更换文档检测模型

paddle_detection/ppdet/modeling/architectures/centernet.py

@@ -0,0 +1,103 @@
+# 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__ = ['CenterNet']
+
+
+@register
+class CenterNet(BaseArch):
+    """
+    CenterNet network, see http://arxiv.org/abs/1904.07850
+
+    Args:
+        backbone (object): backbone instance
+        neck (object): FPN instance, default use 'CenterNetDLAFPN'
+        head (object): 'CenterNetHead' instance
+        post_process (object): 'CenterNetPostProcess' instance
+        for_mot (bool): whether return other features used in tracking model
+
+    """
+    __category__ = 'architecture'
+    __inject__ = ['post_process']
+    __shared__ = ['for_mot']
+
+    def __init__(self,
+                 backbone,
+                 neck='CenterNetDLAFPN',
+                 head='CenterNetHead',
+                 post_process='CenterNetPostProcess',
+                 for_mot=False):
+        super(CenterNet, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.head = head
+        self.post_process = post_process
+        self.for_mot = for_mot
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = cfg['neck'] and create(cfg['neck'], **kwargs)
+
+        out_shape = neck and neck.out_shape or backbone.out_shape
+        kwargs = {'input_shape': out_shape}
+        head = create(cfg['head'], **kwargs)
+
+        return {'backbone': backbone, 'neck': neck, "head": head}
+
+    def _forward(self):
+        neck_feat = self.backbone(self.inputs)
+        if self.neck is not None:
+            neck_feat = self.neck(neck_feat)
+        head_out = self.head(neck_feat, self.inputs)
+        if self.for_mot:
+            head_out.update({'neck_feat': neck_feat})
+        elif self.training:
+            head_out['loss'] = head_out.pop('det_loss')
+        return head_out
+
+    def get_pred(self):
+        head_out = self._forward()
+        bbox, bbox_num, bbox_inds, topk_clses, topk_ys, topk_xs = self.post_process(
+            head_out['heatmap'],
+            head_out['size'],
+            head_out['offset'],
+            im_shape=self.inputs['im_shape'],
+            scale_factor=self.inputs['scale_factor'])
+
+        if self.for_mot:
+            output = {
+                "bbox": bbox,
+                "bbox_num": bbox_num,
+                "bbox_inds": bbox_inds,
+                "topk_clses": topk_clses,
+                "topk_ys": topk_ys,
+                "topk_xs": topk_xs,
+                "neck_feat": head_out['neck_feat']
+            }
+        else:
+            output = {"bbox": bbox, "bbox_num": bbox_num}
+        return output
+
+    def get_loss(self):
+        return self._forward()