更换文档检测模型

paddle_detection/ppdet/modeling/architectures/bytetrack.py

@@ -0,0 +1,83 @@
+# 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
+
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['ByteTrack']
+
+
+@register
+class ByteTrack(BaseArch):
+    """
+    ByteTrack network, see https://arxiv.org/abs/2110.06864
+
+    Args:
+        detector (object): detector model instance
+        reid (object): reid model instance, default None
+        tracker (object): tracker instance
+    """
+    __category__ = 'architecture'
+
+    def __init__(self,
+                 detector='YOLOX',
+                 reid=None,
+                 tracker='JDETracker'):
+        super(ByteTrack, self).__init__()
+        self.detector = detector
+        self.reid = reid
+        self.tracker = tracker
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        detector = create(cfg['detector'])
+
+        if cfg['reid'] != 'None':
+            reid = create(cfg['reid'])
+        else:
+            reid = None
+
+        tracker = create(cfg['tracker'])
+
+        return {
+            "detector": detector,
+            "reid": reid,
+            "tracker": tracker,
+        }
+
+    def _forward(self):
+        det_outs = self.detector(self.inputs)
+
+        if self.training:
+            return det_outs
+        else:
+            if self.reid is not None:
+                assert 'crops' in self.inputs
+                crops = self.inputs['crops']
+                pred_embs = self.reid(crops)
+            else:
+                pred_embs = None
+            det_outs['embeddings'] = pred_embs
+            return det_outs
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        return self._forward()
+