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91 lines
3.8 KiB
Python
91 lines
3.8 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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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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# http://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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import copy
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from typing import Union
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense
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from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters
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from rl_coach.core_types import MiddlewareEmbedding
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class Middleware(object):
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"""
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A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
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after they were aggregated in some method (for example, concatenation) and passes it through a neural network
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which can be customizable but shared between the heads of the network
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"""
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def __init__(self, activation_function=tf.nn.relu,
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scheme: MiddlewareScheme = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_embedder", dense_layer=Dense,
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is_training=False):
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self.name = name
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self.input = None
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self.output = None
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self.activation_function = activation_function
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self.batchnorm = batchnorm
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self.dropout_rate = dropout_rate
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self.scheme = scheme
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self.return_type = MiddlewareEmbedding
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self.dense_layer = dense_layer
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if self.dense_layer is None:
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self.dense_layer = Dense
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self.is_training = is_training
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# layers order is conv -> batchnorm -> activation -> dropout
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if isinstance(self.scheme, MiddlewareScheme):
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self.layers_params = copy.copy(self.schemes[self.scheme])
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else:
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# if scheme is specified directly, convert to TF layer if it's not a callable object
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# NOTE: if layer object is callable, it must return a TF tensor when invoked
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self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)]
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# we allow adding batchnorm, dropout or activation functions after each layer.
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# The motivation is to simplify the transition between a network with batchnorm and a network without
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# batchnorm to a single flag (the same applies to activation function and dropout)
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if self.batchnorm or self.activation_function or self.dropout_rate > 0:
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for layer_idx in reversed(range(len(self.layers_params))):
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self.layers_params.insert(layer_idx+1,
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BatchnormActivationDropout(batchnorm=self.batchnorm,
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activation_function=self.activation_function,
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dropout_rate=self.dropout_rate))
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def __call__(self, input_layer):
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with tf.variable_scope(self.get_name()):
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self.input = input_layer
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self._build_module()
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return self.input, self.output
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def _build_module(self):
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pass
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def get_name(self):
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return self.name
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@property
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def schemes(self):
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raise NotImplementedError("Inheriting middleware must define schemes matching its allowed default "
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"configurations.")
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def __str__(self):
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result = [str(l) for l in self.layers_params]
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if self.layers_params:
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return '\n'.join(result)
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else:
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return 'No layers'
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