Evaluation on LineMOD Dataset: RuntimeError: Legacy autograd function:...

[ZhouTao415]

Hi,, i try to evaluate on LineMOD Dataset, and use this comand "./experiments/scripts/eval_linemod.sh". When I run the code, it can still be processed at first, but the following error occurs when processing No.7192. I have tried various solutions in the issue, to no avail. But i evaluate on YCB_Video Dataset,and use comand "./experiments/scripts/eval_ycb.sh",that is avail. And Training Process is also avail. Any idea of what is wrong with my iplementation? Thanks.

My platform is Ubuntu 22.04 GPU: 3070ti python 3.6 torch 1.7.1+cu110 torchaudio 0.7.2 torchvision 0.8.2+cu110

• set -e
• export PYTHONUNBUFFERED=True
• PYTHONUNBUFFERED=True
• export CUDA_VISIBLE_DEVICES=0
• CUDA_VISIBLE_DEVICES=0
• python3 ./tools/eval_linemod.py --dataset_root ./datasets/linemod/Linemod_preprocessed --model trained_checkpoints/linemod/pose_model_9_0.01310166542980859.pth --refine_model trained_checkpoints/linemod/pose_refine_model_493_0.006761023565178073.pth /home/zhou/SA/DenseFusion/lib/transformations.py:1912: UserWarning: failed to import module _transformations warnings.warn('failed to import module %s' % name) /home/zhou/SA/DenseFusion/datasets/linemod/dataset.py:66: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. self.meta[item] = yaml.load(meta_file) Object 1 buffer loaded Object 2 buffer loaded Object 4 buffer loaded Object 5 buffer loaded Object 6 buffer loaded Object 8 buffer loaded Object 9 buffer loaded Object 10 buffer loaded Object 11 buffer loaded Object 12 buffer loaded Object 13 buffer loaded Object 14 buffer loaded Object 15 buffer loaded /home/zhou/anaconda3/envs/densefusion/lib/python3.6/site-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead. warnings.warn(warning.format(ret)) ./tools/eval_linemod.py:59: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. meta = yaml.load(meta_file) [0.010209865663, 0.024750624233, 0.017249224865, 0.020140358597000002, 0.015454551808, 0.026147178102, 0.010899920102000001, 0.016462758847999998, 0.017588933422000002, 0.014554287471000002, 0.027807811733, 0.028260129399, 0.021235825148] /home/zhou/anaconda3/envs/densefusion/lib/python3.6/site-packages/torch/nn/functional.py:2952: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead. warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") /home/zhou/anaconda3/envs/densefusion/lib/python3.6/site-packages/torch/nn/functional.py:3063: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) /home/zhou/anaconda3/envs/densefusion/lib/python3.6/site-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument. input = module(input) No.0 Pass! Distance: 0.004216850118266611 No.1 Pass! Distance: 0.0038697024826312826 No.2 Pass! Distance: 0.006223264002854911 No.3 Pass! Distance: 0.005435459421881504 No.4 Pass! Distance: 0.0028559770089771563 No.5 Pass! Distance: 0.004135905930436273 No.6 Pass! Distance: 0.004435833969654068 No.7 Pass! Distance: 0.0036375894008631392 No.8 Pass! Distance: 0.001638323984075058 ... ... ... No.7184 Pass! Distance: 0.0051409396661699835 No.7185 Pass! Distance: 0.003015364929576589 No.7186 NOT Pass! Distance: 0.011457473328095534 No.7187 NOT Pass! Distance: 0.011731082900323854 No.7188 NOT Pass! Distance: 0.013262642515697212 No.7189 Pass! Distance: 0.008656459834607513 No.7190 Pass! Distance: 0.008892003837428275 No.7191 NOT Pass! Distance: 0.031596818093702995 No.7192 Pass! Distance: 0.008258320402724294 Traceback (most recent call last): File "./tools/eval_linemod.py", line 126, in inds = knn(target.unsqueeze(0), pred.unsqueeze(0)) File "/home/zhou/anaconda3/envs/densefusion/lib/python3.6/site-packages/torch/autograd/function.py", line 160, in call "Legacy autograd function with non-static forward method is deprecated. " RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)

[ZhouTao415]

I have solved this problem. For me, the problem appeared in tools/eval_linemod.py line 126 "inds = knn(target.unsqueeze(0), pred.unsqueeze(0))", changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“. It works .

[ZhuangTingT]

I have solved this problem. For me, the problem appeared in tools/eval_linemod.py line 126 "inds = knn(target.unsqueeze(0), pred.unsqueeze(0))", changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“. It works .

hello, I have changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“, but I met the problem "no attribute 'k'", do you have any idea to solve it? thanks!

[Lance-123]

I have solved this problem. For me, the problem appeared in tools/eval_linemod.py line 126 "inds = knn(target.unsqueeze(0), pred.unsqueeze(0))", changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“. It works .

hello, I have changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“, but I met the problem "no attribute 'k'", do you have any idea to solve it? thanks!

Did you solve it, I have the same problem

[Harrydsk]

I have solved this problem. For me, the problem appeared in tools/eval_linemod.py line 126 "inds = knn(target.unsqueeze(0), pred.unsqueeze(0))", changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“. It works .

hello, I have changed to “inds = KNearestNeighbor.apply(target.unsqueeze (0), pred.unsqueeze(0))“, but I met the problem "no attribute 'k'", do you have any idea to solve it? thanks!

Did you solve it, I have the same problem

did u solve it ,my friend