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73 lines
2.6 KiB
73 lines
2.6 KiB
# Authors
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# * Elena Rastorgueva 2020
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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# Modified from speechbrain(https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/lobes/models/VanillaNN.py).
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import paddle
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from paddlespeech.s2t.models.wav2vec2.modules import containers
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from paddlespeech.s2t.models.wav2vec2.modules import linear
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from paddlespeech.s2t.models.wav2vec2.modules.normalization import BatchNorm1d
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class VanillaNN(containers.Sequential):
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"""A simple vanilla Deep Neural Network.
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Arguments
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---------
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activation : paddle class
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A class used for constructing the activation layers.
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dnn_blocks : int
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The number of linear neural blocks to include.
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dnn_neurons : int
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The number of neurons in the linear layers.
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Example
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-------
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>>> inputs = paddle.rand([10, 120, 60])
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>>> model = VanillaNN(input_shape=inputs.shape)
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>>> outputs = model(inputs)
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>>> outputs.shape
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paddle.shape([10, 120, 512])
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"""
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def __init__(self,
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input_shape,
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dnn_blocks=2,
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dnn_neurons=512,
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activation=True,
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normalization=False,
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dropout_rate=0.5):
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super().__init__(input_shape=[None, None, input_shape])
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if not isinstance(dropout_rate, list):
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dropout_rate = [dropout_rate] * dnn_blocks
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else:
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assert len(
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dropout_rate
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) == dnn_blocks, "len(dropout_rate) must equal to dnn_blocks"
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for block_index in range(dnn_blocks):
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self.append(
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linear.Linear,
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n_neurons=dnn_neurons,
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bias_attr=None,
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layer_name="linear", )
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if normalization:
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self.append(
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BatchNorm1d, input_size=dnn_neurons, layer_name='bn')
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if activation:
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self.append(paddle.nn.LeakyReLU(), layer_name="act")
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self.append(
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paddle.nn.Dropout(p=dropout_rate[block_index]),
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layer_name='dropout')
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