remove final_size parameter of resnet (tensorflow#5326)

ohyeat · Sep 19, 2018 · 8ff6115 · 8ff6115
1 parent 630c4ca
commit 8ff6115
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Showing 3 changed files with 2 additions and 8 deletions.
diff --git a/official/resnet/cifar10_main.py b/official/resnet/cifar10_main.py
@@ -184,7 +184,6 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
  first_pool_stride=None,
  block_sizes=[num_blocks] * 3,
  block_strides=[1, 2, 2],
- final_size=64,
  resnet_version=resnet_version,
  data_format=data_format,
  dtype=dtype

diff --git a/official/resnet/imagenet_main.py b/official/resnet/imagenet_main.py
@@ -232,10 +232,8 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
  # For bigger models, we want to use "bottleneck" layers
  if resnet_size < 50:
  bottleneck = False
- final_size = 512
  else:
  bottleneck = True
- final_size = 2048
 
  super(ImagenetModel, self).__init__(
  resnet_size=resnet_size,
@@ -248,7 +246,6 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
  first_pool_stride=2,
  block_sizes=_get_block_sizes(resnet_size),
  block_strides=[1, 2, 2, 2],
- final_size=final_size,
  resnet_version=resnet_version,
  data_format=data_format,
  dtype=dtype

diff --git a/official/resnet/resnet_model.py b/official/resnet/resnet_model.py
@@ -354,7 +354,7 @@ def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
  kernel_size,
  conv_stride, first_pool_size, first_pool_stride,
  block_sizes, block_strides,
- final_size, resnet_version=DEFAULT_VERSION, data_format=None,
+ resnet_version=DEFAULT_VERSION, data_format=None,
  dtype=DEFAULT_DTYPE):
  """Creates a model for classifying an image.
 
@@ -376,7 +376,6 @@ def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
  i-th set.
  block_strides: List of integers representing the desired stride size for
  each of the sets of block layers. Should be same length as block_sizes.
- final_size: The expected size of the model after the second pooling.
  resnet_version: Integer representing which version of the ResNet network
  to use. See README for details. Valid values: [1, 2]
  data_format: Input format ('channels_last', 'channels_first', or None).
@@ -422,7 +421,6 @@ def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
  self.first_pool_stride = first_pool_stride
  self.block_sizes = block_sizes
  self.block_strides = block_strides
- self.final_size = final_size
  self.dtype = dtype
  self.pre_activation = resnet_version == 2
 
@@ -542,7 +540,7 @@ def __call__(self, inputs, training):
  inputs = tf.reduce_mean(inputs, axes, keepdims=True)
  inputs = tf.identity(inputs, 'final_reduce_mean')
 
- inputs = tf.reshape(inputs, [-1, self.final_size])
+ inputs = tf.squeeze(inputs, axes)
  inputs = tf.layers.dense(inputs=inputs, units=self.num_classes)
  inputs = tf.identity(inputs, 'final_dense')
  return inputs