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