Open jmckendry-aw opened 3 weeks ago
Never mind, I figured it out. For the sake of anyone else who runs into the same problem: if you unzip the model.zip directory by selecting "extract here" in Nautilus. the weights file model/wifi/b32-256-100s/weights.pt will be empty and you will get the EOFError. So you should unzip it from the command line, "unzip -q model.zip" if the zip file is already in the RF-Diffusion directory. The weights.pt file should be 234.8 MB.
I am trying to run inference.py in Ubuntu 20.04 with an RTX 3060 with 12GB. I created a Conda environment with Python 3.8 and set up required packages following the instructions here, downloaded and unzipped the dataset.zip and model.zip files, and got the following traceback: (RF-Diffusion) jfm@jfm-XPS-8940:~/RF-Diffusion$ python3 inference.py --task_id 0 inference.py:278: FutureWarning: You are using
main(parser.parse_args())
File "inference.py", line 278, in main
checkpoint = torch.load(f'{model_dir}/weights.pt')
File "/home/jfm/anaconda3/envs/RF-Diffusion/lib/python3.8/site-packages/torch/serialization.py", line 1114, in load
return _legacy_load(
File "/home/jfm/anaconda3/envs/RF-Diffusion/lib/python3.8/site-packages/torch/serialization.py", line 1338, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
EOFError: Ran out of input
torch.load
withweights_only=False
(the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value forweights_only
will be flipped toTrue
. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user viatorch.serialization.add_safe_globals
. We recommend you start settingweights_only=True
for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. checkpoint = torch.load(f'{model_dir}/weights.pt') Traceback (most recent call last): File "inference.py", line 368, inWhat am I doing wrong? Also, I tried to pass a --model_dir arg with "--model_dir '/home/jfm/RF-Diffusion/model/'" and I got an error IsADirectoryError: [Errno 21] Is a directory: '/home/jfm/RF-Diffusion/model/' So what is the correct syntax for passing a directory?
Thanks for any help.