Merge branch 'master' into intro_torchscript

Author: brianjo

.jenkins/build.sh

@@ -15,6 +15,9 @@ export PATH=/opt/conda/bin:$PATH
 rm -rf src
 pip install -r $DIR/../requirements.txt
 
+# For Tensorboard. Until 1.14 moves to the release channel.
+pip install tb-nightly  
+
 export PATH=/opt/conda/bin:$PATH
 pip install sphinx==1.8.2 pandas
 
@@ -35,6 +38,12 @@ pushd audio
 python setup.py install
 popd
 
+# Install torchaudio from source
+git clone https://github.com/pytorch/text --quiet
+pushd text
+python setup.py install
+popd
+
 aws configure set default.s3.multipart_threshold 5120MB
 
 # Decide whether to parallelize tutorial builds, based on $JOB_BASE_NAME

advanced_source/torch_script_custom_ops.rst

@@ -999,6 +999,11 @@ Naturally, it is best practice to only use
 ``torch.utils.cpp_extension.load_inline`` if your source code is reasonably
 short.
 
+Note that if you're using this in a Jupyter Notebook, you should not execute
+the cell with the registration multiple times because each execution registers
+a new library and re-registers the custom operator. If you need to re-execute it,
+please restart the Python kernel of your notebook beforehand.
+
 Building with Setuptools
 ************************
 

intermediate_source/flask_rest_api_tutorial.py

@@ -130,9 +130,10 @@ def transform_image(image_bytes):
 
 
 ######################################################################
-# Above method takes image data in bytes, applies the series of transforms
+# The above method takes image data in bytes, applies the series of transforms
 # and returns a tensor. To test the above method, read an image file in
-# bytes mode and see if you get a tensor back:
+# bytes mode (first replacing `../_static/img/sample_file.jpeg` with the actual
+# path to the file on your computer) and see if you get a tensor back:
 
 with open("../_static/img/sample_file.jpeg", 'rb') as f:
     image_bytes = f.read()
@@ -168,11 +169,12 @@ def get_prediction(image_bytes):
 # The tensor ``y_hat`` will contain the index of the predicted class id.
 # However, we need a human readable class name. For that we need a class id
 # to name mapping. Download
-# `this file <https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json>`_ 
-# and place it in current directory as file ``imagenet_class_index.json``.
-# This file contains the mapping of ImageNet class id to ImageNet class
-# name. We will load this JSON file and get the class name of the
-# predicted index.
+# `this file <https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json>`_
+# as ``imagenet_class_index.json`` and remember where you saved it (or, if you
+# are following the exact steps in this tutorial, save it in
+# `tutorials/_static`). This file contains the mapping of ImageNet class id to
+# ImageNet class name. We will load this JSON file and get the class name of
+# the predicted index.
 
 import json
 
@@ -228,7 +230,7 @@ def get_prediction(image_bytes):
 # method to read files from the requests:
 #
 # .. code-block:: python
-#  
+#
 #    from flask import request
 #
 #    @app.route('/predict', methods=['POST'])
@@ -242,25 +244,26 @@ def get_prediction(image_bytes):
 #            return jsonify({'class_id': class_id, 'class_name': class_name})
 
 ######################################################################
-# The ``app.py`` file is now complete. Following is the full version:
+# The ``app.py`` file is now complete. Following is the full version; replace
+# the paths with the paths where you saved your files and it should run:
 #
 # .. code-block:: python
-#  
+#
 #   import io
 #   import json
-#   
+#
 #   from torchvision import models
 #   import torchvision.transforms as transforms
 #   from PIL import Image
 #   from flask import Flask, jsonify, request
-#   
-#   
+#
+#
 #   app = Flask(__name__)
-#   imagenet_class_index = json.load(open('imagenet_class_index.json'))
+#   imagenet_class_index = json.load(open('<PATH/TO/.json/FILE>/imagenet_class_index.json'))
 #   model = models.densenet121(pretrained=True)
 #   model.eval()
-#   
-#   
+#
+#
 #   def transform_image(image_bytes):
 #       my_transforms = transforms.Compose([transforms.Resize(255),
 #                                           transforms.CenterCrop(224),
@@ -270,25 +273,25 @@ def get_prediction(image_bytes):
 #                                               [0.229, 0.224, 0.225])])
 #       image = Image.open(io.BytesIO(image_bytes))
 #       return my_transforms(image).unsqueeze(0)
-#   
-#   
+#
+#
 #   def get_prediction(image_bytes):
 #       tensor = transform_image(image_bytes=image_bytes)
 #       outputs = model.forward(tensor)
 #       _, y_hat = outputs.max(1)
 #       predicted_idx = str(y_hat.item())
 #       return imagenet_class_index[predicted_idx]
-#   
-#   
+#
+#
 #   @app.route('/predict', methods=['POST'])
 #   def predict():
 #       if request.method == 'POST':
 #           file = request.files['file']
 #           img_bytes = file.read()
 #           class_id, class_name = get_prediction(image_bytes=img_bytes)
 #           return jsonify({'class_id': class_id, 'class_name': class_name})
-#   
-#   
+#
+#
 #   if __name__ == '__main__':
 #       app.run()
 
@@ -300,20 +303,24 @@ def get_prediction(image_bytes):
 #     $ FLASK_ENV=development FLASK_APP=app.py flask run
 
 #######################################################################
-# We can use a command line tool like curl or `Postman <https://www.getpostman.com/>`_ to send requests to
-# this webserver:
+# We can use the
+# `requests <https://pypi.org/project/requests/>`_
+# library to send a POST request to our app:
 #
-# ::
+# .. code-block:: python
 #
-#     $ curl -X POST -F file=@cat_pic.jpeg http://localhost:5000/predict
+#   import requests
 #
-# You will get a response in the form:
+#   resp = requests.post("http://localhost:5000/predict",
+#                        files={"file": open('<PATH/TO/.jpg/FILE>/cat.jpg','rb')})
+
+#######################################################################
+# Printing `resp.json()` will now show the following:
 #
 # ::
 #
 #     {"class_id": "n02124075", "class_name": "Egyptian_cat"}
 #
-#
 
 ######################################################################
 # Next steps
@@ -342,4 +349,4 @@ def get_prediction(image_bytes):
 #
 # - You can also add a UI by creating a page with a form which takes the image and
 #   displays the prediction. Check out the `demo <https://pytorch-imagenet.herokuapp.com/>`_
-#   of a similar project and its `source code <https://github.com/avinassh/pytorch-flask-api-heroku>`_.
+#   of a similar project and its `source code <https://github.com/avinassh/pytorch-flask-api-heroku>`_.