mirror of
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88 lines
2.9 KiB
Python
Executable File
88 lines
2.9 KiB
Python
Executable File
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import ParamAttr, nn
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from paddle.nn import functional as F
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def get_para_bias_attr(l2_decay, k):
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regularizer = paddle.regularizer.L2Decay(l2_decay)
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stdv = 1.0 / math.sqrt(k * 1.0)
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initializer = nn.initializer.Uniform(-stdv, stdv)
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weight_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
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bias_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
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return [weight_attr, bias_attr]
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class CTCHead(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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fc_decay=0.0004,
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mid_channels=None,
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return_feats=False,
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**kwargs):
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super(CTCHead, self).__init__()
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if mid_channels is None:
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weight_attr, bias_attr = get_para_bias_attr(
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l2_decay=fc_decay, k=in_channels)
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self.fc = nn.Linear(
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in_channels,
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out_channels,
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weight_attr=weight_attr,
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bias_attr=bias_attr)
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else:
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weight_attr1, bias_attr1 = get_para_bias_attr(
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l2_decay=fc_decay, k=in_channels)
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self.fc1 = nn.Linear(
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in_channels,
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mid_channels,
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weight_attr=weight_attr1,
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bias_attr=bias_attr1)
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weight_attr2, bias_attr2 = get_para_bias_attr(
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l2_decay=fc_decay, k=mid_channels)
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self.fc2 = nn.Linear(
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mid_channels,
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out_channels,
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weight_attr=weight_attr2,
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bias_attr=bias_attr2)
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self.out_channels = out_channels
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self.mid_channels = mid_channels
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self.return_feats = return_feats
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def forward(self, x, targets=None):
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if self.mid_channels is None:
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predicts = self.fc(x)
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else:
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x = self.fc1(x)
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predicts = self.fc2(x)
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if self.return_feats:
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result = (x, predicts)
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else:
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result = predicts
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if not self.training:
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predicts = F.softmax(predicts, axis=2)
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result = predicts
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return result
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