GoogLeNet源码与网络结构的对照

官方代码地址

https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py

网络结构图

 

 

 

 

代码对照

 

  1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  2 #
  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
  6 #
  7 # http://www.apache.org/licenses/LICENSE-2.0
  8 #
  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 for inception v1 classification network."""
 16 
 17 from __future__ import absolute_import
 18 from __future__ import division
 19 from __future__ import print_function
 20 
 21 import tensorflow as tf
 22 
 23 from nets import inception_utils
 24 
 25 slim = tf.contrib.slim
 26 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
 27 
 28 
 29 def inception_v1_base(inputs,
 30                       final_endpoint='Mixed_5c',
 31                       include_root_block=True,
 32                       scope='InceptionV1'):
 33   """Defines the Inception V1 base architecture.
 34 
 35   This architecture is defined in:
 36     Going deeper with convolutions
 37     Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
 38     Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
 39     http://arxiv.org/pdf/1409.4842v1.pdf.
 40 
 41   Args:
 42     inputs: a tensor of size [batch_size, height, width, channels].
 43     final_endpoint: specifies the endpoint to construct the network up to. It
 44       can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
 45       'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
 46       'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
 47       'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']. If
 48       include_root_block is False, ['Conv2d_1a_7x7', 'MaxPool_2a_3x3',
 49       'Conv2d_2b_1x1', 'Conv2d_2c_3x3', 'MaxPool_3a_3x3'] will not be available.
 50     include_root_block: If True, include the convolution and max-pooling layers
 51       before the inception modules. If False, excludes those layers.
 52     scope: Optional variable_scope.
 53 
 54   Returns:
 55     A dictionary from components of the network to the corresponding activation.
 56 
 57   Raises:
 58     ValueError: if final_endpoint is not set to one of the predefined values.
 59   """
 60   end_points = {}
 61   with tf.variable_scope(scope, 'InceptionV1', [inputs]):
 62     with slim.arg_scope(
 63         [slim.conv2d, slim.fully_connected],
 64         weights_initializer=trunc_normal(0.01)):
 65       with slim.arg_scope([slim.conv2d, slim.max_pool2d],
 66                           stride=1, padding='SAME'):
 67         net = inputs
 68         if include_root_block:
 69           end_point = 'Conv2d_1a_7x7'
 70           net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
 71           end_points[end_point] = net
 72           if final_endpoint == end_point:
 73             return net, end_points
 74           end_point = 'MaxPool_2a_3x3'
 75           net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
 76           end_points[end_point] = net
 77           if final_endpoint == end_point:
 78             return net, end_points
 79           end_point = 'Conv2d_2b_1x1'
 80           net = slim.conv2d(net, 64, [1, 1], scope=end_point)
 81           end_points[end_point] = net
 82           if final_endpoint == end_point:
 83             return net, end_points
 84           end_point = 'Conv2d_2c_3x3'
 85           net = slim.conv2d(net, 192, [3, 3], scope=end_point)
 86           end_points[end_point] = net
 87           if final_endpoint == end_point:
 88             return net, end_points
 89           end_point = 'MaxPool_3a_3x3'
 90           net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
 91           end_points[end_point] = net
 92           if final_endpoint == end_point:
 93             return net, end_points
 94 
 95         end_point = 'Mixed_3b'
 96         with tf.variable_scope(end_point):
 97           with tf.variable_scope('Branch_0'):
 98             branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
 99           with tf.variable_scope('Branch_1'):
100             branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
101             branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
102           with tf.variable_scope('Branch_2'):
103             branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
104             branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
105           with tf.variable_scope('Branch_3'):
106             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
107             branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
108           net = tf.concat(
109               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
110         end_points[end_point] = net
111         if final_endpoint == end_point: return net, end_points
112 
113         end_point = 'Mixed_3c'
114         with tf.variable_scope(end_point):
115           with tf.variable_scope('Branch_0'):
116             branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
117           with tf.variable_scope('Branch_1'):
118             branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
119             branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
120           with tf.variable_scope('Branch_2'):
121             branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
122             branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
123           with tf.variable_scope('Branch_3'):
124             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
125             branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
126           net = tf.concat(
127               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
128         end_points[end_point] = net
129         if final_endpoint == end_point: return net, end_points
130 
131         end_point = 'MaxPool_4a_3x3'
132         net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
133         end_points[end_point] = net
134         if final_endpoint == end_point: return net, end_points
135 
136         end_point = 'Mixed_4b'
137         with tf.variable_scope(end_point):
138           with tf.variable_scope('Branch_0'):
139             branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
140           with tf.variable_scope('Branch_1'):
141             branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
142             branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
143           with tf.variable_scope('Branch_2'):
144             branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
145             branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
146           with tf.variable_scope('Branch_3'):
147             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
148             branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
149           net = tf.concat(
150               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
151         end_points[end_point] = net
152         if final_endpoint == end_point: return net, end_points
153 
154         end_point = 'Mixed_4c'
155         with tf.variable_scope(end_point):
156           with tf.variable_scope('Branch_0'):
157             branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
158           with tf.variable_scope('Branch_1'):
159             branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
160             branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
161           with tf.variable_scope('Branch_2'):
162             branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
163             branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
164           with tf.variable_scope('Branch_3'):
165             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
166             branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
167           net = tf.concat(
168               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
169         end_points[end_point] = net
170         if final_endpoint == end_point: return net, end_points
171 
172         end_point = 'Mixed_4d'
173         with tf.variable_scope(end_point):
174           with tf.variable_scope('Branch_0'):
175             branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
176           with tf.variable_scope('Branch_1'):
177             branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
178             branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
179           with tf.variable_scope('Branch_2'):
