更换文档检测模型

paddle_detection/ppdet/modeling/architectures/detr.py

@@ -0,0 +1,118 @@
+# 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 .meta_arch import BaseArch
+from ppdet.core.workspace import register, create
+
+__all__ = ['DETR']
+# Deformable DETR, DINO use the same architecture as DETR
+
+
+@register
+class DETR(BaseArch):
+    __category__ = 'architecture'
+    __inject__ = ['post_process', 'post_process_semi']
+    __shared__ = ['with_mask', 'exclude_post_process']
+
+    def __init__(self,
+                 backbone,
+                 transformer='DETRTransformer',
+                 detr_head='DETRHead',
+                 neck=None,
+                 post_process='DETRPostProcess',
+                 post_process_semi=None,
+                 with_mask=False,
+                 exclude_post_process=False):
+        super(DETR, self).__init__()
+        self.backbone = backbone
+        self.transformer = transformer
+        self.detr_head = detr_head
+        self.neck = neck
+        self.post_process = post_process
+        self.with_mask = with_mask
+        self.exclude_post_process = exclude_post_process
+        self.post_process_semi = post_process_semi
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        # backbone
+        backbone = create(cfg['backbone'])
+        # neck
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs) if cfg['neck'] else None
+
+        # transformer
+        if neck is not None:
+            kwargs = {'input_shape': neck.out_shape}
+        transformer = create(cfg['transformer'], **kwargs)
+        # head
+        kwargs = {
+            'hidden_dim': transformer.hidden_dim,
+            'nhead': transformer.nhead,
+            'input_shape': backbone.out_shape
+        }
+        detr_head = create(cfg['detr_head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            'transformer': transformer,
+            "detr_head": detr_head,
+            "neck": neck
+        }
+
+    def _forward(self):
+        # Backbone
+        body_feats = self.backbone(self.inputs)
+
+        # Neck
+        if self.neck is not None:
+            body_feats = self.neck(body_feats)
+
+        # Transformer
+        pad_mask = self.inputs.get('pad_mask', None)
+        out_transformer = self.transformer(body_feats, pad_mask, self.inputs)
+
+        # DETR Head
+        if self.training:
+            detr_losses = self.detr_head(out_transformer, body_feats,
+                                         self.inputs)
+            detr_losses.update({
+                'loss': paddle.add_n(
+                    [v for k, v in detr_losses.items() if 'log' not in k])
+            })
+            return detr_losses
+        else:
+            preds = self.detr_head(out_transformer, body_feats)
+            if self.exclude_post_process:
+                bbox, bbox_num, mask = preds
+            else:
+                bbox, bbox_num, mask = self.post_process(
+                    preds, self.inputs['im_shape'], self.inputs['scale_factor'],
+                    paddle.shape(self.inputs['image'])[2:])
+
+            output = {'bbox': bbox, 'bbox_num': bbox_num}
+            if self.with_mask:
+                output['mask'] = mask
+            return output
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        return self._forward()