更换文档检测模型

paddle_detection/ppdet/modeling/architectures/fairmot.py

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+# 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
+
+import paddle
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['FairMOT']
+
+
+@register
+class FairMOT(BaseArch):
+    """
+    FairMOT network, see http://arxiv.org/abs/2004.01888
+
+    Args:
+        detector (object): 'CenterNet' instance
+        reid (object): 'FairMOTEmbeddingHead' instance
+        tracker (object): 'JDETracker' instance
+        loss (object): 'FairMOTLoss' instance
+
+    """
+
+    __category__ = 'architecture'
+    __inject__ = ['loss']
+
+    def __init__(self,
+                 detector='CenterNet',
+                 reid='FairMOTEmbeddingHead',
+                 tracker='JDETracker',
+                 loss='FairMOTLoss'):
+        super(FairMOT, self).__init__()
+        self.detector = detector
+        self.reid = reid
+        self.tracker = tracker
+        self.loss = loss
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        detector = create(cfg['detector'])
+        detector_out_shape = detector.neck and detector.neck.out_shape or detector.backbone.out_shape
+
+        kwargs = {'input_shape': detector_out_shape}
+        reid = create(cfg['reid'], **kwargs)
+        loss = create(cfg['loss'])
+        tracker = create(cfg['tracker'])
+
+        return {
+            'detector': detector,
+            'reid': reid,
+            'loss': loss,
+            'tracker': tracker
+        }
+
+    def _forward(self):
+        loss = dict()
+        # det_outs keys:
+        # train: neck_feat, det_loss, heatmap_loss, size_loss, offset_loss (optional: iou_loss)
+        # eval/infer: neck_feat, bbox, bbox_inds
+        det_outs = self.detector(self.inputs)
+        neck_feat = det_outs['neck_feat']
+        if self.training:
+            reid_loss = self.reid(neck_feat, self.inputs)
+
+            det_loss = det_outs['det_loss']
+            loss = self.loss(det_loss, reid_loss)
+            for k, v in det_outs.items():
+                if 'loss' not in k:
+                    continue
+                loss.update({k: v})
+            loss.update({'reid_loss': reid_loss})
+            return loss
+        else:
+            pred_dets, pred_embs = self.reid(
+                neck_feat, self.inputs, det_outs['bbox'], det_outs['bbox_inds'],
+                det_outs['topk_clses'])
+            return pred_dets, pred_embs
+
+    def get_pred(self):
+        output = self._forward()
+        return output
+
+    def get_loss(self):
+        loss = self._forward()
+        return loss