-
Notifications
You must be signed in to change notification settings - Fork 6
/
adapted_resnet_v2.py
150 lines (128 loc) · 7.15 KB
/
adapted_resnet_v2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
# Copyright 2016 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.
# ==============================================================================
# THIS CODE WAS HEAVILY ADAPTED AND DOES NOT CORRESPOND TO THE ORIGINAL TENSORFLOW IMPLEMENTATION
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import slim as contrib_slim
import adapted_resnet_utils
slim = contrib_slim
resnet_arg_scope = adapted_resnet_utils.resnet_arg_scope
@slim.add_arg_scope
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
outputs_collections=None, scope=None):
with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
preact = slim.group_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
if depth == depth_in:
shortcut = adapted_resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
normalizer_fn=None, activation_fn=None,
scope='shortcut')
residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
residual = adapted_resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None, scope='conv3')
output = shortcut + residual
return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
@slim.add_arg_scope
def bottleneck_transposed(inputs, depth, depth_bottleneck, stride, outputs_collections=None, scope=None):
assert stride in (1, 2)
with tf.variable_scope(scope, 'bottleneck_v2_transposed', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
preact = slim.group_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
if depth == depth_in:
if stride > 1:
raise Exception('We cannot do spatial expansion by subsampling!')
shortcut = adapted_resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(
preact, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None,
scope='shortcut')
if stride > 1:
shortcut = tf.image.resize_images(shortcut,
(shortcut.shape[1] * stride - 1, shortcut.shape[2] * stride - 1),
method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
if stride > 1:
residual = tf.image.resize_images(residual,
(residual.shape[1] * stride - 1, residual.shape[2] * stride - 1),
method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
residual = slim.conv2d(residual, depth_bottleneck, 3, stride=1, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1, normalizer_fn=None, activation_fn=None, scope='conv3')
output = shortcut + residual
return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
def resnet_v2(inputs,
blocks,
num_classes=None,
global_pool=False,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None,
checkpoint_backward_compatibility=False):
with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, bottleneck,
adapted_resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.group_norm]):
net = inputs
if include_root_block:
if output_stride is not None:
if output_stride % 4 != 0:
raise ValueError('The output_stride needs to be a multiple of 4.')
output_stride /= 4
with slim.arg_scope([slim.conv2d],
activation_fn=None, normalizer_fn=None):
if checkpoint_backward_compatibility:
res = 0
res += adapted_resnet_utils.conv2d_same(net[..., :3], 64, 7, stride=2, scope='conv1')
if 1 * 3 > net.shape[-1]:
print(True)
exit()
res += adapted_resnet_utils.conv2d_same(net[..., 3:], 64, 7, stride=2, scope='conv1p')
else:
res = adapted_resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
net = res
net = slim.max_pool2d(net, [3, 3], stride=2, padding='SAME', scope='pool1')
net = adapted_resnet_utils.stack_blocks_dense(net, blocks, output_stride)
net = slim.group_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
end_points['global_pool'] = net
if num_classes:
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='logits')
end_points[sc.name + '/logits'] = net
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points[sc.name + '/spatial_squeeze'] = net
end_points['predictions'] = slim.softmax(net, scope='predictions')
return net, end_points
def resnet_v2_block(scope, base_depth, num_units, stride):
return adapted_resnet_utils.Block(scope, bottleneck, [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': 1
}] * (num_units - 1) + [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': stride
}])