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| # -*- coding: utf-8 -*- | ||
| # Copyright 2016 The TensorFlow Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================== | ||
|
|
||
| # modified from tensorflow/contrib/layers/python/layers/layers.py | ||
|
|
||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| from tensorflow.contrib.framework.python.ops import variables | ||
| from tensorflow.contrib.layers.python.layers import initializers | ||
| from tensorflow.contrib.layers.python.layers import utils | ||
| from tensorflow.python.framework import ops | ||
| from tensorflow.python.ops import init_ops | ||
| from tensorflow.python.ops import nn | ||
| from tensorflow.python.ops import variable_scope | ||
|
|
||
| DATA_FORMAT_NCHW = 'NCHW' | ||
| DATA_FORMAT_NHWC = 'NHWC' | ||
| DATA_FORMAT_NCDHW = 'NCDHW' | ||
| DATA_FORMAT_NDHWC = 'NDHWC' | ||
|
|
||
| def _model_variable_getter(getter, | ||
| name, | ||
| shape=None, | ||
| dtype=None, | ||
| initializer=None, | ||
| regularizer=None, | ||
| trainable=True, | ||
| collections=None, | ||
| caching_device=None, | ||
| partitioner=None, | ||
| rename=None, | ||
| use_resource=None, | ||
| **_): | ||
| """Getter that uses model_variable for compatibility with core layers.""" | ||
| short_name = name.split('/')[-1] | ||
| if rename and short_name in rename: | ||
| name_components = name.split('/') | ||
| name_components[-1] = rename[short_name] | ||
| name = '/'.join(name_components) | ||
| return variables.model_variable( | ||
| name, | ||
| shape=shape, | ||
| dtype=dtype, | ||
| initializer=initializer, | ||
| regularizer=regularizer, | ||
| collections=collections, | ||
| trainable=trainable, | ||
| caching_device=caching_device, | ||
| partitioner=partitioner, | ||
| custom_getter=getter, | ||
| use_resource=use_resource) | ||
|
|
||
|
|
||
| def _build_variable_getter(rename=None): | ||
| """Build a model variable getter that respects scope getter and renames.""" | ||
|
|
||
| # VariableScope will nest the getters | ||
| def layer_variable_getter(getter, *args, **kwargs): | ||
| kwargs['rename'] = rename | ||
| return _model_variable_getter(getter, *args, **kwargs) | ||
|
|
||
| return layer_variable_getter | ||
|
|
||
| def depth_conv2d( | ||
| inputs, | ||
| kernel_size, | ||
| stride=1, | ||
| channel_multiplier=1, | ||
| padding='SAME', | ||
| data_format=DATA_FORMAT_NHWC, | ||
| rate=1, | ||
| activation_fn=nn.relu, | ||
| normalizer_fn=None, | ||
| normalizer_params=None, | ||
| weights_initializer=initializers.xavier_initializer(), | ||
| weights_regularizer=None, | ||
| biases_initializer=init_ops.zeros_initializer(), | ||
| biases_regularizer=None, | ||
| reuse=None, | ||
| variables_collections=None, | ||
| outputs_collections=None, | ||
| trainable=True, | ||
| scope=None): | ||
|
|
||
| if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): | ||
| raise ValueError('data_format has to be either NCHW or NHWC.') | ||
| layer_variable_getter = _build_variable_getter({ | ||
| 'bias': 'biases', | ||
| 'depthwise_kernel': 'depthwise_weights' | ||
| }) | ||
|
|
||
| with variable_scope.variable_scope( | ||
| scope, | ||
| 'SeparableConv2d', [inputs], | ||
| reuse=reuse, | ||
| custom_getter=layer_variable_getter) as sc: | ||
| inputs = ops.convert_to_tensor(inputs) | ||
|
|
||
| df = ('channels_first' | ||
| if data_format and data_format.startswith('NC') else 'channels_last') | ||
|
|
||
| # Actually apply depthwise conv instead of separable conv. | ||
| dtype = inputs.dtype.base_dtype | ||
| kernel_h, kernel_w = utils.two_element_tuple(kernel_size) | ||
| stride_h, stride_w = utils.two_element_tuple(stride) | ||
| num_filters_in = utils.channel_dimension( | ||
| inputs.get_shape(), df, min_rank=4) | ||
| weights_collections = utils.get_variable_collections( | ||
| variables_collections, 'weights') | ||
|
|
||
| depthwise_shape = [kernel_h, kernel_w, num_filters_in, channel_multiplier] | ||
| depthwise_weights = variables.model_variable( | ||
| 'depthwise_weights', | ||
| shape=depthwise_shape, | ||
| dtype=dtype, | ||
| initializer=weights_initializer, | ||
| regularizer=weights_regularizer, | ||
| trainable=trainable, | ||
| collections=weights_collections) | ||
| strides = [1, 1, stride_h, stride_w] if data_format.startswith('NC') else [1, stride_h, stride_w, 1] | ||
|
|
||
| outputs = nn.depthwise_conv2d( | ||
| inputs, | ||
| depthwise_weights, | ||
| strides, | ||
| padding, | ||
| rate=utils.two_element_tuple(rate), | ||
| data_format=data_format) | ||
| num_outputs = num_filters_in | ||
|
|
||
| if normalizer_fn is not None: | ||
| normalizer_params = normalizer_params or {} | ||
| outputs = normalizer_fn(outputs, **normalizer_params) | ||
| else: | ||
| if biases_initializer is not None: | ||
| biases_collections = utils.get_variable_collections( | ||
| variables_collections, 'biases') | ||
| biases = variables.model_variable( | ||
| 'biases', | ||
| shape=[ | ||
| num_outputs, | ||
| ], | ||
| dtype=dtype, | ||
| initializer=biases_initializer, | ||
| regularizer=biases_regularizer, | ||
| trainable=trainable, | ||
| collections=biases_collections) | ||
| outputs = nn.bias_add(outputs, biases, data_format=data_format) | ||
|
|
||
| if activation_fn is not None: | ||
| outputs = activation_fn(outputs) | ||
| return utils.collect_named_outputs(outputs_collections, sc.name, outputs) |
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