mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
78 lines
3.4 KiB
Python
78 lines
3.4 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.
|
|
#
|
|
|
|
from typing import Union
|
|
from types import ModuleType
|
|
|
|
import mxnet as mx
|
|
from mxnet.gluon import nn
|
|
from rl_coach.architectures.middleware_parameters import MiddlewareParameters
|
|
from rl_coach.architectures.mxnet_components.layers import convert_layer
|
|
from rl_coach.base_parameters import MiddlewareScheme
|
|
|
|
nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol]
|
|
|
|
|
|
class Middleware(nn.HybridBlock):
|
|
def __init__(self, params: MiddlewareParameters):
|
|
"""
|
|
Middleware 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.
|
|
|
|
:param params: parameters object containing batchnorm, activation_function and dropout properties.
|
|
"""
|
|
super(Middleware, self).__init__()
|
|
self.scheme = params.scheme
|
|
|
|
with self.name_scope():
|
|
self.net = nn.HybridSequential()
|
|
if isinstance(self.scheme, MiddlewareScheme):
|
|
blocks = self.schemes[self.scheme]
|
|
else:
|
|
# if scheme is specified directly, convert to MX layer if it's not a callable object
|
|
# NOTE: if layer object is callable, it must return a gluon block when invoked
|
|
blocks = [convert_layer(l) for l in self.scheme]
|
|
for block in blocks:
|
|
self.net.add(block())
|
|
if params.batchnorm:
|
|
self.net.add(nn.BatchNorm())
|
|
if params.activation_function:
|
|
self.net.add(nn.Activation(params.activation_function))
|
|
if params.dropout_rate:
|
|
self.net.add(nn.Dropout(rate=params.dropout_rate))
|
|
|
|
@property
|
|
def schemes(self) -> dict:
|
|
"""
|
|
Schemes are the pre-defined network architectures of various depths and complexities that can be used for the
|
|
Middleware. Should be implemented in child classes, and are used to create Block when Middleware is initialised.
|
|
|
|
:return: dictionary of schemes, with key of type MiddlewareScheme enum and value being list of mxnet.gluon.Block.
|
|
"""
|
|
raise NotImplementedError("Inheriting embedder must define schemes matching its allowed default "
|
|
"configurations.")
|
|
|
|
def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type:
|
|
"""
|
|
Used for forward pass through middleware network.
|
|
|
|
:param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
|
|
:param x: state embedding, of shape (batch_size, in_channels).
|
|
:return: state middleware embedding, where shape is (batch_size, channels).
|
|
"""
|
|
return self.net(x)
|