Please tell me. I see that your data preprocessing is processed as uint8. I want to process lung data. The uint8 precision lung details will be lost. Is it okay if I save it as float32? After trying it, it seems that the GPU will overrun.

[yuanpengpeng]

Error reported during testing

Namespace(name='dif-net', epoch=400, dst_list='knee_cbct', split='test', combine='mlp', num_views=3, view_offset=0, out_res=256, eval_npoint=100000, visualize=False) mixed_dataset: ['knee_cbct'] 输出dst_name: knee_cbct CBCT_dataset, name: knee_cbct, split: test, len: 1. load ckpt from /mnt/d/谷歌下载/DIF-Net-main/DIF-Net-main/scripts/logs/dif-net/ep_400.pth DIF_Net, mid_ch: 128, combine: mlp Traceback (most recent call last): File "/mnt/d/谷歌下载/DIF-Net-main/DIF-Net-main/code/evaluate.py", line 110, in model.load_state_dict(ckpt) File "/home/yuanpeng/conda/envs/python310Tigre/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2152, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for DIF_Net: size mismatch for view_mixer.layer.0.weight: copying a param with shape torch.Size([5, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 1, 1]). size mismatch for view_mixer.layer.0.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for view_mixer.layer.2.weight: copying a param with shape torch.Size([1, 5, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 1, 1, 1]).

    May I ask what is the reason for this? I followed the same data preprocessing steps and ran it on the 3090ti server and 24g video memory.