60c3ed2e34efbe428ae04ab99ccebd795187c12f,slim/nets/resnet_v1.py,,resnet_v1_200,#Any#Any#Any#Any#Any#Any#Any#Any#,292

Before Change

                  reuse=None,
                  scope="resnet_v1_200"):
  ResNet-200 model of [2]. See resnet_v1() for arg and return description.
  blocks = [
      resnet_utils.Block(
          "block1", bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block(
          "block2", bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]),
      resnet_utils.Block(
          "block3", bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
      resnet_utils.Block(
          "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, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope)

After Change

                  reuse=None,
                  scope="resnet_v1_200"):
  ResNet-200 model of [2]. See resnet_v1() for arg and return description.
  blocks = [
      resnet_v1_block("block1", base_depth=64, num_units=3, stride=2),
      resnet_v1_block("block2", base_depth=128, num_units=24, stride=2),
      resnet_v1_block("block3", base_depth=256, num_units=36, stride=2),
      resnet_v1_block("block4", base_depth=512, num_units=3, stride=1),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope)

Instances