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2024-08-27 14:42:45 +08:00

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Python

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from ppdet.core.workspace import register
__all__ = ['COTLoss']
@register
class COTLoss(nn.Layer):
__shared__ = ['num_classes']
def __init__(self,
num_classes=80,
cot_scale=1,
cot_lambda=1):
super(COTLoss, self).__init__()
self.cot_scale = cot_scale
self.cot_lambda = cot_lambda
self.num_classes = num_classes
def forward(self, scores, targets, cot_relation):
cls_name = 'loss_bbox_cls_cot'
loss_bbox = {}
tgt_labels, tgt_bboxes, tgt_gt_inds = targets
tgt_labels = paddle.concat(tgt_labels) if len(
tgt_labels) > 1 else tgt_labels[0]
mask = (tgt_labels < self.num_classes)
valid_inds = paddle.nonzero(tgt_labels >= 0).flatten()
if valid_inds.shape[0] == 0:
loss_bbox[cls_name] = paddle.zeros([1], dtype='float32')
else:
tgt_labels = tgt_labels.cast('int64')
valid_cot_targets = []
for i in range(tgt_labels.shape[0]):
train_label = tgt_labels[i]
if train_label < self.num_classes:
valid_cot_targets.append(cot_relation[train_label])
coco_targets = paddle.to_tensor(valid_cot_targets)
coco_targets.stop_gradient = True
coco_loss = - coco_targets * F.log_softmax(scores[mask][:, :-1] * self.cot_scale)
loss_bbox[cls_name] = self.cot_lambda * paddle.mean(paddle.sum(coco_loss, axis=-1))
return loss_bbox