XdBwcTRlkmnZ1iLQ7yBiRssRXir18j19+9jf52+/6q5yNQ2RbsIyBcztDln1MBqQgKSrBeKVhdaVCvqgR87uxgzeyf+OA8xfuYvelBTGCyRUhCLo+MFhTRO1pY0fQHlGkI7ASkBWKYigIFsoYiZIERwsntUGpmKDoS4kdeuoi0teeabfPail5zN4Hh4K6VLxgnuMonzExA9R+S78+4gV7m4dWd8iOFMIarr1Y8w33fy0G+JWjj/Gk2ufHb/wad5/f5rXyNKfHa3zg5U/w2CtfxQ9893fyz378/8X6X6z46p03MptOGaqK5WLO/WdexXhnlf3nX8Bda+n2HBdfdZYqVBy9MOX8Wx7gngt3s7a1SRMCIwIbd+8g3/gKfupXfp1L596JH+X0ePLxCn17SNPWyAb2x0vWN0a87TvfxdNPPQM2IrMCioLQ25T8JESiePoISpGVFaIY4X0g1Bbf9+gcVJ4Sm2wUSWlhJFKlxrn4EjfAf/YhBV0IdK0lCxIhFS4EXPCgUnNTiES99N6nXNA8o7M9XdcQvMfkBlOV9AFmB8d0ix43twgLMpMUWUZHoO4sbd/gZI51Idn43UlADTHRQqXBtelBM8xKyiwnuoBtOyYbq8QQOdqbMa+X2C4Q7YlzWKZ6eNO3jFcq7r7nLHmhMSZgY6DzloODGf/2J36Sey/cyzu/5us4ZUoyldO0Hd55pMlQowwbxcn/L1COSgqVc3y8pHcdUTpcaCmKAVpnpA5B2qF757C9o6oy8jxLiVM+afjLSuFsasAGl0iNZZWjTUnTxvSw0A4RLIORwTkoqwrrFIeH+wyGFUJFjmY1WaZYW6sSxBQ4PLrDcFRw6a7z7B3uUlQZUkUWiwX1YkExyFEarHUIFMYYhJKoTBBVIKsMoMiLDKMVzlkEGU7Bcj7n1uUDgvBUo4LhqCAzKhm0rEV2CfHsOouvHfdfuIsyz/jYb3yE13/za1KC2hcZXxGLewiOZX9MPjjF6uppYoSqbFkupiznc0ajNXQuUMqBzCCmOlyMHu8Ftk86UxCQaUaDIh13RY6KPXhHnmXsDEdcbid8+vgz/B8f+Jf839/zwwx3I8wiGigHgraNtC4i+ogxBVLD5HSgvT7mNZO3oeJNstIgMxhkOW3scU7io8OMBSGKlEeZgSoF3dIi0SgDMhdgBYaIyjV7ruWZ/ZtsrIwRVnOkIyZ32ElgERfsFzVXxS5nV1aTU66e0mSRUA4JYUqfKYaqYFANeGn3JucHE7brnArFvFXsv+T45gfeyTl5iV+78QS/XX6Uf3zt5/nh+76fuzfu4sZiztMvXuabHns3t2/P+Rf//t9Q3DPmsUdexeFz+xSu4ODKAWSRtXs2ePqJ55n0Q7pPBzbvO8vLL7xE/sqSNz38Bl7oLVJ4SiF4XtW8dE5g7l/hN/75v+Atf+EHuW48sV/SR4+wnuA98/mSeiPw1m9/F7/96x/is594ClEmU4fznijlSSPQn+iURUq6L3JEUERb0y1rWteSoTFaABKhAjJXiJDUDl/y7PqfeQglafuOvu5SHVmCP9FvKy0RSSiUwsO7nsIUqLKg85Zls0y5wFohM0MfA7t7BylyD42WCq0T5dTZHoEiNxqjMrx1eCFovCeEQOCEjikUMkY0iiJLJ0ktFWWZM18u6F1LF3vavk8njZikk33scLFn89wG2zvrhCxiVcC3Lb3ztC6ysrrCd73nO2gPF6i6Zzgok8wyCLxWNMEhM4koFNJK3DCjq2vqPiINBOmIwqMLSewiWoPWir73Jw86R1FqjFHEYDEGykFGiD3W9TTtgpXJKpubE6bTlt62KJ2xvjEk4vGxp7MeT814dUzX1wxGmmq4QdMtqQY558YTAJyTNAvPbDajHFasro8xueBstUndLOhsy/raOJmrYo9QAWOyBEEMHh8sy7anHJaUZZ6w38LSe0vf9UREElqInHqWkq/2bxwxLTRr6ytUVUz5uSHhNGzv2b15wMHuEQ+8+kHaWPP4Bz9J/BIpY18RiztREkRkWu+RV5tU+SrbO2sgd+i6mth5ZCbA92SZSXAhIiEK+iABSfSWfjanD5HeeQqlUPlJuG6IlLnm0XseZPXOGi8vPsenbz3HT334p/lrj/0QtlF0M5jcpQhe4ixYLIOB4njRMz6bs5wH/MGAzJ9GD3LMKLA51kytw9cKYT2xKAjSk7uccR7JViTtEpQJqHVJkB6HRJeRICKZznjl1hZlUXHlSsepCGvrGbeip7cZLjtmI6t4NNviNjOOz/QsuyUmGoq4wVQf4NYrxixhJfLZ3Vtk5jSnJgX5UjKbw+Xnlzx67h625BrxTs0H2if4yWu/xg/d98287tTDNKpFmIq/9K3fz/hXf4Z/9mP/mo2/ss55tcbR7h36NnDr125w/m3rOAxCC473HOws6aYLrn/4CveMLnG1/SxVFEgvuHbtBq5wnHroPB9636/wi//oH/PmP/9nCaMhXdeS+Yh2gq5uqV1gdVBy4fxpnn/yRbqBTpFvEUKliFmiAyoRk5OyhFg2RKFRWlKagtY2RJOCTDKZVBgxaKTUJ0HpX76hlCQER1c3ROsQWUxALJkeVlqBIKQMT+eQlSarSmZ+yrKpqXR5wgfPCVGwd3CIkgYVNd4LhNQ0XZ+4+CoFQmfDAuktfdbSNJYsM2QICqHxbY8ho8xyXNuRFQVaKeqmYd5MEVLQBQgiZYt2rqPpF8hccPbSWTZ3VrHRozNF5yxeJit6KCtiFGzeezd+v0ko4nCSjytlUtSYkE5kKqnJTCaohjnNwqK1xEeLlDAqB9RHIdXhTUFZabSWLGYdw+EAIaDrHMNRRVFm6CwjhIjWAYHlaDoDAVIHfPS0fYM2inKYMpSLIsfoSElG13d43yN1lyBhpID30bAE0WMKw3hlSNt3ONcSYofJPHmpGIwqIpG2Cyij8OELgmZOMhyCdNRdQxCeokylMk3Au0gMUGYZRZbT9x1VNmS5XHB1ep3haMDaeMBqXjJddkQLpijZ3X+BF3/2l3jD2x5jMszp2j955O+f6FDCsDU6R2OPaMIc5QyZy1kZr7AxnmA7S+ctgoLcSEBjCdShJ9qE1FyKQBstUUIoTuRIRuCiJAZNZwQvHe/y5nse4ur0Ms9M1nmuXfKBvc/wDRfeSrPvsC4wWs2Z7oMIkqghZBY1yhmfBRCEKMkrjVzNWOZLJhsj5lf7lDCvFdFEVAFmohBlykmNuafYUSwPLTLXDCaC+Z5NeNYswzuBJjBZL4kFLI57+kKyMSiZt5pbex2yixRdxWhpuPt0zrOzFzgctATZ0DSB8dlVrs73GfcjhjO4WGommeb6TPLsSw2vuG/Cn8m/EbE/57PdU7zv9ibfM34z+bBi7+YtTutNvv+bvhf1MckvfPCXeM+rvoVBb1jsN9y6cgQfkwxkReg1u8s5VoyovWL2zIyRWOWhex9MIc+NZ3hlxq3tiBnkPPL2N/OBn/oNPvoP/0++573fx+qpChEcnddMD2acamtGSC7fehk78VBEvAm4zEIp0CJHywSAUlrgZcTKJQKFrHr0OKN0Eh9TOHbvI8F6aHuClqASP+XLNreVJDca2/cE58FEYgxoLREynrDrU08BIibPMGVOd9jT1DXj0Yh8UKHLgtl0ztHxlDwriV5hfaSxHmUynFS0bYf1jul8Tt4BQrAMHukDm5lCQlpsvSfKgCApQ7q2p+4XmMqkQGynaLsO1zdgIoOtMeONMdZEPrd7jT5YXHAEKVl2jqOmx0pDUzvi3LIucu5f22FFGorMEHVSn5RCoELER4v3J2RKEZAafAjE4FndGBOsxDfp5KE0BO8wOqca5PjoyI1hUlYpZheHVBCCZTA0KdVNpxxfpRVSRuaLGVoL1tcratvT1HOkLgjeUw0LMjPC+gwfW6S2+NhzPD/CBxiOR7gwZdHM2NzcQNaR0cnuvnf2JLA7YROcdwQf0bmhKIokATWKIAzWdwThiSJgMpAyYvuAigopNKYaEKInL3N8tDRNze07h7jRGnk5IEMTbcvp8+d56pmn+dn3/QJf9c43JvrpFxlfEYu7QDBUKwwHI5b9AcF3RG/JoqGQGUWVg5uzv5ghKfF6QJAGGwJCRqJWZKMhIQYCEq0zjNbkMjnRuuBw0TONHaujVd77Vd/Kz13+KHfymg/sfZZHTt/H+dPnqI8dazs5WZ6gRm0MVGsFvoK4Yck7Taw1XYD1121CZdntI2piODhsWItDlAoEFdFjoBJ4E5ATQbYaae5E9ESw3zTgBFr0KGU4nC4YTyRm5HlqbxdZFtw1LHlBHPOWrbPkTcnEDdid1xzViuEVw6a+l93jbe4MbzM1HaOF5Bl7h+e94ry7j9UoODUQnCtzLh/VfObJmgdetcpfOfO9/KtnfpaP7v4u22c2eMvmqyg0PLv7Mvefu5f3vue7+cnf+TleeOkKdy8ucOf6Me3ScvVTx7zy1aeYHVkWdUveDvEo+v1IpwJnXvVqpgjMvOVD//Z98LZXcOaRi5x66BKvfeQW33vfWxn1kRshcKeLxLZG783J7r2PzsDt6W26cY80qQYts4gk2dCFSOBXLz0uxhMAlCdGizUgPKioCDYkbXIUJzsjT1RfSiz2n39IBWoYsbFH+IjxiUgqDHgdEOrEtNV2oCL5wJBpja89fd+j88hgpMiB5cGc5aLn1M4pBDlCQxs90aTAdSMkG5M1FvtHdEFRlQUiz2BR03cOFSWZNIQeGttjheLg+BhyyEYG3zpsdOALXIi42GFtyxTJteMbtL6l8z1N26HyPOWY+ogshsxtpG4DFTmmMrRC4/KMZfRAuk+NC2ihQQZctAijCMpTjTKkEjgka1uKmzcOqGPPcDKiKpMPBNkzGeYcHhyiomFQDGjqDqUFMldEOrQC73ps7DFG00dPkWesVjkRTzSW1bUBa2pIZ3syY+i6jsXsmKoqKbKKoBxd6JGNpCQjhBbrLMVA0sUF5WpOUJbOWnSpKQY52KT6EbKk7y1CgI99iinE0bsed0KyFVqhlEJrRZEZgk3JVDFGglQEEbBBkosMYTTz3tE6GOsMPak4ff9FlliGs1Xe9+9/kXr5xXHWXxGLewyOfr6gqjYoJxpbH2Ntk0KuZYZQFm9bpssjvG8YTobkgxR+bYzE5AZPYOYEvbV4RHKouUjwPfP5nKbradQRv8GHec/r3sbb/KP80w/+GJ8rI7/+1If4s695D0VR0TYd2cQw2w9kQTAYGHrlyNcErvaYTHLsLe2WohGSeVTosafMPL1yGO+QAlyuUdGDChQbJZ4amSvMWLLcdZTSYCqD7yzZMNKvaj65d5PjKuM+U6CyyEoP582EQy94pp0lVCkzRu0pxLWMM+0KZ1fWWOQLfBHo8ppPL5/ndrHGar1DoSybK5pHxwMOFzVXLx+xsr7CD97zp/n53V/mN5/8IOvVkAdHF3CXj3hm9yXun9zHt7353Xzqtz/J5YPLtE3k+Kgl+g7FObxb4nxyXHrn8V5SB4+pSnIHLz33LJ9+4nGa4yt847m/gCtzvvnb3s7Z+YR//fKHOHXPo4x7SX3nmEtHBaOZI6xJxDCDBogBhEjmJZKmGZFeT4FK/BPEyfXkTkSSGksRpAh4IVJgt5J43JcTCgkSxAr0qke4iOo00UViIQhlaqz7LtAs2zQ/BgIDhFmCb1E4isozlpLZ7pQYDaaaYJ2i6yAWEhsCUkjWyhLvexYBjrsWPSgwSpL7SKEVmc7oZj3SKpazBbdu7zFaH7N6doWaDlkqbAyE+YJhVTEcjMmrdUYrA8pRQeccvXUIoYlICp0xDFB3lr1Fz7XdQw72poS5pZk16GpApkCZiOssWuaIICkGBiUkC+vRRcR1PgH+SokXNdWqo1wZsZw3eHqG44p2UYMpOHV2jIiRvl2SVZFqXOB1R15IiiKhhp211M0SZ1sW7hjf94zHFWa0itM9qjJY2yKLiLaCcV4hnEBqiVeSqDTDQUVpM4J3NF1D51q8iuSFwsXUB4laph6bUbi+T9mvbaCqShaLBYKIiJAbQxY18cRvEWJCHistIXiUlnjv8cHjCQhzwtcJDqMLsDBrloyrIXlRcua+MxRHBW9eUbzvn//4F516XxGLu3eW6Z3bxLxAro1oek07m7FeLcmLAUJGOt/hnUuTQ0gKCZJAHiWZUFgPhdJI53He09Y186amnh6wmM5AZbRZxgf2P8Xu7g2+/1u+mx943Tfx9z74b/lI/F2+euc1PLJzH050DLYNs67H2RKhBZGOcphxLBuysqStIk4GOieRBIzI6MaBpQys9KCjxiMJnQfXk1UFzjnUSNHbHuED+VhhNjXzeY3Ncp66tQu+4CFbMdeRDSMZWUPVFsilpJUVbtqjGbG8GvB1CtyWRzmVNCitecNoyKobUs8X7MWWwoxZznq2VhRbOyuMm4qbd47oqfiu+7+Hu/c+zRMf/wzbr97i0ugMT7z0NLfGQy7dcxevuv9hPvPc53hx7wa+MSjZc3TYJTu6lOhpj28CKivY3X8OPgdbl07zc+/7FeJwhdn+IS9/6BNsf/Vr+XR/zGevfILy9BmE9fSx5dLKecrZPi8trnC+Ok8QkVym3qcXMkWwCZGO/1GkdKEo0CISlD+hBUoIKmFuY0hsEqkRWoA4wUFEif0yqmWklBRViXAC29nU3PQeZSQmT/p127ukvMo0sgBKwdFyisg1apCTD0uElly+dpmgI9nYoAYynQqkJMRk5pl1gfZoSdd7jCnxrUfOe1ZkSdYL2nmT0puUZlKNGd8zQZWabJThc09tl4ioyAYlW1sbbG1tYDKBdRbbtGQukMdU+lBSY5c9feep245bt+9wZzonCg0xJm5/iGRSIW1Ae41rk2FPBI/3KSZRSIFzAe8S71zrjFwN0UVGWWrKrCB66Pp5iq2zLcE78omhKkuySjFrO3wIWNETQsQFiywEO6e30AbqdomSkTZavA8oFxJG2EpUFxnGAukFMki8hIXrkLKnLAswGVVuCGJAwFP3bTJCAb5LDmotApkN5HnESEMeBUiNUgprLZGINin7t/eB3geU0EggyJM6vRAIrREnYDepkrxSRJn2OAb60FHqnM3xJuwHsk1NUeZfdO59RSzuwTsObl9D5CsYr1m0DTQdB2Y3TeBRRu0shc4oRIVRBkVAx0AuU2OobVuIkSzLUX1P5xyzo11mxwtkEOQi0reWo/mUZ5/9JC8+/Rn+7n/7P/M33/0X+efv/0mevv0sE7XC+XObyLxn9ZJh/4UOs8zQ44CSEZMLpnVLmxs0ilTWtNxp97kx3+OWdzxanWU9TijqpIQwxqCJRCfQA8XxYY3EU21k9FXDnW7Jrf1A2Q4ZNSpN6KGgjUvWVUVmNcfHPXQtm6cMsxsB34EIhigEvVcEFCoo1HHBeXmeujxECsvRgWVTZyz6nm7/kM2VIfde3GR3f4/jQ89j9z/GWrHB7edvMxpdZKc7zYsfepkiDNkcVlzYOsNT8Ubiehg4XM7YXB2RzR2qcRTkxDwjW4Xs8oz5v/gk7d4x3//3/huCjmzpAS8cH/CcbdErQ+5bPUeYCWqhePH28zz+sffxijf9EOvLoxStJlWKYNNDfN/hRCJ3poQmAT4SRUw0QNLGHSlP4uRS7yXKmGzscLKTF19WnrtAUOQF3jm890gtE69cZ4l2GFNsJIA2BlVKOtly1M6QZY4ZDJB5wfFyyZ3pEaO1TQbrOaO1DKVACzBaI7VExwxhHDoPNI1DCslYlRS1Ry4C4cgThWBqjxGZZOfcDtkgx/kOP+sQtmNtZcjGqU1W1sYYJalnS9q6xgjFOC/wfcR2llwrtFVcu7bH7fmMhQg4KRisjtm7eYP9+pizcR3bgwIyWRFlxIeIP4F4eW8hKrxN9we9REeFlwLjOtY3R1Sl4PCgZzDOmUwGdG1N2zlsqJn5JZnL0cMcGSRZlmrtPmicc4Qs0GJRlSIrDJ21hBhOjFAJDSF6x2L/gHhsMUKRlxmViYQ80nbTJL02EmQkeMfAGISUCZ3gwZgshXGHJcYmLo70DhElRZbTRYGLJ0wbmx7iSH3if4gEmbwNSqeg867vcCGgVXK8ticPk3yUIXxa4IOWnH/kPE23ZGVt8kXn3h+G534O+FFSTmoE/kmM8R8KIdaAfwNcBC4D3xNjPBJJVPwPSVz3GviBGOMnv9TPkELg2hnt8g4h1wgZUJliOrtFV99CjYeI0YhRtUHmCqSQ+K5FF4qm71g2DXXXEk8Cll3d0M+XxLrHhwwnNdYFZtM9ZnvXaA8OefzqTf67H/nv+ZH/4f/JX/267+NjT3yKejsw3e9RmxnFjia/E5keWnY2Snq7pCgzjo47XGnIiCysI88cCz/n3OYA37Z8ePdFVsUKb4inGJsxoZPYRU/MDb3qaJYdVZURC4dVDrfoiLd7iliQl5JqnOz/oe5YlSNaF9iVLV4LlNEsjwJdU6F1jw8WGyJKZODABYmQmqFex+s5CJju94zWM9R2y353QFtP2NrepslqjvdvcPeZM1xfBm72R1SDnM29Fa5/6ibx9CqqE5zbXuXF2R7WwGF3zM7KhLzVyFKSqxRbdm7rEutiwo0nb7P9tZfYO6N57Px9nM0rdpYND+ztE5YdfR+YHi44uHbMv//ErzCPUx41mquHt+hD5OKlR/iOd38n95y+h9/6lV/g3//m++hFTxQnjJkYE7lQKEJIkYtBJPhWktcIpIjJAESSIYoY+bKu7hGGgyHLbnnSTEiZAsZkCYgVPHXbEEIgyxPu9biZ4kQgq0rysgJp2D/aRWrFyvqEfKzJRjIZd2Kk73u6AJ2zHB/PiE6R6RLRRMLUIReBZndJrgwHi2Oq1QGnL55BFAmKFWxHFiKTco2d9W3KUZHCs4/mdIslRmmqLMcvPL7z5DLDLjvu7B3QNRbbRxa+4dQDF3j82SfIjeLG0U3uOXOKSg/IZU7oQgrLAbQCI0BYxaL3LKNF+BzvJLVLxEqlA7s3DplMRvS9QymJJzWk8zxLhMvYI7JAMCnwA+EJMaVwqVyzJLHclZK0tqHtLVIb+r5HxBRnqDuP7LsUAuQEdBZTaIgJEa6VgSgRQZALRbSJld+3NiV+SU2WKzITEX1PpiRCC/KiSlgHofFS4QkJKhYD7gQBgYyYzKCiTOlTIZCrDE1AKEUQEXzEBpeOtDJhEKSH8WjMzl3rqOyPF7PngL8RY/ykEGIEPC6E+DXgB4DfiDH+iBDih4EfBv4m8C7g3pO3NwD/+OT9Fx15MWD91Hl6abHxECsCNvbYZoFop7hjzbl7X8lotAM99F1PJyJ98NR+yaKu6a3FBo/wkbzz3Lu2xdff/SgfeOoJrs+nHB0eMb98E9cc8/DOXbz3ve/ln/yr/5P/4e/+Hf7O3/ofOfVt30xzY0Zdj9DXMtY2BSt3afae7umOLWK1YKAFRRGxIhCDwckWbMv6YMKWzgg5jMsx7//sE6zWglefGRNbi48C0Unq2NLXLSNR4qwnz0rUImfVDBEiUowC2ng8glObp7j68i0KW7IZFX3hGEpDJSRL3ElyUcofdbZHYZAmQxeWc5sFGYHee5a9ZP9Ky1BZVi8Yal/TdjVjNKtUxLlla2WTa1du06+UiANDsdQ4obmzd4tmYQkK8rGmMBnZQMKKYmhyxCCnd5KdwQXKmeMT5Q2adYMVnmgEXgXGI4MebKGAJjp2nKU7XEeufyu//oH3U5mMl68+w3hlm6//M38dvTpCrW3wzu/6MxzP5vzm7/4WSxUx0oMEG9JOx4gMRcQSTtKEEq5WihMW7ElGqODz9fsvz/DBUxYlbuCJAtxJXVVmCqElve1ZLJdY7xgOKkymmc9naXcZCowuiUFxZ/8YhCQrc4phTj5IMs/Po8CkKdAmQ5mGpukJ1rLiM+LScv25a5RiSBCCbJCxsjHBK0vb1akBHQKTcsTO+hZDU9Iu59RdR9/bFLuHpm88PiSY33TZsbt7h6ZpE82xMpy5cIYPP/04rgBtPPPaMVirsEcd1gkqrdMDV8SEF8gEWSnQWSpyxCgQXhM8CJVh546mb8nVkLazLOoZaxsRnUNWGfJcgyyQJrLoFngsUablzEuPFwLvLCbTeJleh5gbetdi26RwqZSmMAoblphMUhUFnRA00qOVpChzRCZxIRBjSHmlPqVrKakQSuJ7j40WJTWBiIyRtu8wMeCJKCPxIjF9iqpkUhoyGej7lr7rENITnaXzDVIqioEBqbG9o+0dg6Ki05a275BGEqJEZYord27xwq2O7o+D/D2Jybt18vFcCPEMcAb4NlJCE8C/At5PWty/DfjRk+COjwohVj6ft/rFfkZeZmzctcrucp9O7eGjQ9hIJjW5qlgGT2t7ahcYMqRuLH0zJxrJvOtpm5SR6bueSglWzAqjeIb7J4/wxne+iav7d7g2P+Lq7h5GFVTK8sa7X8vZH7qbj33mI/zqr/4a7/3B70Wfz7j1mQXNnmb5uYzBw5rVs57ZtZq1cowPNZXQ1E7QZgHw9N6xUuS/d7wqoqQxls+2N3mgvUB3sGQ4HtJSI0uQM4/NPCpqmjsWO4tYH1AjwayAfV1z/egWI1cxlw3L+QGvHp7j/GSD7giCs2TG0J/ElQklU+NGQYwC3xuO7jScXi/ZGAnODpMTrtERt5hTbjaIiaO3Gqs11eaAgYXTByOu3Fmizwzpb9f4vmflwTGf/eDL0EUGo4Jiu6AZ9iiTnKClNJQLyfpgg/3pFe7oXeLhCqJ3THvLSGVk0dOHHi8E2oMIGjea8PBb3sj2w/fTCsXhdM7F+x9GlGMOO8up2LJRZXzTe76LJ559Ct/cIMoIOkAfESIFJUtkkpoCkDAPIqYFPZVl4kk48pdveO9RmUJmmtb3VGVF1AKMxEZHHxxBRnSZoasckLje4WxgVBUQBU3bslgsqYZDTJmhc43KNSF6ehfwMVC3Ddo7nPcMhyPs1FLqkpdffJnJ0RJfgh4UXLhwgWKjZO6mBO1wvmeQF4xWKnSm2NvdZdkdU45KhNZ4EbDSEU52oLt37jBdLKnblr7vOLu5hqskn7nxAmtnJtxe7nP74IDvevu7kHmkix02avxJM1zKgBAhYbCNoKoUZWlo+xRv13cpdcmYDBNG1PseGwRGj3BNUrcIIU/SpCC6jtFoiI89zluWzQKlFTZYgvK4wEmNPILTKF1isegMGt/Ty0C1XSFaTW0BbYhZwh50okf0LTEElJRoFDJIkBJUcj5LnaMziVcB791J4pUmKoWPHqV1ijN0iU3jpIVSMpmUCF2CjNiuY3p0BASsrQk9VHmFkSVdT/Iu6BxdGWRhCOLElCb1SdD5Hzz+k2ruQoiLwKuBjwHbX7Bg3yaVbSAt/Ne+4Nuun1z7fYu7EOIvAX8JoBwNiaVAy5KuW+KnHStiwM5kByMM+/0R6MC8mWNCct/ZrqE/WVxlCKgoGIaMR3ce4ZQ5w2Z1mt0bK9gs59zZDS5cCBzutMwXklwp/GHHo+ffxIMXH+X6/h0uf2qXe950D+dfVXD78Rl3Ls+Q4wGj9Zz+DnR7Hdlqjupn5LVkPgjkJBmakZE29rQhsN+29K1GtAX2uKc+aslCRa0syuQ0ux3jMxO6hefwzhwpJKYAlUMZA6cmFWe376Z28GI4Zi5hZnIuiAwpBINK0tap/NTZHmSqaUYRUSqipKEOhhcPPPKoY1w1bKwbxmc11XAN0ypaP8OqJa2c0tcNo3KN1TMbHB0HlqGjOF0QlGftvlVWLlXcvHqD9cEKZjNnFmsWi5pBPmBrMqK0khgalrGm6Au2D7Z4Yep5eXcXub3FqlB0fU/nLZ119CHdaKV06Con1i1EwZ07e2jp8MFhnWU8HuBOb/O1b3obv/ir/4a5cEQp0BKiOAlyiAFBIggKREr0CSmP8/Ob9fTRH7x1F0L8c+BbgL0Y4yMn1/7Eyo0n/x5BRrzyHMwPsNqTD3OCCtR1zbybY3HIQqFyTZ4XHBzto7UhiEiQgS527M33UGNNuT5EDA1WJ4146x1IcYIwsIgQaZsloYc784aDoyNyLyiyyPl7zxJzwbyZEoyj6xpGowErwzFSCfb29zjeP2RzYwXpFN6Dyg0Iw6xruH14yKxrmHUNaMXm9iYfffxj3KkPePBtb+BDz3+Gan3EvfddYjwZcfP6DYqFJM8k0UaqLE+L+8mJKobkKE4hzyfKqBghCkJniEFQ1xahJEGCa1PvRyGQTUinmEGGsJboHUUxwIwyfLQE4bHBnmjofbpXYspRzbIMVUDXLemUxcVIPigJQWGlpZMR5S3DEySyMRotgehTTd8HJJrCVAjhaYVDEog6nSCFFIn1o7IUiuIcPkSET6Edse9xOpBJjc4TCK3I1gjes5w3LI6XNMeH9LViONxkdWWTab+kDh3GGHrS74uIf7wM1S+YpEPgp4H/a4xx9oW8jhhjFOI/zeMdY/wnwD8BGG9txvHkDNpF9nb36OwdLmyd49ypc/QiIPqbTEXN8Z0buG5GpSqUzJJzLwaMFJje8eD6eS7JB+BggAoRIS1LNeL2Tdi2ORdOVUxHLbNDQbcYcfNzPTuXxjz4iiGXX9jn8kdn3Ps1Y7Yfm3D4mYbmuY7y4YxqoqhvNdRSUJWK6cECs7nKSCk6AXjHwjmOjmc0Vw55R32awXVNYxz7x0u6LqMftOTrmsO+Y8UH+r0ZTdfiM43UEYWgyHM2JhVEweFBx6mbAi0Fk7ND+kOPn6dSg48eoaAyGhtsspQriZCBgEWr1H2PoqDtAzd3A7t7gvHQsb0VGQ7HjMcjgrFM9465GfZYKVdYWR9hL1vcjidmClVqHnjTRT7zwZeosTShZ/el2wyrMdN2xmQlZ7g2ROiAWuY8tHiItXCa52bXuJLtJhlpl/HsZ17gd5/8MPbUmMnahLODIULCse1ZWd3k0a/5ai5euM3y8DrRaKaZ5FgZeqN569vexmcf/zCfm17BSYfUDh8jgeTyFdgU8hFAqdRw5QtcqeGkPPNFxr8E/jdST+nz44f5Eyo3QqqtdqEnH5Ysjmra0GMGObVviE5Q+y41hjOBFQ6nIo3vUZViaRe0umKvW1BnC3S1TltZ9tyUNT1gJU8PNmUyBkYhBamZ2lmsiozWxtTB0uuMnUtnEZUCnZRFvnXkJmOokwzw5StX6OueC+fOg9D01hOlwklY1jUH9Yz5ckETOrJSY6qMX//gLzMZGL79+76DX/jYhyjKEoVmZbDK80+/yMFz13js3lfTeoFRknBCdoSk7fYWbBsJFoKNRO8QIiKiIFqJFIZ20SYzkpGEHjBQB4+joxqAHQjyYWAwHiWSZdtRDEqUDgjR4+1Jc1PnGFUgo6GXPV2skzcg0ydKFkXfW5be4Ywk19D1ARkDXqade3AeYzKCSOC1KLoU9h1cavpLgRYKYSPKpnOlloroAyImc2NR5OiiAAL4gLABCCgEwkEWJJmD+fGS+thz9elrHDVzBtsrjE+v4XMQeXLzihi+ZD7wH2pxF0IY0sL+4zHG/8/J5d3Pl1uEEDvA3sn1G8C5L/j2syfXvvh/QueMizNknWXpa9ZHhrvP3cN4NKT1ltj3TOfXaKb7+MWSWK5TDScoCcJUZLliu9S84exrGdXbsDHBiZbxoGAwKDHa4INk/1bL6nrJ1oOSO8cdBzc9l6/XbO9oLr1xm2ufWnL9kz0XXjdk65UDZp/xHL3oGO947FSh+h4/kfhrDZweEYeRXkRmwTK73VFd7jj38oSdi+vsL/fpjjv6RWR3tmTlgiRcGFFcGnLjaos+sFitsCcgw1xlbG1PkFpw+/oRt68vyY3kvlMTTAlCBZw3iXmuJZ11FFqRo5ORKyq8V2ihCL1D5YGsSKUbKRQq98hxxl7fMZ+Dbhwra4rx6gaj9cA8zHEuIvcH2P0pg3tyWuuwbcf65gqFypk1M0yoWPQLRhPNdH5InS3ZXLWUa5qL/gLuoQl5ccC0OWL/csNLT97gic++yM2iZnx6gAoNt46Sbni2bNFzSX9+wv2PvYbSGBbHR9zZv82HZnc4u32W01vrPPZ17+DGz/0oNQkA5b1Lu3MhkNEhJcSoUlkK/n+7GRmTeeWL3AAxxg+enEa/cPyJlRsBQgws7JLheEI/tfhuymg8Qg8y2mDp6CmLAjPI8DE1JkMlkCuS5XJJM1py0N6iXWk5dWGE3imZyoasqigUdDawrGuUysitpK+XSBRFWfDC8y9xbBe8/fVvZrA5oXctvo90dUsmBWtrq8Qjz5W9K3gfOH/XRXqbkMQqK0FKjo9m7B3eYdbOQacEosPpHX7m3/17vue9f4rHvu7NfPCTj3Mwm3L6nntQOuP65/aoesFQjHnmmcs8fOlBzLAiD5BLiYyS4BVuCd08YNu00IuQTGiQeidKSWRUCC+pygpUpLVtyl1VGQHF8XGNNJ7FwGNKSRcjbuRQWWSwMkIKjwguraV1xKiMstAYnZq2WgI2kElDphQmOqwBg6AI4PqeGANRBKQRROnR6KSn9y71CoQ6oW+mcmB0DuUS/VVKTUaK0TQ2wtzSu4A0ELOI70mJbQjoA6bXTNQIXWka25NZiZCW6eKI9uaMfK3g3L0XKaoxgyJPOOgvtq5+qYkJcHIc/WfAMzHGv/8Fn/p54L8CfuTk/c99wfW/KoT4SdLOZvofuwGESBFYs6Mj6uWcyeoqMpfUbsHCLlm2U2yfjiIhNigTKauM3ntms32i1DT5KmaYsbWpERaOD3KGg4rBwGBySSTQ9xmzA0HvLRs7kvx8zs1KsLdmWdKw8kpBd9nz0qenXHj1gME5OH7OUR9FCik4fnnO4NKAUV/xxJMvk3/VJlZb3KLBPNfygD7NnekRi1ZSB4E9bggxUs+mTPwat1+6iX3DWeY+Y9KDai3RK0oPW+sDnPDsXl4yPVywsqXZObvJ/qzF4YiVotOBEB2DQuG6hITVBoyWaREHTKYStyQDqTzoiPVJZdCGmnKg6aOj84HlQU/lBUOTMd5ZR48CZVhw48kO6TJqtaQdBzKt0cLgFnOWt2fISwbGkhCW5FFhjw/YmQyoBhX1/RucYUpxO/D0z7/M/GXItzYo8yl7tw45mEVWBgXRerbDKhv9BHd1wcuLl1g9u8U9Z3Y4s3WGmwf7iNgR88Ajj72On/uln8a5gAgRGZL5R4hUf0966VSqiSRDCEAIJw6n/zS1zB+r3PgHjUW7QBSGPlr6ukZVhqzMsbNj8DAYDMgGBV3fsYwNjexwpUQXGjvsGG0Peeyxu+kYsnBwXLdkqiOPLShJEFBqQ4nm2Fm6pgXpqPsl973iIbLJgLqvUQiW8wWyD2RZSbfX0DYN42LM+s4mXWcJSuBzw7Jz9L3leDqjXjYUmWayOuaVr76PG7cv81Wv/Ousn9nhyt4en/jMZ9g+c47pUY2RgTJUbE82eP53P8tIVYzzO7i1SD5eSdF+UYCDbgndUhCdQcST3pGIEAPOeaxNcXsxJBlzXmgKmaO1QJlUcqsXDcIUEDOaZcfS9ug4phUtXb1kMBlgQ2C+XGAwWNGcBGFkaD1ECwfCkUuDV5FKaBahwweX+iOFxjlLCB6tThAPwSf8hdYJd2AMQmWIBLoE4zFRIrqAchFpHcIDNtI20M4iQToc9uQkZRmUAzJpKHVJLofkowkh61gfj7i7vIDaKGBs8ANQlQERsX2bYHpfZPxhdu5fDXw/8FkhxKdPrv13pEX9p4QQPwhcAb7n5HO/SKpLvkiqTf65/9gPCESmiwP29q+yaA6YVoHnry+om2OWXUM1HJMNh6yPNlj0h1jfEEnwovnNPe7s3sbtnOdDVz9Kdt/ruG/rLibrFaF3FLkny0TSlUZo2oAQkjt7DccPCV5Yv40cCJa3Onb2DI9dPM/iquXosx0bF3PG657+GMxI0QnB/md3OXPvFtgeq1KPL960mBcc8ZKisYG8c6AFbdsSjKQPPcdNz/JWh/SeWnRkRqCWltLBqBziuo6jKzV1b5mcHrFxesS86zk6OOTe8Wn6MmEM8lHKmxyhMVIiRfr9QrQnEy2ii0BeJtlVMVR0rUIagRMtaI8XHqTEVBo07N+o2b0xoxhK1oKCmeXgqRnbm5tsP7TN+qP7iGhY1I5OOYrKsIwdspRgakKdszfVbJ5aoekD58/ssOl7xg+M+OC1z7HfzzmzdQo/azjY22dReNbFmJ3xOm7ZM3MB5SzHx7e4cnXOw49c5PTOWaRLD/TJ+grbq9sc3lykdPuQbNpSRIjqJDEuJPxEiAlXEOOJYOaPHlnwRyk3wu/vJ21ubDN3LdJ3mEFBv3DUTY0QEZUJmr7hyM+xlWNGQz5QzETSo5vRgOlEEEaCm/EQJ2qiNsjVRFYMIaTFzktsGzhqlxx3C1YmQ9rZjIvnVvn/MvenQZpl6X0f9jvrXd4996y9unqZ7unpmZ4NAxAAQXADKYqkScKkNstBWrZCinA45C/+ZNlhW7YjHLY/mArZFh1B0wpCEmkuABeQBLEQBAYzmLVnpvfq7tqyKvd3uevZ/OHmgLQCA0Gm6JlbUREVWW9VVlae99xzn+f5/37jVRz6E3XgyUePyYNkf7RFc7Zm0zsODg+Ybm1TuQ49LalTz7JbsdyscV2HJLC7PeLGtR1u3dwhCc+rz9+jl47jruKXvv4VpteuEZNgmk8gWTJyzuuWw+efZxwlnetYXpxSa4u0BToJkhN0XcSHoblqrbpKdApSCCgUvfOUZUEMEecczkWs1SgpCC4SWlAhw5qMzUVHVAGhM6rzDmEivheMMjmMODaS8XSGax3tpaetHY6KPJMQHVZ7lJHYcc7clvTekdo0qA2FIgqPVhpbZvgQiFe1eyE0PkSc80PyOA1KQe/BXK1N7x0pJbSWZFqjhMAzEG6FHmJLKg4NW+HFIPiJQ2M5STFM+jg/4JeDJvQeYyQWjfgdDi6/m2mZX+V7H31+/2/z+gT8+7+bN8E/+zOB1eaEqn6Gj2sen55z9LQlxsTBzg32dm/iTEFVS6qLMzabExCKfDxHq6H+5GWgHcGDdsXo7IxbU0WelZTzBMoRncRaRbEHIiqOnOTZuKH2HWaZ8ex0xS22qZ8EZsbQnHY0jwPjMqPrWirhmT0/5vJLSx68f8T8D8zYqMTFuuf8/hmfM9doVw2qCIjeI/NEt27RdoIqYLmqkbsTegQpJM4EbPlIqB3PNuecIghSki80z9KKhw8uOVl7wlqyu5AUMqLLSDYXjDJFs/LoqNEJ8lwgrCKGhFQds12DyCPCK6L2TH0iJI8qLS5F+uBBQsSRiFij8E5Ru44nXcLuL3j4xgPW3+i4+akD9r6wz4NffUS1bJA7lsa36Eazcjmu7MmiYrOO/Mbb95FPL/jC/+DTdFsbZp9a8vAoR8kJk30H2YL+a2fsL7a5kc9wy8e80y5pXYVYOm7c/BSzz3yOX//O2/zwK89zZ2tnAGv5yPXdA95/+C61ABWGJZ3E0JBTDKWtHn+FJLian4npn23yv/vrX7jc+M/3k+7dfSkpq+lc91sn065rSSKQrOBsfcnj/pz53jZr2ePlirSrsKOMTZY4DxUb7+hERMgW6+H2ZI9RkZO6jnExZm+8YNlGzkJHmuZ0osHohtJrciHpqoqLD09Jmx5tSqpmhQ5weHhAMSrxRMw4Zy16ni5PaLuGvq0xSrA9n3D3+j43DrYH+YURXLYVD5YnfPHdtzhqaorRDOsFse8HQf1YU7kaKRzEyI4S1OtLNqOSwlqMkQPiOHVIAzIZAmLY2DzEK+JlisPpXMiEtoKIp3eJEDRd50kR8iynbTxCa6SIaKOYTjICLbZUxC4RfaTUBbGLuB5CL6CV1F0ijCVZWeCjI7OG5CH0buAXJUXXDGlobQxK5WitCCJSNRtcEzCZHmQxSaKkwgoLSeD6AcGsJDjpkQjClVdWS4lKIKUe/h98oPc9QgqyLFAUBSY3kCy9d1SuRrQCWyi0l2SZom8Crh1Svd/r+gFJqDqq5gjUisSarunRXjIb73Nt9x75aGsQDShLlk2o3Yp1dUEfB2v5dLIg1wVSKs4552EnWFRz/GWOOavYumWY72Z0oSZpQUgav294yhGFivjVmr6tme0esntQsrlY0VzUrL7ZEe7lyFnk6M1jppM9rr+8w1e+9k1G+jb4xONnx6joyBYZdR3Ipxmxj8hcE9aRoFuy7ZKj8yU3dg+4JNH1oBpJcTSk1qbXpsiUOLi9i1aAE1SbjvPTI47dhs4FpqIgWUgi4ivN4mAI7AgcMguIqzCDttDrDqUi+aOeIkj0IkcWOS4EYhz0X8IKPJoa6GNCBI8JGamMxFHiMy98hqPHH3D+7UfcObzD6eKIegXrTYOqC5pW0lUtm7NIfRpQPsepXXwl+NgTx/xwRnHdcfe1HcbZNnYcuXQtBzsTUiU5Duco8Yjr2nJ3eptXbtyljROOU8lFoVExcPL0lK39MTazTLamKCLxajpIBPAJkBGVIkkkEhIZ0zD6FhOGRBT8N+W5/7dWbgTwzqE6hwoCZS0py7io1iybllYl1lnCW4k3PS4TeDWUH5zoqfqOVkW8EmDsFU4WNs2Gvm8ZWwvCg4iEGMizAp9NGAHjYkY8X1M/azh654hFCxNjaVcbQlmwd/0QDmZ0hULkUHVLTi5PiARMFxnbMbPZmGuHu8wnY6ra0/iOZycXfOfpRzxt19QKZvmYLCm0Twgf0TKhYyBpgWscmZIs8oKD3T0OdvcgKNbrNaEXyMwy0pbYKmoXaDc9eImWhhQTwccBt5DZq+9hwvswTEeJobcy6PYGHr42g2qPIBiNR2grqLuOi7MNeZEz2p7iUhzUerFlnG0hQkR6waZbYbSlbrqrun9CS4GSkrbr0Ebj+iFCIdSAWtZaQxQIAplVpDA4kaMUCDMkjqN3KGmwRqO0wLmhJArDukxiYN1HPfhik5F0OELTknpB23miAULicrnGVUsaX0McGrJd/wOO/HWupa4e4zqHImd7skuppxgyNhtHqhuariY1AWV2yEp7ZW1JGJENMoFqTb1asZ5K7rs1YznjML+B8Xs8fbujveyYHOSMdiSVbTgZ1TSuQpOouxWyC8iNZX1Ro7Y0u5/YZc0p999+yGz/kIObc97/ymOmtxY896PP83SuWXWRjx494yAUMAZf92SZpo+RTnqiFfSxJzdjepso9izH3tH0EXVcc/7BKdc/cQNhO/btAvmswhnLk+Wao9MTTsyGy7KjThUi5YzGkjxTLJ8pQhXAespdTTlViLKhSw2NdPgkicmR5yBDRi4cmZfIXqHiEHNWQqOMYjRRjCeCvmvQS0dVK6qJ50I1TJ/b5+SNR3zxF75IaTLyzPD2m8c8/PIZnbJsjXeYjHaZz66hbUmfJCenjl/9G+/wx/+9jxGylvGNEdFAUj3XD3Z48SSxPZrxQdnSh23+yI0X+ZHp88wdnIuaX4qX2GKOShJtLEdHZ2zvbfHpH/shfuXXfoHWNSgTiY4BDpcCICAZFAkrHCElwneP67lFZb9900kI8VcZmqc7QohHwH/If4vlRhjqxXHVXZVRPEfLE94/fUKc5uT7M+LUkHLFWjkcniKzw8y2C9hJQZEJ1pslneso84JRUZI6z2q5ZHd/SojdFWgtMR2PUD7Rn65xy8Redp1f/uLPY8481/d28asNxc6Uw5fuke3N6EdDyfDi/Cmu2ZCainFWkMuSndkuk8WEDMvpyZJlt6ISjg+Wz3hYragt5OMJZe0ogsAEgUhDU1SlAdFcFoZbiy1e3tplfzKjjZK2CYQ2QDJYbdF+4KBLKQgehBcIIxkCaEMvKZmhjySlHJALCowZmO6JiLEa5wNSDEG2tva0XaQPLUlEQlCs2obq8hQlMhCCtq9Z7EwpRprzo1NsaXEbRdMGogiE2DObT1C5QaRADJLgBHk+sON9CEQ/QL+UEaTg8b0n4FHCoAtNjB6ZS2SZc9XaJ9mcNIhth4mvlBgoO4kYA92gLx8AYkpDkYEVeA0b7+i9Q9iBCKoGvsb3XHs/EJu7957usmVa3max8xzldIbSOf1myWZ5TFgpLquWeJYQeoQLLYmeKBJtgNY5VEos18/QrWGTNG+t3mS6JcmUQbkR1ZGhWyaa8x77vKDfiqjak4ymbgeew2gsyFRivWpwyWHuFtwpr/H+N5+yLnMOX9nlo28/Rc1K4sjw+OKCi6VgXkUup0vkSLERPS2ekHniTFC7nmX2jHjL8HTW4YRFBcey33DrzgLfdWRLzWVboUcjTtYndFlNnLQsXYPwBV//1odMb42YU+IqQXSO0GtClKRK0B0HsnmOmkvMbOj0p7JF7fXUJAKONsbBgJMG83vmBEUjsSEjRSi1wDhD8/QMtSeY3Z5hXeDg5buMLyzffONdNt6Dt+gqJ58v2JvvUZRb6KLAjktkJzDLirf+/gd87sf2uPbJOfPdJcL3bCrNF6YvUFYXXGebPb3i3W3Jm9UaX7/J+dl7fLh6wJMIP/pn/12qGBhnGaKqOV4uuXbnOteuXWP94AOEiIBERkFI35UihCHoJIdEYbQGM5sxuXWL5qu/9tuuu5TSv/Y9luR/K+VGuNLTedhsGtbViqNmST/JMNcWLAvojCdKh4+OYpSDCpRWYnRGBFzvyU1GbjTz6YztfES4WNGsNtSTDZORZVIUHF+coaQlUxkhtoz0mLd/8x3Oj1bcMDNOn53wiY/f4+brH6OdWS5Ux6Vbsd6saNfn6LZjIS0TWXCwezhIQJYVl5cXbOg56Vc8cxue+TV9mdEIj2tbboiCwkVMAuKwWYk0RPife/Eu+1nOljKIGKh8y9q1LOs1ymdMtUb1lr4W9H0kCQVovB82e2sNMUa8j0gFTdsCiSzLUEqi1FCPdr4DhoZ6npdXI7KRTd0gpGI8ntL1HV3rSUkN9qRW0q0TyQW0GFPqnPOjE5QWTGZzsonG+46ugsRQarFZQb1uBzevsfRdR1AMoDASRmUkBN5FnGuGUo5UKCHpXXeF9dW4OMCRfBwSVlJJhJK4OGzwQimUtUPgzyd8HGr6HQJhcqQStK7Be4f77lPAb3P9QGzuyYNo9hntvcx4tI8qLVlZMBpNMbkl5AHTejpXsdps6JsV5QiE7emkwCc4Pz/m+GwbPZ9hyzEfxDPKy/f48cM5o9JQ1YmUMvyl5NmHa+rdHi8iQkqCga19y2Nxigg9eZaRJUOzbsgzy81X93j0/iWPH5xx9+O3eSd/wlo6Hh0/o2p6lpXixLSMBdSuxRFZ01HPoZ1JNotIlUeKUUfGFr3y7MwVm4+WqN6wuHOdNos8Ozvi9uE2k705v3ryPt2pIIs5m+A5vzxDKoNAY5QkdQKtBaKHvhZ0l4LRviBuAgiFV5FsnBjNhlOd0hJhBw5L8t9FInt8Gwh9pGGIye/s79HtS7AQ6orz4zPeeed9jp6eouYFs5Gm6jTZPKeNa7oLx0LeYOkiy1VP3zzhx16bsfXogvzFLUZ6zGZ9jL04Y/ykJ7M1768f8ca332d5Y8q9T32CD+sNDy5XnMQz2osVb/yTv8XrP/ZTtL1js1pTdT3b13fZOzzkvfvv0eCJeojN+6iGE6MbTnZOgxlPGF+7ht7ewTlP33Xfv7UN1NGzCYHz2BLmI4qdgnWROO4uhnSqUYDHSgPW0ERPFgbctRWavCiH9HOQpMoRq57V2SX64C7eeUQWGZcZ2ijqumUyKhBtT4gVbb/CZZaXvvA6z718ly4XXPolle95dvx4IFR2nlB7itmC+WSHNniqaonQkiZ2PK0uOPYb6kLQaUgakvPkOsf0CdMnRllGOc7Jt+Y0Gs67ip39LdK6YtNtcFXNk9WauvW0VU8pJhRqTNv3dA2IaNG2QDB4hiWDLNs5+VsxBSkUiYHvH2MaxhPlUKFQShH9EIDqqp5Iol46tDWMc0UmS7QZbpZCCCbFFCslbdUiTaKrAn2d2Nmd06xalLAsq468sJRlSXUFUKubDZP5mNHY0laDus91GknAWAVKAAqEIiKp634Qt0uFkMN8v4sghSRKeXXyN1RNNcDOUuLqjELbN3T9AJtLcuDAKATBD4J1Za5QG9/j+sHY3ENCtIogDW0KbGnLOC8wZU45KmjFBpES7SbQPz4jNg0RgyNC7mj9inZ5wtnTBYvFFin0iCznzfohxUnJa9NPIuQYndKQiisDG9Mhw4DuvSgkubG841oerTeE055xGLNTF2QnMM0n7O4taM42HD/9iOzzYzZ9z+Mn55yuarIm8aS75JqW1F3DR6bl8X4kziTTaQm5QeUZzXrFbHefWbHg/L2nFI9btr5wm4/Uiuxkw43tAy5XFb948g5nquNuKDBjw607e8yxnD49I893CE4P9noY7PAqIaXCbwTVOgyqTJ3hxgpx3pONBcqKQSJtIsZEei1xM8M6CTwCnzwhOZwQ1Bct/bpDpA6uGW7966+x+5Hn+K2HLOMpZ6s1Rw9PiSi2ticgPmTVdKhR5LM/vsXt/Z55vSbdP2X31V0yAqbS/Orf+scsLidkNuML2SeJEwnrnMvtEjF5nfJ+zvWFJD/P+cr/4z8j7I2Y3jxk/OJNKu+YzbcQMRKEwxPQMmGEwyWJlxZrc7YOtpDXDul0Rn18zvqDB9B+b/7Gv+wrkHjkKmqj2NhEpR21d1ysaygEeWZQRlDqHOEdTUxYk1FoTa4zpFBEB13vEbWjrtbkPiDWgcfvPOKVT36KiKZUkqqvhpu4Dogs0YoN+/fGfP7Tr3Hw8j2ehZbl6pKL5QVdWyPbjtB05HbM4e4dpJd88OAZcRbRhST0nlW9ojURSsWyW2HmJdoIpItMgqdQJbs7Y3bmM8aTMXo24rivOH6y5HxzjqwbTk7PaS+WLIMnz8fko+zqvedIUoGWyEyQXEBIiVQDhbF3VwnMBNENBzGjB+6KlFDklt45onMMtwNJU3mkMmQGYrlNiJF6GQgpYYwaoGNxMB453w1iHyPZrDYYlbNZ9UPwzCTadSL0keQCiYzgAyqNOHtWUW88MPR6MmFpu0gnIzobmDPSKtougMqGPpdQNFVDUzeMJ6Ohri8EMUbOLs6JDLTKECMCeTUK6vExoYOEIHBuKGklfdXg/a/ZvX8gNneIrC6OMGfP2FosWEwmjMoCRyBFiepbRkWJ2RaEtmIjFH1KbDZLkvckekLb0206XO+hbQihJ43gneoDfITb9g63iwNG8ymbw4KaM5T3vPnuI8zuDn3tqIQiGoGLHSex4l2pSVPDZHXGrsu5u7vPKNvl/XTMZaO5PGk5P6nJfOJpMabOAo9Z8mjkcHlil5wUJdt1SXzaEfsloxlUo8TeSxOmBwXvH93HNAE1XfDm6gEb1bIpIy/Ptrg72+dc9zyrnrHedNg4BWVIxRTZGbwfGj8mEwQf6ZaKkAq88Mgs0vUCnVuqi4jOBMn0GCuHqYBi0BF2XYu1kkDERU8Xh3q2GkvsNCMTmvOLZzx7/zEnD8/pQyKTDtFFRvM5y9WGVbththf5iT9xA6nO6WrB5Tqne+sR29eHp6mtmzl/4N/8/YS3LnCnFaPxlO39jOLuFl+rn3JUzNl78VPsPfUUj885948529lGlTnKDKeTz33m8/zTX/gF6sbhGRC/SIjaokYLzHyLZprTXqzojy4Il8+QrkL9/z4N+S98eeA0jyxVpJKOTkHQgmgGaUWKHld3JJuT24woNFbnZDLH9Irhh0RWntT3WJ8Yocl8zoM3PuCzn/ghXBJ415JlOUkrqk1NVgi+8JOf4/L2DotZyZN0xnK9ZnN6ju0DcdMQ1g0HW7ss5nt0feTZ+Tl6lNOIjuR7hA8E7QhG4VSgHBckKchcZLcYM7clB4vrTPKS0upB/i0DQQa62PPRk4fIukVerpjnJZNyMliHQgThad2a0k6xU0t34YlGEILEZOq35CsxDKaqGBOx93gt8d6htboSWgwNdNc7sizH+4jrIr1UaG2QEkKKhN6xXi/JCk2WW4RKXK7WRAJSQxKBrLADBRLJZuVQjMALfDd4GAJhaIp2QAqMxiVCCNoKcjVFIFierZgsJviYkDobKgNX5cO+60lB42tP33cURY5UCh0lUpnha45ADyF4Mmvxoce3DgJIP0wDZdLiuo66qZD/IqOQ//+4VKHR84gJDQeTGdOyRCuJ8x4Xe3rXEEMkJE0oxqiZZKQEYz3lfHOC7jyvvvAat3ee48biLikbmrSZK8lKzYV7hg6B3YXFkFhKQ7VpGDvJZu1Y+Ibt+QKpFFVsB2yoj5z5ivoqjpwLy7cvzpmInGKv5LLpqdc1Z4+fkJqSb28b3G7HelGTbKTsFEmOUA2EZ+eMG8XIKvTHWsy8xB9u8fAb36B575L9u3v09hwtFNYWbCvDfixZXjxhbQPW5OyMpkinSd0KtS3JNjPcShDjwDMXUeK6ofveR0lXD+TBsHID+CiPTHdyuqqjzyPCO6RXkDRdH0BGZIKpC/TnHbU8J39uzJwRzQcRzltme1MWh7tsTd/HvrPh/skpZ61j97rgU3/wEMcludAkEUllYJESqy++y/Yfeom+EOx88hpvffOI+EBx7e4M0Qq+/Svf5M3Ne4gfvofdXXCWerb6nHm6wzebNfemU6ZWoUTizvPP84XP/jB//1f/IQ5N0pJUThjP5jgpWG42hONn2HWP7AFtEYVFyOr7trYdgRMbWJpAm/qBmOkBEVEuI5eD+k4FSeqgTx4dPXokMUHiVjX1pkMhoQ9sLpZM5wtuTHcJBNxFi97OmZclx5s10o6Y720RTyvOLp8x3h/R4bn//rvgNYUTnD46QdWBm3sH9Oued4/fYbK7zeRwRq8j664iuZ6Moe5ty5yLdkPKDUQoleGl3WvslBOaKJAKQnS0XcvZcsNRsybg2dQbTOcZiQFp670f8LkJBBJET5QtLjiEzYmtR+XFMHUSI2kIqw42qwBSDpu+1RJjFUZJpLAQrxKkQuGdw/cJkykCifV6g9SayaTAJz/U3l3DuoK2b1lszYc/q4Y0d1CKJBRVtSHPCqq2wdjhBlBOJmgjUCqj6WqajQcBsQ/0KlGMCjI9pt70NH2DMMMJPqkBNtb1Pck5QjWgiK3MCSJgdAYRXN0PPYYQWW2WZGVOEmCFQQdF6BOxcfSrhq2DGSMrUOJ7n1x+IDZ3oRWTT+xRSE0xy1AKUuoJvSN2Hf2mwrUtrgeCQGhLMZ8x29smvzxioz7g977yBV6ZvMBi95B8WmCNQmWWPM8xQlFkGeM8RxYZR4uKduOxXSBXOeuzwA+LBSpLXGg4iT2nrqNpelauoU+CEC3PkiVzS1421zlbHdNMn7H3quf8yw/4yodrZjvXMbpnHidsNSX2qGf9+DGjpNF2StzPWD0+x+yO6CYGZxSLV7YoCsjOS4Qcs9p3vFJO2N4bU8x3WcVAs9owEzn1ZU9nE2Fa0XYaW4zJLDR1IkU5pFFdoOsTISkUCWMcPgynm2q5YTLOkMpTZle8CyWYHmQUo0R81nD5XsWzVPHyT94im5Ssjtfs/b4b7PzUdT569wmXT9eILUmeSa6VFuYrPvt7XyLTHbFPeCWQvaQ6a5GHU9I7F7SLI8znD6kmNS/89Kf5jv0Ov/7WG9xTB9y5c4MLc8o//OrX+PRP/ARqq+CXHr3N17/y83DvGvYwxxVmsCmNIqu+AqUw4ylqe5uQj9lsNoSLc/R6ReYiQlpEkQ08ETJqnn3f1rYTiTPdUgkHFmQGQXiInrYNaJMjtCUmBdIyH48xKKgjwgVsLZGNwgiF3zi6s47CCNrVBXkvaM6WTEaS+WzEyvVsOofWBegcM1tw8ewxpx99xHV9iL/seXz/Id1FzawY8+jRBcpKdm5tI+eSi3SKix6VC4zMyaIYQFlJcHmxZk3izu3b7M+2WJgFonLIHNaX5/TNhqZvOO02LPGM9nfxLiBTYlKOED4hERijkWnQywnhQHTozNL3NdiM5AVJWazIiVen8BASIURgsG4JAcFDzxBeE0JijR7YY35ILTebBmMtKQm0lDgXqDYNymZkucFklhAVbZMQcpivbboOJT3GGvpeIkKibSJd2xOSo6p75vMx0kjayhOTH4TWXSDSYW1O1VX0qUMYhkOVMMik0MZSGEsUEiMlWmuUkmyqDVpD0zW0XUNe5Ng8YzpaoHSC4Iebno+MZY7VapgWbFsQnuT991x7PxCbOwLsSLLpnvHW0Rf57As/RPKSru6ouobeRdrGEzo9dJSveMrjckyW3SCIjp9569fI119kMd5ib3+PUVkgU4sMiVwXbE0XLOa73HzueYqdGySZ2PgWKRW7WD51eMiOzTjtW95dnfNhfUzszqlkwrmOWSaYHRomkxErv2TdXtKEDRfugq48p3lyyqQ5ZF4vmB1llKeK0aZnk7rh9F047NjSHzeMoqSysHf3kMuvHrFeRqR1bA5P0EJS1T3u6SXpwkKu6bqKfPcQPSmwwpDkkPrzyZHbnDxZopTYkAhJ4leR1HtG2jMtYL6jUSOBtSXSRaTp0RJsmRGNR46gJbJxPYt7c3YX25yfNTx4s6FrW2b3xjRVx9ff+Dqv7X2GS7Vi5xZMpo47+1uURpJnljwbQxiaVHUriM2a8Znm7O/c5/asILw85nxf8cq/8UMc/+zXuXExpUTy+37oJ7l1fMS3nqyRz4+49+onUBcVX/3WGyw/OsHXHavygm89PeU3v/kt9GKPxfYOvda01Qp5viTzgiJbDB5MbwjRYsKYXIw44evft6UdhWAjejo6rNJordBX0m9CwBOG3hEJaw1N3SPRxJDYXG6Iyw7ZJpI0yD6xY2ZkraSrakbTks3ZBVu3FnTeIZPAaItUlstqRWwCLihu3/kY3VeOePK1DwhOYGVBWwumN7aZHsyQs0AtV3RqjchA9gbpNLm22CyjNZrdnWvkMTAuFsiYUV206K7lqD/FhQbfNQMmI5MDGqSuSAFi3dErkEFgixHJDcJsIzWZ1pgkCL3nb/+dn+XP/on/Hr4BkwQmSqIciJ6ChEcihCLFgYAphcD7YYzQ6OFVrndobQhRkI31MDdOwtpBqDEdT3FJsVyuUVeCD20FvevRRhEQA6JZDTPum64FBEZqREqDmGSUyK1lMd/B+Y62b0kpIo3m6pEEYzXlKKMJDcpIkhwyKVIMQcUBzBYRIZJQRAQRwWg8xVjLpq6I3tNuLtiZjNgqF2RYQu3ZnC+pzz2yhK++9SUuz8+/59r7gdjcpZKDF9N4jqu3+bVvX/D8jdfZ9HKwoeQKvw74IIhX3WERI1oqJuMdbHA8vFhTdysu1scoGVD71zAmI8WOVXXK0/UJ+uKSN88v+PwNQ7Gb0RpPioEX7D7qQWJTVZhVx71KsHAj5uPES/d2SDczzBj6GHh8vqRvaj7+wkucXdZ0MiIPPe79U3aqgvyRpLnf0toRo/2C0hpOj0447Z7xyeIu+nxNWXUIFzl+8gzdtkwPZ/h5QMsRz0222Z7P2bqdY+YaZQxN35KLjLQROAkuJnrp0RNBWzW0aZihzXpJCpJcKmajyM3rBkVF1B6pJFoOtUvfGlovCHVHp6BdVgTZsZuXyFJQn9Y8eXPJ6fqYg9d2cUHy5X/0T/nCJz/Lr/3MN3jx9btMFyOe3T+hWzs232mJ+xl+0VNGg954ym3JeCxIvUCcJd75T7/B6DN7LD63y0m34u3feJt8/gL7+ZwnbzxkHXtuH27xtc2SfGfMzR/7IX7sJ38/p6dn/NpvfpE33vlNlvUamY+RZkK/DjRtg3COrTBBJUtmJ+R6hCyGN3hE0blmMNp8n64oIp1wBJ/o+zCApUREK4FCDIhYABK+78ibDFk5Vpen+PM1IyxTM2acFYyzAiUim9Ul43GBnYz44P37XHv5JrooB51kbmm6jmxeEKLnYHKXs7c+5Mu/8g3KziIzyWyv5MYLN8kXOVWo6OqaKBzaaHQr2JMLoo+IkcZLTe96ZsqwU4wYuYBfHvOwqUm+IZ9KJiNLtrUFViKLHDudcl5XjIoR12Y7jJJianLadcdmXeE6h1YGiaXZ9CSt+ek/96fY2Sppa6hOW3wDUpih9KIFmRq0lT4MM+CJhAhDw9UHSDGQkhik6DHiXLoCeyVEEIQukZJGRlBJo5PGGEtKkWJssblBKuhcTe9apNB07TCaCAIfI0ILjLV0fT+MOmrFKC8IGSw3a0ILi+0pnhaVK1TvsJkd+PMRvAtonSOQdG4z9A4sJO2ZlXYI53lHUWjmky1op+goWF80PD0543K55GJ9xtHZhwTd8tkf/xQHh/vfc+39QGzuQirMbIpfrlH0LNMxX//wS0wn17l2cGOIC+srWJRUBCRZVjAbTchzw/PXX+IP33wF03aUwjI2Y2w+QYwMWW4QQiJCpMhzutLSTQ2PaDlRiW0Ln9y/QUnicgbNjqAxlnprh/liG5PVtNWGdOppnnnO3l1RlRU39g/4/Mdf48nyFC87TKwpGsXx/XPGywXHsaeqe/Z2h0c8CTivaTcNq28/wnWK7kOPOlOcb1aoviTb7enqFafCcXY/EaOAJIaGjEgQFbQWEwpCreg3AhHhpF0ylbArJkgK7EggouDDoxajNK3QRAshOawS6KyD3CByQZHBRGq2tjNKk6g3PcePTmlmDdd+4jrSW46+/CZb+weocsHJo1N+6qd/nC/+0pe59bEbVOcV/bLlcrlirBIurBjt7EJpCX4AHNjJFvPRCPE0Uv/sQ95+8i3eePJNvr75Gp9f/AivvfwaX6oeEbIGYRXeSu7ducl1cnZv3+Llj32M//gv/d/5jQ/eGXyjmxYVc0appLC7jGyBFhlS2oFzL1pcqtj4hou4xInv37RMEokgIzEI8AO7XIgh2VgWBbkw5FGTK43YdKhLibxIiHXPJBkm2qIrj99UdHlAGqi6eqjJSzDasjxbM5mOmBQjTnyHysyA9OiWTPWEwpYcbSo+fucm+7e30eOAmzRU8QxjNXmy6FAgO4X1mrEr8CqyMbBWQyPRxMBOUph2DSYxuXfAeGfE/qxAShBZjioyolQkqXj3vfcJnWNnMsYi0VGQoymloe2GunjTODKb4RAs9uYsq82QKC6hrwPCaVQsICmkSBgrkERa1+J8RBiNkdkg3hYCbRWdiwQBnkQfI0oqOg993RH8kPHQUg7wspjw3mG0IrkhAatET4oVnRNkeoaPEq0N2EQSnnW9om0rtBLMJxNwkcZ1YCReRVZthckSkjTcJLRAC03XOqp1g9YdPnh0LlBZZDzPKEpFaTS5UMgu4jcdum/oVhXtuufZ4zNUPuZsecFX3/lNfuSnPs0X/sCnOby5i/g/fu+19wOxuasomY7mVDoSl4LkElHWrNqHjGuFkdnVqKseastCMy6nFHlJMJFTF1lveuhrlmdHXB49ZXe8w97hDbbGGVNtOFzsMc32mY9zRJmxXxRcZjmYnpuTjLpqCbaj2tX0E0FLRzjyVL98RnoU2RNjbmUF1zPL3310n+M3z3j187f4qY99hv/s7/2/KLYOCJPhG6m8RRQJPcpRhWZUQuocZycbFgd7xDfX+NPEdJyxdXtKYYZu/IKMojPY3KKkodjNKcsCpRWQcE3CrRL9MtAQOW8Sde1ZiQ6rFe+cPqXJOm4upuy5GacXPR5Pplqstbjo6FSEXKJ0IveeG4stxmXELhTd1HDxbI16bcLOKINac/r2M26+eJd8XPLOz3+T1157CTMuWD5Ys7d9jdlsTpcviUvNst+wPduh7wX5GHJdEH0DruP0fs3OwQE7+89x8MnbvPbCx3nkHvJwR3D06UM+V95AyRXKDjiEtQucS0NyiZOjM7pOI/opU6mZ5mNKPcakDCssSUT61NGEiiUtVVjTUtHpIUgmfgdy3r/sKzE0VaOCwhhG5QgdwATYEhNyJ8kdiC6wOV5zfHaBbhOzIiMrc+TVPMRquUQmMYg18hlSCYILGGF4eP8Bn7hzjcJYXLWCbGg+jmYjulWPziW3Pr7HdMcgJy1BtiA009ww0hk0Au0FshfQDjYkn1vaJlB7UNYyKwomdsRWWbA4WBBHkmADWg4oXGlykBrEgL3VwrJebzh6/IzSapp1i4qSvunp2mFzb5uAdwklc/rWI4UluEjOiMKM6duAIQwz7IDSILXCmJzQDpyZ2HvkVTCp73ukvGq4qoEaGkOE6CGFAS4nhnl5UiDFhNaGunEYP0ydRSTRWVSSKDmEj4IXoIcbdVt1OBewRUZb9WTGUGSG5CsKYckkqOjRzmOjgybS+TR8XUKwNbWYrGA8LzClIJ8ohAwQPL5usblGmhH18QqdIjoEdqZjHp6eoIuef/d//G9z9/XryEkk0P/gJ1TdumOyyonTBe1seCyNziNjz7OLj1AiR6UZMeYAjMqS2XxOkWU0qWUtIqtC0UjFE7HhwbO3+IJ+hS/sfoabh4csxltksiTPc7S2hFXEnCYWdaIPgm+pp4jnS7pdTVKeetMjPqxQDxJ3VgvsPFJoyVIkvn7ykPvdEvfGh3zi07f47AuvwJ/80/zs2w+oR5HpSQ6bjnExYjrTUELsetpVTcwKOlcz1lucHz2lubhkrQx7u1vMpxP0vCDfKpkdjJleKzDTwewCEDpwPpE0iDKjM4FSdHwge0adwHaGp6nja+Y+f/3hm3zcP8dBusEiLymUIG4c1ggmec5YwcGsYGHmjKykkTWn3YqLy56kBFXWMPWWo59/gLZjLkcSc/+MPTfmxZ98jhpPe+YJ68DyeMmLv+c60p3R74zwbWB7MqLIE7FJFMWIZtUi6p7N4xPaixoywdkY7N0dPvfyLZajOVUm2JElnVvSpQEA5k5bvvj3fpF/+q1vkZLh3uwehSqxQiGEx8eOs/acdaqpZEMtG4J2ZNYikyTDIBEsv8+CbKk1WkkKW1DqAuUithOMU45ae/rTNWx6bJ8IbaTuGmTfIbqGLCkKmdN3Pc57hJAopanrir6HXkIdelSIiCCw2hCUou8cQgk631GOLC+9fg/lh00kN1PmZUkWFbKNxJVDNgnVB1KETgRCZkFLlFIYI8ltQQiJ5arCFDkpKORIcl7X9L5n2VRc1hvWVY2/2jj7pmFzsUakSN92lEWB6wPJD1J3hcW7SGEU6SqRWvsOFz2ZBZFL+s6T+iveujZIJTBGDUTYdaBd90ysRggxmKO0vIKNDdpJT0AogbWaPhnWvSMx6LwGZLYFcZUZCQwazqJEyEhVrQh9TVmO6H2PzSR5XlJdjaWOyhG5zWjdmrGMlEQygBRQMTEZlRhjkFKhhEEpTZZLXGwYTyUyA2kCfegJeMbTEY/e/pB207FlZ0gRia7j9Owpt1+6xeduv0Zxo2BTnWMzA7n53hoafkA29+AdF2969j//Aqf5h7RqA62HPgzcBdGRfIXMMkQQjCcTZtMpUin6LuBCQCZJYXKm+YKXrn+KH7n3E+yYu8SVYlNL6uCRpidZj1GSoBMr3dFse+zzI8IWyNgT7vfsvqOQj3P6S8+62eC9wOaKD8tzvpOdUu84xGbMh998xvNfOOSH/tDnsDcW/NM33mL2qQXJRcaNJM8jNia0TMz3JhweTMlHmiA8N185JDM3aZzDFEPT9ExuOGtaxAcn2MdyGKWSw7yvJsekAoMFB5qErhNtCKh+cDuOS0Hbb9i4HmULsjpH95q6cyxKzb3dMdd2J7g2UJ0HHmRnZAeSVCY64fAu0JMo+ozVG0viWcbiE9dZfnDC7Ts7NFs52c0xl+9tKMkZjcd8/R98i5d+3z3EWjEJBjmNqM4Qm0gfegqZkSmFnlmOn5yyk8DGnCJKvvwbX+HtL/3nvPj5z3Ln1c/S7x2ykIJ5iNQPjjl69wh9v+fAHBAyRXYls1h1Sy7DmirVJDnIuGWhKKQdUookvAs41w3+yu+jR1UgkK0kE5ZC5mTeoKpEVsPmYkleJ/zpGt0nxjanTz0pROq+RzqHzEbkmSTPR3R9YLnckI0VWmtin8iLAqQiNglhFLk2rINHaUE+KWhWDe2mZ7KYMxE5ucrQTtFdNqyfregvK4qg0A4yIZFWcCpOEKnAC0nSEak1m7rG9ZFCKfpnS84+umTjq8EEpTVoRTQSbQ1CKbSSPDs+Js8zMq0Zz2ckAUWur9LRidgNkhqFGDbivkOJBMnT0yCMJgV1dbr3gzMVAVEgpWJcKnJZoK5kWz5InO8QQmCkIbcg8mHzbrthmuu7sX8tB2Ry8B2ahCKRekfvO4IEbTzjPJJnCdiwWIwQEnxsmW2VKARlkaFQjJUmK0bYTKKtQGqNzRXO92SZJS9KEIrNcoV2CW09qVsREai8ICtzeufoNw1CSvZ292jXHdVmjdCRz/yeT6GnOY3u8N2GYB0P7j+jmM3pun8BcJgQIgd+BciuXv/XUkr/oRDiLvAzwDbwFeDfSin1QoiMQVv2GeAM+LMppQ9/p88hpWButznkFqUUPJb3qcrNEK91keQdsuxp5QWZmDDd2sKWI5wMdCHS9Z6CRBEcN9KI519+hWuTG3irGJWGkZaUKSMrMuQioqeK9ThSLzpUoakzR6gd/oOGO18X+LOMOrTozDDJp7SZ453ylK/WH1CXkp1QsB9L+vfPEHf3SIeGL3zqBZ7b32V92WJ+ZMrjLx2z+vZT2rOabt2w2M8ptgzz2RZKZJx+dMxl4zG+xOYjFtMZxUgQZQSTEDnEDGIM9K6nDYnWeYyPZMkgRYZLAldfYKwB75EykR5suB0X3JGHmJChY+SlazNubA0z7u+8eclJl7AzzeIlBVOPtBFtQCZL8oLJMpDWc6avLzhfn/DcK1Pm+2PWq8jx+yuqZx0vvXyL3Tv7ZFHz+PyE7Zs73P9H73Lj1RtUzpOHhMg1667CphHrdcXWbIvNxZoiOO4e7pG5j/P1d3+dv/+LP8v4S7/J7mSfW7Mt/siLr3Gjz3n7w8ecUSGzSN1d8LQ752lY4tXwRJOVCiMHLkfjhk2x7XtC9MPjdAxIIQY58vfpEkkycRNGZJQ+R1QJd9mjO4WuIIuWkZyjjSd0PVIKRsUIYo+WAq3tMBWSBIQ0MM2jRomMpq4Yj2a4kNicX5JPtsm0wSGo+nY46StACbZnB4h1ZHW8YfnkhPNHZ6QqsD2aYYt8kEv3Lck3NIsKlTmSFiQZ8UHQuYjVGX2MhB5MntjOS5QskNoitEJYTT4qsHlG3Wx4//47vPTyC5R5PqQ7hWaQoSaiBxUUOmlCGzEio920iCjpG0ffJ/oqICRomaB3NF3DqMgxOqNve7o2kYImJj2MQ2aDONtqidYS568sXEpgjcQj6VwihaH3FES8wo0oSD0hNOQmsphP2d2bMVl8V0YdCSKhrUKZK1F2EtRVg+968myExBGuxreT8wg0o8zg254QBSbLMCkSnKOPPZnJwCvowlC3bzqIkt3DG8Q2cXlxQrY9Y/faNqYwxOQo8oKL1SmrswptCvyZGm5Y3+P63ZzcO+AnU0qbK93erwoh/h7wHwD/p5TSzwgh/hPgLzB4Jf8CcJFSel4I8eeA/z3wZ3+nTyClZjqfYIRiEmbMmi1SLul1Q1BhGKkKQ+MjK2C0m6EyTUhuCAP4xL4peG1+g92dOVt2gcmG11jtyaUgQ6DzhN3NiVPJCRVMFUbBum04+uCI/fsjlrJk+5pA9Bn+wtGcr3kga766OGG1Z9l3ME0FL+zsI9YVz37lffZ/78fYHBquH+5g9yMXbcPWwQHv317x/rcuyL+c2JkvKGYWpR1KCe58/JDlxYbqIlDVG+pmxUxmHO5us3tjRlSebJxRTgxJOs4vay6Pe9xTyF2JMpHaeWQKjG1JpiPLyjNpC64fPE9ejQnJozUcLxPPnl3QdNCljNk+zJ7zqEWLyCRJJGJvUKkgD4nZuMDteVbynNv3Fuzd2+aDt49Qq5LwrEWSuPfxF9iMPdu7O8Q+Yp5zjG5M+fBLj3nhh+9w/OQCvRqk1XW7Zme2y/nTY7Z3d/ABzj5Y8ok/+jL/wSv/Dv/Pr/46GwSYEasu8g/e+g6fOrzDi68+T3f/W/y9R9/kUahYEujHOSLLyYVFJYePDTF5mq7F+UCfAiE6YvSDKCEOY4bfr0tEwVYzY+pymmXL5ZMlVB5kxlY5IssLxiOFwhOTwwhPR08MPYoIURGiHk6vmUVikcmAH5LEbd1h85LLs1MOr8+RKVFXa8rRhK6pB6+nNtTLil/92V9hebKCGpRTXNvdxxhFtD35WGFyjcihnGoa2ZBiT4ai1JYysxQM44vl2JLNLDJTIHNMlhGlwMeAsoq8LGgaTVlqlIqMJhkyDTwkgkBmEhkk9JB6iDqRHBiTk3wiyyW96al8DyJirCF14LoapSJaD9gQLUGokpgYUq8wSCy0JAXHuLQ0vaNzbvAC1J48ZYjgiXVDkSWKXDDKE5kVjPIJ82lBniuEcoR0iZaJJAVoMZR8xBDC0sqgMk8fO0T0KGlIAfp+KLEkHwh9oO1bpKoZj8cYa/ApEZEgRkht8AGcj/g0fC/X5yecHp0zLkZcu7FDrz1JJrJMsVyvuDhZkZsJuh9zdlIhg/qea+93I+sYnkuGy1z9TMBPAv/61cf/MvC/YNjc/8TVrwH+GvB/EUKI9DtouoVUg+EkdrRVRzyXmFkG00gdW4JSA0PFeTq75sI95Ya9TuzA9x7QOJ9oGkEoCjYh4TZrSqkZlZJiMSYIge8ilw8a1qLH3bSwqwm+p9psePDoI243r/PcZkET1kgVCTMDt0fcrx8wtRUTNNLNuBfHHMScyre0Txo+/IXvcOtPv04YC4zO2bKWTdkgXIHMcyanHZPDGSkGZGdoNx41i5hY4Js1EsN8MsNsCy5lj0wV0ifkU0exVpQTy87OmGs7mvvVM9QmEPGUU8UNs0XsIloLbAu3itsc2uvoi6EcJAFXRZoqYUrJ1jYsbgUmBx29StAbepfwvcIGx/MvzFmdQxN7bu1fZ//umLeOHhKixj2pUEZzeH2GOBb40LOzv4VdCszYsPuZHc6/fcHxuyfYrSnNyZoss4gYWJ5V7B7c5OzsiOt7W/Qm8ehbZ7z6+z/N//TGNX72n3yRjzZL1mHN06bii0ffZN9q/uhLn+bPz34ff/lrf5c3bUsjCmwaUZgZou9om2NcavFxkGPHGAcRsxAoMdD3uu/f3o5MEn0hqZ9uuDy+xFeRUuZIqem7CNuKoBV9GmrkRhcQND4Igm9ZtS2u3hC6RJk1hBhRdkY5MsynC5CStm548vAZ+y8/jzWDR7dvO4oswysHyfH+e+/wza9/mWs715hP5uzNdxgVlvlWhp0o9EhAHohSkMhQXpFr2J5MmOQFmTCIkJASJqOSvLRE2dMJj5CRMs/RtkQogTaKcTnns595nZ2txVVwSZEbCx5iPyCZfYqDeUmAj4HvKlQVoCWUhUFlBps0znT0WiKEw8UIQqFzS0LQdg4pFbkxSAF906HkgA02RtO5ODBovCA0PZMix/uAiA2jzLCzGHDWWvWQ1vR9BBxBeopRPoxgxohMCu8iWmqEUUODVwT6PuAQZPkIm4+GzV1EAp68yDDWILSi7nrqCHI8wsecetny+PQppxfnwxiqUCzPLjncPuC5u3dxoqX2FaUZ/u9FUIz1DNEXuFqi65zQh++59n63gmzFUHp5HviLwPvAZUrpu/Go7/ok4Z9zTaaUvBBiyVC6Of2v/J2/pSIzWUEk0XYNXdvg+0AWNLFJqC5hxzlN6lBaE2XkrHrK/Sdvsje9iXfgReJI1sxEi+17toJGe0Gvc2QUnPqAVokkhoCLQ7IKF7Bv2Nie482KvobdEvoiYVWOzzrWzvMdf0RbJkqR4ZNi3AT2Y4GWoArLRM1xyzXZOlBnGmMDuQhoEelFZBMDt1/epnIth9kWXbNhHOd052v0XsH4+Zx+6emrNdlaMx/NEJuA1wFloE3gK8e68eTREoMhs4boEshImUESUCdPHz1zu4upLaHbkJVDfb7ZRDyacpyY3/TYvR6ixC8loVU0vUIQOFgUxGPD8dGGg70xe7nm+N0lLllG3nC+OebW515ka1vwzoMHVI3g2u4u68tL0rKA+Yqbn73B+7/4kOc+N0GLIRW72JmyXjacXq5Y7C+oQ8PBjRkP6vdZPTLce/06/+aP/yj/+c//HH/r4bc4Sht8qLhIjodH32Bnts1ZdU4TpiRdMM+2yOMEqUCVI5bVMS6eEFX4Z6OGBEyCTAqa32Gi4F/2JaOgeVrRPagIdSCnREaNS9D1cOorTB5QeaQcW0yA4BPeJ1yfqDfDCJ2rA5dsqJsW5x2LrZKtrRlCJEIM1JuOrmnpgFxbfILL43OKoJnmJcfHR3zitZe5vnuNm3sHyBCZjkdgEmokSUWiFg1N56hOPIUumaqCwlvkRpCkRpsMawxWFxTWIKxnMRFII7GZRWs1bNRhgHy99vKrV5NeIJNA9RKhxYAUUBBkGuijDTgSQQAGYkqY6EkWjNAYJNF5+r6kdz19F4haEvqB456Mpu8coe3RyOFpxA/BR52ZK4G1JyvyYU1kir5XGGu5fmPKaCQJzuGuZNgxAsLgg6bfROJVScYIPYSchCAm8F2kaSJS5FhbUnVxaCbLhFCCPiRc9EQ6ohjCl00INMdr+uSIMpJPcg53b1FVKwptuPOx6+RS0a022IlBmYx+09B5j6sDbp2olxuWFy2rZUXff2/i6e9qc08pBeBTQog58DeAj/2LLfn/bxVZXk7S+dkZU5mIwSONQqmOsGnpTiKjbUMxU3S6w0hJiBWPl/dZthWjfBe6jjpW/OqDt9mo57g7eZFpMSUXgbItKGKGtAIlIyZryUvBjd0Jb7z/Ht+ujjm99Bz6fVoKnooeoVsmTtD1DZemQVnJMsJuNeL18R3CkxVaSawqWTaBUTahW/d0C4NwHqGhans2bYvVObN7d5g9ceR+Ctrx4VtPmN4aI590lLrg2vUtVjdbiNBpz8HWgskioxMt6/WaGEDGnPbYY3SGsw6dS1TwWKvwOtFvIm0l2Mkn+DOHwEAUtN2Q7jOlYLQFdhQgSsKFRlcl7Sax7iQ3JpFZN+Hi/Z7ZjmRhJetmzWUUYDQFmoOXttl7dczRt49ICQoUi52c9pHEnbXEQjJ+bs721y44fXfFrU/N+OA75zx6dMmLr+5y/KDl3J1wf/qYHzOf5rlXb/He6dss2gVbkzl/5rU/RL8+4+c++iWeiZ4gBGfO87g5pbcaX04HBkeUBAfJFGAKtiZjxl3JefOAyIbSGAplGWuBCB3n38dhmegjq5NLYuPR0RJiIvaRTGb0PtHXLdkIiokkuA6rA+BxvqNpG5qqpa08rgvIBEo1TGcdi/mEmMAYRVla7Bg26w16NEVLgWs6jDS4Tcdl1fDiS68yfiGjQDLROdujMa5rqOoVq7MNLT2tdHQx0TeG2WxCqQ0jpdmazMjzEVJr8olFlwI7Al0k0AFhJEKKKysR2DQ0QXNlcT0kIi4kqqohhUR0EB30zTDhk3pBaBP4IdeRIoPUPQiMgMwMvZVeaDoRcBJaIekSdM5DSoMJyUWIoLRBEPEu4TyECN4LjE7IXIGBosgxVpOMpPGR5UVFU60xekAu9DHhkhzkG5lCmoiLa6y1g4heJQiJ4AQCTwiX+OCJJMQVolde4ZGjGPDE2ih8VBilmI4zvAy40OCqjlleMs4Me9PR1VRToG971hcV4DFK4TaJ5rxls/KcnS354Ml9Wtd8z7X332haJqV0KYT4ReCHgbkQQl+d3v95n+R3XZOPxHCEmjE0Vr/n5V3P0wfvY/MSlUuiisjkcW2HSmPqJ4rN/Ut2n9PEbUUnI152uPYI5yrKOKE6r3jy/nd47+FXONx7jXs3X+DVm7e5PttnokeMleVgr2S6ZyjnijoJbj2+hfpwC98oxiZHGsh2IofTMX0veFddUheCPkhio7j5YJuzx8+Y3RqxvGyZzMccTCZ0MtI8aiiu7bAMKxrX82y95NGzSw5Ht+jyBfX6jPKjDf7Ccv1jC97/xlN2n5shc0FcdxxsT2hEz1JH7lcn7HrN9s6Ymzf3ESKxPu5ZfZjopYAdT8oHS3zwkdhLokuDYNclZAokAThF7D124ji4axnt9IgoqI4MqR7Gf2unWBSB5xdzjk4dyy7x8i2L6VpOhSFZhckiOZLZjQNO1uesLjsynbF9uyQ+XrIXt1mv3RAOEYKdOzs8fveYGCW7t2fIGs4eX3DtpXs0F56vr75JH3r+reKPcfPlVziqzrmxvYeMHX/m9T/GbjHiL33r73CSNjTtwM2JWxkyh2RaWtchwgQlBv3aCM/EjEhdyVRWjK2kVJIYIk1QxPh9TKj6QF03eOcwMZFihuwFSkJMgEjEEAh9QmeRLFdkuQapB35KHwmRAU2rDEZrtLSMx3NMpgaoXpNwMnH/vfd4cfd1urpmvazJMBhhWJ1fMp9ex1SSIgk2p+dc3n/I6uxs4KFLgR2PKGdzcivQo5b5tmV7UVCKhKC6mjyyEDNMkoTgEW3Ce/nP1HdXJEfnA5uqoW4azi+XVHWDD4Esz0kRpNDIqAgdWJWhkyF5iRIakSQpiKGE0jPQEBmcBFmukVrgQ6TrJL1ThB5EcEzykkIYrJEDsx05ABZbh/MQk6B1nqyAZCRouFivWVae3Ci6tiWFSJkVSCSNS/RIpC0x0hBDpHU1qh1GkzOtUVzZpximbqTWZFmOsoYgAi46pJZEMQhFfBRYM0wPpTCQKo2RjEc5eztTcg2yb1AikVQkVI6RtyhtIURc74m956OPPuLND97l+ivX2DnY+p5r73czLbMLuKuNvQD+IEOT9BeBP8MwMfNfdU3+28CvX/3+P/6d6u0wjEIeP36LvVuHZMWcLrVE32GlQuaG9WpF9dEjLt8+5vYXXia/u0cvI170rNwpzx5+RHxySXdxRh8NH9VPWL5/zuXJ2/wrX/gJXr33KaZTgy47np6cs3xH0lUGvxwh/By5X+FGLXs3Fyz2SvrLNQ+aE+6Xl2xMj1nB7hsjutMV84/NOH245Mb1PVarS/J8jO87rBeM7wQ2C8mbxx/xcPmMrJhwcP0erpKYvS3a8zPE05rqaeD2J3epvrmivWfop4HiJGdnu2BSQh06fCs4elxz8qxnNLFMTYmxnmBavG3Q4xHBD6CkID0uc0zKEXGZhtNR1PStQ+rEZKopp4HkI/VFTmgswhu6kIjOcXNmaS4dy6pn71qB1Z5l66lGGdIEjIiUypAaz9HjU6wcYXZ69l+c0WyBUpHV/ScU44LGJYqDETcuFtTnDXsf3yI+c6xOe+y0YzLb4c/Vf5j/23t/g1c/9ip/8OCTHPfHyLmCwxE/99d+nldeeI3/ye2X+Ju/+F/wGyffoFYKqQqikiRZU4clmZmjxAZBQ9c/I/YnqLhkZAMiSjyWy95x2jjc93FzT/EqFNRLlDAIYUlC0MWAVAkXOtymYpQMM5mhhCe3CmsNIc8HT2gRr4w+CqMFUUp8BGVyXL1hvbmkX3WM3Az1bMO2Npi1ptQ5sfO89c0P+PDt9xEXCddUFFqSQs/W1hSVScw4RzLw1Ff1Guaeo9MVq6WikBItJOpKLhG1IKpIEB0J6OpIDAPlUUlBCHGAdRmDsZYsyxnlO5jM0Md+wAGgkGiSEaQg8EEilUKoqxpjn8iMpRWBug1EN8yg904iUyTKYV69ipEoIyLVLOs1W3bEWFhyBrZ/khKTNKoXGG2IIuFFgJRQUSLDIPjogkbbxQAz0xqjNE44ghiS4XUbkEYCBU3XDwYkO2QABGBUABEG+blvkSKiMoMXw01caIHODUWZIUPENxWCjbWQmQAAoU1JREFUQJFJ5rOC8dgS3QaUwitH07doBNnOCOUFm8sVddVyfHnO/ScPaceeP/Xv/auMrpf8n3/uP/qea+93c3I/BP7yVd1dAv9FSunnhBDfAX5GCPG/Br4G/KWr1/8l4K8IId4DzoE/91/3CYQSBL3hdHOfcX5INJGmukR0PdJovDyhrZ/ARcWH//g32frYLQ4++3GW2iMFmFnCP7rghhGkVHDz4JCf+MKP8JkX7lKnwBcffImvfPUrLGzJKy9+inl+FxpJaBp43tL+sGfTNVwQOT9+Cr3kOFvx1K4xQjM+h70yZ/KZOe03Trl7Z5+LJzVy2xL6DpkS8rhn8+5TZp+/xa3tj+HsNuNsgdI545miSIaTzSPuvrTg5DcusaOW7c8skE8yilXG6e4KEWZcH+9T4zir1sQW2l4SNo6qbRjJHCkiI59jvSHgiUKQZKKqW0InBrCS0vg+EUKinErKwkCfcJ3CbzKSgygtXddzOIZRynh00lJsGQ53JUEILpImmojUPdNRRt5Lnjw9J/QFQgquvbzH5foSoSKpjMynI9ahJ7aBbL/glt3j0QdniCIwvzEh2/XkrWDrR7bJzwOvfucuf+3df8SPv/gi2zuHdMuasZzz6bvP8b/6a/87/ti/+t/lf/gH/w3iX0988fTbNGJgxcQUcGKNVBeEZkPnjlDpnNL0WKuJcoKye5y1kePmlD58lxv7/blSSnRtRMUcRYEUBqUVkUgvWrwJaG2HNKrSKOXRSqKVoixKTDaM46UU0VJi1OC+Xa1rbJEBGaWecLizx3gy5smX3uO9dz7CN/Bjv+fH+Se/9Cu888ZbHIRDst6SkqOzgnKW05sAWSSOPDFr6NkgSoFKgbbxpF4TjEGJhBBhSJ/KIXAUpcFmOUJE8nwI6AgB4gpBK5XE6OHj3nvatqGc5sQ0KBJJ4so2PSRaw28JqRWkdAW4GxSFLgpMMsSeoYFoFK3zrHF0usPanswNPZa2XZMlg1U5RTbCSEteWmwAF9Jwk+o90QnGqmRZralahyklLvZY5ZiNSqRJBN/igkdqNeCGhUAIMYDC9KCv7PoWQ6AcKYpxSe8TtXOINDgUvHAoAgpH1WzQKZFpwXiUMRnlZDYRw3pAG7SBgCcbj1DjET5G1qs1J/6S9x88pHM9dz//ApPDMWkMT/ujQS35Pa7fzbTMN4HXf5uP3wc+/9t8vAV++ne7+AF0bshvlFz6p4QqML+xR3Xe0F+umB0qJtuWzU5J1zXIAMtvfkh9vuHGT75OU0YYj3jpE7+Hz+ptPv3cq6RszBsPP+D/+nNf5kl/TsoU82zCK9c/xiheQ6xzoghc/7hi+4cL3holHjnHu3XNnpbMpKLuPKGLzNaCO5MtbhjL0TeWXNu7wXq5oneJycLz7Bsbtm7MqKo13VPP4sGM7Y/t4GxO1QVMkpTFIAEu5zOOHp1x8OKM6sGGerFm755l0Vt2ZMFZveaDh56Dm3s8d3OPy/WKi2VLrA2hFjR0jKRmpjLoA04IWidpLhx+ZZBJ41WHS4o+RqSBYqIwBtzKEhyE3iOTpvUBbQIv3pvw5MOeVR94ZVrQLWuYCIKRGOHBKjIBvq5Z9p5uY9k90KS+5/FbZ1y/tcNoodBNyYOnR0yKbdJOZP/1A8pfyHm0eUh+d8ZIzjg/XSFSYnFjwR+XP8r/7Et/iV948k1+7NqryMuAaBx3b73ID73wKf7qd36BL6UZP/0H/yTHP3fJt/sKq8FIgfVr4vptlG/IzZKYPC6OyPJrZMU9fJwi7AqZR1J/RvodVGT/si8pBVoIlNDoNDBNFHJohAJZljGd5WgGvZrNNSrXSKsQMSGHOT9iEGityUwOIbG6qCAZdvYWFPl4mGbpFJunNboZcf7kgp/7q7+Ac57b115A5ZL5YspsMsIYhTUKm2uSiqhM0/qes8szptMZM5mhQ0JJMZAbjUZKhqi7VUijhxi/1QgD2qjfkmdYY38rVS3kcOIPMeKDx+QS7yLeBaJPuD5AkIQ+4bsrTIALiAi5KTBGEwmEvoOYEFKhDFR9TRCeKAI+OnanE8ZJYt0gz25XHWerc0ze4IPn+eduY4RA+kQpFbFKFNLQNwEvDDoJGgIoRRt62uoUqSMhdNjMMp0t8DHgQ4/rW5COIAJCCmwRMaUlZYJVqAd/rx6AcG2IuL7GGonCY7Vkez5jezQo/3xsaZuWJD1ReqY7c7L5GGU0Lni8C1Su5bxZcfPlO2wf7LJ2azrTEQnkRfY7Tvn+QCRUkxzu3H69IbdjklF0m5rmfM1sa0wxNhw8v8+mKGDlcZcdzWnP0195k4PPv8rr127xhRs3OG4a/sobv8w7b31A3ydG1w+Z7eyxJce8PLvLVryJ6S2z3chzn7tGPje8+/YRbeu5+eoW9SJyTsvaeYyYcf0iYteOUo2p393w8vYhy7zj7Fsbdj+/zcO3HjGejFg9WVFsZVSPVrQ8Ibs158Z0wpGssSpHK1iHiu2X9nn29jEX22uu3dwinjuq6QlirHhucYPxZMqj46c8fPiA2WbKwa0tplsZFx91rDeePC/IraHrAioIUCPqjefytENuMmITsTKjqh3JJyYLQ1EONcjQSaJTSGUJwqCj4+b1oSxwVDv2tkeYfkMvDEaBKSKySOhksaeJ1XFP2xliHSiyHHrF08ctB3ctxV5EicTFhxccXL/FpVthCs/WcyX1quTIHHPvzm0erb7BJD9AFy3PvfI8n35wi//3l/4uP/ynXuHxO+/x8uFzrIPgT/74n+bbv/Af88vf+WXOjs+YjieI9gTrKqxQ6BhIyYOOyAhJ7TGbv0xmDujimI6WKAxZNqHuL76va1tKgTUC4SWxj4Tg0VKijBi4MSlCYghcpUiQBq+GBlwfA857SBFiog8e4XpUkqCH/MCy9uzP5uSjjNA7xmNLJnZ46c6LaGkhQtWv8YsAuaCwGbnWWBRWaaSAZlOT9T3TfEwmDfOQoQKQhhCYkoMcQ2hIViCMJJBIJpJvZ9hck+UZWl/1BLQe6JcpopUaBNAiIaTAOwgukVzE9xG8IDlwbaSrHV3T4doe33UE35EJzSSToCQmKlzv8X1LPipoYyR4gYqRFBK+BRktLkqaJPjw+Am9aDG3S6BnYQvM2jMxBVqP2VQ1dj5iXbVcVBuszSlGA98mpZZMQZ5LZNaRKYWvOkalQTuGRKl3xBTpnMJHQESKskBbhesrRIoI+isoGSym2yzyEcb7QTASe0wmsNMxdmuEHhmwEi8Sm7qnrtcErbj24h2EUaxCRcgiboiok/qI/IGXdfhItlScna6xqaVdd1x8eEL3eMl0MmNyfYKaWMrrC2LR0LkIa4E/b3j2T77Jlz9Z88t7b9HLSMQj5x3maaCIiZ18wQv5De6Y6+zPJjx3d5t8VPD4gxMePGhoNxn5RNCfnrP3iQnyuQX92ACRab5LM6mgruhsyeNN5PJL59z8+C7ruqE9URy8mvHgyZrRSzPMV1bEhSN89QHq88+xm5XUIRC9oGoc2wcF48Md3HLJ5Y2GO+0eXbBc6iUn4xGHi31e2rvFRw8fcXmxpF53bG3PONyeo9s10Uv6LuB7T1EUbFzgg6eXdG1ANRodFaEVxN4xGpmhHOMDvVNEJ1HSgIw4GRgVG64vZrz9nSVJ5Nzcgcp5qnHGOPeIQgwhkZUjnMKzy8RZJ4mrDrrIkw/PqStFygxiHrBRkReK0dTQrwyShHl5xG5/wBe/+Q947oduc2474mHFow+PWFw75I9+4cf43/7Nv8gbb79DfXyC0SVTlfN4teLf+aGfpvrgjHc++jYhT9ibE3TTYbVFK3/lfM2xo0P06Bq1HFO1l3TxiF6uaUNNSB2ycMPg9PfpUkahM/CNAxRSDFRCJQUojbTflTqA0gqHRwmwSjG8gwO+D3RVQ2wduIQVhvl8QrE1QmSw6Tybpz2jrOBw7zrCGawY01UdEoE2GmcN0mrG+YhSD5t3bB3tpiYtHXmSZDbHtz1KQ6Y0QmmEAo0iNxqsQGaCbJKjMoXIBGncozKB0gO5UcqAEEMpTCmFlIkQ+oHmyaCDxAtEEmiG6RgpJLlVjFF4bfFFoK5b6i6QomadOvrGo40aplD6QXriNx3TUUahDKIPyKiQWIQRUAqOLx7SZS22f0TlLphUgnvjA0S5A0KRlKDuO9Z1xaVfEdcrcm8HHEFsMbJDtBDOh2ZwTAnnPTa32DxHmSGB7HUiiEiIDhUiGkWWJdrVhlxpdBCMbcHMFNgQ0SmQ8BgpUJnG5IZkBJSa2rdcbJY0ziHl0Pzd9BUeoEi0scJkEpUEmbID3fJ7XD8QmztJUGYFzWgwmFTVJfVmRexrzh49xRQSZorW9qTS0eU9oQGcICw3PPjSt1i89jxmf4tgGvxO4PLylOxkwvUbn+al2RbPXz9kvjPh4qTl4bdOaRqBzoZ04GgmGe8U3JqNGOucB0dLnl7UjOclo2uHdIuGuJNYrhuy7W1WZxX1b3bsPbegPm6Z3cmJdUBZPYzo3a8wL1SwN2akLWd1R1M1nNrArXv7dN8QHHUXjBeau/k1cml4enlCMcnZycY8f/s6z04uOH225vxoQ3XWUJgRKiaUlGTWcrlp+Ohkw9mmZbsckcLACq+qFm0F+VihtBpOM34IdygTCToQ+yXP3Zzx6GHDajVmchjJteM8auKOois8SkmCE5RtJMXEuo/oOqfpGqKJPD5b0ncaJxNV37M1GnFtdx+lNYd7W/gQyK8rrMt59uvPOK7PuXnrgG8/fJsXD5/jn37jn/Anfvxf4fXRPX72q/+Qf/8L/x1+9m//I/7E7/1juFhzfHzK//zP/y/59ke/znvP3uc0rni7P6eRASXAyYxeWzbe010+BJXQOiGsQJl0xcYOqGjZfD/LMlpiJppQRYyUGC8gBkIAW2iyUiF0IM80Rgl66iHKbxQpBnyIOOfou57YOlLnESqi0nS4UWiJcx15pphOpkPJo4Xz8yNc47g8P8f1LTEN0yjRe9qqBR+Y5CXjoqTMS6bjKV3cYI0hloFUaLIixxYaO9KUswyZS5JJZCOLKSUqE/i8I6mEEGIoQZkB4kUa+g0gsEISoyJ0ES8iPg7pzdBFQhsQToAXxI7BISokRZExnmisVtSbEc5FLlcdtIJS5NQEXF+zt3uAiYHgIiJoeuepW0911VvwM8WZqdiIFV+//x5vizlzSj52+ALV05r1aUNZzJE2x1pL7BxaQKYjtsgwVqOBuh7SvmhJVAKX/PA1qyEsZdUQoqtXp2zOGspM8/yt2yzKKSYqJvkEGSV9tUSYhNKSpBJd7NnUDTYraU8uOG/XNNGRlCILGhkFzgz+hja0RNXjI4wpEB2o3+Hk8gOxucuRQX6sZMdnVw2Wmmuv3kI4gZSRWCZ88qAEfmaxzy0IdSS5iEzDI9mmOmayDuRbltM8YBaGz4z3+fGXX+a5vZusNpJvvXHO8iIh04gsV5RTyWRfMDuU3Hhpynia8+zJBfWHa8rKM20UuIpjY/DeM8lHLKcJ9VGHNRXlfMHxyZrDlyecvlsz2p+x2QQWW1NW335E3h8Qb0zRKmIVxL5BCcnN6ztUvuKiuWCnnHK4d8Bm2fLR04fYW3eQQaAzzeHuLmdPa/CJLjQIEkrl9F3k2ek5XlrGkxl+05O6jthZYhCUIzM4JD0EpwFBkj3SCnxs2J9kdMvIhw97uqjZHUu6DNLYIqcRrCR2gvrMMWohNIq4KpC1xGaws7XDG93joeZoJW3fwnTE1v4Wm67jcDHngzcf8eJsQr5bcPv2de4//pBPvfAC/+XP/iw/8pkf5r2/8x7n52v+tU/9Gf7ir/+nfHt9zLVbW/zDr/08P/WFP8ovfPH/wH/57t/n99z5Ue7deZ1p95T3P/wyTjgaqamFpImRxAalJVIlvBaYrCSJgZ9PTFdjdN+/zV1owf7z+7z17H2skrggUCLHZhlKC1LyWKsZjXK0AEVEm0HDNoy6apLXWDOMCSqVkSvLuCjBG+rV0Jec7W+Ryx025w3PHp5y/PCMarVhs1oR+oYsSDKhhySkMUOUfhSHgYHRhCACTVdRqAJSpGlbRpmjEDlNEENDPy8piozOeDAaYYcDhNACrRViECddNUvT8A9LkBIIFFqq4SlKBqSO2FzgUhhuCrlBFgLfedre04tEs9mgUFihWC8bzi83kBVgBKuLJWVmsFKRWkemMoRQtCGRSHgRsdOctJCcxkvi2PHm8kMeiRnTOOI7T54w8yN+zyd+lPqypTCGFIfR4vEoR0hFHSX9usM7hxSD0SmzllVV44NjMZ+hJDTVEiMDSsIiM9y4eZ1b1w7o64ZCWQjQbzYQElmhsIWkSx2rbkPtW5IQbI4v8BZSNgDYXHT4MOCMnQh4GYe5eN9DGPzSc2aE/gdcsyeEQBo90OWIhBSJY4nQBlKgj3GgwTHU/8RIEfNESIPGTESJCIFL94zZeclzdo8/8unfzx9/6Q/TnY14981Lqi6jRyKUIi8E45FgvqvYu50xOzDkmeToo0vuv3lMW3tu3tnh+rU5m7Xn5KuPuZFniEXJR8cbwgnYgxEpRKYHBXk5JEZHt0qa99fkE42qNP7DJX46NEBjb5B1h5KJ6f6E56odHnUnPNmcMN8b8coL9/j6+9/h/aNH3Ll5h+ACy7MLSjsn+Z4+dUQE65WnbQVKZhgdES2IRmF0zrL2jPKCLIcYEiIqgvegAtomEoK+Cig55p0Ha9beUC4ki4mlzRrklgHjB9n2BSw/6rl1MGOz6fDLgug904WmO+8ItUYrQa4Ny3WD3w3YRcHTj1bs6jmPPqi4/kJgugOffeUz/Oq3fpPF6ztsh4xu0zIpJ3zt/e/wRxY/xB++9Xv5+a//Xf7C7/vv8zf/+l/hH3zwi/z5P/U/4n/zt/8jfubR38I/7lAypzUjqjDE1p1SRGOxWULIYa5fGjukHBk0bINFAr6f0zI+BsyWZvfeFs3jBmVKpI8g3NW/b5hzH6oWAqGvVnpMiJTQSiAyTakmCBcRXWR7smA0HiFHAmkjo/GY7d0dpFDUTcNqtWLTbPDRYUqFzXOME+gkEUYgconMDXqeY3ZLGttz1l3QpppRKCiaHCElpS7RXuGkB5MoLnOKSY7JNaNxQZZrbCGHL0MMmzhpSGKmq3cs6arnFwTJDZISkSQyCmSSCD9wZlRygwtBKITNEHhsyrFJ8vCDx/QebJZRpQBKDIyXGEl9RKGvuPcSJQNaS5ILFKMMp3tqVyOip9xdcPasApWzvTPnR175UZYfLkkOrEhoIcm1olAZXiRqH+mdJAVNIOBdD3FwNojooesZTSdcu3bI/taU+eT/w9yfB9uaned92G+N37CnMw937hlAN2ZwACfDlERJNB2JohTJljVEsa04TsVVSSoppSrluGwnpSrLQ+xIciRLsmhJMM0wlDiBIkWQIkiAmBoNNHq+3Xc659xzz7inb1pT/vh2NyEXAZIlUcCq6rpnn7Nr39v7fHt973rf5/k9QwprcG1DfbHAty1JObzzBB/IywJtJbVydMnjc0EQkk4GVJnhhKcNDucCPkWMtGgJLnmcCyAShe7bO+IyMpBDlvPq61573xKbu5SS4WiIB2KMyNAnv4gYSMH0FbtIBJHwCVRMiBD6RHigSwGfPCIEPlre5Ec+8CcY+l3eemnKYt5gywldKxFSMywMwzKxt63ZvVL0m95C8MabF5wctnROcOvZHfafHtBUNa+8fMTW9iaDzZzD4wXmUmMLQRh5ssoghwKZFOPhBLsmyTNFKgNqoTBJkl45prt0PJFPcCRSFlhQc+vaVRaHHbNuzlvH93jf/rt5+onH+Mqrr/Hg4D43dq9TrK1xdnfKqCxooyfESOwshS+Q2tB25+g2Z3na4ZqEwmBMQkpJSorOe4TyvSnCe6pK4JuC18+WNJ3BrinKYWJkE10e0YOEiBDnML/X0hwL4qZiXjvq1mM1FLYPdIitxhYK7Q2Ly474mMSOLNOmRpcWkQrqKjBCMpZrTHyBukx85/67md8549pwny/ceZE/sP/tfPvV9/Pa9Db/8Pl/wg//0A/z//o7/zXrf2CDf/9j/zb/4a//19xNS5ato55f0tiEGBUoU5LlCqH6zUPrDKkMSSoQPRpW6d5Uw9ep3IUQfwv4IeBRSum51ff+78C/A5ysnvZ/TSn97Opnf4kejBeA/31K6ed/22tbCbJtw/X3XuGev0feKGyjoIu42DC0JcXAYIwgtl0/PJWaGAOKHqcrlUKEiHARaSI2l5SlImQdZB6VSc5mBySf0LnlxtM73Hhib6U07IfduH4Y2YaWoAQmN2QDy2BcEpPDXQRUVOQmQ7USiYZSEYwkCk3SUBGp5wv8Zd8PhoTUEknqFUmp39BjSrxtbRGsfgVJkesRCo2SGrPS7YvYb+4pJqJP/SatVA8X6wJ+XmFFH4nXrmz/SSjqZYOSug/28YLgEypGlIRcCUqlGBSG06aiFTXawBOPPc15ueD05QOefs8z3Hv9LtnSsp5toJxESUWeFYjQ57E6wHU9U11LKHNFFiLJtwyN5PEr++xsrkHoUNGzfHTGPPStEiklUhfMl0uqbgkG8pRIzYKuaZCZRA0MMTN4+pyGlCCFhKAffGtt8CEgkZTGIiVYISlajU2GT3/iN/D/vGyZ3+sV6SvNmpX0KwRi7LWiMQli7D9NSQhCjAh6wX2fjdmznTMpyHzB1b2b3Hn1ENFZCjmhsBNCq1FSMB5mTEaSvaua9QnQVcwfKg7PllR1JAbJ4+/dYf/JkllV8eqvHpHLkhtPD7n7YMqjoyk3NzeZhjmjrCR5R7eRMWxzuq0OhGQyLtBCkcYaYRyZVKxZw95wzOG8ZpEEU++4MtHc0Bvcfqtldum5e++Mp951hZv7V7l774iDdMxWucX+7pij4ynL4EBEhkYwyYe4RpFpy/kldJWkjAKpPcoYfARhHLpIeBcJRLoVL8P5iBcaM1IM13IGI0myHsZ9IhZLw+mbnkdvJQqdI5IkLC3lABCCciCoqgYvJEOtSLXj5O4FT7/rSYSIVMuarumYrCna80CcB07eOOPpwWNwHFgfXMe1gpuTXX785V/m0J/iL5b88FM/wP/75X/Ar75p+Tf+9T/Dx3/hR/nz3/9v8e9+8M/yV//p3+Ct5gzZpRWfPe8jF1egMKstxlqiUmjoT4CijxqMLnyjwv3vAP8NPaL6a9d/kVL6z772G0KI99B7Np4FrgC/KIR4eoXm+AbXdqSVFXaUMd4vMeeKzChssghRoDIBfVlDluveEIcgs5YYHXXd4F2HEqIPjrYa7/sTwHB9QMWUkGqStEijSMKjM8tkMEYqkCqitUSKgpQkQUS60AdE+OBJSkCUZHmOSBaVBGZgSEn0/JZVehFJ4LqIEgKRDEb173EIidi/QUjZpxdpuVJwpL41I8SqSo85JEmMAk8fx5cSSCGRSvQcFhdIMRCjZ6Q0SlmssrgInQ84JC5GqqplZ2OEFhofA4SEiAEJWJUY55aTpsEvK3aubPL6was8fPiQd998D8+97zuQ05ZxyLi5e4OwBNdGZAKCpO06Wtf1hULrGGSWUWEQoWGsFE89+SQb4wEKj44dkUAXIsJDaCIhKZTRnJ1dMm2nONORr2dYCULGHtlgE9pIooy0sUOEns4qEGggk5oueOb1EqUsQ2HA+dXw3fDg5fscvPoALb7+Fv4tsbmTBCFIpIhoH4ghIaUmKYh4fNOR3Eoy5cHYAmkNHkckUGjT28+l4o17C0pZUpJTDDKk1kghWRtn7G0L1rcjRjecz2E2jyxmitlMUEjJjQ8Idq8bLu41vPbFJbVXPPtDm8yaOfdfXbK2kzHYUFzcl2ysb3LUHjMocgqdoapI6iST9YKAoBhlLFpHEIlyp0CVgoHJmV0saduWWbVg79oWp5dz/OmSRw/P2N0ccWP3KtPTJctZyzRcspmN2Z4MSFNYJMdFrFm6gHGWICHGhEwJrTJspgg+gXAok4gp9slnGqw0LM891aLBlBlKWYRIFCoRVMRkGrf0HL8BD15dYMIYPbQoNNWiPzkp2zAaFlyeTBH0A0KR4OHhAaF5N7b2LGaHTE+vsGZGWBFIF5Lp7YYb18bUVeD+6SU39na4trVPcJHPHL7Mu0XOfSf4E+/6Q/wXX/gxljcj/9p3/wh//Rd/lH/7+/4cf/LmD/BXv/D3OVMeNReEUuCLvu2iRF/paanoEgQBMqZ+gJwEMvUW8d/yskvpnwohbv0Or9I/Anw8pdQCb61Met9O78T+ukuQ8KImmcDe41vM3YwQHZkpGRQlnWuo67av1vKcKCAETzWd0bY1PnQgPAqwRYnNDKOixOYaV0dsMWZQFKytjVHSsJi1dE1gMY8YK1nfmFAMMuq6o6kbqqqiblti6F2gNrMIL9gQmxghSSlQx4qoBEr3Waxvu0oFEXzPbxFoYkrIzBDfvnsmCfQbdkyJFYHra2BuWX8zCBEpRZ9dmnqRgBCSIBLJKKRSBN/S+sQ4y1EoYhAYkRAhElxACsl4OO4hg1lOig5cS0yBmAJKBGLbsDi7YDxZZ/bwkss7x9x7lHjiue9i0GVcH+9QdJrZvCHTGSholi0pOpQW2BjJjabUku1hyfb6Frf2N1DJ08wv0FqSZ4YmBIKHatmynHumsynzusWMMhhY5MDiSkilRNiEVAJp3nb6BvyqULFKoYUk+V7i64MnkciM6XMtlpHCDLg8uuTo9Yc8feNdKPUtvrnLAN25w2wMOb17m9Q2bO5tU4xKUjQ8PJpycecc3fWGit0bT7F77QqLMOPu4T2c8GzfeIrJcJ2NapO82SLXFisMZabYXLdsbmYoG5nNG9rGUdUaHwt8G8npuHZ1wPpQc/TGkoM7S9pF4pnv2SeqwFefPyBhePJdT3Dn9kNGmznKBhqT2LYFZZGRzhssGaN1xVnwFIXivIpIFI4OUWTsDNc4nVV0UXByOmfn+oir17dZLDxN1/Lg4BytLI/v3OD2q/dpZMOZTAyzgu1RiaznTH1k2Tna5RIhMlxToFPCZoqEJ8SAKvpNPXQRqSCajvPzC9Iyx5ChZEaeFRTDyNog4LUgVhnTO56j2y1GDPuBm+xRqiEZIg5FTa5HHJ2fo5KiKCRaRR4eHOCWHfLcc+fBazxz671cGWYcvX7J1vV9ko9czBuuXx9y6h7QzBc8vnWNG3aNn7/za3z4u/8sP/nJf8Qf+9D386/f/B7+1gs/TbX/EdpFy3/203+F/+D3/bs88cp1Ti5f61tCukYMBghtSKIPRw7Oo4wGo5EIVOolhj54fhv6xW+1/ndCiD8LfB74P6aULuhpp5/5mud8LQn1667gOr7rQ8+yubFNHgqqD3c0Zx3nB1Nuv3ibMs8xAaxQmCSou47gQp9M1EAIPVp5VGi08EQ67GgNpwOL5ZK1Yp3MlghhcD7RNB2Pjk6Znc2I3rM2GjEZj7i2c42BGTASY9rUsVhWOOdZuIa6qdBagEwUpSWzGil65VFpC1SmQQkCHp1ptJZ411J1TX99RYdUvTGqb8L0qOkYUn9zQPStByF7NrpIJOhVT96T2tCfBGIixYhMhnxlLgpe4LShcZ7OBfI8wwdHSp421kjRtzISAScDKNBlRtfVZMayPhjhli3GCXYHm3zn0x9kN99iI9tE1TmLaZ+JajKBshLXNhA8hZVkuv9zd3PIzWvbDHNLNb8kJCAq5ovA4dElJ8uWy5BYLh0iGWxWokYlrfFkA4WdgBo4pPVI04PNpBIkEUkpIFNAC9lv4gkcgBQoUVCagnFuUU1Hllv0Em6/fMxWdgWZBij7Lb65RxcJVUBtGlztyLsOFhU6s1BoNvYmpDqQVQVXt69z67Fn2L6yQ80SR8v9g/tMfcU1+yTD5grarIPWrA9L9jczRuOEFxXTmaSZW0KwBB+QJLTquHpdMdhwHLzZcHTcIDPB1lOeyTX4ymcPaM4E7/nODZZNQ11Hbjw34uLBjKgTw2HZA5OkpiwGyLHDzxxZrumaiFWWTnum8xk3rl9hODTUVeDyomF62bC5M2Hy8IL6yDFdOI4fXbBrRtxY2+Z+8wivEq2GwkfWfQGh41IApcV1CpyAFNEq4VKLMIEkAy5ZlBA0seVs8RDRlVDDMCvxAbQVDHYiatIhzZiDl1qOXwkEN8GniEyOzaEhxECQnhA6UtdgUDTzRAyKwViznF0yu7zEUNK2HXdP7vPW6RGPX1une0MhhGI4krz4+iM2b00Yjyf81Eu/yp/76P+SD117ir96/x/z5eO7PHdlm49/6Rf4C+//EQ7uHfL3v/xxmqpvNf0nP/XXced1D69yIE5mBK2JWpNvjuFtJ3sEYuxDO0IAJEmI361D9a8B/zH9LvUfA38F+Au/mxf4Wpz1+t4ue5OcGGcIG1h/coR6fMLmE+s0Zs6Dl+4xHheIJuBTA9KDSrRVpF52FJlFRo2JAp0iNhMkEwkmMhkPKUcamwuyQuNDYrwxoBzkXAxPmT66YHFxxtFbd/ly+2UUGeNizJXta+zvXGF3tIGZWEwhEVqy7CrabtnPu+jf0+A9VV2TdETnfXszN5qU5ZQyQ5aaqlnSdR3QEEJf6UspUVr08kEgJE/rInHVm5dJgFerMWhCJ9GraYTCSg1+NbiUvcKoaQPOR5yrcbHj4uKEd733aWLboluPKhStSrSuP88XwyHGXdBVLYt6wUgX5Bm859a7cI8CyWvaCpTKGYwlAUeKHUpFRuWQa3tjdjYMhVFYnTDCsbicE2Mf53c56zg+WzJfelqd01mLE4FEQiOxMpEXfc6sKSLKBJSOK3pmRJCQ9MWXURKlNUqAj6lPfgoCRE7sIrWrKEKL9pb2ouH65k1CW0CWozPzda/Db4nN3fmOZiiYuxlNXTNUllC3tM0Ca4bkA83m9Q327A1ubT/F5tYGw2GJY8i7bz3H+ekJDw5fYy/tUNQ7qLFgb3uLyWaOkDWzWaCjoGsUrhUIAS4GlG7Z3R6issS9Ow0XC0cygbxsufn4DT776ReZHSmuXR2ztbvFC88/YHtvQjG2vHLeMBgbhpsl548uQSWyNYEfCJhKpJI08w4ncrTKmNLgk2C8VnJ6dkm7jBzdmTJZL7h2Y53FWUdwiuXMUY96J+K1wSbH3YyQh35DX2pKXdKplhgCUSkKLRiu5ZhV76EY5HTBEUxfAhykI2b1lH23jRFrJC3JcsFwO7F5SyD0gKPbLQevNGjGKJlwPiJEIteJ2Cmi8DR1w3Bdg/PUTQJlGA4KLk9O6ESDTopOJU7qU16+eJnvefJdrO1FnOuYZDmXZ3MOTi/ZW7/CK2cv8U+OPs2Hn/x2Nt76NT7+1Z/i//bd/w5fuf+T/L23fpY/+n2/jwd3vsIn6pdZdo5ZdURqO3QCYsTOW5rqhDBvcE9ewVzbwGWKtGz7TE7VB2BIoXq409s94N/BSikdv/21EOJvAD+9evg27fTt9bUk1P/5a7yDs37mQ+9P25tjmtgRIkwvH9HWgfVym4/94LfzyvaIw1fvszi+QMucWi45P5kiRY4XkbpqWSssw8wyGuRs7mxiJsNeQTPQZIUmJs/l9ISiHLG9t4mRmt3tCdXlgtQF5osFt88OuFwsuDyfcvLolC/ffYFSDtgebzEZTdjc2ODmzX32d/YpSkUSkShCvyknh8cT8ATRm8IigaSgGOXs7K1hrcEHR9PUOOeom5rFYkbT1AgpUCpDJ41AI4XpxwxdwIheqosXICQajVWGbJD1zmq3Cl7RitA16CzjrZdvMx6NeyRBkn1kXhcIrusHsyHRVC2+6YiNR1rJ4Z0jvvMDHyY5AUHi24gO/cxGW03bdRi7wkVoSVYMSdpSBUfjHDIFQic4P7/k0cmMppNkgw28LmicJxERosf8ZmWPcLbDQDZMCBvwOBBgjCalRCKSRN+eElKjlSGEBElilEFESMFgEViR8E2DRzCerCPqhLcZZjyAfx62zL+UlRJdIalPzpDzREtEk2izFrQFK8D2uZ9re5sYMyBqiVFwdfcKt65d58svf4mDozco01VuTm5hy74Xtlx4CH0fMPmVGkdHjImsbxiSbDk8apktLQ2OxClPX73Ob3z2eb78xpu894kP8MR7b3D7pVNSp9i9ucbZyZQYI6NJSV5qXOpQRWSyq5lVLYSEkYrQBJSM4BNCQucbhsMSbWYooTk7qlhc7Xrk52jEw7OamBnOFzVDBOm0YjTOaX1AFRkLJbBKsTlUhPM5NQGtNAOt8K5BK4vrAmZg6ZY19/2bvDJ7i2ftt5HqAmUydCbIJ7C2l8iNYnogOXqzIbMDlBR0zr/Ts1RS4Z3H+0DbOLaspWkdiQwpE2VueOXuGWLdIhtP07Y8ms/44oOvMvuOH8CMA1EJivEQKxV3b19y43ueYW99lx97+RM8+z3v4wcf/z7+/tHP8T+89Al+5AM/wF/+/N+jMAP+7Mf+HC/82H/E3UWHdw5CwseI8BHRJpTvSPMloa4JmaTdGjAoS/RgSIixH6wKiRTqHZjV72QJIfZTSkerhz8MvLj6+h8Bf18I8Z/TD1SfAj77272eFDAoLKHriN6RlxCiZ+lO6NpLrj+7zdZ+wS///D9hNj/huDtn6pasyU1G2YhhPmSgMzJtMMqglMV5R0iOSESZAYPhmJg8FxePqKoZ21s7lIMSHwzJC3a2NikeHzFrG5rLhuaiJSwSaRmpLyvO2kdMH57x5tGrlGXB7t4a48mQ9Y01huMSnSmMNegsA91fy0ImokyIQqKtRirIs4zNjQGIhPeehKeuKy6nU+aLhqqSeJ+QMaKVRkiNCpoYUq9nl1mvphECKSLWSirX0vkOaQx5kXExm/PGa6/xXR/7VxBeYKVmYBXzdobVEqMVdXDk2uAbR6lzlouKrWKDZ26+m9PDC66V16CVjAZjfBOoqwqPJ8sNUgjatuLo+IwHx6lH+cZIbxqJeJ+IlARpqWsJypDQRNdgcoGyEVsk8nHCDgNYT5ABYSNJ9fjfftC9mjOQiDERGk8IIJXFmoyes2lZ1BWd8KyVa0zMkDRX2FEiGqjjAh+/1XXu1qCNgIendNOa4CrqhWTk14jSEgeRJBJH00OutwuKcoMoE8omSj3m+s2nuf3gHpecobegFYFm6YkJopPICCI6IGEyiTAd6xsWHzyH9+e0KVKJh9TNOR949jFevfsSH3/xl/jeD3yM9zyxy+xkSXUAV2+tMSg0r94/J8sKRmt5PxhBkmRC5Ykw9/3ARwo0hlgJYi1IpaLrIlluKUpLOxW0Z4HTOzXNqGPdDjmJ8/7fXRhEJvuLYO7BRfTIkw9HtAuPdJHBoODVw1P28l1kB94lRAKVQRsd56Lhi/WXeXL0LINHaxBz1ECSjSUbVxXDjUB1JnnrhQoVRwgj6dreCZkQCAXBBdqm39hTgsz2bPSQDEo77ADOm5ZrH3kfsVMsnGPt1nVOc8frJ8e8t7hK4xNJJtYGQ06OOi5Plnzf4x/l15//Uf6ntz7B/+H9f5R7F/f49NELXB1d4fuuvp+feO3TTK4X/Ilnfoi//qm/xxJBSpqmafrwYd9rpU30hIMFHbcx774GTwzhbWZK4h1g19fb2oUQ/wD4GLAlhHgA/IfAx4QQH6Bvy9wB/iJASumrQogfA16irzv//d9OKQO93j6XilQOyFJgPJK4jUjXepJPVLMpds/w+/7Uv8Jrr73OY+YZCjXm3ufucv/X7iBV3oerdJEQLYmEsba3vouOi7NzUoT1jU0ymzGdzbh3/y57u3sUec78sqKeN4ClkJahKRBbirhGHxsXPM53xBQ4vzxHkHhw+RA5E2QnhrX1Mbt7m0w2xxQ669VKwaONBAQ25n00nlY9zVL28XNFbtAGxOaIvf1N2tZxMWuZXTbMLytc5XFdQIq8H7gGiREKIwxCJLxbILUCEYj0DJWEpygtP/iH/xAu9sPVtGoDWa16QYGA5WxJCAkZYXOwwXQ64/d95++nPm0YZWsUZkDd1SSZMMpQxYAxkhQDykisNnQ+0jiIUWHoKasxCKzNaV2kdf3QuNfveIoiUQ4M2TBQbCSKTVBlIKg+ri9JgVSCKFLf7qJXvfrVMNbYvIfLiQwRNMJFku+QwTEc5gzLHJkMXgv0QFEvZyx98w0Net8Sm7s0ina2pDuvcJUj+EC16Ppq0Ris7K3zTX3Bg/uvsTnZAWmpowME480dnv3Qh7l3+CZv8RrK5EiWFMFgEOQiodMQLQpGmSTfyjlcnrCcNsy7mrv+LrP2IX/4ie/goXvI3/7Kz3DtvU9ya38P4y0HbywoVcn2uqSdNrSXUA4TWmqSj0ShkdKjoyS2kuWFI8ZA2wEzyyIJjB7gLhXDNUEpFTPpiQFOHrQU1yyL5YLNbMTBfIELmugUa4OCbjpDaYvsYp+i5ARVZbm7OOTILtnWkgCESmLLhCwSl5cNn11+iixfY7+9SYiKQZljMkFeCtb2BSaLPHzJoVyGNZboPTIljBL41AdJhE4Qa8982uBXU/vWRaDtXY5Gcf/RA7w2xMpDFxBZTsoTX53d4aNXbyFdx9x5Hr9+k/M3XuFzX32Db/vYh7j+wk/xT+5/mj/5+PfyF5/9Ee5+5m/yk2/+EjfzHWam4W/9xo/zF9/9hzEYZHD4EEh+FauTBInUm2aaQHzzlOX5An9ewTPXydbHUFikVAjnvu5ANaX0b/wW3/7vfovvvf38/xT4T38313ZKEU/EyhxFpA19Hz3LLF3bgclZLOa0saW4PuBwcQE24+nveBJ7EWlvz9gcbzMaDMjX8x7zaxQxOYSQGGURUdIuO4TQbI53OLs45/z0kq3tTYQ2iOSoHlQoH9DWYvMMnWcE3Qd1UBqigPX1bYSAq+kasXG0rsVYiRoXFBtjNrdH5IWgdY5ltaRaLKkvF/SKGLCZoSgtw1FBObArsxFYpVGFwhQ5W1tj8IFmHrh8WNNMHcvzto/LC30cnQTQPXfdWIk2muAFJkmkVujQW6RCiChij0EuDF3ocQ1lnlHNp2TCcDKrmZgJcRZYG61zfesqci7ACNp5A6mXVed5jlAeoyVaabrOI5JBCYtc5bGG4PFeoHSOlYnWBWLoyKxibb1kuK7QZYsa1IgsYAZgjKD2CecCchUwLoiEEHEhEGPvSQmdBKEIDtqupWs7tE6MCtjenDDMCkQnWbSORw9OESZDK76uhwN+F5v7iuf+eeAgpfRDQojH6IM6NunzVf9MSqkTQmT0uuEP0ycw/cmU0p1v+AEQMD8+JVwuEF3/wZVS93IxvYZ0nra7RNmO08u73Hmwxe7+45jMoLRhLd/g1sYt3njtRV6df45uf042eg4Xl8zOL6lml2zLXd67eYti8hSfOXiTuvSc64aDxTFVc8L37X+IWAh++s6vM/mOx3ly8hg39Q73b8/x0ZAPEikGju9VGFEyzC06CWIX6KqI8IYwEzTTxHLWkjxkxtCpRNt6picd6+sFZXIYp9EKykKxmHrcZomrKwbrE4a2Y+k9bVDMY6JUJXXV9mqEIlAaTWs9r8RjumFJagLRe7QJZCPLQi35zPHn+PLDV/hT7/63SI9K0KbHslrPYFNRDBSzA0F9DnnWkwG7LiJlRBURYsTX/YXcLDyuSwjbp880TYeSgqKwSAdd23B1uIWUgerigjffeJ3z3PHLl4k/ffN7GHQROyiZbCXMK4GHR4HFWeTPv/+H+C9f+DH+2gs/yX/ybf8mf+6JP8BfvvNjfE48wFnNNFb8P/7Bf4VrOkL09OjEHgdLSiuudiJGMCmhLyq6z7/O9P4J6b032Xr6BtJYFi4QYvydXub/4pcQaJ3hRR/flqms144TEVr0baNkiDrw4OCEqWqpY6Jezlm/Oubg7ikiT5SbA+wog0wyX85oupa10ZBROWJtsMlyUXNyckJZlIyGE6KIuCqhTMZkVDDchOq05uTRGcu2xZQ5g8mQcjKgGJXE5PGdx2QGk2lEASY5EoEKz8PZGY3qmKwVKC1xWhKMprqoscqSCCwXLbPLxEWuKQc55aCgKC15YTC5RCiH1gJjDEZqhibnbn1AVV8wKjfIUBipSUQan4jBYU2OCxFjNdL04Dy6SAh9e8ggUCqhpVpp6QMyUzTRcefefYa65H3v/iBffekV3nV1Hx0NbVVT2gJnHJeXU4YbRa85JyKEJIaE79w7UtD5dNZTJ52n3Oh5O7PlAmjZ291gOMxQxiFNSz5MZGuaVDiS7OjHRIHMFlg7QJFIoUNERdd4pFBIaVksWoxMiOARMaER5Fnk2v4aa5MhKSq6mDg9P+lx3TLh2oD4FwQO+w+Al4Hx6vFfpjd7fFwI8dfpnXt/bfXnRUrpSSHEn1o9709+w1dOEffwHLtwBK9AS4rBmLXNPUb5BuPJiPPqkMv2EJHOeevBy2xu7pKbTVIHQsHmeML73v0Mrx69Rhxe8NLF56hDS4rQ2gYnBO/eusn/8KUf5173gGgVk6vX2XvyKu8RN7hq9/nZu5+luVVybbTLc/ljLI8qutZiJhIhI90SFpcOYzWFUlB1dCTa80TmDX4Z8I0jhoYwT+Sphwwpq2iamvllx85ogI0alQIuJVKrOX/UsrEx4ODgjI0rI5qzU6LSNK0mKw1GQ1N1GKWQRnLenXEYT7jeXqfcFsgYMDbhdcNrj17m9vKLfM9z385utU6rFQGJAPKJZLIv6ZaJxd0MFQzS9NWVkIlAQKRA17XvnDyWURADKAIy9P33mASFlYSmpq4WbGeWLjmciKASRlnuHR/y0uVDnsu3yUcD2nrO1b1dLu8seO35c/7Q9/9+7p0c8nePfpX/5+f/Ht+98W18bPO9fOL0C8wzsGsjzlyNbl3fJhICueKUQA+lSitHZEKggoQK0p0p+uxlpg8vGTz7FHJtzDe4/n/PVwKiWIVTEHvrPQklBEkpHBGfoIkBp8BnikXb4OoleE0nG5z1tLpF5RZpQThJbgyDcsBstmBYrLEx2mR+uuTs6IKm7Ni/vo9Kkq5qcXhCIWHfMpisEy+X1IuGs/MLphdzJsWIUT5gMhqTj3KWaUmn+1aNUL2ct3ORhyenPLro5ZIm03S1w5L1UDKlKexqk0yO5dJRVQ6pIMsNWaEZbmmGZUHrHJk2zJqWk5NTykGJ1YrUOqS2LBcVysgeH2w1sesQKmGkRCmJ0Stzno/9DV/00K4k+kFlTJHOtXStY22yxfKsYS3bxC8jIXMYqVhMZ6hOMSgKko+901MlouiFnFqYPg4vChZuTkqJtbUxQkQuL0/Jh5bBOKccRKKcoYygSxW50tjckHJF4/pgklwXeC9pF643RoWE1IaMITFqfBdRKV8phCS+bUHA1mTCKLfU8wXFcJ07bx5wdrGAZLi4uGBzfaMPN/k663c0aRJCXAP+NeBvrh4L4PuBH1895b8H/ujq6z+yeszq579P/DbkpuQD4rxBtro/poSIkBoX4OHBEX6ZePrWByiydaaLisNHd7lz/xWcW+J9zXn9kHuzN2hUy9rOGnaoQSesUZTWMrYZXWz4qVf+MS+5r3ARLqgWDcu3DhjcPue9+U0O7Bz3njHD9TXel64xeGQ4qzpk4SizhDGW5bwlJtB5QBPwU091GqC2CCcQOEJqENJRn3lEIxDRYI0lywzVssJ1HSoFMiPJrSVXA6p5QihD20ROjh5ihMe1SzyC2vu+Tx8Efu5YisSXpm9hmsC3bTxBXgjqtYDfTxy5I/7hr/yPnL15h+vuBqLKENFS2hIBjDcLpE0sTwV+3vetuy7iY7ey8SdCq4mhl6AllZjOWmLq48JkikSXAEVhJd7NESJQJKjqDlHmlFsTstGQzkg+f/cVouw1zkuRuPHULpv5GH86Ynqh+OFnfpAP5jf5xdOX+W/v/wQfmjzL9XaLKRXtyBKtJXkBUbzDJCGJfjieEikmBH3oRRvBJ9n/bNbinr/H9BOfo/6NryL8N69yj4BfqSO+VgOuUi/dNLJ/r32CNiUa37uJl01N5SqKzYJ8O4c1jSsiVapxvqPQlvFohDaa+/fv0tQ1j12/xe7aFrOTS1594WX8omNnssmgKFnYJcfmhPlgidozDK6NGGwNMFbRzJbMH16yuD9lcecCd1ERly2iDdAE3KLBVS3CJ4QH30Zc4+kah1CKkCIuBUJKuBAQypAVA5S2JBR1E5jNa06PZxwenHHy8JLpWcP9u0eAxtq8H3pLiXOO6XRK2/StibZtcb7rA699i/MNJIfVkBuJtf28q/MdjWvofIcPrjdRISiLMfWsY2OwSykH5KafEeAT3jms0SjZYyoUCqIkepAoku+9IiJJpNRoZWnaBmRgOLIY68hKz2hDUWwk8jXAeJpQ4zqPFIbQQWwF7TzglxHRapppZHpcszjt8JUgtZpYQzfvkJ2gvpyzMRqxNZ7glxHlcy4PGy5Oa5QsiBFGo4Jr+xto9c/Pc/8vgf8zMFo93gQuV+HY8M8aOq4C9wFSSl4IMV09//RrX/BrtcDCKvKgaGNE0B9VfbVgvor7MsKwf/0KT1x5Ly+88RvM2lO+cveLqEGOVJ6Zv6CVCzAZIs/IpCD5vq/VNo6uCfjughBbxmabnck+A1OyoTf4yN7j/PqrnyN+eIOUw616kyvdGucXc4QumAwsIhiUFixqB0NDYaBdOoSyeJkwURN8YrFUPJqfM523zE9rdIDoPDLmaGsRwdPUkRQjw2jwXmGMwHUC7xPZwPDqvTcZbEfuH97jyae/B8UmCzwbRcHZoqabCqbzJX/wsW9jN5TMl5HTeY1PF3z8Z36UNw/v8n0f+AHG3T6iG2BtIM8gXzcMNyP+RNCeSOrQ9agCOsgkPngWtUdHgxkYOu95ffoaZbtHFDkxOFJQiLZFRotRgrbrOL88xRSK5eUchMCOJxS6JbSC3zj6Cn/sPd/Bg7e+wuZoG1uucfPGJg/nFQ+bKdTn/ODT38vzL97lpFvyK5cvszXZJflj8B6V5SRXgUmIFSxK0oOpYowrgmhfuyuxkkMJgUsJOo84PGP56Byab2bMHjgCWiTkamNH9M5VEeMKlSv7mymSrksUMkNpQz60jG6OGEyGBBnpUg0xUeaWnckmWaYwea+qCKEjoNheX2eYFdy9d5fXX3oJLd/FxpVNwtYmYnnOfFr10ZBKkVmDmeSEiw7ZgBctnQosT6cMNgdsbm1gS0tSkcbVeO9AJELVoZNlkJWk6BFSIbUAFUlC0PmOFBJaqf66F5CIdI2nXngKawhVx2LmSdHQVJ714aTPWEWyt7dP8B0+9gY0IQT93p9Isg83kf3e3RcdWvZv9Kr9FlPCOcdwMCYFiRYZuRogMXT1ktnlJWLlXBYpYU0/qFRaInwk+ISMgkXV0HaOmBQCxcVsgckl0mi62FKMNCpLiKwjmI6slEjbQ9Ok0AghaboexxGcIjO6V9ssBUL0iAeXIIRA07QM84zFdMaV7V121zeYHs/IS0teDji4c0BoDTFB13XcfPwKo6Eg/fOoZYQQb4OVviCE+Ni/iAse/lktsMx0wieiFGgB0gpsaai6JSF0XBzf597Lt7n69OPcuPoUh5eS8caI8+6ApH2PHrUFSEv0gq71BNfhW0/0Ee880TlUlBS6ZGzXGaqCG5u7vHB+h/OnBn2aTKd4IqxxeXJJ6xUb+QjlLdFFfB4JQaOxqBioaocdamIEESzOtTSN5+zihLbL6RqPMYboHYtzUEpSlkO6kSXZGoxHZqA6hbYZbRsZlgXKGP72j/1/ODm5wweefYE/9sP/G1S3w1kS5GVBO52hlWJjOCJrFbGFsRG8cviAIx6x9r7rPPfstyMPDEJJrJGgBOVOg5QZ80eGxXlEm4xAryCKLqyCfg2YgHaOh81b/Niv/x3+9Hf9exgKUjRUNdAKIgm/kplW9RKhM45mJ2QDQX38kKlcsLm2w2GqeFhf8okXf4E39SV/6LHv5ntvPMfzD77ML9x+yIP5W/zpD/5BfnD9w/z/Fp/lk9Pn0UpQdJHmtfuI6YxkJVKk3/yQC7Ey2PQb9tu80LRiDvV4WXqmSRLoro91+2atJCCIiBIBVhTcfjwHRghq1+B9g5QJbVSf7esSVlnWdrcY1D1ysXUdy6pBNgnDgOA8QiWygeJ8OuXenQVbwx0mg3XGowEf+tD7ODi8y1e//Dx751fYedd1ro62aa3j9PyCpmsR1uGDw4wMMgi6zuF9x8jlZFHgZwtUyBmMy36j1BCkp+oqYgik1BGEIMszlJHE5Pv/PykhJqLoZwusVCFKZhhZoqJFohnk6yzbiiIvkdIgVCQzBiMtTkpCW2FWfoWUYh/lJ/tEKxHevrknQgShJMbmPa++rjg9v2B7Z49mISisRWOoZzVSN7jWIZ1glA/QWvWbcZJIJDHRqwnoOUxdJ7B5QZKCqp6xPh4ynAzwzBlMBqBa9ECCSZBFhOoddd71g3+cJrYBKwtEp2gah/CGrCiJLtFVER8SpR0Qm5q1csgoL1icTzFak+Uj7r76iOU0kNsxzncM1hWbm0Pm5yd8IwvH76Ry/27gfyGE+EEgp++5/1fAmhBCr6r3rzV0vG32eCB6oMSEfrD69T8AIZKiQlmDzQ2Pv+fdBB+4/eqrKK3o2iVvvfESrgzsPL2DWXuMZD1JBYSWgETEPvhWxQyTChrX0FSOZtkgQh84HGLF+fKA2k0pzYjLdEb27huoNcnpV27zrNpjcTUS/JQR6wzzkugsOqXVEbTAJANtTdNK3EgDHuc8VbVknYxpdYxii7pdkGclJIeIEqLFzSWzU89wX+N1Q1AKjCQ0gdmlZ2cvRyU4uPc6l5cH/NLJPW7ceoqPvvdHCM0AP+zASNbkgHEwyCTRMvbDq/V1xHrGzfe+i1G7gQ4GYYAkUUaRlTA7lCyOBVobpA49G8TROwRX1vEUElOm/Pydn+He4Jw31Zs8bbdBSh5NGwZKIpVGhsS8VeSjISko7p3e45n1m8i2om3PeXj7IdtPP8OnH7zAe556Fz/x6o9zdOcfc+t7dvjli0/zfHZKVTia13+G/+0zf5hfffkOx27Koy6gH9U0h+fYEAkq9SyT1WbeU2X7xnuMkSQgxfTOhhm/ZngqUw8P+0Y5k/8y1jutGbHiyydwweFjxPke4OWTI8SO0NbYPEdkiv2bjzF94xicYqIyypghhKCkpHaQkkAOBxTZkvpwQT1tqOSUYi9S7EnWntrCDzwP3zxmfl5z8/HH2b66TVFqpnJGQ4tQskdM+Ej0ilB3fWtLJLz0SNkSksKKom9h5JbJMKeLLZVrWAZHFxqS7OP1EBDo26pSalLUQI8F7oLDCoWSilJpOmWo8WRDQeMWpAyChOA8wTuatiGXFm0V3ge01hhjcNH1ShmtCSLRRkcIAaH7DbmtewPQqJjAskJZzcAIfBPxi4RxGVlmkEpTtTWZzpAIkhekJIkx4ULCRYWyGpOXhOgpyhKlJTa3KGnpQosxAWkkttSk5Ffoiz7YWwtL8hoiqJQTvMAGiUwwv4xMWwgrQuZI9tLevIuUTtE2gdHamOVxy+JsgR3kNLEi0PH0kzdJnWdtOEbpf46ee0rpL6WUrqWUbtFT8X4ppfSngU8Cf3z1tD8H/MPV1/9o9ZjVz38p/TZwDxF7hv9ka4Nv/97vJWnD+cWM3p+l8QQYOIpdkFkkHw4wWY5QCt9FQuXJWstOvseNyQ22sw380nF6coJzHoJkbbhLMboC2YCFdLQ7YN6/w2le8eAzX2TvfsuHii2+cnKbroisFyOEF7RtxOYZbReJUULnCXVHlIaAoK07fNswqx9RuUsuzh9xML1NSB26gLpb4EKFEH1/r2slySt6hbRCREWKmthlpKgYDnu3qUqa1sOP/8Tf4+D+a9BB01rOusiu3EZdCvJk0EqTOsV2vsluGnDyyy+w1Y7BC3zoDUmDzYg1iXbmUNaD6gg0eN+SYoAI0UekCkzFGZ9LX+L42Rb9nVc53lwQy4AQ0JJohCJET7PsmDYgjMVXgTcP3kCVGZqAUtCdzjj81Kf55G/8Eu8aP86zdp+FcdyeP+LGxmMM5IiBH/JQ1PxPR19kkmfIJjA47kgPlxB6zbJSgKJ3TKZAiJ4Y4zs29xT7Fk2Isd/0V5VXCpEYe0T0N3NvjzHR+ECTIj4JUlJEFN73/ekQE8oa/IqgWFqDJBKVwllLsblFGxI6KbaH6zy+d51bV28xGm8SQ4ZMA67t3STTOccPTji8f8bxwylnszl3jw9wOvLEM0+TkfP686/x1U9/hbySPL52lRuDHcYix4REnhnykWG0OyauCdxawo0gDCDkYEaW4caIclQwGBesbUzYv7rLtRv7bO9tIExgXl9QuwUhtUQ6Ev3vK4T+fKWznlvjK8fZwTmzR+eI0EPrVCEIytGmjmQTdmgpJzkyk/jkEKthvxSpD/jONMpIEoEoI0hJ2zjq2nF6MmVv6yqzszkyCHbX1sgJiKrDX0boNFYXPYpYCbx3fZ896b514yN124HsoXQhBnxoe359LlEmYTOJDw3G9kTLGBLR916n1IHoJMZbTMzJ0gDlMlKtCRXUc89i5uk6i+ss7TJSnc8oI2zZIXm0rBfbNBeJ6WFFbgraZk7dnLO/P8EISTNdEOvu9ywg+/8CfFwI8Z8Az/Ob+uD/DvjRFTXvnP6G8A2X1JLHPvAsW089yVtfeIHhlW0uLs+xSWAE2FHBreeeZOPqOtEkVJS4GtqqY7GYEuaewXiNrWwPGxVtVJRxyJX9x1BlxoM7DxgJxXD9CpNSMN4esbY54WLWcvfuy6g7hzz5vh/ib/zCT7L/gWeQm9cp7YDQCAieNjS0SVJIEM6BD0g1JPOSuDB4t+D5g0+xduujHJ8teOXiq3z7Ux+hFGvUbYsIlkwEvGtZVopNbzGiAiShEcgAEUU9CwzHJWay1fO8Q8dldcbf+gd/hX/vf/UfscWTRDOg9Ir5eaSQHisSobaElHH21inX1nZRoQApkVojTKIYgbuMpFojbURqTddBSg6hesqFCy1kMw5PPs/P/9O/RfbchGJzxIIL5CASK8OwyPoNNUQuG1g2fWqOmC554/IAqQwDY1DCIIaW8HDJVz/56zz6yB/nylHiftHx8uA2T+R7XK1HvH74BpVv+IJ6RKgc1emczA7QQWPQRCFJOhFC6HkccaVtRyKE6Cv3t2WRvaEbHVcD19W1JYR4J8Djm7H6VrDEEVFS0gAiRFqfiELRdIkmRk5Pp8ymDT5GtApMygnnl3Oe2LtO5TVndw8wA02SMJtf0FUR1RQUg5wkHePr27RccPf2Md1Ji08lN991hQeP3sIPItvv3sfff8iD+/e5/NQpTz35FDduXCNXhrlsqHXHXFQkG5FSI6MkRUGdIq2vmE47xn7JxsaEIrNYqzBWUuaCyJCNjQHLasF0NmW5WK7QDz0ki+gJUaApEQFm5wua04o8y/vEMKf7ClQGnA8I4ZFWk9kM51tcGzBGE4g0qee4y1wTfSLE0OMNpMR3jrZtWS4WrI+2mc/m3Ni5jtWaLtYMhiX18gKBwLmOkPpcVmsM3kfqqiHF/iT4dkUcYofzLUk4UAptLEJFhPYIHZEqIoXuXfABwgp+hk/41Kwqd0mM9Jt/UDSdIyZJka3YTU1NpiProxHDwZC6cvjUMr9sULZAG0+qLtlcG3L96jrVwxl0HULnxPD1xQK/q809pfTLwC+vvn6THnn6P39OA/yJ383r6tGAyXtuUZ+cYvbW8Zlhe2OT2eEZUgj2b11l++Yewmpc7GinFae3Dzk/PqdqK/COOAys6XXWNnaIWCbFDqcnb2Ji5Jlnn0XKnMLklNtjxFBzfv+c87dOuXj4ADmb83c+9RM4FPm0otiyaKVYzPrjpZ87TJkRY8S3gRA0yhlEEizrhpfbt/jMg8/xMf1dnMSGN7tzvvjwC1zfu9mHF4SE8zVIj04lXdv34LVSdKEPIJYy0iwS21f3KQYbtO2SUikIC+6evcrHf/a/4c//kb+EZpOmgTZJ5hcRPej7zsE1TG5ssnflKSS6BxBFx2jNYorE2e2IcJKkHK71aKvQqyNojB3kLefdfX78F/8Gi8V9Tj5ZEV1gPniBj/2Z70PoXUKtMDmI2BJir2a6cHPuXbzF7eqAy2rWB1hLhdooCOdT2rri5z71CTYGA45+9jP84pU7fD5kHC1PqJslTb0g0TO9jc7wRYGXkRg7hFLE6PvWixCIFe/6HRnkqgf/zteIHjWY+g9YjLHH/X4TS/eQIkvnGeUZIfVWc4nCS82yqVk6z8J5FkuP1iU2gnSSvStXmZ20XHQN1554jOnlJUdnZ4yLEUqCDgG5DMRlZJlHzPqIvXHOIiyZPZyzOAg8jKdcfeoxHjUHvHl5h52bu2xsTzh9/ZjD1x8Qz1v2b11htJnThYZ8pPAqUtoSGSUhCbqYaEOgjoGqXXB5WpFrxcbaiM31EaUwaC3QxjKebLGxOaSua9qmY7Go6LolUvZoAREC9bSDFsbFOslLusZRLTzleo/HaN2yV5yZHAEEGfs8WfobpQ+9KkdJQVL9Jhxjol20SKOoqorBYIBRmsz07ZambvDOkbrU33CkWDltI0ZnlMWQxaKibRbEKLDGorTGpw4hPVmuMLlB2UA+SkjrKIaKfDBA6JUkEwNeEl0guISIAufjquW8gthFBaoEY6DzqOTRoSWzgmefuMn2+gg8Pf7g/jldK9EZtIsZ4+0Bj1/fJc0qdFdT5paUIPyL2tx/r1bSirmJpNRinr7O9I0HnBwfEyIUuWZydRtyAa1neTznjedf5vjVu4goKHdGGBu4nD7ktde/xK0n30s5XscIC3XNwf1XufvKizz+3vdz5SMfofGJ86MZFwfHuJML0sWSetaQjQpkUXJ+fsnofQbXdMQ4pPURESOjTJLqBq2hcxq/DNTKc5COeIGvcmFBRYXv5jSx4vajF7HiRxiODX4JwSd0logq0daOIgetA8oYRKdxzhOCZlx7vvuDH+XRlz0uBDq/hlQjfu3FX2d79Hf5Iz/0FyjVGqFVLCtPWQiSdGgZuQyX7D77DP6lFUdbC7JhYjH1+EYiZUXoer5FALzvegWCdqRizi9++v/Lyw9eoI0OESGTisqcs1QH5PkmVZMYaoESCR/7xPdr77/Jz6bbtBue47OHGKXR1mDWC+LA4quGX/jsL3JlawczW/Bg8TrHQRCTR6WevRJ0H1iRpKdr+9gwUVjI9dv6EoSI/U0ggUjin+mtv6O07dvzwNskyN9MBPpmLSF7vbNVniAFIkWkBCehIlCFwMnFJcfn5727EkuhSnwnWNvcZdp1bBnJxs2r3H70iJPpGdeHO7RuQX3W4ruKwd6EGCPlmuQDH30XX/rVFzl+Y8rZ6RSdKa69+xr35gecXJyya9d5/Okn6M5rmtmCpl4yFmsMTU5uLAtfod9WI0kJRhCU7I3BEgSR6BzupGZ6fsLm9pid3Q2MNSQhKPKCIs9IgHeOrnPMZwsWswZXLUlBY7UhFzldAIWkaTpsSqjcoBA9hKuUCJHA9zf+EFakyiAAQYiBFEAKRag60AmlFK3rGJZDzk/PGQ9GSATL2QIRelWM4G2FkujVPMrgWk8MCSNtjwkQmuA7XFqSlX1BMxgbiqFGmK4fomY52vTKINcKIhnJS3wTiC6hkIikISpCEkQf+oJLQOtTPyCONQPj2d8YMikMvm5JQTNftNRdom4ToanIx4Lt9Q2GVnF2+IhxNkAJwbRqV5LP33p9S2zuIkW6TNGOCtJ0TicSZnOAafs7dMg0s6pm+eCMO196k/P7h6TWEYTAzRvURNHJlqODN9FCsLf3JFrlFLLoNaw2Md4ecHx6SnuvJkqJqzvOp8fExRLhesSqKSReaKZdwySCMobkGnxKLBc1o0wSnKBzAtF5DutTfu7ln+fO2hnKWoxz3Ln3ClV9RLt+jbq7xOsZWm4itEbJhJYSFxNZJzCZQehIag06alo8b77xBk8/doPiRYOXYHSBzEYUQvFzn/s5Jls7fM9zf4JOgS4k0WqMzpjOI7vvfR+F3UA1Gq0U2UChTWJ66vBBI6VDyEhMnoigA5QQ6EHD829+kl9+/hMordGhNyO5BDFXfOr5n+Hf/Pbn6E4iMWmIBp0Szs14/v6XqG4adm5e40V/gN8doxYLbIjEcYk/r7lYXHK+uEDFSIqp7z0LQArSqrSOAoLouQcm9RWQyAxJ9moZKQS9UPY3eywpJViFYYvVEDCkvrrvE4DkN31zB1jWNUYGtJI43xLxNK4jKVj4movllDZ0CBSX51M++B0fQaSc2bRBo7l38ogb2xts3bjC9PCSTVOhjCKZjm4Z4dhga4kwINYD3/avfpBfDy9xenfKg9sH+HrGxpNbLH1DE1rqsiHfzpETwVxUaGcZDwcIKRkIy7Je0iZHUhKR9Wz3qNIK+dCHa2RCoAScPDzj4uwSmxm2t7dYX5+gjURpiVYGU1rKomB7M9FN4fC1036IKQtS7KWNLnY0vkLZiMgCiEgn235gSp8dKnTqRSwJtJRE11v6jeyvlWJS0NX932uFwYuEVZqu6chshq8bmqbuU6nkSjqZwHehZ8G3KyaU6kPEY2yxVvaOXBuYrFvWtwfMqzOEMuSZRpBQaFzojYExgG8gup4pFF2HUll/+vfggqcJkdYHxqUkk4GN3HB9a504r1k0HVEOOT6eoswIg0LEjtGoT7ian5+RS4EWK+69+sZQvG+JzR2taUxGVQyJi5Z8NEDvD2FesbWxgxORh199nfnLh2wNt5i6REj9cbxeVohigMgSnW55+eB1TivP5to1olKUW/vc+rYncUTe+NRnWesmpEnJ8No6lDndaXrHWiybmouTc6b1EldqTFAkL0kpokIiiwGZPKHJmYuH/MJXP8HPvf7L7HzoOjeuXWWxrDh1HXa8iclGHJw/4CvHd3nP6CNIbxF1IrSRTBQMlEAqUDbhqhYlNMJHvvrai1yOX+XwjZcIVqGLISobkLKCtD7iJz/5E1ycVfzAd/1xzHCPUEoW7TmPzJK1PGf2/Cv49DiKhJCxVwDEAFLQdRGd9UxpJQRCJWTWcXj5Kj/6k/8tCxrsaEymeklbjOB94PMv/Bp/7LseMtIj8BJjV1pi77i4vCRuWO699YA3j+4x2d9lZ/smp8cnVI/m+AdnyLaXIrqVbDGS+up7pfdWK533O310IYg+okMkLzO60PZHXSX6jEklUKrPGY2rKv5t/Thvc93pwUzf7CVF335rXduHIaeOLnQsu4rGtzx4eMz5YsFgWLCY13zogx9he3OPxdITRQlIfNvQiMRwc4N24elSosw1dgfOppe4M9DVEC8TlWgZ3yx47l99D1/+p68we+2E+oGgIGf7xjaUgjrW1KKBQe/6DNUFTdewPdhgpPJ++K4cNYEmgTUaRySGgBECm0AngUqCQE7beJraUVcnnDyaUQ4KJpNBPz8yvX5fK4EeQJYrnAk01Zy8GONjS14KompIOJLuN3WZ9cNSKXvpbYr9sSwl8Mn1pwgjcNERbR9u3S5ahOppoKPBEOU13jtC6Igh0VYtRZZhtGW+WK7mAVDVDUKoXnfeOYqioFosGG9b8lJTDDOGa5a6m9KFJbm1OOdwne9jA70hdf0NBy/RSSCjglWWMVH1OGIUuS0RdNjkmBjDlbURou0QQpDbEa/dewgyw/lATD0ZsywMEo9ve+REWLbMZnMYb/RH36+zvjU2d6lQW5uUow2M0AjhSIsN4vkFXRV4cHFG9eic9z7zDK+//AZeRJLsTSGqC4RZi5mAMYbN7XVcs6B15+Q39rjx2LuYhhlvPP8F5OGUujmB+YCB3MbmBdcef4b7dx8QvSL6SFSJw/kZe1mF7SxCSQqrmdiMFALHy5YDf5tP3v05fun+p5iVMJCOna1tzrsasT0hDQzD9U0O3ZTnq9fYGl9lHGBcbFAvWqq5YZRZYhExuSZlHjrIibx29AI/91P/I43oEELRmgvMaEiwZS8dsYmf/o2/z/H56/yxP/i/Zmd4hbvVISeDKbd/43l2F0/DNY0IEms0rJgxLTVCalJQ5LlBKocwLUfc5W//yt/gkbwkSUFoWxIdxiiMtuR5gVeJf/j8x/lTH/w/0RwX5ASSsMSUYYThbDbDDizez7k4aEFnMBgwuLqDOm+o75wRut/KbNE7UVLqK/O32yueviL3zlGoEpGVzKsluL5niugrL6FUP2QNv1nPC9krFySrG8Fq2PrNWikldiY73HnwClW7ZHN/k9p3nE4vOT4/wScYjka0jePGtZsoldEFsHnJyeUZrnNsDEsq17K/v8fdByf4KBBlhiwik6sls/MlcWaZhQ5jEmfpHhtX9nnvR5/mtaUjHXec3z7BLx2bT+1gtiyXYUZHxyDLMdYSOqgvGiKKtObJhposy6mFp4l9WHQSauWuTX1ATRI9A0rnfeRGitTLwHIx5+JsSTnMGY4KRuMBZW7RHryPJAJFkWMzCAhSlojKk4RHiITW4KPDmNUA33uUEgjV/50iij6SL/StIiU1hETT1UwmY6gNMhiMzOk6SVtXJB/IjEEh6TrX33SFoes8RVbgfcDkpo/pC47MamLwGKXY2BixbM5AdxA9MmUoDMErXAfBa4TXqNhHPEbfV/FSWmJMdMEjtSEhqBYVRil8M2dtfYNCSGLrCdJyPl+QZE6MguAd1mrG44ws69HDea4RRuNDTUdfAH3Lt2WSD/j5HG0E9fkMdz6jWBsiFyCEZefx6+jrj+EXc7rTDCtHiLoldB4fEiLLkWtDtFZMlxWj7U2Gj40ZXR2xmC945Vd+HRscddtimiU0lwjZkg/WOZ22bF65xvnBGSEE9rc3uEgNj5YXjL1ke2uCiIJLf8mJu8/nz1/gMwef4ZgFbnuAFYr5xZTtW2MOLx5idgxCS/Sw4DguuSuPuMgekXU5kSHWZlwuOrI1i3GQG4XSEYKkDlPevHiF5m3iYQSJJ87myLwm2JIuGyBKy2e+/Cu8/NIXWb+6zpPf+x0clS1hQ3H36JjvzxW+WR0NW4FI/Q1TqrjqVzu8bqk55c3ikPY9JZncIB4vqS/OCb4maomXitpY5JURn5t+iceaX+E7134QVUd0GVDRkKmMennMYDigqVcMeATNcoFva7zs9d18zUX4tSaktwNO02rYCBBW7k1cpJovsFsTzHCIny+QK8quEL25R4QESq06872nP/S1Hsi+iv+mSiF975Le27zKq2+9yotffpVoE8cXJwzX1rDaMJ/XBBcptkeM1zZZtC1lbtne3aFZ1rSzKedtw/amZTBe48GrDwjjbQbrgr0nNtHTc6avXWBSRn0UgcDMnrC3scP7vusZ7n3uHvGylwwf3zliJNcpNkuCT3QxULm2t95nGpPl1NGRukiRa/IsY+kbFk1FIKGNRWqF7zxN59B5toq5iyipybICqSRt13B+vuRyumAwXFDmJSMzJAaDsQqDJSRHEo4QW7xziJRQWiDRWGn6SD0viN4hWZ3qfCKteEcpRHxKeAmucyyaBWW+znLaUYisz3sNfdWfYkTE/sSplcbkOTFAVS/Rg4z57JIkItoqyrKgHOSYskdGnJ6eUo7pWTtC0taerlLETtMs+xONVHqVcy6RqTdeCVQf2tMtiSRQAhkjoanYm+RMrEF0gYTksmmpvKRqA7XzDIY56xPD2ppGZQ6TCcrRkBQCndA0yeHhG6aMfUts7kRPjC0uSNzlJcsXX2fze7+T0zePqeqEO6/Y3yp5dHrM7nueQhhLNZvh5gu6usYoi1nLMdpQ5jl7N6+CSnzhp3+KcuMW4u5DaqGx22NU7pFVh29aUu7p5nOmyw6vJeVgzLJecn95zJM7DRPnmcdHHCzu8qXDL/Pl8xc5ac9xJOxwiDG2n7gPSsoI96tjFgay2LDoOtbsjFlq+OzlV/jhrXVm/oKy3EQJzXJasWYMer0k2IQIifvVW5xsLxDLgu68RYVIFg3JB8KyxS8bKFqycoAqx5wtThivrdNlDXc/+SluftdzPAwnCFmT2xFGappqSZShZ3CEiHeCpq0wsmWRnzLbrHE60tYt7ckp0bXo1Pe2HZ4oJcOtAXGv5JOXn+KJ9z3N5OwG1amnTkvq1BJIdHVHlpV9deU9mZDo3NAQwIe3i/R+iRXXGhBCgeAdXowQgrdtGTJI/NJB4ckmIxKOWC0QaVW5CbGaoPbMeCEFwii0BCUTEUWQkfhNZMsoKTHJUhRDxsUZBw9P0cqyt3ODw0ePqNsZN689zlOPP83dN+9TlZ5BOUAXBbH1uK5lNCwZj0ekKNi9coXDlw9oAoyQ6Dxw4717fPnR6yymLf4osD0YIYuGM3efG1ef4JZ+gqMv3Ccj43R5yexwxqbeYWOwjtKazjUsY8uFmVPHlsm4ROueXV6UGVmwWK2pvaPxntb7VcWdEWT//oeQ6FxH3XUo2Qd4lMMxUvWxcfNly3IZyYIlkxYtLZ1vQCRi7OmGMklkkOA1xhqsMsjUYxH6bFVBEnIV3de3a2L0uNQwXVQ473CpwzlPqfsWS3QeJQQu9APYohzQdY6UAjH2VX9T1wTv0VZirSEvbB9XSUcIASVE77HwgSIv6epEaCUi5OgkWdYtMbWUWYlVGSmKPqhe9gUISdC1rs+giBHahs3BDqJ11K0jKUuUBW1ILJtAOSwoS8OwFKyNJE4mTGFQ1lAtPPMu0KReg/8t35ZJAqRNtK5BjDJ2P/wczaLCbkzQdeLdN27w6d/4JO18Tnl4Sbm+waJtSMkjupa93S0ykfPmW69h97fxkxFHJ8fU53MmW5rgPN5Lhg0MdkYs5QwfYNl1qM0Bg80thpnFjkpiJpkPG77UfhEz15ycPODV89vUJjHvWmIUjIoMoRWBuDJlVBy1Z3z57A3iwCK6wO3qASfdMV46XndHfKF6me+dDBDBMJzkNHNLW48YjFNfDXQdR+0p86fGqBuW/N5DuntT6rMG7TzCBVSE6Fpc12DGI/ZvXeEDf/D7+bX//uPUlzXpmYZUKmbpmK0w7oN3dcSjeuVN55DCIFWijsf82vHn4MltBuMBa3s7NNOWxckUKQ3JCtSaIbsyZHRrmyA1x/MH/Mr8nxC2vpetZpuvHn6WC3+ENP2xUds+0ACRkEIRlcSuDVhmFyT/dsX9dsW+cpymPmTka6vrt+WNaSV/ccsamxcUWU6Vlggf+speiF7FwYpoKQWqMbBSpIQIMZcI9ztPYvoXvSSwnC6xOuepx97D+s4e9x/d597JEe9594cYDMZYcpplyxM3nmG2rGjaDqMaNsYTbEpcHD0k5Dk2H2B8QtqMuw8OMWKN0f4EvSnZ/8A+d58/Q88M4dAjZCDElgfndyjXdrj6/ms8evWY3cE2SuW0Dx3ZRkG5UdJZzTzMOWeOEQZXedZHo75lkCDPcrIiZ9m1nC3mNE1Lt8pJRQT6NBzVK1nSSqkk+56zj44kAkZlpGhxjQIXKUrZD9VFP5fo4V2C2EQ673o8g9YoCZk2xBj6E4IUSCRCqj5sG0eQ8OjoovdvVIE8y3FVS9d4FAqlNBjT3/iNZTarMNoiVd96kUowHA5JMvRobCPxOLKs5+JkNsPqiAuRZulJQfdu3iYhgkYDdbekjS1JJbS0SKlxHuq2Jq64SKFrkNHz1M0r7G+Nkd2iD3gSGcs6cD6vGEwmSB0wOrGxXlKWiTaCS46mkwShWPqILEqi1t9QMPAtsbkrKemmC8xoQJtpKtexfrJgNCzZvrrJmy++gFewc+M6JubMTi8IszmJxJWbNxnojJdefYm1d19ncu0Kr33lZYY6IzNDitEIOxyTugIvM2qRwzjHihw9KBhul8iRQQlNUIJWeKam4aCc8mh6TJMvabdKludzskwTRYvJbJ+kIiSLpk9g/9XjF5mVNcElIoa73THCCWSZ0yrHp2YvsjkY8pGNDyA6iXI5SIHrAsEL2qYjigo7Erhhhiz2kJvrdG+do46nxMspKQQgElzEV44PfNd388rnP8vs/JyUZ1S1p5aJe/41RvExTBAkCclLlMjJrSGojuVgxldOX+DAnLOrt9i+cRUnNdMoEcUxrYuEYcbw5jpqSxE0tLMFWlteOHyBKlzQPTjnzsk9al0hfIbM+tiwRM8WkUIhixx21mm3LvDN/G2d4mqDF6uKvVdBvN2q+VqAaF/JS2LT4mdzitEIXeR0s0V/zE59GBn0bG+ZRH8DFKugYSvIr09o70z/pV/Tby8rFaN8QBUC4yznyniL7fEak8kWZ11LPt7m3u173NzeZ2QKBtmQk9NTBmuarlrg2yVb22t00yWXixk3h7vs7e7w0ptHnB9nlHYEO5HhdsnW9Zrl7SWiVjQHgvXhNotFRa1P2NtYZ/PpCfWhR1eGiRoR5y1Czih2JaFMzJUnaMWyW8DME0VgXa0xyEsQoE3Gxroia5bM6yXOO3Qq8D7hffqajV2SgsAlR4wtSkek0iShqF1LQLCkJQiPD47CWDK9kikqgTKapBSp7sPChfeI5CEmtDaklAjOk5IiCUFTOXwryOSA5dJRRsWyXvZhOgLa1pHlOVoaFvWSDgdCEV2k6lq0lhQD258mVSKJgNaJLJeUhaXQOcl7YpXwbYeSPf8G2XtEstyCKMF3aOl6x6rSVAGCGeJ9RyYUuCVbE8GVvRwRPUKWVDWczGbMRcTLhFs2TCYFg3KE0Yq2W5IIZErju0DdBJoAUUJsXT9o/jrrW2Jzj61DVYGWmmx9TD5WLF//MtmaYNnM0Lnh1s1nOHlwxNmDuwyGQ3Yev4bJMqrZkq/ce4Wtp/fBNxy9epsQJHPncbrgYrlk/amnuZwl7MiiBprJZNKnJw01YeCYdxeMihFVV1N1LYUoSEvP8cEh5WBEPi5xMSA6EGQkIQmhN9dIKSiVYemWKCswdSJ5QdB9fKCMCUvgJC75+YPPszna5H36/aytDagWNW1jkDLHq8Td6hApQx+/lmmyKwOKYGgv5qiBBQJCKIzW5OMRG7sbvPgLz6PWS0oZuLh3Bx81n/H/mMff873QZhi9ko6hEaEhqYp7R5/l7/7M32T89D5PfPTd1MWAzZvXGa1NOP3qK7z15h2Gt3bJdwYYFXGhJliQweNmNaflnGrimVYOOoFOYI1BSdvTDnVfXSUSaliSb4yojmtwPdyrn4ryjsOUlfW+X4K0krlI2d8scIFuukAZiy4Kuq4l1T02IcX4TvXXx5f10rloBeNbm5grI7p7s3/5F/Xby0XWbckRLb5rKY1Ga8vO2g6nhw84PblgPNkgzwpc7bm8vMRag5ESIyP5oFdOJCP70GwS+9e2uVNa5heOgzDF+cC1vS2uPr7JnUdz5KVC+wGP3lyQP21wuuJs6Nne3mGix/BIM04F1hQ0akZVLymMQmhD3XqkELjYcTG7pHEda2GD4XCINophYRiPcyZtznyxpLoM/XBSSwKix/56T0BijIQgCS7QpA7RtgQCyWoa6YBeX54lQS4MRgHEleRRQpKk4AjeozQk1SdbEQRaGGKShNZTzz3V1LG+PmEZL+laT5ZlaNP/3S45MlXiY6BqG2yRkdmCtnF9rF6WoYzCWoUykUCHFG6lhc9pqg7XebpOEIMBbfAEYvTEFIkhIpPFWIOREYcjiozKJ2bLlv3NCYtHD9ifZNzYzygLR6wMdSc5m0XmjSDkCm0jSXZsrw/YGA9I0fduXxzjjTEiJeqqIUpFlGJl7Pv6l963xOYuVpVbmWc455kuF8SxYfPKDkJlyK7l8M07SCV54oPPkaTk3tEhdEtEoVm/+TjzuiEtI2W5RpsFvGuQWwVkim5QsL41IrcKZSN6bcQST2cCi3aJcx11d0o0lvXNbbTOmNc1BEFdtzSdR1tD0J5S6D4UIPSxW1JJOgHRJ4aDAW17iVW6PzaGCKqnvilheKQDv3j0AtlWzpPGEWLJ6XlHUeyztA3n4hSXenngIMvQreb0wUNM3aKtQRpLJBBl4smPPMvzz38RFwJ4z7LroD3Ft4Ffu3/Ju8a/yHfs/X6UCqToWXSSgQyciRN+9Of+LtOLM8yipEuS2asHfOVXPsX1b3uOZ77/o2w8+zhLt+BsfgpRkckCjCcEh5KB45NDrl55D8vLiqp9iM0USudEFEIGJAFFwruAiB6dGQK9koCVtK1HCbyNc029YYXfDLUASLFvC4gowAfqeU2xPiErczrfojqIqtfMCyFWyoFeeVPuThhcXScrC+bim3eZt1XDOCpOQ6ALgTzL8CRsnnFle5s3Hxww2tii6TomwwGZa6nmc4Lz5IWhi57MFiwWFTrv38f1K5vsPXaN4y89pL2suDCO8TBjZ32D3Sd2OX31vEdNJ0H1cMEoy6lEzTkXbA4VJiaaOmBMySiboJKl9Q7bwEBDJ1pQAucd7eUlLnjatmWyNsLkJSmBtTnb2zkLu2RZNVR1C6E/SUkrUaJPRuq6hPAGpTKQCiE8iR7JG11LZiQh9NZ/WVqs1WgpaJxDrtgxJCCAlJoQBcHRexmiJHQwvViQ2wHRg1U5KfXZqL5rCa4nn5LiSsGikKJPFKurGu97uWUXfH/a1xqpJSrTPd44BtpVYpJRtoefmZyuaxAYlIi0nUdGgbICF/uYSh8iofVsTMZU1RRUw3AyYWNziMChreH0tGLeObzU/fXsIuNxxmSQY2XENy2xi1irkTpDhpXKR0O0CmHlt/7mjlJEoZldLMjXRzQCtj78Pvy85cGDE4KQXPng+xkOCupFx9nZGXprF5MZiuGAetExLNaRY0OdAsvlJdmaZjIsGRY5NmWYaGi7llgamswzS0uWzQKZIApFkVnWd/Y4v7jkfPqI/Z0NrNWrsIneUqxin5qjjEHJ3iATQkDIRNs6hqno7c1GEgSEFBAxoWzf3/PR8eXqNnfv3ef95gmeU8+xvrzBUCjOiwccVY8QQ8VQDYhC4uYLZOURaJxcmXVCYjgaQZs4ev0umReE1hO8xwwNKURqUfHS7JN853v+AA8PAsNoibriyNzl5YNfw8mKje0xxc4aQURe+8znWLz1kNeERN/aZX1zjVujazw4PmReV6SuJbQtQXqEkCjg8P5ttia7+OUcYSIYiRCKVRgTKYCPkSgiFAryXrpFFCQXV7921bdh3mEErP4Tb2vUf7MZnxCEuiEWeY8+bld88fibSAKREjIlGFiG+xtgFKNighRfn5z3e726ZYs7nDG+ojmvFlSDgiglSghGxvD0lX3uHT6E0QSdGYbrE6ySnJ2dYne2EEJQdw12kHGxnFKODKHxjPbXuPdrbzG0hgzDweEBfqdl+8Y2rgssDudsl+s8Oj9Engjy4ZA6tsz0JeP1EgaaadUhK8XacJ21PMPHjqVbcqpa2tj/rrVWtG3Lo0fHzBdTtv0Wa+sjsqKnjm7sDClayaISVHXDclmTfE9Bjc6gYobCoqOh7hZ0TUNmBlilSTIjt5roOyI9XylGiVYSKTyh60AmclPggidFiUQRvMc1Dil609Dp4SXPPPEcD15/RB4GWG0xQpGkAynJrUZKqNuuvzEo6FoHSVAUPd8mJtBGIrXA5r22HeFxnUNJhdEZXRtwLpAZIBmIfWZr9AmlJM4njNIgDK5JZFqjYovNAkkm1vctk90MlprZZeL+o1O6NEBmOSF6ZPDsrG+QiUisKubzObVrKXcmpNYxX0zpXIcwGVFEUorf+j33JBU6H7IxyFjEjnwtJ2UWF2DjievkxYDQBprLBbbMeWy0QRcjyWqsVAgX8Qiq0JETmR7M8DJQ1RWCQBMdsavxVlKWhml7RuM6Mqn6I5U15Lni+Pgedd2xNhqjbU8/7G32fWc3NxZjLVHo1YCy7Sf30eOjI4QR0UiWsUMjyIwhtzlOQd1VGECiOUkd/3T+ErfjI55Rj7GlrvHW/C0uzYwkJLksCSkxu3cHc15D3atZVPQImXj8gx/m9U8/j5i1mMk65aggzzMYZOxe3+epdz2GLNb56vZXGHZjllPBonuLT77+s3z+iz9HcXWbW9/33Vz9wLvQOsG6RX3kFvbJfabdJSe33+TG/lU213fZ2d7l4PABj6TCCchjREuNjw1HD98C4XsnrzYkJDImUhf6Tovu4WVmrUAPDa7u+sC/JP6ZHnvi7T78Pzv4jKlnYksgSYEIkW6+QGQj9GhA28z6fqzoTwCk/nc1WB+TjXO0zchVjvzGQWC/pyu4wKNXH3DlxjO4LFB5jwMKm7GZlzRIimvXefXogLW9bUIdGZYDMq2IsY+LK4ZDkmq5uLhg5Jfsb66xdnWT7a1NOOtQUdGGxPHlCXrbMrhekoInzhz7wx2WZwvCOshNRS0WCFsxKIZkaojQGYumxSjL9sY6O+UEvzzirJnhXH8yRYA1lhAi9+7dZ75Y4+q1XYaTHKBX1JSasjXoKSzmLe2iJoaAFQaVFF3T0VYVUkKeZyghMFKTWYPrBJnRq2DqjhD7zNSUIjEGtLAI3dMagwPXCFzXq2zms5pcDQhNop07ROoospwYwjuICiEkbdtRLRuGxRoChcO/kw3QdQ3FQKNVQqqIzWyPFnYO5z1lnuNXklaiwrnY6/pDgqCwK09InxImkBgkARldz48RNRt7JTef2iJVHSTBg8NjZk2HLCYQEnmWMxpo1oclKlSkrsa3LQKJ0hkkQQgeZRSDYU6rJV6llXLot17fEpu70AozmGBILOlQSiIVNCJSKItqIms6J9sqcALmyyW5MqgiQwGiC9TCYyJ0tUekQHAtddvi5wq8QW1ts7a7xqKrcKFBr/p1xmratqOJHi0lxiou5pfkeUbbNAgJ2hbkZYY0AhcTkojSkoEqCMHTtjWkiPe+DycQibIsyBG41ODa/3977xZrW5bed/2+cZm3tda+73Opc6q6qrrbbRvFMcYhsWwsByNIDCIgQmSEQoIs+cWJgoREbPLCAw/hAYKRgiUDBieKcCIDCkIWEJIgcJAb3zp2u6/VdemqU+e2b+s65xxXHsY61dWdOuW23afOqWL9pam91pxz7fmtsfb+1pjf+H//fyzKhlLMPXQsRK4HzRkrNafKX2CsU1lVz4VhEFee/s176PNLxBUdF5Mjk2snbC7XXN6/LGOnLQfXTvn4pz7Jvfv3ePvOfe5+9S7t/hT+DcsXPv8buIsVV6sz/tCP/CA/8M/9WYy1rOcb7v/qZ7n1g9/H/qdeIMc1qqmwUZW6qet5++23mezNOLhxg/roiLtvvsGwuCrc8eygckjK1FVFUhnnemJI5ZZcK7IWklaYrsNMWuKDDSZBkC1tUR5xhwERlJR/DvIjPRkhbztWMxTj7sHBeqQ72iN2juiXW4MOhWQh14bJjUOsNVRVVRyCnqIqZKUr5m/cp3llwuzFG3gUIRfvT+89E2tQSjFrG1brFcftARNVJDDauqFpG7CaILB3csT5Ysle2zBWie/87u/k1f/7c1ydX7H3sSl93HAxXKInRzTXaoZxw2FzTLNquPvgjKo2mFnEy4aNSjgF00nDZNaRgF717LUVz5/colp0PHj4kL7vS0kylQaxuq5ZLpe88krP/tEeN24cYWtBV4q2aanrmqPDyOWDFYuHI2nTl5uxnGhay3Q6YTLpWC2WEDNtU6N1afYJPpGSYMQQvAOEFAU/JpSpSD4wjomUDDlDP3oW854Xbj7P4mzDtJoR1omsi8E1MVEZS0iC80UcJ7rMMKzZbNZFwndSk6XY1lWdgHKkGBiHgPeenAVB4V0oTVNJ6FfjVtqiLBfZWqN1uevoNwGlEgphYhUuzLH1wCc+8RJ+3KCT4v7bF3z17n3a/ds4qVhv1symDSf7M2oFeI93A6I0tupQtiGbhKkNk7ZibQCVy53J+3RxPBvJPWcaY9icn+Nlg3Sapm6Kseyy5+6DM+6MAy4NSBaGYaCqKgzQmRoXM8EKgRGspT2qGdYblK1Zv3YXCcL+J57jMizxYUB0Qm8JGyEEwuhQdkIIgXEcigbEuCEDta5oHi3OpERO4PyA8w5jDC54IsULMw2eCZaqq0gijN7jnSOFTKfttqMvYZsKYxty8ricCMqRE6RIkQTQEdl4tNeFZ5sTBiErxfGtW7z62S+QE0TvWbx9F9Y9b3z2d4g+0p5eI0jmu168yfmbb9GedmibuPbdf4QHX32b4eqKPI7c/dIbrJXn2/+Z7+bw+jGbsxUhRvw4YrUle0G05d7lGWazIo2BF567zWXbcnb3HilTzEKyRbwQnC+UXgWoIhGgtl9myghmb4Izl4jPW480ikjvI42AXBpMcioMGaEsuKatlolQjktKhFVP2JtgDmckNxRWxdYzszloqA9qtHn0+b6/5vUTR4aDesb9L3+Vl6+fYnKmaTVaZbqqodKKJIHTw0OWlK5rUsSa8gaUVowhcLVcsDebITlz5/Kc5/b2mF3vmB5PeeXt+3Q3p0RdJj6VMdy+doMmVvR3HTeu3yJvFIuzM7qqhk6xcj1eenQzUNuBhGBai9iIqqbcun2d/cMpb731NhfzS6IHYw3BBZRSuNHx1pt3WVwsODk94uTGPrYtPQttZbCnloMmcf72nH6+JiRPVRVbvuX8ipQSbdtt19IVKQZ8LBaKzg80jSKEBEpjbU2/CQwuok1DZUsOWK0dIQgHk1P6hw8YfE9rW9arNckHuqZiOtln4TZ4N6DEMPYOJTCddIjKGA110xBCaXzSxuDDiNGWFMH7yDJt6Nee4POWNFA6pNuuJaVYmgVTYnSJwQeUJCorVNqjxPHc6Yy2Uni3Qfmat++cY6spSRlccMz2WvamhsZAGnuGfsn5xRm52cPaliRCDI4hepq6ZXQDLhdTlPebuDwTyT14z/niHv38gpNPvMT09Iic4P5bbzFcXMIQmLU1WgJXD8/wq00h/4+e3La0t68jsWZ59gB71DG7ccJq7tH1Pmra0U074lQYo8MnR2MqskjxXkQQUyham3FNyIEcI8N6RdM2TNoZSTzj6Ig+EVPGJYdS5VZPRGGVpRWFCdC0E3xMjMGVumBSVMbQGYtWUmrTxpQt6VLzz6XLLcbScRuHgcWrd/GXm1LHbms6JdQHe4zjSOiH8pnm8s+wXi5KbXE64fjGda596gW+44//Ed5+7TWy0Xz5H/0qIUQOT044e/iAOPSYg4aT7/12XAVNYwnJFzGxuKQRzeJqxd5xR9o6BalN4NW7b/P8x19CX7/Owzfv4JNCoyAUHi9a0FqDxKJ+mBUSFULG7DekVhPGQBaF2i6CKqV4ZwE1FzXLR52q73ZcegRJiTSMDIsl5vQAPZ2Q3BIdhKgSemrQjWArUxbd4iNj6qcDbTVt1/LK669R7X2FF7/vO7lzsaA72SOEgKotRltmdUsaBuIwQDuhaRq00sSUSAl8iCzWayqlODg5Yrga8VMh7yvqece4DFSzluwdm37NZX3FyY0TBjdytVnywo1bnK+E+eIcqSvqWrOOnkE2uEmHiKFvM1IJOgzUQdg/7Kja52nuWh6enW3ZIeB9pq4brKlwfeatNx4wn6+4fuuYw+MpgmCV0EyhvW1ZTpa89uW3EGkY+p7KWtq6BtjqsxRZguhLqcQYRYwaRJFiJucK5yOjLwqPm9Ex+sjlfMmk6VherohDIvmEUqUZiixUukayxrtEjlsxupwwxlDVGud7lBaqxpJVea1Q/oZzEoIvNOIkCiUWo4sYmtG2fLbK4EZXSsNKsE3F1DZsNmuqGvxwQVNtuH3jFmkcMHXFlz73Bst5oj444MJ5bFujZeRkv2NqHZIdm2Ggmu2zxhIFekmIJFJVSjFJJZQ1aKvft+T4TCR3pRWTF06YPXfMMAQe3L1Ps7fP9OZNzGxKnG/oL68wlWW2f8xm7alzZrlcktsGnz3j2YKZmqKqGZkaow8woUHdfpH2ZMrKFBZKbQ0qRLIRXAoYUYQUyQpiiqTgyzqeh6ZpSSoyxojfCvGnLZMg5gSmiCdNsbRVzZgTi9Xwjt1bK5aqs+UWygrJFC44OaFz3IoICb1bU1WGGEsXqV97cj+Q+zXG1tSTfTSeMQbCnbvFFdgLylpuf/xlrl27xnTSYboOO5mxTGvisufOpz/H/mRGVVnWiwUP3toQQiQeTzHf8zzx+SmeyN70iLaa0buyOEwujvIwUufiQUuMJO957fXX2Ts95vqt53h49x5xdIhVWC0kYlkIzQa24mAqb/W+ZzVq2pBXm0eTbOBrnPdH6fe91ofendzJGZMgXq3Qs456b8awHiE6sBrdNaSsUFRI1sRY6GpPC6IVuq04nZ1w57Nf4cWXb/OJ5w75yv0L1H5TmmuU0CqLMpmHV3N8VRcTaR9wIaBrS1t1jN6x2CyZHFYoraiu1zS3p1zjOfrznriASdURQs9FfwkTTX08wTFw2uxxrTklbzIXyzlZNLYWXBxYxznSTohNjdeC1cXYWdmag8OWenKbblbz5ptvMrrIZDZlHBxKF+1+lGU571ktv8rRyQHXr58w6xpCiLS1xRxOuDycoHLNOBZ2ijGmSPn60vnpRlcWL7UBatw4MJvNqJuKxXJAVQ2GxPzsHoMPXM6XLFcrnrtxiwdfucfU7mPEEZzHKE0368gps5gvGdYOyXUxV09+68daEVO5Y4CI1Vt/1hRLNymhcC9TUYIlaRS6GITk4hVQHMAizgcUCoxmjJ6IY91v2Kscn/q258usPWcWFz0X5w6l9lgsHX2OzBpBy8jBVNNt+wMSCj3dLyqTGi7HDY0O0GhGAmIVymiUfv+S4zOR3FNKSFTc6y+RqyXTk2uMyznuYo60NcN6zRHQX66Zf/UOeu0YyDSmItRVKTWsBvxMI97SypRpt09a9hwfTOi7lhg2VJVBEjjvinOPlKQlqTQSqVR8FMUaQk6YXBEyuJSL9gXl81ZKkzxIjiQCyQhrP5LCoyYLxbSZIDog1iA6EXNA64pshGoreCUpY+uKbDqc79GmIo0bNhdzmsM9VA02asTo0tp9tSiLwEYj2qCNYd1HvvD5V3DjkqP9fZZ9z3f98Pfz+qd/Ez0EPvsr/6gYFfaB0Sgmt46Yfc/HkOstSCTmQjVzzgOFjZBV0cbo1z3bgikhjYhE6NekueZe3zOZ7pMaS/AjOceSoJWCVDpHU4yIUiQUZjLFnkyJ5yuIW4eiXOhsIo/S+9e6Vh/9zDk/Enss7KRM8an0CX+xpL59gzybFGGy/Q5/MCEqCCkW1cL0eE13EXke+BvA9fJG+dmc80+LyBHwt4EXgdeBP5NzvpTyTfTTwI8AG+DP55x/4/3+trOGniIM9+bnXuEfT1r+6I/8ENdm+zzMgeXoEA+zqmJiaxZaE6InKkVOqSQrbRg3Aw/OL7hxfFy8SVUkzoT6Zse+s+jFks1yQX1YoyQypp4zd8mNkxm2a3jw8D7P7R1x8/A2rDrO/QW6AqMjq/U5bixdtM10RhZI2TO4hB4yymhu3jphOmv4ymtfZbVco3WFqLI2Y3VD0zQEH7h/54L5wxXP37rJ4d4UbRI5CrduPsf9e5ut5K7ecsiLv2hwgZxBaYO1NdY2mKZFG8Nq7UEsl/OB9TgwhsJYW242NNMJyhq894TsqaxFofG9Q2uN854cC1c9RIV3ES0ZowWlMl1XYyqhsoYsnmEzgEqlmxaN80VkOhYRF8hsmVdbJdIsZW2n1tha0yfF0G9AeVTuOT5uOT2aEFzPdHbEF77wJS4vBp47fhGvMkkcShw3r81oTcCqSD86XMqslz1+MkG3lgGPSMRUGpeLSxVKbQkHj//beyaSezibr778Uz/zxUfP36uf8OHjXvyFO+88XPI6AO9uWbl41+PN7yEmDywfcyx+7eEJcPa+7t9/QKzpYfG1d/GN7+HB4vKdx3fm5Z3/8i/83ff+ZREWr949Wbx69+zRrr/DP/jWBfv7QnzM469hS5A8Ac6gSAcTgfMVF+evfO3E9ZzVnTmrf/JXfOwxFw/Av59z/g0RmQG/LiJ/D/jzwN/POf9VEflJ4CcptpJ/EvjkdvujwM9sfz4WooWowG0CHRWycAwPFnR7R7R1xcPlBSLC4ckJjVIc7e+ziMUublK39Hjm8xUxwtHBEavlmjoLR13HIg/Ykwnj23NOj6+xCAo1RqRRpBp6Rh6GC/abPbr9zIPNQw7a57h+/RZxLly4u6iYaRpb+ksul9g0oT5qMZXCR8d8MTKZTphMW46O92nbb+etO/d44/W30NrS1Q0ZTwgRsqa2LdFn3nz9Puf1Oc/dvEZtpNjuKU3SQmVrqqrCu0CMkbS1T7TGUtsaow2i4GrZ04+BkOH+2QXLfoNtG3o/ghJe/sRtHt59QFu3VMoy9h7nXFl49wnvApItMUSGvlyr6QzTWcfoNqAitq63ssKq3CEmtlII25JiKk5qgkKJoq5smTVv15Zyhqq2RBKrzYJx3GC04+Sw4qWXbhP8QErCV1+7x/2352iZEYJhiBvUNNNUmYNZBX5NlshmsyaK4rIfMW3L4bSh7iqSrAgSiJJKB28WVH5/psAzkdyBL+acv/dpB/F7hYj82oct7g9jzPBk4s453wXubh8vReTzwC3gTwE/tD3t5ynWkn95u/9vbA3ff0VEDkTk5vb3vHfcZHRQrC5GbJ5wOruJ7oWr1844/I5rpL0Zq+gZUiBgaOoGFS396HFbxVPvPfPVGkioFFmPG6zJSDNh/9By6+MTzu69wfXDKeerC6SqSRZGcfTpIUxHrh9eR0xm6a+4ZmpeOD3BXoys45IUM1E8c78gjBnGI24cHyNeiN7jh4GV8+zN9pg1NS/fvkVnKt66c4fNZs50sk9IiRwzle1Q1jD0A4t1jzq/oJvURRMiVRhTExNb67tAChFBaKpChVQSSSGx3PQMIVJ3e8wXa9Zjqbl7Ii4EKl1xtHfAvS/e41AOCL0npwASaacdSmm0t2xWjsE5Ys6YKqMsZJUwtUYbg5hyl5dzQqkKpTVu9ETnqXSLxIzRGpHS05KTx7mRujZFfiBnBgIPF3NaI8zMhpmNvHh8DZxjQHP/4ZKHZz3OTTnYu0Xvi0lJbRNHhzWTVhGHSAiBKDVRBSaHLewbpHUEk0hEtKLIXEtCI6T03mXMR3hWkvsOOzxViMiLwD8NfBq4/q6EfY9StoGS+N9818ve2u57bHIHweaKceWQaOnqGXlIjH7B8Kpj+tJ1skhJHNZgcqYWRVCKMRVnqZQze7Mpw7Amjp7LqzPc0KCPK6ZMET2Q0orjg1usNwuCL5aPyXtUBRs3Z6mnHLZ7hNGzXJ1z/do1vuPlT/L2xX3uLO+BSkTtuezPUPOAMnC6f0itFCpm3DDyYP2A6XRK0zbcvn2dvb0JX/7yq4QQkSRoZUpncgikGIvui3O4VFyOpuYAlxNudFitaeqaqqqpbIXZMnC88yCCtprgHFdnZwwRXMwMIRYBuhw5PTlmfTmUW+yccYNDtk1JJOj7gc3asVn1W5aL2So+WkJ0iM7UbUvTWmIqphsxJJzzSFa0VUsO4P2IVRoBjNXE5NCVJhGI0aG0YuU8Y3LocWQSez7+wk2ev3bMYrNh2SfmK0HbY6pqxAdNykLdVVTVgFahyFRLoWsOUYhK0c0mpGkE68kmI0qhquL9GlPEKIsx5uucyb4RT08ub4cdnhGIyBT4H4B/L+f8dUI021n674luIyI/LiK/JiK/Nl9cljWGkOmaltlkigAqZhb3L7h66z6TIPQPrujPFwTnyUSsLXotJI8iUVmF69fcunGNm9dPaSpLciM6Re7fu0uInsms4/BwH60ylkxnLLUyJB+4XFzhCEz2JySdWY9rTG3Y29+jMgYNVGhqVTEOIxeXF6z6NRhdFnRnEzbjwKuvv858tURZoaorvu3bPs7RwSHWmOIT6gMaoTKlZFNpg0oZiYm+3wCZ2WxC13Wlf0EJYsxWL0WDMURKt+vgHD5FtDWYypDImKq0Au7vH3B1dYVWmkwCScXCMEXGvi9yvyFhtKVtOipbY60tLCqdMVYXqYNtP0RVtVvv1IxQmDqjD+jKYmvLEEZGP4KmNDgSitCYAS2JaW3IbqBrGg72DkpXuRPu3b/kwf1L1mtPihoXMoGEC47RD/jgiSlgmopsDVQa3VrqaU3dVmirwQiowqrzMRBzZhgdQz++b4fqs5Lcf/ZpB/D7xIcx7g9jzPCE4hYRS0nsfyvn/D9ud98XkZvb4zeBB9v9d4Dn3/Xy29t9X4ec88/mnL835/y9e9ND1ldr0hiYTia0bUOOkUZX2CHz+f/nM6TzgZcPjvEPr1ieXxCiw2hFY8stuOSIG4rm+MXFOW4c6dcrNpeXNJLpGkvbVYjJ3Lp9g2nXoGJgamuMT9RZk8icry652MyJFsbsWWxWxfv05JSuKjK5KgreB64WC9546y2uVnM8GZcSp8/doNmb8Nuf+zxfeuUNkoK2bTk9PuHk6JTa1oXOmDK1tdsvDUWlDZO2xRpFygEXRlb9muV6xeAcq6HnfLFgOY70OTPvB+brDYvVmphT8ZuVxGTWcf36dSbTCYJh6CNkwY8DOQWCDwzDiPeRSTstVNKQkbSt+WtFyh5jdDHx3gpxLeYr5lcLxiFQmQZjanzIJDLaWgbvWI89USK2tWSdwCSqtjDhhIiEga6yfOzWLWpTs1oM3L97xdmDFX0PzilS0vTjyHrckCSytz/l4GAPXWmCJLxOBAvJCkEHogpgip6HGIVoRRahshXaWJSqeD+6zDOR3HPOH8qE82GM+8MYMzyZuLfsl/8G+HzO+T9716H/Gfhz28d/Dvi779r/70jBHwPm71dvhyJw1c97rGgOZntoK8TosUmYSY1dRt78zJdw9+Z8/Np1pnXNGEZC9iAJrQvvu64MN69fK4qYOXN6fESnNWG9QZM4Oj1ksZ4z2Wu4ef2EzhhMyFRJYTGshw3zYcViXJOtEFVmsVmirOb2rRu89PwL7LdT4uAwWpNyYj1seHh5yXLcILWlj4HD01Oe+9gLvPrVN/js577A1cWSuqk5OT7i2skJXdMSQySHBBGSC0RXNICMFpwbWG/WOO+omwa0xsWES5kxJPoxcrXaMJ+v6PuR9WZDzonZ3pSjowOQXOQFQoKkERTDMOC9K82GSpNj8RMILuLHImcgotBGI7q4PWUywzAyDg5jLFbXKFWa7obe0fc9KIWPgX7sqWpD3VoyEedHshJCDmzGAT/0uPWS0/0DDmcH+CGzmI+88dUHrDcJYyZ4lxl6z3K9ZuN7REHTGOpaIxq8yowqEmwmN6BqyCoSsidJQluFrSxGG7yLEBUqm2efLbPDDk8J3w/8WeC3ReQz233/IfBXgb8jIj8GvAH8me2xX6LQIF+hEJf+3W/mInEIzOpSMhGdkZzQKNpouNWd0MSG4e6CSTOh3bfM3QoXI107o1aaAUFri+sHbh6fcnV1SZbEyWSGu1xgcsC0Cu88zVRze3rKMi84Wz+k7jqi8khdavfnyyus1ty6doMscDE/5/rklNNrR2zChq+88VqRfKDIMN87e8DoHbeeu8XedAZZaPYmvPSJl7n39j2+9JVXON47pq2maKk4PjpmvewZ+gFikV/OW0VMbRpsozG6ou8dV8s5KSm03fqGxuLotFoPiLbsT2e4nDBtXTqfJTO/vMKqGpUVtaqJOhKNJ5OYTaesrgaCi6xXG1KErpsyaaaklNE6Ya0tJRzviCFhqxqSIqdMCql4vG67pSWXpidthW46IeGJZLIqctTOOcYxFI/XDCcHx1uVyp57D6/w3iJmQs414xBJUajammZfUdUJUkCLRleGhVvTi8fpTDBCZUMR5FNpK3WQCKHEqLLF9QkV0zsNf++Fpz5zF5E/ISJfFJFXtrSzZwIi8ryI/EMR+ZyI/I6I/KXt/iMR+Xsi8uXtz8PtfhGR/2L7Pn5LRL7nKcevReQ3ReR/2T5/SUQ+vY3vb4tItd1fb5+/sj3+4lOK90BEflFEviAinxeR73vSY51z/uWcs+Scvyvn/N3b7Zdyzuc55x/OOX8y5/wv5JwvtufnnPNP5Jw/nnP+QznnX/vdL0LRw0mJuq1waURVihQ82sO17pDWK9zFiodv3UGFxKzqkDES1gPGZ1QfmWDJG0eTKmaqob9YMVzN6edX5OyRStEedsz7FbYyXD8+pjMVNilM0ihd+iWUVSyHNcu+1M2zzvTjgFbCiy/c4uUXXyzmzNvmKmMNg3M8OD9jsV4ScqKbTuj2phwdH5MRzi8uuf/gIQ/Oztn0A3t7++zv7ZUkGSJqu+yXiYiCmALaKJQqap7e+9I4FAEUxlQ0tnToNk0LOeHcgHcjWhQqC27jiGMiDJH9vX1mkylEWM5XVLahXw0IiqZpEDJKElbr4hGQMimBMRW60GeKL2suSbuqLE1TAaWxMedEP2wYxoFJ19K1bfFHFYtgyEGYNlO6Zsp6Hbh/tuLsYkDMjJAtq40jpiLf0HQWW2usFSZNRVMZhmGNS45q1jA5nlLNKpQFbUBpQUmhZ0oCIxY8mGwZ5o7g3ps+XEbyKUJENPDXKfzh7wT+LRH5zqcZ07vwiAP9ncAfA35iG9tPUjjQnwT+/vY5fD0H+scpHOinib8EfP5dz/8T4K/lnD8BXAI/tt3/Y8Dldv9f2573NPDTwP+ac/524A9TYv+wjPX7Ioye2mqarkJq8MoTciT7RN5EJqpmr2oZF0su3ryLWXmuVXscUXOUDC9OjrkuE45jw9nnX2dvNBzmBrUeqZWAJILJUAtrv2HTrzg+2OPawSEmKypVFRncrbVcyJHL9Zyr9ZysE0kFxuDQRnP79nPcuHZK9CNu7EsyFFj3G86vLtmMxau0qmuOjo/YPzwk5Mxq6Bmc42qxZL5YYKua46NjunZSqJYpEomE6FiulyQp2iyQ0Qi1rYqZ9RhpTcOk7tBiSCGQ/IikgMoRiZ5WG/qrNX7lCJuAZEWlG4LLNHVHCkXgq65qamvQRqhqXUogSJH0oGyPErsxlmo7qw+xNOUJESWZtq3IOaKVMA4j68WGOEbikBiXgRwMRncsloGvvnnOm29dse41VXuMmA6fIauMqYWq0YQ8oojsdQ2NUdRW45IjWlCtxnQadDG0JyWi96RtqSuNiTRmahpW5z3v13z9tGfu/yzwSs751ZyzA36BwiV+6sg5333UfZhzXlKSzSMO9M9vT/t54F/bPn6HA51z/hXg4NGi3AcNEbkN/MvAf719LsA/D/zi9pRvjPvR+/lF4IdFPliNXBHZB36QUv8m5+xyzld8CMb6d0POmRgC02nH7HCCF8/ar0lb41iVBI0u+kNNg+k9mzfuw70rDkePPVuz+fLb5DtXTOaB8dUHbL5yj8NRs6crGlsW9Vz2BJNwOHpXurFPjo7Za2dYsRixGF0W4hKZ1WbN+fyCq/WCMTpcdPjoqRrDSy9+jL3JlBxjEdLzRV9osVpxeXlJ3xfp3qqqOD69zsHxCbZuGHw5597Dh9x78ICQEofHJ+wfHBZNmugJKVLVFudGhn5DWzdYbcFFYu8Y1z2+d1hTMWsn1Frj3Uj0jhw8bV1Ra0O/WONXIyYbksuslwP9eiB5MMpS2apIQMeA1hljIURHCGk76zZUtujjhFBUW/O2GzsTicmjpMz481b2t2taxk0p++AVYQAVaxq7h3Oa1167x+XcoewMlyouFwPzdc9mGPBxxNhM1Si0ytRGmNYWFT0pOkQBGoIKKJtBeYwWjFIYZbBiUElQQejshM1ljwqmyB48Bk+75v5evOH37fh7GnhyHOgnhv8c+A+A2fb5MXCVcw7b549ig3fFnXMOIjLfnv9OF+sHgJcoTcj/rYj8YeDXKXceH4axfl/knFFAVVmq1rBQgaAiohUhOrpuQt009NkRU6D1itZlNg/f5otvvEnoIx97/mUObzTUa/iO/ZvgYX9U2KrGMRC14IJnHRT7tkVZjeTM8eEhYxgZzx+iciJJRutiMhGTY7lZkc4jzcRgaoXJZWZ7sLfHd3zqU3zmt3+LxXqNtRXjOBJjZJ4yRoSuqtFSxKsOjo4Qqbk6W5Ryk6rYDCP+4RnXTo7ouglHOnP37B4+RmaTfcZxxG0GrIJxE4vqpFS0uqgu6iw0tsJni+nL4mW/Gjjevw4uUmtL6Mu09epiARH8mAmDYLXFaksMofiXVjUigvcObYp5dQyJGDPeeYIvyV1b0EYhScgxFJ9UoZSDjMWHuK15azbLETckBMM4FDP32DvikIGKkCxDH9BdRVdr6lZTtwpTCTkZJq3FCoRxYO2XtPsNsbX4rdkPhK1CaMagkJCRACpr5g/nrC8DJtak+IxL/j7L+EYO9DcYOGcp/nDPDETkXwEe5Jx/XUR+6CmH883CAN8D/MWc86dF5Kf5WgkGeDbH+ptBSonptCvc6Fy0/lHFpQst2wRpEFWchWwGek+dLWb/Ohs2HETFidSskqer97haXNG5Us/2ySGqUOk2fmBatSSVCTmwP5lwFA/pg6NfXRFzYZEoY4t+ekhs+p6zy0t0bchWEUm0TcNsNuETH3+Z3/nCF+k3GybTKRnBjcU0xCB07RQjXXGQmk0Z1o44ljsVUiJn4d7ZQyprwGRcDLghkLLQVh3JZ/wwkLPQ1RMmzbQoOibFar3B+xEnQ2mMigmrDV3d8dabd8EZok8QE8E5DveOCOOGqrIEH7HaUjeWnAKZhCiFKEWmeAb4EEnJMY6uqFDm4slbmUIbzTERYyiU1MYyDD0+FstD5xPLRU9b7VFXHcvNkqxK0o45kbIQpXDTp7MpxiZIG0bX42REmYQ1NbL1nwrBo21XKJEaRDJ+9FTaYEUTXWS5WZNGaNWUMArLyx6/6ouV52PwtJP7N8Ubflp4Pw50zvnu74cD/QHg+4F/VUR+BGiAPUo9+0BEzHb2/u7YHsX9logYYB94knI574W3gLdyzp/ePv9FSnJ/1sf6d4UCrIXJ/gSVQAWh1Q3RJVSjCSqDGlA60OhMEpCmpsoGnTTaCXEzYhOczPYYG4fbrEneo0Whka1HryaLsHKButGs8OB79k5bBplyGa6Ig0NQpKxJ3lDpipwT80tP2wQmE02yBh/Kgufp6SkfW2/48iuvIo/s5ExZgHx4ccV0EumaRGNb2q7m2skeSxHW8zXOZVCKMWZWofDUjakQVRqUvE/UqmIyq9FbT4BWZyqlGdaeWd2ycit8HGi6CistQkO/gmGtEGdwo6cRTaUmLC7WtE3DerPBWINPS5Q0oIsMUaWKgXWImZAjLg7UtkIaD1pR25IKE5mUiimPrQXT1mz6DefLFW03IYVMFo2qKmxbFQExZXCxWPDZusKIRmswKqOUR1vB+0w0Bq0idSNMJjVRJeabFWa/wh41rFVCtEEiTNUBKkTCuCH0GRsbcqyZP3BcPOhZXyasain1nMf/7T1N/CrwyS2TowJ+lMIlfur4IDjQTwI555/KOd/OOb9IGc9/kHP+t4F/CPzpx8T96P386e35H+gMOed8D3hTRD613fXDwOd4xsf6m4ESwVhF3RaBKklFe1MphbKC6SzZQpaAlqLAmKwiqEzW0M06Dg73gURdW2yl0VZwblPqyUpjlcWoqmiyO8/KDXjJRJ0xreLodMbhQUtdCVrytp1e48aEYElecXa2YLkcWK8do/MopbDWcvPGDV64dZv1Yo0fXRH/ikV3v+97LudXrNYrnBuYdBUnR3sc7E1orC76/KJAaXxIeJ/JWZMoRjZjDNt6/0DCgwrFYF1nuknLdDZlOpsiAm50xAjrtSM48K4Yz8cAVtV0zeQdZkwm4ONITB5rTWHcoOgHxzCOaKNoJw0xj0ymNXuHHUmKzK/SRZ80SzHDcCngyei6peommLbDNi3tZEKC0rWqFCKCrizd3oR6WhGzp2k0TWVoaktV13SzKXt7e8y6hrqtEKsYs2cUz3JcEsUjmlL6iQodLTZ3VHToMCFtKoal5uqBw200Kre83/LYU525b2u8fwH43wAN/FzO+XeeZkzvwgfCgf4A8ZeBXxCR/xj4TbaLl9uff1NEXqGIaP7oU4rvLwJ/a/sl/ypl/BQfzrH+OmgF3bQt1L9YFu2M1mgxdJOG0W0YEmQUzgeyaOqqRiWFMpmJbXHJ0VUtRmumhxPmmwUhRlLOhBDJkcIVD8W+r3hrlmN1W3N6/RpDiMyv1ig0VQUhFoPlTKbfbLi4uMIY3lFsrKxm2nW8cPt57t99wGrdY8xA3TTFKSqD94FBBlTIVN0+dVMxm01QaGS5pveRmDKaInKVyYBGFPgUi6uREpQu2uUQiiFFcMQcCn10HFlfrTnY298ahkRyzEWDndJ9mmPhtosoKlNxsH9ATkKMRcsmhlQEx5oKrRUhJWxlOTk9ou839P0Grc3WPCZT1YZM8bBNKVBVFqU1OYF3npyKpESRACgfctM2ODfivEdpjbUVxhq0UhgTqaoaox2K8qUfgkOsZnrQsbEJjCLmQIyefnRUpoPUkV2mXwTm5z3zi5E4GjQVKje83/z8aZdlyDn/EuWf9ZlCzvmXeXxv7w+/x/kZ+IknGtTvETnn/5OiaEjO+VUKO+kbzxmAf/MDDew9kHP+DPBeqo8firF+LHJGGZgdTAlSGmRIRQq4aWrqxjL6/I6+faL4ysYciSS62rJ3OGO9XOFkJKpIt98wH6/IWSNbL+CUQbDklOmHgU2/pqknDB46W3F4vM9is2a97gkuonXhcztXSibaKi7OL2gbw6w7IAyeShVj5oO9Gd/+qU/xpS9/hfV6wJqaMYeiqiiCcw7xkTUaqac0XUNlK0QU4WJOioGqqnGSGL1D6cKf98GzCSMDntZk0AqVFIJBAmSTsLWhtRPkcoGxhpRc+ZIUmHQt4hPr5RqFEJOn61qQXHRiKLaYORensEcuYT6O9MOabtJweTkn5YC1RYgrpUQmYYwmpVi4/pWhtrbYDPoi3eBdIDsY+xEUqMoQNxHnPNoYGtOS8/aLgLKW4ZxD1MjBzKBEmK+XZAvdbEJUA45IjhFrFDpp3CbjNol+Gbh4sOT8/powKGo9QSWDH4rZ/OPw1JP7Djt8lCECVa1ppjXzuN5yqyMpJerGorQQUiABMQvaaHKKZVYeHa3RdIdd0RgBSJnZbMbF+pIhR2TLcJFcDMZTTAzDwGJpmE4rUtJFRbCpODjcY7lYMb/cIEpR5e3CahwRiqbMxfkVs8ZSGYsfHUobUhJuXLtOv3G89tobjJsRawUqQRshkZDKshrW5BCZNVOUaNrWMps06Aw+ZwKFT55J+BjJSkhacK5YPIacyBF0sIRVZP9ojxwTujZMZlOapkHJgLUWt3EEP+BXjolpmXQTRtcznU5wfgQEpTR1ZXAuMIZS8tC6NAdNpx3GaoZxIOdIXddYY4mxUFRD9GgLdWNJImhdFTGxwVFZy+ayp1+OJJ+wbYU2QggRay1t1yG6zKhDDBgpHHqlMsmNVKIgBXwMtEdT0JqcMhrF6BxxBHEav9aMi8x6GVhcjPQrT22mpFCSukuB/D4dqrvkvsMOTxBaa2YHE1QFcV20y1MqSoKVVSCFzRFzIiYFCYzSGK3JGJJOJBVRtTCOPahM1U2YHUzYnF+i1Fb2NUMMcWvCXrw9vY8kCjOkUZn9gxlDf4QbPMOYqZRFlMb5Ug4oM/nA5cWcSTshJ0XTaFIuZYbjg0MuZ3POz+fFBUgySgl6a/c2Bk9wjhACja4xyjCdNKgMq37E5UylDS56vPeoSqGtLa5CSdgEX+r6wSBRsM6Qc2Z5tmKziuDbogM/eoxSSAYtslV1rHBuIMZIVVlge0ex9eQVEUylyTKQclGaDNGhFIxjwBhTulEpXaEhRsQodKWKU5kf6De+GJKoDqM0CkVVVShT7gq6dkLOAiiMreiHNcpk6k6TJRETdEaY1JYQBiIRqTvm6w3J5sLwESFrjaos52Pm6rxntdiwWTtyUkDRyDFSbAqfeZu9HXb4qEKJMJm1+OTx0ReZ2wjqkaQvCaXLAmGmWA5qStu7FiHmgIsDzbTCxUcdo5H9gxnz1RrnM0YpYih2grayGKPwYWS97jk+npUW/+DQ1rK3P+V+dUZYbzBaIarosIftF4O1FZvBM1+uOalrgi8lnBwTjW147toN4pCZXy1RsdwtYDUx9tSVIQms/JoYA41qqExF21Yggt8MhJSotKB0xRAdfmvFKEoV/Rit8SESXeDq7hW6VizWK06ObmFMhRLP0I+00uD9iMrC6Ea0KkkvpUgO5UtVBJSUBC0SCWnA1Imjk2PGwbFaObwP73xWzjlijFhrqBtLVIFEIkvm4uqczcrR1jOGUOQEKlthxSJW4aJndJ7K1GTAbJO9qTNKl67XqjZMGotOgeViQZh4AkLM0FZdoXY6Q+hhvehZzTWruWOzcVhdYVtNW7d07YSxL/z991MOkw+YGLHDDv+/gogsgS/+ric+ebxjU/iUsYvj6/EHjeNjOefT9zqwm7nvsMOTxTNhISnPiL3iLo4PLo6nzXPfYYcddtjhCWCX3HfYYYcdPoLYJfcddniyeFacr3ZxfD0+8nHsFlR32GGHHT6C2M3cd9hhhx0+gtgl9x12eEJ4mhaSIvK6iPy2iHxGRH5tu+89bQu/xdf9ORF5ICKffde+D9ya8jFx/Ecicmc7Jp/ZKqc+OvZT2zi+KCL/0rcohqdq1blL7jvs8ATwjFhI/vGtL+wjqt3jbAu/lfjvgD/xDfuehl3ie8UBxWryHb9cgO3n8qPAP7V9zX+5/fz+oHiqVp275L7DDk8Gz6KF5ONsC79lyDn/XxR10W/muk/MLvExcTwOfwr4hZzzmHN+jaI2+k+I7P0+YniqVp275L7DDk8Gj7MC/KCQgf9dRH5dRH58u+9xtoVPGr9Xu8Qnib+wLXn83LvKUk88jj+gVefvC7vkvsMOH038QM75eyi3+j8hIj/47oNb2eQPnCr3tK67xc8AHwe+m+K3+59+EBf9RqvOdx97kuOxS+477PBk8FStAHPOd7Y/HwD/E6XMcP/Rbf432BY+aTzuuh/oGOWc7+ecY845Af8VXyu9PLE43s+qc3v8iY3HLrnvsMOTwVOzkBSRiYjMHj0G/kXgszzetvBJ45mwS/yG+vW/ThmTR3H8qIjUIvISZUHz//0WXO/pWnXmnHfbbtttT2CjWAF+CfgK8Fc+wOu+DPzj7fY7j64NHFPYGV8G/g/g6Alc+7+nlDw8pWb8Y4+7LkWN/K9vx+e3ge99wnH8ze11fmubSG++6/y/so3ji8Cf/BbF8AOUkstvAZ/Zbj/yQY3HrkN1hx122OEjiF1ZZocddtjhI4hdct9hhx12+Ahil9x32GGHHT6C2CX3HXbYYYePIHbJfYcddtjhI4hdct9hhx12+Ahil9x32GGHHT6C2CX3HXbYYYePIP4/nEryNVhytI8AAAAASUVORK5CYII=", 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ghIe+/u0rvhGLwCeBUyJygjRAfpCvVr0XI+JDGrcEVA6CxitNZgSDJmpQypBHMKpEKY2JgvMOHyzRNuAiIUYynUH0BARLJGio7RiUYbB4gPXNDZh4QsezuXuJpeO3MVg+xPb2DJkotG7RMX1a7S6iNUVh6PZyWp2GsiO4ok2jA9vjbcazCUUTMZ1FvB8S8ilReZy1aBdohRbOV1RhQu0dMtWYosDqjFIbHI4QLIjBBQ3BowgoARVlbt2BE0dmNO1Wj9FolyBCDJHGOVRu6JgOpq4JVQNWE7IMn0VmmaN/fImtyyV2t6Klc6pgWa826bYHFKZg1AR2L10kyzT5wgKlKbDjy7hGcHWNsg50pJoNCbaht3+Z9QtP8PjHf4d66wq5KXBZm2A8aBCJqCxDggatyFoF5Dn4iNcKpQ3KlHjfoPIMcpUWx6jBgg6OIld4yYhZQxYiwQVENJnK8E4hEog6eTI6UyidPDhCxEdBgplbS5Eozxki92vWbRHSwrhn3auINoIy6f2FEPDWI0YjbYOKCm0KYgiYmKGMIighy3N0ZtDB4LRBSjCtnGBdsgYbi9Y5Mc8I3mGyDpkWaBrirAYf5p6rIkZBtEY5jbEVOw9/gdnGBide+VZWlw+xNVqnmg4RHRHr8OtbjEYTTB7JuhmVb2i2t7HW09t/gNIU1NMZ4+lu8hBrw2x3hi5L+ieWmPUc3moK3UdZC8ajWjlFUSBKUztHLibZ71Fot3oMRzs4566+QxWFICFZ6ShQAaM8Njq8sngMlXdYb3F1TRSPCpHSd2jpFjiPlRmmnRG0pWrAxQU2x1vU9SW6rQ6L5SKmp2nnU9SkQVSOHkGuC6L3zMKYsRvCZIK2OYPlQ2TtFltXHiV4DxMPM0t/+QBoQ+3GBJMM9wyF8hFEY71N4xuFznJ0ntHWBieREDwqeBpJdoxRGRGFCxFFRFnwdQAiSs09gq9TnvNFIKade/48iV9dA/90XuX2TL8BO/eCtMK5kDwA73FRUBKJEvESCCYjywxODF5pJGRk5JjQIzqLbRzeRTwRHx1eKTQF2lim7NJeOIK59DhhY4JTOSO1SVM5Dh27jfHFDUQFKHJUO0N6iqwQOp2CrNR4BXVUNLZmPJ6xPZkyqxsWlNBkHpoGk4GWQMgDUQIFGh8GiC+Y2QlaR7SP+FAz8S69Qx/w4ojY5PkrhU6+dWL5UwplIt4HJBq0KphVNZFIADwRFwJ5WaKKEju1OOvwITJ1U7KVAftuvYFzH/886vIU32szNROabcugt0J/5QjeK+JkRNlpk+V9nJ3gZ7tIBG0KKBZYPXUTZmGJ7dE2584+ynAywqCQTKANojRBebJc0JKlkEGhUaUhoinmyuxjgI4hz3KQnFBbQuPIdYeyu0CZtyiKEucsO5vrTDfPEmNFVmRonUMtNLMZsXaoQqMzk8JkRCSLaeB4TfAepQTG4ZnU7xuq24hCFQUhxBRC1B60JwBEQWMI3uFqhxhN3ioxRVogbW2J2tBePsrC8irGZNR1RdXMqOpdGjfEFDmqn0Fs8DbgSYtloQwQEBRFZaD2xLk+2Ya0QAcIU8E7xXAy5tzZRzG9AXqwxMqp27j08ANQzxAi3luyYkDW60AzJIwbsoUe/ZVDuGlgd7RBTUPuLHE0pSZy+CU3EFY0UztGBVAIpp2TtTOCQOU9BgWeZCAhtMoCiQbv5yEZlxZKEQGt8TEQgyVicUjSJe0QcRA8Ogp4hfealurQkpIiU4Tco+ZjUzuNaxqaxmMrYThtsDOBjqJbtMijIhRClhk6pRAnnqZWqJAhkxwKR5a1OHj0NprKMxxu4meesDHFuC6dhSPM2EUbS5ACD0jwaDHomFHkOXluEJPhVMChqZUmBodEobIO71JITYIQYiSEFBpyPoX8xAfwXwz3fT3yDckJxBh/ncQ7/ixFENQ85g04D9YhVhF0ij8qZYg+eQqOhqhssmiU4FWgiSo9jBFMplA+JzaKLKRQQpBA7bc4unYT1dZNXL5yH4WDXCKzyS6La0eRhR7WbqILiysjtgCVa+w8h6AUzJqaWaiZTGvqyiaL00Sm1Ta+NaPVCAowuU5WQAYSA2VlUE0LZ5oUs8+TFetdAO9BaYJOC0B0jugDep77EBHEQZQGG6EoO8zqOl1HK3CKEKCmJuLI0Cit0BIQYCQ1rYML9A8eZPbwBVA17eWSajJme+xY3HeAfYePMhlv4ySSDbo4JcxcTawcvdUb2Xf0JAsHD3Blc5PtS5dw9QhTlIgRfB4IukGipJhuVGBS2ENEEx3gPTY0hNJguiU2akJTI9MpcasiTDx1HpC+RgYZWa5otfv4JiCjKZN6nXrcoLUnNDXROvCB4CMugCkVKheiTjqAiimnFMNzSiP2Neu2ACogWkDHeb5LUF4INqbJo5rHkj3YUOFtxHuPVjmd9grd3jKtdp+6rqlrR707oh7uEpoJsa0JyyWxrYjdAiMaN6kIM0eu5h60aGI2zwPYgJJAwBJ0JJYgqsToAluPePyR+1ncv59DZ15Mp7/G5ScfZTTeQkqDWemS9To0u57eoQ6d7gJu5tm+fAmfVbQ6OW63xk0i/RsPog8tMJIaLZIeWysaPJNZg2AQNE7Z9I58QInCtIXLO1eYzYZITHHwOA/vehFEz/MFIaA8uOBBB7RRqBiJTcQ4Q+ly8sIgRSB0QJmUk1OAaxwza6mrbXARcYbaNez6Ea7d0FIFJheCgDUKl2ssAVdGQmHxLUeWLbGw7xjb21coJNI4aJywtnaG/WtHeXj9owQCymu0gIghyw1K67QQx4CLNTEGfFBEGxGJSEg5BfEpzxGCS2FVL2BDmhtjipTIl2ZA/9DygtgOTkRQYnA+xblwDqxDBZOStSESXQoHgcf6iqjTCqiR9DlqgteATy64FGiVI9YRRYhi0Bp6SwUrh08zai4TVQ2ZZVits2gO0ltb4PLZ8+m+2qJzBRLxwaOD4EOgsYFp7XEOgnPJ8gCCVNjYkLsMLeCxNNFCZtA9KNst2ATdBGa+wUVHEMFbT6YNShSeFM5Kub95UiwkT4AIHkdV7bJ/YZHK2pR8tqScgUteQRChDhYlkRQdcVhGjLKS/o2rMKyJrka3DXmrRbXtGE62WewtsnJgmdFkytTuIG1F/8ACqik4duwM3d6Ac+ce4fxjD6acQ2NRwRNFoSWHxhJsQxRFzAxiIh6LRIWgiSHiTSQugGkbxArTzR3isEJPBBpPmFma2ZRq6xIjo1Mi3Udi7dDOQxT8rCaGJuXaYkrS+eDm9zGEliASwURER/Dxi4m550OutgWQMI9uCKqJ+JnDzQL4ObAhRoJviE0KIWnxVLtb1NWQLZ0Snd55lHWEJlmK0Wqcq5F+QXulg840zcwhOzXBJSMp4ImSvILoItE6YgyoLEerDC8OFTxhPKRuZpzfPos6eYbDh2+kO+jz5JMPEPIaWp6J3aHTa9PrtJnOGoaTbXSp6SwOQCxmVCL7ClqnVrnSbCKTGRlpfCdjNgE/VLQowJsUJlOkxapX5mzvXEBHh44p+RxCmD9HwgGkMSKoKEiIWGuJmUbFiGkMRTToPKe91MLqGmscBEdOhgkKHyM2WoJo8Ck274PDuYzp1KMKS1QKQeGDB4lpLtCWGCw2VCytLYAxjOp1yCxRBdoLK6wcPkNvKUdvg/ZtJLo0f2WGKJHGz+YRHI2PHsQTveD3EsNeMBjEG0JIv1Uxzt91mhclCNFHtDbJQ/o65QWxCKTsV5wn8yLiItopjG7hM08kEKNPrl+ICYkTQVTK+kvj8USst0TnkQgqWoSMLMtpdTtEZbBVw4NPfpKOPoLOSsbNLmJnjKsNnJty8OhNrF95PM0ZNt0rytwamU/6vq5pKo9zDdHVCTERPCFaEIu1nqA0UQliMpQ2mFyhgmBqQY8Ns8oSpxqJFmUitfJzN1cIMaF1jAjRBUIIBElJz5gFXAyIzghRUdukOjp48JYg4KJOfYPDN03yKkLEtypmxSJyfAm1W7G0b4m8KJksVNSuxukpLneU2tLUDUXRZ3HfAXTs00zHPPz5z7F58QKxdvP+CMSqIipJIY+o8LOA9Q3RGHQ2n8RDIBiFaWvamcLWERl6tDfIOFJUmhAgqOTyqxCg8WBTiCsGT3ANxIDOSxJAKKEnhHlINArBKXwlCb2VQdQuGUn6mwYK+ooieo4GijENbgu+8gSnrsa8ZQ8RJQllJhKxTQW2RtkUO1dKoWJAxYAWISpQQZFV0Ajo0iDGkdWRPMsJBNzUI86BAh8c3nqccykvIyotTPMFJegJ1Iqo4NwXPs1s5xLLh05w+NRpvAzZri5S10PKIkv6Yhv6BzoUpqAzKGnqmu2wSRiUVEVDPdxGzyxOCWLSxBwxRAEjSc99yFJYF6HIFKIznG9QMeLtHAk2D4lGk3Q+AQg1PoKLAeMjUnuiZCBCXhqkK+iORlQLFwPBCzEK1tn5xB4I0eAbj0RHjDU2QjR5CiPHAm3ypOt7aBwb0txStDl49GacnzGabdDYGVVT0ckGBOV48MlPYpVNOcXgqMYTXFURqzRGo4CYgApze15pYnTEIOAFrxzMkUmiBa1zdNQ4N0kJ5D10mVJXUVVfj7wgFoEY5/AxRXJxfCDUHu/AKgcElE7WsHiFRhNdBEkJEkJScEFhSAMmLw1aBIzBqkhlJ8Tg0F5RqGWULvFTiNZi7ZStnXUO7D9Iq7dAPdmmmta0PESjmFQ106aB6AjOEl1DdB7nUqgha2mMISF6ELz3RGWISqNFEUNkOp1S+zENM6wIHdMlOkOFxUdHICB1cou1UpgsWU7EmJAcREozwDVQDcd0shI7rQg+EDx4yYlUKCLRuwRvU0Kr10tut5lbNKttWoOCvN1CI+RWkEaDRGajXRpbI0WLVtajzAq2Ny5y9gv343Z3k0saVLL2g08TVox4a/EhJNRSTMk9CQpXlLjCkGsDxqcFdFQRZ4H2ymFM0SPMhnjcHM0V8M4BCYJLTF4YMSTr0ShC9EiYQ+YABLTWCApXe8QHspZCl8lD/JKdIJ4nUTKPSnnBVx47C0QX0UohGnwIX4Q8zxcBZRTOzhPd1qJVwIukBCTzJK/MLUgxmLJHpkqmV85hqwm6LJGiAF1Q+RxTO/SsmiPPko7SNKkNc+xjCA7xmugC3tVcfvwhNjcvc+SWW1hcWaLlezTVhOHuNnlW0Cq7EHXKAZWKvCxpZwNmucaZmrZuYfIuEU81m+GrGhGHaIMlIpRIExDtUFrRafeohmNU3SLPoGp2iVHQRkMEExLyK4SQ8ipGIVEwZJQqQ4xh4seMqMl9iziFot1CK4VXBucD3juSC+kxBjKjGc4iLgYUDRHPzObUMYK4q5549IFqWuODp9VbZHFpP5cuX8DaKdFafA26V+LilKndpZIZtbWUWU7WKcmcwcdIUyU4qrcWbz1a6RS6ROberBC8I+IBUrgYBU4T6oR6E62S8Yl/4eYEvmaJMcGmjCI4n0IjIeCbGnScJ4gSlt5ojceSZRnOzXHCAVplG51nCBleFJJDcJYgARsDQQuEhCZq4oysXEJ2LuOriqzdZlpvI3KI5eVVLk63aWzDZDqhKEtiDESVrMwgmmjSACxMTqk7ZDqiJHkhtUuTr8RA1GnSIkRcXVPZGTGHTrdDGXKqYURqmxJRIaKjShaZT2EDr5JiJCiZx1WBpe4qzCz7l1aZ7WyBQNFdIsSM2m1hlEeRE2KgiQGUpomChIALMwAmswl+MqbZHjPdnlDmbVYPHKTTOkijK7wyNMOGC1ceY7q7gfgaHITGYpRGtEaMQJ4l+KH3BGsheFA56Ba6aGEW+5hOBpMxs60xYTojOk+p2xidUDB1iEAgWDe3+sIcIeDnsd853JPU59qYa86NKEkwihBjwnrbQGOFIpboUhNVQL55JQL/lUgUcKCCxlchLYIugiiC0gkqCFfrPlIi3lyNe+MDqJT0jjElSeeFIWnBKMAFTaELjFFoG6mGTQprtAP54gJldxE9scQwxMcZ+CmEhtA0qCxZzybLUEYSOs17nPVIbpBQs3nxEWbTZUzZZmBW0KUjz0tcXbF+6QJVPaW10KFY6lIpT8g7ODtDFDRiyERTlCWZJMBDQFKbzSJKHHW1BTGwf2mVzfGUpdYy4/E60QcUBqJKiK8GtBMyyQBJXeNTvoxCU/ZzUF3qZkRlZ0itiJIMCC8a8V/0erQBLZbMCJ12hyplb4laaESjQiSECgmpDqGuKhrbgMDy8ioiwqTaQfD4qkJ8SVYs0jDDiUu1EVqooyObw3zFFGQadAxELL6x+KomqlQRZIxgrU1hKO+vvnOlhKap0wIgKoXFjL46Br5eeUEsAnsFEinsQUr2Bo8OEY1mLzYuSkEQTKbJTIYxKc4WrUWynJDrNEjC3F3O5qkTJUTn0UqIwRG1pTdYZTpaYTZ7gtw3VPWI6XTCwsIy6xc1lXVMxgmDqyVZalYMKisIpiTPhYKI2JhcuRgIzkMDIVhCVRO0oDOZ1zN5umUHlbWQPMM2NaojdFRBcNAEQbRDTIrjRi0oFciioIuEDHLR4v02p4+9iE6vzcVzj6KyjAP7DzHc3mZrFIlag1LJ63Eu4fUlI5OAUQoJjnpjitupqS/vYsdTbNlhuXuY3sJBnJ6ysbvJZLpFXe+QhWTxee2o62myzLVGjPliUY9z6JAWaW9yypU1il4Pb2v0zoTR7iaMa0yIeFF4n95JFg3eOogBtwfnhGSZxpA0I8ar8Mo92SuikrknEv0e2maeB7GRerdC1QZTZs/FOPlDS4zgZ4KrGkLliH4+6YeQEEOk503FUV98NuCLz73XHzGm5937DWBCBOvISAuH9ySYaRPxbkYdAj2nUHkbfeAgajTCXbmAaSwxBKL3SGawwRNnluhcQqUpTdEqaZVdJCiGo0sUzQDTWmNpYT8mb7O58QSjx9bx1QTfbWP3VcSFgqJXoA14nwyRGG2CyRoDykAIZD6y0M3pL+3j4sUZwVkOHjrGYDTlocfuxWhPaRbRWuGVYCWSBYX4OC/WihhvyCWiWhBLwRWRVt6lzAqCnRGixzVVQtDZiPIRFQJKZXN9scSoyFTEFAU1QhMi0UesrVAxeag+KibjMc46SqNZWFhmOp1Q1UOCb7Czik7nOL2FVZxaJ4pDKcErEJMiAQn84gmSIOtKmbnHHDFZRhYFwRFDwFtS/iakZLlG40MqQtUq5bzCvEDwuTBvXhCLAIAOCTmRwm8BvIVJjck7+DIHAzkg0SBGsBIQNVdYsSl/AEQV0goqBqUyPJAFoatSIVJWeHRWQQy0y31Mty4RJzWxNWHSbLK0cpSi/QB2ZxtmDa6oUCYDQrJ+QyDTGqNSot5biKomuJBcOJGridNYJChcrjQmzwkqhTlcsDjXkOmAtCBYhfEa8TblP3QEo65i3lXeI7qI8hOUF3qqTbffRxvDpJPjjKMKYxwVwSskpvZGnZLF4hzWg5dIkQm6MEw3d6l3LKoJ1LMxFx55BN0uMe0+Smm6ZYmKHSbjCjNr0M4hIRCsBxcwISImgxCIc88FZcgWF+kdOEAUx/iJc8TtHQxxHs7RyXOLER0MIKiQkDBfKpLmvL1kmUgCBYgiSKqUVHPvMQ1m2KuqvVpf6SNhErC1m1ddPk8SInbXpRzKvJJ1L4wFXyz8hPRMISZjSIlCK0PwNnkA/mrAiC/+gmTcaAUIOpjkWWQaTfL+mNZUkwvI0gKLx28k73Zp7IxmvYaQwmeiUqwbZ+eLckRlgnKWMKtxktNZ6tAuWyitqTy4zUtceOQR6u0xOkYqZ4mmor2vQ8yE2kZCBFGOYEB0qgr33hGDRRGowpi27jHt5HTGkX6/j5qACoLSOVF3iEbwdjRPsAcgxdIlCjHToD06C0TtsS5ijErIPF2iQsBZRxU80SWvOyiFLcIcFVTTeE0dQBsweg+oFBDxaW5B4VwFswbdRIqFPksrR9kabRHDJM0ddUl7aQ2dBXRWsUwHGzVWBeoY5sWsc6SP8vMkt0ZrRZAGCRGMTpN+liES0QIVgANTRfykBm+vsgvomHTruQA/vyAWgRhT5lvl2dWEn8SIm0yTS7jQIXY1jYkUKsXh91APCTcaiOIRDHvsAN4nyzI3Ga284NjiftayFWbNDlemG1zZnCBZgYSSZjpF25rhbMj+AzfQ7q0wXd+CSQWZJvYMJs/Rc0/ENRXWKXA5yucpWRn2Cjg0OssIpHgiSuOUYL0neoc0DU3TXKV/cJXD6BaYjOgNtpoSG0dQkZgrhAwpMqTMmY5rsnbOxdkWp1wXyRfRvS7j8ZTaeyRrkYXkNcQ5PYTJMqJX2MbivccDTiKz4YhQW8w8UTXe2ebcI4+weOgoWduA0mQ6p9BNQqSgMSbD+ZTAzYxBjMHtPQuQd7v0FvoEXzPcWqeZTdJkTYqLi9nz69I9s6Kglr1K0S+lNACu0gRoUWiT4HVqbjXvTfgIX9YikigIAWy4er3nQ2KISLDz9snVhl5tr3xxSpc5AECJoLTGGIN3HhfTIvn054gx4kNAmZysKBIFCfNwEhCbkCZiImE2YffKJfpLq3QX+oxnY+rh7tVwQmZMMp58IMaIMRkaA85TaEWmc5BE7bK7ucH2hacY72wTU9oCVzeE4YicpfnDKDKjyfIM0SFRlPiE9DM6Jyih9p7xeIrurSHNCHGBi7MtpGwxm9Z0yi4SG2LlCN7SNPMFIBiyrA1GEULDZDbDlAbvFRNryfM8JV9JXjUkzxg9rylRQojJ6hYU4nPcbAamJoZAHnNQGd4oXJOMUSYVykK7t0LeXWB45UkaW9NMayS0EVNSxwlr3ZLV9mHa+QJX7AZPbl9i5mu8s/g9Coi5cxvFp1AzFgippiVGUNDECF6QsafameCrChXnJsDcI7waFv065QWxCDB36aOdh4N0miyCczCe4p1DNS10rw2lnqMtZL4ARBQ5BIUYNQdhpHJ8bUBHhXHQ1oZO2eLK7mVGscF1oBPWmG3vYzx+ElNVjEc7jIcVa2vH2XzqCbAVzbRCTE70EbIMowvEKbTRaDIETXA+tRVHVIDSSGaI3lFPpkwFtE4FHs65FNtWGkcgekemhbzbRXUVcXeHZrKT4Eje41RCbfT2Dxg3Ga3eIhemI476gtuO3sXj9gqz3ccQr+j097G7vY5RKoWnfETNYZRmTudgg0dlWUKGIKhMk+U5ooTp5hZlt0vWXUMkZzbZpB6NkSZQFl2yvIvzFu9dKut3TYLrzUM1RgQ7HjFaX8fVMyQEJM/YC+d5ACVEkRR+UIIYTdqo8Mtjnq/yRQHBh3mE5Ks7wWlCjPN593nMCShJcN/5ivXVWrK3EAS/xxskz/CreWjIJDSanSPJUIIPkTivohYEcYFq/QpuOKJslej5fWTON+VDRPI2ZauH1gajE9S5aSrcaAyFJu8ugMBsd4vpxha5UsRWC99YVJNyeZJn2OAx80Uszr2wZCMJMs9RDfqruOEIW09Y7u/jxNHTVL7gwnREp7/AjtuiGLQYXd4m1E3yahxE0WSdPq3BAoFAM6wTa4BLFbg+eOrZLN1bK7wP6AhaZcTM4ABvkzeCiwQnZGgiWTKSHIhWuMZjbYNvXJoDbABTsrZ2nPEozRWuqrBjRbfcR2epS925wig2tGY1q/0W7ZBhHGgUPpIoQvZyOaIgKBRl4vRCkZjK01jRVSSOpvjhDKkaIKaQmtbJQ3GO6P0fr5zAHk46RpkjgSIJ85GgiN46VA12qUNWGoKLeMK8MKpA6b3SfEWIexOtIfrIrJnx2FNPMFm1XKlHbEudCs7I6fX2MZ1s4UcTfDFlOhlRthYhbyFNg/WOzDpySWRn7cEAN5tRdgpigHqSYJvJE0n5CB/mYYkIygdc9CnO5wMeIcsyvHfYGDBRiATqUNHpLbHYPcTuFc10d4fQRKL21NWEfW3DOM8YLK0x297FlKucWj3M5bvPIhqWBos8vrPJtKnJmBediEdUxEXB6MTDZEMEZzERRBui0Um55tQMo61N2vuXaHe6THXBznBIGFlaxQDTHVB0BmitqZuaeucy3qZwRQyRejyhno7nCB+ICDrPyNttFAnnjpDQRVFh8gJdFOh6lpBWT/MA9iSGgG0sWiWkiNY6TZJXFyCZpw++zIAoDdjnMRykFWQCs70++aLIHBe6B3WVuQcgkCzHxqbJb+/8eb/M08SApAR7UWDyYr4gK0Sn+hqTFwQizXSarkWkmU7wo0R4mDzVdF2d5RQLaxR5ifceO5vSjHeZ1buIy+jsW6Xd6jKdzBhtbSIuoFBg0iJS+FToWTsLIUMh1N7ivMNIJAZB4nyxQlC7u9ywsEzVTGhdrDl91y1cLhuULhksDtgej2i3DeuzCcYK4nOUEcrBAgtr+7HRMxttAWEO/XRkkiZS5yziaqJWcy809amNYd6DKawUJeU+jAhFp0RUSTWpMa0WuxtTtA1Y63DekWlFzFuUrUWm4zF+NsGNZijfo9fbR8UOFWOsi8Q4Im4+xeX188y8JeYaLQaRZLkr0QnQoA1KFykHUQciCuXBVp64NcWPR2jv5m2ek+SJIgSPcC255NcnL4hFYI/9MlUIJm6QKEKQOc8JIEERZha1G6AO6LbClDmmUNgs0MgMFTQxgadR2mDnpegRT/CR0k2ZUCHeoxzU6hJ5dpT+4iF2Zw9gZmO2dy9x7OjtLK4cZPPCNsE56mlFS3JaZYvu4gJ17olZIm6rnUNLjlFzVsIYEfFXLayIoOffB9Fkc6x7CBETDEYbsizHxxnT0SUk69FaWWX/sRPsXtqiqw22aejSoRqskve6DIDt0Q778pO8/dAb+NTO73PPU5+lCuOUfIoJwhlwiIqIVoSQoaJJlqJ4okmL7R4HUfARspyoSiZNZLFf0OsO2NGGxk6Y1BuIq+iySpYViHjyViuR2NkGW9U4W5MFUhUxGh2ETGUURQvVaiUakKYhzqvDdVEiRTkPM9VfMkNea6VGIHhHmAUkyxKnkkq0GjKHk4rR85iT4BubOHUyRbbQxW1Nnhe1BhJ30kIHa0dgIyoz6HnYkxCJzhN8qoVBqcSRUzVYayGE1FPX9sW1C52AMRlSlOiiJM6qVEtBwOQ5SmvCbAaVxapIUBFNqhuwMS0SWVkk/poiR/DYusLamvHOOnE6RqtIrlv0uwuUumC9mRJVCVLhXY0iLfZKK4KZh2gJKScTHASL9akGRmEQpREJ1E3F+fGMlxx9MS9deDUj1WVn/CiDxQF5r0t/sEqfDid6h8mWc8beMdi/yO5kwnA2JNoRKnp0lmNdAOWIKiV9M5URmgrlE9JMtKQwIuFqH4poRGlcgDrOEG0oypyWySk6CzTbO8ymNfW0IrjETbS8coC8tcjF9c9hZ2PsLDBYPETINI06h3IKASZqxq7LGIaGGpdQScREBOh9goSHQBAPLUVuC1TtiVUgTgOxCoSZRYIiohNprAh4kJjoP1BJH/741AmQCjvjHjNeCAn7nBWIGJQUFEWPVmtArkpm421mkzF6MUPpHKMCtUpwUNmDFjKvPpY5jYTAqN5OL8An9wotqE7FvsWDTJ44i51MaBFYbHU5sP8E25cfQ5oxeQatdkk+KFEdja0trpphpx5rhTxvJWgeDeiInrt0IHgnKMkQJWi1Z7EGMpWhck2u81Q04iNkMBpts7u5Tfv0GVrHlvHbE3rGsLZ2gHq4Q9nqUNeWejqkM865YXwDj00eYXV1nXrzLMFbtIJoNN4nVkLrwTSeWjtyraFlmLQ0cVolRYsRokreQrmIUy0a71OoqOxRyxiRgESLm+6mhLiviKLJi4Ky06Fst2kqS4wuxYEzg60t+JDIzwZ9ImCahuAcxkSyXKPzjGae1L2q0DLXgThHdsW9WGrEWZcmni9ZML7IkBlEAxXEiOmV5L0WbvPrZ1r8Q0uMFL0WUnncsELnBTovUdHPmVavSe1FUpjNcxUVFUgLYkhFIwkjfs3DBwGdZ2S5Rmyk6HdQ88IsIV3P5Tm6VZIVGdE6bGPJxZAXGSihaRqmwyESt9G6TCSA0c69tqQDKtPU3uNUC9VaQvQEX1fEmPJyTgKhLClbBkKD9Z7CC1Zn5IDSkuo5YgqNKJ2xtnyQpck+bjAneWRrSqWGlHlB2eowWFplX38B2RwyyaG1NMA2gcsXzlHm0GsZcGkMt/ol1qfcVYxzRI3O0yIbY8LaZ/VVz9Ej4ISSlPeo7Bg1rYlUmKJFq6PJByX4kul4iHeC5CUH9p1gsdXligTsZIIxHfYdOciw2QKd6g+CF/x8rlEZmKhSpCDG+ZyUOJGiCDEqipChnEKNG9y2o4xdllqLNIOK2WyXuh4RYk2MCekX50VkPqR8z3Ph474gFoFU4ZojuoXJ22SmRJkSk7XRwaBVjsmSy1uYHGUhDh25ysEqfB7wLUH18qv5giwzMEdjZEoleKTWZFGhAimurSB2Rki+zMLSCbY27qEeXmK0fYXV5SOU7QV09OR5TsgVDTUxzGiCx84aqIUYFG0TWdMdKq/ZCTNqcVffjskSZwnzBGgijldIEIq8JC/7WBsxCkwrYzI5T4lj5+JZ+gcOEHs5rLW5YncZrC5SmDa5srSKDt2tPv3JEkcXT3H3hT8gz4VAlhgKRRF8qo1YlC6mnvKoGqPwZAH2lQus+3XGMaDV3LX3nlyDUYGqmRGVUHQGVNkWrqlQShFcwM+aRCUtUFU1ut+jv7hMa6FFI4FsXvHazGbY6TTRYQSPNhmmVVLqVJwW51A3P48bi0pc+KI13iXXOe6FSFJ56dXEWAgJQhpjxHmPNE1KiCME5dF9QzbIwaX6jedLovPgLNkgJ+ASVHSWiIKDvzaumxY+tdcHCMyp0UMIKWwzT5CnPR+S1+zDHC4okTyDfGFA5X1K1jpLJGJaBVm7Td5qpdBJgDwqlJ0x3N6kGo6I3qWifQO6lad3rQSTFRTtAV4JVTNL+SYVqefnx5igrt2gWW0tsBsNVkBMznHp4so223EMKqJ02utCx0QmV09GHDl4iv5ojW5nm/ZSN3FjFR0Gq8KV8Q4cWSDMpkjTsHvxIiUVyhuKzj7czGIDFJkgsyGVn4LERCanmBeZxVSRHubJdYEyahZoUeqSK6ZiXEVCHajF47M8jXFqQq7I8xxcxLQWWV05ymh7nXp4mVBblpaPIx0hZqM5lBO0MmiTIyLkkidIatgDJ+RY5slcHwkjSz4D3WjyaUEzEbr9RYr2Mso1GNWnldf40OCVw9kpwVdYW+GaGRKmEOuvW0dfEIsAolldvZF2dx9IgU4mCA4hNBFCSO6xODqtNrQ72NmYViyoN6dUwyvExYL2TYexRUByocxyFBFtEtxKS4aLMVE/OE0MHiceGyZkdovFxYOMR2e5vPEYebaPwydvptNeYGyHRAO1t+Qho57O6LWX2NyuqaOjHzyvWj7Fj337n+TclXP8s0/8ax4Nl4leiMonCy6SaglURFSB0SbBHNEQNe2FZdbW9nPp/MMpJ4LHT3cZyH4ePfcEu0WX1RtO0kSLWM+gvcRC0SEfR66YMYvVMtnU4UxD1IrKCW0iy3mXvhScXDzMS7Ib+PlzH+QiIwob+b/9wJ/lF/7PX+De0VMEH9Aq4ppI2UzI4wJNhEzldAcLuF6P0Y5NBXsh0WUESTmbGDx1XWG9pb24Qp5nCRFtm9QH89yNNCFVRau5lyYeg5AV7USvsYfymYcF9+gU1Lx6Wkic8mqefwhzPH2cQ+VicETnE1FZV6O6BUF57HaV3MznS3yk2h6TLZSoriG4GsZV8nyfFtoBUPNNjcK8MC4ScY2dL3qpf1AJQhrRiM7IijYGwXkLUc9DMTFRTCOYvEWWtzGtXor9kyDM040RdV3N6csThFi5mlAnGg8lmk6vR3dhAVROEyEn4Jopzk7R0SXqY+Bk/zA/+QN/mr/52V/Ei3CQHj96/G3c457gka2zDFXNZjNiCmQ6FUTmzrFYLXPFjMnzwMLCEtQ5lXWMsETvWH/qSVw95uShY2xPd0kMWxkRw6Ebb2F9/RLNaBNF8iqRZOBFX+NtCvuEGOc8QwbRkeNqjR99+fs4snaEf/bb/4IPDf8LwygUPqPfXqKeVoknzFuiSZ5Wp7OA9w3nn3yIK+uPkhdrLC4dYmLXmcYJMu8vlEbN82yFCD7aeVJYCAiYhthEsloxfvIczVZNd3CAomwjmSZvdzCZYTKb4mzESIcs66FywbQiSGJIJtZMx5e5cuHer1tFXwBF9WBMwfLgGN1ymVxKcIKvE5Om0vOYHhETI8aDNhntQZ88N1TbmwzPX6G0LVbbB+lIF41C6QR7MzrxkGM0Jk9hmaAiHiGKQrRQhx10y7KyegrrNDu7F3HOsrZ6jKzVw8UGvEW5iB07FhbWMEZYCwu8YfBm3rL6Y+zee4hT2et51/HvomVzrIo4NCGG5LoRQKcJURlDq9MnZlDHMS6OmDElW+yx/8RpOt2DtNur7Exqbrn5NhY6HUz0YBQqN8yaGbQ1o30jNpanrOhVjmU3glcpvuwjLlR0dMkP3/Z9PPz4E3RXlnjP2uvoVcLuZIet8Zgf/d4fpq00V2FryqB0jzIfkJV9otaoVkax2Mf0E/7b0xBlblkrmbOFGqJRgE9Yd4SgDNFkBJPhRCEuEutUIVlNp9jKYSSn7CyQle0UK5/H9YNLDKESYqpGdinElxdFwlFnc7joNQgXIK0IZU7R72HynGZY40dfv6X09Yof1TSjGp3n5P0elPnVkM7TYaGSGVSWkRdFCgOl2Nu8L0Lqm3mCXGWGrGxTdhYwkmMrRzWdpirU2iIu4kQRTJbehUqblagYgZQXksykmpT5bn5RfHrHmaAHHYrFPqqVEbUmK/uU2QCtu4iYq4inltL82Pe8j63xhN3JDr0K3r36Onqryzz02OO879bvpWtKXKjSAuU9eMXR7CSreo2N5SmjtRG0FbNmhkruKCZ6Fjodbrn5NnYmNe32Kp3uQfafOE222GfGFBtG1HFMzKDV6aOMwc/HWmA+9mKia7Yq0rI57zr2Ds5kr2f33kO8ZfXHeOPgzayFBYwRFhbWsOM01vEWFxuyVo+1lWN4Z9nZvUDjNCurp9AtRx12EJ3mEk+aW0QJJs/AaIIEjE6QaqUTUqgtXVbbBylsi9GFK1Tbm+S5oT3oY0yG8WDifP8ALSgtRB/wtQMn5FLSLZdZXjiGMcXXrZ9fdREQkX8qIldE5PPXfLckIr8pIg/P/784/15E5H8XkUdE5F4RufNZNUJM2v7ReYK3+GCxrsYHRzQBlQtFnlFmKc6ZFTmtXg/na6ajdShyVo7ciIkFccsm2mYPuaQ4t9KGxlmapplvORfTjj4RlDicmlGFdVaW99NtH2NU7bK9ucWBIzey/9AJoujkiTiPn1WURUEn7/LeW97Ddx39cWaPrLGz0aXZ7fDqA6/nhBymdKBiwvunPxJ8VNIWdgv9BXSRkxlhoZNTT7e5/Phj7K5vsXbiNAduvRNdLDC2iaZhd/0KGQn+11tcInMGhabcKsl3WxwLtxNroaobom/wNJzfvMxAr3Bm4Rj/5vMf5I1nvp272jdh6sADFx/jjjvuYMV0KSVLm3zs65OtGWJrjNUbVNkGdjAmO2Ho39yjd0MXtVYQuiYVfqm0GYzKTLLAgsP6BucbfPSJSC7PiVphbY2fTvDVNFVbzmoiimy+vaHW+upkHsIXsf0hBFzTYKuKYO28xiDFlzNtUFqlHSVV2mmMhRJV5tipI058KsJ7HnVbAOM1ceJxU4cqc1goU1tV2l5UaUWmDVrrFCsGgrXYqsLNdRYSKuiqRzDvA5MXZK0OEUU1q5nO+9hPJ1ibEDI6z4jG4KPH+SbFz2Mi2Nt7f1EplNHp3a4V9G7oMri5R3bCYAfjpAt6g9gaY9YMxb4+qpVTSsaK7nLHHXfw4KXHMHXgzvZNvOmmb+fffO4/cXpwlIVsjfMbV/BYom+SjtbCsfAi8t0W5VaJEk3mDL3FJVCKDGF3/QqiFROr0cUCB269k7UTpxmub3H58ceop9ssdNIY0kXOQn8h0S5LICYE5tXxp6KjdHBcDvHqg2+g2e2ys9Fl9sga7zj647z31vfQybuURYGvqrQJjbVE0ew/dIIDR29ke3OLUTWk2z7GyvJ+qrCOUzOUzENpqKuhyqZpaJxNBJLakEtC/pSVgS2LCQUrR26EImc6XMf5mlavR1akOa7Mcoo8Q2VCNInlNM2JNhUQOo+hRMnXH8x5Np7APwfe9rTvfgb4rRjjKeC35v8GeDtwav73k8A/ejaNMFrRaRfkxqTNVAiJDljmoZ12TlkWlHlJnmUYkypxd3aHNHVD79ABfL/NY488xPTyNjKCOFNo0RRF2pGpqhuqqqJpmpR8m4ck8B7nPNNqRDQ1a2vH0FnBdLZDTAxTlJ0FyFo0eLzM0Lbh1OGXcLR9KxtXPFPbUOaKcR2xk1Xee/rHeFv7TRwJa4nQTjJiTMUseI0PgcpOaJU5+/oD1HhK32gCNaON85y9/1PsXH6C/UcO0+oucfDgcZQ3aG9QFOisy2p7kVP+IMeurFJMDYe4CW2T5+FshQwbmnrCh899mB+84+2c37jM765/mv/2bX+OI/UiFy5f4tCJY3zX676DV9z6YlaPrJAdKak7E3bry4ymG+xOttia7DASR+yXFId79G5aZnB6jc6+HmjBhYB1jrqpmc4m2KbC+wRH1CIUWUZuDM7W2OmEZjxKpfyhZtqMceLS1qHzBSBeLaGVq4BRIgTnqauK0DSIc4lrp8gxZXkVcdPu98i6OU01w+3OEtoiqGdC53/DdVtIeaNYBdzujKaakXVz2v0eOs9QmcGUBRR5gng6R2ia9Kxz7vin90W8xotQmcYpx7SZEEKNayqa8Qg7neBsTW4MRZanugDS5jC2qZhNJ9RNjXUOFwJoobPWY3B6jd5NyxSHe8R+yUgcW5MddidbSSfqy9SdCdmRktUjK7zi1hfzztd9B4dOHOf8pUscqRf4y2/7c3xk/TOc27jID77k7fzu2Q9T12PUsMHZihADymoOc4Ziajh2eZVT/iBr7UV01k067g0qGA4ePEHZXWL/kcPsXn6Cs/ffzWjjPIGafqZR4yn7+gPKMqOyk0TG53XaJCkaRDIMBUfCGm9tv4n3nv5x7GSVUR0pc8XUNmxc8Rxr3capQy9BuwbPDIuHrDUf+4ZoDJNqF21yVlePEU3NtBri5p4akuYU7z1Nk+aaqm7QmUlzkGjiTCEjmF7e5rFHH8IP2vQOHaBpGnZ2dwlztFeeZbTyMs15nRzJJc2FEoFEiZMbQ6ddYPTXH8z5qstIjPH3ROT4077+buAN88//Avgw8Ffm3//LmLT04yKyICIHYowXn+keSguqKFLIRWbJDZpvViJlcllzrclDjhLw1ZRqPKG2ntbCPopjxxlXI9idUC4t4azBVCCD5BqOhmOmw4rMpJXa+4Qi8DEmHHYINMyY1Vssrexjc7LIaHyF8e4WEttEa+gvdVlY6REEJo3l5qWTqO0OoQGdIlhUM5gZzcnsZRw/dRM3bH+Mf3fh/VzRV2gwRJfqGJQ4JqMtTh2+mTMLN9FrDjByW+zW5/GxxHnPlScf4NLZc5y57aV0jh1hucjxKlIiDHoLdKJhX16yIdA4RV/1WQotNpsrZDby/be+g48/9Ek+8fjH+LOveC8v6p3kP3zht3jHLW/g3a96J+//zV/igfXzvO2Vb+ItueHvfOAX+HxzGbyj1gppZsTdTZpxBNPCtTQhD6g2dNZalP0OSsHupV382FM5j5t52gOh6Oaplk8LIoaSgmY0pZ6m0nfytAHIZDSkzLqp0A9A0tZCSs9hq3veQCSFneZudfAx5XcItEyOmICUBtXO0bOGentKrBJdhJf5ZuHPk25HSfsqxABUAb89pRCD6uTkUhArhyZj5pqEZJP0/F4lmCUu7EVqElx0vimNzLljCIowaajsGDub4cYzaALojCIvKYtibunLvG4F6knNdHcXNxslq1cCvf0DBidXqMrAZDKj3nGoRsHM07gZeVfwg2Vcrsl9QHzk2OAAf/GdP4luHA9sXuCxK4/xw2/+IbKy5Nce+BC3d29k/6GDfOIjH+OQHvDK0y/nn933/8NljuXQoq8GNF6RGdiXl7SznEG+QBiOGKvI8qnTdHorTJ88yz2f/xQqjFkIHRbjErrOuZ076O9fYpRf5MGdB3n43P0o74hOoaJGRJHjWAtrfO+RH+auxVehfY+ZVVSzNGZ1CTQgts3J5ZPcvfMJeotrDCLsbIwZb42Q2GI83GI0vkzRWWR5dYHd0WUaPwPsHNkISMCLSqFMkQRdFU++uISgkcrhrKLM+oy3thgvjiiOHccPa+omUO1OKLo5Km+TZS2CaJrCI9aCnVdLE+cb9GSoIkM9B1Tpf1hfYt81yn8J2Df/fAg4e8155+bfPfNAiRC8xeQBHSRRQEdLNAGJBSrsccmkP2cdwXnybp9sdY28M6AezTh+5EXodsm228aPR9iuZzTdYnP7CjTg9d7jzpEXMVXUEgNBOWZ+h+X8IGuLh1i/8CDDzUt0Bz00ns2LTzIZtVnddxjT15zODtGdddlRaRcs55vE4ZJpqrGiZIW7Bm+ksjv8yvr7qXOLcTl5t4ePNUu24C65gxtad6Lp0SmnvPG7XkxeKD720Mf5wKc/yKwTGI+2uHDvkIMnT9A0DtdYdDtS5po8M0gBynVYyGpOZac425yn1HBlx/HTr/sp/tEH/zGfOfcEb3/Jt/Px3/p7/KtP/lv+xFu/j088+El+7/Mf5fvvfCuj8xu85MBN3P/JJ7H9nLVOjyN6wOd3J9jhFMQRVEPlJqiuojxhkH2L9OpVRptj3MQS/JRQ+xRiU5pWv4fOTNozAWiXkZgnGl3dVvSWc5YO5SwtdqmPHqZp1uY1GVOa4YhqOKXemdJMGmoPojJUVpIVeWKVrWoSMZRGdVrQ0VR1RdweQ2XJSZvbOBPBfk3ooOdUt5nrROYECR5bWZrtIbLSxQxKxHiYgPFpE/qsLFBKE5uGUFdEZSk05J2cYqFN2W+T93oUnTbKGPI8p2i12dpu2Io5ozjDS+KnaZcJMWRyATF465lNp1Q7OzS7O1iXaBJMJ6N3eBVZauEvb1M9vkMYB0rTQYVENd32LW5bOMS5yrE52SUbVtz5spvpmRa9o8v8m3t+k9NHTvLOt76bf/Wpf8to4xzf+cb3cs+5J1A7gZ96+5/hNx/8JB2dUTm4Mb+RhayLqjtIEciNolQa3Vjc2OJRZKbDZz93H33xDDKhnPT5rpd8J6868wqaOmC3CiahjW+doDdssWmfYD3TFEWRPE5j6TQ537n6Du4afDt60qXCYxZB6xQay3NNE4TuTDi9dph73N24ScP65XNU4yntYkC33WO0cYkwq1g7eAada6Z+lxCnafIPKsHTRXA+4OZGh/WOqtlFXKQflwiTSCY9FlaPsNKpuDTZougNUCdO0syS5+esw+TzuS4y50nyKDPDFCptUVkIJg9zXqmvRbW/vHzdAaUYYxT52rduEpGfJLnV5K0OdTXEiWXmLUE1aO3IJCMPOWIFLRrn57v0xESD0FlYQC32qCYjBqrL0r5jNDSMNiYY32W2PmOj2qKOMzJlmGIpixKT6fSa5lh0BeSZRrUclRuzurif8cZTTEYb9NaW6fUWqJsRrrbsbm2x4AcsDRYpZ4bcBDJtECytskXZsUwmHl+V5HXOK1dez/3ju/lU8yB02+RFi27T5aQ5zpnOnRwuDkFQ9LMFsm6gySx33fkG4pEjfOzRe/ncw3cz277EoF2Sr+wnR9DW0i4WiKFAWjOYFGTRcFN5J/fIZ5nlNRtum/3lPl5xy7fxOw//Pn/52/80N330AB+++8O878Xfzfe84l386h/8Bt/7uu9jY/d+fuDN7+Sjn/4EFySjqxf48dd/J3/33/0LxnKeGDSZa/C1xl2pqfSI1vIyZqVP3m0TpxNCiETb0FRTdNMhiz3yPIXx8qKLumlAv38LrU6LfNAm76QwXZYZlAqEmOKqEiIxGLQVGFu2L2zwxBee4tEHn2I6tmnvVQnIHG7oS1DdgsbX+OEEVTUYURiTQa6hp3Fb0+dNt8kU2VKLbOShUUTXEKqGMJwQlnvk3QLvG+KcdiFGN+eQ93SX25w8fYTjNx9j8dAKdDN8FhHlEv+NpHxaCAprj+DtrTSThmY4ZTaeMRzOCOOMpoaqitTNjLqpaKop0TYQBS2GrNvGLPdxzlOdHRGvNGRFQdbOcCZHq5zD7UP8+Ou/n7/7u79OJTUHtOYH3vxOzp8/S+emG7jnU5/nPa96F846fudTH+ZMaz8vOvUS/u5v/795xS0v50C5n3W7jXTaLDUFNxd3kkVDIENaM2Jo0ZYW2o7Io6DRTC5e4OLnPsnOwj5uv/GVvOLGF3PX6klWw4A8y7GVYmiBok/egUeKR8A9wThPm8bk4ym3lmd41cobyOucJnikrCg7Ct8YdrYtmVFgHOXMsDRdoDm3ze7uVjK2RNPtLaKLnPFog1Zesrq4n4nbRLUsudM0lSfEcJXuRrRKpHV1hQZs5dgcbZGVA4q6S9Q57d4ieS9ndrZiOB1T7l8l3x5hXAr1WZd2W9BaE2ykiHnaVCi3OF8TfESaBhMzriko/0PLH3YRuLznCovIAeDK/PvzwJFrzjs8/+6/khjjzwM/D9BeWIlVM6XWFq8ysqwgA2LooFyRKKQlQPTUVY24BlHQ6rZpxFNvb5ItdGloqJpR2kdgFnFeUKFNDFPq2CBFInMTrdK2bSrx+WhRtPMOQUWG0y06nSVW1o5wZXgRFwLL3YNU9YRYaHxoOGj6MNH0ui1arYZJk+EiIA1tU1BJYhMlGhaKFd509PU88YWL7BTC4sTxvtvfQr2+j6xZQ1U5TAO7IXIqL2kvdMmsQS8W6JsMqsw4/9Tj2JCRh8B4a4P9gzXKUKAkR7LRHEuecWJwG/uaYzw2PcuLjp6kN8l47x3v4X/6wM8xtpa33fhm/u5//AUeefIh3nbXm7j701/g9z5+N9926ih5rXjna9/Cv77vY0yN0Okt8prbXsnuo5+mk7c4VrT4zBfuYcOuw06D35iilnvo3JApjQ2RKA6Lpd2BlZNd9h1apNvPaLc1WSsVftnosUSCWIgObyEolUr7IineqSymm5EPMo4eOczJl57idTsNj3z6Me795GfZ3NzCE4m5gZ4imICaeLQVvFI4l7acLHOhtdxmtDt73nRb9fLYWi5wzZiqSqSC2ihyK8TKEwYBeoJ4gzQOrS3Ly0u86GUv5saX3EC2mGN1qk1pfNpKsZlzY0UBjUu7sQmEIpLnQmexy5oMiCFiZzCbekZDy+Xz20yqdeyGRSSiMGRKMLkhCPiNKWw3ZCpnpb3CHafu4KlmxqSZ8eob7qTTW2JihG5e8M7XvoFcNPtOHuEjH7+bI+VR3nbXm/m9x/4Lk6c2edt3voextTx64fP8jXf+LGrD8+JjJ/n8A0+yb+EIJ/LbCZVJ+4hkDqVyypCjg2W8sQFLJTZkHDn2Ig4dOcGrjt/OS3unOVL3OKR6zIaOh7ftvADTYOo1XjZ4Ja9ZPckvfu6DbHYyBrbPm258HQvFClWVwAsaaJuMCQ1GoCwzOr2AyQqqieGgGfCoBNr9ZaT2rHQP4kKgDjWra0eBwHC6SdCRdt7B12OIgahS9X1QggupMtjVHpEMpdo4J6g6olqWqhlB3iMTqLc3aXXbtLptwnBKsA11hLwsIKTiOhUKcukieoKYgsZHJm5K4bPntVjsPwB/Evhf5v//99d8/+dF5JeBbwN2v1rMFBJPStVMkeUlZKYITY3zHkckeotSnk47p5NnxKohErEGQiFMtzax69uMQ4cgkaYZMdrdIaoc1c7SBt1xSpBdMqNRJkFGFSR8uQ+odokqchoXmNZDZtkOy8sn2Z7u4oZjvvv2N/GhT07YGQSyXsaBbB95MOhMOHCow1NnHbZWaCJaaYyKqDwiQegVGWeK27h56VY+0TzIWmeZmxZv4ny1QG5zqAIqDwynliemigXtyRcz9kuHl/ZuYXRyQLnvMOujDYiO1cUDuBjZrrbZyfcjHZMqn2PJIPY52b+Ny4MdfuvJz/Ajr/s+VtqL3LJ0E7/9mQ/xPbe9g3/xq+/n7vvv5rvuehM/+u738r/+f/4Jb37Ff8Plzz3Eu9/43Xzs/COcyyMPjS7zytvu5BMb51laW+bnvuuH+Nm/97f47fEW+AZ7echdR07xhWKDy2YC0WFWOxy89SgHbjrEwmqfTpmlXZkEmhpmtWU2nFLtTJiOKvwcgdHUlhAiojXKGLK2prfQZmmlT3+hR7sXKBc1Z779DKdfeYpzD53lofse4+LmJmNXYyc1xgI6R2SvItOj+jlZT3+tBCvPqW4jkPU0cSzEkUeCwojG6BwshElN1snpHlzkwPIyp2+9gcOnjyAtRS01YzdhujNjuDNie2PIcGeKnSbCwug9Sgl5kZFlGdpo2r2ScqFDq9+mVRgwUPQUWSejWBzQ33cDlx4oOH/fU7j1Kblk7Ct63NzZz3+5/zx4h9I5dxw+xV//ib/EX/uPv8zmlU1eeftLeGh8mVZHczhb5T1v/G4uXjjPvttP89F/9W/4yz/wp+gtdrn7/k+z3LR4821v5f/7mQ9yy+IZjrdPsLGyzW/93qfpHOhxMt7GYNZHxRZeC9Ix7OQVO9U2LkZWFw/wZNymXF7lyLGbONU/wUtbB9mvS3KdcXG7ZncKk9CwUGaECnKXs9I7waHFRfZ1PslOs8HNi6e5aeF2emTUCjITCTqNUU2gJYqsCBw50uHKFUfuNQdW1ziycBw7sizsKt5y+5v49/d/hMy0WVk+ye70HNNqhBSQFzmqznHTaaK+UZEgCdaZG41tPCq2aUkfN5nRTIfITgPTIXneY7q5id3cZtrtYJZWwIC2qcC1yDMmPjCZJnSY6IjB47VCdAHtQLW5lTzjr1O+6iIgIr9ESpStiMg54GdJA+RXRORPAU8C3z8//deB7wQeAabAjz2bRrhmxvDcWRZabZrdGaEZIjim1ZBAi6LbI5oltO6hNFTO4nUA3zC+cpHqyjmubG6iWz1CaLDO0hos01rZT240nbykNjWSKUSl3Y1MiIlHqsjIOjm18oRgUaIYNxssDw6wtnSAWT2GKvIDt72bf/DRv09Y3cfKLatkdUY1rWh3lml3HFVUGONRpkZlNYVuERqPKjz5ZI07972aSxsbHFg4hM56tMqCwikW8ki/K/hcM4wWhU500y3PkV6XG+lzbpyz3O6xWe+wPZkRbJezMqTvt+nnKtVABEXpCvaxhuoNGOspvz66hz9h3sj33Pp9/IPf/Xu880Xv4Xtf+U5+454/YPsHR9x06gQvO3ULH/7Q7/KWV7yKts74jlu+jV967F4e2LjCm1/1Em64d4VtBapd0ityokpkV4Oh56df8z38w0eGbE12WD1zgAO330B/uYduacR56lFNtCWuUuxszzj3xONsPf4U9foQHyK9wYAogqtqjAg+RqISGknhIVGRLFMs7Vvk8JnDHL7pCMv7+qy9+BAHXnSM2e4OT973FJ+/+wFGdYVXCktDVBHVVailElvwFZkWvxm6TYzYAmSpRO1aQgMRhVYZWil6/ZLb7jrDsVuO0Ros4JVjVE/ZemrI2QfPcu6Bc2xd3sbaOf+OaPKokZDQVy5GTFkgMTLa3UUroVjts3TiKIePn2BhsYUpA5LVSOHpH+rRWTrF6k37uXjvY+w8uM3phaP82Vd/D5/+rU9gm4AqIt08R3XaRCWcWFrhzPHjfOhjv0FLtfmOm19Er91BHT/Bb33o93jpjTdz06kTbNsx93z2Xr7nld+FLQs+/sBH+Auv+0vQwAfHn2W0olDtLmvDVbquwGmFUxWzrOKyn/KUGzKyjt1qhu8Iy0WPlhTc2OpzZKHH9qSimdVEcnbx9BY0h1vCcBIZG4U3BTrrsX/hENOtyJ0rrya3a6iuR2vIckWtapQxGOMpckXZgXanoKrHmHbGcmeN+gufwl25zPe/+v/KVhVo6hlrSwcwmWfcbCTeJAK1SnOH8w6sRwfBmrSZjMoMeaEoXEneCHHqqTeuMNvdZGwylMoJszG1nxI7is6gB1rRWE1Ld6hizrgeMZpsU49HKGa0S0dkiMr7tGKL3fPncM3X5OV+WXk26KAf+gqH3vRlzo3An/taGxGdZ7qzQ6caUbYio9kQN7VMNyqiN7jBgGhn+E6ftsmwxuJNpBkNqTa2ibMpMz8jDkdoCajcEMsWyo+wo0BsN8ggSwmzMKcewBKN0G716eku3lYEJ1hg1Owynq6ztnSA85cf5v4r9/HTd30ft95zkvufeoTB0RaDrMPMpWrE/qJmNqrJ85x2F1gqCGOISpGVkbgDJ3vHOb15iEP5CgUd2mVBpxHyjqA7iq7N0JszBoApCrarGcPYsGZb3NG9gbuHj+PVFDXoMjiwyObj55kUDUdW1pg+VRNtTmCBU+omDtZ382i/5sOTT/Pd+17F7QfOsFqu8p+/8CHe+bb38tH7P8enH3iQb7/1Lt73He/mn77/X1K/Qlga9HnTna/no2ef4vJwF2kXvO+Nb+GDH/0wygdKpzBWUSEUxrC6fICXn7mDlZce5fLhiqgUfioMH5uxcfYKzcSSqTZF1sEaxWBpkQWlOTd5hOFoSAwu7bUQLdY5bAiYPCMzBcZ0QWA6GnPh8jnOf/4C93TvZuGGfRx62e2sHFthf99w7OWnOXDrCXYv7PDY5x/mqfseY3c0o1jtoEqTiqu+wqYy3wzdnvM3oEpDZ7VL5Sb0ewVHbz3KDbedYnBwgbyT0EGPr2+x8dQG5z/5OXYevUw9Tvqq223a3R6QqCAaX+EaS6ZU2g0lakyIqBjotvscOXmCuNBn/cqYnQtDajvBhil5J2PlyBqd1Rbd/Tmn1xbZd1PJDaMlVpcPUGSpmCy3itIplPecIONtb3wDqlNwabjL0WyBN931espBn+2tbbbPXubHf+T/QojCpx98gK70eNfb3st/euC3WSlXuf3gTawz5XfGd1P3NScnA06rmwkspD0ljKO92udiuMLG7jYLNxxiZGf4ECmkzx3dE6zZFsNxQ1ELi67AjSJhGGktt9CdSB4D3UmEvKRAOJ6vkEnkZO8EsYGsVLTKtOVsu1vQ7sJoOxJjQ3/RJLix0wzosDBpEZ68zC0rN3D70TP8o7v/LUbB2tIBdsaXGDe7KKCwknZw021iS5jGHQSPhCzRecicvnpW46ZbZF4R6ylxOqJqHD6qBJrQkWpjm+n+IXneJhpLdLtMtyyTyZDpxjp2dzftw7xcYDo57VYLW3mm2zvPCSXKC4I2IsaIsxZbDWkv9cgyRRM8eWiIzqLrjHr9IvXGBcLyCrGXMJnb58/BcIQKEAioaFFI4u6PDXa2hSVQTwTaLVTHYJRBxYhTilaecax1mJcffxnR14x3r7Dut3ng8hPMplus7L+Bvl/l3Gydz64/xI+8+Qf5+7/0dxnQI9Rpx7DRsGbfwZLJ7ohWu6TTzaEOjEcOk2uyTBDjaFHy8v130W578pDTz3t0RKGNJxiNiZKoLaY1xZUuB1c11kFrrNmOnixrowNEadDRMfOWe3bOc6J/ANVRhF3Bh4wVv8ZtHOERf4WL/hyf8Y/yNn0H73rVu/iH/+HnefMtr+UH3/JefvM3f4NX3/wiBu0+737Pe/jwJ/6A977j3exbPcxrjpzhAxce4MLWJt925haajXUOtHvceduL+dX7f5+WaJaPrFK0O3znK1/H703u4YNbn+fyxSGzKwE/MdSznMpDFqYsr2i+40+9h5e97FaO95f4wu9+hr/9V/46G5e2CGZe5Wx9IhK0HqMDYiBqje6WeF+jakddwbhq89RZx+fu+zTF+BJL+xY5duo4B47u42VnXsVL3/hizj9ylstb26wPdxhubaV9ep8v8QEzdvSXlli9ZYG11yxy+MYjyFKH4c6YR554iicffpKtS1vU3X0M+oeYzTrUVUAFTyxSH5BrZN5P3qaqcO89ghDqhugCRw7v57/72z/LLa9/CU8Mt/jkp+7jQ//nr7K7NcWqjHKS04ym6M6Q1ppi34Eet5+4idd37qBod1g+ssbG7jYqau68/cUcaPd405lbecWZW/jEE4/hXOBVh29i3+phnA783ic/yrvf824G7QHjUPGff/M/8wNv+T6cUfzGH3yAn37XT6BEc49/jIv1OSLCrfYIK36NJuaIsqiOou5r7tk5T5VbdHRE3aAV5Fmb1WmXY9JjIZTkAep1RV2N0VJgoiaYiDaBXmFQWY88RE53DnFo/1HalIixZFkbk0d89LQ7ik43p9WG7qBmbV/JxsWKXKWN3Pu6x2ps8SNv+gE+u/4Q55p1+iurmG6b2ZVtCh+4ad+NrOpFuoM1RJd84olP8GiYMmssJgpGGYIJhDHU6xN0GJOooOcIQgIxJuZYFYQ4HLN9/hxLR45gfaQebrKzuQFRME2DuFnaUjIaskyRlzDb2sXZ5o/PpjKiFK1Wjt3eZhwsOIWJGabsIIUh6wyYNpZ6OqYpJkgGbtYwu7KOaipyrdFzLnWtNUonC9BNk9tGo5B9JaIKlEl7DKgorGaLvOXEWzjVu4PF9jK9gxZvdvn9hz/CvdtP0Om3MPtO8+RjD/D75x/gZ978g7zk8Evp5Es0U42RyHhYcfxUj95SqkzVGlptwxhPxKN1liB/Vc6+7BjLiwZvFTSeIkuVkcoDwZGZSIyGxx8asz90yHqOQehxxuZc6Y4Z1k9x6Qv38eL9B9hotTg33GVjMGV1YHC7M4wEzKTH8exOBuV9NK0J//Hix/m2o2e4c9+LefGBm/nNT/8273n1u/jkfR/l80/cz20nbuHoTYc47W/gU/ffzUtvfAmve80r+fSvPcHnLjzFzauHePWrXkdsPG9621v5Zx/9VVZOrfLa21/EUrmIW1Dc99EHuPe+u4m15hWvfBM33/Ii/v0HfwM3HCH1iIO33IQ61qVZNtRtzV3f+Rp+4tKf43/5H/4G0YWrZGl7++cGH2gaC1niwNFK4yWSLa7SWjlErMCfHzK9skP14JhLnzhLvlKwfHo/R245zPEXHWFVncSNK9YffZzfefDjz5tu58bw8rtuZ/XkCUy3JISGB89e4KmPnGPz4Us06zWhgqAcrLWJObRWDtJs7+CurKctJkPEOgfWEXyqnhC1VysgaetKgT/13/xZXvqdr2HbVDSZQR3rcujWQ2x96B5Mq02v1+W73/5WvnD/vXz8Y7/F5cJz5FbD973jbZhywLve+Do+srzAxsNXePNb30oEXv2q1zEcV9x7/ikO5SWvf+0rceL41Bc+w6kX3cDRmw4zHlbc98T9tKLnFa96Fb/6Bx/g9v1nuGvfHWyriv947mM0xjGYtTkxu4vc9ohS46gx/Zx1NeVctcu+QYtB8Fz6wn3sv+kmbs/2cWa8xiArsOOK6chw6ZExq0cNmYkQHMprMqAwMGk8URTHFk+zaR2xylGlQWuINIDQaqd/i0BvSdPpZDy+uzvf6EjRKRd5yaGXcuL4GX75N3+ZptfnwA2nac8Cx4v93L7/Fbz21GvRbsBIMranmwxOLDB8cJ1zbpsoOlHdBKCqiVt1qndCE5xL+55kWdoYJia6kKapsFeuMOv2ySQnjie4SU3R6tLutLBAFAcxA6sYb2xhd8dpzhx9E4rFvhmijCJrZYSmwc0sMWZgQchQpkTnHTLlyPIc0yrRWYvcGeqij2tHtHNoUWRFB2UKTLtEjAGpoarJxOBqwVqhMYnfphDFjf0bOJndghkusTPKcL7g1ME13n5gkdPdIeeKdZ5c3mRrtsy5p3a498JZvv3Fb8bu5ISYEC3VLDKtPd1BwXRigQyTC1mh5rUPGqPSDkNK2vTzHpszizaePFNkWtNUgaaqqXzNrNJsBwiXLXE2pVqqCIuBma3otDqsHTvIkBnb1YSsv8bnZxd43cpJZL1ioWdpG6g2D3ByeoDPlw9z7+7D/MHss7w9vowfed0P8f6P/SrD6S4/9r0/zC/82vs5+hPHUZcsrzn9Mu5/+CHowpEbTvD6R87wibMXMGg6knF2chnRkZ/6k+9jdxFe0T9E7oT7HnmE3/6N32OnmfLy172Wv/I3f5ammfAHH/tdpltbHD5xnDOveyXDxnJhaxuDInRyXvmet/Oaj3yU3/nAb0NIk5rWer5naiKLC84ldmmtUN0u5dIq4iyjp57EXb5MTiRkEa2FZjrh7P0Pc+Gpx3no8BIHjhzi+IljHLrzDL3/9IXnTbd7SwMO3XmG7Z1dnrj3AS6ePc/2uS382GGatAFMyEDZSHPpMuOY0T94kHJpjel0BrEB57BzCu20ZWJippUQEse8gte+44286nu+k80scHGyy4WtbYa15fRrX8n4sR3Ond1kZVDwtu/7Dt6Vv4ef+TMX+cTv/h6/c+4j/ODpd3Ln4kt5w6130jqyj95WRDLH5nidoytHqHCcXb/A6249w9EXnaBZH7Ovv8Itp04zubTLUFt+7T9+gD/9fT/CaLrL5Y3L/Mjr3kdW5fyBfJLP7j6CyyI3zQ5yXB3gwEGYOsvmKOBXMz4/O0vWL9murjCkYO3YQTqtDjMqrixO2BzOyDc8athmJ0TixNJqB/7/3P15kGXbdZ+JfXs4453z5pxZc716Q70ReCQe5geAIEGQIMCmSKlB0U2NtjW5bStCUrtblkN2SFaEHaHojla3LIkttUhRlCiJk0QSAEmABPBATG8eaq7Kyjnzzmc+e2//cYoiHW6yKUEiENoRFXnzZGXec+9da++99lrr+7ncp87DppHUMyhnsFYyiIYk9Rxbe43v+QqpikZu1W8Og52pGp8tDHlmMSisk/i5zwee+g5e2bvPfZvS2RiyPhxw7njI9pl3c6nTpWWWubafslAW7Za45D/K5d5FDoqvU1iDtQJRCfxCoGXY4NXDAM8f4B5sTm1dUBUJOIuvNdrv0pINTNFEjnZfgvBRWmOKEmtyXPUARGcqbFniRR5Sf+OLwLcGQM7TGAW1pzG+R1HmJPMpSTIjNwUVhqAdsby1xmBrg7XtM1y5/Bhvf+Y5ts9fIO4tEXa6BP11upuX6axfJlo+i/NbGBRaeJh5hc5BOYdnIfACBsEyA5apMkm9qKhKj9nMsdbb4Jy6yNX507y9eJwn1x4h3hzy2uldrj56lZYOkdrhtEFIxSIpidoaTzVvqNKgvCYRJZXA8xRaWuJAEgQClCXwJIEHwjWOrIQg7rSYmIp01TBuORYqYlJbXjsZMbUNLEu2JLM6gUgS9BVvFjskvQx6kumiQvmWx8/3edo+Rlx4GFnwr3Z+mT15zGa8ziff8QlWVcRWf5unLl/lx3/6n1B4UOUZDz18EdmX1EHFO7/jPZjxCbvFhFOb0rm6ya3ZHT70sQ+QHRywsnkR5Rm+/MZvkpqUyA/4ru//XsJhF0GNzmfowPKRP/4jZH7MbG/G/t0jXrt9n+vTEfMlxZ//63+RC49doDb1Aw3kGvWAoqmEhLrG5BUOnyBqoa0hPdrFTg8IdY1tK+gpVEfihwpfSTzrkY0Lrv3mm3z6n/0yn/3VL1L+/wnZ/8GN0hg++6tf5NP/7Je59ptvko0LPOvhq+aeVUdCT2LaqnlN0wPSo120NQRxC4ePySt4sNERSqCkbJTpyka56/yj5/nz//e/yHxJcX064rXb99m/e8Rsb0bmRXz3H/8RdGDRxRyoiYZdPvKJ7yXyA1KT8uU3voT0DKubF0kPDvmO7/sgt6Z36F7d5NSm7BUTzPiEd3343dRBhexJHnr4IlWeUXjwE//iJ3nq8lW2+tusqIhPvuMTbLXW2ZfH/KudX8bInLjUPGUe5fHzfVRgmS4q6EqSXs6bxT2CvoKosW3ZklR2ztSmvHYyYlxbEhUxbrnGN2xF3I6bplLT+FDgQeDJxrcCQRxItLR4XrMz14FEeY1vShpR+aitWSQlQiqcNkjtiHXI449d5dXTO8RbQ55Ye4S3F49zdf4059RF1nobTGeOqvQwi4oqkwxYZuAvE+gAzzZzjM5p5hw8DArnt4iWz9JZv0x38yGC/jphp0vcW2L73AXe/sxzXLn8GKvbZ+hvbbC8uUbQjqgw5KYgSWYk8ylFmWN8r5krVTN3fqPjW2IRkFqjwg4yCJFa4HseAoktMkSa4NXQ7/TpD7pEyz7rGwMun7/Eww8/yePPPMu5R66ycuYyvbUNZK+Ha3UweJTzimK6IF2MEUcL7KQgSGO+a/tdrNFDGovyZ3Q8QUf6dDsBzpOolmZpKOnmPbZ3zvBMeZXHtx7mVKaIdshgaYDnGbRXowNFnpfEbUWrA54n8GKF33F0hxonbLNzU4JuO0JKTRxGxFqgpKUwBlvVCA/KylFPJD4KwhIvLjFVSelnHKT7iMJQW0F2mmOzBeOja4QDyb4+wsYGaULuHjlyGfGBhx5luxriRM0dc8jPTT9LaSvOx2tMJyNKYfjYez/G6ek+//wr/xpWA5xwtIc9glDSG3T40PPv4frOm5SuIjQ+a/1NQt1hoLskriBcarG5tEqkYx565mme/8T3o5THq196maNbe3znd38EvbWGUBFlUnJy75Rrb+1w441bHC0mBOeW+PN/5S8QBwHSgrI1UooHRFGNFo3GgXQGYUqq6Yh6NkMqjWyF+LFHGAiElhipccLHSiitwhiPeuHYe2WHZDL7ptl2Mpmx98oO9cJhjEdpVQMTFD5GaoQnCQNBEHvIVohUmno2o5qeIuoS6Ro2jX6gJSC1RkrRvFcW4iDgL/yVv0B4bsjRYsKNN25x7a0dTu6dUiYlQsfIjTW+86Mf4fjWHq++8BJSeTz//Z/gyjNPE+mYjeEq4aBFQkFftYl0h9XeJqENKG3F9Z03+dD7301v0MUPJe1hDyccrAb89Ff/DcfHu3zsvd9LKQzT6Yjz8RqVrfi56We5Yw5xoma7HPKBy49SyIi7hw5pQlxsONCHhAPJ+Og6NpuTnebUViAKw0G6T+lnmKrxBcISH4WZSMrKITywVU1hDEpaYi2Iw8bHGl9roiYnLN0ljd9xeHGTp2t1IG43vqsDhfZqPG0YLA0Q7YgTkfL41sO8rbzK1s4ZunmPpaVmbnCepNsJaEufjidR/gxlLGv0+K4z7yJIY+ykQBw1c08xXVDOKwwertVB9rr01jZYOXOZc49c5fG3PcvDjzzF5fOXWN8YEC379Je69Dt9vBpEmjRzIRLf85BaNHNl2EHqb3wR+JY4DkJ59LYuYuoUQYHwwKSS3M0xpkIr6HR7uNChPEPgBQ1cKY4ZrDzCxsY2R4cn5JUhKWrmac4sF9huB02Ochbt+SAVThgut7b4trXHubX7OpHUXN1qcTxz6Nin3fZQzrA89JCuIE0V9v4al1ozPnfjS+xfvs9gaYN0avC0hwCKHBCasG1AO5x2lKogCsJG8g5ot0LiDkgnGkEPYRryo3GYukZ5HncOx1gZEtY1nlIc1SnHUcahO+QiWwzUJV42t5mLMUEroF3mxKbkzXSHreEQdWyxpcf+bsGVJy7yfavv5J8ffoajNvzE6Musxtt8f/RthKtrfHn3VZ596En+1I/+cf7MX//LdOJlvveJD1AfJHQ3G0H5t116gtsvv0ErUky/fJ8zszUOFyPe8/R7GN85wjx8gWe/453EP/9jnLl8DtHukQvNmZUt/g/f88PYzYvcqQxxO+LZzqPMZhMmAbQ7XUYnY1bCmLe9/1089eyTvPzCVzGqUUQTUdigc6sSqRW4pkzPlgKhPXQYgzQ4k4MwCN00AErZNFBhXdMY5MkGuPZNzAs7SxPVeA2rprk3idQgrGi0DmqHMwrhhQilqPOcusgb5LNzSK0wSqIC70Gjo8HTPkrAk29/kre9/10clBWjkzHtTpflQUm/gG63z7FTzGrDUxuP8rbv/WHWVrYohIZWjzMPnWd+7QbPfuhdmAImt49579Pv4eQrI86erDEd3ad1pc2G7nHh8qN4RhIRMTtcYGaCX/j8r/BT/+Kf89//138TE8NXrr/K41tXKXP42ezr/MToyygithL4vrV3cq57iWuvFFjjU/mWclnyRrJDJy5RQlC1QgpXscwaT6gLTEzOoTsgiiJsZlhVEVVtyaXmzumUq9sDsrpCKYUvBZ60KKeQTtDqBOR5s8t10iICR2kLrNZIIQjbGoSmyEFK8KVHbQydpZj96X3mN/Z5Rr+T9WSN1VgRDwqWhj6VFaz020S6YqB9VruSW5kmrDJ++Or3MV1M+Jz4KoFU0OpS+yVGSIJuh1AJuqFHJ+7QCjShp1hdW6bba4MNya0hKAOUp3CBT6cbkRwdYJxDxwPCfofWoIvzIWwNkTom3Tv+hm30W2IRMLYmWlkilGvU2Zy6KMmzAKdblNkMv90iboWkXorQCq0a1rofKoKgg9Y+6sGHWGQFp+MpLV+TD9rIagPpHFJGmFhxFBzxi2/+Cv/X7/5zbLs2Mg9JnOXMRpukdE2jl5WEsaDbzxj0Y+QNjZhfpGq9k5PdU86eu8DhrQXOgRcorBUUpaW11ISVuSuw2iDCBv+rBPihImwLbAWRczhJA8mzDq0VgVT0UJSRpGob0tBRpoZpccIgkjxXXYGRII0U1723GAczel4LdZJTDjtcrw54bLCBP1aIymPnRsp3Pfw8HvBL4y/xqjrhx3c/xcWza7xdbrLZXeKzt7/Mc08+xY/+4A/w93/8f2T4p2LevfFOZtMpbRWTLOY8vPUU3Y0BJ9euU+/kFEc155/aJrYx4+tTzr7vES6fu8jS6gqZtXSwLF/cQL7zCX7qlz7NpTMfxnQCSgxBt0+Zj8jyFJnBSTdhuNzhgz/w3bz+2huNBq8fQhhiy6pRtxLigeSiA6XwoxgRdjDGYtMKU5boAFTQ8PgrJ5AWpCeR6oGYyH8AHdZ/3yGEwPMVVjiElI3KlWuKCpwxmMJRFwbwkXEX5TelmWWRY4x5UO2mmhyA5yF9D1UZbA06FnzgBz5C1Q05PZkwn8zJBJTOEnSXKGVAgCJa5Hz+y1/gD3/Xh1m6uAlYMmtZWl3m8rmLnHvmEcafO2HLbrGULnHn9SMmRU7xmGBtY5OHtyNY1MQqYjad4nzNF29+ib/34/8Df+wP/QC9rWU++/KXeai7QbeEr5b3+PHdT+FMzsNmyHetvoMPrX6AnbdSRBnh+Y5yYLjOASbyCE8neO0WR66kV3S5XF3mkWwb0XN8wXuZSXZCz2ySxgbdtrRrHx9FJ1YU1lJD0zTXEEXQTiHbgnBuMRac9yBqqA2FyAi9gGhJkUws1ip04CjzCpyj0w+5sXPKc/FzPDK/yLpss7EpMaSEocYkAl86upGj1WtxepKg8pD3bX47rZUOf+0rP00U9FgdPoQKDdZmTSex1yL0fPrtFsNBjyAK0ErRG3SIooCisBSZRaugwbR7jrgd4bdb0OsRRF38bkw86KIDHx11yG2FsfU3bKPfEouAtTVJOSForTMYbOIcxFFOspiSzOd0OkvoQKBUDdIHJ5sqCWcwRlCVFfoBXRFf02mFoDVWBChXgqkJfJ+Ndoc7eY8XJy/xP3z2f+K/+cRfpn3oYObQQNQS5Lkjrx2idHheiNTQ27Tk97u8rfdBlNvDjzykDy0/IHcldS0xrsbrCqwTiAKUDyoSFEmFRKM8kIGASuDhUIHmqM5542SP5X4XUWnG2uEFNVXPsnALTsKUe+KQ7f6ALDdM0ymZ77BRG2unlL6irUJacYtbh3ucbfVYSwNiFPNccXKr5nse+TBn5CU+tfsyvx69wN/Z+Vn+8pUf4eLyBXYXc16/cYePPvcxDg7m/NjP/1PCy12ee/wpRm+dENYhp3dPwXcsXV7m9Zev0SvbFC9aVq5sc/v6LYInI9519R1cLyukMERCcE2l3Doj8B7u85l/8GO870/+Ce57BlcmlM4gqkZcfT5PSJct7//+7+bXP/15Xvnya4hII7xmQW9a8SVGGJwDpZq+fxkGCKtwVUqRpOR1jo/G0wKQCGWRgUJYkJaGR/HNGsI1mgeyUQ2jsFjTSENWRUWZ1EirCFoRImojpMHkDVnXmEYOUYsmEaylRHseLopweDzxbVd5/vs/SoplNk+wRYlQitIY7qcJwtdsVpJf+Qc/xqVvv8obZ+CiTrmCIREWWxre9di3k09STq6fcuHSJdIXF9iF4K485rHLVxi9PiK5M2d4ccho74T44SVeePVlfuznf5KPvuc7+Z53foyXbt4kXmpztn2BG4tT/s7Oz5KVcz6aPc+Ht57kqc2L7LxVkOaajjakgWHUKriV7dFeX8JO5mR+hhUOG7fJMstBfUqv18NGmp3qkJZqoV1Fuxfj6prEWO4mI5xXczKZ8djyBmtBhMmahV8GDuU5XAGVrQlaHqoEoUAE4AmHGdeUdYXA0ooCFmWNF3sM7DZv72/QIqS3adGRRNYhaWnJK4Gxgqgl0JWDxDLsOIL1K/z1f/U3OLYFT3ef4vzyKvuLOUVZgtIY4SNdQSfQBH4jdqWloCoqfN/HOYM1tsFESAFeM6d11tYYxh6tTodWu0fcCRESZlVKkh9j/wMsAt8SOQGcxArHND2iqBK08lnb2OTiw49w5YmrDDZWke0I/AjfD9FaAQ7rBKWVgMSZinI6JU8WlHVNqBSdyCOMA7yWImprnr78KM+sPUJPt3lx/y1+6gs/TWslwvMUxQy6XYXvSaxRVDjCliKtKrrbPu2eIjQtWmYT3QrwOpaVFU13IGh7ClEZXKixLQhUQDfQ+H2JA5RnUUOw0lBj0ZHDCYuvfZ5cXeVSdwm1CFh3IQ8NIwZO41c+tSxZDmKe9lc5CGfsb5UkcYKHR+iWmeqMyVBQk0Df8Uq9z9gr6a46liPJfA53riU8PbjMD595nve4J7mX7/GTO59iauY8u36Vhy+dR3Rj/vT3/Qgfevw5/v4//glu3LuPViHjw1PK3HLjU7vko4waD6EFk6OadJ5QTBfc/8JdLncuEeQVsRNII9jZ2aXWNeuPneXoaJd//d/+HdqTgpZqg5H4xqFrQZHmpLXFb0WcO7tJEAW4VoALwAVgY4XratTAxx/6BEsarwcuynDtHLUkifohKlI4rxHc8SU403TXCtkglL/Zw8kH92IF1hh8SSMo4wlUpIj6EWpJ4toZLsrwehAsafyhjx74uK5u3ouweV9cKyCIAs6d3cBvh6S1pUhzdC3wjQMjaak27UnBv/5v/w4HBzusP3qWWht2dnaRVhA7CPOKh7qXuP+FOw9QHgmToxqhBbXzyEc5Nz51nzK3jA9P0Srk5r1d/v4//nE+9Phz/Onv+xFEN+bKxXM8u36VmZnzT3c+xb18j/e6J/jhM8/zzOAyd64nzGewHEm6q46xV/JKvQ99S03CZCiY6ozQLePhkcQp+1slB9GMp/0VloOYWpb4lc/AaR5ajlh3IWoRcKm7xJOrqwQ6wAmLF7lG9F4a1LDxPQf4fUk30AQqwLbAhRpRGdqeotMXrKxodNvitQJaZpPQtGn3FN1tn7SqCFvNnGBNM0d0u82c4XmK1mrEP/vCP+fF/bfoqjZPrz3C0w891hSLtBRhHDRzkVaUdU22WFBOpzhTgZCURmKdAOFQWuH7IfgRsh0x2FjlyhNXufjwI6xtbKK1T1ElTNMjrHAPlKq+sfEtEQko4bHaOUNWjcnsHFV7+HVAv9tnudujKioKUyEImwoANBWW1Ja4ymGEIxGW3FWNolCo8D0P3xPUTuKspvAEtyaHvOfyY9yb3uGN3pC38oTPHr3Ed517P9lJTVVbOoOA6QkIK3EarF+hOgHdbQCBdZIg1siBTxIk9JY7zO+VGGH+bRinQvB6ChE1OsIuMIQbimRUIQNNqyeYH1X42kf4PqYWaCy9YYQLYTEpKUPJcitinmv2jwpk4QiLmE7icXEz4M3ZdUatHCszsszS3R5wb35Ct+zQnsH5SNPzNfdnkjdvZTxxpccPBx9BnMx5pXiNnztY4Ye67yFoxxzt7bOpV/iRj/4R1Jckv/C5f8MnnvpeWqXH4iRj/+4YviRpyRhbag6TOZXokBrF7I0ZHTHgsYceBV/hMkP77oz9NYfXCnj8Q+/hsz/1GV7423+PH/rkH2WwHiNsTWE009MZ63lKB8md/dtUPQOhw3iW2q8gEmgRoKV94CACIx2VTBAoZFyiuz5RLTGu0dMtjcNWBvISqyWoB3mCb9JwAkpnGr3f2lILg/UVSkl0O0A9SPjWumwSxhi8lsO3Aap2zR+wEuMctVchfIGsJJW23N67Q21Lprlj/3ROaCVS1lwyMb1bC37qJ/4xO6dHvO8HP4hsB0wmKSuHAp40ECkevfAo7WsDTt6YoU3IsagYJXNaoU/LE+z85pj9uxMC3SJuB+xnU37+S/+G568+ySff8YepTc3R6T5Lmyu4ScnPHXyZV4vXeD68wH8+/G7Od3q8ci3DZD5nepKKmjuzmuvRnHvxCWvbXeYnY6wqiGyLQdLm0cFDHO8WhC0PG+TsBSXKaZZbISWSRV4i+4KVYUBSZNhaEIc+rpYUVUVnTZNPLUVdE274zGe2QbFEjU/qUGICkJXCCIPXg87ZmORkjpI+QawJkURDQXfboTpg8wqnA4QVSGnpDKCqCwpn6ZzT/NLhS7yZJ5w9/3Ye8dZ51+XH+Pzd1ylC0SiLSYkWgtL3KCtH4WpKB5WwtIRD1SXCVVTOAppee5kisLSICJSHF3gUpWEym1DUCzI7JwwjBt6AA+F9wzb6LbEICARt1afd6pCUp1hT4EyF7zxC6RPGAdRzThYzJBFGt7DSo7IWIR1OK/xOG+ssFonWPp7WBNJihaCwNbUzTF3BoDPgk9/+ffzMnRc4DlI+e/QKj29e4ezmGdJJzdJGgB8YTCXInSVeCjExuOWKoNC4VFNYGD67AnHFYelQPY/TUcaSa6OUxSqH7gKxwHgW2RP4A0d27NA9wUnWKFpoUaKUx2i6oNuTeB3Da0eHyCjkQjviupjwvtVtgiyiV7c4nKeMU0X7rseKfojDyRrH7QOmXkFnIXmjOuaaUZytrzBwgvWW4EwUcGec8tKrKY88NeDPbP0R/uEb/4oXDr/C2tYy71t5ilDDm4e3efjMQ3zyEz/IT/7Gz3D91l0uLs5xfH9CnlTc+/qEJ59ZZzauWKQ5Qd7GoChPHIWybD31DFME3jzn8//s5+CDT7D1+HnWH7vE2x/f549ceT+d0rFrLceFw+Up+miO/9AVCg8OpgcU3RLpNY080m8ol841nHaHw0hD7RxY1wiOu4rKawTilFPYyjaYaScayqY1OPVb0jTfnOGAkhphHzR3aQG60bxWWjUsJtWE9FKAwWJko6nMA1U05wRaNNEvosT4UPkP3rO8IKgE+tYRbrWNCz1WrGWrdPyZ9/8AP3nts2xcvQxWcfT6HQ4/8wpve/47mEYttp56hvkLdwiNQww0ae6YpzkqFHTXfV7+2iF1XXG0MyZcjrl16y6rS33+yHs+TnUEb+3fZnW1jfAln0vf5IuHX+Yd3gV+9InvJ1p0eemllFopzq8FKFFykhhGdcV1u8OoOuKhRR8yn+2qw8pinTWvizfz6RaCgVGsrfeZthMuRjH7ScmFTsxMZNya73F1ZQ21gOl0wfJSm9qUFMZQZBWtXpt65vAHDtlrfJBYoOsmGpdKUFeWUZRRPRGRdiX9S32GqUeRQdDWiNjglmtMHBAvhRS5wVYSP7D4nmJ0UtPa1OyYXT539AqdrVUuFuf5+Pnn6MYdJq5ARRFBrAgeUAqklai6bqq+sPidNk6phpllBU56GFtQ1QWZyVhqd+joNjiBcyXOVFhZ4MVtev4QkavfSzXv9z2+JRYBZ2vK+YI4Xibqaap0QlVljVi89BGqwlQ502SMMRntXpug1ZyreZ7ECzwMllktKKsKQ8P1MLXDmpL5fE5WlGRqzGf4Ap949oN80DzN/+dz/5ibkePTr32e/83bPkEYxuRZgd/zmJ1YfCtotTxKVRMsCerU4PmSianIVxWZkMydQncNkW8oVY1naqSAOtAoZ0BZwuUIQ4oMFF5XkhzWRNLDiz1MUeG3HeVA87WjPSaxzxUvRPmOfglnvR4jI3gjn4FSZMzo5OuIHZ+tvM92f4lFsMCEliJIeTG5xkG4xCDdIFQVK33N090Wo0XKvTtj+sM+f+Lyf87PHv4iv/Lq5xjGbR7tnKO+M+aNw1s83LvCx9/zMb7+61/jzukd8swxGec4U6A4g6kTamMpJwWmNhgjSa3BiyOCGm699SYvvvxVssldPnLmT1JHAd/z8Q+xPe/xE7c/z/rlp+mWkvR4wqVxSGdWY5ckou1DRlNOI0TTJIZt4G+i+TwFCpzDIR5cb6p/kCBpHkthMUI0wvdKYqj/A7jJv/8QgPAUylNgHEJalHZNA5xWGOwDVbUmdSGFQiBRQuCUa1DLziGEbV7og4ohQomMfKyztGeOh8cet8WYeG2dg9Dx9b0X+eSFd/G9j36IVyNYTHO++C9+nujuCTfffIP20rMUcUhZWTynMHXdfKbGYuoSRczJ4RQhLWEUcWd0h2gbPv7uj1EUHm8dXsemFVFnidePbvKZVz7Hu5ce5uPr30W273F8OmZzNWap3SKvDMeTitMU9sMT9pMbPB09xKWXV1H5Ou2ijZu0MWGNOONIxSlO9xnlM855MeueT+aOUb6jE4Vc1xWLfI8nN9fxxwXWVHixRzk3ZLOa/pqkLhSGknAlps4yjDOYQONVBmOgkpCt+2SrPrVuqgajtmByqyLsS6qWxV9qZB2Dlk+a1FTWES0J8qwgDHxKL+XTr3yeVw7eYiUT/OH3/jBbgxV+5qu/wv2DXTLjiAIf3englN/0eQiF8hqZ3G6vj0JSFRVVZamtw5UFs+mYhcpYbw3xdQ9nPApVUpHjwpAo7uMVAen8BPefSk7A1BXT4wOSyQJThWSlZjSdkWQJFY5KOgpTYOoaYSShkIQSfCyhg0goAisJlSYUksBazGLO+OSE/Tu3Obl1i+TgiPHxmM+++nX+x1/4nzm7ssKPPvtR5GnKF+99hZv795ChpRYF4RrQKakridACR0HUVlSyRESCPIZaWgojkdbiCZ+iG5BICyVo14jA28JBXeLHjrquUR0oqxJhLEFb4a1o0l5FtR7w2ugQTMhjVZvMOQJP0BEecR6ynESsyWWG0xbn7RnsXYVZSGoLZhwQHw3o763xjpPH+UD9NtL5gqM85zT3uTMrGZmS1Y0+Z9dWWIwXTE8kf+jhH+L7z36Il3/zJUbzjEudLea3Dtk/2KPtdXnq4asEYcj9oxFFpjG1YTwqEOpBgnJaYjKL8kMOp/e5f/N1giTl0z/3S7h2n9nJiNuf/zK2crxYjvmpu58m2lxCVIbSJlzqn6WtJbcWd6lzgxWOQIL/IIHqKdWgvxUPFgWNQKNF0/avPIf2GvEYpXRzlKIaKVIRakTLQ4YKP/C/6dVBftDgC0TLa+7N0zglsBKU0mjtoT2J8hxKgX7wWoXQzQKowNMK70GVkC8hkGCEo84Mt5O7tJTgcv8cpU0QlSHeXOKf3f0MXy/HmMpx+wtfYXYywnX6fObnfokgSdm58QaH0x2UH2Iyh5qWaCkRSjIZlZiqpsg0u0cjgjDkqUeu0va77B/sM7+1z6XOFuN5xktfeonvP/shfvCRH2J6oliMF5xdW2F1Y8DYlNyZlZzmPkd5Tjpf8IH6bbzj5An6e2vERwPMOGhseSGxdxXn7RmG01Zj82lElId0hIfvQeYcj1ZtMCGvjQ6p1gPSXoW3opsdfN2IvOsO1HWNHzU+aAuHRKHRUEIiLUU3wBM+0ja+XEtLHoOIBJUsiNsKR4HUgrqS0GnmhloUyMhyc3+HL979KvI05b949qOcW13l7/7C/8yvvfJ1xsdjkoMjTm7dYv/ObcYnJ5jFnMDYZv5SmsBKIqEIHQRYQgGhkAjTdMznpqCSjgpHkqWMplPSUmGqkGSyYHp88AeDkv6DGNbUnB7sIII+ntEs8gyyglPvEKlAdXzSuiLUPqGI8ZSHwqKdJZABdV6Q5zk4h+8HqLKkqGtm40NmkwXSCgLRlIGN51PefPNr3Hj9Jf7W/+n/wV/62J/iH/zaT/L6wZv0VJ+zZ1aQQcngksfJ9QIv8dFdi5IOLxBM05w88NAoCkCIiuP8hN35Efum5ul4m6HrEaYSIcDzPDQOVwt0SzEZpUgM8bJPGWccFwn7J5Yob9PJFFGkidqC3CUMVYxfaSaTEoqclXWP2a7FFDS0QiEojcKiUFahJiFn5VnSaIQUFePTihXtsyhLipMRK/02D51f4fDkiMnI8NzDz7EULnNw7YBO5zwbxSY3Pn+b0LZZacecW93iNbdLXTfNbKNkxsqggz+vUVlNSIALfPwB+HdmzH/sa+RHE37k//V/xGrHqm5xfXLKW1WO7re5MjiDnQlSobhxcI2vfunneOJdf5ZhMsYZgycVSig83caUBbVoSK+N4lhTU+9Eo7Ha8HMAKXHGNUA14XDS4USTkG8iA/HvqifwH3YIEJ7APjjeB4FxFvtAQAfVdAEjTIPPoIkIfounJGVjR4FteFoqCKjqBcYBxrBIJ9x1FT/3pX/I2/kY1foanlMsD85wrZhSVzkPa8l7PvZdvOej70HWklf+p19n/mNfo1NNKQaCMGhBVaLyGt8TeB2P4/H0AQC1RjnHudUtQhGze+2YG1+8xUW9RVUE7F+7w7uvvJuHz13m6OYhYZVz7vwqSVmzczKisgJT+oxPS2RQscUKcbqEsSGl0ZhaInFNp7gVmKJC1ZbBNoyLOcUkpOf7DFVM7nJW222oHJtpyMx5vHUyZmNZ0ok94mWf+cyQpSX9Ydz4HA7P8xG5A1cz1Y5RNeXF6X2OlWZLrtKOehTOpwvkgWVaVkQtgZKOWlqSWU1alSw/5CFlief73Ns55vWDN6mrlL/0vX+KR7rr/KW/9X/hldN9+kvnaXVifBxF5RiNxtS2puudIWhplO+Dc+TzBTIMCaTCuoZt5CuPMIgJXU5Wl8zKBDMvGZ0cMp8XUGtkmlGNJowOdrDmD6BEVAhxBvhHNFqrDvi7zrm/LYRYAv4pcB64A/yQc24smm3X36Zhr6fAjzrnvvZ7PYcUgjqfkSfH2EA3IbOvmM72KdJ9VLeN6HToxMv4dYgUElPk6FCRlQVJlpEWOe6BUHmdZpTzBJeWGOtTS01VW2bTI2ZHO+SnI756b4//6m/+1/zNv/o3+HMf+KN86eWvk65ZpiclasUn3NAEx47pqGJjOaKsEsLIZzwpqCMPH8eiqgn8moWZc2alhclzvnB4g4Ho8w63TtfrYgtJtShxgUepCrKkII59XFhTqZp6UeAOSkIXEkSSuNtgG2xaMJAd8tpyKHOMFihPk4wtRRajdYmxFZV1KOFDDbWVCKlp6yFGz0HA9KSkM/RRazknxSl52mN1bY3MT5mc7HJxa4v7iWWvHBO3AlaO+tz/+h5uc4AqBGfWBtyYHVF5MCombPR7BLlGRpJAOYSOObN6iaHosfvqAWvPX+JoS/Pc2StsBzEbScYjRyfYpKAsLdPRgtOdCT//5V9i7qY87WnujfYpreP8pcf5zz72A1zevMyv/tIv8PO/8nOUosSJBwwh53BO4oTC2kZq1IqmUawRqhZI4UA0IDrxAEr3u60CfxC2zQNxeCUEzlrEgwyFRYCUCKVwOKRV4ARS6oYPJAAlcAIEmhCf7/nQ9/LBj3wvN/Zu8C9//qe5f/MOO6d7qE6La27K/pd/ke/+0B+mf6ZPb6nNpcc/hGwFrK4uM2hFdMuUF+5eY+35S6S/ccKjj1zm9PyUaBY32IRAEyxZdF8xOphQeTUYOLO2iirg6OYpe3tjVoo+9AL27JiVfoftrW0mJ7v0/YhorcfpPGOhp6h1n/IwYHJSNuI6ThHWfVylGlu1AucUpSvxqFB4FFmbZJIyuCQpq5pDmdO1moGOGFVz/LhGdz2MkYQLxzxJqUOParnCCyVx7JOmBVFH4RlNVZTYQiBywaya8SVxwNhN6K/1OBOGzLI5Pi2KSrCmNXUEuTAMooCyyvD9FqejiqAvCYeact8wPSlJa8P+6D5/7gN/lIuDdf7SX/8rfOXVr5ILR5FmdFfP0O2t4GSEsDUuLSnnCxIh0XGMxSGcoyxLWlEItsY8kBj1vZiOlhRFxf2T1zGzBXkN2vcxzlEkE8rkmCqfNZucb3D8fiKBGvg/O+e+JoToAF8VQnwK+FHgM865vymE+MvAXwb+EvDdwEMP/r0D+DsPvv6uIwhbDNfPUsqKyo2ohKVyJVW2QORT6onmzENP0ulsQAllUVIIR2kNqUlYpCllVVFZgzCOoDA8tLTKd158ms++9jL351PGozHzO3vU2YSrGxf45Cc/yd/9h3+Pv/q3/hp/7a/831j/+PeQ7c5I0w56x2dpRdC/oDl6vaSYVIhBSEsLwtBRCYuzHrXMocoZtnqsah8bQDfq8muvvMwgFTyz1cXlFcYJRCFJXU6Z5nRERF0ZAj9CLQIGXhshHGHHoj2DQbC+ss692/uEVcSKU5RhTVt6xEKSUD9Q4mr0eeuqROEhPR8dVpxZCfGxlMaQlJKTuzltVTE455GalLxI6aIZEOPmFav9FXbuHlD2I8SpR5hoaqE5PtonW1RYBUFXE3o+fktCX9H2AkQroKwlG61zRLOaL0e7ZEOPShicJzDK0u146NYqCshczUZdUYyGyOH38enP/hqx53P73ht0+2t85w//BfSgg1pa5sN/6IeZzOb8yld+lUQ5PGlA0uwsAU/4KBwVDUFTKflvRVcaaU8HuN+eUL9Jti0ESJrEdrPFl81k75p7FgKEE3j4GCGoaMRiPNmIX1dO0TIe73n783znD/4wLo7QnubDn/wL/PR/9//k1s49tp96iuXNS3zofc/z9ndcJRj2UNojEhoDxFJhhMVpwVwY9LJgL9rlsell/FafsK7xdaeJVIICvyUJ/QCvqymqmmxRsXd4yOrZTYLEx6qasq9QWcbK6jp6UTIgJqVkj1PsMkjlMb5XsbgL3Zag5Vt81aKkxc5BRZ362MpRU6G8Jg9irUNSE4uIlrYsqRTPaGazBZmXcu7CJtMkQ3uGsGMprGPgBqhFReDixg8qKMucdCTpikbYyRUO4zTXDw/5anyb9z/xBD2/i3TgyZK8yqmlaDS7RU0YOlpaYQgb3wf6FzxsLZjtQJrWuPaMj3/8ewjmjr/6N/4qdw7u8d/8xb/CP/mJf8Kb+3eZp4re+Q5LSwO2+6u8/+oTXD/Z49roiCJJcErgSYXvpbSLmEh5UFuKokJYTey1mcz32bn3OrqqcUEP02o3UEwnMbJmuH6W9PDw9zK/39f4/YjK7AP7Dx7PhRBvAFvAx2lUmQD+IfBrNI7yceAfPRDheEEI0f8tzdbf7TmCyGf5woDD5IRCHWFcjagcvtQEKiaxhrwqSWtLmzZpVlFmc5wnmRcledao65iiJFaCvten47Z4uPc47/zwu7h3cszOfMy9wyM8FRKrindefDvbf/YiX3rpi/zyL3+KT/6JP4I+67P/0oLsSJPc9Gld1Qy2DbOdlKWoi7EpsdCktSD3GzHw0tT0wwArm8kpdJLMq3gl3+OR/BzFaUK72yYnRUYgZ4bKNyinyY4rqpmjMhbVEcxCONEp98f7dOqYucxI5qc80z7D2d4yxRhsXeF7HmUJprbNbheHUOCcwJQe4+OMzWHEckew3fapFWTaUS/mRCsZoldTVppKa+KVFq0KNk873D1O0FttyoMUU5b0H+3yyuduQ+FodULCtZCsXaK8pjM3kh7RQjJsLXMyvcuxPsSN+oiyZlpWdJSP7wylLTFCoA0Iq6k7Pa6+752sXX2YXChG0znnH76KiLqMiop1l7Mc+3z0E3+Il998DZPt4qRrqmpKhxCqacJCNiW4ADR4DuGaib85DnK434MZ8Qdh2w/+NqJBCyJc871EIPGQNAuBFO7fJomdq5G6iRRCIxi2VvjoJ/4QURxw7HLGRY2Muly48jijyYyLUcQP/+//tywvL1H7EaHVCONYqArlHAqfUghmZQVljTtdcKwnmLJka3mD1J5A25LZGoQiC0rC1YDWJKTYmzNL5/Qfu0hdltTS4q+3KacF51Y6tLdaVD6kx3OczAn6Nc4ospOIZX+JMw81n3u+KEkWjvFphikjnBMI5RBGImqJMQ000Pc9bF2jrcfZwTK70xFfnd+ntSw5mhYs5inb/Q380MOUDjN3qJljcVwRRZoqS5GlQQaOPEsJCMlnCW6oeCXfJ+2VRE5hHhwThlozL3LQPrmzqFoQC42xJaqIGR3OGJyJUS1N8npNdlRil3KuPH0OO6v48Z/+J1w++wif/Ngf4+zqBaoPDkiNR2Vyzq6tst0ZcHZ5hXE6Y/9YEruExWJMWjtU4AM5s7SmE/iI2lI7H6liFvWCvCqRThEpn0IZiuq0ySVpjd/XLLeWObrm/16m9/sa/045ASHEeeAZ4EvA2u8w/gOakBoaJ9r5Hb92/8G1/x9HEUL8aeBPA0SdNi4SaBlRFAlmWtAXLTZ6G3jC46Qcg7bMszme9TFVTVVklA8mYWktygna1ufpjcdZ97ZYiTc53O1T+QFntpc5d84y2siZLySBUphRwdNn38Wj55/m/skxd75+yOV3XebsUyEHX51xfGeG7LboDAPKYyiOCvxBgCpnBKlk3rIEGJyn8aQjdyW5tZzkOWWuEXlINSlJxzm+jUlVhfICssOC7laPYmEYHc+RQuKFoAKInGW9F7O9dpG0hht2wlzCzAs4J3ykELRiSZ42x15FVYJsxLOdcCjlUNIjtR43Tg1yXNCNM5aHHt1tTdxewssVuZlRqYRcTinTjE60xGBrmfHEktiCcDPEKsPSlQH9SzF793YZtvp4KwEzl7JYpLSCFqu9DlElcTYjcSlhGbJ2usr1qeH24SFybZWBUBRlSWEqiqqmtGBxRLJGxwEuzcEJjo+P0LLG2Jqqruh2W9Sbazz/rg/yr3/5nzIXNU4KdLORRormbF2gm05LRKNnbM0Dzv4DO+NBJ/k3ybZlqB7kJX77PpxzzVk/onmsJAaD+q1KoOYFIqygbXyef/eH6G2t0Yk89mYJztVoKTg6OWK7vYqzNboVklU5WVFzisCTEHqaQHmUvmDscm4dHuGmhounq0zLnNRLcTbDBAVZZDmazkmKhLYf460EyD1Fkiw49+1bLF0dMLo5JdwMMaam60UMtpZJvJp5NgZbEJYhXtYnVF3qliGpa6b3a05OK2apwNoAbISSgHIY4/CVelD1BYHno0JFK1ZEwkMKx6wXYuMhcRsurvaJN0EWgtlRQRaArEAYyehgxvJKF2l9ssMJrUGXcl4gjE86zVGTEJEHVLnmXpaz7GlCKfGkJPA8cAYjLHFqUWWJ9LoURwVhGNPuBczvFxzfTtC9mjNPL2Mqx80Xd3jqyvNsv2uFwMYc7SW8/cJHKIyh07YsDUPsVHJ0J2dUepzvPklbr3BQ7fLi/VeY5zVGOLLKkCiNj8ILBLXnmNs5aEe/P2TZG1BRsT/bZ5IskL0uOo5wgcC4P0BlMSFEG/hp4L90zs1+Z8WFc84J8e/Wm++c+7vA3wXorq64bm8LXTuODo8oqmPOrZ7hzPoZSmER5R5TkTI53qUuZsQqRkkf6WuUs3hS4JU1jw7Pckk+AqctlHUIWZGoDgd7sFYFnFuPmXZyZiNBseiwd7Nk41KXR59oc+f6CXdemPHQe7usPddj9FJG9lZBdNUn7inS/YxUCuJIMT1d4K0M6ChFIQBTs6hrxpMZ2d0R35Fu0rqvybyak0lCUfiUrZxgqBmVBX1jKY9mZEWO8TVSOxSCMAhY7sXgBKPTgvW9prW8t92mHBnMvDniMM4gFMSeprIVCIVUEiEtlgqtLE4InAjJS8veoeXwSNBt16ytOtrtLt1uB+tVTI8m7Nkj+lGf/rBDdaei3jA4X6EizSPvOs9Ln7tFSkVmSw5vHdCOu0zzGb1+QHupjdAWlQQ8tniMJbvJW7Md7vqHTXlt4fPmS9f5yqtfoFrv0lvqsd1qIyRMqpL+YIWn3/tuzp87IBndx3maqS+ZKI/S07z/gx/kla9+gZvTu9SyRuoa4xyWRtRbUDViNBaUahLH8NuOYR8cC32zbNvr+U1Rq/jtiEQqENKBqB8kt8W/vUMlQAmFlBptNWeHZ3j+Ax+k9ATTJGE2nVMlp9RVzXuffxf9s+vcmBwzuX1M3/NxFu4nC6ajGd7BlGcffxePPPUQo6Dk7skh0UzxuL3C2LVRXR+xZFG1pjQLptMZSMH9W3t0h+skVFSq4JF3nkdFuulWlgZ/T9E/3+GYnMntCasyou8PkaVPPhNMFzWHR4bZQmAczeQvJEo6hKiprMRXGmd9rGnKgL3Aw1qHcTUgKeYVOhL0Bi22dicMZ45u5LG0EUDgKKc1ZVngAoetLWleszgC3wSMygJfGMp5xmLhmGcJctbmkfsdtjsRx9cPOT5XMeh36WhNoDSBU3gIxCghjnzSmUEsSuL1FvXUkb1VEA4lw6d6iMpy+4UZtmrz6BPLFLuSvZsOY5YJo4LVVUcvDpkcGA6PKzLjI6oFqnDE4zUu9trky1NeOb1B5WkqBEhNXtYk5SmpSUmDOf2VgKXNDdb8TXwn8WWb4miHQK6wurRKrMXvppz67zR+X4uAEMKjcZIfd879iweXD38rFBZCbABHD67vAmd+x69vP7j2u9+EDuiGW/hFRWJShh2Pi2cu0+20yU2FK0um8x2y6QlmkeCiIXG7h5IgvBg/UKxFmndsv51OugbLPWqR022FtFoRnvYwVnKynzMYRqw+KjmeFJzuGe7cT1nb0Fx65xo7X0+4/7WSc8+2WX2yxewlw/hGTXfDUE0VqiwxPYnZyWCzg2s7SuGY2YrZQUF8p+DM7R4b54ecJCcUk4Jy4TicJfTPSey5DuGlNrv3cvRpRaUVlWqOiQPls7rWQ2rBwf0xB/cTAk9yZb2HF4FQltp4DXNfS4qqJtSKAN00zDmFMQotFLasUYHFD5sjIykUKjDIrs9RWTCfg85q+kuK7mCZztAyt3Pq2iFPWlQnU1qXA/KqpsoLhit9QhUwy2Z4NmZRLuj0NNP5iNRPWBlUREua8+Yc9WM9gvCUaTbm5E7GrVd3efmVG+yFKd3NFspm7I8zijpnluTouaQ82+Ph595G5HksJmOOTw74/OyY7bVtNleHPPeB72D3Z/4RKQ4hHcbUzW5fCKSrkRKcU81xGPy25J50OMzvKcH3H9u2nXNYLEposOK3nhOFRgiHkAaEwwrdII5co6QmUITC47nnP4S3NmRvNuX+4X08azi/0qfdH5BVZ9i5vUty75S7k7uMWyGBDllomMua2TSl/MwNFkea3uNLlEVNP2zTurpML/Hwl2CkxhzPRlRlgWgVzKc1nomZZTNCFTJc6VMWBXlV48UByY0Er92CZUswEFwebiFGksXIMBkV1LUmq0pkN0BVJa5o4HlRKCkzMKVEo3FOohWNjKgVOCRVYQi0xhhBbTRaVSzF0NnqMz+Ycv/+CbVssb49YHWtR5JOyEyBU+ChGOcV9WpE+OxD2Fpib8w5PEzQ0lGOC+JUcpZN9l8/5TQrmJzPcJshtVD4zsGsxtxLMcMQd1xSZwqxbBjvgIp81p5UYCR3v7YAKbn0zjVOjxMO79cEXszquZqVfkx2ajnZNwgXMegFtOuKJOkyS3w6yyHIGZ2lt3NczziUNXVhMFWOqUvSxZRFdkrZzuivnGG1u8zA7xMqD88LWGSG3AT0vTXCwEPr4Pcyv9/X+P1UBwng7wNvOOf+37/jRz8L/BfA33zw9Wd+x/U/J4T4SZqk2fR/7cxUCElZGWbjMWkypzcYIANJWi9YVAlJPqUqK5ypsS5DeY4o9imNYTY7wUlNFgzw2j6rKxpRweQ0oN2KabU8vEDisJSlz+xUUJqK5Q1JcDZgLxYcLVUkZPSfFBR3DLdenHLumRatMzB5qyYdO0IpmNye07rUolPGvPzqbYJvX6HSFfUiw3sr5xG9yfF0zCKXpFZQTTKsc6SzKT2zxMGtPap3bDM3Pr0SVF7hjCIysDpsUQvD4Z2E6WhBf1Wzsb3CySynpsbFikJbrKtphYq6aCiU2gNPy2ayBzxfIWQDsJOqQVtXxoDnyG1K1NKUrqYwluS0JDaCtufT3RiiO5bILth9tUDWPqlKyLsPlI+ER72YkxzMkJc86EqsTQicopqcstFrEbdi0oeX2WJKeGB5/WdvM78NweoyUTDlaH/E6czRb4W4yrBmByyXPep7C24vbjHYXuXy1gZbq1vsnZ4gXIELLI8/9yw/829+mrq2COuQtqGwCtHkBywWIZsjIofEPBCRsfZBJ9nvXh30H922m+f+raRncx9SSpQSD6IWgRTN0ZBwjSyhFQonFNqPefy5b8MFFpEWbK0sszlcpnaWG3v7jHeOYFQxLHvsVjH3Z2OEt2CS5JhEMAgGBPkyb31mSvvWlKvftcFwewPNMvFRgpEpe5OEzBkKLyEJHFlakNxbEIguWnTwtKboWlKVoOuItjBsXWnRutimRjLdz1gc56RTC0Y3guhakM1T8BSihthX4BReoDACnFVUhWmiHi2pakdVGZR0tMIGKFkoCZHDaIPoOi5ur7J//5iTowVVLRmutVjttMj3MzIMRhiSjsd0y6czUKS/eZ+BgXQ2od0LGU8yKiNY5AKmHg+bVV5/65B84HBtSVF7zF69zRPlCvXcktxO6G/2SccWUzo6DzfHj3dfXCC1o/ek4F6QkWxLgrjFZurTSWCyb8gnXoO39i0CSVV4CFoY6+gPwXmKPAvI0pKkGDO3NZ04Iop9isJh0wxX11RlSZJP0Sis10YGkrDTYjQaMxuPkcMhQvzBsIPeDfwI8IoQ4sUH1/4rGgf5KSHEnwDuAj/04Gf/mqaE7gZNGd0f+197Aotjujjl6OQei+yUaWy5dn9Bmk1Iioy43cVvtxl2llmUIyqT4TBoXzHfO+L48IB64yyfv/cC/pVnubJ6gd4wxpY1YWDwfYETTWia5RYhJMdHGZPHBNeHB8iWINkv2DjyeO78WRb3KsavFCyfD+gODeUEvI6iEIKTVw7ZemgVqpJKNblKt1fhXa9xlxRZZQmKGrQgz3OsJyltySQrSfYLpDGkosD3BCqpiGroRG3qomB8NyUtK3qbHZY3O8yLkvHpiIe6m5RRg58IOpKiKOmg8WSjS+D7AusqpNBoz6FDSxApjLOEbUWRK6QnqEUOunEYpMSLNWg42U053J0RtiVLVsGs4vS1GWsrK6w9tsbw6ROE81ikNYWqCWOPxBXISIKXYtOAo6lmZb1PVlrObm2wYkq6j3T43M5NTso5W6vrmFnG6dEJi9AwFF02ukPqpGRWW1RdMZnsc/fenKuPn2dzYxtZNwt/b9hnbbDGaG/RVNZYhRW2KQd1TWLYYhtsiHUNZsK5BwVCv6eT/Ee3bWjuoakOaqIAay1GugeVQ01yG+EazVn7YNGygtXldXrLfWos68urWO1x/2TCa6/dwU4XeGWBKWvqombDDJlOEk7tjDxPGMbLbK2uMTnOWdYhzz1yia2VgGhlg2zP4rV8jg/GpLKkbBdkVUmSW0QMhaqRaU078vGfHrD22Bperjl8/ZC2CanLmttvnpIvLArdaHsMJFVqwFYoFH5HNZrKlSMIIV8YlNAUmaHOJXUF1tVI6VMWDV67shbVKvEDj6gviGKLX8H12YhgsML6lVWO9+ZMj+ZUeU5HxXRURJ5NyXSF8XxSCiIjKffniLQRxTF+izzPUVpSloasslAqwps11dkKcdlRKaAu8bTHvVcOabc6eB1FmVi6Q4fSmpNXCmKl6Jz1eGF/h/2VktZmgBUO6dbJbzsCEeP3LFEoUYIHZaAGFVb0NzysTrk2us/n732VnaPr3Nu7R7i2zqB/EWcMlcmQ2jLsLuPLFgfHx9xa3KQVRMRRn0lesshOEScOETRz5zc6fj/VQb/B755Z+9D/wv93wJ/9d7kJ5wyzxTFJekht5+yejNg/yLHWsb68zerKGSovIkklyfiUxeIYhCJs99FKYlxFLQ15C+7lM1qnp5ztKsIgJu47UFXD/fAV0SoIq9ivJIftjLQu8KYBhyczzjIk3TP0PI/spCDbNbTjgKLISURN73KbyW9OuXdzn/539Fgox3heMrp1yrd5m+SzDBUZRFkjQ0cxz9F+BxXBdJYiVzqUCJxxnApYqi0mrThcjDhBYKQkHGgO3YydexOO5zVmLlkZSCJp0bEl6AtagSKb1Wir0Q7CUCB8hTUOqQp6Kx4itIhaYXVNt27OWlXsUzlLaWqQYBsuIr6nqCtFWhXsFQ5/bcDOK/eYv1Rw5ul1Vp9b495v3CeZZshln6zO0ZlmVoVUcUlgFYu55Utv3UIejHnuT76NYmlB7+kpO/shSnborFUQDCi/fsraYMh22KOa7nItn5JXCWJasX3maXpv/za++PpbvPOxy5xfWkaqRkh9a2WdmzvXSQUo06R7nWh22YrmSK2kflCC+aBeyLrfXgy+abYNxlqEAKUeXBQCKy2+VEincFYCtukXMwIjILaOrdU1pBQEvod1HndPTnjh9Rt4dczszevs7Hwd1/MIvRb9sMcFt4yfexyMU86eG7DcbxNkHmdMwGNPtQiXugT+Er/x6a9j3xpxZjOGTk2Rl2QpmLpsGgPbPslJhupNOPuebawPN168z3h/xpOPP8leUqNCRycM0B44XSCQxB0PrMJXGk9ITJqjhMZpQa/WOG1xuWR6XGCNRiqFKxt8ey0stayJuoq6EOjY4nzHREiuzWtu3NhltaPxlUR1JYejjBObYI4bIm/dsowESOsoEWhiprM5OgLpKaoyxevEUNaoyJDPMga6w1du7dPfiFnqeHTXe9z79D4Ugt5TbZKiRheCMAhZ7JaoiaW9HDDfM/gCDk9mbLWWqUTBYTunM4zoexonLeRgS4PwLNoAE0FtCnZOTtkpZuSthoVlXIVWkiRdkC8mLBYNHqPXXqbVXceLMu7MX+fmzg2kFFgdIpwgSWtmiwj3B5kY/o85rKlIsn1QMxxziqxE15Jee43NlUuErSVcXeErnyDokFYz5smY0gq01HQ7A0IdIaVixIidQjBI+tSTEO80YemsR38loDApTguM09RrHgfsEylLPZtT5im9lQ1W1mMW4xnZOGX2coG5FCJ7lv03juh2Vtl6dJmvfv1lWvoc1I7dwyOUrQgGAWlqCLsBtrTIUGPmFqNzgmHM/mjK9so6ExxFCSqTRPtQVzXdzS7SOdbPraAVUAmSRcHoZJ+jakFRGboiwvk0Bp9oButNY5SgQgYG4Tcfpfah1AVKWcL7JZGR6EGIjEIq0whxS99H+IIaTQqU1iFMjWcCXGyxLcfbH3o7+7u3Gb12n/Mb5zkZ7JPOYL7IUGlElkuKJGdxaklPDKoOqdQKdSJ4ZK+iv9Ej2qq48OQy7WCI37ZMqpz15Q4ukRyZEUrcZ0v7XOie47HtC+S2w5GLGUcaZQ3HBycsrbXxA5/OUheFbVALNA22tQOkRTmLEw6HRFqHoFkAPFxzDP9N1hPQwiFpJigpmyjFOokz4JzFCMCCZ5sUtpUgjaU76CIDn9IaRodTlDYMoy6D0me1fYHw6jKvz25zO99lN7mPcT6uWmV9eRnd8rl07gLlULJSCAbbMdbvM9mvePNrGr27ws4bE4xeEC8ntJckQVshrMAuMjoiJB4ozm+sc/zqHeJOyKX/7G3khUX6Ak80aA+tNJ4UtACNwpUOW4KUDi8OsbmlPskpVUa+7WNCSdyW1GWTKHdlii4UDg/rNIvjByReX+KZkKnLOF3M2ChizughrX4AkaPuwMGtY2womO3NiGYS25ZUZVM0EK+22Hlpl63hgELnUFtkqHClJez6pLkhXAopbc3e4RFL0TkybUnGe7ztmafI9iyz+QHnHl1lemw4vZnjbRu8VVgZdNk78CiPU0jmRF2PA8Zc2O5SjiuUqJu+n7bP5LhkdK+iSiS6X7JT7DJihJSKQEV0OwO01MxmU4psjBCOIOjgKx8tNWFric2VSywmC6aLA2qdEIQ+KEeS7WHNfyLYiKrKSZNdqqJCETLsrBDrLh4Bi0WFSzOyIsVlBuUtE8Q+ZV3gpMMTAU5KymROOpsx70puVXPassdGuI1Xr3LwVkE+Keish7SWJYmfcdxKyaoEjSMtZsjCIBc+83GKWtKsPLHCnBNuvbVDb22D9TN9bn51l+7ZARffc5mDvmZWWO7eP2TdRNCGOi0JAk1pLYWssb6gtCWh16b0HdGqz1FdkZUWdZQyun3C1hPbCL9gzR8gDxMqz2dvOmf/5Jhjb8EkLkhdgnAhrbYkDBTTQ4VJDPg18Yom7ipEnFG4jExW1E5iXUUYgjQBoagIaoksFcp6Te240ChP0eoo2h1BWWToaUWSKpJOzVhldC+ucfzKfV74zAvEXkAYeLz1xhE7Xz6lUD5L7WU6rRX6vU20H1M6yfFJxW/8y2t83595BBPktLdbWA+cKtlaX+bKsWPY6nE7zinNkO/evsK7upfpVzASKb9mJ/hRH+Uk2vPZ3z9luLrE2977Dj73hc+QVxnKs9iKBhLoDCDAeSgcvqgwzmF+a/sf+g/qsb85QwU+hD7kJc45FA0uonRec9/Y5rpTCByebkpd4yDkbe99joU1jI5GxH6HypWsRT264ynve+gKSy7mOe99fHF2nV+4fw1PneFiGnIaTknWBe1BiOj5tEqHCdoo1+Y3/uWb1Cc+g66HL2Lqss9kd5+9a8eMFgcEpuRM2+PJRzv4HrzwmRfYemSLlYtrjGWO5/u0akUrNrR7Pn4Q4uYOOTcN1r2oMaXAyIrENxTCkocWqwpyB1J4aN+itEcgIpyIyOeC9LiG0qMqPHpnDa22oHaWxKVMogkqTXnlZs7G8gqbvQ6qKlnzWxz4Y9qDmN1X7pN1PcxqQFlXRKs+VQDO88nNAt8XFLJGKA/P8yiyEtoRSWnY3znk7PoZ+v2Ii++5zHSnYnbviEtvX+H4yDI93GX7/JDO4ytUnYL5KEUmPrIwpPmMVrdH5gzHrTlrdUxYRiQzy/ggZ3Jo8b2YKjxmJ9/lVn6HzLOkszllMkcLiacDalEiPQ9ft1FelyIzlPMZZRCTzGt60Spx1COtZ2TVnLJIcLKkqvJv2Ea/JRaBuq4pJjnd+ByD5YvE3R5Kh5SLKYvpEWammCQ59tQhdIvK5DhKrHDkBvKqaYqZzg/RucfCad6cvUF3SRIoD1W1SPY9iqkjG5X4lwXlkkWlNc7TpHlBbS2ttiBQjvkso3IV3oWI8/EmN18+YB6HbDy2wt3XDlC9GNvy2B2PGU8F/cQy6U6RLcVClOTUmKDG9gRpVTINDrFnPQ56BZXwUaZiWi44e35AXRQEU80kT9CtFsfzY4ogxXZyplWGqCNefPUO3bMt+sRUicBWFabUGCtxiaA4MgT9ENWXeD3wELg4R62WpDgMFbm1iFAhXY1wEFSCKJP4JsBZiLXAqzyyg1PUqqB3rodfGdYfvUB77PPyK9dZ1DXUPjoJCfsDVvurRPESOorw2zGyEHjThDd/8Tbf9t5VNp/q01+ZIuqSRaJ5rvsQcTJmiyGresb1oeSNZE6dvsHo9AZ3ZvfYs/CeP/y/I7GGdhAgkpSj6ZTN81tsbm4yv3f73xI1pRUYJ7DOYKVpGsqkpLYW63t4vR6ds2fJvvaFb5ptqyBi+PiTzO/eo5pOqW3DyXEYrG1w0YhGF1cKh5IghWRzY5OV85vsTafoWqHaAUmWI4OAX/zNf8HLEs53z7I0vMx1l7O+eo6HpgMeNT12E5+0O2BiJa2Wot8L0KLP3itj3vw3N1kRFwj7HfwAyoVjSawReR4m1dSzMYSWpK4R+wlPPvEQFx/doh5KSk+xuDdDHloGZ4Z4taDOC+QMyqQg05YidLgIrKhw0mCkpO5JYnyk8BFpCFjqqWAx8SkmkjrVVKlGSYkVFdVCUc5hQs5LO7cRbZ+ZylgNNbN0RDHOWOkMqJMEXUBRFMTnl5hWc3qmTSUCDrsl9imPw8khuvBRnk8aFBTSECqNaikm1ZR8bpkYwe5sRHewxGRWY+7OOHd1nd29hDLJufzkJmJNktoF9ayioyLaLUF9akiLgrgWqKqmCC0mF5zcKEmOFXXqoWSJCcckZpc3Rq9zTEZtaqbzQ9IyJa3qRnJUN3oYlfGhbnF0miNFSb+VkyVzWsMz+O0+ps5J51PG41vMJnep6/9E5CVdDSJbo7X6KO3WGir2CeKIVquLF/qY0ODlNUWVMFssKLMZcQuEX1JIQe1gNDri6HSI7vfw4za37Snx5Abv2+jTij2S1OFcQD2RHN6Zk66U1MIipMR4sLTmsytOEKYkDAIC55HNM8LA58zjq9y/OWH33ikXrp7jWrjHXFbcPzokyUqmieLYy2kLSKucCsucgrQPeU+yGFiS0BK1CgKWKFXNcl+xuDtFlR6D81vkgeXwdJ9zG0M6q31+4/gmxYkgsCELUzOanCKVh0DjKYkrBFoLRAllKigmgtaawC4MCEWtLEHb0eoJhHAoLRF+w9lx9W+htmvq3GBKS4YhCiOW11Yp1iT4YNKE0dEp167dZP/gBNWP6LU0SaEJ+iG5nVOMKwZym2llmc5KymyP9z7ZY+n+mPDKEi3dZjE/wh+f0t4rCfyUm/P7vPLaTabbXS49/QR30gX3JjOO7Sn5eMYrv/4zPPPej5CXFYvZnKQoGW6tsLqxwY1bN8iosdoirKW2TReuqJqqm0qD1+7Q3txED5epqpqyKL5ptl0WBUa36D3yBPXpCcXeHlWWIKvmyMpIcMKglaGUEukkUS1Y3digEHA6GtEqPDzRxpQVr/z6LzId36YYdFmoHmfbJSrucvjiq+T3J/jDyww6beS9knTTR8slWoMhYaZZ2hnx3is9Xn55j4MD6HV9fFszGR3hvIxBP6SofXqtmoyaxcGcKBD0L4f0hyu0WiGttZAAi0AxP8hQKJSVGCRS+3iRRWiHMxpXO0xtcU5QTh3FwkcbH5yhniuSQ4WoNUoLtAJXC7ygSZjP5hVjM2JRG3QVUpgc25M8sbLJ/Ljk7t4+g7BHlEcc39ll4VUsP90lkTUBHvNWwY0rBa3c0R4XnE4z4hw6VUBaSWIvZJHBNDEkomTn6JDtYQcZVly5eo6de/v4LY+zj68iAkc+z4jaIakw7Ban7IoZS2s+paoRSlILS1aV7NxZMJz0cc5iZUErrkj8Ka/t3+B2fkohHWWWcHR6yOj0CNcZNBoBrsQUhnkyxY8c7cDHkzE6jOmvdWj31rBeSJFmtE2PsgyYn+S4+u43bKPfGouAcYhcYaRH7gxL2qcdRnhxSNyKyMUC4Rz5wlDunmKzDItHhYWwIq9n5NNjTg8GDAZLOFMigpA30h2i45gnu08hZBv9QLyB2LDwCqRpKIHjSBJ6PteqnPvzBeakpG3aLKcRwTF0ww4rqwOy0wVHB3cJvr3NoizZ3RtxMksJMsdeMWFTS9Ii466Xs7tmsT1JtxtD6KHCgGw+o7eyRi8aMLpxQLSbs/TcOe6qGcHxgu3hOpNZwq8eX+NUFVwwEV7b4+z5Vfr4nBycEobLmEo3O0dACItQDikV9UKQzE2jPaIDqrZCjEqCtkD5ohFj9yyeZym1pOp5zJ2gRlC7GuMqKiFIx3nTbekK2PQ4+8knWblbc/TmDlN7wulszv7OCRbF0rAD4g6zrEC1LM++b4lzayX9dI67dcLK4ysEGLxE8xs/8ysMJh0CP+C54ClsR8I8ZDKMEZ1niG+FbA0k4Sjkq//gxzGrLbpnNmhfOUNSV/T6SwhrMaKixqClwxMVlZPU0sf3Q5bWl5CbGxQ6ID0aMb99D/Lym2fcecnoxdfpXDhDuLpKf9DD7u0zOhhRlTlKlHjKYgXUCLRTCKfo9QckdcW4SLh3bYfZzi+jjhKeHG5z+eK72Y0sKxcfpacG9E8tF6cXkVNLflhwv5wxfmWX9/yXH6Qa9OjGK7hXT+incy4/XOL1Pb7yxbfY35F0I590njM6nSOp2ejAxnaP3sUhqw+fJTqvOYklp0mCS2v8OiAWHp6rUKGPFhpNk/dwxkFpH0SrGlM4TOkoFg6X+VQLjasBAYHwmohI8aDfQ2IcmMphw5oTd0p/0+fpzTPcOzykmgZcP50wOnqVZ4Ittv0eu8eHHLZbrF7a5PSFt5i1fJauDgi1ZTqeosOAlIKsWzIlRTrL5oHkfBVig5L92nJkUk5cSrDnWFwqWR0GHN28y1K3TzRsczpNmB3OKVbgpM5YqAQVeKQtzXTgk1eWZWmR2rHwCog9nM1xzlHKgkIuePn4dV5PdyjrnKLIyadzTg8OGE+PCUNNu46p8hqXWHRWEXiC3toWUa9Lf2mN0LXxdEDlBJSGkcsw0kfkqnnPv8HxLbEIgGU23sc7PWRpMGDQ6dCKIyoMzkpUmdOKYryhwOQJC6EonWOxmOLqGkeJyUuKRUFV1pBnGFPiWnAtuU1t4Zx/nnPROq1+l8VGRMopqq554/p9vJVlyrQiEQrrCSpbcGwTrkuN63p0ZqesVCEXVtZoBSvcdEdMMs3kOGd0nBLUjoOoTRoYdplyv1VRhY4VQpyVDNMYe1BgyymtHiQtx+rDHbrrETf3b+FlBtUd8MbsHguVs4gtj/aWuNBbY6RLDpND5osC33ZBebioiyw86toCDeLa1JZiqjAuohY1MrAUpUCHPsnYogOB80o8XxL4XhOyh4qiyPF9icFS2ZrCNuftqi3xuwGB0IzGhxze3OV4Z0RpHIGsEIWl1e8znS2Y5Qt6q5bnP76NVCOKVDCZhxRv3me41URnS2dCvuOPfgjz5pjqJKHV7jJcC4guLPH19ID9qM/qladZPaiJdkeM6l1Ol4eoOER5TQHPt7392/n8Zz5DmlXUNOhoJFjto1oDvP4SWTckH88o98eYySGyShpMwTdpKAlyccD8zTnpwRr+xoBweYDnR5jJCJuMG4ywA5xAoejEXb7t2XeAAOUJVBzSXh6ytJvysNcnW1uiv65RYcRG5fHMhXWyrMPpUkGymOEtt1CPDIjPrnIaBriJ4OjN+wTzlCKf09+wfOjjXX71X97n4EjiSmjFHvk4IehJ5nVFsXOI0yXnett0W2sUQlHOZLMR8wyBlA36G0dZWoIgROYGkXkUZU1Z1sjap84bRa06F4haYAuJdj6FkEhpmz6BorFj5UlMUJGrGTN7wsnhjLTts7rWZWl5ifbkiFvjEW9O7tI2IXHVIr97wM3omO771nE9jddytEowb0zp+CC7MaduTkXFcZBz2BIcTwq2CpgVipMsYbTIiAPJJCvZczWPXV5lWlS8OL7JsZcz79YIWaFFTeBpPE+T1RVlVSE8nzeu3+Ty+joJBf7GCq2FopjMOJkfcHdyh1vZbXKTk6QzqspQlTVFUmDyEidKssUIkWvafo+WH9Je6tBdGuDHEUoptB/hS58WCt8K5rMF09EBs/E+8LuzsX6/41tiEVCRRvctnslY7/ToxjFaSaq6prIlZZVhjcU4jYnaqJ6kpQRt3WW0OEYXNY8/9CTnli+yPbiAC5pkc1DFBLFmXB2ijWFl4OPhmEqPZJHRriSLecWgzhj2B0ilSGyOdU0Ye1onpKam9iSh8HltPKIjQqLVmElWks5TTnf3cFnMa0OPaqVgPkhxviUuFE62UBmYwxHtTNHyFfqRHK8fU28ssfPSS2Q3JqxdWKX0R2ih8P2IofJYszHT8R5z3+B7IcutLrLSuGKGGkqCRY9qJrC24ekLK6mKRomqtJIitUhfYWYVzlpUaOkuhxRJQRlaRF0hawVOU5QGpEU66FaGclSQyhHhxTZ9WmS3LYxyeqtdBhsrLHVv4l9bcOv4hNO8YmVL8PSHN6iYEAqNExYXGwbOMXvhOsPvfJgyEiw/tcmbL+9j7yk2L/QQueC1z73MG4sbiHdewl8ZcOpKlsqQvjvPy9mcS90uXV+hhOP85cs89+w7+cXf+BQVGqclLu7Q7vWppGC6WGCODvHnJbIEtI+IfIRMvmm2LaSHiAYNyeJ0Rj6fMev4qHaL9tIArz8gn04Q6RxZWwIU73j2OS5dfog75YSOr0i7cOd+ysXt86QrIaO1gHLgUx6PmXzxJn77EpfaV+jlIfv3EkQn59EnN5lGmq5pcfrCWwycI4kNbtH0yRRMePrDIZ/75T1ObjpWpcdDF0MuP9xm+eolxvsnMC6IbhsGfcmEBdmtBbFdwl8KqDxJLRy1dQg0RVVgMwOJR5E7lAgYn5SYXDYobaexJQSy+d46RxhInBEoKZtqoq7FthcQzehHGuv1OBAp8+khqlSs0WGq+nh+hckrxsEI6zSHrx0R5QW9734az/fwJimtexVVWTOPEtSah5OKskiZ+IaTSHJzXOCdBOyd7DOKUpZ620yyFOHDncNrzF3O1C/JdYk2lrjWDGsP5ztKawkIGRrFycmYRVahh5LFImMqS2ISUj1mN7vJ2J7Q0hqdxBQCdCtEetDfsqz5ETeYoaI2SysruFpha4eJYgwaZyxllaN1SSA8Aq1Qccx6p8v9OkP3LWrxjU/h3xKLgNCKzhOrRFIT9QKUAudKTFlhi4JykVDlOVUJGIHQPlG/R291SDjZZ6Fu8/7HnuOxzkMMVjYIuxG+p1CBTxiGeEIRBQHtMERGAfuDhHxR4xeGUIXMTw3vFANU4BhrOLYlJ1VBlpXMqozSCYz1OXQ+QTXlUW+L09kRWfeQ1cdrRl++x1fvzOktb+Hpkr7tsJTF+Psl891dWk6j/S52LWC2O8JbaVF0PCpPMXhsiSiCYBQjZJvZWsVjcYfhapuov8LMGrLZgp4ISSclhe8w3YS80PhRm8CHLHVNrbluOi+L0mFcs0vzvIraNGWTyXRBpx0gVU0cWIRzKCXorgdELYc9zJjcSDh0CY9+8CxBJ2Z2NGf1A9ssf2SLu9f3mBzMEUuSMJBsxj70Zzz7/ocJdIEtHbUSyFKSnObIjS7u2ph8sI/37RsknZSHfvBtvO6/zhfffIVLap3z57cZeyd86mtf523PP49aivi1+2/x4ld/CS5t4m+EVJFHJQS0LLMyAaXw2l3UcIgJ2ywWC8x4hJ7PCCqLkD4iCgh1gEdAyjeO2/33HRKPjn+GioK6LnCuQCcFpqxIK4vqL9HavoAqFpiTU1gkzMqEO7NTbiQjDienHIwnnO7d4l/d3OXpj34nzyxt4hUVL3/tRT7c3+b88Ay7d464eXBA921bPPbxx0i6HsbEVF/Zx701JtsOSU5z6lJQRwZblgTa8m3vP8NX8mtsFC3CUCKWJKpbc+nKNuce2kIWAhEozq5us34+5eandxmOW/QvLyHXIrJUMNsvMabGBY40r/GsYr5IEHhI49CAqxSmEhTCIXxH4Kmmz8OzRLGgLiH3EupugvIdvlTE3QjpNFF3k65UZBODznxulXNax12cXVAslQyiJeZOINsenieY7o6wlUEdl9hixvxGjb8VsNTvU5Iy0XNGTjC9fQM6Y1au9sk7h5zOdvC9NV4vDym0orYlypX4pUO4iFwrhtZjtQoZ1G1Mobh9ekLYCSmLmlxIGBiWLnXoZz7Dsz2yoqByhjzPMUVJWTU9CuP+eV6fn2X0+hdpbV+g199genxKOp6CEVRlBWmFZyF0HUzg45RAK03cC4i2PDobq6QHe9+wjX5LLAII8FuSRXHIm/sv8OxD78DVkiItSIqMsrLkWY0pNJV12Ad8lXbcJgi2MaLgJ9/8AuH8BQbtJVbXVmnFEdLlSOMIdcRSd8Cgv8KZi5eJlrdx0rGoc6RUrODz9MYGy37ASZlzfTbiTnqELUYk0lFVBb1A0Nvw6HRazOop83xCZhaMqzFFPCLbO6GTbdBPB/T2A+ITRWtRsnBFs5uPKvy2T3mU0bKSxIfVCxtMvrbPfGqRfsVi4xgtJElaUh1McGMfQk1RJIQrG+hOhC88nJQUVUHtKkI/JHQ+Vkp84zBOUs8srqxp6ZpuBP1ljWoJfD9GVhbplWgJfhzw/+XuT2NtS677TvAXEXs++4z33Hl483v5Ml+OTA4SKWokKcmULFmSrfIkt11WF9oowHB/6Q+Nrm60u+0GqlBAV5fLbZcaZbuMkm3JLomURMmiZolkZpI558t883Dne+Zz9rwjoj+cZ39olKRqUyIJB3Bxzzl34+442P9YK9Zasf5/49bIBuQYFlVJ91KH1e4Ko2HGo5sZRZ7TvhSTJQVvvP0Gz619iIma0d+DZqvi/HqPyJUEvkfgx6AleZKT5gKTzYmHDsNfuse5doi+HjNaVzz9lz7K6efeYGfcIkLy3R/9HvZOj3jncI683ODSjWdR44SvvfM204dn1GnBLBrzzvGA1956B6e7RnelT+k45MkMOZri14LQ7yIdia5dtPFwdUwgGpzxxjcN2hKH2OyQ24RKLlCyRDkVRhmy0lCNpigpCRodvI2YfDjg9996l+IXP4e/scosTUjOZoizBS9+7BOcv/EcUvk4hwl/4+lPc2l9E3O7xmk0sB+JWPvsC4ziGl8r/A8mHH/+HkElmZZz8lxQakHQbhDEIUWxgFLy7S9coT7JCToenmrTFB3eePurtLb6hF7I9P0Z/oOS1TXFMx/eQ481SVXSwmclUnSa4FQhRVpQBwqjPMxqQFnW6MQyHcDUltS1g+s5dFsSJSxCCaRvcMKcXOWojiGKfUTsYuKKwtGMzib4WckgrxFFSZlqEDkjaYhPHOYHhqItWHtpA+EvFdiS04wqsSzmFfOsoC5r4jMPHbu0VxrQcTjJFkzmtwnWDaO6pqu7zNMpURBw/vkW83nJdFSRFOB4Ad2izUXV43xnjSutHn0vYFgWvDF4wEQaFnWBlS5TW/Abt+/y+N4dxpMzRvMxeZVhlMCIgCTNODs5ZbQYkTcN7Z0d1tZ3cFXIQkww1lIZS1FALWusMiRphtKShqMRnuHm0VdYyGO8UP6JqOZ9SzgBqSRGaXBrTpMP+IN3x1zeeZFFKdFC4wSKeq6ptcA8YXgUxuBIRTPu4+mKx+M5aTFjPD9FSY1a38J1fawpmCUDjudnOOMJN0djPrLjEq765G6NNZor3jrqkWWRJLizgkuJoFs16MSWa5f62F0fN4bSaA5GU8os5Zkr1xhOUgppkJs11d0B/SQk2Jdk93Jyr0FjPSTyXAZHZwyKE54PL+CM5kRJgagMp4cnOHlOa7NN3dE4ssHF5gornQ69cwFux0G5LlmZEwgfuxBUEipjKWWN0xTkSUZuLRqLX0qslgRS0W4YdrddFAnGqZFK4sglrUKdu+S1QKcFhYJ8mqBlwWoQISNBOkg5vDllMD9l47lVKi159dd/n489/zJ/8LNvcvXFC7S6DU7unVHMKxbv5Zh1n7pbEhkXZ1ETrUjiWGBLgRhabv33b9L40BrdD69yVsz44CsfEHSusB50OHz7MXNTcm6zx+uLKUE/Zvc7Psp3fM/3MhgM+YPXvszbt15jms6RQYx0m0tR8TxDVBU93URZD99rEjgNZOiijcCgKKoM801sFjPCogKfnttBsuTHMaYiFwl+OUfrknJYkc3mEHhIt4UIBK/+/ldoR01uXL3OZ17+BP3v+7M8qBbIps/JYMqLbo/Z0ZQ7p4/ZqtYYnE744NZtxFNdVNDi+NUzkq+eEliF6AlasUDWknJWY840qawox5b0JMERHs3VBusX+3h+wBu/9Rbf/hdf5tVf/wM+9pnvw/Mqjt44pGqu4V8XhM0IZ+Hw8O4UZXyCQuJrjdIOTu0gRYUjBVZVKMew3nfo0+DxoaCoFVIKHGUovAoVloTNgo1GSO1ZvMDBbQncHojYsr7RI/QCdFVRTWrGD3Makyn30gH1pYLWxCMdTTk7OGX9hXVsUrAYzUlnlg/eP8F60FlZpS4cskeGrMgJLir6vZBDkRNu9Ak6bc5t7PHMlWucPZoSNgMun1vHkw7VAsTjguZdzYWzBr1DgdtIWbQ0sqG47K3zqjnCuAGh8Dl+fMIrr73KbDqgTk+RUuC5MUL4VHXO8OyI0XyI9RXdqM1uf4+43SdLc8QT7QsjBJXWWLu0fZqCWVlj0jF3b73BuDoB12CUQP4JFLy+JZyAkAq33aKezlGUTO0pbzx4hVZzm62NHXQpwHlCGiYVGonvh7QbTYLA5fL2NT6z+zRuXhAJj9iN8YImouHiBy5CSIQ2hEFAEXkULZd9cs6UZcWD59d3iLBM2pD1BZnrkfb6dLoruH5Kniywg5rspGZ4e0YSJeysb/CRZ57jcDqglgWuSQkzxem9EfG0y6kpSdKStVWXspRIoKod8kXG7N19qkJRPKhRQ8VoMUOVEf5qSZHOGIiK4T27PEduBdqIZRHUKMg9XB2iU0W5WBKOneVTWhJWRRNJiNdYctE/OMpxlUMuHIwH2lZ4SuD4BQQuIhCEPjSlQ2/FJ3It6aLkdH9A1s7Y+q5tZO1x9OpNeusbqKjL2f6A7/+JT/Ll33qVvad2SEYJ5TRnMp0RK0ulZzT6qxB56HpJTOE1e3QaDcSxIf3cYz44fIe3D9/ijcXrfKT77Tx3/TleSfbRfobwFLUnuXR+l20CVs/tcf2pp/gHP/OP+cr9W9RVDYscZQIaNiL0Vml4IY7wkdJb6iyInMomLOqMsZlSiW/e6aBKlJyqY7pem9gJCUQD37aI6WH8ktoWJDojKxLqokDLnFIAQczlvcv8jb/8v4FGSFhXOOTcmQ8RnuJtP0O2JJ9sbPBbN9/ilf0/QM+g/Ceaa1s3aNGhuX6eNEkoxBS3NtDwiESHZHRGrAIGU9hY6eO1AxrdBieHR3zsuz7M7//iK6iwy8rGBsev3WTzw5fYOucxe33E2X7N+fNbrLY9GlGL0VAzN5ZhBTaTkBfUhaDUoISHLCGwUOkCxw3xfKjQFCJjoOdMZ7BaethRghdbVKSpRhkcliA1yi61fhEWKS12ISjTDHTJ4syij2fYQlFQU70/YuYbskVGVTnLpsnSko5KTs8SkrlHKSSL+yP6YRfXRDgyJg4bfPSZ5/CLkOnNR9RpzfrliMaGxes5BC/1aDwTkYwFdmQIK0HoQcezPM8u98YzQhWxSoOdVsCN7/8sflqS5TlWSaw1lHmFSSrKfM6iWpDakirwOTGaU2o84TL0QgxTlFRLyivH4oUC5UkOjx9z5/QArcYoVWJdD9VuIqT64yD4x45vCSegjKTV6JA4BjMV2MpiZMosf0ycKlzpP6FJcpa5b+EQRy3CIEK7hkFlmC9KKFOmwyMmR8esxn3WNnfoxT4tx2Wzu0bLX6cTB4jIZz0MmfgBuCW7TZ80ydFeQbLqUDYFOQX6qCb57SF237AmYvb8kG3f45f373F6c8iNj+zx/U99iH/+K/8jYW8D3RQ4wkHVHiK0OI0AFTo0IrBFxfBsQXdjDXNzTj2wtGKf3rkWoasoq5IuPmHh4gUeSrqEqwFRFKIcBViqzFLNLOVUk2EYZZY0rZmJAs9R3Bock/kFu90Wa1WbwbikpsZXOZ7nUZmKQhkIJMqxBHXNTrdHHBm8rqJouYxP5qjnmvQbPqQOgw9O2L16gSCOuPWrb/Hcc9dw45DpozlrK1u02x2KYIqZOkzLBSvtPmUpCGIInBBTZ1AVDO6l9Dc26K9fZOP5czx35Rn2q8c87guOXtrkw9EOSs5Q3pLGYl5pRtLFVpazoyFF4SDKFi3p0ApiIifGtT6e8LDCUNqCTCdMyUn0nJyEwlk27An1zVOaF0pQt2uGesDCOAQ0aKgmbQJCLyAWXbp2hdKWVKIgrRfMigXzsqYsXB4eDVk9v03muvjG8tHVXfx1gS5BmxbHqebk7F0+ceMT7Li7dKPz1AvF7GTO4PiYqKdQoiJwQ4o6pywFYbPPcDantdKl1Xbx/TZVXjN9OMdthDz33DWGX77LS595nnyRcvbBCf1rq6x8tINKPMaiQK038GY1vczgW8mdZETuOOhAgDFL0RzpkOuKcR3gCIf1lmDoTnk8nhGmPmHfZ1Zr4twhajq0IkHY8vDa8TIaCJdCPLrWpGlGNszRukIVig18xisF3sYKWaUZDWfUr4xx+uCZkEdnpwSNAOG7KOvjNBSiBJUpHOGgmxD2NjC3j/mBz36KK5113n31Ed6+x6c3dune8UneMZzaBclOgfcSmF0Hueth5gL3LKNwfHYbAZ8KNiB06bgRgTLYWJOkFcPpkKPxCbO6YrQoOD064GxxRmdzg/bKxlJOMnQwniLEpdFogRg84ZJysFSUJqVKx8zyxxg3X2pOuz5+O6Lhdzgy/5FEAtW8oDkLMK0uedtHJhWmqpGm5GT8ECUClG1jTABAI4podzqEvk9mc+bCMAsVmVQcigWPTt7nY87TfGz1Q+xubtKNe/gyIggCHMdDzwzuwNJNLaUWvKOOEZcjilUHq2rSRYl4kKAeWc7PungdQ+hIpsLyxtlj7hVTqrcf8OxLe7x85Wn4kR/jcx88Im0YWmcBLArisEGr7UAEpijJZynGDymqlNjpMTo6JhtPmCuXtdUenVYTpxMS9CLaGzGtrRC3tdShBdAFVLXFOiAin8LVRKLgvixpFAKvcDm2Ba+79/j5xzd5pr7Iht2hG0SESmAWFZ4raAYBsYKNdkjX7dDwJJlMGRQzxpMSqwSJn9GqPY5+9RGOFzNpSNx7Q9aqmKvfc5GUmnxYo+ea6emUqx/fRlZDyn6DOtesNBuEgcVkljBskM1yRFqyODgjH6fgC4YxeBf6fPj6HtNGh8QX9GVEUU0p7JIIrhrkfPlXfpPff+cdrHW51L5EqCI8oRCipjYFw3zE3KYkMiOVGdqp8D0PaSU+LhLB9E8icfofOASCRuhisE+ilIJZOee0dolMSMOENEVEL2gRyg5Nr0831OQ6YzzM+P/89z/Hx2/c4GM/8N04Kw1KY1DWwQ/aDIxDKSwfvv4io/IR+/chWwwRhaVKU0SRYBoeYaeBqTStMMTtRoznCaubEV5qWN9c4dbvHxCvxeTDmtzWfPR7n2XyxgnDt4ZUqw30keLgwSM2f2idQWNOVoQMJgnd2qMtPdpezFP9DuMq4XiSsXBhmueUlUVKl6ycM8lTfj3d5115j53edV7Ir9OuXGLf4PqGnZ6i1fOXBWNHI1xwI4HyASQtrahaMTM/Y1ouyEdznEnNaDbm9GxEoSvCbpu9lU2KaoYtSrJZSrDeQfkurdrF5DVzm9A675PsGjq9XX7o2sd5+cozlGXNq2+9hSq6vD495XtWL7DVEHRrl3Immb2ZUk0Ek/MNoo5H0nTwz0rsrQkt7eMpHydyl/04ToSKW3jOCuub5xgvRjw+OuK9+2/xlZP32duK2QocQl8hpCVGEvo+7XaHRtQgK0u0sVhbcjQ6RdscqUqQEsdpImKXwI9ozQKq+dffCPkt4QR0XTG+WbP+kSsMggfkagF5DaXGYjCiwNYJ0vcRWhA3m7RbLaRSlIWm0nrZaekGtIIu17Zf4NsvfRd99wJmplikklTXSLfEejWukmjHMnMKspUa73ID3QNpSvS9ktVbCnkQUE5q5tmCuhZ4geJBNOI9f0DarxCLmAdvnXD5Y5t89NMfxtvp8vtvv0/7hS62MsSZJAgMnrE40tJZa7K50SJoOGhRs/v0Jr67S1ZVuOGy+DuUC4ZZjrh/hncgEa5ESgkWHAJcG+LiQbXcJzipJdcaVSqkkcSRIC8XLKoS5YX4aYBTOqRFRTdyuLQas7XapMo1yUjzyB/ib0hsZClERV1pSixh6TN7e4oZ+nSf3WZ6/4xz5/tkvQB/N2ZyZ0FEQCOOeePX3uHad19CzBVN7SJbBlW4mMxQ6pJQ+vhK4bQ9Tg8H9C14JiA0kle/8lU+eOVfcPUjL3P+xsuUa5t0paCjDemjU45uH+HcK9lwN9C+wreWhc6YFVMmek5iU6xcitrLUBFKD1MvlcTqSlNVBUL80TrDf9rDWkNZJlgLrhvguIIocJCOhzSGLJuRV1MG+SkNEdFRTRp+mzUvphRNVKFx7pVkX7jJ5pVNor0VcgWHZsDB6REP3vkqt155lWv2Mh8OPolT1JRZzmg8YG2rh1IKYRzyRYqXKVypWAl9zExSphXCC7jzxgM+/qMfJRIB1UjjX45pAZEMuXX/lO7KNidvP2D2xozwuTapzfEqwUhokqhGVBnFxBAXTS5GAW5Hcajm3D1aME4NrnDx6gAnClmUJXm5II6WtNmyUuSOJllkTE1OjQAXqscllcioyZfM2sZgK0NZGmwJhDWVr4n3VvGzLqHrUlQ1WlWEDYeNzRZalBTSooxGBILWqkSGDuJFF9EY85mPPs+Lz1ymVDUPvnpCVcWU/YqbdsDaUcCFtEeZaxxlaZ6EeMcOZl8yupqjznsUGw7KazC5kxEOoTUGVQsqbRClwVQ1RkqMjum7F/n4pc8wxsMNOoRugDEWYw2lhIaraLfbxM2YfDwGP6dWC6xbPMGvi/QUMnAI3Ji+Oc/JzQfo+htAICeECIDfAfwn1/+ctfa/EEJcAH4WWAG+CvwVa20phPCBfwp8CBgCf8Fa++CPuoeUgo63wiZ7RFJwIO+RRAuUq6Ay2LpCRiW5HOOLJq1eDy9qUElNoQ1FWRNiCXXFjm1w+frTbDV3qD1FI3JpOJLI+vihj+wanJZiHhvSboEKHVK/QqcV9f2M828I6qFPqnMc36UZtMj9ilvRgK+l90kjSV+HrJuI8u4QcWENu+nysReucHF9lfkkx/32FgevnDJ795h8mFLMM7rrAWHPpdPuoYTP4OEpk6zGrSO8oEG31SZsLCmGcS0iAOODMZqyKsm1Xao71Qbfukix7CCs0jGu50JdL3OmjxacM13Oy01c7eMYw7WtNju9ZY/ArZsTzgqL13boXlPQqpGewXFBWg9bC5pTjZ13aL3YZTQ/4+LTLTrrMfOZ4fTujOSk4Nr1PVbPr+Mbh4PRGSu7fe79+m12buyQVDWBtojAYV4keLbBfJ7Qa/dYjOeEuuLC5hp+9Qxv3P4SX/jNzxG/8hqrzXX22j1+4Opz7JQBHzw4YEiC9A1pMea4GHGsp9RqGSH5kcKVEqEkWVVitSEvS7SpKasCbTRSiD+Uc/0bgW2DJS2XvSeqLvFqHyUdQgtWSMLYw2pDbWCczhnoKc58nw3Vpuv3iP02A5Mwe3DA00HM/skZX7j1Fg+nI87mJyySIzZLh09c/rNseV32b5+QzXJ67R6zeU7Tb5MOU1zfocoNubKEbsDRO/tc/L4rHIzP8LVD//wG167PSU4yTjBEOwGtVoOLUYf7d0dsv3iJcj6mOTHodoCnLFJqalNjmpI8dzj6oKB8VLPqCza7Hh+9sMb+KOfuYUKofc7Vm+wXD7BnC9Sexaoa14EyLaiCLtSW0hYUdUXlLNeBowSe6yGlQhZPWMJrgXQlWWKZj3KyPGPmTHFDh/65NQIbUPbOiEaa8UmC36wJViLWPrbO9ofXqYIZcdtnfau7JLU71pR3hjwTrXAcpiRK86q6j+4rrqV9gsJF25rFIse/AyvjggdFinMhQvVd/KaDk0F77NBaSOqZxowlRSZIhUHXLgmwubLNp7zv5M7iETJfkCiPgqWEbCU1fhjR6vUYlgOKxowgKDHUKNcFd0lv06DBtjxP2+xR+nOk/Pqj3P81kUABfI+1dvFEiu/3hBC/Avwd4L+21v6sEOIfAn8D+O+e/B5bay8LIX4S+H8Af+GPuoGUDq1OE1comrpNO+thA0npZGilsXpJN+06Cj+ExqqP8h20rbBW49WWdTfkuc4Oq/0OPa+L6y+v8ZyaQAp8BE5g8VYDTEtyRgIthatgnmcc3T9i/V6DqYxY2RKI0qceV2SjOY9kyte6Z8zWPNYraNmQK/11xDzh5Hfusv6dT7HYdNne7OOtG8Z5Rm9jg7vnZtx9Z0zwqqXf6RK2PZRToZTg/DObTMcLkrEmSRek2Yy29NlcXWF1p41RNX7sEzVdrKwYTVImpyXVMQRVhHINaVUjrSb2InzHME1qmnnI9sZlgiRG2xrHgdOp5eRkTFZAYX3a69C+WKO6OcKXWGExpYuyIYG2tOOQaq1mJkecu9Rl7dIK9z84Qs0i9EmOxHLpmSss4pqV1T6mNLgXKxo7LR68csCVbzvP6eEYZ7ZkxEzzOf32KqPjU1ZW+9QahvenPPuD1/k7T/9N/unXvsQCAW6DWWH4tfff44XN81y9cZni3jv8yv5b7OuEKZoyDhB+QCA8lK2oTYaxNVmRU9Wa0mq0qTCmxgK1WXajfrOwDZbyiTOypl52Y0uDqTWuo5ACpBUoQtxGRG5LyiJnf3HKvByykzb4+M7LXL14mdfnI16/94BZZcCNWW0Kzsfr/NRLH+Oac51bv3KG68SEqzHD8YjexhqD4RDXExQ2p7aW1a0ud7/0gNbVFs5uhX1gWFnrY+OKS09f4dTMMWcgWx6PGXHhhU1Ma8jx0YhW2KbddCjqDIxAiwwhKoRvUd2K9sWAwfsOd09gf5AS+ikiCHAcILcEJuap3tPsHx8gqWkEAld6LPIcK2si18GrInJ3gbuu6Kw36HUihHFJFxXFvEBqh8H+jMNHA6ZZgXUk3ppDt9ul3Y3Jygx0Rdj26He6DGeH1C8WbNzoc+n5DbympBtuUUrJGIM4rjj57bvsmJjtfoM7xQlTlTHpweunJwRjw56J8HoRQd/H9wTDSsG9gn0O2by8SRyE4EKRG4IwRPqGsirwHIGHS2AcOlET3aw5123zbLnNWT3hrcWQwyrDWo224PgOjVUfv7YYpallBcpFKAclFJENaOVdmrKztJXtJlJ+A5rFnghpLJ68dZ/8WOB7gL/45PN/AvyfWS6UP/vkNcDPAf8vIYSwf4TQq5AKL/KpTUGeFJiRxG370DKkJkcrteTIqWoKb864OmbH28YUUJc14FDVliwT6DBkoS3VYk4kHRqRJOzGaCGoC8PkUcZclFS7Hqw66LokWSx4tP+Qc9mLXFx0yfQcqQy67cK5BvfSR7S8hCYOsmpzycRsmICkzskPMx588T32fuxFdCxwnYCe57GIMkQVIoOA5qCgudnGGo0sXPJFjWobXBNSZ3MkLp1mG3dFMJEl0ibI2iKPK8K5Imp69PsxW32He8kJaqEx1EQtxY7bwxQGxxF4OeyF59j0tnHGyzSUBKrEkCUWN5L0VqC7p2luFJTKQulSVpa6VHi64vKVDrMRZKZkb32b9Qsx7x89RhuH6jBBuQ6b223EqaDWJf31Ht5U4MYuqx/qM3p3zOntM7xei+xsju97CKOZDhNWN3YZDo/YXutRupb9d4bc+N6X+N/vbPG53/0yDxdT5nrOcZbw5aO3WPccfvDaS/z19nfzT17/ZW56OZkI8WyD0G0jyoI8O6WyObVZiswbYzDGLHV8hQALxR8uKvOnjm0seFItUxp2OT+BwUpJXVry2uCKgCDs4bg+STmlFAWuqtgrXf7ac9/FUBT811/6nzgpa9qNDi2vwYoNOdfZ4rPf8TE27Ab3v/gYp6uIKoeD0xGrG7uMRiOwGqMdiqImXG1ydmdAXdWsvtTHbSrciaS/1luym0aKzbUO08Oa/DCB2OXu0QlPnd/Fq5scHc0otWJvJebO7QmlUjgeeK5AujnNjQLSJfVFOhLMZ4ZQG6Tj4EsQpWKnew4VOvgK4lhR15LOVo8wAqUrXM/BjS0Xn1mnpCYZViTzjCzRmEBjEIxlgbfapiMt49mUOqtw2yHV3JBPa/zYxRpNc6dNU50yfdlHXAyINwJiJ8RBktkKu5A8/I33CE8K4lZIwwQYs8Id5jScGXIr4W5fcDm6TjRxUInBmQkuqi7vKM2jx+/R2ohpOz4uLtkc7j8a07QegQZhBMI6pOMFSZqRmhpXuRgbkWVTqtoCirqslhGOXzOujim8OcZUy454KVFW4kkfdxZgp5LcFKhGgRf537jTQUIIxTIsvgz8t8BdYGKt/Xc8pvvA9pPX28BjAGttLYSYsgyrB/8///OngZ8GcP0QgyUvMoo8oy41vnYwmUUVFi8OyGyBchyMNAyTY+4d3mSttUtdQS0sRzKlLXK8sqSnHZxaUDoB0ggG9TKvZ8WykahCMtNjWHdZeCWnixllCqsRlKHFUwG1XzCvat6rj8gjSyR8aquIM826CXHkUny6qTpU0zn+XJP6Dq6nCYTGEYZSGBZGc+76CkmVs+n3KLIFselQjOY4ayHx5YByWlMmc/y5Q6fRRiw0taNRLuQW6qRintUExsNod6k0VVmQhsgHKyC1NaWp6XiruKmHLhb40bJ+kC0MNQ5RbOns1nhrJRhJPZXoXJGVCoFmoxtiTl1OjxZsrMWsBQ6nt6dU1qNRu4wWp+x9+Cq9FcGtR49IMsHW6irzyQQ7DaEzY/flHe7+5mMufriJI5Zdyt1+i/k0YzCZ0V3vkuqMjZ02j9K7zPZdLr24zV/+5Cf4F7/6eX7h8Tsc2QW1ThjbisdHb9JvrzBMRmS6hXVCOn6PwDSRClTUYJqcUpkzjNLLUynCATSuBV8Ksj8iZP7TxraKfZpSUlhLJQWg/r0urDQunlyhHa3hyRhjoCMVp2VGmg04ygf8ozf+FYPpkMQxzIWLkzTYFDE/vPshfvKTH6e/ssHh6wdMvFP2di5xcndKZ73LcDAjzzKabY/xYEYQejgaHn4w5tJ370LXwDTGn07or/VJTjSBmHP16h7RiuXhK7fo1edIphWn0ylrkQ9rMcf7KbGO6dmQx2cZJT7W09jAxWtavLWCjvWoCkE2d8jmFj928RQUaYmbxnS8NQpdoVVNw/EQnkFKUEahXJdau5w8KshliTWW2mq0q9F5haoVXXxG8ymlrnHWLF47QBpNcZoRi5BFNqHVjDkshnSeWuHATCmExREGJWoyq0gr8OcaZ6ppdtoo38OpYL0KGOoJi6aLIySZY3mbI57ub9NxHbzCp9SWNQFlBieLOc0oRlaG8n5F67DLDEXiVAirqbUhz33KwqLrhMLRjFXBgSg4kim1dXAqQVElPB4+ZpgcYxyDNC61rQmNj16UKN/ia4e61BR5hnKCJ/0v35h0EHapYfaCEKID/Bvgqa/3xtbafwT8I4AgatrRcEhLWoyuka5CqQK9yCnODI0Vl7CtKJwCV0q0STiY3mOaJzSCVSgKUpPwe48+YKEucqF5lVbYIhCaKA8JjY/0BEoaXD8niAQ7q03evnuHd5NTBpOazXqdnJBjUSKcnGYlKMqMiZuhPMnUwGrS4MX4PPpwhqMknoqYZpqG36SYlxRdF1HVCAeSvGSR53hOQPvSedqHFUHdAqfiwfuHtPZi5GFB5IRsbfeY7eZgoHBqNnpdml2fQuTM53OMBmkC8tN6ySboVTiBROkaz1PUjqVcGPJE0A+a1MMKgQtGkBeWGokbCRo98BoajESPl3wm+cIyLyQ7TUO7aDK+W9LuS7qeZJ7NmRgBrkOIw8a1FdZuxBy9e4S1EKLo9gPyfUk1zDGhJL7YYeX1MYPbM/ZeaHP/vRH7+xOu3ljl9FHOqDrjXuuA73Bf4uKNPe4MPqCbd+k1O/z4c5+mnA/5/MPf4kSUaCEYVjUH2YDSc6ijFhiojERXYN0Q3JBeMyYuIkbZIwwLItclVB6xIxC6YPRHrJM/bWw31pt2I5BY5bOoLZm2pJUmJKQX7eH5G2gVomswZUalFNlSLoGsmrB/fEaAh+s6KJuxZnO+/9wNfuK5z9CLu+R5yp3sDhdvXGP0OOGV+C0uzrap0ibr50JuvXOGqxTbV2IevzGl34lpXuxQJwVVnuNqSXcjBCTW1pwNZ2ze2KSYraBch8SxTKqKICvpeCFZJBnfrejETaaFYX8ucXxDEAfoOgW3xmtAvOKQzqFKJDazeK7AES5iUtNfiVmkE0praDfA8QQajQwdKlHhGJ/8pMZuKIzKUV5JZ62JbwPm44KTegy7kob02EwDRvsj5nWGQXL0aMzujXWE8ui1DGprHc+8T5LlJHmBE3nM6pqFVvjzkoYfU+UunhfhaIleWF7cusLX6gecNmaEUjIpM4oyQ9PgUKRYHWBtyHq9zuO3Tsg6Q55prPHs6mWKpCZPK6pi2d9jSkteliQmIy9zZtMZ9+e3eFPfw12NcWWDyvcZnD1kkZ+gZYKSgsoawjpCTCXFMMfv56ggAtdH65pksWA2GGH0N1hPwFo7EUL8JvBtQEcI4TzZMe0AB08uOwB2gX2x3JK1WRbR/tBRVyXHj+7iBREqkBhlkLamyguUjUkPFYt7E1YvOpgVRSENtSyo8iOqKiEyTZJRwuHd97jz+Ktsrj3Hpd0r3Ng9x3Z7nabTIFYeG2sRrTWXqKNIrWDvYA/1oEedKWJ3Sezk9w2brZiyFNxWE9JQUGqJyRS7j1YYHpzQ3mswneQ0OzEbzSaFNGT7GeFWn6mekVUlJ/Mp+ycTNht7FEGXdD4kerigHntsP9Xl7pvHrF5sIwOBmRdsrDTJRMnUMdxLzlitHVb6Mbu76whhmZ+WzB5YSimgX2MDC9aga4MpJaayOChEZZFWYwVQKUxZ4zUrNi54NPolwgiSIxebgqkhrRTdUHO52+FoUDEtLNf3PNwiZyBcrKdwfUOApL2zwdl8xGxS4Ds+K+cizMGUNbPCfF7hSIkUgv75Pge3TzFGsnqujUxheDBm69olsnHNG7O3KHXJXwk/y+71pzlKRuysrCFNwY+/+FlWwwY/884vcWYXZPmSF8n0fGQA1s3JqwKhmyjhgbA0qGm6DWwR0ZIJsSeJlMRoQ6YVxvzxHcN/Wtg2xuJoRSih4ylSbVhYgzIRTbdBgUumXagFuirITY5wc4QFM/GoF5pE1oSBZlXE/LUbf4bveuq7kaag9gSHoxG715/GXzT4ov48787e5zl9mfDaGid37rKz3sNEYIxCVxUbl9eQQqBKgZxb1jb7hJvQ2G4yf5gwmxY4sxFbT20wnZS4OFghmZeauMjZijzemxp0XnN5tUMynzCeKKLEIB2BiFwaTUurX6PwOL5fUs9dQCExCKsRlYPjOhjfUvcMxjPggBY1OhcwcPGMpd0UNNfaYCXj0YzDwTFnRY1pSNq1T2g9FscLhCPQheHs3phLz2+QHwuc7oJ0T1F0O6zk2+yfPOJBd8a6cCmNR8PGFPtDVoIOvidxjcd0ssD3FMOvnrCzvcLxlYQykiS+YJxVXHBiVjzL8bgkHzi8WF3jSpXjDDTb52Pa2x7RDUs21UxPKg5OU+amZF4mHE5PePvxQ+4+vsPx6ZuonZAt/TSNniUtj1joGZWq0MKgtMAfas7uzYiDFZTnUxULrK8xyqCNpExzjh/dpa6+/kbIP7bTQAix+mSXhBAiBD4F3AR+E/jxJ5f9FPALT17/4pP3PPn7b/yROVOWR0RPD94nzwfUoqKwOWVd4ElFELjofEbycJ/bv/IK9Z0hfuVgjKEWJbNqwP0H73D8/usUp4dkZsHD9JCv3f0Kv/e1X2ORPebcZsCFHZ+wU3B8dsLrrxzz1m8NGLymEMcdZCCpWjlrV0POP7WK8CWP9IB70YSFV6Iqy9bbDYqDGd5TDQbDKe1WxGI2QRSSelrhPaiITzW2lNx89JA3H97FD5tsbF+iCgLctR5506KLlOS44Nzzq9j7OXlWcObM0Wc1fR2y44V0lEOdC44OUm7fHHDweA5G4XqgwhzrpTixRIRAaNFBTeVXNKMGsrJIbRFaUaYVEkOz5RC1NLY2pCcuehhh522KhYtZGHY9j2xSMU1K1tY8PAdmVU3iKqS7TG9FjsRmNUcPBljp4vYF61fbdJ5t097rYJOKUC/TVOFGg51+l3SUsXaxRW81pBkEeK2CtfN9fnLlM3z5ztd43T7g/MYWjX4D2VWw2eDzX/1Ndree42//mb/DhxtP4c9LTK6RKgQlsTIl1VOMW4NYIBhQlPdI09soc0bL0ShjqLXDqBQ8TiuqP8QJfCOwXRnL47RkVApq7aCMeTLHM9L0Nnl5D8EAxALj1qR6ipUZKIlUISbX+POSDzee4m//4N9hb+t5Pv/V34SNBrKnaKw0OL+xxRv2AV+68zV+cuXTrJ7v47UKmkFAby1k7VKLdJSxs9Il3GxgKkukPWxS0d5bPsP1q228vgTpcfRwgM1qIkcSCI10NYmrmFXL0zxrax7TpCQdV+x6HmZhKBYudt5GDyPSExdbG6JWTbPlIDHL46haIbVFVpZmGFH5FXVQQ2gRITixxHopKsxxPcAoDh4vuHVzwNFBSp0LOsphxwvpmxB9VnPmzMmzAns/59zzqyTHBbpIKJoWd71LFQRsbl/Cj5q8+fAuNx89xJaC+FTjPSippxWikMxnE9qtiMFwivfUcq1vvdNAlZbEK7kXTXikBwhfcu6pVdauhlStHBlIxHGHwWuKt35rwOuvHHN0ekLYKTi/43NuM2CRPeb3vvZrfO3uV3iYHpCaBcXZIcfvv879B+8wqwbUosQYg1851HdG3P6VV0ge7qPzGUHg4glFWRfLAr+oKPIBpwcffGOOiAKbwD95kjuVwL+01n5eCPEe8LNCiL8LvA78zJPrfwb4Z0KIO8AI+Mk/7gZCCbSzYLC4RxxsYlxDlkwQRYl0HWp5Rp4ewjjhwW+8Ru+pPTZefoapUyMFuG1LvT9mxxVYG7K7scl3fezb+dCVC6RW8+VHr/DVr32Vrhfx9NUX6AQXIJPoLIPLHvm31SyKjDGG0ekxlJJTf8axN8cVDvEI1qKA5oc65G8OuHB+nfFhilzx0GWBtBZ5WrK4fUz7I3vsrTxF5a0Q+12UExC3FaF1OVvsc+Fal7OvTPAaOSsf6iIPfcKZz2B1htBttuN1UiqGyRyTQ15K9KIiyTMaMkAKQ6MO8GoXTY0RAistSZqjC4E1AqEc6tKitSVqSaLQhdJSFYp64WMrMNKjKEo2Y2hYn/2znLDnsrkq0UIwtg7GNUinpNXwCUrJ4fEIXYYIKdi6vsZkPkEog40MnVaDuV4abH89ZM9bY//+EBFqOjtN/NWaIBf0vn2FYKS58d4Ffu72r/PJq1dZ6W9STFNi2eGlCxf5v/7c3+ezP/Tn+elP/SXMz1u+PHiXTCy5gIzVVGKOVGN0tqCojlB2ROSWeJ6DkU2Ut8YwN5xmA0pt+CPypn/q2AZBoi1VptGqQydoY8oThDen1FPSxRFarOC7m8gwpq7nGFsDCoQgrODllWf56U/9Jd6cPOQff/5f8H/84b9FLDsUiWBldZNivuDnbv86N9oXuHx9l05vhfEfDHGvNwj9JnMnIRI+Oy+sMOoaijzDuopOq4ENNaWyFPMJW9fXuHc4Rc9DTssJWxs9WqFikBQY6zO2DrEQbK5K5jOXo7OSnThgPaw5Wghc30OUNXXlIwx4viIKBXlUkY4NtbD4oYMxGlsuMbshYgIhkFhU7aLqgEoYKqmYHGXooKIqJVI4qAD6jSYRLgeTEwbM6M9aZGlB40MO2TihHEpWP9ri/mzMartNu+2Qobm0/RKLYsxec5U2LRa3H6FOS0xk0UWBcj0WhykXNtd4dOeEzof6qLMJ5bhi1oVjb86piQgWEoozRMdl3M5w/JDwS5r8ToHSmiwrebh/n3dvvcG4TPjQSy9z7elzvPjMX+Zrt+/zm1/5Ax6XJwi3ZmzGOO02qa0wFbRrxfFr7zJ6/xEmq9HdilrGoFYps4zC1LgKplnGYnGEdud/It3w/2tOB70FvPi/8Pk94CP/C5/nwE/8/zWJwCXYiZjUx+hE09lZIxlllJMZ7U1Fc8Vj0Y8oigypYfrWA9LRgp3veZEsMhA3uPbsx3nZWeGlizewfszbj+/z//78qxyWI6yv6PhNnt5+iobZQswDjNBsP6NY+baQ9xuW/aridpqy5kjaUpEWNbowtOeC880eO67H0ZtTttZ2mE9nlJWl2a05eXNBb6dNkswpjmu6j9qsPNWn8gKSQuNaSRQqtIGo0+Zof8jG1TbJowVpd87aJY9u6dGXIcN0zv3HNRu7a1zcXWMynzGe5pjURaeCjIKGdGgrH0pNJQR5JcnGFfXMRVqHWhVUVlEag3QhbCpcF6qZh65AlzXSOuS1xnE1Vy81OXxQMis1T7dCimkKTYF2Ja6owVP4Auo0ZVrWFAuP1Q0HW5YcvD9ke69Po6twsohHx0c0wxVs37D+4gbRFwP2F48JLrRpyDajwQxhLd2dLj8sP8H/4ZWf4YuHb/EdWzeQE43IKi7sXeWjV17gf3rvi7xi2/zEp36E089PeLdM8BxwpcCr55j5B6g6I3CnGFtTmQZ+sIUfXqI2LYQ3QwYGWw6XlMXfJGxbIbBugPR6CG8PZAs/2qLO7lJVhyiZ4Ioc8gRdhzQdS+kIKgeqsuRic5s//+kf5R///s9zV0z4vksvcPHcNUReYSeQyprfO3yHh/ce8J995K/T3epCYilWM1b6bYxxmNxPuXp+l+b3NBnuH+ENFONsyO7GRfyeS13Awe0zrj4V0IhcxseCqShZTVN8X6E8haprtCups5p6XrHRCnjvMOfYVFy9FDN4LyUvNaGU6LJGLULKAly3ImwqsoWh1AZhJa7SKOtTzzyysabR9fBdg2stoYoYy4KEAp0KhHAJopJuO6DTbDEbZdx/fECd5ezJDibSjDs5p+kcc+LQvxpzfDgk2umimy5RKKkzi++G9OKYFb+B/GDA5HhMkBQ0Gk2G+1PWX4iZHwvm04yttR2O3zvh/PMxTuBxZzFi1DSk3RrhC6ZGc+pVZJHLthtx/bsChn7G0W2NnAdEaovNnZyHB6/yb976IuJVzZbX5eruFX7qz/wgovgkX7v3Lq/VA+5GE4zICDPJvd94jWp/gOtITCTxVyKaKx7CSZifjfD6LYJ2zGT/lHl9RrC9jLq+3vEt0TFsJWgs9XxB4MVYV1EsUrLRnHYvJoxdNi6vswhDmNVUk4JsUHL8OzfZ+MgNXtza42M7O5xmGf/s7d/m1vv3KUtLY3uTdn+Nnoy53r5Az+zilh7tVcPFD28RdFxuf3BEntfs3uiRdg0jcuZVjSvabI8N3rwiUjHp7QXXVzaZBgXDdxasfmSFx+/vEzcbzA5nhD2fZH9GziH+XoedVpMjmeKpAEfBXCesXFvn5INTxitztnZ7mFFF0jpDxIqL3R3iZov902MeP35Ee9FiY69Hq+czflgwX9QEQUjguRSFRmkBqkG6qJkMCuTCx2QGT/okaYWtLc2uSxhJqrJGFxJTKaTy0MLFMRW725ZKFxylFWsrDdxyQSlcXAVuaJChxbEe3sAyOy3JCxeTakI/gFJxfJCzccEjXDMoYRk/GLOxvcekmuGGNb2LEeks4sg95dL5c+zP3qQZbOCEORefvsxLj/b416/8Mt/2557m4NYdrm9eZK4FP/LJH+PdL/4Dfvu932Z4OqQVNxH5GV6V4AmFYzTW1uAYpAGr1mh3ruO7GxQmpiDHCBffb5KW4282vLFIfL+JES4pLr7YJorbKH+LyeQmUo8wzgxrF7i1gzAKYTU6z2jGG/zjL/5z3jl9h5ef/jA/8r0/xlwLeq7izq07bH37Ff71K7/CS+t7XHr6ClmRoHXIvnefzUuf5O6DR/SaEb3tCEJNpZdF1kd37xBt+8hVh7KQHB0UXL2giHwwaUGOy7wqlwpeTYc6LDFWYDNBVQncckG73eBomLCqLed3LI/vabTwEUaSLwzS1ShfE0aKZstlelZRppZGy6fODHLhMR0YIscliB1qrdG1IPAbaFFh84xm7NA918ZUsP/4iOlojoPk3LltnDncd/dJ8jM6ox5y1+UwGlEMKtavrjMMDU0/pOn4eDpnM4zQs5L89UP0/gyv3WR2NMNrezx+f8TOtR3OXhmy90zAdbnF0e0zohdi2iQErQZut82+FVRug4iA/kSSvTbj1C+48uImWxcq7r16xOTMo2d2ubFqeG96n5G/4L3BkFd//fN4nuDqU+e5fvki3x+u8eXRPq8f3uP4lXdgUNJqdnE7PrQc4u0WYSyokpx0NMcGDnZdURUZxXyBcOSSaO7rHN8STkDUBn+qGA7meDYnnxeMH5xRHExpNds0t5uopke03cWEGUVlYC6oRxknv/sWrz6f8ttr71NKg6FGdgrcY01oLP2gy5Vgh/PuNuvtJhcvrBA0Qg7un/HoUUa+8AmagnIwYu3ZJvJilzJ2AUMrWCVrJpAmFF7EwcIweWXE7jOrzNOM/EyxccPn0eGcxrU27ldnmG6F/toj1EcusupHpFpjakGSVaxshMSbfarplMlOxvl8jUJ7TJwpZ3GDze4619b2ePh4n8l4Sjov6K202Vzp4ORzTC0pC01d1oRhyKLS3D+eUOQalTk4RqFzgSkrGg13mQaqNWWlMJVESRekoZKaRrhgu9vmg/emWBGw24ekqklinzioEaHAkSBmFXoAJxPLsJCYWQGF4fDBiDRRWN9FdDSeUQShotFyKWcuEot7vcFqucGX3/o1Ln70HCOvwGwm7D84oru1yQ9+7Dv4e//zf8vbH9wiPT3DdSJaKuBgNuNvfvQnSO4PufXwXXRg8XabOFmB53g4qn6iiRzgNTZxGlukMibJJxTmiFLOyXWKtgUyrODrP0r9Hz4UyLAitacUekpAiFe18GSA9GP83jVEekS6OARyPAyydDF1ieeXvPH4NVQueLp3ib/5kZ9gPJvRa6xz5+yYx6cnDD+wHO3f5a/98N9iIOaMh0esnd9k9KDArsPtV97nh174FL4fUZHQcB0aLZcgUHgrAbojMBNFligO7p7ROt8hGVRkrZDAN4SiRghwWlCFkFjNwnWJkprdPpyMBPdvL7j2dJuzcEpStfCMAOtSZaBrjedCI3SpIkgWGp0rXCUxmWGalOQHE8TGGrHrkmXZk94DSdBx6K80OXo8YTScUtY5zbbHud1tTCU4tCfM51M6egN/I+ZBcEq1b4g3+7AZkmQFDQ8cYWl5EaQG8/ojTFLhnljkhwIm9yfs7TQYvKOodgp2b6yx/3jI4iMeZRzBnmErOk8YN2iqCJB4eYW5t2D0zgQ7cLk71xy8cZfdvZBLl9bJNzLu35eI6Ta6BbfzfSZmwNxkVB3FIQ/5naMDXCNonGqGb96hTgpQDqbh4K/HqF6Iaip0YUhOMqb3h+RlTby2TTYoSB4lrPQ7iPo/EnlJrCDyQ7JGgXAhSSakixmmTBnuH+OGEtqK3CuxUUURlOgMqAR6uuDRK+/Qfe4y7noP7WbUfc1kMsA/a7K98xLX2j0ub2/S6TcZn+U8fmdAlgkcPyRuKBptSdwP2Ws3iJ2AR0dTjscpcSeisbVJ0c0wfct0nuGvrDAbJqSvFaxd7JKe5rTPB5hUozxneXTxXoJ7JYG1mIbjMUwLsiRj4Gn2Lq1TvCk4KsbEXYcLwRaBdDmenBE2A/p+zOVz25ycjRmczBkdLUiGGaHbQBmLkhLf85gsMh6eLRguclaiBlZrLJAkOY4nCGKFchR1DrJ2kQKUa9COxpRTLu622X+cMZvFNDcNgVMxMg6mryjCGqUkuhJEucEay7w0OGlAVmQY13AwnFIWDpW0JGVJr9Fga3Ud5ThsrvWotSbYVnhVwMmXTjhNR+zubfDu4w+4unmR33/zd/mzn/wzvNi4xOe+9m/5Wx/7UT73i7/On/3Oz1KZlNPTAf+nv/5/4d2HX+LOyV0GZsYH5YhMapSASvqUjseirikmj0FZHMciPIFyLQoQtUYZj8Ufkg76RgwpBEHoIWSFdAwVGXk1xpaWOhegBb6wuK6PWy95bjCavnZ5av1Z+pstLq9f4plz38bde2+w2u9TO5Jf++0v8kM//H38gy//G16ILvHc+Wf5hd/9Zb79xkd47/Etdvc2OMtGnCQnuNshylWkB5qNtR5VDVtrazgNj1mZUMmAslAcDKbEV1qYArzUYy5SrAUvF6SBQnmWIrKYviKbO3SdimYsmR3FPH6ccXEj4s37Q6zXxhEgagVFSK0rlKMIGoaiNCRJTivykdqipMdgkmDrEedWY1qhhxUWbQxVbblz52gpf+pUbG10WF/rUmMYVwuOJ2es+32a/Tb380NOpws2TRf/4hqP65SsLMmjFv3IR+kad5xQ3E3QJSh/GdW2zwekp8u1nBzk2Jct/o0VpnshzWYP5QvaJkQcztCTARvdiL3VNos2uH3BojI4xlAuJHdfzzgMH7NzrsWF66t0BwHuQUaaTnntbMA4GBD1+ziuQmUB5mTEo7fuoPInNSDXUgQlZVQhPImoPOppwXD/iKpM0QtBkkwQriVqhER+uGwS+nox+vXD/OsfsuEin4rov7xOvOshSdm6scfuJ56mc7WPaVhKW4MS1G0P72IX73ob93qM/1STcDdgkZxi52cEsiQPNG7X5UPn1vnk9es8f/UK2rq88/aI924mJEkD12vSaHn0thzWLjpcfbFDf73J9HBBentO9LBg7ahg/WFCsK/wjqBZN8hbMXrh4Lk1rU5EnlWs7TXJhymN9TZ2Ad2gRfbuPs7+BKFLHGXw1PIcuBIlu9t9WniMszFzuWBjbYOG6/Pw+DGzMmOSpTi+w+bqKp5QUFuKPCMrczJjmBQVR4MRtYS42abODHVRUBU1RgvChovrK3QNunLQVlCLCuvV1KSsN32KqeHBg5JZYQhiSeGD7XnIlgFfLqOXYYVINToTmFmInEs8F/q9PkWx1Ir1PEle5hBCb73HoigImiH3b51RLGqCVsi5c9vcO3jAhcvnuf3BXfq769x5fIfRaM5/8sKPkw6OeXd+ytZej3/7+q9y7eqHePvO2/y9X/37DHPNpfMvcnH7Go5wqIRgIV1mwmFkDDOzoFYJRiXUTgY+WLXkE0JKpFSIb6ITEAikVPCE48gqCT7UTracs0qYmQUjo5kJh4VcSmm6wuHi9jUunX+RYa75e7/693nz9ltcu/oh/u0bX2Brr8d78zOSwTF/8YUfZzSac+fRLVb3Nrh16w7nL53n3v5D9s5tE7ZCikXNg1tnBM2QRVHQW1+BEPIyw3OXz7IsJf2VPq4Lci4xs5A6E4hUk4wqTC3Ak8iWwfY8Ch/CWDIrllgqpob1ZkBNusSaqNBWLDFYg+srwoaL0YKqrKmLgjozSwxLOBqMmBQVmTVkZU6RZ1BbPKHYXF3FCRwmWcqszHl4/JiG67O5tsFcJoyzCS3rsbvdR8kSXWZ4Clxllmtwf0L27j7dsIVNoLHWIh+my7WbLdey59bohUPeimnWDfwjQbCvWH+YsHZUED0sSG/PmR4u6K83ufpCh/VLDr0th0bLw/WbJGmDmzcT3n57hDYuz1+9wievX+dD5zfwui55oAlkiZ2fsUhOCXcD/KeauNdjvOttvItd6rYHSlDaGtOwdK722f3E02zd2EOSEu969F9eRz4VIRv/kdQEhBBI10E5DmDQ1mBiiXBcsJrSGJYquUtRatFQmMCi7VKFRxiJ0JpJdUJ7FHHRW+MHXvpefvjaZyiGDW7fnJAUPiUSoRRBKIgbgs6qYu2cT3vDJfAlRw8n3Lt5Sp7W7J7vs73VYTGvOfvaATuBj+hGPDxdoM/A22hgtaG1ERJEy6ORjb2I7O6coOmgEof6wZS6tSzkmtJFpgVKWlrrTS4mffaLMw4XZ3TWGjx95RJv3H2Pu0f7nN89j6400+GYyOtg65LSFhgE81lNnguU9HEdg8hBZArXCZimNY0gxA/AaIswCl3XoDSOZ7EIykSjZMytR3PmtUvUlXSbHrmfIXsuuPVStH4M04clexttFouCehpi6ppW16EYFejUwVGCwHGZzjPqVY3XDTl+OGPV6bB/P2H7iqbVh5ef/hC/985rdF/ss6J9ikVOM2ry+t33+IHuR/nM3nfyq2/8Mn/ju/8a//PP/zN+7f5v8tf/3P+W/9sv/t/52f1foD4oUDIgdxsk2lJbQ6UUxvXwfIuQy74I6XroJ/ZeCgHy38nvffOcAEKAWtJGIJYC61qAdBXalAgJQkGZC+q6Itcax1gKafiXd38NbTIc63PD3eZv/PB/xr998JvsTx7x8vf+VX7mN/8HPrP7nVzqXOBX7r5CM2xRJDkrtUev2Wdy+gqfuPEhrNUkE83+/YS1Z2FapLTXW9S+Jktz2l0Xx5HoRFAMC9qRy2IiqKqAvJ3SbIdMH05xlUuwJpBuDX1FPirptCKcsGQxdrh1N2Njs0mZTHGbID1LnVagBVI4GGsIAgdhHao0o90KqDKFiMAJLKr2GU0zgsASRBpJiSd8hOtxNhzT2olxfMXDxw+QEq5fusTsdM7h4hQlXfY6fVqNkKkd0JgLwiigXYBMU9wHU8LEwW06qBIauxGLuwlB5NLaCJHG0NxoUJ5B+M6Cc2sudpxykhe0n9km3ooQesKjBwOSaYkpBaubEdu7MbFXcWqLpWSmI8gyh9nCkC8SGn5Br7fK3/7sX+firW2+cP9VTstTplWK0xFIpUBaXAtKLN9LKUEItLIYZSFwkUJh6wpHWZRwAIkW/IlscL4lnICUkrgZU7OkjJVa4xqNMBqr3WUEICxaWGoLyliE1qgngUxpNbWtEVrzbdE5fuyFnyCu17n/3pTFPMeL2pTFUuQiDl3iyLKx6rC+FS6N40Jw596Ys8OCshKcf2aNzasN8jTj/ZtH9FdXaKwEHJ4scCcOXijQzRo/dZGxQFpFK27jdSSBr7CRRi0UrpXY908oJxWXgjYVFutrFmSc39lmcVgyK+fcP3nEc5vXuXrpAm9/cIv9g8fsre8SdjoMH05pdnSCyAAAb0hJREFURiGFqdHGYEqPsA6RjktRjnCKgGRQUuUWhYvrWqSUWKso6xqhapSw2LomTQV1HnJ7mJCXLl5HEcWWpmcpA4PTsAgDZg7zRwX5icCsKOZZRVbUeA6EnoDcYgoHL1Q4tctiUmIuSLymxzTPcCIPYUOyVNNE0pId2nWImlg+tnmd+YMhO/EmX33wDp/a/Agf2X6eW9O7/MLrX+RHP/uj/D//h/+G7qd6/K3v+k/5L/7gv+GhTUiKimw+Ifcsohmi3Ag/UAi1JPtxHB+pXOwTnh6LQDlLGm6+iZEAQiAdZzknCWARFoTyUI6grgukAj9Q1FiKOidfZNSloAw9PL/Flmjwv/v2/5S3s3v83u9/kf/8p/5zfuH1L7JHj4/sPE8dWL724B2e7mwwvz/iY5tPoyaWdh3Skl3AkqUaYcNlx2ye4bX6GClZTEpU08VTHibTS6I3DxIBeVEzzyoaVUR+IpirAj/wcJtgGxZSS8u1RLElSRVnE5gXCY4fktYFnmdxhEOtBGUNSjpIaXBdg6kc0kmFzmsasYeQOU2vh6k1uszI3RIlDUL6zCdTeuc6eIHi0cFjiiLj2WtXscpw/+QRpTG0wzYXtrY5nk6waHbOFK6tmH9wF6/jEloXIxU21AS+wutIWs020iriDYFbuJQRhNbBThTjVsX2+S7dYc77Nx/x7Ldvsn2jiXQlD94dc+erM6ZbBdtrLZquINwKCUXG8YmB2meRVWSmRluXwZ0R8XHBx9e+i+dXr/Pzb/4cX0ruYtVS4MYTy6KVxmCVwkqBI0BZgWPFEjPKYH0PpEI8cRQhS9v59Y5viXSQYblzzbSh0pZaa0pdUZqawlZkuiSvK8paU1UVla6psVTCUgmQSuF7Lk03ZnvjHA8+OOTdm2MGIw8h2ujCQUlBJ/ZZ6Tic2w3ZWFOIMmV+XPL+6xNO7tTUc8nFK2tsX26zSEve/d0jAhuxdzVmmCWcHk1ZWYlRTUnHjwg9sD2f2AbEfQ+EpN0KcYTCaXkoT+NLxZoXcSFuEVsQVjCtKmTbYW+vR6g8ZhPDw0dDWs0W5za3SWcpB8cnOB5srreYpymTpGKeZwi3ot1SBErhOx7pBMpUImoH6dQoV1AbME6FE2usY9BKkxnQwqWqLZVY7ojiTkijueR+obVUeJMzn9Fdwel9izQBwkp04hE1wAkEUUOQpjm1kESOwmYVZw/H6GxJ8pcmGWVe0u4oipHGzHPO7gy52rgAJ5puY5e4aHKuvc77R7c4rAeMxwk/euXTTAaP+d17r/Of/NBf4Zd+91+RhxU//eJfZXvhwHyGTDNElWHtE6lRYTFG40iF63lY5eAAruMgXQfpuiy32t9EcAtASKTrIl0H11nO0SoH1/NwpMJojZAWKwXWWkSVIdMM5jO2Fw4//eJfpQhrful3/hU/+UN/md978DqTwSN+5MqnGY8TjuohN49uca69TlzEdBu7cKK52rjA2Z0hep5TjDXtjqIsSpIkRwiDzhRnD0bYrCJyFLWQpGlO1BBPnjXUCw+sRJqA0weW0V2BnPkoI6EFxtPETZ+4E+I2XSqxJHPUwiUzUKslBp2GxjgVtQHlCqRTI2qHMpWkE/Adj0Ap2i2FcJdYnyQV8zRlY72F48HB8QnpLOXc5hbtZotHj4fMJoZQeZzb6yHbDtOqQlhBbOFCs82aFxFIhfIMTsvDEYpOKwQhiVc8Yhtgu/5SLjKIUE3JykqTs6Mpwyxh92pMYCPe/d0jFknJ9uU2l66uUc8lJ3dq3n99wvy4RJQp66uKc7shKx2HTuyjpEAXDkK0GYw93r055sH7h2yvn6Ppxviei1SKSkAl7NKm6ZqqWtq6vF7avsIubWGpK2qtqZ7YSqMtfxJKGd8STgAr0FoijcCpDbK2ODhI5YEU1GWFSWrMuKIaauzcQxYN6tyhzEFWLi3Tw5Nd7jxaMJtHVEUA0kc6DlJJeu2AnQ3B3nlDI8wZzXMeHJfce1ByegB6Itm95LC+6zJ+lHPzCwnJVHHl4yvMijmPP0jorPk0thQGSa+7QukIGmFA2PBRgYMtJe1uiEAQNX20AC0s0VqI6gkavQBTWrKsYJYu2Njp0+7GOMrl9HjI7GzO3vo2nahFPiuYzicI37LabhApDyMUY5Oxnw8YVWO0NBijkdbiKI9GFAMSRI1yDcZqpAMyAC9yqaqadJFi0UuxEWEJlUUrg+s7mKzm6I7m4c0FJo9wiFA4pAtBXVksGc04ZHI2RWCWXP4Wjg8P0PkCL6tZzA6ZDkZ03Cae0NixZXo3p103yVLN48EEz7js9DfRleHLhzdJxIC71YifeOr7+e0PXuerJ3f5Mx//Mf7hr/8z2nHMXzj3aVoLhdSg5gJRCGotKKXEKgehFI5chgQasMYijEVZcKzgm6gzj3gyB2VBGIs1Fg1LoaAnuzrrOJRCUmkQhUDNBVJDa6H483ufoh03+Ye//k/5wY//Ob52ep/ffv91fuKpz3CvGpOIAV86eo+60uz2t/CMy+PBhCzVtOsm07s5dmzxhabjNpmejVjMDnDTCp0vODo6QFiWzxLD5GxKMw6xZNSVJUsECgeHCJNHPLy54OiOxmQ1ru+gHUOg7JOsl8KiSRcpVVXjRS4yAOmAsRrlGhA1IGlEMY7ykNZitEZLw6gas58PGJsMIxSR8lhtN5C+ZTqfkM8KOmGL3fUdpoM5J0dDHOXS7sZs7PSZpQuyrMCUlkYvQPUgWgufZBGg0fQBQasXYkuJChzChk8jDCgdQa+zgkHS2JJ01wIev58wy+dc/cQK6VRx8wsJ40c5azsuu5cc9FRyegD3HpQ8OC4ZzXOiMGfvvGFnQ9BrBygll5Gg8qmKgNki4u6jOZ7s0jI9ZOVS5lDnDrJoYOce1VBjxkubV5cVSIFU3tIm1nZpI83SZv7HUxjWUI4qXBsyfHjK6O4hYl4RGZemCamOMs5e22fy6jHVOwN6g4DLcpcd28ceFFSPMlarLa74T7HFJYK8T1B7eMIl8hXb6z57Oz5hUzKbVxwe5Rwf1MwmPmUmCajY2fboxiFHdxJuf3VIMS+49qFNjNK8+/oBlozLT21wOp/SXPFRniZ3LQ0vJGr5WCXw8Gl2A7SGMFSU2iC1pKJEhDVrmx1ctezqPRvMQcL27ip+4FNXsH8wYjHLuLi2R1R65FnOsJ5ifM1qM6LlSqSERFecJGMmi5QqN0hr8XyFpUabEuVrpGOw1iCkwbglo+kZeZLi4qKkT+CHhLGi0zDUjqBKA6YPHI7uFrgiRjlyeaJFGrR1MVYDGYETkIxqlFWEocRRhuODA6qkRI9qHuzfYjwqiGLF0e0J1D62NoznOV4Eg2qfW/N7hKLFntfhVx/8PtGVbX7n3mv4cZMfOvcJfuXN3+aNu3coFgX/5ef/K3Yv7nGpsYvJKxiXmGGGqEA4LtZZdtbqqkbBMgKQEoXAGkOta/4YZoc/1WGtXc7BGBRLfQXpOihAVzUIgXUUwnGRFZhhtvyOecWlxg57l87zX33+vySfF7x19y6/8sZv8UPnPo4ft/jte68SXdni1+7/AXtem1C0uDW/z6Dax4tgvMix9TI9cXRrQtRQjEcFD/ZvYcaaOik5OTjEUYYwlCirSEY1gRMAGcZqtHWRUiNkhXIkrmhydKdg+sChSgNqR9BpGMJYEvjBslaFS56kjKZnGLdEyCUWpWNQvkabEkuN5yuktVS5YbLIOEnGJLpCSmi5ktVmhPE1w3pKnuVEpcfF9T2SWcb+/oi6Aj/w2d5dBQmDwRxrBK4SrG12EKGmpkRqSakNQajQ2tLsBHgs12zU8ml4IblrUa6m2Q84nU+49NQ6Vma88/oBtdRce3mTYl5w+7UhR3cSunHIzpZHQEWZSWYTn+ODmsOjnNm8ImxK9nZ9ttZ9Il/h4RLWHkHeZ5NLXPGfYrXaon6cYQ9ydmyfy3KX3iCkemfA5NVjzl7bpzrKaJqQyLiIecXo7iHDB6e4NqQcVUj99WP0W8IJmMqgU40RLlVW4eQlLFKcssb1oLfRprPeZmWlz/VLz/Ls1ed55vIzPHftOba21slNxrROaXorxHILx+2C69KNI7ZXfFY7YETKaFYyOPWYjBtkCwedWhxKzu0qmr2Kg3sL7rwxp8gs/Ss17R1495UD8qHg6gs9kjwnywzr55vM8gXGscRxhAgdkA5R2EC2FLWx+IFDmRtMrijzmul8RtwMiGMXKWEyzplOclbW2rS7AdZIpouKk9MxNq3Y66ziIamVpQhAuIauG9IWS3F1P4oRKoBKgLU4yoIsEG6BlTmVNSghKEzJ8eKYqtTUGfh+BFrgeILGmkF1S6QbcfCe5tHrGj1vUyY+xcLgSQdtNFounUtZ5rgo8rnFaEWj5ZDMJswmE1wi6kLw8Owx9wdHBD1FmSqEUMRNyTu3T0nTilarzS+997vMKs1LO1e4M7jLWycPubG1ys++8W/58LkP85nuS/zrt36WN0YPuL044e9+7h/y+OyUKi0QRYk4m6EPJ5TjBCGWKR8hQBqQxlCVJVmekVcllbB/aMfwN2JYIaiEJa+Wc6rKEmmWTW7iSapICJdynKCPJoizGaIoqdKCx2en/N3P/XfcWpzw1vgBP//Wz/Lp7ot8+NxH+Bdv/Do3tlZ56/Qxtwd3eGnnKrNK80vv/Q7NZos0rXjn9ilxUyKFokgVwYriweCIh2ePqQuBQ8RsMmExm9BoORi9fLYuirLK0aZ48uyXWCgWhjLx0Is2j17XHLynkU6E0y1prBkc78mRVz+izqAqNceLYwpTosSSGdPKHOEWIIslZq2FWiCUjx/FeMKjJXy6bohwDUUAtbJ4SPY6q9i04vR0zDSpsEbS6gasrLWZTXLG4xwpIY5d4mbAdD6jyGtsrihzgx84aAOypYjCxjJECR3iZoR1LLNiwfq5mCyzLLKMq8/3KEaCd185oLUNq1dritxy5405+/cWNHsV53YVDiU6tWQLh8m4weDUYzQrMaSsdmB7xacbR1jXxXG7NOU2TW+FWZ2R6YytrQ2eu/bc0qZdfY6nLz3LysoqnbU2vY02jgdOVS9tYl5S5RVGuEubWX39CaFvCSdQ1SV5LBhWSw50Bw+dFRT5AlvXBA2Hld0el5+9xvVnn2fn/BYrvRbrvXWun79Bww/YP7zF8OSYfFSjrGBjtU97JULIgtksYzJSZGOHMhGYQlBVmtom9FcDlO/w6EHB4VGGUTVetODcxTVe+dI7nOwv6G+E9Nf7PHwwpr/RJmx5nIxyGqFLvBIx0zkoi98R1M1lBVAqST4vSaZQZD7TWU1tBa1OhFCaItccPZhiasPOXpfY85GVIplVZGVNXRXsNFbwS4EWmonQzBNFJCIipZaFI6UIHUm308BVFteBZivAcz0c3yKs5cAccZpNkFUDX3SwzlKWMV61rJwXiNUGR48KDt7LwUQoudTkFcISOHbJUCpq8ixHug5UNVluQbnEjZDJ2ZxS5DhWIZTlLBtwc3yTIjB0NgxVVdL2AybDOQeDCRvtLd4fvscXj77Es5c/Qq9u8LPvfo4r51/CrUr++f1f5lOf/F4+6V3HCMu8rLg1OuJecYrUSyPvzQvE3TP0u/tUhxO0kFS+oigLqJ+kunwPFfj4jRDxJ1A8+w8dQkr8RogK/OWclAO1pigLKl+hpVx+h/f2EXfO8ObF0klouFeccWt0xLys0MLyHe5TfPqT38c/f/DLOGXB1fMv8bPv/CK9usFzlz/Cbxx/mfeH77HZ2eZgMGE8mNP2A8qqpLNhKALDzclNzrIBQlkcq6hEzvRsRhyHoFzS3EJVoRyHLMvRosaUksCx/16vWUkLJuLgvZyjRwWsNuifF8SrFj9SS4yJDrJqcJpN2NdHCGtxfIvnejRbAa4DrrJ0Ow1CR6KeYDpSioaImCeKidBoofFLwU5jhboqyMqaxaxClorY89nd7WJqw+HDGUWuEUrT6kTUCKazmiLzWUwhn5dIJcEqqqbA7whQlnmd0+w1iEKXk1FO2PJY3Wjz6OGY/nqf1Y2Q0/0Fr3zpbfYurOFFC4yqOTrKePSgQPkO/dWA2iZUlUYXgjIRZGOHyUgxm2UIWdBeidhY7aOsIB9VDE+O2T/8gMgPuH7+BuvddVZ6LbbPb/HUs89z+dmrrOytEDQcqGuKbIHOChw88ixjUE3JY0FVfwNYRL8hw1rKUJKeDZFzSzEx1ImlmBeUWUmta/CWuridjRXcMMY4Dq7vsb2+xfmdXYp8xMHRHUbjIQiBF7nkWnM6qRmMJdMRpDMoMkNZ1rjCsNJzsbLg8GjOaCrIdEVWnbKz3eIrr7zOG197j0akuPTsHnffG2BLxfq5DsOzOcYYmm2fIHKobIkKDe11Z7kd1RZXKnSuoTBQW4SEss6J4wjHlSjhMTxKWZyWxCJgrdlElAJTu4wWGQWGZJDS1AF+bfFDj0opUC4rcUQISw52BQ1PYalwlEdVGtzAQ+eGB/Vt3h9/QNfuYrMQ5fo4vkvQduhsWAJXMT2UHN3L8b0GXiBQjkapJz/SUFc1da0p8orAc8iLCouPlJYocDk6GiK6HjKvyYuC0/mMr+2/y0wmOC2NUYKwFeNJxcO7E1pBj43uOv/y5hfIhc8PXvwkp/kZ/+N7X+AHXvg0b5zc4Vcff5W/+l0/xWYRoiYSkhoKS50bdG6wSY0apci7x+i376LPJiR5hoh8nLgJfoDjBbiej+P4/17E5ZsxhJA4jr+cixeAv5yjiHySPEOfTtBv30PeOUaNUmxSo3NDnZulJFpSoyaSzSLgr37XT/Frj7/KG8d3+IEXP80/f+9XOc1P+YELn6QQPv/y5hdY76zRCno8ujvBE4qwGWOUQLU0M5nw1cfvcTqbkhcFMq+h63N4OKLhu0hpAZ+8qPF9hzKv0PWyQ10pi/PvcOFovEAQeA2O7uVMDiS+p5aYajs4votyfWwW0rW7fDD5gPv1bXRucMMlRh3lYaloeAqpllgOgZU4AuVSKYUfevja0tQBySClwDBaZJjaRZSCtWaTWAYsTkuGhwlKeDiupBFHlFWOkEBtoTToXONKBdogxHKtqtBQ2QI/UjTbPsYYhqcz1s51sKXD3fcGXHx2j0akeOP19/nyK6+zs90iq07JdMVoKjg8mmNlQb/n4glDVdYUmSGdwXQEg7HkdFKTa40XuSAEo/GQg6O75PmI89s7bK9v4Qbe0qaFMd2NFby2A94ynVlmJeW8WNrEiUHOLdnZiDKST0SXv77xLeEEhOfiuAKOB5TTjNlgymB/xOK0IJsWJGlKrlOOpofMiwXC9THSQXiKqNli99xVGs0eE3eIswWF0ORJTZ7VFJWkqkFXFcbWuL7FCUq6qy61Ntx/POVonnJmH3Ga3Wb7XIcPHr7Hz772S6ycX+fpS+vMzhLSA9judWiEDkePR/h+QLMTIF2BQWKlRQUWrWt0pZFS4OBiUoHJBLZSlKXBDzzCyMNRLsXEYfAgY/hoQteLUaYmT2qSQpAgyRUU8xoWGoeaIA4oakGeGxqNkLNkhg2W3cB1ZdE1KBcKUzESOV/L3mKvuUdj2sExASqQ+C1Fb9sj7gnSoeT+mymUTYR1KIuKMiswtUYY0NXS+Bf5Uv7O95bc/Nq6KGXxGjDKC3Zefg5TKhZVRef8LoOg4vbZCSr0yWuLkZZOI2ZwVDI5S/jkxW9jki/4V/e/wI88/z18IniG147e5MuTO3xy+3k+d+tLvJof8RPXPkucgVsLXOOgc0OV1VSlAS1xC4s4WFC+dhf1cARaAgpPefjSI5AuofS+qSCXQPhkLr708JQHKNAS9WhE+dpdxMEct7CgJVW5/I51bnCNg1sL4gx+/Opn+Wp5wi/e+hLfsf0cX5nc5dWjN/hEcIMffeG7+Zf3v8A4m/PJS9/GZJBwdlTSbcQYZSlqixP63BmcMggqOhf2WFQlplTsvvwcoyLHbYBSy2erjSXwFNYK8ifPX5c1GIupDWVWUBYVWAfKJg/eSkkHkrgn6G17+C2FCiSOCWhMO+w1z/F69hYjkVHoEuWCrpeYlQIIXM6SGY1GSJ4biloQxAEONcw1xbwmV5AgSQpBntQoU9P1YoaPJgweZBQTB6VcwsgjCDzKymArhckEJlmuRSkFutJoU6MCsNJihEK6gmYnxPcDjvbHNEKH7ZUO6QHMzhY8fWmdlXNr/9/2/jvYsuw67wR/2xx3zfMmvSnvYQoECBIgKVo1RIkSTQ/VarXUoxjNdE9HaKJ7QiPF/DURE92hmJiW1NE9CpGiKLoWREK08ABRAFkFFFCFyjKJyqys9Pm8uf4et938cW5mFSlSNCBRVWJ+ES/ynnNvvrfPuevstfda3/oW/+75T3Dx+qscP73AXvE6++Em2+OcazeH1M6xsBqh05ooCfhgccZgLFRGUhaWMrdUwqGOwTA6oNVd5NSZB2l15xCxaua0KGFUTdgZbVG6nGmeUwwrxnsVBxs9RgdD6mEBOwdoLRDxN18s9rZwAjJSVKMpdS/H5M1ENDwYcnB9n/ywxBc10lSUkz4bty7hfImXgcIbKhxzy2s8+t4nSY90uKYvcSW6yE1eZ9Nd54BNRuIW03BIxYg0GTG/Jtiq9rl2uM1Ouc+L0xd4/vBpTh3psmN2+KlXPsHcY8c5c/QIkY3ZfXVCK2hWFyXVsKQagBIBLTXBerzQSKnQXuIrybRv8N5R1VCNYia7EjdsYwaKqBK0pEJKj3ewv1HhxhGT3QnLSReTV0wngdFEQZpRG4OzilB74rZDJ4LSx1we9tiOpzgtcTE4IZGtgOoEBmXJ16ZPk6QLHK1O470ia6XEiSBtCRaOCqLEc3ClQpmEWMZgQYZApARSgg/gaoEvHONhiTWBJIoa3SaqpvQ9Utza28BOBD63UDtEkhJSzTdG1+lkEdLU5MZyz8nTKF/z3Dcu822n38tJucBv3/oKm/kh/+dHf4xjZp1fv/oFXuxfZhSV/Ouvfoz06DwREdIFgrEEa8F5CJ6AwwegdPirB0yfucjkuYvkGzvUkymlN+TBMamLtzwxPK0L8uAovaGeTMk3dpg8d5HpMxfxVw+aawgQcBB8c43WEoxFukAUIrJjC/zMs7/CMCp5qX+FX7v62xwza/z9R36MrbzHF249ywm5wPtPP8lz5y+jfMXZU6fJjUWYmk4WcX54jZAqRJJC5fC5xU5hY28TFSl0pAhU1MYT6whrApNhiSsdzgh8EEg5s5EQwEIsY5RJ2L9cESWexaONjcVpU6zlveJodZokXeRr02cYlBWqE5CtgBMSFwWslmzHUy4Pe5Q+RieCuO0IxuOsojYG0ozRRDGdBExesZx0me5OcOOI/Y0K70BJTyabZ8z2FW7YZrIrqUYxVQ3eO6YDg68k2jeFWV5ovPVoqVEiUA2gGpasLkhaQbP7ak5kY84cPcLcYyf46fOfYMfscOpIl+cPn+bF6QtsV/tcP9xhq9pnbk2QJiMqRkzDISNxiwM22XTXucnrXIkuck1fIjnS4bH3vo+55TUqHIU3eBlwvmTj1iWKcR9pKnxRkx+WHFzfZ3gwpC6qZo7s5VSjaROi/Sbxx/4NM83154HNEMIPCyHOAh+l6bH6deBvhxBqIUQC/DzwJE3Xpf9DCOH6f+x3BwHj3QPcYIKoBYGAlJokatHWC0hjqeoBKq45GNzg+sYK60fvIUoilI5YSJc4s3SGy5fO89r4OeqjY5LuYxg/ZdQbkI8GrMp1Hl8+QzZ/P89uXqVoWXq6ZHOyS17u811H34vPBB+//mXmP3AP982f5bRe49aVMdZHpO1A8I7dmzmRaNFJY3QQ+NpR5x5hI9xIUA4D01FFsJBEUaPVXlmG+zWLixmtYIiMRitoZYrJ0GKWW5gip704TyeumVpL5RRjH2ipFkVeIZUizhytSFPFlot+l7rTIpQOby06ciTdmIma8uzuc7y8c5GffPi/JOy1QEfoSKFjS3tZkbUVo01B0YM00UgBde2R0qMyD943ksFWUE4spg6IGBKtKcsaJQVZFiMN1FXJ8c4KUjryfp+rl1+nlxq+OAj8rdMfol174naL+ZVAdNGxs+2YHHr+7rt+mH/20i/zL176df7f3/Zf8Hfu/QH+yfVf5jmxgYk1Q5/zP/7bf44pa5y3NPzPZodCCAjRcOq9hygEdD+nfv51hrf2CY+fZuWBU8goZmIczv/hybM/T7sGcN4zLirakcKZmoNLtxCvXMftDtC1QzqBEQ4luXNNwkOwDhNKDIJa1vyP//afY584QRTP8Vy1wdFa81/d+4OkS5r/33O/zLLV/N0nfphJz7OzXbHiYWElIWoJfF1RaMeXXnyaK5sXWSojpo8OkKsnON6ZZ6u6hTTQymKKylIWNYmOEICpA+XEYo3AhQqdamIlCaaxEx2DlJqybxltCDrris6Kpxp6QqTBRIRhxiPH3sVHL/wi+kibj6y/n040hy9rfGogElQduFjsck+8TivS1MFSFwGfOxLdYjy2VKVABEs7jmlHHaaTIZHUTIY5rW6KVo7YaKq+YTqA4UFOVQmEUsRRRLAwHVaUwzauKxp6Zu4JtUUH6KQpZirZuTlkrZOQpoHxQHLrypjTp9a47+gZxi3Hx69/mb9+/Ls5vrbEl7a/wsX0Msdb6yzVKelA88TaPeyOLvPK4TUO/C7Z3AJzSwtEcZsLm+e5un2RxW6bM0unWUiXZuqpBlNN2N2+ysHwJoQaN/UkYo5Ia8bRgFrWBO8QtccOasa7+4Q/A/7zn2Qn8A9oui7dxj8B/mkI4T6gD/y92fm/B/Rn5//p7HP/cQSP2emhJwZhHQJotedYWD5CN13i6Py9zMVHKauacdXj2sYFbD1FeUWoG+rx8tw8Tzz8IEdOdvALfV7tP8fLvXNc89e4GW+zFe1hVwK/+OLH+K3rv8lnL3+G1+1V5u5b4H2PP8nxY8f45ObXKM+0ODa3zmPpWabbOXUliFoSIT31FCYDQxQrMqUgr6mHhqoXkGWEnTpsafCuxI0DaZBoJYhiRVkaxoMa6SJir1GhYfWEStPbq4iSNpubhyy2OyhTgHOUlcTJpOkrnHtCrZCRpKcO2fL7ZJWntSqQ657oaMCmJZf6F7gyfYEPPfZ+1qtFIq3QUiKAdF4yf1RSTwOTGwnKZU1jdglCBjwOHxx1XWJ9RVWXTPNmx6JwSGexlcMHQRZLXFlQ5BNWk5g6GIzwoAKRirm5u8Wrgx2K0qCzGJ/WHD+yjs7bXDrX4wdPfj8/euxDXDi8yv/0/C8x0I7vWX6cxTKQCWgvdJmYAleVBOPABqSnqQCmWWGHEGiWDALlFCqHcH2I/uIFhr/zMsXOEOnkH0Wl/vOza5oFjnSSYmfI8HdfRn/pVcL1ISoH5RQB0VzD7Hqa/9SklrCBYByuKpnUBe35LpmAxTLwPcuPM9SO/+n5/50Lh1f5G8c+xA+d+n4uvdBD522OHVnDp829L6qaC4MdbuxuE6kYVMAIR41hJYkp8im2zMliiQ8CWzuktygc3kGee6q6wvqK2pT44PA0BW5CNlWrymVMbibU08DcUUk639iclpJIKdarRT78+Pu5Mn2BS/1XsUlJdDQgj3iyVUFWebb8Pj11iIwkoVaY3KN1gpMJZSXBOZQpWGx32Nw8JEra9PYqQqUhBGQQxF4jXcR4UFMWzbOqlSANEjcOeFdiCoOdOEQZUfUC9dBCbsiUIooVk4GhnoKQnqglqSvBdDvn8fQsx+bWKc+0+OTm1zh27Cjve+JJ5u5f4HV7lc9e/gwfv/6b/OKLH8OueLaifW7E21zz13i5d45X+8/hFvocOdnhiYcfZHluAWEh1KC8wpqcaxsXGJeHlFXNXHyEo/P30k2XWFg5Sqs919QeWoeeGMxOr9k5fpP4YzkBIcQJ4K8A/2p2LIDvBT42+8jPAX999vpHZsfM3v8+8UcIXATrEL0SWWm8lwTnEVJjHOxsbmOngQfOvJssWWQ4ydnau8H1WxcxZoq1Bb1ih5ujy5SqYmFtgbijQQfiSNGKY+bihNqX/NbFz/KqeYW+65NPSqbXNmlf6fF4eprNeIx5ZI7O4gJPhBO09yIO8xqZGVpJIIpipuMKH0CnDo3DDi35gYMiRhiBwOBCiZCG4tAiSoHwEXEUkyQR+TTH1DUqOJJIksYxqWqTjwNCRVSlZ397h0hYTDXFIiisbfIITmDHhqkIvDi8RlQ6vm3pXtJMUCw47NHAttnmN7707zi8ep2T5hQiTxA+phW3EMDccoaMA9MDgR0LRJDUtcf6eia/EHCVxjuNkJKgAsNRhQ8CJUEGjzcBUGSxxJoxQjiyAHlRI1oprZV5km6HOpI8f+MiXgosgakInLp/neV0DnvQZdhX/I0HP8J70tN8/uAC//LWr/Le+Uc5Wa0wJKfqxvg4JlgBvvkRoSmOCWHmBHzjBBy38++yeW9UYc7dZPjp5yi++o0/VG73z9uuoZFJL776DYaffg7zwk3CsGpYkUFS+aa4TdBcS+MIgEBzrbPrDlbgk5hqLmZIzolqhffOP8a/vPVrfP7wVd6dnuJHH/wIw77CHnRZTuc4fd8RpqLp5uWF4Pkbr1FHgqTbIVuZR7RT8qKm5QUIizUTsrjJqXgTkN6jJPggGIwqUB4hBd4pXKUb16vA+pq6doggsWPBdF8go8DcctYs5uIWIsSIPOFEfYrDqzf49S/9Mlv1FvZooFhwpJng25buJSo9Lw6vMRUBOzakTpCmMYW1OASmnhIJy/72DlXpESoiHwdS1W6epUiigsPUNXmekyTNsyd8hChE80xKg6dAYBBGQBGT7zvs0KBx6LQJzU3HFVEc00oCMjMc5jWt3Ygnwgm6iwuYR+fYjMc8npymfbnH9Nom+aRk4Pq8al7hty5+ltoXzMUJrTgmjlQzJ3U082sLlKri5ugyvWIHawuMmXL95gW29m4wnORkySIPnHkPdtrMgcYGhNQE5/FeIiuNOKwI9psvFPjjhoP+GfAPge7seBkYzBpxA2wAx2evjwO3AEIIVggxnH3+4M2/UAjx94G/DyBiReoUlfeN6qJQ2HzCODjKwhKJiKMnj3Hvscd56fJXGVUHvHLjBVQ7RSrLyPap5ASiBJEmJFIQrMc5T1Ua6tJh6z7OV8xFq6zNH6UdtVjSS7zvyD18+bXn8E8uEVI4UyxzrF6g1x8jdMZ8O0a4CKUFk8JAJyKLoJoahIqxMjRJSxuYTBV74x7DccX4oEA78MYifYqOY4SzlIUneE/HR1iriCKBqQXWBpJ2xGs3r9Je9dzausl9D3wIxTITLEtZxuGkoB4KhuMpP3T221h3LcZTz8G4wIY+H/3EL3B16wbf9e4fZK4+iqjbxLEjTSBdjOgse+y+oNqXFK5GRRGWGhKJdZZJYdE+ImpH1Nby+vASreoIXqR4ZwhOIaoK6WMiJajqmt7ggChTTAfjhpU1N0+mK1wl+Or2K/zoIx9g49orLHdXiVsLnD61zM44Z6ccQtHjIw98mHPnb7BfT/nS4AIr8+sEuwvWopKUYHKIAkIJhJBIAj4EvG+I9o2wYECJAKEpvjIhQG0RW4dM93pQ/qFz9Z+5Xf9+20ZJpl99FWkDIPBCIIVHhTAbPwQUiEDwzapaCnEnNBR8AAMqSZs8gVOszK/xO8OL7NcTFkTCRx74Lg6GPYRISeYURzrL+LakMCNevnadE+tHeHbnZaLFDioJJDYBIZkOJkRdSa9/QFXXTazfC0RlCCrG+4AXmu2iIPfX6Kb3EuuYcgrWWjrzDq0agoVCUhiDPoiJYkFn2dNdiyj7BrxC1DFz9TEePPsEv/PiZ/nFT/4CP/kjfx8tFlnrxqx3JD909n18ZeNlJsOauC5Y7mT0cktVC6blIZcvP83JY6eY7AseOvUQ1ga8FUSxQFlFx0HwhqrwCBtIVIyvNd6UqAjGBwXDcUU07rM+fxRXByItqIYBHQSVq8namqmCycTQiSJEHpG2E8ZVRTUYcyxZ4DQLXG7ts60G7D5/lQ8efy+RWqBne0xNwf50i54ZolxDnY61QacKpTRaxzgfGPoxg/4Bqb/BnF4k+IhXbrzAyPToJivce+xxhFVsXLvG7q2bpJnGFgXSg/eGioCeOqo/pH/2nwR/pBMQQvwwsBdC+LoQ4nu+6b84Qwjhp4CfApCJDtiAnwknybiheOb1FOdq+ru3uHnhCscfuIdTx+9nayCZW+rSqzcJ2iJihYozkDHeCurK4kyNrSzeNjRHbwzKSzLdYi5epKMyTi2v81LvOr3728SpJqkV97oFBvsDKqtYSrsoG+ONx6Ye5zSaGOUdeWGIOxrvQbgYYyrK0nLY36eqU+rSEkUR3homPVBK0mp1qLsxIS4gssgEVK3QcUJVeTqtDBVF/Owv/xT7+9d596Mv8aN/4/+Cqtc4DIK0lVENR2ilWOp0SSqFr2AuElzc2mCbPRaeOMljj74fuRkhlCSOJChBa61EyoTxXsSk59FRgsMAAW+aEFykIogc2hh2ymv88pf/DX/rO/4bIjKCj8gLoBJ4Gn2n6cSSF1OETtge7ZO0BcXuDkM5YXlhja2Qs1MM+PT5z3FVD/jLZ7+TD596jHMbL/O5KztsjK/xt97zQ3xk8Ul+bfI1nhqeQytBVnvKS7cQwxEhlkgxC/3MJsdGkLOZ2G/rywbvm3/DbeFQgQgCXXus+Q9XS39edv0f2LYUQdeNrPXtsFQITQ1HCAGUbHJgCJipQt6+thBCs/qOBWIwwly6RfboCV5Qr+MGgVhrPtJ5EhfF/L/O/RInumd5dP4ID5x4gt/e/gafuvY099gF/urcX2XL5yS+5HB7D+07BFuynZd0ljrkZc50bLHONd9tJbBAcBESQR4P+aVn/g3/+Xf815xI78clEZGPGrFB72bfg0PrhEnPIXXE/FlLa62iHGniKGqKtIzm8Uc/wMvhAtuDffYONnjo2BLtSJKkiqWki1aKMB2Rznc5LAWlEZRuj9/6xL/kxfNfZ3X1DD/5A/8nOq2MqvJknYQgAjJxEFuck/hRjOl3yHODcyXeW+JIU5eWqq456I8oly3WQCRivK/xQpMXJWkWo1E4G7B4fOlRUUSsI3rlELtfcO+RRW5VI/LEUd/f5sVr1zm1fAR/6FEiYTwZUJgxXtZYImQp8TbgE1A6QcUaFSvQkrqu2K83EVazsNZF6sDRhXtJwxw3L1yht3ML58aUJqbVTvCuJNTggmjChf+RfNcfF3+cncB3An9NCPERIAXmgH8OLAgh9GzVdALYnH1+EzgJbAghNDBPk0j7QxGcJ3iFiiPiNOKeRx7GWceV115DaUVdTbl2+VVMy7H2wBrRwllCbAnKIbQEJMJLfG1RPiEKGaUpKXNDOS0RTqKVwvmc3nSTwgxpRV0G4ZDk4VOoBcnBK1d4VB1hctzj7JAui3TSFt7E6BCoS4NQGVGIoCooK4npasA2mjz5lEUShvkuihWKakKatCAYhJfgY8xYMjqwdI5qrC5xSkEkcaVjNLCsHUlRATZvvs5gsMkX9m9y6sz9fPDxH8OVbWynhkiyINvMuQgZJFp6Wu2U1uIiYjHh9OMP0a2W0C5CRECQqEiRtGC0JZnsCrSOkNrhQ0AakAR0pIBAcIEhQz5z/RPcbPe4qq7yQLwKUrI3LGkriVQa6QLjSpF2OwSnuHlwkwcXTyOrnKrqsXNlh9UHHuQrGy/xyP0P8auvfYzt65/lzIfW+GL/K5xLDsgzQ/n6J/hvH/zP+N0L19k1Q/Zqh94rKLd6xM7jVAD/xqQf4E7s3HtPEI1WkJy959/0UMggEF7cySN8q+36zoB9k7nwb0riyZmqnfceZCMxLaXEe49Q6o6jCyEgVEB5T7nVQ6+tsHW0xVKkWI+WeN89j/MvXvsE57M9LlQHHFQr3PvIEX725c+wU/f54Qe/nWc3X6ba22D/0mv45RiZSFQiuHnzBo+ceJys22ZcKzo+IJVmYjx5WTaOK3ZcU1e52e7x6euf5Cce+DvMs44QmgSFNR6cQETNbkz4iMmuRySSZE6jIoUvm4p37SI6ZokzTzzKjVcuki0s0GqnaCmQXjLnIhZkGyLJpPJUkxgnSp5/5Qt84alfwxpPXdQoBIlO2DuoIUTIVOJUwGpHcCnjfYsZy2YHQg2hQEURRZXjyBnl+wRhyfOKllAECZaIspKkFcQqxnhBnRdN324j6KQZvUngwA5RLmXx5ojzbofFh04xEJ7dC+fZO9gmN2OKetrU7dDC5I68mpK2UxKpSFWGrz3elkjd9JgQWQDrWIjnWZhbpi0XuX7pMhtXLlJXU5RWiADHTp9Ga82VVy8QSkMwhvCtyAmEEP5xCOFECOEM8JPAF0IIfwt4Cvjx2cf+DvAbs9e/OTtm9v4Xwh/B0RO+6QA4v7LE+z/8YYKO6PVHRMQINBYHbUO2DjLxpJ02UZIilMLWHpdbkipmLT3CqflTrCZL2KnhYH8fYyw4yUJnnax7DJI2E2mo1iB61xoHac7Gsy9w5FbFe7MVXtm/Qp15FrMuwgqqyhOnCVXdxOKoLa6o8TLCIaiKGluVjIo9cjOg39tjc3gFF2p0BkU9wbgcIUyzGqkkwSqCA1AIrwhe4+uE4BWdTkokAipoKgsf+9VfYvPWJaihrGIOa8+6XEUNBGmI0EoTasVqusx6aLP/xZdYqebACqyz+OBoL3viKFCNDCq2oGocJdZWBO/Ag7ceqRxDcchz4UV2H63Q336c3eUJvuUQAioCpVA4bymnNcMSRBRjc8fVzcuoVoLGoRTUByO2nv4KT331Czw0dw+PxkeZRIYr4z1OLZ2lLbu0bYcdUfAr2y8wnybI0tHerQk7U3AKN5PiR0EQjSCe8xbvPc65O+ES7z3O+8Y5BMCHWey0kR7/g4zvW2HX0PxtG8DPxjTjghJC0z3Le38nH3D7mpxvvrcwezCUAicgOEXYmdLerZGlYz5J+Nj2ObZFTtt2aMs5Ti6d5ep4j3FkeSQ+ykNz9/CFZ7/A1tNfoT4YoRTNd5QlXNm8jM0tIooZlYFi2jCxKqGoaEThQsuxuzxtbOHRiufCSwzFIVI5vPXgIXiHtRWOElSNii3VyBBHgfayxweP9RacYLXqcvDFl1gLbdayFULdiP+lRKiBYF2uclh7yiqGGjZvXeJjv/qLVBYUmgjotJPf88wIrwg0z1SwirqSxJFGCINxOUU9QWXgQ83m8Aq9w13yesCw2MNWZfMMQ/NMFzXUFu8llZk9+5VHWMFi1qXOPK/sX+E92QpHN2o2nn2BgyQnetcq1RpMpIGkRdY9xnx3HbzEGNvMRZOa1WSRU/MnWcuOkFQJLrfYunH8UZKSddrIxJOtB2gZmmyIJiKm1x/jdcT7P/xh5leWmu55fwYyot9MncD/A/jvhRCXaWKjPzM7/zPA8uz8fw/8oz9yEFpy9j2P8uiHv51rr70G0tEf9BBBEAloz2Wceew+lo4vIloBFUmCgWpUM9rv07u5RziQrNgjLFRzLPgOLd/h2NGzrJ06zSQYKqHoLB1j/Z4HeOj9H+Dhd7+PfBq4cf4Cg+u3uO/4Pfz0536dfm8DaTUt3caVAoylciVVmLFojAHrkCohsRI5ibCm4tzm0wzNgN3DCc/uvMBhNcAIKKqKuqoJzmFNxTS3SBsTOQFIXNkoRgoUxcjRmWsRza/gF5ZRnXkG+SH/+t/+f9kdXcFPBd62adkFxj2PLy2xCLgixtmEw2sHrIlVlMtQUqK0RsSQdcEMPKEQyNg37TqBEAxCNW0pjavw8YCtyfN85tM/Q371Ftm4YDLuI9se7ySdLEFoQRCBQRmYji1lbRHDKZcHm0gV0Y4iVBQhOjEur/jGU19mb2eLY9sBtVdz4cYVjkfzHKcLW33yS9t8/cWvceGFiwzPbSB2p2inidAgJEE3qzwnPV44bovnvhEz981r0YSGlJ9pCIVGwVMK8SeVkv4zs+tmoM0YRGjGJD0o34w1zMI/YebA3tjtNNfqZLMTClqCkMRotNWI3SnDcxtcOHeR5899lfzSNmz1OU6HE9E8r968gtqrOLYd2NvZ5vwXv4zLK0QnRkXNdyR1xJXBJgynlLVjOnYMykCYNUbpZgneSUTbM570yMYF5dVbfPrTP8PW5Hl8PMC4qmnXoBwhGACiTCLjxtZM35N1QcQBpTRKSKTLWJVrHF49wNkYV8TEEnxpGfc8LbuAt238VLA7usLPfvR/ZpAfojrz+IVl9PwynfkWxcghUEgnmuc0SCInUDZimjeyK3hHXdcUVYURcFgNeHbnBXYPp4zMkBc3nsaaCjmJmmdZJWAdwhiEhCpIKleCsbhS0NJtpNX0exv89Od+jXuPnWVwfYMb37hAPg08/O738dD7P8D6PQ/QWTpGhWLiDWunTnPs6FlaocuC77JQzbFijhAOJL2be4z2+1SjmmCauU20AkvHFznz+H205zIi0UjQ9wc9UI5rr13k0Q9/O2ff+yhSf/OlXn+iSoMQwheBL85eXwXe/wd8pgR+4k80iG6b+UfOUOwfEB1ZxCYRq0vLjLYOkUJw9MxxVk8fQcQa42uqYc7BlS16uz3yKgdr8B3Hgl5kYWkNT8x8tsbB/lUi73nw0UeRMiWLUlqrc4iOpnerR+/aAf2dDeRozL95+lcxKNJhTrYSo5ViMmpYSnZsiFpNWbmtHM5plIkQQTAtSi5U13h24zm+R38H+77kat3jhZ2vc/LIaQRNrwRjC5AWHVrUVZMj0EpRO9A0xWPlJLB6/ChZe4mqmtJSCtyEG4ev8dFP/q/83R/5x2iWKcvGQMd9j243E4ozJfOnljly7H4kGusD3hu6CzFRFji84hFGEpTBVBYdK3SQTfLP15BW9OpbfOzzP81kcov9p3K8cYzbL/E9f/u7EHodVyiiFISvcL5hb/XNmJv9a1zJNxnko6YRvFSopQzXG1IVOZ96+tMstdtsf/JZPn/sOs+7hO3pPkU5pSwmBEAKSaQTbJZhpcf7GqEU3tsm5CMEQswIleH35gjuvEaAmlFvZ6tsMWPbvBV23fzHWdgKj5Si0TESDS309sBuNwa5vbEQCPxM+E7MyFFCKnxZkd/aRnuJKwomtsKHJheSZh3Op5c4aD/HUFUcbu2w9N1/nU8/82mqagqZRC1l6FgRyYhhPuJyvsnN/lV6Zowx4FRAeIsiwRRgdaDUfZ7/xX9Lb7KFjBSZbvGxz/8U/92Pn2ApvQ9fCqRsOv6pSFJVJVKAMBH9Xc/yvYH2gmS4W8/CdpojD93PaNPgTIWPA3UFNvdUhcQLiIViUuzy0U/+b1w/uIheXATVIXeOrLPMavco+VZoGsd5hXOQaoXykqpymFBjfSNn7e2sJ2EIfH3nBa7WPZb8Kl7Ds5vPceTku+jkD9CKMlQRUUUa5R0+eCBiNK5wVhJqTydVZCKmP8q5Zob8m6d/lcIUeOM47HcIZ1fpHFsi013y/RFJFjG33qW3u4MZVhxbvQfnYkpnGfb2uPHqq+xNNkBrWkmbpfUlVu49RrLQIo1jVk8fYbR1yMb5a4QAq0vL2DgiOrJEMThg/uEz6MtX/sQm+fvxtugsFrRiHHlCqIgeOMnw8gb7u7s4D1mqmT++CqmAyjLdHXP53AV2X7uB8ILWWpcodgyGO1x6/UXO3Pc4rblFIhFDUbB56zVuXDzPPY+/i2Pvex+lDfS2R/Q3dzH7fUJ/SjEqSboZMmvR6w3oPhFhyhrvO1TWI7ynm0hCUaI11EZjp45CWTbDNi/xDfrxjOtbjyl9zpW988Tix+jMRdgpONsIaHkVqApDloLWrlk11xpjLM5p5grLd77ng+y9bDHOUdsFpOryzPkvs9r9eX7kh/+PtNQCrlJMc0srEwRp0NIzcAPWH30Q+6oHBFILkk5gMrTYUiJljqtls8oErK2RMhC0IWRjPv+Vf8+FjZeovEF4SKQij3pM1SZpukxeBjpaoETA+oATnhPvOs0nwxWqJcvu4Q6R0ug4IlrM8O0Ym5d87muf59jKGtFowsbkdXadwAeLCiAFOC0AS5CWusoBEFkMqSbMNqtC+MZZzBrzvDn2f4ep2aQPADFTDn0T9/4tRJhlq4MQs59mov+DGKZSSpANSeJ2n+Qgmx7AojT4YkoNSBfAe0AibcBOhgzEkJHYw6jAnBc8ff5Ztg72ccGg2ynRYoZOIyIXsdPbpV6yfJKrnHzXaZz1WN/E9Z2FvAz41DFRm+RVD5dX5N4xlmOG/R6fe/5j/MT3/TcI6wk+wtjGrXkLMiiUMPhSMxkY0o5ifChwVmCNZ/2RB3nu1pfRwhGkwXhBPg04p2glHRADPv7UL/DM+Wforp3Et+eJtWJZKT70+AfRhaYuNEFJYqlRsUXpppCwLA1eNTsaZz1CQqfdBJav7p+n8FOMmaC8oh/Di7zKMTp06oxqpNFtTRo5SlvifMyk8E3hp1QYU9BtR/R7AyQtfDGmGJfEVjJVh9SxZ1FoFudbLB5fYPvrz3Hl5ZfQ1rO2copIxOSmIh/3uf76eQbDHYQ3mKlnZ/+Q7VdvsL7V4753P0x6ZB4SwfyJVW6+dou8tJQ7u4Qjc8w/cJJ8YwuhfSOl/k3ibeEERPDUiaLqZoThmFoEouU2URWa7jyJZpQXTDcOuf7iVXq3tgiVwQmBGZeoeUUtK7Y3r6KF4MiR+9AqJZNNWIQ4MLfaZvfggOpmgZcSU9T0hrv4yRRhAlXehEms0AzrknkPKooIpsSGwHRS0E0kzghqIxC1Zas44FMXPsP1hUNUHBMZw/WbF8mLbarFExT1AKtHaLmM0BolA1pKjA8ktSBKouaLrCK011RYrl6+zANnT5Gdj7ASIp0hky6ZUHzquU8xv7LGhx77CWoFOpP4WBPphOHYs/74E2TxEqrUaKVI2godBYYHBus0Upqmv0Cw+CZlhhIC3S45d/Upvnju0yit0a4p+jIBfKp4+twn+C/e/xj1vscHDT5Ch4AxI87depH8dMTa6ROct5vY9TnUZELsPH6uhe0V9CcDepM+ahb/tqGZCJFiVvEY8LMGPPhAFGTDWEoigmzYQVIIGgLxGxNnM7lKJE3oJ4iAC81uAdFQSt8OTgAaITlmDiCIgJrtXJowFhCasJZQzW5GqYBUonEGQiKDIBiHcmCEx812CASPlxCCwIVm9xdCoEBw/vprzb2IAnouI24nqDTBdeY4bzZZO32C18MeW9c3ee/6B9DRHM5H+CCoraO96vjN5z6JTxVm3OxeQaCU5ksvfob7zzzIe8/+EGaicXigAMKsxkfinWByaJlflrTaispAKDVZvMj6Y08wrAPL7QQfO2xmqb3HmopnXvw4n/zaJ8nWTxEvHsWLgBA1GREP3HOKq1c2aGWPkHjd7JZUTSwjfFVhXPOMIQMi0mjrsXpEXkuquiLf2+TG2BMZg4pjboyu8MmbfboP/+fE9Qq1FES2IRtMqwITGiFFFaXUHoZ1iaFZnNS5RxiPn07pxbssLbaobg3Yu9EjOZnRXe2g49CwEmWKHdWU/RG7O1fY3rxCHSqC95ixweY1MgR2X7mOGdacefc9tE8s42KNbGekmYdEUAuYDMeIbta0V/0zSAy/LZwAWlNGCXnWwU8q0m4bfbQD45yVpTWM8Ox843XGF7ZY6awwNAEXmjBAMc0RWRuRBGpdcWHzdQ5yy/LCCbxStFaOcubb7sPgufz011io5wnzLTonFqGVUh80jBhrLLIs6O/3GBZTTEsTOUWwspHPdYHEO2SwuDJlLHb43Dc+zade/yJr7z3JqRPHmUxzDkxNPLdMlHTZ7G3wyu4NHum+D2ljRBFwlScRGW0lkApU3GihKKER1vONS+cZzL3G1uVXcbFCZx1U0iYkGWGxy68/9av0D3N+8Dt+nKhzBNeSTKoee9GUhTRldO4iNtyDIjTNPKxokr9SUNcenTRFP0oIhArIpGZr8Bq/8Ov/kgklcXeORNkmDOHBWsfzLz3Dj37HDl3dBSuJYvAhYKyhPxjgl2JuXtvg6vZN5o+us7Z6moPdffK9MXbjEFk1NEIzo3N6QrOaF02hlJISISVCiBnjRzR6Ls6TthJqV+FMUysQXPOvUhrvm+pl732zXxDNz+1k2Z/B8/FnB8GdpvJhRmUKoXF+SIkUEilnDC2aa/RCoLQiVgk2r5qVbdOEoKn0Fm+EvXzTWPkOZdYQEL7ZPXktCIsd0tVlVtZXGRYVv/LyJyknJatnT1APasySgSgQxQJnPEo7Cr3L8y89g3WO7vICciZaGZxmYkp+/jd+imN/9zTH2k/gqwglRNNNz3tsDVo5gncEG2YV6QEXFONzF1k4krAXTVmNe2StRcxQM653+eyXf4UvvfxpwkKXkKQUkx6ummKLCePa8erFF5gfjvnAEw8hI48TjSiktBDyQDk1DAcFyleUTuBVyau7r6Li00Rxl3humf28ZjKdcuzEcW6aLS689gqJj/mBU3+ZbrmCkCVJ0OSuca5BSNAKE2mG+ZT+QQ9XFo3Crgs4B+1WSjbXpbe5hxjkDG4OOfP+e3j8L30X15+7jHWajf0DDgYb7PevgTIEEwi1p5jms8JHgbKBRd3iyrPn6T50jOWjR1m5d52D3h50WkRzbSZOIDsdQpSA/hZqB/25QirUyjKt7hKRaLL6YbKE7/Wpc8dG/5B8r8fjDz7I6xcuY4UnyIAkoGqHG1VE8xBFEcuri5hyQmV6pKeOcOrsQwzdiMvnvo7cGlKU+zBu05arxGnGiXse5NaNDbxVeOvxKrA1PuRIkhPXMUJJslgzHycE59idVmzaKzx141N84dbTjFrQloa1lVV6dYFYnSe0IzqLy2yZIefyS6zMHWfOwVy2RDGpyMcR3STGZ54o1YTEQg0pnkvbL/Gp3/p3lKJGCEUV9Ym6HVzcamgiceDjX/3f2e29zo/+0N9jrXOMG/kW++0hV756jvXJA3BCI1zDkGCmCVRRzCoOFWkaIZVBRBXb3OBnv/TT7MkBQQpcVRGoiSJFpGPSNMOqwG+c+yg/+Z7/O+VuRoojiBgfEiIRcTgaEbdjrB3T36xAJ9Bu0z6+huqVFNcPGxXK/wBNAVUI4U6BFIClWeFbY8hUC5G0GOdTMKFhC4lmNhJKNS0b3Rv7g6aqteHdBzfTGPqjkgJ/zhCz5LRQAq9oaIFvclBCiTd6HggJwWOFgEjRarXQFkpjCEJg35QYh8YZMyuY+4OgYk3n2DLp8TWClOxfvwm2wvuauN1hMhqxTIdAgnMpBEdZaOzaiN8491FcGmi7jLouMXWNMQ5BTEgU+37Az37xX/Hf/sj/wNHoLMEkeBlRTA1CBixF0/azFsSRotAWlCa/cZMrB6+x/H0Pc7MwiLFhb3ebX/3sz/Dcpa+h5+conYDBIarOMeMJ0tTUwfFzv/Rz/OUnf4LvfvLHEdqiY0uUSoy1uEqQTwx1WTHX0RxOeoz0DufyS6ybeTpLy4R8BTE19KqC9ZU1LuzfoN+q+PdXPk6/6vOXznyE4/k6i62M+a7C1xVlHRjWOXWUszU+xMuGTedR0Opw4vQJjA5sb91gemsfhlN8ann5XM59732SUx/8AONru5RXd3B2wpHVRQ4PdzG1wY4qdO1wstkRBuHpjfo8+PCDvLx3gyKNSVZbpKfvQy4t0uqskYUIEywqESD/EwkHBeuw4zE6EhS9EaY3IlvoICcgRMzaPSfRJ89iJ2Pqg4RYdhFFhasbTyySFLnQQWvFcJrTXV2mc3aO7vEuk/GEi1/6MrEzFFVFVE6hHCBkRdpe5GBYsXzsBL3NQ5xzHF1doh9K9qZ95qxkdWUe4QUDO2Df3OL53ks8u/ksu0wwq21ioRj3h6yemWOrv0O0FiG0RHcydv2UG3KbfrJHUqd4OsRxwmBSkyzERAbSSKF0I41cuCFX+xcpmckHeJBY/GiMTAtc3KJO2ohWzLMvf4kLr77A4vFF7vvwB9huVbglxY3tXb43VdiyYaX4SiBC41il8rN4usHqioIDrmZbVI+0SOQSfndK0e/hbIHXEisVRRQjj3V5bvgiZ8sv8e0LH0EVHt1yKB+RqIRiuku706YsHEoqLIJyOsFWBVY28dk3656/udjrdgPgRgyuee1mq1iMJx9PiFfmiTod7HiCbLi1d8IrwgVQapY5CCA8TclTaNot+z8yL/znDtGQVwiqmfBVJFGhCa00hW0gbye0Q0Ag8VqhOx0Qkrw/bFpR+mafoADnPHdSCgIITRL59+jLh+beWxmoqgI3hRSJVjFOKKIsYzqZEusVpI4RSU2dC8qFiq/VX+JrwxcpIos/GENd47zDWo/SKdncMvJIi/KhFldbW2RFl0ysNDkb4ZG6oe7KkOAr0Th5JchSxY3xLu6U4vlLn2OzSPjZ3/kq/c0+QztCLi9SOwjVBFXn+NIirWvonwJKAlf7r1GLMS21CFqipKQ0ltrCYFwTxxGeikr06Se73JDb7Lgc3UkR85K4HbE53mF1qcv40ohobg7TdTxdvsTrF67w7cc/wPvid7MqTiKSjDgL7B+MGE379ENFZ22R7V4frzTLx9bZ3tqnO59QTftUuweAwAhNtjXh4s6Xefx7PsTiiS5jkTO91md4MEJnbVzk8KXAW0ekBCrWhCyhXk4wxzo89sAHsXFMf2sXNXGE1DLcukS0NEeyOI8d+2+pbMSfL7zF+wrjJGYwYHr+dZY//O0cXN0lLwKml3N0pcXewS7rj9yPiGLy0QgznlAXBZGKiRZSIh3RSlOOnD4OKvD1j/8WraUziBs7FEITr86hUovMa2xZEVJLPR4znNZYLWm155gWU25Nd7lvrWTeWMZ+j83JDV7cepmXe+fZr3oYAnGnQxTFBOeJ2i1aHm7lu0wiSHzJpK5ZiEeMQsnXBq/wN1YWGdk+rdYySmimw5yFKEIvtnBxQLjArfwa+6sTxDSj7lUo50l8RLAON62w0xKyiqTVRrXmOJzsM7ewSJ2U3HjqaU5/x2PsuH2ELEjjLpHUlPkULx1x3GgyWSMoq5xIVkzSA0bLBUZ7qqKi2j/Am6ppzo7AYPFS0llp44+0eGrwNPc+8QDzh6fIDyxFmFKECkegLmqSpNUkM60lERKdRpQ4sO72or+BaNgvjTNQTdJ0Fh4RQnB7bSOdxE4NZJZkvkvA4PMJIgjc7baRTUCdIJvqWhEptAQlAx7VUEv/EO2gbwkEyFihIomMAg4BQeGNn4VymvCOk00VsZzlNtR8iyRqUw/H2KlpZMppEv5wm0kkmviSgIC7k3B+Q2UPsA4nLN00QmkNQSB1QuI8pqhxBMpQUvop+bSitaI5WL7JF15+Gn8kQ1Vtip0+EotCIr3AmBJ3cEAyfwSjHMPlgvHOAWmZUpYGbIqOAzqWeOcwxpC22qQxODlmx+0TqZQbX/xdlj/0nfgFweG1A7LOKjhBNe5hiyngUAFUkFTaYBUkSxn7a1Nu2as82novIRZoqShzw3SYoxKPzCwjP0Atjvna4SuMQolxI8a1IQklkyhmo9hlzR8jamdIlxMpjTGGq5Ndbt38LZ7afYZ3LT3Gu489wfHOaURbUUYlG/s7TPMpUTslr0cMr29S+wo6CltWxInGt2KE1xSbQ1Sw3Dr3DfLDazzxg9/LmaVH2bmxSV6WxNaSLhQYVxNnGVG3Q2tujmBq9re2WF227B4W9Lb6tFLByuoS++dfo/XE/ci5tKkW9n/QDvtPhreFEwgCZByoTInoJqw/+RjlJCdemkcXgYdPneIrX32KajymtTWgtbjEpCoJwSLqiiPrKyQi5eq1S8RHV7HzXbb3dyl6Y+ZXNM5YrJV0SmivdZnKEdbBtK5Ry23ayyt0kpi428InknGn5MXqBaKxZn9/g9d6VyiiwLiu8L7hUAutcHiSWGPznO3qkJcPL+PbMaJ2XMk32K93sdLwutnm6/kFPjzfRriIznxKOY6pii7tuYAQkqqu2a4OGN8/hzoVk97cob45pDgs0cYijGv45abC1CXRXJejZ47x7h/6Xp75uY9SDArCgyWhpRiFXVbcHDI4tPZYVMM0qg1SREgVKPwuz+w+B/et0p5rs3BkjXJYMdkfImVEiAVqISI51qF7ZhUnNbvjDb40/m3cyodZKVf5xtbX6NvtZnKzFToOeCdABKRQeCWJF9pMk/6dFcsbO4A3SSP8voKuOzUAM7qPmRbEaUaWpORh2ijNzrSD3Gx17HBNrqOMIHikbGT5fSoR5i3sLBZJfCygdAgzI/SIgAum0QVCNbFg2dAYhZQErcjiFCqLmRZNyOu2sNzs3gB3iuMafzCT03hTiC0I8Ikkm28jVKNJFHDNLiKEhh1GoG93OD/6Kt3jP8jGygG/O/oddscbSKWb7x6H3ZoSBjUqCghv6CzPkR5ZozPXYdSxfHn3OX4g0yTqFN5CXVviOEIrhZYeGRzKCfpqF9+ScFhS3OjxyuDzfOjv/iTFYMKgX2IGA3yZo7wFCVYrbKwRyy3SU/PIU0cYJyk71QH31TVxFhNcoCo9xpZ05msqX+LVkBeGF7hcb2Ol4cXea4ysRSQKH8e8cHiFdy8n2DwniTXOWbRWyCRlUNSM6gH7ved4Zvd5Hli6l7XVE5iuZdSpUGtt5uYz0skclDXDwwOmtcEGSDoRc60u072CPPcIbYmEpuhN2Lh4maOr69jhkN7WHvecfIA6jri1exNTOsSgxCUT8n6ffHLAYXeXD37ge3ml9IgMyknO+pOPMU2hdiVJHP1RCrl/LLwtnICSkno4Ieq2qRJNbmoW9yd0Oy1Wjy9z9fxLWAVrp04S+ZTRQR83GhMIHDt9mrZOePW1V1l4+CTzJ45x6ZULdHRCEnXIul3izhyhzrAyoRApzKXEIkW3MzqrLWQ3QgmNU4JKWIZRyWZryN5wlzKdUq20mPbGJInGi4ooiXFAEJJJWSAF/O7ueUatAmcCnogb9S7CCGQrpVKGp0fnWW53eN/SuxG1RJkUpMDUDmcFVVnjRU7cFZhOgsyONFvjaz3U7hA/GBKcAzzOeGxuePd3fCcXn/8ao16PkCbkhaWQgZv2El1/lsiJJgxhJUqkpHGEUzXT9ohXDl5iM+qxrldYPXUcIzVDLxHZLpXxuE5C5/QiakXhNFSjCVrHvLT1ErnrU2/0uL5/k0LnCJsgE9mwcbgtgqaQWQpri1QrfWw5vs3fvJPcbHYAAR/eCBG9mTbZ7AwkvqywozFZt4vOUurRpCkSCyBmghFCNjIRyjesGesDPhakJ+eprg+/5TZ9GzLVJCfnKW/0sXWTEJeeZjfEbSXURvuoEQ0VxFlKpDXFaEwoqya/8aYs9x0nOVMdDTPn0DiJN3ZUSNDLbdprS8RZ2tzLWUW1EAKpAr7yFLris3u/xaXwEnF7icu7NxshNluTzGV0HjiKW3JMbvSRk4p2LJk7vsb84w+wemIdpwO7sse5ycs8sbJAW6TEroUQiuDBa6hcI3Z2017iUFZkpSAknnG/x8Xnv8bZh8/y5c8/gzAVCEvQTRWtmm8TjswTn10iXknwrYREgBc5VVmhOxHGNNIbKpWIrMbGU17tXeR3R+cptUGKlPP5VnO/dIQjsNGaMto5z8QYfDCkcYILTfvLRJaokDC1Hla6XEy2ue5GrLXWGRYlVduTtDTJXBcXLIsrLer9HJW0IZQUDqwM6NYicVyQdbskUYcwyLl0cIGTjz/CJFFcevVVHjnzMGvtZbZu3EAgqOa6LCwv0lnKOBwNuHL+JR595HH2hoeYwzH99QyfRCRKUw8nDfvxm8Tbwgn4yqByR0VBsjhHOqeYvv4yyYJgWo7QacSZ0w+yv7HN4cYN2p0Oa/ecIEoS8tGUV25eZOWBo2BLtl+7gnOSsbEYndGfTlm8/wEGo0DcjVFtzfz8fNMNrKNxbcO47tPNuuR1QV5XZCIjTC27m1u02l3SuRbGO0QNgoQgJM41RUxSCloqYmqmqFgQFYFgBU6DiCOkD8Q49v2Uz2w+z3J3mSf0u1hYaJNPCqoyQsoUqwI38i2kbNgUeaJJjrXJXETVH6PaMeAQQhFpTTrXZWl9ifOfO4dabNGSjv7N61ivedZ+lnse+TBUCZFuNN09GuFKgsq5uf01fv4T/4q5B45y7wcfpsjaLJ8+SXdhnoNvXOTa1et0zqyTrrWJlMe4AheDdBYzKjhojcnnLcPcQC3QAeIoQskYQVNx2uh9BlSnRbrUJd8twDSFTU12d1ZE9cYsNrOGNyY8KRungnGNwUcxOsuo64pQuJlkgZ9V5UqEaHrVIhoHMHdmmehYl/rm6Ftv1LevRgrax+eJhWR0/QBRN/mKZo6WuNu1DLLJG4gkaq6xrKiHE3AOpLqjK3TbMd7BLCzUTOqz866pAieSpMtdsm4LqaOmCA1JsE3iPI4svqqwUjBMDTtzhtZkTD0qkMGiY9BRkzswLUlIYybXt1m75wwrjz5EPD9HK47oWMXXXnqZly99hv/ur6Q8tvi9RC4hyBiJpXYeYy1FVPPVVz/H+HCTUjo6LajSFhsXL3LPD3w/i+tdihFYq/DBoVC4RJKdXIdjXXJRkQSPVIIb+RZPZs0CwhYVpi5YWGhTyoLrZoPPbD7PPlNSBMYLfBYR6hppBZEMqFZgaqa0dEzhDC54gndIpVHRHAJN3IF0LqWe5Az7W6zMLyMEjOsJLkroZil5UdBdWSTNlvATiwyW6XAIsYW5moU5QX86xeiMcWHxQrH92hVEIlh84CivXH6NldY8Jx6+D1NVjHt9dm/doH1ihTMPPkjYGzIuRhSuohoP6bz3DCWOqj8izh2+Mt+0jb4tnICYrQRbaYIxluF0gp+LWD62hlAJsq7YunodqST3vucxgpTc3N6CeorINIun72FclISpp9VaoEoc1pTIlQwSRd3OWFzpksYKFXv0QpcpljpyTKopxtQU9QE+illcXkXrhHFRgBMURUVZW3Qc4bSlJTRBNLRS5yxSSWoB3gY67TZVNSBWGiFVoxWjPNYFlIjY047Pb79EspJyX2RwvsVBrybLjjKNS3riABOaLX07SdCV5mBjh6io0HGEjOKm8YsM3Pe+Rzl37gWMc2At07qG6gBbOZ65NeChuc/zgSPfj1KO4C2TWtKWjkOxzy986ucZ9g+JJi3qIBm9tskrX3qak9/2GA9+7wdZevQepmbC4fgAvCKRGUQW5wxKOnb3tzh+7BGmg5y82iFOFEqneBRCOiQORcAah/AWnUQ4QPqmOswTZiGOWaWvDAgRaGbChkQKEHyzchVegHUU44JscZ6klVLbClWDV+KO/IKfxUykELTW52kfXyRpZYzFW2fmWmi67S7VcY0tDNXGEB8CITTJUqWacJgk4CKI2ylCRxSDEVRudl1NUd2drVRQNBO/b4Rf/O0ua54gZ3sj3whs6CRCeAumERIEj5cK75vvLE4svorI0lXmF4+xsfUqWjqkEERRSqKyhgUTOU48dJL24w+zur7Ora9f5LVf+zzv+e4PsbC8Tj4ZMuwf8guf+jn+4U88zqpNmNYVndgThMbWihd2fpunX/wSpZ6gEwWmQsQxTmjOnXuBe9/3KC9/+SsIr4iIkSbgi4rJ5jYrpxfRCThTYRAccsg0LmHaoigO0LpgEnIul1f5/MGL7GmHshHWGZCW4CRIhZOOurKEaIlJPsGKhq3lnUXQJOhbUpMHj1Ka0eGo2YE7waQo0HHCwvIq/d4BRTFpyBdqSmd+nrgV06ZFkghcLSlrR2XGYAtYXWDsPDpKkVqRjwaItmTx4bOM9vYZjnsgJaceuAcRAls7W2xfvcWx02eQK4t0XIueLygHQ3Q7pZUmuNzc5lV8czb6zf+KPwPMen2O+hPSxS6lgJUnn8COKzY29nFCcuw976LTzigmNYeHh+iVdaIkIuu0KSY1nWwRORdRBMd0OiBZ0Mx3WnSylDgkRD6iqit8K6JMLKMwZVpOkAG8UGRJzOLaEXr9Ab3hHkfXlohjPWuK4nCmUXK0oSkiU1LeEf0SMlBVhk7IUApUJBu+dHAIH1BxjFQa6w0v51e4cfMW74ru5TH1GIvTU3SEopdtsJ3vITqKjmrjhWyocblFoDFyVhTlAp1uF6rA9us3SKzAVRZnLVEnIjhPIXJeHT3Ftz/yA+xsOjo+xuuc7egGFzafwcicpdU5srUFnPBcevY5Jtd2uCQk+sw6i8sLnOmeYGN3i3GRE+oKV1U4aRFCooCtW1dYmV/HTseIyEMkEUIxay5GcGC9xwsPmYJUzATHRNOaEFBKzeiTtyUUbge+b3P830gWBASuKPFZ2khqVwXWmia0MYuBN4nVAO2YztEliBTdbB4pvnka3Z8WUjRjqExN59gS9WGOGle8WdzU01QIk0SkrYy6qLB5gbqdSQ/hzt0REu4kCJiF0N5ki8xYNKgAqYZMYYRvmsRYjVKKIBREqilQsxGxT1hZWGfr1lWUeoN5pXVMd34BEad0sxbH14/RG0/Y7vW49PyruGs7XIqf49h3vptsbYGlvSGWgpevP8Ujq99B25xhPG4xkYal45oLrz9F4aezDkACMylR2qCSiO3RDY4dO0GnPc9kMCYoTZPK8cjc4UclyZEOUiY4CnYme9zYv85S4ZiEDfrtm5zvf4OXzGWG1HipUKkmOPB1jZ3Vo6iosV+Co6pypG/qTJSQqNsUZVujBDjj8Xi0Ekih8d6yfXOP9vwcJ46eYLC3S15XGG84rA5opx2UaKNaEkrDfCvByIxaSERX0Z/kTEuJbi8wP7eGNwYTKhbP3k8xmWIqQ09IVlaWue/sCSbTgkF/zHj/gIUTq8w9+QQHgwMy65n0J3SEvrOr/mbwtnACQSp02mGpnTDxNelCSkhijIOle0+SZm1c5SgHE+JWytnuErX3hFgTS4UwHosgdzUpnuHmCCsdeZEjaBp8+7rAxpJWK2JYHVKamkQqvPPIOCJNFbu7NymKmoXuHDpu1DIbeYRmdZVGMVEc44WeJVorhBTgLdYbnOviI8nU12gESRSRxilGQVHnRIBEsx9qfmf8Klf8Hg+qs6yoE1wbX2MQjQhCksoWLgRGN68T9QooGvaO8hYhA/e850le/8o5xKgiml+k1c1I0wTaCesnj3L/Q2eR2SLfWH2FTj3HdCiY1Nd46vVP8vwLnyI7vsqZ7/pOjr/7IbQOsBij3neG+L6jDOsB+1eucurocZYX11lbXWdza4M9qTACUt805ba+ZHvnGgjbVFbriIBE+kCoXRPh0RKpNdFC1sRui7qRUA5v9AQQs8YqTS7g98Y3ffANmwUIUiCcpx5PEEkX3W1TlSOEtY2mUBMMxwlBe3GOZC5FxwmpShsRubcIUghSlZLGKbYrSBfnmE4O7lA578T1I0XSbWP87BpdE+cWoWmiczuEdBtNSCi8kQ/gjfCZEI08SdTRRAspMtWIIAkWcCBiiVcShAar8LVhe+caSUuilcR5hxEgpCRrdTh+7CTOWrZ3b3Jje5Nkbon4/aeolyJCFKF14LG/9v1snrrI9d95jp/9wv/Kk++5zF86/Vfopmfx84HtYyOWP3SaH3/yJ3j94nV2b23BtKIsK0xZUg/7vP7lc9z/5BN8/YtPE3yJk5qEiKhXUNzap3N0GSUFU18xiEZ8cfwZzsqzHLgNXutdY1MeUMeCWAR8KKk8pHFGJjNEXVGZmpJGpM25GlPmaBnhEThv0UlKFMckkcTUNRNTNX0VQkAj0LGi200ZDA8w9Yi5LKMkQjhPJCTj6QgTlcy3O+QHBXo0RcYOJ2vyosLKgEgkS+tLJChaKkbTCASaNQe1JZaSyllK62itL9I9paiKKaUKhCQmnZ/D55al5XX8tCL8p1InILQias8TEZhSo5REKiiFJ1MxqvQs6JRkJcMIGE+npCpCZUkjp1o7CmGJPNSFRQSHMxVFVWHHCmyEWlllYX2BSZ1jXIkOEldZolhTVTWlt00/1FjRHw9I04SqLBESdJyRthJkJDA+IPEoLWmrDOcsVVU0BT7WNjQ+EWi1siYeGUpM5ZpVmmia0CgHAs9eesBEDonDRarE41XTQtF6g5sYils7qMM+om50enRwtNdWyPtT+rv95t6piIW1Ve598H52dnfY2txl++Y22XwHfizi4oUXqHsTBpMDHv/Id/GhD/9tdBQxHebsPnee49/1QeYfPEVwU2QaEzmJCwFTF2xtbdGe67Jw5AjJ0hLbt25QjgYN9z7UENcIH0jiGC8DdV3grEcJhVSSoAReSXSrhW5nuL0c7cGKGZ1TgA+NTDWiCY+EMCPVwxsKmzNHgffYsoZpRWtpDteqcWY8ayQjEUEQEk37yCJRpInjGKnFG9TUtwIClBYkcYxwjvaRJaa7Q0Q5E8ZrikHQrZQs65D3Rs01ek+4nfSbsYbu7IyEQIqm9SMh4INrciIS8KG5x1Kg2i10q42PJNI1+QDvPc6WgCROInQcY+uaIC1Bp/goxsuY5fVjHDl5mlYc0xv0mY7HKFmjQkA7yXw3xj98hAXZJsstu8+dp7O4xAf+5l/FGoOqA//6E/8fFjorxEsdHjr1Xr76pS9SDCYEJVlbWuHII0e4+tpl9je3sC7Q3+uT96e0kzbT3QOcUNggIRe4WxL78GlCJ4IgqRN4Tb/OdXuFWpfk3iAqj6oUtQInAy5YTPAopWjHESpWjPIcT/OsEjzeW5IkQyk9Y095jAjITJHqFJ9XuLpxKGVV0x8PiOIILSRlXuKNJ4njRpRRN/TZiZdk63MMN3vYnQPQBoPDJ0njvJUjyhRaCrIQEWJFDLiiAudYXVgiDlCHwKisEEpRirpRGNASC6QkmFaK+E9HOyiQak1+eIgROaKlSJOUVGfYccH23gGbVUntS0QQlGVJHMdooKUTahewkcBSQRSRLSWU0xwZJUyvbSOsYP6+Y/TtGGNLhPKoGUHFWoutamTUxlpLVZUYZymrnAAkKiZNEnQkG816D7UpqU2N1praGhwQSY0vDW0i4laMF4LKGExd422gpaJmVSs9URqjo5TgDXXwWFk3LApHI+WgHCI3KKNwzqGCRyMIUrJ8/DhXz18keHDGMNrahmnBjfPfwBlHtrqGFYEnzhzl8NYG2WoLFXnW3v1t7N3cohwMCFXF9qUbTKXhoSffzeL6MvnBBOscpqqIVEQwAqEidvoH6HyCryynjp2gn2UcbO80ceog0SFCGIGtzZ2iKGQjeyBnTk9qgZ5rU+s+wsw0lWkmwDusl9BUvwbfJD4FTeLYh5msBM37wnvspMDOtdGLXXxdEgp7J0GaLqQkCwlK3/5+67e2WiyAsfXMGUCyEJMupFQ7kzsJcZFp9GIXax12UjQOIMyk82ZSGtI3TWduq5IKOROYuM0SErM/JgLCC4SWRHNtpG4mTal1o0vkGtqt8GBzQyIkUYiwQTRd8hCsrh9hcXGZne1d+onGViXzUYdgDJGKMFUzWcVxYHFliXxScO5zv0Pba44+cBqRJKQLCzzwoW9jvLNLPN/i8NYGy4tzvPTaTXQQ7L54gVcjRTvOGA0HWN908Lp6/iLrp08y2e+hvYfgcU6ijELnBtGWCOexzjTig6rZMSrdCBc6Y6irGm8FCE1dGSrt8LFERhFLrQ51UeNLQyQ1dQjU1pAIqK3FeUcURQQCUSRppwmlc5S+oqxyalsRgiNBNTviqkBJ2TQMCgHnHeN6SqkdaiEiv7BN0IH07FGkCk2ryrLHeGzQJGgTiJUgt82uo65r0jQliEAsU2SS0l1bYKm7SKkCk3yMnUypQqC1vIT4M9DGels4AWsMh6MdimGPlfvO0lldInjY3dig7PWhtHSzBCUsg/0DzCSnqAyhMoQsIzuxjnAJ44M9oqUW3SMrTIYGlcwjOy1anRau0/QQNb4m1TFBiJkWt0BoRWEq8mqKDZbgHOV0QpqltLMuXhiqqsYZj/OB2tdIKamqGiEkkYzIhERbSLM2xnkqW2NKg/SSWGtaOkJJ0cTOtW5+vGpyEsHjncO5mRZJWTK6uo3p502cPUtoSUGyMEdVVdiibBa3s1DBdDwCKUk7bZaPrLP24Cke/kvfxta1awSteP2Z57DWsbiywsH+Hq4s0AspK+97iDqGNI2w3jSicm5MKhSjwYS55Rbe1lhnkbnl6vYWJ+89i1pfZ//WJsZLFBKsmK1wBEopEA4RPDJIhJMIAno+xWcKW1mCkI3csBAzGeVZIjg0xVC3K4ff3EHsNoT3+LKiHI3RqwuoThtfj1FW4KRHdTQqFUSxbgqnnOct9wLOoxDIuMkLqY4myNBUDauA7LQhjij3hw0lNNDoJ73pum8nfuENGuhMJ69xmnfabQqsDPhMoedTJALvJLJ51exsRdQsOHyAIFBEeO+JpGR1fZ0sSrj60stknS7ORU1YTsNoMAEdqN0YHwI2GJI0po4FK0+cZfD1i1z+8guoJGNlbY3XDg7QWvHIj3w/Tmoe+qHvxict9i7e5KCYYiZTptWoSeiHhipri5KqLJk/sk41GFH5QLAW088ZXN1krnUMG1kiBFIqpNJEImpyLLKpt4iUAOObr95LTGUpjCFKBFkUM5e0KYuSVMaE4LHBMq0MSjVdDGMTo2RT2a2Vpt2dh2JMNZ0QbI0NjrFztJI2QjdxeUGYyXpLSt/kJJJOTPvEMvkkJ8QZthqSzMUMdw4wvSndlTVCXVNs7CCKApFofBJTd1rMr60gXMXocMRo2iNdWmT5zAmOrhxh4mIOLl+jiAzO/CfCDpJK0j61QvfYMmVp2dveJZ2bp3P0KLrbwQ1ziv4AHUd055fJp4YkBMbjMSFLMcFQHYzoyg4y7hJI0GoBbVPkiTNkKx0mumHdJJFuytC1oPYWLSTWO4IE5x3emmbXbSBNM7x0VM5hHDgf8KICJXDBgxaoIOgQkcUJVfCMJmUj2uU9mYiIWxHIQIgEXjdceoJHBUcatxAIinpKHGuca6p6zdQQipJQTNFRQtKeR2GonMVuboOSYAQyijhx7z2sra3RabfQrRZRu8vYT3Hjgs2vvsp8u0scR0xHI/Y2cqx1uOUO+r0ncSc7GBxznSWyuEtRN0lugiTIADSUPGssOIc3hmvXrzO3usz68WPsb+/gqhoRSSIl8LgmoRs0zETiZIjQWqC7CbKTEib57UU78EbNwO1p+g9a2Lx5MiQ04Q43mKC6LZK5LuW0AldDpFCtFB8kkhgRFM7ZmTb8WwMfPM5apFQzvSWHamUQjRDGo5OYdK6LLWvqwRjtwp2k8Z3+Ar8vp/FmRu2dmovmoPl/WiA7KbqbEGmNDbrRKhICgQYR0HGTnxIuEKQgSTJWjx5hYmt2treRxoCziDoQawlUM5uQCBmQXpDFCfOdJSwWd7KDCKepv34LdThhb+Mm3gdaK0v47QGbl2+ytnKEGMHDjzyCOX0Sm+dMpjn7e/vcuHKluQ4lGW/u4NIY1eqQEOGnQ0wxxRcVpraEdoRUklhJnA2kcUYgUJociydEiqAFwXpSGZP6hLo01KWnqGtKIJKSTtJC1RVjHFbMnmkFFRXSJygHkXAkSpKmGa6ZBJqQmhbUwTa9k73DBt+IMlpHEmmCChgt6N53D+5ggjQCpwSBhFY8h1eC8cE+STcDAmJc0o0XqUREa34ZVERRQ3ZkHTXXIu12GRcl5WiI1hFrT9yP14JD9XvzaH8avC2cgPce4SQ7RR8xGNNZWaMaD6l7Q0SWUE6nLAFFf8rw5iZq2iR4Uh1jk7gJcUxKTFchTEQmOnRa8/hxwfJCm6KV4WxOHDcPQ21qmp6vzeQmfFOwJb0gWIGINDZ4dIixAWofsMY0RT0KpFR4AyI4PBavBVNT4W1A64gQJJ20jVAWEWmE8rhgUSomaEE8Ez4TPhAlMUG3qE2B0jG+ysl7Q9LFOWQCkVMIrahNTTEYNclsrRBKo7RmWjguXrhMXY1Zmp9nXBQ88X3fyfWvnkOVlvPPPtM06CwslZa0jy/Rfe9pxHoGwuFCI+lQ14aGua4IUiGFopg20sCIgPUVQjgopvihYqcoaHfm8WnTWS2EGTddSvBNJa93DiElHolud4hWOrjDScNgCbOYtm/4/bcrXeGNye9O85jZOyEEXGj68yrjMb0xyYkjhG67Eaibb2EW2jgJ1jtUEDj/1vYUCCFQe4dA4EQjFGYWWrDQwvZzVKeNihKK3T7KNDuhRg77tvgd/8H43+ifEPDM8ijSI0TTaEUpUCsddLuNp1Ebtc4jVdOXoGHiegKWEEmyVhulNDtbN2llGRRThJRYX6FEhgiBYlogVIQXDhnA4alr07TNxINwdI4v0tYp43M3mG700NZTHw45/9vPcObdj3Dj2RfwCH7nt5+im2X0hkPipNvIoKQtsAaHoKwNVVmSdeabOgWlcAqyxTny3hDVXUQlKQFL2moRiSZEpaIImcTUziFtILgKnUQEJ1FCkTpNUZYI4anrGqsFaIG3tln4oJCappGR9RjjCbJhAuoALhiUjAjGIqXAWof0AesKnFIENMIH4iQixJraBGi1WG17BodTOt15hIDalgRTEsZDaleRJjFax5TjAjEuGU9LFk8dZ77dpjcdkCrD5GCPsDSPSDSjg10GC13WW4u/d4H0p4R4O+itCyHGwGtv9Tj+FFgBDt7qQfwJ8U4cM3xz4z4dQlj9sxzMHxfvUNv+i2gjbyXeUtt+W+wEgNdCCO97qwfxJ4UQ4vl32rjfiWOGd+64eQfa9jv1Xt8d958Ob52y1l3cxV3cxV285bjrBO7iLu7iLv4C4+3iBH7qrR7AnxLvxHG/E8cMd8f9rcQ7ccxwd9x/KrwtEsN3cRd3cRd38dbg7bITuIu7uIu7uIu3AG+5ExBC/GUhxGtCiMtCiH/0Vo/nNoQQJ4UQTwkhXhVCfEMI8Q9m55eEEJ8TQrw++3dxdl4IIf6X2XW8LIR471s8fiWEOCeE+Pjs+KwQ4quz8f07IUQ8O5/Mji/P3j/zFo13QQjxMSHERSHEBSHEB98p9/oPwtvVruGdbdvvNLuejeVtbdtvqRMQQijgfwP+M+AR4G8KIR55K8f0JljgfwghPAJ8O/B/nY3tHwG/HUK4H/jt2TE013D/7OfvA//iWz/k34N/AFx40/E/Af5pCOE+oA/8vdn5vwf0Z+f/6exzbwX+OfDpEMJDwLtoxv5Oude/B29zu4Z3tm2/0+wa3u62/Uabum/9D/BB4DNvOv7HwD9+K8f0HxnrbwA/QFP4c3R27igNDxzgXwJ/802fv/O5t2CsJ2aG9b3Ax2kKbg8A/fvvO/AZ4IOz13r2OfEtHu88cO33/913wr3+Q67nHWPXs/G9I2z7nWbXs7/9trfttzocdBy49abjjdm5txVmW8n3AF8F1kMI27O3doD12eu307X8M+AfcqcVFcvAIIRgZ8dvHtudcc/eH84+/63EWWAf+NnZVv9fCSHavDPu9R+Et/v47uAdZtv/jHeWXcM7wLbfaifwtocQogP8e+D/FkL4Pc1qQ+Oq31b0KiHEDwN7IYSvv9Vj+RNAA+8F/kUI4T3AlDe2x8Db816/0/FOsu13qF3DO8C232onsAmcfNPxidm5twWEEBHNQ/JLIYRfnZ3eFUIcnb1/FNibnX+7XMt3An9NCHEd+CjN1vmfAwtC3Gm2++ax3Rn37P154PBbOWCa1c5GCOGrs+OP0Tw4b/d7/Yfh7T6+d6JtvxPtGt4Btv1WO4HngPtnGf4Y+EngN9/iMQFNlh74GeBCCOF/ftNbvwn8ndnrv0MTT719/r+aZfe/HRi+abv3LUMI4R+HEE6EEM7Q3M8vhBD+FvAU8ON/yLhvX8+Pzz7/LV2VhBB2gFtCiAdnp74PeJW3+b3+j+Bta9fwzrTtd6JdwzvEtr/ViZI/IHHyEeAScAX4f77V43nTuD5Es0V7GXhx9vMRmrjibwOvA58HlmafFzSMkCvAK8D73gbX8D3Ax2ev7wG+BlwGfgVIZufT2fHl2fv3vEVjfTfw/Ox+/zqw+E6613/A9bwt7Xo2tne0bb+T7Ho2lre1bd+tGL6Lu7iLu/gLjLc6HHQXd3EXd3EXbyHuOoG7uIu7uIu/wLjrBO7iLu7iLv4C464TuIu7uIu7+AuMu07gLu7iLu7iLzDuOoG7uIu7uIu/wLjrBO7iLu7iLv4C464TuIu7uIu7+AuM/z+tcnSvxrRG4AAAAABJRU5ErkJggg==\n", + "image/png": 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", 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\n", 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", "text/plain": [ "
" ] @@ -459,8 +459,8 @@ "\n", "本节介绍了数据预处理方法在数据集中的使用方式,可先将一个或多个方法组合定义到一个实例中,再在数据集中应用,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图1:数据预处理流程
\n", + "
\n", + "

图 1:数据预处理流程
\n", "\n", "图像、文本等不同类型的数据预处理方法不同,关于文本的数据预处理可以参考 [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data_prepare/overview.rst)。" ] diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index 58263ecfaad..e54c1a01dd0 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -134,8 +134,8 @@ "metadata": {}, "source": [ "通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 2个`Conv2D` 卷积层、2个`ReLU` 激活层、2个`MaxPool2D` 池化层以及3个`Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", - "
\n", - "

图1:LeNet网络结构示意图
\n", + "
\n", + "

图 1:LeNet 网络结构示意图
\n", "\n", "另外在 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", "\n" @@ -372,8 +372,8 @@ "\n", "本节介绍了飞桨框架中模型组网的几种方式,并且以 LeNet 为例介绍了如何使用这几种组网方式实现,总结模型组网的方法和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图2:模型组网方法
" + "
\n", + "

图 2:模型组网方法
" ] }, { From db8c0d0a95fb9e392f66c90855ee2d6795122f66 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 13:24:49 +0800 Subject: [PATCH 41/63] Update 01_quick_start_cn.ipynb update image format --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index cd959a834ab..615d8b87415 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -90,8 +90,10 @@ "\n", "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "
\n", - "

图 1:MNIST 数据集样例
\n", + "
\n", + "![MNIST](images/mnist.png =600)\n", + "
图 1:MNIST 数据集样例
\n", + "
\n", "\n", "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" ] From 133b48fa075feafc0409dc1e39ff3747a35b082c Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 13:51:13 +0800 Subject: [PATCH 42/63] Update 01_quick_start_cn.ipynb test image --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 615d8b87415..ce24f2575d4 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -91,7 +91,7 @@ "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", "
\n", - "![MNIST](images/mnist.png =600)\n", + "![MNIST](images/mnist.png)\n", "
图 1:MNIST 数据集样例
\n", "
\n", "\n", From e4f2a591c3591768e93ddc0a464d17d82cbbca4f Mon Sep 17 00:00:00 2001 From: WangChen0902 <827913668@qq.com> Date: Tue, 15 Feb 2022 13:55:14 +0800 Subject: [PATCH 43/63] updata 05 --- .../05_train_eval_predict_cn.ipynb | 311 +++++++++--------- 1 file changed, 158 insertions(+), 153 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb index 1807d86d7cf..4a115f4a5be 100644 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -35,12 +35,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a88a8752-bbab-4ecf-b384-4199626210df", "metadata": { + "execution": { + "iopub.execute_input": "2022-02-15T05:48:34.721177Z", + "iopub.status.busy": "2022-02-15T05:48:34.720958Z", + "iopub.status.idle": "2022-02-15T05:48:36.730839Z", + "shell.execute_reply": "2022-02-15T05:48:36.730090Z", + "shell.execute_reply.started": "2022-02-15T05:48:34.721148Z" + }, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", + "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.3)\n", + "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.19.5)\n", + "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.16.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (3.0.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2019.3)\n", + "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (56.2.0)\n", + "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n", + "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], "source": [ "# 使用 pip 工具安装 matplotlib 和 numpy\n", "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple" @@ -60,15 +86,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0da73e3c-adb6-49bb-9da2-637ab920f558", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:07:59.984715Z", - "iopub.status.busy": "2022-01-19T13:07:59.983585Z", - "iopub.status.idle": "2022-01-19T13:07:59.991700Z", - "shell.execute_reply": "2022-01-19T13:07:59.990882Z", - "shell.execute_reply.started": "2022-01-19T13:07:59.984656Z" + "iopub.execute_input": "2022-02-15T05:48:36.733300Z", + "iopub.status.busy": "2022-02-15T05:48:36.732654Z", + "iopub.status.idle": "2022-02-15T05:48:38.199626Z", + "shell.execute_reply": "2022-02-15T05:48:38.199024Z", + "shell.execute_reply.started": "2022-02-15T05:48:36.733265Z" }, "scrolled": true }, @@ -79,7 +105,7 @@ "CPUPlace" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -104,18 +130,7 @@ "\n", "> 注:\n", "> * 本文仅以单机单卡场景为例,介绍模型训练的方法,如果需要使用单机多卡、多机多卡训练,请参考如下章节:[单机多卡训练](06_device_cn.html)、[分布式训练](./06_distributed_training/distributed_introduction.html)。\n", - "> * 飞桨框架除了支持在 CPU、GPU 上训练,还支持在百度昆仑 XPU、华为昇腾 NPU 等 AI 计算处理器上训练,对应的训练指导请参考 [硬件支持](./09_hardware_support/index_cn.html) 章节。\n", - "\n", - "\n", - "# 备注\n", - "\n", - "2. 这俩改放到对应需要的位置,另外totensor改为normalize\n", - "\n", - "import numpy as np\n", - "\n", - "from paddle.vision.transforms import ToTensor\n", - "\n", - "___确认下哪里需要np,补充代码。\n" + "> * 飞桨框架除了支持在 CPU、GPU 上训练,还支持在百度昆仑 XPU、华为昇腾 NPU 等 AI 计算处理器上训练,对应的训练指导请参考 [硬件支持](./09_hardware_support/index_cn.html) 章节。\n" ] }, { @@ -130,15 +145,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "c20487eb-ae05-4a35-a459-a00484f02f1b", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:08:15.735730Z", - "iopub.status.busy": "2022-01-19T13:08:15.735285Z", - "iopub.status.idle": "2022-01-19T13:08:20.338671Z", - "shell.execute_reply": "2022-01-19T13:08:20.337632Z", - "shell.execute_reply.started": "2022-01-19T13:08:15.735677Z" + "iopub.execute_input": "2022-02-15T05:48:38.201208Z", + "iopub.status.busy": "2022-02-15T05:48:38.200733Z", + "iopub.status.idle": "2022-02-15T05:48:42.416167Z", + "shell.execute_reply": "2022-02-15T05:48:42.415347Z", + "shell.execute_reply.started": "2022-02-15T05:48:38.201177Z" }, "scrolled": true }, @@ -186,15 +201,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "a7705595", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:08:27.651130Z", - "iopub.status.busy": "2022-01-19T13:08:27.649829Z", - "iopub.status.idle": "2022-01-19T13:08:27.660944Z", - "shell.execute_reply": "2022-01-19T13:08:27.659530Z", - "shell.execute_reply.started": "2022-01-19T13:08:27.651041Z" + "iopub.execute_input": "2022-02-15T05:48:42.417342Z", + "iopub.status.busy": "2022-02-15T05:48:42.417126Z", + "iopub.status.idle": "2022-02-15T05:48:42.421002Z", + "shell.execute_reply": "2022-02-15T05:48:42.420385Z", + "shell.execute_reply.started": "2022-02-15T05:48:42.417318Z" }, "scrolled": true }, @@ -214,34 +229,29 @@ "用 `paddle.Model` 完成模型的封装后,需通过 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 进行训练前的配置准备工作,包括设置优化算法、Loss 计算方法、评价指标计算方法:\n", "\n", "- **优化器(optimizer)**:即寻找最优解的方法,可计算和更新梯度,并根据梯度更新模型参数。飞桨框架在 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer) 下提供了优化器相关 API。\n", + "- **学习率(learning_rate)**:在每个批次或轮次更新模型参数的比率。较小的值会导致学习速度较慢,而较大的值可能会导致模型震荡而不收敛,为优化器设置合适的学习率非常重要。训练过程中需要根据训练进度设置合适的学习率,或者指定合适的学习率策略,如果需要使用学习策略,飞桨框架在 [paddle.optimizer.lr](../api/paddle/optimizer/Overview_cn.html#about-lr) 下提供了学习率策略相关的API。\n", "- **损失函数(loss)**:用于评估模型的预测值和真实值的差距,模型训练过程即取得尽可能小的 loss 的过程。飞桨框架在 [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) 提供了适用不同深度学习任务的损失函数相关 API。\n", - "- **评价指标(metrics)**:用于评估模型的好坏,不同的任务通常有不同的评价指标。飞桨框架在 [paddle.metric](../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n", - "\n", - "\n", - "# 备注\n", - "1. 学习率这个重要的超参,补充代码和介绍\n", - "\n", - "————补充下学习率的介绍,代码也补充一个" + "- **评价指标(metrics)**:用于评估模型的好坏,不同的任务通常有不同的评价指标。飞桨框架在 [paddle.metric](../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "ccefe291", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:08:30.072813Z", - "iopub.status.busy": "2022-01-19T13:08:30.071887Z", - "iopub.status.idle": "2022-01-19T13:08:30.079309Z", - "shell.execute_reply": "2022-01-19T13:08:30.078609Z", - "shell.execute_reply.started": "2022-01-19T13:08:30.072752Z" + "iopub.execute_input": "2022-02-15T05:48:42.421929Z", + "iopub.status.busy": "2022-02-15T05:48:42.421750Z", + "iopub.status.idle": "2022-02-15T05:48:42.433745Z", + "shell.execute_reply": "2022-02-15T05:48:42.433112Z", + "shell.execute_reply.started": "2022-02-15T05:48:42.421908Z" }, "scrolled": true }, "outputs": [], "source": [ - "# 为模型训练做准备,设置优化器并将网络的参数传入优化器,设置损失函数和精度计算方式\n", - "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()), \n", + "# 为模型训练做准备,设置优化器及其学习率,并将网络的参数传入优化器,设置损失函数和精度计算方式\n", + "model.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()), \n", " loss=paddle.nn.CrossEntropyLoss(), \n", " metrics=paddle.metric.Accuracy())" ] @@ -273,15 +283,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "51021638", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:08:46.924974Z", - "iopub.status.busy": "2022-01-19T13:08:46.924271Z", - "iopub.status.idle": "2022-01-19T13:11:05.819413Z", - "shell.execute_reply": "2022-01-19T13:11:05.818324Z", - "shell.execute_reply.started": "2022-01-19T13:08:46.924912Z" + "iopub.execute_input": "2022-02-15T05:48:42.434747Z", + "iopub.status.busy": "2022-02-15T05:48:42.434575Z", + "iopub.status.idle": "2022-02-15T05:49:30.599976Z", + "shell.execute_reply": "2022-02-15T05:49:30.599106Z", + "shell.execute_reply.started": "2022-02-15T05:48:42.434726Z" }, "scrolled": true }, @@ -291,7 +301,8 @@ "output_type": "stream", "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", - "Epoch 1/5\n" + "Epoch 1/5\n", + "step 10/938 [..............................] - loss: 0.9679 - acc: 0.4109 - ETA: 13s - 14ms/step" ] }, { @@ -306,15 +317,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 938/938 [==============================] - loss: 0.1962 - acc: 0.9300 - 29ms/step \n", + "step 938/938 [==============================] - loss: 0.1158 - acc: 0.9020 - 10ms/step \n", "Epoch 2/5\n", - "step 938/938 [==============================] - loss: 0.0445 - acc: 0.9689 - 29ms/step \n", + "step 938/938 [==============================] - loss: 0.0981 - acc: 0.9504 - 10ms/step \n", "Epoch 3/5\n", - "step 938/938 [==============================] - loss: 0.0638 - acc: 0.9780 - 29ms/step \n", + "step 938/938 [==============================] - loss: 0.0215 - acc: 0.9588 - 10ms/step \n", "Epoch 4/5\n", - "step 938/938 [==============================] - loss: 0.0035 - acc: 0.9825 - 29ms/step \n", + "step 938/938 [==============================] - loss: 0.0134 - acc: 0.9643 - 10ms/step \n", "Epoch 5/5\n", - "step 938/938 [==============================] - loss: 0.0786 - acc: 0.9860 - 33ms/step \n" + "step 938/938 [==============================] - loss: 0.3371 - acc: 0.9681 - 11ms/step \n" ] } ], @@ -352,15 +363,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "70f670ec", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:13:03.190834Z", - "iopub.status.busy": "2022-01-19T13:13:03.190285Z", - "iopub.status.idle": "2022-01-19T13:13:33.046705Z", - "shell.execute_reply": "2022-01-19T13:13:33.045836Z", - "shell.execute_reply.started": "2022-01-19T13:13:03.190759Z" + "iopub.execute_input": "2022-02-15T05:49:30.601360Z", + "iopub.status.busy": "2022-02-15T05:49:30.600927Z", + "iopub.status.idle": "2022-02-15T05:49:51.509694Z", + "shell.execute_reply": "2022-02-15T05:49:51.509091Z", + "shell.execute_reply.started": "2022-02-15T05:49:30.601333Z" }, "scrolled": true }, @@ -370,9 +381,9 @@ "output_type": "stream", "text": [ "Eval begin...\n", - "step 10000/10000 [==============================] - loss: 3.5763e-07 - acc: 0.9810 - 3ms/step \n", + "step 10000/10000 [==============================] - loss: 2.3842e-07 - acc: 0.9714 - 2ms/step \n", "Eval samples: 10000\n", - "{'loss': [3.5762793e-07], 'acc': 0.981}\n" + "{'loss': [2.384186e-07], 'acc': 0.9714}\n" ] } ], @@ -403,17 +414,10 @@ "* 模型是单一输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)]`\n", "* 模型是多输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), …]`\n", "\n", - "numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,数目对应预测数据集的数目。\n", + "如果模型是单一输出,则输出的形状为[1, n],n表示数据集的数据量。其中每个numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,类型为numpy数组,例如在mnist分类任务中,每个numpy_ndarray_n是长度为10的numpy数组。\n", "\n", + "如果模型是多输出,则输出的形状为[m, n],m表示标签的种类数,在多标签分类任务中,m会根据标签的数目而定。\n", "\n", - "# 备注\n", - "1. 这里有两个数目,待确认是什么区别?\n", - "\n", - "返回格式是一个列表,元素数目对应模型的输出数目:\n", - "\n", - "数目对应预测数据集的数目\n", - "\n", - "——————更新一下描述使更清晰\n", "\n", "\n", "\n", @@ -423,15 +427,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "d6318f18", "metadata": { "execution": { - "iopub.execute_input": "2022-01-19T13:28:57.526737Z", - "iopub.status.busy": "2022-01-19T13:28:57.525675Z", - "iopub.status.idle": "2022-01-19T13:29:18.834313Z", - "shell.execute_reply": "2022-01-19T13:29:18.833570Z", - "shell.execute_reply.started": "2022-01-19T13:28:57.526676Z" + "iopub.execute_input": "2022-02-15T05:49:51.511077Z", + "iopub.status.busy": "2022-02-15T05:49:51.510653Z", + "iopub.status.idle": "2022-02-15T05:50:07.724522Z", + "shell.execute_reply": "2022-02-15T05:50:07.723742Z", + "shell.execute_reply.started": "2022-02-15T05:49:51.511050Z" }, "scrolled": true }, @@ -443,32 +447,47 @@ "Predict begin...\n", "step 10000/10000 [==============================] - 2ms/step \n", "Predict samples: 10000\n", - "[[ -6.5593615 -6.4680595 -1.4708003 2.1043894 -11.743436 -4.4516582\n", - " -14.733968 12.036645 -6.582403 -1.8672216]]\n", + "1\n", + "[[ -6.512169 -6.7076845 0.5048795 1.6733919 -9.670526 -1.6352568\n", + " -15.833721 13.87411 -8.215239 1.5966017]]\n", "true label: 7, pred label: 7\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " from collections import MutableMapping\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " from collections import Iterable, Mapping\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " from collections import Sized\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " if isinstance(obj, collections.Iterator):\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " return list(data) if isinstance(data, collections.MappingView) else data\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n", + " a_min = np.asscalar(a_min.astype(scaled_dtype))\n", + "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n", + " a_max = np.asscalar(a_max.astype(scaled_dtype))\n" + ] } ], "source": [ @@ -476,6 +495,7 @@ "# 用 predict 在测试集上对模型进行推理\n", "test_result = model.predict(test_dataset)\n", "# 由于模型是单一输出,test_result的形状为[1, 10000],10000是测试数据集的数据量。这里打印第一个数据的结果,这个数组表示每个数字的预测概率\n", + "print(len(test_result))\n", "print(test_result[0][0])\n", "\n", "# 从测试集中取出一张图片\n", @@ -498,7 +518,7 @@ "示例中对测试集 `test_dataset` 中每一个样本执行预测,测试数据集中包含 10000 个数据,因此将取得 10000 个预测输出。\n", "\n", "打印第一个样本数据的预测输出,可以看到,在手写数字识别任务中,经过模型的计算得到一个数组 [[ -6.5593615 -6.4680595 -1.4708003 2.1043894 -11.743436 -4.4516582\n", - " -14.733968 12.036645 -6.582403 -1.8672216]],数组中的数字表示对应分类(0~9)的预测概率,取概率最高的值(12.036645)的下标(对应 label 7),即得到该样本数据的预测结果(pred label: 7),可视化该样本图像(true label: 7),与预测结果一致,说明模型准确预测了样本图像上的数字。\n", + " -14.733968 12.036645 -6.582403 -1.8672216]],取其中最大的值(12.036645)的下标(对应 label 7),即得到该样本数据的预测结果(pred label: 7),可视化该样本图像(true label: 7),与预测结果一致,说明模型准确预测了样本图像上的数字。\n", "\n", "\n", "\n", @@ -508,10 +528,7 @@ "* [Model.eval_batch](../api/paddle/Model_cn.html#eval-batch-inputs-labels-none):在一个批次的数据集上进行评估;\n", "* [Model.predict_batch](../api/paddle/Model_cn.html#predict-batch-inputs):在一个批次的数据集上进行推理。\n", "\n", - "这三个 API 与上面介绍的三个 API 的输入数据的维度有所不同,详细介绍可参考对应 API 文档。\n", - "\n", - "# 备注\n", - "2. 为啥概率是负的,为啥概率会超过1?12.多?----- 改一下示例结果的解读" + "这三个 API 与上面介绍的三个 API 的输入数据的维度有所不同,详细介绍可参考对应 API 文档。\n" ] }, { @@ -521,38 +538,7 @@ "source": [ "## 三、使用基础 API 训练、评估与推理\n", "\n", - "除了通过高层 API 实现模型的训练、评估与推理,飞桨框架也同样支持通过基础 API。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础 API 封装而来。下面通过拆解高层 API 到基础 API 的方式,来了解如何用基础 API 完成模型训练、评估与推理。\n", - "\n", - "# 备注\n", - "1. 都用前面那一个网络,同步更新下面基础API的代码" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "da17af7e", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# 定义网络结构(采用 SubClass 组网)\n", - "class Mnist(paddle.nn.Layer):\n", - " def __init__(self):\n", - " super(Mnist, self).__init__()\n", - " self.flatten = paddle.nn.Flatten()\n", - " self.linear_1 = paddle.nn.Linear(784, 512)\n", - " self.linear_2 = paddle.nn.Linear(512, 10)\n", - " self.relu = paddle.nn.ReLU()\n", - " self.dropout = paddle.nn.Dropout(0.2)\n", - " \n", - " def forward(self, inputs):\n", - " y = self.flatten(inputs)\n", - " y = self.linear_1(y)\n", - " y = self.relu(y)\n", - " y = self.dropout(y)\n", - " y = self.linear_2(y)\n", - " return y" + "除了通过高层 API 实现模型的训练、评估与推理,飞桨框架也同样支持通过基础 API。简单来说, `Model.prepare` 、 `Model.fit` 、 `Model.evaluate` 、 `Model.predict` 都是由基础 API 封装而来。下面通过拆解高层 API 到基础 API 的方式,来了解如何用基础 API 完成模型训练、评估与推理。\n" ] }, { @@ -580,9 +566,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8419b510", "metadata": { + "execution": { + "iopub.execute_input": "2022-02-15T05:50:07.726048Z", + "iopub.status.busy": "2022-02-15T05:50:07.725599Z", + "iopub.status.idle": "2022-02-15T05:50:52.862759Z", + "shell.execute_reply": "2022-02-15T05:50:52.861931Z", + "shell.execute_reply.started": "2022-02-15T05:50:07.726018Z" + }, "scrolled": true }, "outputs": [ @@ -590,11 +583,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, batch_id: 900, loss is: [0.03877499], acc is: [1.]\n", - "epoch: 1, batch_id: 900, loss is: [0.04977579], acc is: [0.984375]\n", - "epoch: 2, batch_id: 900, loss is: [0.01578258], acc is: [1.]\n", - "epoch: 3, batch_id: 900, loss is: [0.10209924], acc is: [0.96875]\n", - "epoch: 4, batch_id: 900, loss is: [0.04281481], acc is: [1.]\n" + "epoch: 0, batch_id: 900, loss is: [0.06991791], acc is: [0.96875]\n", + "epoch: 1, batch_id: 900, loss is: [0.02878829], acc is: [1.]\n", + "epoch: 2, batch_id: 900, loss is: [0.07192856], acc is: [0.96875]\n", + "epoch: 3, batch_id: 900, loss is: [0.20411499], acc is: [0.96875]\n", + "epoch: 4, batch_id: 900, loss is: [0.13589518], acc is: [0.96875]\n" ] } ], @@ -603,9 +596,7 @@ "# 用 DataLoader 实现数据加载\n", "train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)\n", "\n", - "# 声明Mnist类的一个实例\n", - "mnist=Mnist()\n", - "# 将此层及其所有子层设置为训练模式。这只会影响某些模块,如Dropout和BatchNorm。\n", + "# 将mnist模型及其所有子层设置为训练模式。这只会影响某些模块,如Dropout和BatchNorm。\n", "mnist.train()\n", "\n", "# 设置迭代次数\n", @@ -657,9 +648,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "d27f6ec2", "metadata": { + "execution": { + "iopub.execute_input": "2022-02-15T05:50:52.864342Z", + "iopub.status.busy": "2022-02-15T05:50:52.863912Z", + "iopub.status.idle": "2022-02-15T05:50:54.048046Z", + "shell.execute_reply": "2022-02-15T05:50:54.047104Z", + "shell.execute_reply.started": "2022-02-15T05:50:52.864305Z" + }, "scrolled": true }, "outputs": [ @@ -667,11 +665,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "batch_id: 30, loss is: [0.12935154], acc is: [0.953125]\n", - "batch_id: 60, loss is: [0.19010888], acc is: [0.921875]\n", - "batch_id: 90, loss is: [0.07307276], acc is: [0.984375]\n", - "batch_id: 120, loss is: [0.01087341], acc is: [1.]\n", - "batch_id: 150, loss is: [0.11148524], acc is: [0.984375]\n" + "batch_id: 30, loss is: [0.23106411], acc is: [0.953125]\n", + "batch_id: 60, loss is: [0.4329119], acc is: [0.90625]\n", + "batch_id: 90, loss is: [0.07333981], acc is: [0.96875]\n", + "batch_id: 120, loss is: [0.00324837], acc is: [1.]\n", + "batch_id: 150, loss is: [0.0857158], acc is: [0.96875]\n" ] } ], @@ -714,9 +712,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "1d79305f", "metadata": { + "execution": { + "iopub.execute_input": "2022-02-15T05:50:54.051031Z", + "iopub.status.busy": "2022-02-15T05:50:54.050435Z", + "iopub.status.idle": "2022-02-15T05:50:55.278437Z", + "shell.execute_reply": "2022-02-15T05:50:55.277810Z", + "shell.execute_reply.started": "2022-02-15T05:50:54.050992Z" + }, "scrolled": true }, "outputs": [ @@ -731,16 +736,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] From e7d39602cb25d5b5430f3347e024e0164b0ea7d7 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 14:17:11 +0800 Subject: [PATCH 44/63] Update 01_quick_start_cn.ipynb test image --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index ce24f2575d4..2ea63785e88 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -91,7 +91,7 @@ "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", "
\n", - "![MNIST](images/mnist.png)\n", + "![MNIST](https://github.com/PaddlePaddle/docs/blob/develop/docs/guides/02_paddle2.0_develop/images/mnist.png)\n", "
图 1:MNIST 数据集样例
\n", "
\n", "\n", From 57ae32d0a8c54256740541e15911e37d05e399c3 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 14:49:31 +0800 Subject: [PATCH 45/63] Update 01_quick_start_cn.ipynb test image --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 2ea63785e88..665703e7cb3 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -90,10 +90,8 @@ "\n", "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "
\n", - "![MNIST](https://github.com/PaddlePaddle/docs/blob/develop/docs/guides/02_paddle2.0_develop/images/mnist.png)\n", - "
图 1:MNIST 数据集样例
\n", - "
\n", + "![MNIST](images/mnist.png)\n", + "图 1:MNIST 数据集样例\n", "\n", "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" ] From 6dbf53a2d551ef4bb7ce96acaa01358db3118c03 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 15:24:32 +0800 Subject: [PATCH 46/63] Update 01_quick_start_cn.ipynb image test --- docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 665703e7cb3..f1d8f6bf23e 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -90,7 +90,8 @@ "\n", "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "![MNIST](images/mnist.png)\n", + "![MNIST](images/mnist.png =600)\n", + "\n", "图 1:MNIST 数据集样例\n", "\n", "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" From a321ec4338536fbfd2ea525008fe73d0a5048029 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Tue, 15 Feb 2022 16:08:54 +0800 Subject: [PATCH 47/63] update model_develop images --- .../01_quick_start_cn.ipynb | 7 +-- .../02_data_load_cn.ipynb | 5 ++- .../03_data_preprocessing_cn.ipynb | 6 ++- .../02_paddle2.0_develop/04_model_cn.ipynb | 11 +++-- .../05_train_eval_predict_cn.ipynb | 42 +++++------------- .../images/data_pipeline.png | Bin 147904 -> 77675 bytes .../images/data_preprocessing.png | Bin 150760 -> 83104 bytes .../02_paddle2.0_develop/images/lenet.png | Bin 150249 -> 35826 bytes .../02_paddle2.0_develop/images/mnist.png | Bin 260311 -> 86849 bytes .../02_paddle2.0_develop/images/model.png | Bin 104897 -> 56718 bytes .../images/model_develop_flow.png | Bin 190267 -> 97242 bytes 11 files changed, 29 insertions(+), 42 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index f1d8f6bf23e..8f9a093fd8d 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -90,7 +90,7 @@ "\n", "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/),用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件,每张图片上是一个 0 ~ 9 的手写数字,分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n", "\n", - "![MNIST](images/mnist.png =600)\n", + "![](images/mnist.png)\n", "\n", "图 1:MNIST 数据集样例\n", "\n", @@ -662,8 +662,9 @@ "\n", "至此通过飞桨几个简单的API完成了一个深度学习任务,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图 2:模型开发流程
