更换文档检测模型

paddle_detection/ppdet/modeling/architectures/s2anet.py

@@ -0,0 +1,83 @@
+# 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__ = ['S2ANet']
+
+
+@register
+class S2ANet(BaseArch):
+    __category__ = 'architecture'
+    __inject__ = ['head']
+
+    def __init__(self, backbone, neck, head):
+        """
+        S2ANet, see https://arxiv.org/pdf/2008.09397.pdf
+
+        Args:
+            backbone (object): backbone instance
+            neck (object): `FPN` instance
+            head (object): `Head` instance
+        """
+        super(S2ANet, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.s2anet_head = head
+
+    @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):
+        body_feats = self.backbone(self.inputs)
+        if self.neck is not None:
+            body_feats = self.neck(body_feats)
+        if self.training:
+            loss = self.s2anet_head(body_feats, self.inputs)
+            return loss
+        else:
+            head_outs = self.s2anet_head(body_feats)
+            # post_process
+            bboxes, bbox_num = self.s2anet_head.get_bboxes(head_outs)
+            # rescale the prediction back to origin image
+            im_shape = self.inputs['im_shape']
+            scale_factor = self.inputs['scale_factor']
+            bboxes = self.s2anet_head.get_pred(bboxes, bbox_num, im_shape,
+                                               scale_factor)
+            # output
+            output = {'bbox': bboxes, 'bbox_num': bbox_num}
+            return output
+
+    def get_loss(self, ):
+        loss = self._forward()
+        return loss
+
+    def get_pred(self):
+        output = self._forward()
+        return output