Hi guys,
Very interesting paper. I was looking to duplicate your results and test your pre-trained model with some of my own point cloud data. I am having a pretty difficult time getting the example running. The main issues where with spconv versions (I assumed you guys used V1 since V2 has changed VoxelGenerator to PointToVoxel) but I solved those.
When loading the pre-trained weights using the information under 'Running the pretrained models' I am getting the following error.
Traceback (most recent call last): File "test_recurrent.py", line 28, in <module> model = BEVNetRecurrent(MODEL_FILE) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 97, in __init__ super(BEVNetRecurrent, self).__init__(*args, **kwargs) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 31, in __init__ self._load(weights_file, device) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 53, in _load net.load_state_dict(state_dict['nets'][name]) File "/home/franc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for SpMiddleNoDownsampleXYMultiStep: size mismatch for middle_conv.0.weight: copying a param with shape torch.Size([3, 3, 3, 4, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 4]). size mismatch for middle_conv.3.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]). size mismatch for middle_conv.6.weight: copying a param with shape torch.Size([3, 3, 3, 32, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 32]). size mismatch for middle_conv.9.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.12.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.15.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.18.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.21.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.24.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.27.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.30.weight: copying a param with shape torch.Size([3, 1, 1, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 1, 1, 64]).
It looks like the model is all there but is in the wrong shape. Wondering if there is a quick fix for this? I havent modified the cloned repo at all so I am trying to find the reason the example won't run as outlined.
Thanks in advance.
Hi guys,
Very interesting paper. I was looking to duplicate your results and test your pre-trained model with some of my own point cloud data. I am having a pretty difficult time getting the example running. The main issues where with spconv versions (I assumed you guys used V1 since V2 has changed VoxelGenerator to PointToVoxel) but I solved those.
When loading the pre-trained weights using the information under 'Running the pretrained models' I am getting the following error.
Traceback (most recent call last): File "test_recurrent.py", line 28, in <module> model = BEVNetRecurrent(MODEL_FILE) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 97, in __init__ super(BEVNetRecurrent, self).__init__(*args, **kwargs) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 31, in __init__ self._load(weights_file, device) File "/home/franc/Semantic_BEVNet/semantic_bevnet/bevnet/inference.py", line 53, in _load net.load_state_dict(state_dict['nets'][name]) File "/home/franc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for SpMiddleNoDownsampleXYMultiStep: size mismatch for middle_conv.0.weight: copying a param with shape torch.Size([3, 3, 3, 4, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 4]). size mismatch for middle_conv.3.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]). size mismatch for middle_conv.6.weight: copying a param with shape torch.Size([3, 3, 3, 32, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 32]). size mismatch for middle_conv.9.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.12.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.15.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.18.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.21.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.24.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.27.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]). size mismatch for middle_conv.30.weight: copying a param with shape torch.Size([3, 1, 1, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 1, 1, 64]).It looks like the model is all there but is in the wrong shape. Wondering if there is a quick fix for this? I havent modified the cloned repo at all so I am trying to find the reason the example won't run as outlined.
Thanks in advance.