\n", + "![](images/model_develop_flow.png)\n", + "\n", + "图 2:模型开发流程\n", "\n", "如果想要完成更复杂的深度学习任务,开发更强大的模型,飞桨提供了功能丰富的 API 帮助开发者完成任务,比如对数据集应用数据增强、使用更大的 CNN 模型、调优性能等。飞桨官网提供了丰富的教程与案例可供参考,欢迎一起探索深度学习的世界。" ] diff --git a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb index 524be7e7706..06d35825518 100644 --- a/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/02_data_load_cn.ipynb @@ -596,8 +596,9 @@ "\n", "本节中介绍了在飞桨框架中将数据送入模型训练之前的处理流程,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图 1:数据集定义和加载流程
\n", + "![](images/data_pipeline.png)\n", + "\n", + "图 1:数据集定义和加载流程\n", "\n", "主要包括定义数据集和定义数据读取器两个步骤,另外在数据读取器中可调用采样器实现更灵活地采样。其中,在定义数据集时,本节仅对数据集进行了归一化处理,如需了解更多数据增强相关操作,可以参考 [数据预处理](03_data_preprocessing_cn.html)。 \n", "\n", diff --git a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb index deaa723197d..4fed2a146a3 100644 --- a/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/03_data_preprocessing_cn.ipynb @@ -459,8 +459,10 @@ "\n", "本节介绍了数据预处理方法在数据集中的使用方式,可先将一个或多个方法组合定义到一个实例中,再在数据集中应用,总结整个流程和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图 1:数据预处理流程
\n", + "![](images/data_preprocessing.png)\n", + "\n", + "图 1:数据预处理流程\n", + "\n", "\n", "图像、文本等不同类型的数据预处理方法不同,关于文本的数据预处理可以参考 [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data_prepare/overview.rst)。" ] diff --git a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb index e54c1a01dd0..a8ba18700cf 100644 --- a/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/04_model_cn.ipynb @@ -134,8 +134,10 @@ "metadata": {}, "source": [ "通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果可以看出,LeNet 模型包含 2个`Conv2D` 卷积层、2个`ReLU` 激活层、2个`MaxPool2D` 池化层以及3个`Linear` 全连接层,这些层通过堆叠形成了 LeNet 模型,对应网络结构如下图所示。\n", - "
\n", - "

图 1:LeNet 网络结构示意图
\n", + "\n", + "![](images/lenet.png)\n", + "\n", + "图 1:LeNet 网络结构示意图\n", "\n", "另外在 [paddle.summary](../../api/paddle/summary_cn.html#summary) 的结果中可清晰地查看每一层的输入数据和输出数据的形状(Shape)、模型的参数量(Params)等信息,方便可视化地了解模型结构、分析数据计算和传递过程。\n", "\n" @@ -372,8 +374,9 @@ "\n", "本节介绍了飞桨框架中模型组网的几种方式,并且以 LeNet 为例介绍了如何使用这几种组网方式实现,总结模型组网的方法和用到的关键 API 如下图所示。\n", "\n", - "
\n", - "

图 2:模型组网方法
" + "![](images/model.png)\n", + "\n", + "图 2:模型组网方法" ] }, { diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb index 4a115f4a5be..7bf7b850f42 100644 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -30,12 +30,12 @@ "source": [ "## 一、训练前准备\n", "\n", - "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库,matplotlib 库用于可视化图片,numpy 库用于处理数据。" + "开始之前,需要使用下面的命令安装 Python 的 matplotlib 库,用于可视化图片。" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "a88a8752-bbab-4ecf-b384-4199626210df", "metadata": { "execution": { @@ -47,29 +47,10 @@ }, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://mirror.baidu.com/pypi/simple\n", - "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.3)\n", - "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.19.5)\n", - "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.16.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (3.0.7)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n", - "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2019.3)\n", - "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (56.2.0)\n", - "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n", - "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ - "# 使用 pip 工具安装 matplotlib 和 numpy\n", - "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple" + "# 使用 pip 工具安装 matplotlib\n", + "! python3 -m pip install matplotlib -i https://mirror.baidu.com/pypi/simple" ] }, { @@ -228,8 +209,7 @@ "\n", "用 `paddle.Model` 完成模型的封装后,需通过 [Model.prepare](../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 进行训练前的配置准备工作,包括设置优化算法、Loss 计算方法、评价指标计算方法:\n", "\n", - "- **优化器(optimizer)**:即寻找最优解的方法,可计算和更新梯度,并根据梯度更新模型参数。飞桨框架在 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer) 下提供了优化器相关 API。\n", - "- **学习率(learning_rate)**:在每个批次或轮次更新模型参数的比率。较小的值会导致学习速度较慢,而较大的值可能会导致模型震荡而不收敛,为优化器设置合适的学习率非常重要。训练过程中需要根据训练进度设置合适的学习率,或者指定合适的学习率策略,如果需要使用学习策略,飞桨框架在 [paddle.optimizer.lr](../api/paddle/optimizer/Overview_cn.html#about-lr) 下提供了学习率策略相关的API。\n", + "- **优化器(optimizer)**:即寻找最优解的方法,可计算和更新梯度,并根据梯度更新模型参数。飞桨框架在 [paddle.optimizer](../api/paddle/optimizer/Overview_cn.html#paddle-optimizer) 下提供了优化器相关 API。并且需要为优化器设置合适的学习率,或者指定合适的学习率策略,飞桨框架在 [paddle.optimizer.lr](../api/paddle/optimizer/Overview_cn.html#about-lr) 下提供了学习率策略相关的 API。\n", "- **损失函数(loss)**:用于评估模型的预测值和真实值的差距,模型训练过程即取得尽可能小的 loss 的过程。飞桨框架在 [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) 提供了适用不同深度学习任务的损失函数相关 API。\n", "- **评价指标(metrics)**:用于评估模型的好坏,不同的任务通常有不同的评价指标。飞桨框架在 [paddle.metric](../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n" ] @@ -261,7 +241,7 @@ "id": "6a73ec3f-8e7a-40ab-bf92-01ba7247c507", "metadata": {}, "source": [ - "示例中使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam) 优化器,并传入封装好的全部模型参数 `model.parameters` 用于后续更新;使用交叉熵损失函数 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) 用于分类任务评估;使用分类任务常用的准确率指标 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) 计算模型在训练集上的精度。" + "示例中使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam) 优化器,设置优化器的学习率 `learning_rate=0.001`,并传入封装好的全部模型参数 `model.parameters` 用于后续更新;使用交叉熵损失函数 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) 用于分类任务评估;使用分类任务常用的准确率指标 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) 计算模型在训练集上的精度。" ] }, { @@ -410,13 +390,13 @@ "\n", "高层 API 中提供了 [Model.predict](../api/paddle/Model_cn.html#predict-test-data-batch-size-1-num-workers-0-stack-outputs-false-callbacks-none) 接口,可对训练好的模型进行推理验证。只需传入待执行推理验证的样本数据,即可计算并返回推理结果。\n", "\n", - "返回格式是一个列表,元素数目对应模型的输出数目:\n", + "返回格式是一个列表:\n", "* 模型是单一输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n)]`\n", "* 模型是多输出:`[(numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), (numpy_ndarray_1, numpy_ndarray_2, …, numpy_ndarray_n), …]`\n", "\n", - "如果模型是单一输出,则输出的形状为[1, n],n表示数据集的数据量。其中每个numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据,类型为numpy数组,例如在mnist分类任务中,每个numpy_ndarray_n是长度为10的numpy数组。\n", + "如果模型是单一输出,则输出的形状为 [1, n],n 表示数据集的样本数。其中每个 numpy_ndarray_n 是对应原始数据经过模型计算后得到的预测结果,类型为 numpy 数组,例如 mnist 分类任务中,每个 numpy_ndarray_n 是长度为 10 的 numpy 数组。\n", "\n", - "如果模型是多输出,则输出的形状为[m, n],m表示标签的种类数,在多标签分类任务中,m会根据标签的数目而定。\n", + "如果模型是多输出,则输出的形状为[m, n],m 表示标签的种类数,在多标签分类任务中,m 会根据标签的数目而定。\n", "\n", "\n", "\n", @@ -745,7 +725,7 @@ }, { "data": { - "image/png": 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b/docs/guides/advanced/layer_and_model_cn.md index e988653f1f2..cd6fe2d22c9 100644 --- a/docs/guides/advanced/layer_and_model_cn.md +++ b/docs/guides/advanced/layer_and_model_cn.md @@ -1,347 +1,559 @@ -# Paddle 中的模型与层 +# 使用 paddle.nn.Layer 自定义网络 -模型是深度学习中的重要概念之一。模型的核心功能是将一组输入变量经过一系列计算,映射到另一组输出变量,该映射函数即代表一种深度学习算法。在**Paddle**框架中,模型包括以下两方面内容: +为了更灵活地构建特定场景的专属深度学习模型,飞桨提供了 paddle.nn.Layer 系列接口,以便用户轻松地定义专属的深度学习模型。 -1. 一系列层的组合用于进行映射(前向执行) -2. 一些参数变量在训练过程中实时更新 +为了充分利用它们,并根据实际需求进行量身定制,需要真正理解它们到底在做什么。为了加深这种理解,我们将首先在 MNIST 数据集上训练基本的神经网络,不使用这些模型的任何特征,同时采用最基本的飞桨 tensor 功能进行模型开发。然后,我们将逐步从 paddle.nn.Layer 中添加一个特征,展示如何使用飞桨的 paddle.nn.Layer 系列接口进行模型、层与参数的设计,来开发一个用户专属的深度学习模型。 -本文档中,你将学习如何定义与使用**Paddle**模型,并了解模型与层的关系。 +在具体操作之前,让我们先了解与之相关的基本概念。 -## 在**Paddle**中定义模型与层 +# 一、概念介绍 -在**Paddle**中,大多数模型由一系列层组成,层是模型的基础逻辑执行单元。层中持有两方面内容:一方面是计算所需的变量,以临时变量或参数的形式作为层的成员持有,另一方面则持有一个或多个具体的**Operator**来完成相应的计算。 +**1. 模型** -从零开始构建变量、**Operator**,从而组建层、模型是一个很复杂的过程,并且当中难以避免的会出现很多冗余代码,因此**Paddle**提供了基础数据类型 ``paddle.nn.Layer`` ,来方便你快速的实现自己的层和模型。模型和层都可以基于 ``paddle.nn.Layer`` 扩充实现,因此也可以说模型只是一种特殊的层。下面将演示如何利用 ``paddle.nn.Layer`` 建立自己的模型: +模型的核心功能是将一组输入变量经过一系列计算,映射到另一组输出变量,通常为带参数的函数,该函数代表一种算法。深度学习的目标就是学习一组最优的参数使得模型的预测最“准确”。在飞桨框架中,模型包括以下两方面内容: -```python -class Model(paddle.nn.Layer): +- 一系列层的组合,用于输入到输出的映射(前向计算) +- 一些参数变量,在训练过程中实时更新 - def __init__(self): - super(Model, self).__init__() - self.flatten = paddle.nn.Flatten() +**2. 层** - def forward(self, inputs): - y = self.flatten(inputs) - return y -``` +飞桨大多数模型由一系列层组成。层是模型的基础逻辑执行单元。层包含以下两方面内容: + +- 一个或多个具体的算子,用于完成相应的计算 +- 计算所需的变量,以临时变量或参数的形式作为层的成员存在 + +**3. paddle.nn.Layer** + +从零开始构建变量、算子,并组建层以及模型,是一个很复杂的过程,难免出现很多冗余代码,因此飞桨提供了基础数据类型 paddle.nn.Layer ,方便开发者继承并扩展。 + +paddle.nn.Layer 是飞桨定义的一个非常重要的类,是飞桨所有神经网络模块的基类, 它代表所有可以用层表示的网络结构,包含网络各层的定义及前向计算方法。除此之外,飞桨还基于 Layer 定义了各种常用的层,比如卷积,池化,Padding,激活,Normalization,循环神经网络,Transformer 相关,线性,Dropout,Embedding,Loss,Vision,Clip,公共层等等(paddle.nn 包中的各个类均继承 paddle.nn.Layer 这个基类),详情请参考[组网相关的 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html)。 -当前示例中,通过继承 ``paddle.nn.Layer`` 的方式构建了一个模型类型 ``Model`` ,模型中仅包含一个 ``paddle.nn.Flatten`` 层。模型执行时,输入变量**inputs**会被 ``paddle.nn.Flatten`` 层展平。 -## 子层接口 -如果想要访问或修改一个模型中定义的层,则可以调用**SubLayer**相关的接口。 -以上文创建的简单模型为例, 如果想要查看模型中定义的所有子层: +> 说明: +> 本教程基于“基于手写数字识别(MNIST)任务”作为样板代码进行说明,通过本节的学习,用户将进一步掌握使用 paddle.nn.Layer 改进模型、层与参数的方法。 + +以下内容假定你已经完成了飞桨的安装以及熟悉了一些基本的飞桨操作。 + +# 二、数据处理 + +## 2.1 加载 Mnist 数据集 + +相信根据前面的内容,你已经知道如何使用 paddle.Dataset 和 paddle.DataLoader 处理想要的数据了,如果你还有问题可以参考[数据读取](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/data_load_cn.html)文档,这里采用前面讲到的方法使用 Mnist 数据集。 ```python -model = Model() -print(model.sublayers()) +import paddle +import math +from paddle.vision.transforms import Compose, Normalize -print("----------------------") +transform = Compose([Normalize(mean=[127.5], + std=[127.5], + data_format='CHW')]) -for item in model.named_sublayers(): - print(item) +train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) ``` -```text -[Flatten()] ----------------------- -('flatten', Flatten()) -``` -可以看到,通过调用 ``model.sublayers()`` 接口,打印出了前述模型中持有的全部子层(这时模型中只有一个 ``paddle.nn.Flatten`` 子层)。 -而遍历 ``model.named_sublayers()`` 时,每一轮循环会拿到一组 ( 子层名称('flatten'),子层对象(paddle.nn.Flatten) )的元组。 +## 2.2 对数据集进行预处理 + +为演示方便先从这个训练集中取出一条数据,简单测试下后面搭建的网络,同时为了方便训练对该数据进行形状的变换。 -接下来如果想要进一步添加一个子层,则可以调用 ``add_sublayer()`` 接口: +当然,实际过程中需要通过一个循环不断获取 train_dataset 中的数据,不间断的进行训练。 ```python -fc = paddle.nn.Linear(10, 3) -model.add_sublayer("fc", fc) -print(model.sublayers()) +train_data0 = train_dataset[0] +x_data = paddle.to_tensor(train_data0[0]) +x_data = paddle.flatten(x_data, start_axis=1) +print("x_data's shape is:", x_data.shape) +x_data's shape is: [1, 784] ``` -```text -[Flatten(), Linear(in_features=10, out_features=3, dtype=float32)] +# 三、搭建一个完整的深度学习网络 + +接下来仅利用飞桨最基本的 Tensor 功能快速完成一个深度学习网络的搭建。 + +## 3.1 参数初始化 + +首先, 需要通过 paddle.randn 函数或者 paddle.zeros 函数来创建随机数填充或者全零填充的一个参数(Weight)(模型训练中会被更新的部分),和一个偏置项(Bias)。 + +注意:这里可通过 Xavier 初始化方式初始化参数,即对产生的随机数除以 sqrt(n)(n 是第零维的大小)。 + +```python +weights = paddle.randn([784, 10]) * (1/math.sqrt(784)) +weights.stop_gradient=False +bias = paddle.zeros(shape=[10]) +bias.stop_gradient=False ``` -可以看到 ``model.add_sublayer()`` 向模型中添加了一个 ``paddle.nn.Linear`` 子层,这样模型中总共有 ``paddle.nn.Flatten`` 和 ``paddle.nn.Linear`` 两个子层了。 +参数初始化完成后,就可以开始准备神经网络了。 +## 3.2 准备网络结构 -通过上述方法可以往模型中添加成千上万个子层,当模型中子层数量较多时,如何高效地对所有子层进行统一修改呢?**Paddle** 提供了 ``apply()`` 接口。通过这个接口,可以自定义一个函数,然后将该函数批量作用在所有子层上: +网络结构是深度学习模型关键要素之一,相当于模型的假设空间,即实现模型从输入到输出的映射过程(前向计算)。 + +本文利用一个矩阵乘法和一个加法(飞桨的加法可以自己完成 broadcast,就像 numpy 一样好用)来实现一个简单的 Linear 网络。通常,还需要一些激活函数(例如 log_softmax)来保证网络的非线性。请注意,虽然飞桨提供了大量已实现好的激活函数,你也可以利用简单的 Python 代码来完成自己的激活函数。这些 Python 代码都将会被飞桨识别从而变成高效的设备代码被执行。 ```python -def function(layer): - print(layer) +def log_softmax(x): + return x - x.exp().sum(-1).log().unsqueeze(-1) -model.apply(function) +def model(x): + return log_softmax(paddle.matmul(x, weights) + bias) ``` -```text -Flatten() -Linear(in_features=10, out_features=3, dtype=float32) +## 3.3 前向计算 -Model( - (flatten): Flatten() - (fc): Linear(in_features=10, out_features=3, dtype=float32) -) +通常训练都是针对一个 batch 进行的,可以从之前的数据中按照 batch_size=64 取出一部分进行一轮的前向执行。由于本轮利用随机初始化的参数进行前向计算,那么计算的结果也就和一个随机的网络差不多。 + +```python +batch_size = 64 +train_batch_data_x = [] +train_batch_data_y = [] +for i in range(batch_size): + train_batch_data_x.append(train_dataset[i][0]) + train_batch_data_y.append(train_dataset[i][1]) + +x_batch_data = paddle.to_tensor(train_batch_data_x) +x_batch_data = paddle.flatten(x_batch_data, start_axis=1) +print("x_batch_data's shape is:", x_batch_data.shape) + +y = model(x_batch_data) + +print("y[0]: {} \ny.shape: {}".format(y[0], y.shape)) +x_data's shape is: [1, 784] +x_batch_data's shape is: [64, 784] +y[0]: Tensor(shape=[10], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [-1.20662355, -4.20237827, -2.47686505, -0.78191900, -5.13888979, + -3.07260418, -2.94610834, -4.91643810, -3.71131158, -4.85082626]) +y.shape: [64, 10] ``` -当前例子中,定义了一个以**layer**作为参数的函数**function**,用来打印传入的**layer**信息。通过调用 ``model.apply()`` 接口,将**function**作用在模型的所有子层中,也因此输出信息中打印了**model**中所有子层的信息。 +## 3.4 反向传播 +这里我们会发现,y 的信息中包含一项 StopGradient=False。这意味着我们可以通过 y 来进行 BP(反向传播),同时可以定义自己的损失函数。以一个简单的 nll_loss 来演示,写法上如同写一段简单的 Python 代码。 -另外一个批量访问子层的接口是 ``children()`` 或者 ``named_children()`` 。这两个接口通过**Iterator**的方式访问每个子层: +```python +loss_func = paddle.nn.functional.nll_loss + +y_batch_data = paddle.to_tensor(train_batch_data_y) +y_batch_data = paddle.flatten(y_batch_data, start_axis=1) +print("y_batch_data's shape is:", y_batch_data.shape) +y_standard = y_batch_data[0:batch_size] +loss = loss_func(y, y_standard) +print("loss: ", loss) +loss: Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [2.85819387]) +``` + +## 3.5 计算 ACC 观察模型收敛情况 + +同样,也可以实现一个简单的计算 accuracy 的方法来验证模型收敛情况。 ```python -sublayer_iter = model.children() -for sublayer in sublayer_iter: - print(sublayer) +def accuracy(out, y): + preds = paddle.argmax(out, axis=-1, keepdim=True) + return (preds == y).cast("float32").mean() + +accuracy = accuracy(y, y_standard) +print("accuracy:", accuracy) +accuracy: Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, + [0.09375000]) ``` -```text -Flatten() -Linear(in_features=10, out_features=3, dtype=float32) +## 3.6 使用自动微分功能计算网络的梯度并更新参数 + +接下来我们将利用飞桨的自动微分功能计算网络的梯度,并且利用该梯度和参数完成一轮参数更新(需要注意的是,在更新参数的阶段我们并不希望进行微分的逻辑,只需要使用 paddle.no_grad 禁用相关功能即可)。 + +```python +loss.backward() + +@paddle.no_grad() +def OptimizeNetwork(lr=0.5): + weight.set_value(weight - lr * weight.grad) + bias.set_value(bias - lr * bias.grad) + weight.clear_gradient() + bias.clear_gradient() +print("weight: ", weight) +print("bias: ", bias) +OptimizeNetwork() +print("weight after optimize: ", weight) +print("bias after optimize: ", bias) +weight: Tensor(shape=[784, 10], dtype=float32, place=Place(cpu), stop_gradient=False, + [[-0.02580861, 0.03132926, 0.07240372, ..., 0.05494612, + -0.03443871, -0.00228449], + [-0.01263286, -0.03029860, 0.04301141, ..., 0.02060869, + -0.00263721, -0.01837303], + [ 0.02355293, -0.06277876, -0.03418431, ..., 0.03847973, + 0.02322033, 0.08055742], + ..., + [-0.02945464, 0.00892299, -0.07298648, ..., 0.04788664, + 0.03856503, 0.07544740], + [ 0.06136639, -0.00014994, 0.00933051, ..., -0.00939863, + 0.06214209, -0.01135642], + [-0.01522523, -0.04802566, 0.01832000, ..., 0.01538999, + 0.04224478, 0.01449125]]) +bias: Tensor(shape=[10], dtype=float32, place=Place(cpu), stop_gradient=False, + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) +weight after optimize: Tensor(shape=[784, 10], dtype=float32, place=Place(cpu), stop_gradient=False, + [[-0.05760278, 0.03702446, 0.06256686, ..., 0.13622762, + -0.01372341, -0.04273041], + [-0.04442703, -0.02460339, 0.03317455, ..., 0.10189019, + 0.01807809, -0.05881895], + [-0.00824124, -0.05708356, -0.04402117, ..., 0.11976123, + 0.04393563, 0.04011151], + ..., + [-0.06124880, 0.01461819, -0.08282334, ..., 0.12916814, + 0.05928034, 0.03500149], + [ 0.02957222, 0.00554527, -0.00050635, ..., 0.07188287, + 0.08285740, -0.05180233], + [-0.04701940, -0.04233045, 0.00848314, ..., 0.09667149, + 0.06296009, -0.02595467]]) +bias after optimize: Tensor(shape=[10], dtype=float32, place=Place(cpu), stop_gradient=False, + [ 0.03179417, -0.00569520, 0.00983686, -0.02128297, 0.00566411, + 0.02163870, 0.01959525, -0.08128151, -0.02071531, 0.04044591]) ``` -可以看到,遍历 ``model.children()`` 时,每一轮循环都可以按照子层注册顺序拿到对应 ``paddle.nn.Layer`` 的对象 +至此,就完成了一个简单的训练过程。我们会发现,需要定义大量的计算逻辑来完成这个组网过程,过程是很繁杂的。好在飞桨已经提供了大量的封装好的 API,让你更简单的定义常见的网络结构,下面介绍具体的用法。 -## 层的变量成员 -### 参数变量的添加与修改 -有的时候希望向网络中添加一个参数作为输入。比如在使用图像风格转换模型时,会使用参数作为输入图像,在训练过程中不断更新该图像参数,最终拿到风格转换后的图像。 +# 四、使用 paddle.nn.Layer 构建深度学习网络 -这时可以通过 ``create_parameter()`` 与 ``add_parameter()`` 组合,来创建并记录参数: +paddle.nn.Layer 是飞桨定义的一个类,它代表所有可以用层表示的网络结构。对本文前面这个例子来说,我们需要构建线性网络的参数 weight,bias,以及矩阵乘法,加法,log_softmax 也可以看成是一个层。换句话说 ,我们可以把任意的网络结构看成是一个层,层是网络结构的一个封装。 -```python -class Model(paddle.nn.Layer): +## 4.1 使用 Layer 改造线性层 + +首先,可以定义自己的线性层: +```python +class MyLayer(paddle.nn.Layer): def __init__(self): - super(Model, self).__init__() - img = self.create_parameter([1,3,256,256]) - self.add_parameter("img", img) - self.flatten = paddle.nn.Flatten() + super(MyLayer, self).__init__() + self.weight = self.create_parameter([784,10]) + self.bias = self.create_parameter([10], is_bias=True, default_initializer=paddle.nn.initializer.Constant(value=0.0)) - def forward(self): - y = self.flatten(self.img) - return y + def forward(self, inputs): + return paddle.nn.functional.log_softmax(paddle.matmul(inputs, self.weight) + self.bias) ``` -上述例子创建并向模型中添加了一个名字为"img"的参数。随后可以直接通过调用**model.img**来访问该参数。 +和直接使用 python 代码不同,我们可以借助飞桨提供的 paddle.nn.Layer 类实现一个基本的网络。我们可以通过继承的方式利用 paddle.nn.Layer 的各种工具。 -对于已经添加的参数,可以通过 ``parameters()`` 或者 ``named_parameters()`` 来访问 +那么通过继承 paddle.nn.Layer 构建层有什么好处呢? -```python -model = Model() -model.parameters() -print("----------------------------------------------------------------------------------") -for item in model.named_parameters(): - print(item) -``` +### 4.1.1 子类调用父类的构造函数 + +首先,我们会发现,在这个继承的子类当中需要去调用一下父类的构造函数: -```text -[Parameter containing: -Tensor(shape=[1, 3, 256, 256], dtype=float32, place=CPUPlace, stop_gradient=False, - ... ----------------------------------------------------------------------------------- -('img', Parameter containing: -Tensor(shape=[1, 3, 256, 256], dtype=float32, place=CPUPlace, stop_gradient=False, - ... +```python + def __init__(self): + super(MyLayer, self).__init__() ``` -可以看到,``model.parameters()`` 将模型中所有参数以数组的方式返回。 +### 4.1.2 完成一系列的初始化 -在实际的模型训练过程中,当调用反向图执行方法后,**Paddle**会计算出模型中每个参数的梯度并将其保存在相应的参数对象中。如果已经对该参数进行了梯度更新,或者出于一些原因不希望该梯度累加到下一轮训练,则可以调用 ``clear_gradients()`` 来清除这些梯度值。 +这个时候飞桨会完成一系列初始化操作,其目的是为了记录所有定义在该层的状态,包括参数,call_back, 子层等信息。 ```python -model = Model() -out = model() -out.backward() -model.clear_gradients() + def __init__(self): + super(MyLayer, self).__init__() + self.weight = self.create_parameter([784,10]) + self.bias = self.create_parameter([10], is_bias=True, default_initializer=paddle.nn.initializer.Constant(value=0.0)) ``` -### 非参数变量的添加 -参数变量往往需要参与梯度更新,但很多情况下只是需要一个临时变量甚至一个常量。比如在模型执行过程中想将一个中间变量保存下来,这时需要调用 ``create_tensor()`` 接口: +## 4.2 访问并自动记录参数的更新过程 -```python -class Model(paddle.nn.Layer): +这里我们调用的 create_parameter 函数就来自于 paddle.nn.Layer 类,这个函数帮助我们简单的创建并初始化参数。最简单的我们仅仅传入希望的参数形状即可(如 weight),这时候 create_parameter 会通过默认的方式初始化参数(默认是参数而不是 bias,使用 UniformRandom 来初始化参数,详情可以参考 create_parameter);或者可以通过诸多参数来定义你自己希望的初始化参数的方式(如 bias),可以限定其初始化方式是全零的常数项(更多初始化方式可以参考 paddle.nn.initializer)。 - def __init__(self): - super(Model, self).__init__() - self.saved_tensor = self.create_tensor(name="saved_tensor0") - self.flatten = paddle.nn.Flatten() - self.fc = paddle.nn.Linear(10, 100) +完成参数初始化后,不同于我们直接使用 Python 时利用临时变量 weight 和 bias,这里可以利用 paddle.nn.Layer 自动将定义的参数记录下来,并且随时通过 self.named_parameters 访问。 - def forward(self, input): - y = self.flatten(input) - # Save intermediate tensor - paddle.assign(y, self.saved_tensor) - y = self.fc(y) - return y +```python +my_layer = MyLayer() +for name, param in my_layer.named_parameters(): + print("Parameters: {}, {}".format(name, param) ) +Parameters: weight, Parameter containing: +Tensor(shape=[784, 10], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [[-0.03399023, -0.02405306, -0.06372951, ..., -0.05039166, + 0.05060801, 0.05453540], + [ 0.01788948, -0.06409007, 0.02617371, ..., 0.08341692, + -0.01115795, 0.06199412], + [-0.07155208, 0.01988612, 0.03681165, ..., -0.00741174, + 0.03892786, 0.03055505], + ..., + [-0.01735171, -0.05819885, -0.05768500, ..., 0.04783282, + 0.05039406, -0.04458937], + [ 0.08272233, 0.02620430, -0.00838694, ..., 0.03075657, + -0.05368494, 0.03899705], + [-0.06041612, -0.05808754, -0.07175658, ..., -0.07276732, + 0.08097268, -0.00280717]]) +Parameters: bias, Parameter containing: +Tensor(shape=[10], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) ``` -这里调用 ``self.create_tensor()`` 创造了一个临时变量并将其记录在模型的 ``self.saved_tensor`` 中。在模型执行时调用 ``paddle.assign`` 用该临时变量记录变量**y**的数值。 +## 4.3 执行已定义的层 + +下面可以看看如何使用我们定义好的层。 + +### 4.3.1 进入训练阶段并执行 -### **Buffer** 变量的添加 -**Buffer**的概念仅仅影响动态图向静态图的转换过程。在上一节中创建了一个临时变量用来临时存储中间变量的值。但这个临时变量在动态图向静态图转换的过程中并不会被记录在静态的计算图当中。如果希望该变量成为静态图的一部分,就需要进一步调用 ``register_buffers()`` 接口: +首先, 我们通过构造函数构造了一个层,并且设置其执行模式为 train(训练)模式(通常你并不需要显式调用,因为默认是训练模式,这里仅仅为了演示),这样做是因为如 Dropout,BatchNorm 等计算,在训练和评估阶段的行为往往有区别,因此飞桨提供了方便的接口对整层设置该属性,如果层包含相关操作,可以通过这个设置改变他们在不同阶段的行为。 ```python -class Model(paddle.nn.Layer): +my_layer = MyLayer() +my_layer.train() +# my_layer.eval() +y = my_layer(x_batch_data) +print("y[0]", y[0]) +``` - def __init__(self): - super(Model, self).__init__() - saved_tensor = self.create_tensor(name="saved_tensor0") - self.register_buffer("saved_tensor", saved_tensor, persistable=True) - self.flatten = paddle.nn.Flatten() - self.fc = paddle.nn.Linear(10, 100) +然后,可以将输入数据 x_batch_data 输入我们构建好的层对象,结果将被即时写在 y 当中。 - def forward(self, input): - y = self.flatten(input) - # Save intermediate tensor - paddle.assign(y, self.saved_tensor) - y = self.fc(y) - return y +```python +y[0] Tensor(shape=[10], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [-2.78626776, -2.75923157, -3.15698314, -2.98575473, -5.58894873, + -5.03897095, -1.63698268, -0.70400816, -6.44660282, -2.51351619]) ``` -这样在动态图转静态图时**saved_tensor**就会被记录到静态图中。 +### 4.3.2 计算 loss -对于模型中已经注册的**Buffer**,可以通过 ``buffers()`` 或者 ``named_buffers()`` 进行访问: +同样调用 paddle.nn.functional.nll_loss 来计算 nll_loss。 ```python -model = Model() -print(model.buffers()) -for item in model.named_buffers(): - print(item) +loss_func = paddle.nn.functional.nll_loss +y = my_layer(x_batch_data) +loss = loss_func(y, y_standard) +print("loss: ", loss) ``` -```text -[Tensor(Not initialized)] -('saved_tensor', Tensor(Not initialized)) +### 4.3.3 构建 SGD 优化器、参数传递及计算 + +与此同时,可以利用飞桨提供的 API 完成之前的操作。 + +例如,可以借助 paddle.optimizer.SGD 构建一个优化器,并且通过 paddle.nn.Layer.parameters()获取所有需要优化的参数传入优化器,剩下的优化器计算事宜通过调用 opt.step()就可以交给飞桨来完成。 + +```python +my_layer = MyLayer() +opt = paddle.optimizer.SGD(learning_rate=0.5, parameters=my_layer.parameters()) +loss_func = paddle.nn.functional.nll_loss +y = my_layer(x_batch_data) +loss = loss_func(y, y_standard) +print("loss: ", loss) +loss.backward() +opt.step() +loss: Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=False, + [2.76338077]) ``` -可以看到 ``model.buffers()`` 以数组形式返回了模型中注册的所有**Buffer** +这样,我们就利用 paddle.nn.Layer 完成了网络的改造。可以发现,paddle.nn.Layer 对大部分的网络场景提供了简单的网络状态控制和网络信息处理的方法。 -## 层的执行 +## 4.4 使用 paddle.nn.Linear 改造预定义的层 -经过一系列对模型的配置,假如已经准备好了一个**Paddle**模型如下: +此外,飞桨基于 paddle.nn.Layer 构建了一系列层,这些层都可以通过简单的方式被复用在我们自定义网络中,上述例子中的 MyLayer 可以用飞桨定义的 paddle.nn.Linear 来改造。 -```python -class Model(paddle.nn.Layer): +paddle.nn.Linear 的改造主要包含替换线性层、调节参数初始化方式、改造前向传播及 softmax 等。 +```python +class MyLayer(paddle.nn.Layer): def __init__(self): - super(Model, self).__init__() - self.flatten = paddle.nn.Flatten() + super(MyLayer, self).__init__() + self.linear = paddle.nn.Linear(784, 10, bias_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(value=0.0))) def forward(self, inputs): - y = self.flatten(inputs) - return y + return paddle.nn.functional.log_softmax(self.linear(inputs)) ``` -想要执行该模型,首先需要对执行模式进行设置 +可以看到,利用线性层替换了之前的矩阵乘法和加法(而这也正是线性层的定义)。只需要定义好自己的隐层大小,以及参数的初始化方式,就可以利用 paddle.nn.Linear 建立我们的线性层,此方式可节省自定义参数和运算的成本。 + +## 4.5 总结 + +至此,我们完成了如何用飞桨层的概念和 paddle.nn.Layer 来完成一个简单的训练任务。可点击此[链接](https://aistudio.baidu.com/aistudio/projectdetail/4508657?contributionType=1)获取完整代码。 + +paddle.nn.Layer 的功能远不止于此,利用 paddle.nn.Layer 还可以进行子层访问、层的成员变量操作、模型存储等操作,具体操作接下来会逐一介绍。 -### 执行模式设置 -模型的执行模式有两种,如果需要训练的话调用 ``train()`` ,如果只进行前向执行则调用 ``eval()`` + +# 五、利用 paddle.nn.Layer 进行子层的访问 + +本节继续基于前面的手写数字识别任务,介绍如何使用 paddle.nn.layer 进行子层的访问。 + +## 5.1 查看模型的所有层 + +如果想要访问或修改一个模型中定义的层,则可以调用**SubLayer**相关的接口。 + +以前面创建的简单模型为例,代码如下所示。 ```python -x = paddle.randn([10, 1], 'float32') -model = Model() -model.eval() # set model to eval mode -out = model(x) -model.train() # set model to train mode -out = model(x) +mylayer = MyLayer() +print(mylayer.sublayers()) + +print("----------------------") + +for item in mylayer.named_sublayers(): + print(item) +[Linear(in_features=784, out_features=10, dtype=float32)] +---------------------- +('linear', Linear(in_features=784, out_features=10, dtype=float32)) ``` -```text -Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True, - ... +可以看到,通过调用 mylayer`.sublayers()` 接口,打印出了前述模型中持有的全部子层(这时模型中只有一个 `paddle.nn.Flatten` 子层)。 + +而遍历 mylayer`.named_sublayers()` 时,每一轮循环会拿到一组 ( 子层名称('flatten'),子层对象(paddle.nn.Flatten) )的元组。 + +## 5.2 向模型添加一个子层 + +接下来如果想要进一步添加一个子层,则可以调用 `add_sublayer()` 接口。例如可以通过这个接口在前面做好的线性网络中再加入一个子层。 + +```python +my_layer = MyLayer() +fc = paddle.nn.Linear(10, 3) +my_layer.add_sublayer("fc", fc) +print(my_layer.sublayers()) +[Linear(in_features=784, out_features=10, dtype=float32), Linear(in_features=10, out_features=3, dtype=float32)] ``` -这里将模型的执行模式先后设置为**eval**和**train**。两种执行模式是互斥的,新的执行模式设置会覆盖原有的设置。 +可以看到 my_layer.add_sublayer() 向模型中添加了一个 10*3 的 paddle.nn.Linear 子层,这样模型中总共有两个 paddle.nn.Linear 的子层。 -### 执行函数 +## 5.3 自定义函数并批量作用在所有子层 -模式设置完成后可以直接调用执行函数。可以直接调用 forward()方法进行前向执行,也可以调用 ``__call__()`` ,从而执行在 ``forward()`` 当中定义的前向计算逻辑。 +通过上述方法可以在模型中添加成千上万个子层。当模型中子层数量较多时,如何高效地对所有子层进行统一修改呢?Paddle 提供了 apply() 接口。通过这个接口,可以自定义一个函数,然后将该函数批量作用在所有子层上。 ```python -model = Model() -x = paddle.randn([10, 1], 'float32') -out = model(x) -print(out) +def function(layer): + print(layer) + +my_layer.apply(function) +Linear(in_features=784, out_features=10, dtype=float32) +Linear(in_features=10, out_features=3, dtype=float32) +MyLayer( + (linear): Linear(in_features=784, out_features=10, dtype=float32) + (fc): Linear(in_features=10, out_features=3, dtype=float32) +) ``` -```text -Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True, - ... +当前例子,定义了一个以 layer 作为参数的函数 function,用来打印传入的 layer 信息。通过调用 model.apply() 接口,将 function 作用在模型的所有子层中,输出信息打印 model 中所有子层的信息。 + +## 5.4 循环访问所有子层 + +另外一个批量访问子层的接口是 children() 或者 named_children() 。这两个接口通过 Iterator 的方式访问每个子层。 + +```python +my_layer = MyLayer() +fc = paddle.nn.Linear(10, 3) +my_layer.add_sublayer("fc", fc) +sublayer_iter = my_layer.children() +for sublayer in sublayer_iter: + print(sublayer) +Linear(in_features=784, out_features=10, dtype=float32) +Linear(in_features=10, out_features=3, dtype=float32) ``` -这里直接调用 ``__call__()`` 方法调用模型的前向执行逻辑。 +可以看到,遍历 model.children() 时,每一轮循环都可以按照子层注册顺序拿到对应 paddle.nn.Layer 的对象。 -### 添加额外的执行逻辑 +# 六、修改 paddle.nn.Layer 层的成员变量 -有时希望某些变量在进入层前首先进行一些预处理,这个功能可以通过注册**hook**来实现。**hook**是一个作用于变量的自定义函数,在模型执行时调用。对于注册在层上的**hook**函数,可以分为**pre_hook**和**post_hook**两种。**pre_hook**可以对层的输入变量进行处理,用函数的返回值作为新的变量参与层的计算。**post_hook**则可以对层的输出变量进行处理,将层的输出进行进一步处理后,用函数的返回值作为层计算的输出。 +## 6.1 批量添加参数变量 -通过 ``register_forward_post_hook()`` 接口,我们可以注册一个**post_hook**: +和我们在前面演示的一样,你可以通过 create_parameter 来为当前层加入参数,这对于只有几个参数的层是比较简单的。但是,当我们需要很多参数的时候就比较麻烦了,尤其是希望使用一些 container 来处理这些参数,这时候就需要使用 add_parameter,让层感知需要增加的参数。 ```python -def forward_post_hook(layer, input, output): - return 2*output - -x = paddle.ones([10, 1], 'float32') -model = Model() -forward_post_hook_handle = model.flatten.register_forward_post_hook(forward_post_hook) -out = model(x) -print(out) +class MyLayer(paddle.nn.Layer): + def __init__(self): + super(MyLayer, self).__init__() + for i in range(10): + self.add_parameter("param_" + str(i), self.create_parameter([784,10])) + def forward(inputs): + pass + +my_layer = MyLayer() +for name, item in my_layer.named_parameters(): + print(name) ``` -```text -Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True, - [[2.], - [2.], - ... -``` +## 6.2 添加临时中间变量 -同样的也可以使用 ``register_forward_pre_hook()`` 来注册**pre_hook**: +刚刚的 Minst 的例子中,仅仅使用参数 weight,bias。参数变量往往需要参与梯度更新,但很多情况下只是需要一个临时变量甚至一个常量。比如在模型执行过程中想将一个中间变量保存下来,这时需要调用 create_tensor() 接口。 ```python -def forward_pre_hook(layer, input): - print(input) - return input +class Model(paddle.nn.Layer): -x = paddle.ones([10, 1], 'float32') -model = Model() -forward_pre_hook_handle = model.flatten.register_forward_pre_hook(forward_pre_hook) -out = model(x) + def __init__(self): + super(Model, self).__init__() + self.saved_tensor = self.create_tensor(name="saved_tensor0") + self.flatten = paddle.nn.Flatten() + self.fc = paddle.nn.Linear(10, 100) + + def forward(self, input): + y = self.flatten(input) + # Save intermediate tensor + paddle.assign(y, self.saved_tensor) + y = self.fc(y) + return y ``` -```text -(Tensor(shape=[10, 1], dtype=float32, place=CUDAPlace(0), stop_gradient=True, - [[1.], - [1.], - [1.], - [1.], - [1.], - [1.], - [1.], - [1.], - [1.], - [1.]]),) +这里调用 `self.create_tensor()` 创造一个临时变量,并将其记录在模型的 `self.saved_tensor` 中。在模型执行时,调用 `paddle.assign` 用该临时变量记录变量**y**的数值。 + +## 6.3 添加 Buffer 变量完成动转静 + +Buffer 的概念仅仅影响动态图向静态图的转换过程。在上一节中创建了一个临时变量用来临时存储中间变量的值。但这个临时变量在动态图向静态图转换的过程中并不会被记录在静态的计算图当中。如果希望该变量成为静态图的一部分,就需要进一步调用 register_buffers() 接口。 + +```python +class Model(paddle.nn.Layer): + + def __init__(self): + super(Model, self).__init__() + saved_tensor = self.create_tensor(name="saved_tensor0") + self.register_buffer("saved_tensor", saved_tensor, persistable=True) + self.flatten = paddle.nn.Flatten() + self.fc = paddle.nn.Linear(10, 100) + + def forward(self, input): + y = self.flatten(input) + # Save intermediate tensor + paddle.assign(y, self.saved_tensor) + y = self.fc(y) + return y ``` -## 模型数据保存 +这样在动态图转静态图时 saved_tensor 就会被记录到静态图中。 -如果想要保存模型中参数而不存储模型本身,则可以首先调用 ``state_dict()`` 接口将模型中的参数以及永久变量存储到一个**Python**字典中,随后保存该字典。 +对于模型中已经注册的 Buffer,可以通过 buffers() 或者 named_buffers() 进行访问。 ```python model = Model() +print(model.buffers()) +for item in model.named_buffers(): + print(item) +[Tensor(Not initialized)] +('saved_tensor', Tensor(Not initialized)) +``` + +# 七、存储模型的参数 + +参考前面的操作完成 Layer 自定义和修改之后,可以参考以下操作进行保存。 + +首先调用 `state_dict()` 接口将模型中的参数以及永久变量存储到一个 Python 字典中,随后通过 paddle.save 保存该字典。 + +state_dict 是一个简单的 Python 字典对象,将每一层与它的对应参数建立映射关系。可用于保存 Layer 或者 Optimizer。Layer.state_dict 可以保存训练过程中需要学习的权重和偏执系数,保存文件推荐使用后缀 `.pdparams` 。如果想要连同模型一起保存,则可以参考[paddle.jit.save()](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/jit/save_cn.html) + + + +```plain +model = Model() state_dict = model.state_dict() paddle.save( state_dict, "paddle_dy.pdparams") ``` -可以随时恢复: +可以随时恢复:参数载入时,先从磁盘载入保存的 state_dict,然后通过 set_state_dict 方法配置到目标对象中。 -```python +```plain model = Model() state_dict = paddle.load("paddle_dy.pdparams") model.set_state_dict(state_dict) ``` -如果想要连同模型一起保存,则可以参考[paddle.jit.save()](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/jit/save_cn.html) + + + +# 八、总结 + +至此,本文介绍了如何使用 paddle.nn.Layer 来辅助您构造深度学习网络模型,并展示了如何使用 paddle.nn.Layer 进行层的查看、修改。还可以根据自己的需要进一步探索 Layer 的更多用法。此外,如果在使用 paddle.nn.Layer 的过程中遇到任何问题及建议,欢迎在飞桨 Github 中进行提问和反馈。 From 5b93c58d7677506502070bdd80505754d6e36e43 Mon Sep 17 00:00:00 2001 From: moguguo Date: Thu, 29 Sep 2022 14:56:04 +0800 Subject: [PATCH 63/63] fix link bugs --- docs/guides/advanced/layer_and_model_cn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/advanced/layer_and_model_cn.md b/docs/guides/advanced/layer_and_model_cn.md index cd6fe2d22c9..ccb67b18257 100644 --- a/docs/guides/advanced/layer_and_model_cn.md +++ b/docs/guides/advanced/layer_and_model_cn.md @@ -31,7 +31,7 @@ paddle.nn.Layer 是飞桨定义的一个非常重要的类,是飞桨所有神 > 说明: -> 本教程基于“基于手写数字识别(MNIST)任务”作为样板代码进行说明,通过本节的学习,用户将进一步掌握使用 paddle.nn.Layer 改进模型、层与参数的方法。 +> 本教程基于[基于手写数字识别(MNIST)任务](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/beginner/quick_start_cn.html)作为样板代码进行说明,通过本节的学习,用户将进一步掌握使用 paddle.nn.Layer 改进模型、层与参数的方法。 以下内容假定你已经完成了飞桨的安装以及熟悉了一些基本的飞桨操作。

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zQv;%-2Bb$lU8$cw6Eu>b1vz8Fpn9oh*4>vT@H=m{; z-#a&w_*%p+(#;V3a3@=<@Yp#rH9h$0u}rFNN|-j;&geFJ&cSHh{KF0Rz1ODixr@nt zdhF$tt?vJcf9d&eGQQ62O}Fc(PY+QkYr?2yo||66`*c!t{#6k&_tr2SM(!NB_W6?j zqMC^v5e{liS7Y&Jbc}GAEPbctf}VS!Ul3#rwD2Ca7eOKBVNQF&XoFZX2ye4}H9t-mC z-PcIA*P?e^b@$g5+w?ibW*tDJsK=Elv3bJ#$KzFv98U}}zv)=`rL|nczAnPgFZVxN nzr?Afc=u11{H<9MzejE!HkVb}(cMtZTLqTyFjzFx$}8&sJgh}$ From a50ee99aeb7892ab1927293dc0ba1f7c12f27ec4 Mon Sep 17 00:00:00 2001 From: Nina_bd Date: Thu, 6 Jan 2022 10:44:52 +0800 Subject: [PATCH 27/63] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E7=BB=84=E7=BD=91=E5=92=8C=E5=BF=AB=E9=80=9F=E5=85=A5=E9=97=A8?= =?UTF-8?q?=E7=AB=A0=E8=8A=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1、更新模型组网章节 2、快速入门章节:更新paddle.text(目录不含.dataset),简化paddle.summary相关代码 --- .../01_quick_start_cn.ipynb | 11 +- .../02_paddle2.0_develop/04_model_cn.ipynb | 408 +++++++++++------- .../images/model_develop_flow.png | Bin 190803 -> 190267 bytes 3 files changed, 255 insertions(+), 164 deletions(-) diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb index 5ce0aa86e36..b77f952d255 100644 --- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.ipynb @@ -168,7 +168,7 @@ }, { "data": { - "image/png": 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