# 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