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| # copyright (c) 2021 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. | |
| """ | |
| This code is refer from: | |
| https://github.com/LBH1024/CAN/models/can.py | |
| """ | |
| import paddle | |
| import paddle.nn as nn | |
| import numpy as np | |
| class CANLoss(nn.Layer): | |
| ''' | |
| CANLoss is consist of two part: | |
| word_average_loss: average accuracy of the symbol | |
| counting_loss: counting loss of every symbol | |
| ''' | |
| def __init__(self): | |
| super(CANLoss, self).__init__() | |
| self.use_label_mask = False | |
| self.out_channel = 111 | |
| self.cross = nn.CrossEntropyLoss( | |
| reduction='none') if self.use_label_mask else nn.CrossEntropyLoss() | |
| self.counting_loss = nn.SmoothL1Loss(reduction='mean') | |
| self.ratio = 16 | |
| def forward(self, preds, batch): | |
| word_probs = preds[0] | |
| counting_preds = preds[1] | |
| counting_preds1 = preds[2] | |
| counting_preds2 = preds[3] | |
| labels = batch[2] | |
| labels_mask = batch[3] | |
| counting_labels = gen_counting_label(labels, self.out_channel, True) | |
| counting_loss = self.counting_loss(counting_preds1, counting_labels) + self.counting_loss(counting_preds2, counting_labels) \ | |
| + self.counting_loss(counting_preds, counting_labels) | |
| word_loss = self.cross( | |
| paddle.reshape(word_probs, [-1, word_probs.shape[-1]]), | |
| paddle.reshape(labels, [-1])) | |
| word_average_loss = paddle.sum( | |
| paddle.reshape(word_loss * labels_mask, [-1])) / ( | |
| paddle.sum(labels_mask) + 1e-10 | |
| ) if self.use_label_mask else word_loss | |
| loss = word_average_loss + counting_loss | |
| return {'loss': loss} | |
| def gen_counting_label(labels, channel, tag): | |
| b, t = labels.shape | |
| counting_labels = np.zeros([b, channel]) | |
| if tag: | |
| ignore = [0, 1, 107, 108, 109, 110] | |
| else: | |
| ignore = [] | |
| for i in range(b): | |
| for j in range(t): | |
| k = labels[i][j] | |
| if k in ignore: | |
| continue | |
| else: | |
| counting_labels[i][k] += 1 | |
| counting_labels = paddle.to_tensor(counting_labels, dtype='float32') | |
| return counting_labels | |