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Factor out Python code from the GoogLeNet notebook
This will make it easier to edit, refactor, and reuse the Python code, and still remaing easy to import and use in the notebook. This also enables training the model outside of Jupyter / Colab environment.
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| # Copyright 2022 Google LLC | ||
| # | ||
| # 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. | ||
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| from __future__ import annotations | ||
| from typing import Callable, Optional, List, Tuple, Union | ||
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| import tensorflow as tf | ||
| from tensorflow import keras | ||
| from keras import Input, Model, Sequential | ||
| from keras.layers import Activation, AvgPool2D, Concatenate, Conv2D, Dense, Dropout, Flatten, Layer, MaxPool2D | ||
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| from local_response_normalization import LocalResponseNormalization | ||
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| class Inception(Layer): | ||
| filters_1x1: int | ||
| filters_1x1_reduce_3x3: int | ||
| filters_3x3: int | ||
| filters_1x1_reduce_5x5: int | ||
| filters_5x5: int | ||
| pool_proj: int | ||
| module_name: str | ||
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| conv_1x1: Conv2D | ||
| conv_1x1_3x3: Sequential | ||
| conv_1x1_5x5: Sequential | ||
| max_pool_conv: Sequential | ||
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| def __init__(self, | ||
| filters_1x1: int, | ||
| filters_1x1_reduce_3x3: int, | ||
| filters_3x3: int, | ||
| filters_1x1_reduce_5x5: int, | ||
| filters_5x5: int, | ||
| pool_proj: int, | ||
| name: str, | ||
| **kwargs): | ||
| super().__init__(name=name, **kwargs) | ||
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| self.filters_1x1 = filters_1x1 | ||
| self.filters_1x1_reduce_3x3 = filters_1x1_reduce_3x3 | ||
| self.filters_3x3 = filters_3x3 | ||
| self.filters_1x1_reduce_5x5 = filters_1x1_reduce_5x5 | ||
| self.filters_5x5 = filters_5x5 | ||
| self.pool_proj = pool_proj | ||
| self.module_name = name | ||
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| def _conv2d(self, filters: int, kernel_size: int, name: str) -> Conv2D: | ||
| return Conv2D(filters=filters, kernel_size=kernel_size, | ||
| padding='same', activation='relu', | ||
| name=f'{self.module_name}_{name}') | ||
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| def build( | ||
| self, input_shape: Union[List[Optional[int]], | ||
| Tuple[Optional[int], int, int, int]]) -> None: | ||
| """Builds internal structures to prepare for model training.""" | ||
| self.conv_1x1 = self._conv2d(self.filters_1x1, 1, 'Conv_1x1') | ||
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| self.conv_1x1_3x3 = Sequential([ | ||
| self._conv2d(self.filters_1x1_reduce_3x3, 1, 'Conv_1x1_3x3'), | ||
| self._conv2d(self.filters_3x3, 3, 'Conv_3x3'), | ||
| ]) | ||
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| self.conv_1x1_5x5 = Sequential([ | ||
| self._conv2d(self.filters_1x1_reduce_5x5, 1, 'Conv_1x1_5x5'), | ||
| self._conv2d(self.filters_5x5, 5, 'Conv_5x5'), | ||
| ]) | ||
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| self.max_pool_conv = Sequential([ | ||
| MaxPool2D(3, 1, padding='same', name=f"{self.module_name}_MaxPool"), | ||
| self._conv2d(self.pool_proj, 1, 'MaxPool_Conv_1x1'), | ||
| ]) | ||
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| def call(self, inputs: tf.Tensor) -> tf.Tensor: | ||
| return Concatenate(axis=-1)([ | ||
| self.conv_1x1(inputs), | ||
| self.conv_1x1_3x3(inputs), | ||
| self.conv_1x1_5x5(inputs), | ||
| self.max_pool_conv(inputs), | ||
| ]) | ||
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| def SequentialPassthrough(layers: List[Layer]) -> Callable[[tf.Tensor], tf.Tensor]: | ||
