更换文档检测模型

paddle_detection/ppdet/modeling/losses/cot_loss.py

@@ -0,0 +1,61 @@
+# 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