更换文档检测模型

paddle_detection/ppdet/modeling/architectures/tood.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2021 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
+
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['TOOD']
+
+
+@register
+class TOOD(BaseArch):
+    """
+    TOOD: Task-aligned One-stage Object Detection, see https://arxiv.org/abs/2108.07755
+    Args:
+        backbone (nn.Layer): backbone instance
+        neck (nn.Layer): 'FPN' instance
+        head (nn.Layer): 'TOODHead' instance
+    """
+
+    __category__ = 'architecture'
+
+    def __init__(self, backbone, neck, head):
+        super(TOOD, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.head = head
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+
+        kwargs = {'input_shape': neck.out_shape}
+        head = create(cfg['head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "head": head,
+        }
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        fpn_feats = self.neck(body_feats)
+        head_outs = self.head(fpn_feats)
+        if not self.training:
+            bboxes, bbox_num = self.head.post_process(
+                head_outs, self.inputs['im_shape'], self.inputs['scale_factor'])
+            return bboxes, bbox_num
+        else:
+            loss = self.head.get_loss(head_outs, self.inputs)
+            return loss
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        bbox_pred, bbox_num = self._forward()
+        output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
+        return output