Merge pull request #1447 from ahundt/resnet_fix

resnet_v1 and v2 segmentation bugs from 7e2435e5 resolved

slim/nets/resnet_v1.py

@@ -202,6 +202,8 @@ def resnet_v1(inputs,
                             normalizer_fn=None, scope='logits')
         if spatial_squeeze:
           logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+        else:
+          logits = net
         # Convert end_points_collection into a dictionary of end_points.
         end_points = slim.utils.convert_collection_to_dict(end_points_collection)
         if num_classes is not None:
@@ -215,6 +217,7 @@ def resnet_v1_50(inputs,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
+                 spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v1_50'):
   """ResNet-50 model of [1]. See resnet_v1() for arg and return description."""
@@ -230,7 +233,8 @@ def resnet_v1_50(inputs,
   ]
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v1_50.default_image_size = resnet_v1.default_image_size
 
 
@@ -239,6 +243,7 @@ def resnet_v1_101(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v1_101'):
   """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
@@ -254,7 +259,8 @@ def resnet_v1_101(inputs,
   ]
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v1_101.default_image_size = resnet_v1.default_image_size
 
 
@@ -263,6 +269,7 @@ def resnet_v1_152(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v1_152'):
   """ResNet-152 model of [1]. See resnet_v1() for arg and return description."""
@@ -277,7 +284,8 @@ def resnet_v1_152(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v1_152.default_image_size = resnet_v1.default_image_size
 
 
@@ -286,6 +294,7 @@ def resnet_v1_200(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v1_200'):
   """ResNet-200 model of [2]. See resnet_v1() for arg and return description."""
@@ -300,5 +309,6 @@ def resnet_v1_200(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v1_200.default_image_size = resnet_v1.default_image_size

slim/nets/resnet_v2.py

@@ -211,6 +211,8 @@ def resnet_v2(inputs,
                             normalizer_fn=None, scope='logits')
         if spatial_squeeze:
           logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+        else:
+          logits = net
         # Convert end_points_collection into a dictionary of end_points.
         end_points = slim.utils.convert_collection_to_dict(end_points_collection)
         if num_classes is not None:
@@ -224,6 +226,7 @@ def resnet_v2_50(inputs,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
+                 spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v2_50'):
   """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
@@ -238,7 +241,8 @@ def resnet_v2_50(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v2_50.default_image_size = resnet_v2.default_image_size
 
 
@@ -247,6 +251,7 @@ def resnet_v2_101(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_101'):
   """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
@@ -261,7 +266,8 @@ def resnet_v2_101(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v2_101.default_image_size = resnet_v2.default_image_size
 
 
@@ -270,6 +276,7 @@ def resnet_v2_152(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_152'):
   """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""
@@ -284,7 +291,8 @@ def resnet_v2_152(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v2_152.default_image_size = resnet_v2.default_image_size
 
 
@@ -293,6 +301,7 @@ def resnet_v2_200(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_200'):
   """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""
@@ -307,5 +316,6 @@ def resnet_v2_200(inputs,
           'block4', bottleneck, [(2048, 512, 1)] * 3)]
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
-                   include_root_block=True, reuse=reuse, scope=scope)
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
 resnet_v2_200.default_image_size = resnet_v2.default_image_size