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model_base.py
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model_base.py
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"""Base ResNet model.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class ResNet(object):
"""ResNet model."""
def __init__(self, is_training, data_format, batch_norm_decay, batch_norm_epsilon):
"""ResNet constructor.
Args:
is_training: if build training or inference model.
data_format: the data_format used during computation.
one of 'channels_first' or 'channels_last'.
"""
self._batch_norm_decay = batch_norm_decay
self._batch_norm_epsilon = batch_norm_epsilon
self._is_training = is_training
assert data_format in ('channels_first', 'channels_last')
self._data_format = data_format
def forward_pass(self, x):
raise NotImplementedError(
'forward_pass() is implemented in ResNet sub classes')
def _residual_v1(self,
x,
kernel_size,
in_filter,
out_filter,
stride,
activate_before_residual=False):
"""Residual unit with 2 sub layers, using Plan A for shortcut connection."""
del activate_before_residual
with tf.name_scope('residual_v1') as name_scope:
orig_x = x
x = self._conv(x, kernel_size, out_filter, stride)
x = self._batch_norm(x)
x = self._relu(x)
x = self._conv(x, kernel_size, out_filter, 1)
x = self._batch_norm(x)
if in_filter != out_filter:
orig_x = self._avg_pool(orig_x, stride, stride)
pad = (out_filter - in_filter) // 2
if self._data_format == 'channels_first':
orig_x = tf.pad(orig_x, [[0, 0], [pad, pad], [0, 0], [0, 0]])
else:
orig_x = tf.pad(orig_x, [[0, 0], [0, 0], [0, 0], [pad, pad]])
x = self._relu(tf.add(x, orig_x))
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x
def _residual_v2(self,
x,
in_filter,
out_filter,
stride,
activate_before_residual=False):
"""Residual unit with 2 sub layers with preactivation, plan A shortcut."""
with tf.name_scope('residual_v2') as name_scope:
if activate_before_residual:
x = self._batch_norm(x)
x = self._relu(x)
orig_x = x
else:
orig_x = x
x = self._batch_norm(x)
x = self._relu(x)
x = self._conv(x, 3, out_filter, stride)
x = self._batch_norm(x)
x = self._relu(x)
x = self._conv(x, 3, out_filter, [1, 1, 1, 1])
if in_filter != out_filter:
pad = (out_filter - in_filter) // 2
orig_x = self._avg_pool(orig_x, stride, stride)
if self._data_format == 'channels_first':
orig_x = tf.pad(orig_x, [[0, 0], [pad, pad], [0, 0], [0, 0]])
else:
orig_x = tf.pad(orig_x, [[0, 0], [0, 0], [0, 0], [pad, pad]])
x = tf.add(x, orig_x)
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x
def _bottleneck_residual_v2(self,
x,
in_filter,
out_filter,
stride,
activate_before_residual=False):
"""Bottleneck residual unit with 3 sub layers, plan B shortcut."""
with tf.name_scope('bottle_residual_v2') as name_scope:
if activate_before_residual:
x = self._batch_norm(x)
x = self._relu(x)
orig_x = x
else:
orig_x = x
x = self._batch_norm(x)
x = self._relu(x)
x = self._conv(x, 1, out_filter // 4, stride, is_atrous=True)
x = self._batch_norm(x)
x = self._relu(x)
# pad when stride isn't unit
x = self._conv(x, 3, out_filter // 4, 1, is_atrous=True)
x = self._batch_norm(x)
x = self._relu(x)
x = self._conv(x, 1, out_filter, 1, is_atrous=True)
if in_filter != out_filter:
orig_x = self._conv(orig_x, 1, out_filter, stride, is_atrous=True)
x = tf.add(x, orig_x)
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x
def _conv(self, x, kernel_size, filters, strides, is_atrous=False):
"""Convolution."""
padding = 'SAME'
if not is_atrous and strides > 1:
pad = kernel_size - 1
pad_beg = pad // 2
pad_end = pad - pad_beg
if self._data_format == 'channels_first':
x = tf.pad(x, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
else:
x = tf.pad(x, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
padding = 'VALID'
return tf.layers.conv2d(
inputs=x,
kernel_size=kernel_size,
filters=filters,
strides=strides,
padding=padding,
use_bias=False,
data_format=self._data_format)
def _batch_norm(self, x):
if self._data_format == 'channels_first':
data_format = 'NCHW'
else:
data_format = 'NHWC'
return tf.contrib.layers.batch_norm(
x,
decay=self._batch_norm_decay,
center=True,
scale=True,
epsilon=self._batch_norm_epsilon,
is_training=self._is_training,
fused=True,
data_format=data_format)
def _relu(self, x):
return tf.nn.relu(x)
def _fully_connected(self, x, out_dim):
with tf.name_scope('fully_connected') as name_scope:
x = tf.layers.dense(x, out_dim)
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x
def _avg_pool(self, x, pool_size, stride):
with tf.name_scope('avg_pool') as name_scope:
x = tf.layers.average_pooling2d(
x, pool_size, stride, 'SAME', data_format=self._data_format)
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x
def _global_avg_pool(self, x):
with tf.name_scope('global_avg_pool') as name_scope:
assert x.get_shape().ndims == 4
if self._data_format == 'channels_first':
x = tf.reduce_mean(x, [2, 3])
else:
x = tf.reduce_mean(x, [1, 2])
tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
return x