# Copyright 2017 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.
# ==============================================================================
"""Contains definitions for the preactivation form of Residual Networks.
Residual networks (ResNets) were originally proposed in:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
The full preactivation 'v2' ResNet variant implemented in this module was
introduced by:
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
The key difference of the full preactivation 'v2' variant compared to the
'v1' variant in [1] is the use of batch normalization before every weight layer
rather than after.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
_BATCH_NORM_DECAY = 0.997
_BATCH_NORM_EPSILON = 1e-5
_DISABLE_BATCH_NORM=False
def batch_norm_relu(inputs, is_training, data_format):
"""Performs a batch normalization followed by a ReLU."""
# We set fused=True for a significant performance boost.
# See https://www.tensorflow.org/performance/performance_guide#common_fused_ops
if not _DISABLE_BATCH_NORM:
inputs = tf.layers.batch_normalization(
inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
scale=True, training=is_training, fused=True)
inputs = tf.nn.relu(inputs)
return inputs
def fixed_padding(inputs, kernel_size, data_format):
"""Pads the input along the spatial dimensions independently of input size.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
[batch, height_in, width_in, channels] depending on data_format.
kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
Should be a positive integer.
data_format: The input format ('channels_last' or 'channels_first').
Returns:
A tensor with the same format as the input with the data either intact
(if kernel_size == 1) or padded (if kernel_size > 1).
"""
pad_total = kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
if data_format == 'channels_first':
padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
[pad_beg, pad_end], [pad_beg, pad_end]])
else:
padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
[pad_beg, pad_end], [0, 0]])
return padded_inputs
conv2d_counter = 0
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
"""Strided 2-D convolution with explicit padding.
The padding is consistent and is based only on `kernel_size`, not on the
dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
"""
global conv2d_counter
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format)
result = tf.layers.conv2d(
inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
kernel_initializer=tf.variance_scaling_initializer(),
data_format=data_format, name="conv2d_%d"%(conv2d_counter,))
conv2d_counter+=1
return result
def building_block(inputs, filters, is_training, projection_shortcut, strides,
data_format):
"""Standard building block for residual networks with BN before convolutions.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
[batch, height_in, width_in, channels] depending on data_format.
filters: The number of filters for the convolutions.
is_training: A Boolean for whether the model is in training or inference
mode. Needed for batch normalization.
projection_shortcut: The function to use for projection shortcuts (typically
a 1x1 convolution when downsampling the input).
strides: The block's stride. If greater than 1, this block will ultimately
downsample the input.
data_format: The input format ('channels_last' or 'channels_first').
Returns:
The output tensor of the block.
"""
shortcut = inputs
inputs = batch_norm_relu(inputs, is_training, data_format)
# The projection shortcut should come after the first batch norm and ReLU
# since it performs a 1x1 convolution.
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=1,
data_format=data_format)
return inputs + shortcut
def bottleneck_block(inputs, filters, is_training, projection_shortcut,
strides, data_format):
"""Bottleneck block variant for residual networks with BN before convolutions.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
[batch, height_in, width_in, channels] depending on data_format.
filters: The number of filters for the first two convolutions. Note that the
third and final convolution will use 4 times as many filters.
is_training: A Boolean for whether the model is in training or inference
mode. Needed for batch normalization.
projection_shortcut: The function to use for projection shortcuts (typically
a 1x1 convolution when downsampling the input).
strides: The block's stride. If greater than 1, this block will ultimately
downsample the input.
data_format: The input format ('channels_last' or 'channels_first').
Returns:
The output tensor of the block.
"""
shortcut = inputs
inputs = batch_norm_relu(inputs, is_training, data_format)
# The projection shortcut should come after the first batch norm and ReLU
# since it performs a 1x1 convolution.
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=1, strides=1,
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=4 * filters, kernel_size=1, strides=1,
data_format=data_format)
return inputs + shortcut
def block_layer(inputs, filters, block_fn, blocks, strides, is_training, name,
data_format):
"""Creates one layer of blocks for the ResNet model.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
[batch, height_in, width_in, channels] depending on data_format.
filters: The number of filters for the first convolution of the layer.
block_fn: The block to use within the model, either `building_block` or
`bottleneck_block`.
blocks: The number of blocks contained in the layer.
strides: The stride to use for the first convolution of the layer. If
greater than 1, this layer will ultimately downsample the input.
is_training: Either True or False, whether we are currently training the
model. Needed for batch norm.
name: A string name for the tensor output of the block layer.
data_format: The input format ('channels_last' or 'channels_first').
