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Deepmind Team
121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
################################################################################
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# Copyright 2019 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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################################################################################
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"""Custom layers for CURL."""
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from absl import logging
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import sonnet as snt
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import tensorflow.compat.v1 as tf
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tfc = tf.compat.v1
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class ResidualStack(snt.AbstractModule):
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"""A stack of ResNet V2 blocks."""
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def __init__(self,
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num_hiddens,
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num_residual_layers,
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num_residual_hiddens,
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filter_size=3,
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initializers=None,
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data_format='NHWC',
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activation=tf.nn.relu,
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name='residual_stack'):
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"""Instantiate a ResidualStack."""
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super(ResidualStack, self).__init__(name=name)
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self._num_hiddens = num_hiddens
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self._num_residual_layers = num_residual_layers
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self._num_residual_hiddens = num_residual_hiddens
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self._filter_size = filter_size
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self._initializers = initializers
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self._data_format = data_format
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self._activation = activation
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def _build(self, h):
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for i in range(self._num_residual_layers):
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h_i = self._activation(h)
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h_i = snt.Conv2D(
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output_channels=self._num_residual_hiddens,
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kernel_shape=(self._filter_size, self._filter_size),
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stride=(1, 1),
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initializers=self._initializers,
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data_format=self._data_format,
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name='res_nxn_%d' % i)(
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h_i)
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h_i = self._activation(h_i)
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h_i = snt.Conv2D(
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output_channels=self._num_hiddens,
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kernel_shape=(1, 1),
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stride=(1, 1),
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initializers=self._initializers,
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data_format=self._data_format,
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name='res_1x1_%d' % i)(
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h_i)
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h += h_i
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return self._activation(h)
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class SharedConvModule(snt.AbstractModule):
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"""Convolutional decoder."""
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def __init__(self,
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filters,
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kernel_size,
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activation,
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strides,
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name='shared_conv_encoder'):
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super(SharedConvModule, self).__init__(name=name)
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self._filters = filters
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self._kernel_size = kernel_size
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self._activation = activation
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self.strides = strides
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assert len(strides) == len(filters) - 1
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self.conv_shapes = None
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def _build(self, x, is_training=True):
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with tf.control_dependencies([tfc.assert_rank(x, 4)]):
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self.conv_shapes = [x.shape.as_list()] # Needed by deconv module
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conv = x
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for i, (filter_i,
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stride_i) in enumerate(zip(self._filters, self.strides), 1):
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conv = tf.layers.Conv2D(
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filters=filter_i,
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kernel_size=self._kernel_size,
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padding='same',
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activation=self._activation,
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strides=stride_i,
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name='enc_conv_%d' % i)(
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conv)
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self.conv_shapes.append(conv.shape.as_list())
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conv_flat = snt.BatchFlatten()(conv)
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enc_mlp = snt.nets.MLP(
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name='enc_mlp',
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output_sizes=[self._filters[-1]],
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activation=self._activation,
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activate_final=True)
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h = enc_mlp(conv_flat)
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logging.info('Shared conv module layer shapes:')
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logging.info('\n'.join([str(el) for el in self.conv_shapes]))
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logging.info(h.shape.as_list())
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return h
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