official/legacy/image_classification/vgg/vgg_model.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""VGG16 model for Keras.
Adapted from tf_keras.applications.vgg16.VGG16().
Related papers/blogs:
- https://arxiv.org/abs/1409.1556
"""
import tensorflow as tf, tf_keras
layers = tf_keras.layers
def _gen_l2_regularizer(use_l2_regularizer=True, l2_weight_decay=1e-4):
return tf_keras.regularizers.L2(
l2_weight_decay) if use_l2_regularizer else None
def vgg16(num_classes,
batch_size=None,
use_l2_regularizer=True,
batch_norm_decay=0.9,
batch_norm_epsilon=1e-5):
"""Instantiates the VGG16 architecture.
Args:
num_classes: `int` number of classes for image classification.
batch_size: Size of the batches for each step.
use_l2_regularizer: whether to use L2 regularizer on Conv/Dense layer.
batch_norm_decay: Moment of batch norm layers.
batch_norm_epsilon: Epsilon of batch borm layers.
Returns:
A Keras model instance.
"""
input_shape = (224, 224, 3)
img_input = layers.Input(shape=input_shape, batch_size=batch_size)
x = img_input
if tf_keras.backend.image_data_format() == 'channels_first':
x = layers.Permute((3, 1, 2))(x)
bn_axis = 1
else: # channels_last
bn_axis = 3
# Block 1
x = layers.Conv2D(
64, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block1_conv1')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv1')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
64, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block1_conv2')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv2')(
x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(
128, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block2_conv1')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv3')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
128, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block2_conv2')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv4')(
x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(
256, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block3_conv1')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv5')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
256, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block3_conv2')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv6')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
256, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block3_conv3')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv7')(
x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block4_conv1')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv8')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block4_conv2')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv9')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block4_conv3')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv10')(
x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block5_conv1')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv11')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block5_conv2')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv12')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
512, (3, 3),
padding='same',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='block5_conv3')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name='bn_conv13')(
x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(
4096,
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='fc1')(
x)
x = layers.Activation('relu')(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(
4096,
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='fc2')(
x)
x = layers.Activation('relu')(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(
num_classes,
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='fc1000')(
x)
x = layers.Activation('softmax', dtype='float32')(x)
# Create model.
return tf_keras.Model(img_input, x, name='vgg16')