1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
7 # http://www.apache.org/licenses/LICENSE-2.0
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 # ==============================================================================
15 """Contains the definition of the Inception Resnet V2 architecture.
17 As described in http://arxiv.org/abs/1602.07261.
19 Inception-v4, Inception-ResNet and the Impact of Residual Connections
21 Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
23 from __future__ import absolute_import
24 from __future__ import division
25 from __future__ import print_function
28 import tensorflow as tf
30 slim = tf.contrib.slim
33 def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
34 """Builds the 35x35 resnet block."""
35 with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
36 with tf.variable_scope('Branch_0'):
37 tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
38 with tf.variable_scope('Branch_1'):
39 tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
40 tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
41 with tf.variable_scope('Branch_2'):
42 tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
43 tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
44 tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
45 mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
46 up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
47 activation_fn=None, scope='Conv2d_1x1')
50 net = activation_fn(net)
54 def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
55 """Builds the 17x17 resnet block."""
56 with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
57 with tf.variable_scope('Branch_0'):
58 tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
59 with tf.variable_scope('Branch_1'):
60 tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
61 tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
62 scope='Conv2d_0b_1x7')
63 tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
64 scope='Conv2d_0c_7x1')
65 mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
66 up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
67 activation_fn=None, scope='Conv2d_1x1')
70 net = activation_fn(net)
74 def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
75 """Builds the 8x8 resnet block."""
76 with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
77 with tf.variable_scope('Branch_0'):
78 tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
79 with tf.variable_scope('Branch_1'):
80 tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
81 tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
82 scope='Conv2d_0b_1x3')
83 tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
84 scope='Conv2d_0c_3x1')
85 mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
86 up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
87 activation_fn=None, scope='Conv2d_1x1')
90 net = activation_fn(net)
94 def inception_resnet_v2_base(inputs,
95 final_endpoint='Conv2d_7b_1x1',
97 align_feature_maps=False,
99 """Inception model from http://arxiv.org/abs/1602.07261.
101 Constructs an Inception Resnet v2 network from inputs to the given final
102 endpoint. This method can construct the network up to the final inception
106 inputs: a tensor of size [batch_size, height, width, channels].
107 final_endpoint: specifies the endpoint to construct the network up to. It
108 can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
109 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
110 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
111 output_stride: A scalar that specifies the requested ratio of input to
112 output spatial resolution. Only supports 8 and 16.
113 align_feature_maps: When true, changes all the VALID paddings in the network
114 to SAME padding so that the feature maps are aligned.
115 scope: Optional variable_scope.
118 tensor_out: output tensor corresponding to the final_endpoint.
119 end_points: a set of activations for external use, for example summaries or
123 ValueError: if final_endpoint is not set to one of the predefined values,
124 or if the output_stride is not 8 or 16, or if the output_stride is 8 and
125 we request an end point after 'PreAuxLogits'.
127 if output_stride != 8 and output_stride != 16:
128 raise ValueError('output_stride must be 8 or 16.')
130 padding = 'SAME' if align_feature_maps else 'VALID'
134 def add_and_check_final(name, net):
135 end_points[name] = net
136 return name == final_endpoint
138 with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
139 with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
140 stride=1, padding='SAME'):
142 net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding,
143 scope='Conv2d_1a_3x3')
144 if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
147 net = slim.conv2d(net, 32, 3, padding=padding,
148 scope='Conv2d_2a_3x3')
149 if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
151 net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
152 if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
154 net = slim.max_pool2d(net, 3, stride=2, padding=padding,
155 scope='MaxPool_3a_3x3')
156 if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
158 net = slim.conv2d(net, 80, 1, padding=padding,
159 scope='Conv2d_3b_1x1')
160 if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
162 net = slim.conv2d(net, 192, 3, padding=padding,
163 scope='Conv2d_4a_3x3')
164 if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
166 net = slim.max_pool2d(net, 3, stride=2, padding=padding,
167 scope='MaxPool_5a_3x3')
168 if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points
171 with tf.variable_scope('Mixed_5b'):
172 with tf.variable_scope('Branch_0'):
173 tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
174 with tf.variable_scope('Branch_1'):
175 tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
176 tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
177 scope='Conv2d_0b_5x5')
178 with tf.variable_scope('Branch_2'):
179 tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
180 tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
181 scope='Conv2d_0b_3x3')
182 tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
183 scope='Conv2d_0c_3x3')
184 with tf.variable_scope('Branch_3'):
185 tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
186 scope='AvgPool_0a_3x3')
187 tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
188 scope='Conv2d_0b_1x1')
190 [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
192 if add_and_check_final('Mixed_5b', net): return net, end_points
193 # TODO(alemi): Register intermediate endpoints
194 net = slim.repeat(net, 10, block35, scale=0.17)
196 # 17 x 17 x 1088 if output_stride == 8,
197 # 33 x 33 x 1088 if output_stride == 16
198 use_atrous = output_stride == 8
200 with tf.variable_scope('Mixed_6a'):
201 with tf.variable_scope('Branch_0'):
202 tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
204 scope='Conv2d_1a_3x3')
205 with tf.variable_scope('Branch_1'):
206 tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
207 tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
208 scope='Conv2d_0b_3x3')
209 tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
210 stride=1 if use_atrous else 2,
212 scope='Conv2d_1a_3x3')
213 with tf.variable_scope('Branch_2'):
214 tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
216 scope='MaxPool_1a_3x3')
217 net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
219 if add_and_check_final('Mixed_6a', net): return net, end_points
221 # TODO(alemi): register intermediate endpoints
222 with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
223 net = slim.repeat(net, 20, block17, scale=0.10)
224 if add_and_check_final('PreAuxLogits', net): return net, end_points
226 if output_stride == 8:
227 # TODO(gpapan): Properly support output_stride for the rest of the net.
