Cannot run eval on pretrained models, instructions unclear

Hello,

I am attempting to recreate steps that are documented in order to test this model. Running the following command that is specified in the documentation:

D:\Projects\SportAnalytics\src\learnable-triangulation-pytorch> python train.py --eval --eval_dataset val --config experiments/human36m/eval/human36m_vol_softmax.yaml --logdir ./logs

Returns the following error:

args: Namespace(config='experiments/human36m/eval/human36m_vol_softmax.yaml', eval=True, eval_dataset='val', local_rank=None, logdir='./logs', seed=42)
Number of available GPUs: 1
Loading pretrained weights from: ./data/pretrained/human36m/pose_resnet_4.5_pixels_human36m.pth
Reiniting final layer filters: module.final_layer.weight
Reiniting final layer biases: module.final_layer.bias
Successfully loaded pretrained weights for backbone
Successfully loaded pretrained weights for whole model
Loading data...
Traceback (most recent call last):
  File "train.py", line 483, in <module>
    main(args)
  File "train.py", line 444, in main
    train_dataloader, val_dataloader, train_sampler = setup_dataloaders(config, distributed_train=is_distributed)
  File "train.py", line 117, in setup_dataloaders
    train_dataloader, val_dataloader, train_sampler = setup_human36m_dataloaders(config, is_train, distributed_train)
  File "train.py", line 65, in setup_human36m_dataloaders
    crop=config.dataset.train.crop if hasattr(config.dataset.train, "crop") else True,
  File "D:\src\learnable-triangulation-pytorch\mvn\datasets\human36m.py", line 70, in __init__
    self.labels = np.load(labels_path, allow_pickle=True).item()
  File "D:\Envs\Pose\lib\site-packages\numpy\lib\npyio.py", line 417, in load
    fid = stack.enter_context(open(os_fspath(file), "rb"))
FileNotFoundError: [Errno 2] No such file or directory: './data/human36m/extra/human36m-multiview-labels-GTbboxes.npy'

This seems odd as I am unable to find the specified file within the shared files on Google Drive to be able to run the model evaluation.

If You have any solutions to this problem please let me know.

[Kaszanas]

Thank You very much for response @shrubb I am closing this issue and if I will fail at the verbose instructions I will re-open it or message in it being closed.

[Kaszanas]

@shrubb

I have made an account to access the dataset some time ago but I have not received any information. So my attempts at validation are currently blocked.

In the meantime I would like to try and run inference on images / video, both monocular and with multiple camera inputs to see the pretrained model in action and to try and export the 3D information out of it.