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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/hikopensource/DAVAR-Lab-OCR/blob/main/davarocr/davar_common/models/loss/cross_entropy_loss.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
from .basic_loss import CELoss, DistanceLoss
class RFLLoss(nn.Layer):
def __init__(self, ignore_index=-100, **kwargs):
super().__init__()
self.cnt_loss = nn.MSELoss(**kwargs)
self.seq_loss = nn.CrossEntropyLoss(ignore_index=ignore_index)
def forward(self, predicts, batch):
self.total_loss = {}
total_loss = 0.0
if isinstance(predicts, tuple) or isinstance(predicts, list):
cnt_outputs, seq_outputs = predicts
else:
cnt_outputs, seq_outputs = predicts, None
# batch [image, label, length, cnt_label]
if cnt_outputs is not None:
cnt_loss = self.cnt_loss(cnt_outputs,
paddle.cast(batch[3], paddle.float32))
self.total_loss['cnt_loss'] = cnt_loss
total_loss += cnt_loss
if seq_outputs is not None:
targets = batch[1].astype("int64")
label_lengths = batch[2].astype('int64')
batch_size, num_steps, num_classes = seq_outputs.shape[
0], seq_outputs.shape[1], seq_outputs.shape[2]
assert len(targets.shape) == len(list(seq_outputs.shape)) - 1, \
"The target's shape and inputs's shape is [N, d] and [N, num_steps]"
inputs = seq_outputs[:, :-1, :]
targets = targets[:, 1:]
inputs = paddle.reshape(inputs, [-1, inputs.shape[-1]])
targets = paddle.reshape(targets, [-1])
seq_loss = self.seq_loss(inputs, targets)
self.total_loss['seq_loss'] = seq_loss
total_loss += seq_loss
self.total_loss['loss'] = total_loss
return self.total_loss
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