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# copyright (c) 2022 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/wangyuxin87/VisionLAN
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
class VLLoss(nn.Layer):
def __init__(self, mode='LF_1', weight_res=0.5, weight_mas=0.5, **kwargs):
super(VLLoss, self).__init__()
self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean")
assert mode in ['LF_1', 'LF_2', 'LA']
self.mode = mode
self.weight_res = weight_res
self.weight_mas = weight_mas
def flatten_label(self, target):
label_flatten = []
label_length = []
for i in range(0, target.shape[0]):
cur_label = target[i].tolist()
label_flatten += cur_label[:cur_label.index(0) + 1]
label_length.append(cur_label.index(0) + 1)
label_flatten = paddle.to_tensor(label_flatten, dtype='int64')
label_length = paddle.to_tensor(label_length, dtype='int32')
return (label_flatten, label_length)
def _flatten(self, sources, lengths):
return paddle.concat([t[:l] for t, l in zip(sources, lengths)])
def forward(self, predicts, batch):
text_pre = predicts[0]
target = batch[1].astype('int64')
label_flatten, length = self.flatten_label(target)
text_pre = self._flatten(text_pre, length)
if self.mode == 'LF_1':
loss = self.loss_func(text_pre, label_flatten)
else:
text_rem = predicts[1]
text_mas = predicts[2]
target_res = batch[2].astype('int64')
target_sub = batch[3].astype('int64')
label_flatten_res, length_res = self.flatten_label(target_res)
label_flatten_sub, length_sub = self.flatten_label(target_sub)
text_rem = self._flatten(text_rem, length_res)
text_mas = self._flatten(text_mas, length_sub)
loss_ori = self.loss_func(text_pre, label_flatten)
loss_res = self.loss_func(text_rem, label_flatten_res)
loss_mas = self.loss_func(text_mas, label_flatten_sub)
loss = loss_ori + loss_res * self.weight_res + loss_mas * self.weight_mas
return {'loss': loss}
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