add keras-in/keras-out to INC

.azure-pipelines/scripts/ut/env_setup.sh

@@ -28,7 +28,10 @@ elif [[ "${tensorflow_version}" != "" ]]; then
     pip install intel-tensorflow==${tensorflow_version}
 fi
 
-if [[ "${itex_version}" != "" ]]; then
+if [[ "${itex_version}" == "nightly" ]]; then
+    pip install /tf_dataset/itex_binary/221209/intel_extension_for_tensorflow-1.1.0-cp38-cp38-linux_x86_64.whl
+    pip install /tf_dataset/itex_binary/221209/intel_extension_for_tensorflow_lib-1.1.0.0-cp38-cp38-linux_x86_64.whl
+elif [[ "${itex_version}" != "" ]]; then
     pip install --upgrade intel-extension-for-tensorflow[cpu]==${itex_version}
 fi
 

.azure-pipelines/scripts/ut/run_basic_itex.sh

@@ -3,7 +3,7 @@ python -c "import neural_compressor as nc;print(nc.version.__version__)"
 echo "run basic itex"
 
 echo "specify fwk version..."
-export itex_version='1.0.0'
+export itex_version='nightly'
 export tensorflow_version='2.10.0-official'
 
 echo "set up UT env..."

examples/keras/mnist/README.md

@@ -0,0 +1,41 @@
+Step-by-Step
+============
+
+This document list steps of reproducing Keras mnist model tuning results via Neural Compressor.
+This example can run on Intel CPUs.
+
+# Prerequisite
+
+### 1. Installation
+Recommend python 3.6 or higher version.
+
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### 2. Install Tensorflow
+```shell
+pip install tensorflow
+```
+> Note: Supported Tensorflow version > 2.10.0.
+
+### 3. Installation Dependency packages
+```shell
+cd examples/keras/mnist/
+pip install -r requirements.txt
+```
+
+#### Quantizing the model on Intel CPU(Experimental)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+# Run
+
+  ```shell
+  cd examples/keras/mnist/
+  python mnist.py
+  ```

examples/keras/mnist/mnist.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import tensorflow as tf
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras import layers
+import time
+
+num_classes = 10
+
+def build_dataset():
+    # Load the data and split it between train and test sets
+    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+    # Scale images to the [0, 1] range
+    x_train = x_train.astype("float32") / 255
+    x_test = x_test.astype("float32") / 255
+    # Make sure images have shape (28, 28, 1)
+    x_train = np.expand_dims(x_train, -1)
+    x_test = np.expand_dims(x_test, -1)
+
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+    return x_train, y_train, x_test, y_test
+
+class Dataset():
+    def __init__(self, ):
+         _, _ , self.inputs, self.labels = build_dataset()
+
+    def __getitem__(self, idx):
+        return self.inputs[idx], self.labels[idx]
+
+    def __len__(self):
+        assert len(self.inputs) == len(self.labels), 'inputs should have equal len with labels'
+        return len(self.inputs)
+
+def build_model(x_train, y_train, x_test, y_test):
+    if os.path.exists('fp32_model'):
+        model = keras.models.load_model('fp32_model')
+        return model
+    # Model / data parameters
+    input_shape = (28, 28, 1)
+    model = keras.Sequential(
+        [
+            keras.Input(shape=input_shape),
+            layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Flatten(),
+            layers.Dropout(0.5),
+            layers.Dense(num_classes, activation="softmax"),
+        ]
+    )
+
+    batch_size = 128
+    epochs = 1
+
+    model.compile(loss="categorical_crossentropy", optimizer="adam",
+                  metrics=["accuracy"], run_eagerly=True)
+    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+    model.summary()
+    if not os.path.exists('fp32_model'):
+        model.save('fp32_model')
+    return model
+
+def eval_func(model):
+    x_train, y_train, x_test, y_test = build_dataset()
+    model.compile(metrics=["accuracy"], run_eagerly=False)
+    score = model.evaluate(x_test, y_test)
+    return score[1]
+
+def main():
+    x_train, y_train, x_test, y_test = build_dataset()
+    model = build_model(x_train, y_train, x_test, y_test)
+
+    from neural_compressor.quantization import fit
+    from neural_compressor.config import PostTrainingQuantConfig
+    from neural_compressor.utils.utility import set_random_seed
+    from neural_compressor.experimental import common
+    set_random_seed(9527)
+    config = PostTrainingQuantConfig(backend='itex')
+    quantized_model = fit(model,
+                          conf=config,
+                          calib_dataloader=common.DataLoader(Dataset(), batch_size=10),
+                          eval_func=eval_func)
+
+if __name__ == '__main__':
+    main()
+

examples/keras/mnist/requirements.txt

@@ -0,0 +1,3 @@
+tensorflow
+neural-compressor
+intel-extension-for-tensorflow[cpu]