| """Similar to Keras' `Sequential`, but shows all layers transparently. | ||
| Instead of hiding all the layers behind another abstraction called | ||
| `Sequential`, this function explicitly shows all the layers involved in the | ||
| model, so they're visible when calling `model.summary()`. | ||
| """ | ||
| def process_layers(input_: tf.Tensor) -> tf.Tensor: | ||
| x = input_ | ||
| for layer in layers: | ||
| x = layer(x) | ||
| return x | ||
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| return process_layers | ||
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| def GoogLeNet() -> Model: | ||
| """GoogLeNet model implementation.""" | ||
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| input_: Input = Input(shape=(224, 224, 3), name='Input') | ||
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| x = SequentialPassthrough([ | ||
| Conv2D(64, 7, 2, activation='relu', padding='same', name='Conv1'), | ||
| MaxPool2D(3, 2, padding='same', name='MaxPool_1'), | ||
| LocalResponseNormalization(name='LRN1'), | ||
| Conv2D(192, 1, activation='relu', padding='valid', name='Conv_2'), | ||
| Conv2D(192, 3, activation='relu', padding='same', name='Conv_3'), | ||
| LocalResponseNormalization(name='LRN2'), | ||
| MaxPool2D(3, 2, padding='same', name='MaxPool_2'), | ||
| Inception(64, 96, 128, 16, 32, 32, name='Inception_3a'), | ||
| Inception(128, 128, 192, 32, 96, 64, name='Inception_3b'), | ||
| MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='MaxPool_3'), | ||
| Inception(192, 96, 208, 16, 48, 64, name='Inception_4a'), | ||
| ])(input_) | ||
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| # Output 0 branch | ||
| output0 = SequentialPassthrough([ | ||
| AvgPool2D(5, 3, padding='valid', name='AvgPool_out0'), | ||
| Conv2D(128, 1, padding='same', activation='relu', name='Conv2D_out0'), | ||
| Flatten(name='Flatten_out0'), | ||
| Dense(1000, activation='relu', name='FC_1_out0'), ## params | ||
| Dropout(0.7, name='Dropout_out0'), | ||
| Dense(1000, activation='relu', name='FC_2_out0'), ## params | ||
| Activation('softmax', name='Activation_out0'), | ||
| ])(x) | ||
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| # Continue with more Inception modules | ||
| y = SequentialPassthrough([ | ||
| Inception(160, 112, 224, 24, 64, 64, name='Inception_4b'), | ||
| Inception(128, 128, 256, 24, 64, 64, name='Inception_4c'), | ||
| Inception(112, 144, 288, 32, 96, 64, name='Inception_4d'), | ||
| ])(x) | ||
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| # Output 1 branch | ||
| output1 = SequentialPassthrough([ | ||
| AvgPool2D(5, 3, padding='valid', name='AvgPool_out1'), | ||
| Conv2D(128, 1, padding='same', activation='relu', name='Conv2D_out1'), | ||
| Flatten(name='Flatten_out1'), | ||
| Dense(1000, activation='relu', name='FC_1_out1'), ## params | ||
| Dropout(0.7, name='Dropout_out1'), | ||
| Dense(1000, activation='relu', name='FC_2_out1'), ## params | ||
| Activation('softmax', name='Activation_out1'), | ||
| ])(y) | ||
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| # Continue with more Inception modules | ||
| output2 = SequentialPassthrough([ | ||
| Inception(256, 160, 320, 32, 128, 128, name='Inception_4e'), | ||
| MaxPool2D(3, 2, padding='same', name='MaxPool_4'), | ||
| Inception(256, 160, 320, 32, 128, 128, name='Inception_5a'), | ||
| Inception(384, 192, 384, 48, 128, 128, name='Inception_5b'), | ||
| AvgPool2D(7, padding='valid', name='AvgPool_out2'), | ||
| Flatten(name='Flatten_out2'), | ||
| Dropout(0.4, name='Dropout_out2'), | ||
| Dense(1000, activation='relu', name='FC_out2'), | ||
| Activation('softmax', name='Activation_out2'), | ||
| ])(y) | ||
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| return Model(inputs=input_, outputs=[output0, output1, output2], name='GoogLeNet') |