Returns:
The output tensor of the block layer.
"""
# Bottleneck blocks end with 4x the number of filters as they start with
filters_out = 4 * filters if block_fn is bottleneck_block else filters
def projection_shortcut(inputs):
return conv2d_fixed_padding(
inputs=inputs, filters=filters_out, kernel_size=1, strides=strides,
data_format=data_format)
# Only the first block per block_layer uses projection_shortcut and strides
inputs = block_fn(inputs, filters, is_training, projection_shortcut, strides,
data_format)
for i in range(1, blocks):
inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
return tf.identity(inputs, name)
def cifar10_resnet_v2_generator(resnet_size, num_classes, data_format=None):
"""Generator for CIFAR-10 ResNet v2 models.
Args:
resnet_size: A single integer for the size of the ResNet model.
num_classes: The number of possible classes for image classification.
data_format: The input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
Returns:
The model function that takes in `inputs` and `is_training` and
returns the output tensor of the ResNet model.
"""
if resnet_size % 6 != 2:
raise ValueError('resnet_size must be 6n + 2:', resnet_size)
num_blocks = (resnet_size - 2) // 6
if data_format is None:
data_format = 'channels_first' if tf.test.is_built_with_cuda() else 'channels_last'
def model(inputs, is_training):
if data_format == 'channels_first':
# Convert from channels_last (NHWC) to channels_first (NCHW). This
# provides a large performance boost on GPU.
# See https://www.tensorflow.org/performance/performance_guide#data_formats
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=16, kernel_size=3, strides=1,
data_format=data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = block_layer(
inputs=inputs, filters=16, block_fn=building_block, blocks=num_blocks,
strides=1, is_training=is_training, name='block_layer1',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=32, block_fn=building_block, blocks=num_blocks,
strides=2, is_training=is_training, name='block_layer2',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=64, block_fn=building_block, blocks=num_blocks,
strides=2, is_training=is_training, name='block_layer3',
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs, pool_size=8, strides=1, padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [-1, 64])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
model.default_image_size = 32
return model
def tiny_cifar10_resnet_v2_generator(resnet_size, num_classes,
data_format=None):
"""Generator for CIFAR-10 ResNet v2 models.
Args:
resnet_size: A single integer for the size of the ResNet model.
num_classes: The number of possible classes for image classification.
data_format: The input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
Returns:
The model function that takes in `inputs` and `is_training` and
returns the output tensor of the ResNet model.
"""
if resnet_size % 6 != 2:
raise ValueError('resnet_size must be 6n + 2:', resnet_size)
num_blocks = (resnet_size - 2) // 6
if data_format is None:
data_format = 'channels_first' if tf.test.is_built_with_cuda() else 'channels_last'
def model(inputs, is_training):
if data_format == 'channels_first':
# Convert from channels_last (NHWC) to channels_first (NCHW). This
# provides a large performance boost on GPU.
# See https://www.tensorflow.org/performance/performance_guide#data_formats
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=1, kernel_size=3, strides=1,
data_format=data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = block_layer(
inputs=inputs, filters=1, block_fn=building_block, blocks=num_blocks,
strides=1, is_training=is_training, name='block_layer1',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=1, block_fn=building_block, blocks=num_blocks,
strides=2, is_training=is_training, name='block_layer2',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=1, block_fn=building_block, blocks=num_blocks,
strides=2, is_training=is_training, name='block_layer3',
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs, pool_size=8, strides=1, padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [-1, 1])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
model.default_image_size = 32
return model
def imagenet_resnet_v2_generator(block_fn, layers, num_classes,
data_format=None):
"""Generator for ImageNet ResNet v2 models.
Args:
block_fn: The block to use within the model, either `building_block` or
`bottleneck_block`.
layers: A length-4 array denoting the number of blocks to include in each
layer. Each layer consists of blocks that take inputs of the same size.
num_classes: The number of possible classes for image classification.
data_format: The input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
Returns:
The model function that takes in `inputs` and `is_training` and
returns the output tensor of the ResNet model.
