batch_gt_instances=batch_gt_instances, batch_img_metas=batch_img_metas, dn_meta=dn_meta) 李峰:DINO: 让目标检测拥抱Transformer
forward_train(img, img_metas) # 1.backbone提取特征 x = self.extract_feat(img) # 2.head计算,这里是DETRHead,包括了encoder、decoder和head三部分 losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore) return losses 在detr_head(mmdet/models/dense_...
第十三个:Collect3D,这一步其实很简单,就是用于pipeline的最后一步,其根据我们初始化时候给它指定的meta_keys,为了我们具体的任务搞出来所需要的整顿好,把dict中我们已经存下的这么多东西进行自定义的收集,放入img_metas这个key中,其是一个包含了我们所收集的内容的DataContainer。 """Collect data from the loader...
bbox_cam3d = CameraInstance3DBoxes(bbox_cam3d,origin=(0.5,0.5,0.5)) draw_camera_bbox3d_on_img(bbox_cam3d, raw_img=img, cam2img=camera_intrinsic, img_metas=None, color=(0, 255, 0), thickness=1) # --- 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16....
img_masks = mlvl_feats[0].new_ones((batch_size, input_img_h, input_img_w)) # 遍历每个图像,将原始图像部分设置为0,1的位置表示pad部分 for img_id in range(batch_size): img_h, img_w, _ = img_metas[img_id]['img_shape']
return self.forward_test(**kwargs) File "/home/y202729/mmdetection3d/mmdet3d/models/detectors/base.py", line 42, in forward_test return self.simple_test(points[0], img_metas[0], img[0], **kwargs) File "/home/y202729/mmdetection3d/mmdet3d/models/detectors/voxelnet.py", line 100, in...
(self, *args, **kwargs) File "mmlab/MMDeploy/mmdeploy/codebase/mmdet3d/models/mvx_two_stage.py", line 53, in mvxtwostagedetector__extract_feat img_metas) TypeError: extract_pts_feat() takes 4 positional arguments but 6 were given 2022-05-03 17:38:11,339 - mmdeploy - ERROR - ...
filename = f'train_imgs/{name}' if name not in datasplit: # 意思是不在训练集里就跳过 continue image_names.add(name) bboxes[name].append(bbox) labels[name].append(label) metas[name] = dict(id=img_id, filename=filename, width=width, height=height) ...
models import build_detector detector = build_detector(model, train_cfg=train_cfg, test_cfg=test_cfg) input_shape = (1, 3, 224, 224) mm_inputs = _demo_mm_inputs(input_shape) imgs = mm_inputs.pop('imgs') img_metas = mm_inputs.pop('img_metas') # Test forward train gt_b...
no_grad(): feat_shape = (img_metas[0]['pad_shape'][0] // self.down_ratio, img_metas[0]['pad_shape'][1] // self.down_ratio) heatmap, box_target, reg_weight = multi_apply( self.target_single_image, gt_boxes, gt_labels, feat_shape=feat_shape ) heatmap, box_target = [...