official/legacy/image_classification/resnet/resnet_model.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""ResNet50 model for Keras.
Adapted from tf_keras.applications.resnet50.ResNet50().
This is ResNet model version 1.5.
Related papers/blogs:
- https://arxiv.org/abs/1512.03385
- https://arxiv.org/pdf/1603.05027v2.pdf
- http://torch.ch/blog/2016/02/04/resnets.html
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf, tf_keras
from official.legacy.image_classification.resnet import imagenet_preprocessing
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 identity_block(input_tensor,
kernel_size,
filters,
stage,
block,
use_l2_regularizer=True,
batch_norm_decay=0.9,
batch_norm_epsilon=1e-5):
"""The identity block is the block that has no conv layer at shortcut.
Args:
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
use_l2_regularizer: whether to use L2 regularizer on Conv layer.
batch_norm_decay: Moment of batch norm layers.
batch_norm_epsilon: Epsilon of batch borm layers.
Returns:
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if tf_keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = layers.Conv2D(
filters1, (1, 1),
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2a')(
input_tensor)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2a')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
filters2,
kernel_size,
padding='same',
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2b')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2b')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
filters3, (1, 1),
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2c')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2c')(
x)
x = layers.add([x, input_tensor])
x = layers.Activation('relu')(x)
return x
def conv_block(input_tensor,
kernel_size,
filters,
stage,
block,
strides=(2, 2),
use_l2_regularizer=True,
batch_norm_decay=0.9,
batch_norm_epsilon=1e-5):
"""A block that has a conv layer at shortcut.
Note that from stage 3,
the second conv layer at main path is with strides=(2, 2)
And the shortcut should have strides=(2, 2) as well
Args:
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
strides: Strides for the second conv layer in the block.
use_l2_regularizer: whether to use L2 regularizer on Conv layer.
batch_norm_decay: Moment of batch norm layers.
batch_norm_epsilon: Epsilon of batch borm layers.
Returns:
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if tf_keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = layers.Conv2D(
filters1, (1, 1),
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2a')(
input_tensor)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2a')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
filters2,
kernel_size,
strides=strides,
padding='same',
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2b')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2b')(
x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(
filters3, (1, 1),
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '2c')(
x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '2c')(
x)
shortcut = layers.Conv2D(
filters3, (1, 1),
strides=strides,
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name=conv_name_base + '1')(
input_tensor)
shortcut = layers.BatchNormalization(
axis=bn_axis,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
name=bn_name_base + '1')(
shortcut)
x = layers.add([x, shortcut])
x = layers.Activation('relu')(x)
return x
def resnet50(num_classes,
batch_size=None,
use_l2_regularizer=True,
rescale_inputs=False,
batch_norm_decay=0.9,
batch_norm_epsilon=1e-5):
"""Instantiates the ResNet50 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.
rescale_inputs: whether to rescale inputs from 0 to 1.
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)
if rescale_inputs:
# Hub image modules expect inputs in the range [0, 1]. This rescales these
# inputs to the range expected by the trained model.
x = layers.Lambda(
lambda x: x * 255.0 - tf_keras.backend.constant( # pylint: disable=g-long-lambda
imagenet_preprocessing.CHANNEL_MEANS,
shape=[1, 1, 3],
dtype=x.dtype),
name='rescale')(
img_input)
else:
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_config = dict(
use_l2_regularizer=use_l2_regularizer,
batch_norm_decay=batch_norm_decay,
batch_norm_epsilon=batch_norm_epsilon)
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(x)
x = layers.Conv2D(
64, (7, 7),
strides=(2, 2),
padding='valid',
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='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.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = conv_block(
x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), **block_config)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', **block_config)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', **block_config)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', **block_config)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', **block_config)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', **block_config)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', **block_config)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', **block_config)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', **block_config)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', **block_config)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', **block_config)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', **block_config)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', **block_config)
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', **block_config)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', **block_config)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', **block_config)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(
num_classes,
kernel_initializer=tf.initializers.random_normal(stddev=0.01),
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='fc1000')(
x)
# A softmax that is followed by the model loss must be done cannot be done
# in float16 due to numeric issues. So we pass dtype=float32.
x = layers.Activation('softmax', dtype='float32')(x)
# Create model.
return tf_keras.Model(img_input, x, name='resnet50')