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resnet_deepx.py
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# Copyright 2018 Thomas Mensink, University of Amsterdam, [email protected]
#
# Beloning to the DeepNCM repository
# DeepNCM is proposed in
# Samantha Guerriero, Barbara Caputo, and Thomas Mensink
# DeepNCM: Deep Nearest Class Mean Classifiers
# ICLR Workshop 2018
# https://openreview.net/forum?id=rkPLZ4JPM
#
# This file (resnet_deepx) is based on resnet_model from the
# TensorFlow Models Official ResNet library (release 1.8.0/1.7.0)
# https://github.com/tensorflow/models/tree/master/official/resnet
#
# It contains the ResNet code to create a deepnetwork, without a final layer.
#
# resnet_model.py has the following copyright notice:
# 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 Residual Networks.
Residual networks ('v1' 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 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
DEFAULT_VERSION = 2
################################################################################
# Convenience functions for building the ResNet model.
################################################################################
def batch_norm(inputs, training, data_format):
"""Performs a batch normalization using a standard set of parameters."""
# We set fused=True for a significant performance boost. See
# https://www.tensorflow.org/performance/performance_guide#common_fused_ops
return 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=training, fused=True)
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
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).
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format)
return 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)
################################################################################
# ResNet block definitions.
################################################################################
def _building_block_v1(inputs, filters, training, projection_shortcut, strides,
data_format):
"""A single block for ResNet v1, without a bottleneck.
Convolution then batch normalization then ReLU as described by:
Deep Residual Learning for Image Recognition
https://arxiv.org/pdf/1512.03385.pdf
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
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.
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; shape should match inputs.
"""
shortcut = inputs
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
shortcut = batch_norm(inputs=shortcut, training=training,
data_format=data_format)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=1,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs += shortcut
inputs = tf.nn.relu(inputs)
return inputs
def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
data_format):
"""A single block for ResNet v2, without a bottleneck.
Batch normalization then ReLu then convolution as described by:
Identity Mappings in Deep Residual Networks
https://arxiv.org/pdf/1603.05027.pdf
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.
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.
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; shape should match inputs.
"""
shortcut = inputs
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
# 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(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=1,
data_format=data_format)
return inputs + shortcut
def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
strides, data_format):
"""A single block for ResNet v1, with a bottleneck.
Similar to _building_block_v1(), except using the "bottleneck" blocks
described in:
Convolution then batch normalization then ReLU as described by:
Deep Residual Learning for Image Recognition
https://arxiv.org/pdf/1512.03385.pdf
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
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.
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; shape should match inputs.
"""
shortcut = inputs
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
shortcut = batch_norm(inputs=shortcut, training=training,
data_format=data_format)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=1, strides=1,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=4 * filters, kernel_size=1, strides=1,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs += shortcut
inputs = tf.nn.relu(inputs)
return inputs
def _bottleneck_block_v2(inputs, filters, training, projection_shortcut,
strides, data_format):
"""A single block for ResNet v2, without a bottleneck.
Similar to _building_block_v2(), except using the "bottleneck" blocks
described in:
Convolution then batch normalization then ReLU as described by:
Deep Residual Learning for Image Recognition
https://arxiv.org/pdf/1512.03385.pdf
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
Adapted to the ordering conventions of:
Batch normalization then ReLu then convolution as described by:
Identity Mappings in Deep Residual Networks
https://arxiv.org/pdf/1603.05027.pdf
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.
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.
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; shape should match inputs.
"""
shortcut = inputs
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
# 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(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs, filters=filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm(inputs, training, data_format)
inputs = tf.nn.relu(inputs)
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, bottleneck, block_fn, blocks, strides,
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.
bottleneck: Is the block created a bottleneck block.
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.
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 = filters * 4 if bottleneck 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, training, projection_shortcut, strides,
data_format)
for _ in range(1, blocks):
inputs = block_fn(inputs, filters, training, None, 1, data_format)
return tf.identity(inputs, name)
class ResNetX(object):
"""Base class for building the Resnet Model."""
def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
kernel_size,
conv_stride, first_pool_size, first_pool_stride,
second_pool_size, second_pool_stride, block_sizes, block_strides,
final_size, version=DEFAULT_VERSION, data_format=None):
"""Creates a model for classifying an image.
