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Xception.py
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# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow import keras
from layers import (add, batch_normalization, conv2d, dense,
global_average_pooling2d, max_pooling2d, relu,
separable_conv2d, softmax)
def block(inputs, layers):
for layer in layers:
inputs = layer(inputs)
return inputs
def conv_block(inputs, filters,
kernel_size=(3, 3), strides=1):
inputs = conv2d(filters=filters, kernel_size=kernel_size,
strides=strides)(inputs)
inputs = batch_normalization()(inputs)
return inputs
def conv_relu_block(inputs, filters,
kernel_size=(3, 3), strides=1):
inputs = conv_block(inputs, filters=filters,
kernel_size=kernel_size, strides=strides)
inputs = relu()(inputs)
return inputs
def separable_conv_block(inputs, filters,
kernel_size=(3, 3), strides=1):
inputs = separable_conv2d(filters=filters, kernel_size=kernel_size)(inputs)
inputs = batch_normalization()(inputs)
return inputs
def separable_conv_relu_block(inputs, filters,
kernel_size=(3, 3), strides=1):
inputs = separable_conv_block(inputs, filters=filters,
kernel_size=kernel_size, strides=strides)
inputs = relu()(inputs)
return inputs
def relu_separable_conv_block(inputs, filters,
kernel_size=(3, 3), strides=1):
inputs = relu()(inputs)
inputs = separable_conv_block(inputs, filters=filters,
kernel_size=kernel_size, strides=strides)
inputs = relu()(inputs)
return inputs
class Xception:
"""
Xception
- Referecences:
- [Xception: Deep Learning with Depthwise Separable Convolutions] (https://arxiv.org/abs/1610.02357)
"""
def build(self, input_shape, classes):
inputs = keras.Input(shape=input_shape)
# Entry flow
filters = 32
outputs = conv_relu_block(inputs, filters=filters, strides=(2, 2))
filters = 64
outputs = conv_relu_block(inputs, filters=filters)
residual = outputs
filters = 128
outputs = separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual, filters=filters,
kernel_size=(1, 1), strides=(2, 2))
outputs = add()([outputs, residual])
filters = 256
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual, filters=filters,
kernel_size=(1, 1), strides=(2, 2))
outputs = add()([outputs, residual])
filters = 728
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual, filters=filters,
kernel_size=(1, 1), strides=(2, 2))
outputs = add()([outputs, residual])
# Middle flow
filters = 728
for _ in range(8):
residual = outputs
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = add()([outputs, residual])
# Exit flow
residual = outputs
outputs = relu_separable_conv_block(outputs, filters=filters)
filters = 1024
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = add()([outputs, residual])
filters = 1536
outputs = separable_conv_relu_block(outputs, filters=filters)
filters = 2048
outputs = separable_conv_relu_block(outputs, filters=filters)
outputs = global_average_pooling2d()(outputs)
outputs = dense(filters)(outputs)
outputs = relu()(outputs)
outputs = dense(1000)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model