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89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
8 months ago
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# copyright (c) 2021 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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"""
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This code is refer from:
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https://github.com/LBH1024/CAN/models/can.py
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"""
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import paddle
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import paddle.nn as nn
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import numpy as np
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class CANLoss(nn.Layer):
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"""
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CANLoss is consist of two part:
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word_average_loss: average accuracy of the symbol
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counting_loss: counting loss of every symbol
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"""
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def __init__(self):
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super(CANLoss, self).__init__()
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self.use_label_mask = False
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self.out_channel = 111
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self.cross = (
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nn.CrossEntropyLoss(reduction="none")
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if self.use_label_mask
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else nn.CrossEntropyLoss()
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)
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self.counting_loss = nn.SmoothL1Loss(reduction="mean")
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self.ratio = 16
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def forward(self, preds, batch):
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word_probs = preds[0]
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counting_preds = preds[1]
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counting_preds1 = preds[2]
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counting_preds2 = preds[3]
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labels = batch[2]
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labels_mask = batch[3]
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counting_labels = gen_counting_label(labels, self.out_channel, True)
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counting_loss = (
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self.counting_loss(counting_preds1, counting_labels)
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+ self.counting_loss(counting_preds2, counting_labels)
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+ self.counting_loss(counting_preds, counting_labels)
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)
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word_loss = self.cross(
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paddle.reshape(word_probs, [-1, word_probs.shape[-1]]),
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paddle.reshape(labels, [-1]),
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)
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word_average_loss = (
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paddle.sum(paddle.reshape(word_loss * labels_mask, [-1]))
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/ (paddle.sum(labels_mask) + 1e-10)
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if self.use_label_mask
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else word_loss
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)
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loss = word_average_loss + counting_loss
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return {"loss": loss}
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def gen_counting_label(labels, channel, tag):
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b, t = labels.shape
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counting_labels = np.zeros([b, channel])
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if tag:
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ignore = [0, 1, 107, 108, 109, 110]
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else:
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ignore = []
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for i in range(b):
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for j in range(t):
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k = labels[i][j]
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if k in ignore:
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continue
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else:
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counting_labels[i][k] += 1
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counting_labels = paddle.to_tensor(counting_labels, dtype="float32")
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return counting_labels
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