180             branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
181             branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
182           with tf.variable_scope('Branch_3'):
183             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
184             branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
185           net = tf.concat(
186               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
187         end_points[end_point] = net
188         if final_endpoint == end_point: return net, end_points
189 
190         end_point = 'Mixed_4e'
191         with tf.variable_scope(end_point):
192           with tf.variable_scope('Branch_0'):
193             branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
194           with tf.variable_scope('Branch_1'):
195             branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
196             branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
197           with tf.variable_scope('Branch_2'):
198             branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
199             branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
200           with tf.variable_scope('Branch_3'):
201             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
202             branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
203           net = tf.concat(
204               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
205         end_points[end_point] = net
206         if final_endpoint == end_point: return net, end_points
207 
208         end_point = 'Mixed_4f'
209         with tf.variable_scope(end_point):
210           with tf.variable_scope('Branch_0'):
211             branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
212           with tf.variable_scope('Branch_1'):
213             branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
214             branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
215           with tf.variable_scope('Branch_2'):
216             branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
217             branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
218           with tf.variable_scope('Branch_3'):
219             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
220             branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
221           net = tf.concat(
222               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
223         end_points[end_point] = net
224         if final_endpoint == end_point: return net, end_points
225 
226         end_point = 'MaxPool_5a_2x2'
227         net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
228         end_points[end_point] = net
229         if final_endpoint == end_point: return net, end_points
230 
231         end_point = 'Mixed_5b'
232         with tf.variable_scope(end_point):
233           with tf.variable_scope('Branch_0'):
234             branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
235           with tf.variable_scope('Branch_1'):
236             branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
237             branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
238           with tf.variable_scope('Branch_2'):
239             branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
240             branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
241           with tf.variable_scope('Branch_3'):
242             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
243             branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
244           net = tf.concat(
245               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
246         end_points[end_point] = net
247         if final_endpoint == end_point: return net, end_points
248 
249         end_point = 'Mixed_5c'
250         with tf.variable_scope(end_point):
251           with tf.variable_scope('Branch_0'):
252             branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
253           with tf.variable_scope('Branch_1'):
254             branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
255             branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
256           with tf.variable_scope('Branch_2'):
257             branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
258             branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
259           with tf.variable_scope('Branch_3'):
260             branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
261             branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
262           net = tf.concat(
263               axis=3, values=[branch_0, branch_1, branch_2, branch_3])
264         end_points[end_point] = net
265         if final_endpoint == end_point: return net, end_points
266     raise ValueError('Unknown final endpoint %s' % final_endpoint)
267 
268 
269 def inception_v1(inputs,
270                  num_classes=1000,
271                  is_training=True,
272                  dropout_keep_prob=0.8,
273                  prediction_fn=slim.softmax,
274                  spatial_squeeze=True,
275                  reuse=None,
276                  scope='InceptionV1',
277                  global_pool=False):
278   """Defines the Inception V1 architecture.
279 
280   This architecture is defined in:
281 
282     Going deeper with convolutions
283     Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
284     Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
285     http://arxiv.org/pdf/1409.4842v1.pdf.
286 
287   The default image size used to train this network is 224x224.
288 
289   Args:
290     inputs: a tensor of size [batch_size, height, width, channels].
291     num_classes: number of predicted classes. If 0 or None, the logits layer
292       is omitted and the input features to the logits layer (before dropout)
293       are returned instead.
294     is_training: whether is training or not.
295     dropout_keep_prob: the percentage of activation values that are retained.
296     prediction_fn: a function to get predictions out of logits.
297     spatial_squeeze: if True, logits is of shape [B, C], if false logits is of
298         shape [B, 1, 1, C], where B is batch_size and C is number of classes.
299     reuse: whether or not the network and its variables should be reused. To be
300       able to reuse 'scope' must be given.
301     scope: Optional variable_scope.
302     global_pool: Optional boolean flag to control the avgpooling before the
303       logits layer. If false or unset, pooling is done with a fixed window
304       that reduces default-sized inputs to 1x1, while larger inputs lead to
305       larger outputs. If true, any input size is pooled down to 1x1.
306 
307   Returns:
308     net: a Tensor with the logits (pre-softmax activations) if num_classes
309       is a non-zero integer, or the non-dropped-out input to the logits layer
310       if num_classes is 0 or None.
311     end_points: a dictionary from components of the network to the corresponding
312       activation.
313   """
314   # Final pooling and prediction
315   with tf.variable_scope(scope, 'InceptionV1', [inputs], reuse=reuse) as scope:
316     with slim.arg_scope([slim.batch_norm, slim.dropout],
317                         is_training=is_training):
318       net, end_points = inception_v1_base(inputs, scope=scope)
319       with tf.variable_scope('Logits'):
320         if global_pool:
321           # Global average pooling.
322           net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
323           end_points['global_pool'] = net
324         else:
325           # Pooling with a fixed kernel size.
326           net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7')
327           end_points['AvgPool_0a_7x7'] = net
328         if not num_classes:
329           return net, end_points
330         net = slim.dropout(net, dropout_keep_prob, scope='Dropout_0b')
331         logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
332                              normalizer_fn=None, scope='Conv2d_0c_1x1')
333         if spatial_squeeze:
334           logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
335 
336         end_points['Logits'] = logits
337         end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
338   return logits, end_points
339 inception_v1.default_image_size = 224
340 
341 inception_v1_arg_scope = inception_utils.inception_arg_scope

 

posted @ 2020-11-23 19:11  ZzUuOo666  阅读(221)  评论(0编辑  收藏  举报