Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

Related tags

Deep Learning semantic_bevnet
Overview

BEVNet

Datasets

Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100.

Training

BEVNet-S

Example:

cd experiments
bash train_kitti4-unknown_single.sh kitti4_100/single/include_unknown/default.yaml 
   
     arg1 arg2 ...

Logs and model weights will be stored in a subdirectory of the config file like this: experiments/kitti4_100/single/include_unknown/default- -logs/

  • is useful when you want to use the same config file but different hyperparameters. For example, if you want to do some debugging you can use set to debug.
  • arg1 arg2 ... are command line arguments supported by train_single.py. For example, you can pass --batch_size=4 --log_interval=100, etc.

BEVNet-R

The command line formats are the same as BEVNet-S Example:

cd experiments
bash train_kitti4-unknown_recurrent.sh kitti4_100/recurrent/include_unknown/default.yaml 
   
     \
--n_frame=6 --seq_len=20 --frame_strides 1 10 20 \
--resume kitti4_100/single/include_unknown/default-logs/model.pth.4 \
--resume_epoch 0

Logs and model weights will be stored in a subdirectory of the config file experiments/kitti4_100/recurrent/include_unknown/default- -logs/ .

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Comments
  • Size mismatch when running pre-trained model

    Size mismatch when running pre-trained model

    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.

    opened by TankyFranky 0
  • inference

    inference

    Dear author:

          Hello, could you upload the model weight and give instructions on inference please? I want to to test it on my campus dataset.  Thank you very much!

    Best Regards.

    opened by rock19970106 1
Owner
(Brian) JoonHo Lee
5th year MS student at University of Washington. Interested in Robotics, Deep Learning, Kaggle.
(Brian) JoonHo Lee
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