Open W-hary opened 1 year ago
I have the same problem. Could anyone fix the issue? thanks.
@hhaAndroid Hi, may I ask, do you have any plan to solve this issue? thank.
I met the same error
Yes. The same issue appears using rtmdet, its a bug. Any one fixed it?
Thanks for your error report and we appreciate it a lot.
Checklist
Describe the bug AssertionError:
binary_marks
must have the same shape with imageReproduction
python demo/large_image_demo.py demo/large_image.jpg rtmdet-ins_tiny_8xb32-300e_coco.py rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth --device cpu
Environment
python mmdet/utils/collect_env.py
to collect necessary environment information and paste it here.sys.platform: linux Python: 3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0: NVIDIA RTX A5000 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.11.0+cu113 PyTorch compiling details: PyTorch built with:
TorchVision: 0.12.0+cu113 OpenCV: 4.8.0 MMEngine: 0.8.4 MMDetection: 3.1.0+769c810
Error traceback I ran the following command on 'demo/large_image' and an error occurred: ’python demo/large_image_demo.py demo/large_image.jpg rtmdet-ins_tiny_8xb32-300e_coco.py rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth --device cpu‘
Loads checkpoint by local backend from path: rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth /root/miniconda3/lib/python3.8/site-packages/mmengine/visualization/visualizer.py:196: UserWarning: Failed to add <class 'mmengine.visualization.vis_backend.LocalVisBackend'>, please provide the
main()
File "demo/large_image_demo.py", line 264, in main
visualizer.add_datasample(
File "/root/miniconda3/lib/python3.8/site-packages/mmengine/dist/utils.py", line 401, in wrapper
return func(*args, *kwargs)
File "/root/autodl-tmp/mmdetection/mmdet/visualization/local_visualizer.py", line 468, in add_datasample
pred_img_data = self._draw_instances(image, pred_instances,
File "/root/autodl-tmp/mmdetection/mmdet/visualization/local_visualizer.py", line 194, in _draw_instances
self.draw_binary_masks(masks, colors=colors, alphas=self.alpha)
File "/root/miniconda3/lib/python3.8/site-packages/mmengine/dist/utils.py", line 401, in wrapper
return func(args, kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/mmengine/visualization/visualizer.py", line 881, in draw_binary_masks
assert img.shape[:2] == binary_masks.shape[
AssertionError:
save_dir
argument. warnings.warn(f'Failed to add {vis_backend.class}, ' Performing inference on 1 images.... This may take a while. [ ] 0/1, elapsed: 0s, ETA:/root/miniconda3/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, kwargs) # type: ignore[attr-defined] /root/autodl-tmp/mmdetection/mmdet/visualization/palette.py:90: UserWarning: floordiv is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). scales = 0.5 + (areas - min_area) // (max_area - min_area) Traceback (most recent call last): File "demo/large_image_demo.py", line 282, inbinary_marks
must have the same shape with image