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mnasnet_model.py
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mnasnet_model.py
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# Copyright 2018 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 MnesNet model.
[1] Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le
MnasNet: Platform-Aware Neural Architecture Search for Mobile.
arXiv:1807.11626
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow.compat.v1 as tf
import mnas_utils
GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'data_format',
'num_classes', 'depth_multiplier', 'depth_divisor', 'min_depth',
'stem_size', 'use_keras'
])
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
# TODO(hongkuny): Consider rewrite an argument class with encoding/decoding.
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'strides', 'se_ratio'
])
# defaults will be a public argument for namedtuple in Python 3.7
# https://docs.python.org/3/library/collections.html#collections.namedtuple
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
def conv_kernel_initializer(shape, dtype=None, partition_info=None):
"""Initialization for convolutional kernels.
The main difference with tf.variance_scaling_initializer is that
tf.variance_scaling_initializer uses a truncated normal with an uncorrected
standard deviation, whereas here we use a normal distribution. Similarly,
tf.contrib.layers.variance_scaling_initializer uses a truncated normal with
a corrected standard deviation.
Args:
shape: shape of variable
dtype: dtype of variable
partition_info: unused
Returns:
an initialization for the variable
"""
del partition_info
kernel_height, kernel_width, _, out_filters = shape
fan_out = int(kernel_height * kernel_width * out_filters)
return tf.random_normal(
shape, mean=0.0, stddev=np.sqrt(2.0 / fan_out), dtype=dtype)
def dense_kernel_initializer(shape, dtype=None, partition_info=None):
"""Initialization for dense kernels.
This initialization is equal to
tf.variance_scaling_initializer(scale=1.0/3.0, mode='fan_out',
distribution='uniform').
It is written out explicitly here for clarity.
Args:
shape: shape of variable
dtype: dtype of variable
partition_info: unused
Returns:
an initialization for the variable
"""
del partition_info
init_range = 1.0 / np.sqrt(shape[1])
return tf.random_uniform(shape, -init_range, init_range, dtype=dtype)
def round_filters(filters, global_params):
"""Round number of filters based on depth multiplier."""
multiplier = global_params.depth_multiplier
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
if not multiplier:
return filters
filters *= multiplier
min_depth = min_depth or divisor
new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_filters < 0.9 * filters:
new_filters += divisor
return new_filters
def _get_conv2d(filters,
kernel_size,
strides,
kernel_initializer,
padding,
use_bias,
data_format='channels_last',
use_keras=True):
"""A helper function to create Conv2D layer."""
if use_keras:
return tf.keras.layers.Conv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
kernel_initializer=kernel_initializer,
padding=padding,
data_format=data_format,
use_bias=use_bias)
else:
return tf.layers.Conv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
kernel_initializer=kernel_initializer,
padding=padding,
data_format=data_format,
use_bias=use_bias)
class MnasBlock(object):
"""A class of MnasNet Inveretd Residual Bottleneck.
Attributes:
has_se: boolean. Whether the block contains a Squeeze and Excitation layer
inside.
endpoints: dict. A list of internal tensors.
"""
def __init__(self, block_args, global_params):
"""Initializes a MnasNet block.
Args:
block_args: BlockArgs, arguments to create a MnasBlock.
global_params: GlobalParams, a set of global parameters.
"""
self._block_args = block_args
self._batch_norm_momentum = global_params.batch_norm_momentum
self._batch_norm_epsilon = global_params.batch_norm_epsilon
self._use_keras = global_params.use_keras
self._data_format = global_params.data_format
if self._data_format == 'channels_first':
self._channel_axis = 1
self._spatial_dims = [2, 3]
else:
self._channel_axis = -1
self._spatial_dims = [1, 2]
self.has_se = (self._block_args.se_ratio is not None) and (
self._block_args.se_ratio > 0) and (self._block_args.se_ratio <= 1)
self.endpoints = None
# Builds the block accordings to arguments.
self._build()
def block_args(self):
return self._block_args
def _build(self):
"""Builds MnasNet block according to the arguments."""