228 raise ValueError('output_stride==8 is only supported up to the '
229 'PreAuxlogits end_point for now.')
232 with tf.variable_scope('Mixed_7a'):
233 with tf.variable_scope('Branch_0'):
234 tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
235 tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
237 scope='Conv2d_1a_3x3')
238 with tf.variable_scope('Branch_1'):
239 tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
240 tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
242 scope='Conv2d_1a_3x3')
243 with tf.variable_scope('Branch_2'):
244 tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
245 tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
246 scope='Conv2d_0b_3x3')
247 tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
249 scope='Conv2d_1a_3x3')
250 with tf.variable_scope('Branch_3'):
251 tower_pool = slim.max_pool2d(net, 3, stride=2,
253 scope='MaxPool_1a_3x3')
255 [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
257 if add_and_check_final('Mixed_7a', net): return net, end_points
259 # TODO(alemi): register intermediate endpoints
260 net = slim.repeat(net, 9, block8, scale=0.20)
261 net = block8(net, activation_fn=None)
264 net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
265 if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points
267 raise ValueError('final_endpoint (%s) not recognized', final_endpoint)
270 def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
271 dropout_keep_prob=0.8,#0.8
273 scope='InceptionResnetV2',
274 create_aux_logits=True):
275 """Creates the Inception Resnet V2 model.
278 inputs: a 4-D tensor of size [batch_size, height, width, 3].
279 num_classes: number of predicted classes.
280 is_training: whether is training or not.
281 dropout_keep_prob: float, the fraction to keep before final layer.
282 reuse: whether or not the network and its variables should be reused. To be
283 able to reuse 'scope' must be given.
284 scope: Optional variable_scope.
285 create_aux_logits: Whether to include the auxilliary logits.
288 logits: the logits outputs of the model.
289 end_points: the set of end_points from the inception model.
293 with tf.variable_scope(scope, 'InceptionResnetV2', [inputs, num_classes],
294 reuse=reuse) as scope:
295 with slim.arg_scope([slim.batch_norm, slim.dropout],
296 is_training=is_training):
298 net, end_points = inception_resnet_v2_base(inputs, scope=scope)
300 if create_aux_logits:
301 with tf.variable_scope('AuxLogits'):
302 aux = end_points['PreAuxLogits']
303 aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
304 scope='Conv2d_1a_3x3')
305 aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
306 aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
307 padding='VALID', scope='Conv2d_2a_5x5')
308 aux = slim.flatten(aux)
309 aux = slim.fully_connected(aux, num_classes, activation_fn=None,
311 end_points['AuxLogits'] = aux
313 with tf.variable_scope('Logits'):
314 net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
315 scope='AvgPool_1a_8x8')
316 net = slim.flatten(net)
318 net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
321 end_points['PreLogitsFlatten'] = net
322 # end_points['yjr_feature'] = tf.squeeze(net, axis=0)
324 logits = slim.fully_connected(net, num_classes, activation_fn=None,
326 end_points['Logits'] = logits
327 end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
329 return logits, end_points
330 inception_resnet_v2.default_image_size = 299
333 def inception_resnet_v2_arg_scope(weight_decay=0.00004,
334 batch_norm_decay=0.9997,
335 batch_norm_epsilon=0.001):
336 """Yields the scope with the default parameters for inception_resnet_v2.
339 weight_decay: the weight decay for weights variables.
340 batch_norm_decay: decay for the moving average of batch_norm momentums.
341 batch_norm_epsilon: small float added to variance to avoid dividing by zero.
344 a arg_scope with the parameters needed for inception_resnet_v2.
346 # Set weight_decay for weights in conv2d and fully_connected layers.
347 with slim.arg_scope([slim.conv2d, slim.fully_connected],
348 weights_regularizer=slim.l2_regularizer(weight_decay),
349 biases_regularizer=slim.l2_regularizer(weight_decay)):
351 batch_norm_params = {
352 'decay': batch_norm_decay,
353 'epsilon': batch_norm_epsilon,
355 # Set activation_fn and parameters for batch_norm.
356 with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
357 normalizer_fn=slim.batch_norm,
358 normalizer_params=batch_norm_params) as scope: