更换文档检测模型

paddle_detection/ppdet/modeling/architectures/queryinst.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2023 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__ = ['QueryInst']
+
+
+@register
+class QueryInst(BaseArch):
+    __category__ = 'architecture'
+    __inject__ = ['post_process']
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 rpn_head,
+                 roi_head,
+                 post_process='SparsePostProcess'):
+        super(QueryInst, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.rpn_head = rpn_head
+        self.roi_head = roi_head
+        self.post_process = post_process
+
+    @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}
+        rpn_head = create(cfg['rpn_head'], **kwargs)
+        roi_head = create(cfg['roi_head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            'rpn_head': rpn_head,
+            "roi_head": roi_head
+        }
+
+    def _forward(self, targets=None):
+        features = self.backbone(self.inputs)
+        features = self.neck(features)
+
+        proposal_bboxes, proposal_features = self.rpn_head(self.inputs[
+            'img_whwh'])
+        outputs = self.roi_head(features, proposal_bboxes, proposal_features,
+                                targets)
+
+        if self.training:
+            return outputs
+        else:
+            bbox_pred, bbox_num, mask_pred = self.post_process(
+                outputs['class_logits'], outputs['bbox_pred'],
+                self.inputs['scale_factor_whwh'], self.inputs['ori_shape'],
+                outputs['mask_logits'])
+            return bbox_pred, bbox_num, mask_pred
+
+    def get_loss(self):
+        targets = []
+        for i in range(len(self.inputs['img_whwh'])):
+            boxes = self.inputs['gt_bbox'][i]
+            labels = self.inputs['gt_class'][i].squeeze(-1)
+            img_whwh = self.inputs['img_whwh'][i]
+            if boxes.shape[0] != 0:
+                img_whwh_tgt = img_whwh.unsqueeze(0).tile([boxes.shape[0], 1])
+            else:
+                img_whwh_tgt = paddle.zeros_like(boxes)
+            gt_segm = self.inputs['gt_segm'][i].astype('float32')
+            targets.append({
+                'boxes': boxes,
+                'labels': labels,
+                'img_whwh': img_whwh,
+                'img_whwh_tgt': img_whwh_tgt,
+                'gt_segm': gt_segm
+            })
+        losses = self._forward(targets)
+        losses.update({'loss': sum(losses.values())})
+        return losses
+
+    def get_pred(self):
+        bbox_pred, bbox_num, mask_pred = self._forward()
+        return {'bbox': bbox_pred, 'bbox_num': bbox_num, 'mask': mask_pred}