filters = self._block_args.input_filters * self._block_args.expand_ratio
if self._block_args.expand_ratio != 1:
# Expansion phase:
self._expand_conv = _get_conv2d(
filters=filters,
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=False,
data_format=self._data_format,
use_keras=self._use_keras)
# TODO(hongkuny): b/120622234 need to manage update ops directly.
self._bn0 = tf.layers.BatchNormalization(
axis=self._channel_axis,
momentum=self._batch_norm_momentum,
epsilon=self._batch_norm_epsilon,
fused=True)
kernel_size = self._block_args.kernel_size
# Depth-wise convolution phase:
if self._use_keras:
self._depthwise_conv = tf.keras.layers.DepthwiseConv2D(
[kernel_size, kernel_size],
strides=self._block_args.strides,
depthwise_initializer=conv_kernel_initializer,
padding='same',
data_format=self._data_format,
use_bias=False)
else:
self._depthwise_conv = mnas_utils.DepthwiseConv2D(
[kernel_size, kernel_size],
strides=self._block_args.strides,
depthwise_initializer=conv_kernel_initializer,
padding='same',
data_format=self._data_format,
use_bias=False)
self._bn1 = tf.layers.BatchNormalization(
axis=self._channel_axis,
momentum=self._batch_norm_momentum,
epsilon=self._batch_norm_epsilon,
fused=True)
if self.has_se:
num_reduced_filters = max(
1, int(self._block_args.input_filters * self._block_args.se_ratio))
# Squeeze and Excitation layer.
self._se_reduce = _get_conv2d(
num_reduced_filters,
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=True,
data_format=self._data_format,
use_keras=self._use_keras)
self._se_expand = _get_conv2d(
filters,
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=True,
data_format=self._data_format,
use_keras=self._use_keras)
# Output phase:
filters = self._block_args.output_filters
self._project_conv = _get_conv2d(
filters,
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=False,
data_format=self._data_format,
use_keras=self._use_keras)
self._bn2 = tf.layers.BatchNormalization(
axis=self._channel_axis,
momentum=self._batch_norm_momentum,
epsilon=self._batch_norm_epsilon,
fused=True)
def _call_se(self, input_tensor):
"""Call Squeeze and Excitation layer.
Args:
input_tensor: Tensor, a single input tensor for Squeeze/Excitation layer.
Returns:
A output tensor, which should have the same shape as input.
"""
se_tensor = tf.reduce_mean(input_tensor, self._spatial_dims, keepdims=True)
se_tensor = self._se_expand(tf.nn.relu(self._se_reduce(se_tensor)))
tf.logging.info('Built Squeeze and Excitation with tensor shape: %s' %
(se_tensor.shape))
return tf.sigmoid(se_tensor) * input_tensor
def call(self, inputs, training=True):
"""Implementation of MnasBlock call().
Args:
inputs: the inputs tensor.
training: boolean, whether the model is constructed for training.
Returns:
A output tensor.
"""
tf.logging.info('Block input: %s shape: %s' % (inputs.name, inputs.shape))
if self._block_args.expand_ratio != 1:
x = tf.nn.relu(self._bn0(self._expand_conv(inputs), training=training))
else:
x = inputs
tf.logging.info('Expand: %s shape: %s' % (x.name, x.shape))
x = tf.nn.relu(self._bn1(self._depthwise_conv(x), training=training))
tf.logging.info('DWConv: %s shape: %s' % (x.name, x.shape))
if self.has_se:
with tf.variable_scope('se'):
x = self._call_se(x)
self.endpoints = {'expansion_output': x}
x = self._bn2(self._project_conv(x), training=training)
if self._block_args.id_skip:
if all(
s == 1 for s in self._block_args.strides
) and self._block_args.input_filters == self._block_args.output_filters:
x = tf.add(x, inputs)
tf.logging.info('Project: %s shape: %s' % (x.name, x.shape))
return tf.identity(x)
class MnasNetModel(tf.keras.Model):
"""A class implements tf.keras.Model for MnesNet model.
Reference: https://arxiv.org/abs/1807.11626
"""
def __init__(self, blocks_args=None, global_params=None):
"""Initializes an `MnasNetModel` instance.
Args:
blocks_args: A list of BlockArgs to construct MnasNet block modules.
global_params: GlobalParams, a set of global parameters.
Raises:
ValueError: when blocks_args is not specified as a list.