"""
if data_format is None:
data_format = 'channels_first' if tf.test.is_built_with_cuda() else 'channels_last'
def model(inputs, is_training):
if data_format == 'channels_first':
# Convert from channels_last (NHWC) to channels_first (NCHW). This
# provides a large performance boost on GPU.
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=64, kernel_size=7, strides=2,
data_format=data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = tf.layers.max_pooling2d(
inputs=inputs, pool_size=3, strides=2, padding='SAME',
data_format=data_format)
inputs = tf.identity(inputs, 'initial_max_pool')
inputs = block_layer(
inputs=inputs, filters=64, block_fn=block_fn, blocks=layers[0],
strides=1, is_training=is_training, name='block_layer1',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=128, block_fn=block_fn, blocks=layers[1],
strides=2, is_training=is_training, name='block_layer2',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=256, block_fn=block_fn, blocks=layers[2],
strides=2, is_training=is_training, name='block_layer3',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=512, block_fn=block_fn, blocks=layers[3],
strides=2, is_training=is_training, name='block_layer4',
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs, pool_size=7, strides=1, padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [inputs.get_shape()[0].value, -1])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
model.default_image_size = 224
return model
def tiny_imagenet_resnet_v2_generator(block_fn, layers, num_classes,
data_format=None):
"""Generator for ImageNet ResNet v2 models.
Args:
block_fn: The block to use within the model, either `building_block` or
`bottleneck_block`.
layers: A length-4 array denoting the number of blocks to include in each
layer. Each layer consists of blocks that take inputs of the same size.
num_classes: The number of possible classes for image classification.
data_format: The input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
Returns:
The model function that takes in `inputs` and `is_training` and
returns the output tensor of the ResNet model.
"""
if data_format is None:
data_format = 'channels_first' if tf.test.is_gpu_available() else 'channels_last'
def model(inputs, is_training):
if data_format == 'channels_first':
# Convert from channels_last (NHWC) to channels_first (NCHW). This
# provides a large performance boost on GPU.
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=1, kernel_size=7, strides=2,
data_format=data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = tf.layers.max_pooling2d(
inputs=inputs, pool_size=3, strides=2, padding='SAME',
data_format=data_format)
inputs = tf.identity(inputs, 'initial_max_pool')
inputs = block_layer(
inputs=inputs, filters=1, block_fn=block_fn, blocks=layers[0],
strides=1, is_training=is_training, name='block_layer1',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=1, block_fn=block_fn, blocks=layers[1],
strides=2, is_training=is_training, name='block_layer2',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=1, block_fn=block_fn, blocks=layers[2],
strides=2, is_training=is_training, name='block_layer3',
data_format=data_format)
inputs = block_layer(
inputs=inputs, filters=1, block_fn=block_fn, blocks=layers[3],
strides=2, is_training=is_training, name='block_layer4',
data_format=data_format)
inputs = batch_norm_relu(inputs, is_training, data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs, pool_size=7, strides=1, padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [inputs.get_shape()[0].value, -1])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
model.default_image_size = 224
return model
def resnet_v2(resnet_size, num_classes, data_format=None):
"""Returns the ResNet model for a given size and number of output classes."""
model_params = {
18: {'block': building_block, 'layers': [2, 2, 2, 2]},
34: {'block': building_block, 'layers': [3, 4, 6, 3]},
50: {'block': bottleneck_block, 'layers': [3, 4, 6, 3]},
101: {'block': bottleneck_block, 'layers': [3, 4, 23, 3]},
152: {'block': bottleneck_block, 'layers': [3, 8, 36, 3]},
200: {'block': bottleneck_block, 'layers': [3, 24, 36, 3]}
}
if resnet_size not in model_params:
raise ValueError('Not a valid resnet_size:', resnet_size)
params = model_params[resnet_size]
return imagenet_resnet_v2_generator(
params['block'], params['layers'], num_classes, data_format)
def tiny_resnet_v2(resnet_size, num_classes, data_format=None):
"""Returns the ResNet model for a given size and number of output classes."""
model_params = {
18: {'block': building_block, 'layers': [2, 2, 2, 2]},
34: {'block': building_block, 'layers': [3, 4, 6, 3]},
50: {'block': bottleneck_block, 'layers': [3, 4, 6, 3]},
101: {'block': bottleneck_block, 'layers': [3, 4, 23, 3]},
152: {'block': bottleneck_block, 'layers': [3, 8, 36, 3]},
200: {'block': bottleneck_block, 'layers': [3, 24, 36, 3]}
}
if resnet_size not in model_params:
raise ValueError('Not a valid resnet_size:', resnet_size)
params = model_params[resnet_size]
return tiny_imagenet_resnet_v2_generator(
params['block'], params['layers'], num_classes, data_format)
