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Remove call to layer.apply and just use __call__ #21

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Remove call to layer.apply and just use __call__ #21

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24 changes: 12 additions & 12 deletions tf_slim/layers/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ def avg_pool2d(inputs,
padding=padding,
data_format=df,
_scope=sc)
outputs = layer.apply(inputs)
outputs = layer(inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)


Expand Down Expand Up @@ -164,7 +164,7 @@ def avg_pool3d(inputs,
padding=padding,
data_format=df,
_scope=sc)
outputs = layer.apply(inputs)
outputs = layer(inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)


Expand Down Expand Up @@ -675,7 +675,7 @@ def batch_norm(inputs,
_scope=sc,
_reuse=reuse,
fused=fused)
outputs = layer.apply(inputs, training=is_training)
outputs = layer(inputs, training=is_training)

# Add variables to collections.
_add_variable_to_collections(layer.moving_mean, variables_collections,
Expand Down Expand Up @@ -1080,7 +1080,7 @@ def convolution(inputs,
dtype=inputs.dtype.base_dtype,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
outputs = layer(inputs)

# Add variables to collections.
_add_variable_to_collections(layer.kernel, variables_collections, 'weights')
Expand Down Expand Up @@ -1440,7 +1440,7 @@ def convolution2d_transpose(
dtype=inputs.dtype.base_dtype,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
outputs = layer(inputs)

# Add variables to collections.
_add_variable_to_collections(layer.kernel, variables_collections, 'weights')
Expand Down Expand Up @@ -1554,7 +1554,7 @@ def convolution3d_transpose(
dtype=inputs.dtype.base_dtype,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
outputs = layer(inputs)

# Add variables to collections.
_add_variable_to_collections(layer.kernel, variables_collections, 'weights')
Expand Down Expand Up @@ -1635,7 +1635,7 @@ def dropout(inputs,
seed=seed,
name=sc.name,
_scope=sc)
outputs = layer.apply(inputs, training=is_training)
outputs = layer(inputs, training=is_training)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)


Expand Down Expand Up @@ -1889,7 +1889,7 @@ def fully_connected(inputs,
dtype=inputs.dtype.base_dtype,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
outputs = layer(inputs)

# Add variables to collections.
_add_variable_to_collections(layer.kernel, variables_collections, 'weights')
Expand Down Expand Up @@ -2221,7 +2221,7 @@ def gdn(inputs,
dtype=inputs.dtype.base_dtype,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
return layer(inputs)


@add_arg_scope
Expand Down Expand Up @@ -2438,7 +2438,7 @@ def max_pool2d(inputs,
padding=padding,
data_format=df,
_scope=sc)
outputs = layer.apply(inputs)
outputs = layer(inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)


Expand Down Expand Up @@ -2488,7 +2488,7 @@ def max_pool3d(inputs,
padding=padding,
data_format=df,
_scope=sc)
outputs = layer.apply(inputs)
outputs = layer(inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)


Expand Down Expand Up @@ -2794,7 +2794,7 @@ def separable_convolution2d(
dtype=inputs.dtype.base_dtype,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
outputs = layer(inputs)

# Add variables to collections.
_add_variable_to_collections(layer.depthwise_kernel,
Expand Down