@@ -150,21 +150,21 @@ def ResNet50(pretrained=False, end_with='fc1000', n_classes=1000, name=None):
150150 n = BatchNorm (name = 'bn_conv1' , act = 'relu' )(n )
151151 n = MaxPool2d ((3 , 3 ), strides = (2 , 2 ), name = 'max_pool1' )(n )
152152
153- for i , block_name in enumerate (block_names ):
154- if len (block_name ) == 2 :
155- stage = int (block_name [0 ])
156- block = block_name [1 ]
153+ for i , name in enumerate (block_names ):
154+ if len (name ) == 2 :
155+ stage = int (name [0 ])
156+ block = name [1 ]
157157 if block == 'a' :
158158 strides = (1 , 1 ) if stage == 2 else (2 , 2 )
159159 n = conv_block (n , 3 , block_filters [stage - 2 ], stage = stage , block = block , strides = strides )
160160 else :
161161 n = identity_block (n , 3 , block_filters [stage - 2 ], stage = stage , block = block )
162- elif block_name == 'avg_pool' :
162+ elif name == 'avg_pool' :
163163 n = GlobalMeanPool2d (name = 'avg_pool' )(n )
164- elif block_name == 'fc1000' :
164+ elif name == 'fc1000' :
165165 n = Dense (n_classes , name = 'fc1000' )(n )
166166
167- if block_name == end_with :
167+ if name == end_with :
168168 break
169169
170170 network = Model (inputs = ni , outputs = n , name = name )
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