更换文档检测模型

paddle_detection/ppdet/modeling/architectures/sparse_rcnn.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
+
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ["SparseRCNN"]
+
+
+@register
+class SparseRCNN(BaseArch):
+    __category__ = 'architecture'
+    __inject__ = ["postprocess"]
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 head="SparsercnnHead",
+                 postprocess="SparsePostProcess"):
+        super(SparseRCNN, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.head = head
+        self.postprocess = postprocess
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+
+        kwargs = {'roi_input_shape': neck.out_shape}
+        head = create(cfg['head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "head": head,
+        }
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        fpn_feats = self.neck(body_feats)
+        head_outs = self.head(fpn_feats, self.inputs["img_whwh"])
+
+        if not self.training:
+            bbox_pred, bbox_num = self.postprocess(
+                head_outs["pred_logits"], head_outs["pred_boxes"],
+                self.inputs["scale_factor_whwh"], self.inputs["ori_shape"])
+            return bbox_pred, bbox_num
+        else:
+            return head_outs
+
+    def get_loss(self):
+        batch_gt_class = self.inputs["gt_class"]
+        batch_gt_box = self.inputs["gt_bbox"]
+        batch_whwh = self.inputs["img_whwh"]
+        targets = []
+
+        for i in range(len(batch_gt_class)):
+            boxes = batch_gt_box[i]
+            labels = batch_gt_class[i].squeeze(-1)
+            img_whwh = batch_whwh[i]
+            img_whwh_tgt = img_whwh.unsqueeze(0).tile([int(boxes.shape[0]), 1])
+            targets.append({
+                "boxes": boxes,
+                "labels": labels,
+                "img_whwh": img_whwh,
+                "img_whwh_tgt": img_whwh_tgt
+            })
+
+        outputs = self._forward()
+        loss_dict = self.head.get_loss(outputs, targets)
+        acc = loss_dict["acc"]
+        loss_dict.pop("acc")
+        total_loss = sum(loss_dict.values())
+        loss_dict.update({"loss": total_loss, "acc": acc})
+        return loss_dict
+
+    def get_pred(self):
+        bbox_pred, bbox_num = self._forward()
+        output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
+        return output