Args:
resnet_size: A single integer for the size of the ResNet model.
bottleneck: Use regular blocks or bottleneck blocks.
num_classes: The number of classes used as labels.
num_filters: The number of filters to use for the first block layer
of the model. This number is then doubled for each subsequent block
layer.
kernel_size: The kernel size to use for convolution.
conv_stride: stride size for the initial convolutional layer
first_pool_size: Pool size to be used for the first pooling layer.
If none, the first pooling layer is skipped.
first_pool_stride: stride size for the first pooling layer. Not used
if first_pool_size is None.
second_pool_size: Pool size to be used for the second pooling layer.
second_pool_stride: stride size for the final pooling layer
block_sizes: A list containing n values, where n is the number of sets of
block layers desired. Each value should be the number of blocks in the
i-th set.
block_strides: List of integers representing the desired stride size for
each of the sets of block layers. Should be same length as block_sizes.
final_size: The expected size of the model after the second pooling.
version: Integer representing which version of the ResNet network to use.
See README for details. Valid values: [1, 2]
data_format: Input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
Raises:
ValueError: if invalid version is selected.
"""
self.resnet_size = resnet_size
if not data_format:
data_format = (
'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
self.resnet_version = version
if version not in (1, 2):
raise ValueError(
'Resnet version should be 1 or 2. See README for citations.')
self.bottleneck = bottleneck
if bottleneck:
if version == 1:
self.block_fn = _bottleneck_block_v1
else:
self.block_fn = _bottleneck_block_v2
else:
if version == 1:
self.block_fn = _building_block_v1
else:
self.block_fn = _building_block_v2
self.data_format = data_format
self.num_classes = num_classes
self.num_filters = num_filters
self.kernel_size = kernel_size
self.conv_stride = conv_stride
self.first_pool_size = first_pool_size
self.first_pool_stride = first_pool_stride
self.second_pool_size = second_pool_size
self.second_pool_stride = second_pool_stride
self.block_sizes = block_sizes
self.block_strides = block_strides
self.final_size = final_size
def __call__(self, inputs, training):
"""Add operations to classify a batch of input images.
Args:
inputs: A Tensor representing a batch of input images.
training: A boolean. Set to True to add operations required only when
training the classifier.
Returns:
A logits Tensor with shape [<batch_size>, self.num_classes].
"""
if self.data_format == 'channels_first':
# Convert the inputs 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=self.num_filters, kernel_size=self.kernel_size,
strides=self.conv_stride, data_format=self.data_format)
inputs = tf.identity(inputs, 'initial_conv')
if self.first_pool_size:
inputs = tf.layers.max_pooling2d(
inputs=inputs, pool_size=self.first_pool_size,
strides=self.first_pool_stride, padding='SAME',
data_format=self.data_format)
inputs = tf.identity(inputs, 'initial_max_pool')
for i, num_blocks in enumerate(self.block_sizes):
num_filters = self.num_filters * (2**i)
inputs = block_layer(
inputs=inputs, filters=num_filters, bottleneck=self.bottleneck,
block_fn=self.block_fn, blocks=num_blocks,
strides=self.block_strides[i], training=training,
name='block_layer{}'.format(i + 1), data_format=self.data_format)
inputs = batch_norm(inputs, training, self.data_format)
inputs = tf.nn.relu(inputs)
# The current top layer has shape
# `batch_size x pool_size x pool_size x final_size`.
# ResNet does an Average Pooling layer over pool_size,
# but that is the same as doing a reduce_mean. We do a reduce_mean
# here because it performs better than AveragePooling2D.
axes = [2, 3] if self.data_format == 'channels_first' else [1, 2]
inputs = tf.reduce_mean(inputs, axes, keepdims=True)
inputs = tf.identity(inputs, 'final_reduce_mean')
inputs = tf.reshape(inputs, [-1, self.final_size])
#inputs = tf.layers.dense(inputs=inputs, units=self.num_classes)
#inputs = tf.identity(inputs, 'final_dense')
return inputs