"""
super(MnasNetModel, self).__init__()
if not isinstance(blocks_args, list):
raise ValueError('blocks_args should be a list.')
self._global_params = global_params
self._blocks_args = blocks_args
self.endpoints = None
self._build()
def _build(self):
"""Builds a MnasNet model."""
self._blocks = []
# Builds blocks.
for block_args in self._blocks_args:
assert block_args.num_repeat > 0
# Update block input and output filters based on depth multiplier.
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters,
self._global_params),
output_filters=round_filters(block_args.output_filters,
self._global_params))
# The first block needs to take care of stride and filter size increase.
self._blocks.append(MnasBlock(block_args, self._global_params))
if block_args.num_repeat > 1:
# pylint: disable=protected-access
block_args = block_args._replace(
input_filters=block_args.output_filters, strides=[1, 1])
# pylint: enable=protected-access
for _ in xrange(block_args.num_repeat - 1):
self._blocks.append(MnasBlock(block_args, self._global_params))
batch_norm_momentum = self._global_params.batch_norm_momentum
batch_norm_epsilon = self._global_params.batch_norm_epsilon
if self._global_params.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
# Stem part.
stem_size = self._global_params.stem_size
self._conv_stem = _get_conv2d(
filters=round_filters(stem_size, self._global_params),
kernel_size=[3, 3],
strides=[2, 2],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=False,
data_format=self._global_params.data_format,
use_keras=self._global_params.use_keras)
self._bn0 = tf.layers.BatchNormalization(
axis=channel_axis,
momentum=batch_norm_momentum,
epsilon=batch_norm_epsilon,
fused=True)
# Head part.
self._conv_head = _get_conv2d(
filters=1280,
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding='same',
use_bias=False,
data_format=self._global_params.data_format,
use_keras=self._global_params.use_keras)
self._bn1 = tf.layers.BatchNormalization(
axis=channel_axis,
momentum=batch_norm_momentum,
epsilon=batch_norm_epsilon,
fused=True)
self._avg_pooling = tf.keras.layers.GlobalAveragePooling2D(
data_format=self._global_params.data_format)
if self._global_params.use_keras:
self._fc = tf.keras.layers.Dense(
self._global_params.num_classes,
kernel_initializer=dense_kernel_initializer)
else:
self._fc = tf.layers.Dense(
self._global_params.num_classes,
kernel_initializer=dense_kernel_initializer)
if self._global_params.dropout_rate > 0:
self._dropout = tf.keras.layers.Dropout(self._global_params.dropout_rate)
else:
self._dropout = None
def call(self, inputs, training=True, features_only=None):
"""Implementation of MnasNetModel call().
Args:
inputs: input tensors.
training: boolean, whether the model is constructed for training.
features_only: build the base feature network only.
Returns:
output tensors.
"""
outputs = None
self.endpoints = {}
# Calls Stem layers
with tf.variable_scope('mnas_stem'):
outputs = tf.nn.relu(
self._bn0(self._conv_stem(inputs), training=training))
tf.logging.info('Built stem layers with output shape: %s' % outputs.shape)
self.endpoints['stem'] = outputs
# Calls blocks.
reduction_idx = 0
for idx, block in enumerate(self._blocks):
is_reduction = False
if ((idx == len(self._blocks) - 1) or
self._blocks[idx + 1].block_args().strides[0] > 1):
is_reduction = True
reduction_idx += 1
with tf.variable_scope('mnas_blocks_%s' % idx):
outputs = block.call(outputs, training=training)
self.endpoints['block_%s' % idx] = outputs
if is_reduction:
self.endpoints['reduction_%s' % reduction_idx] = outputs
if block.endpoints:
for k, v in six.iteritems(block.endpoints):
self.endpoints['block_%s/%s' % (idx, k)] = v
if is_reduction:
self.endpoints['reduction_%s/%s' % (reduction_idx, k)] = v
self.endpoints['global_pool'] = outputs
if not features_only:
# Calls final layers and returns logits.
with tf.variable_scope('mnas_head'):
outputs = tf.nn.relu(
self._bn1(self._conv_head(outputs), training=training))
outputs = self._avg_pooling(outputs)
if self._dropout:
outputs = self._dropout(outputs, training=training)
outputs = self._fc(outputs)
self.endpoints['head'] = outputs
return outputs