mirror of
https://github.com/gryf/coach.git
synced 2025-12-18 03:30:19 +01:00
108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
#
|
|
# Copyright (c) 2017 Intel Corporation
|
|
#
|
|
# 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.
|
|
#
|
|
import copy
|
|
from typing import Union, Tuple
|
|
|
|
import tensorflow as tf
|
|
|
|
from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense
|
|
from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters
|
|
from rl_coach.core_types import MiddlewareEmbedding
|
|
|
|
|
|
class Middleware(object):
|
|
"""
|
|
A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
|
|
after they were aggregated in some method (for example, concatenation) and passes it through a neural network
|
|
which can be customizable but shared between the heads of the network
|
|
"""
|
|
def __init__(self, activation_function=tf.nn.relu,
|
|
scheme: MiddlewareScheme = MiddlewareScheme.Medium,
|
|
batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_embedder", dense_layer=Dense,
|
|
is_training=False):
|
|
self.name = name
|
|
self.input = None
|
|
self.output = None
|
|
self.activation_function = activation_function
|
|
self.batchnorm = batchnorm
|
|
self.dropout_rate = dropout_rate
|
|
self.scheme = scheme
|
|
self.return_type = MiddlewareEmbedding
|
|
self.dense_layer = dense_layer
|
|
if self.dense_layer is None:
|
|
self.dense_layer = Dense
|
|
self.is_training = is_training
|
|
|
|
# layers order is conv -> batchnorm -> activation -> dropout
|
|
if isinstance(self.scheme, MiddlewareScheme):
|
|
self.layers_params = copy.copy(self.schemes[self.scheme])
|
|
self.layers_params = [convert_layer(l) for l in self.layers_params]
|
|
else:
|
|
# if scheme is specified directly, convert to TF layer if it's not a callable object
|
|
# NOTE: if layer object is callable, it must return a TF tensor when invoked
|
|
self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)]
|
|
|
|
# we allow adding batchnorm, dropout or activation functions after each layer.
|
|
# The motivation is to simplify the transition between a network with batchnorm and a network without
|
|
# batchnorm to a single flag (the same applies to activation function and dropout)
|
|
if self.batchnorm or self.activation_function or self.dropout_rate > 0:
|
|
for layer_idx in reversed(range(len(self.layers_params))):
|
|
self.layers_params.insert(layer_idx+1,
|
|
BatchnormActivationDropout(batchnorm=self.batchnorm,
|
|
activation_function=self.activation_function,
|
|
dropout_rate=self.dropout_rate))
|
|
|
|
def __call__(self, input_layer: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
|
|
"""
|
|
Wrapper for building the module graph including scoping and loss creation
|
|
:param input_layer: the input to the graph
|
|
:return: the input placeholder and the output of the last layer
|
|
"""
|
|
with tf.variable_scope(self.get_name()):
|
|
self.input = input_layer
|
|
self._build_module()
|
|
|
|
return self.input, self.output
|
|
|
|
def _build_module(self) -> None:
|
|
"""
|
|
Builds the graph of the module
|
|
This method is called early on from __call__. It is expected to store the graph
|
|
in self.output.
|
|
:param input_layer: the input to the graph
|
|
:return: None
|
|
"""
|
|
pass
|
|
|
|
def get_name(self) -> str:
|
|
"""
|
|
Get a formatted name for the module
|
|
:return: the formatted name
|
|
"""
|
|
return self.name
|
|
|
|
@property
|
|
def schemes(self):
|
|
raise NotImplementedError("Inheriting middleware must define schemes matching its allowed default "
|
|
"configurations.")
|
|
|
|
def __str__(self):
|
|
result = [str(l) for l in self.layers_params]
|
|
if self.layers_params:
|
|
return '\n'.join(result)
|
|
else:
|
|
return 'No layers'
|