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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
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