更换文档检测模型

paddle_detection/ppdet/modeling/architectures/cascade_rcnn.py

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+# Copyright (c) 2020 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__ = ['CascadeRCNN']
+
+
+@register
+class CascadeRCNN(BaseArch):
+    """
+    Cascade R-CNN network, see https://arxiv.org/abs/1712.00726
+
+    Args:
+        backbone (object): backbone instance
+        rpn_head (object): `RPNHead` instance
+        bbox_head (object): `BBoxHead` instance
+        bbox_post_process (object): `BBoxPostProcess` instance
+        neck (object): 'FPN' instance
+        mask_head (object): `MaskHead` instance
+        mask_post_process (object): `MaskPostProcess` instance
+    """
+    __category__ = 'architecture'
+    __inject__ = [
+        'bbox_post_process',
+        'mask_post_process',
+    ]
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 bbox_head,
+                 bbox_post_process,
+                 neck=None,
+                 mask_head=None,
+                 mask_post_process=None):
+        super(CascadeRCNN, self).__init__()
+        self.backbone = backbone
+        self.rpn_head = rpn_head
+        self.bbox_head = bbox_head
+        self.bbox_post_process = bbox_post_process
+        self.neck = neck
+        self.mask_head = mask_head
+        self.mask_post_process = mask_post_process
+        self.with_mask = mask_head is not None
+
+    @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}
+        rpn_head = create(cfg['rpn_head'], **kwargs)
+        bbox_head = create(cfg['bbox_head'], **kwargs)
+
+        out_shape = neck and out_shape or bbox_head.get_head().out_shape
+        kwargs = {'input_shape': out_shape}
+        mask_head = cfg['mask_head'] and create(cfg['mask_head'], **kwargs)
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "rpn_head": rpn_head,
+            "bbox_head": bbox_head,
+            "mask_head": mask_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:
+            rois, rois_num, rpn_loss = self.rpn_head(body_feats, self.inputs)
+            bbox_loss, bbox_feat = self.bbox_head(body_feats, rois, rois_num,
+                                                  self.inputs)
+            rois, rois_num = self.bbox_head.get_assigned_rois()
+            bbox_targets = self.bbox_head.get_assigned_targets()
+            if self.with_mask:
+                mask_loss = self.mask_head(body_feats, rois, rois_num,
+                                           self.inputs, bbox_targets, bbox_feat)
+                return rpn_loss, bbox_loss, mask_loss
+            else:
+                return rpn_loss, bbox_loss, {}
+        else:
+            rois, rois_num, _ = self.rpn_head(body_feats, self.inputs)
+            preds, _ = self.bbox_head(body_feats, rois, rois_num, self.inputs)
+            refined_rois = self.bbox_head.get_refined_rois()
+
+            im_shape = self.inputs['im_shape']
+            scale_factor = self.inputs['scale_factor']
+
+            bbox, bbox_num, nms_keep_idx = self.bbox_post_process(
+                preds, (refined_rois, rois_num), im_shape, scale_factor)
+            # rescale the prediction back to origin image
+            bbox, bbox_pred, bbox_num = self.bbox_post_process.get_pred(
+                bbox, bbox_num, im_shape, scale_factor)
+            if not self.with_mask:
+                return bbox_pred, bbox_num, None
+            mask_out = self.mask_head(body_feats, bbox, bbox_num, self.inputs)
+            origin_shape = self.bbox_post_process.get_origin_shape()
+            mask_pred = self.mask_post_process(mask_out, bbox_pred, bbox_num,
+                                               origin_shape)
+            return bbox_pred, bbox_num, mask_pred
+
+    def get_loss(self, ):
+        rpn_loss, bbox_loss, mask_loss = self._forward()
+        loss = {}
+        loss.update(rpn_loss)
+        loss.update(bbox_loss)
+        if self.with_mask:
+            loss.update(mask_loss)
+        total_loss = paddle.add_n(list(loss.values()))
+        loss.update({'loss': total_loss})
+        return loss
+
+    def get_pred(self):
+        bbox_pred, bbox_num, mask_pred = self._forward()
+        output = {
+            'bbox': bbox_pred,
+            'bbox_num': bbox_num,
+        }
+        if self.with_mask:
+            output.update({'mask': mask_pred})
+        return output