Hi, jiachen. In the paper, it says " PointNet++ and RSCNN leverage ball queries to find neighboring points, which will hinder the gradients from backward propagating to the original point cloud, making adversarial training inapplicable." I am wondering why the ball query operation hinder the gradients from backward propagating. If so, is the optimization of pointnet++ in standard training mode also be influenced? This makes me confused, hope to receive your reply!

Hello, Thanks for your great work！ I would like to ask why the layered phenomenon as shown in the figure occurs when the code is used to process Kitti datasets. Could you guide me here? Appreciate your work and the help. Thanks a lot！

Hi authors, thanks for your solid work!

In the given codebase I have not found the implementation of evaluation metrics for modelnet40-c, i.e. error rate (ER) or class-wise mean error rate (mER). So where can I find the evaluation metric code in the repo?

And in the Metric section of the paper, is the average operation missing here?

when I run python main.py --entry test --model-path runs/cutmix_r_pct_run_1/model_best_test.pth --exp-config configs/tent_cutmix/pct.yaml

I got this error:

ADAPT: ITER: 10 METHOD: tent AUG: BETA: 1.0 MIXUPRATE: 0.4 NAME: none PROB: 0.5 DATALOADER: MODELNET40_C: corruption: uniform severity: 1 test_data_path: ./data/modelnet40_c/ MODELNET40_DGCNN: num_points: 1024 test_data_path: ./data/modelnet40_ply_hdf5_2048/test_files.txt train_data_path: ./data/modelnet40_ply_hdf5_2048/train_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/train_files.txt MODELNET40_PN2: num_points: 1024 test_data_path: ./data/modelnet40_ply_hdf5_2048/test_files.txt train_data_path: ./data/modelnet40_ply_hdf5_2048/train_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/train_files.txt MODELNET40_RSCNN: data_path: ./data/ num_points: 1024 test_data_path: test_files.txt train_data_path: train_files.txt valid_data_path: train_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pct_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pct OPTIMIZER: pct SEED: 1 TASK: cls EXP_EXTRA: no_test: False no_val: True save_ckp: 25 test_eval_freq: 1 val_eval_freq: 1 MODEL: MV: backbone: resnet18 feat_size: 16 PN2: version_cls: 1.0 RSCNN: ssn_or_msn: True TRAIN: early_stop: 300 l2: 0.0001 learning_rate: 0.0001 lr_clip: 1e-05 lr_decay_factor: 0.5 lr_reduce_patience: 10 num_epochs: 300 Pct( (model): Pct( (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,), bias=False) (conv2): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False) (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (gather_local_0): Local_op( (conv1): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False) (conv2): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False) (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (gather_local_1): Local_op( (conv1): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (conv2): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (pt_last): Point_Transformer_Last( (conv1): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (conv2): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (sa1): SA_Layer( (q_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (k_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (v_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (trans_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (after_norm): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): ReLU() (softmax): Softmax(dim=-1) ) (sa2): SA_Layer( (q_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (k_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (v_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (trans_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (after_norm): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): ReLU() (softmax): Softmax(dim=-1) ) (sa3): SA_Layer( (q_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (k_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (v_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (trans_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (after_norm): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): ReLU() (softmax): Softmax(dim=-1) ) (sa4): SA_Layer( (q_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (k_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False) (v_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (trans_conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,)) (after_norm): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): ReLU() (softmax): Softmax(dim=-1) ) ) (conv_fuse): Sequential( (0): Conv1d(1280, 1024, kernel_size=(1,), stride=(1,), bias=False) (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.2) ) (linear1): Linear(in_features=1024, out_features=512, bias=False) (bn6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dp1): Dropout(p=0.5, inplace=False) (linear2): Linear(in_features=512, out_features=256, bias=True) (bn7): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dp2): Dropout(p=0.5, inplace=False) (linear3): Linear(in_features=256, out_features=40, bias=True) ) ) Recovering model and checkpoint from 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Done ... 92% (72 of 78) |###################### | Elapsed Time: 0:01:46 ETA: 0:00:09Adaptation Done ... 93% (73 of 78) |###################### | Elapsed Time: 0:01:48 ETA: 0:00:07Adaptation Done ... 94% (74 of 78) |###################### | Elapsed Time: 0:01:49 ETA: 0:00:05Adaptation Done ... 96% (75 of 78) |####################### | Elapsed Time: 0:01:51 ETA: 0:00:04Adaptation Done ... 97% (76 of 78) |####################### | Elapsed Time: 0:01:52 ETA: 0:00:02Traceback (most recent call last): File "main.py", line 684, in entry_test(cfg, test_or_valid, cmd_args.model_path,cmd_args.confusion) File "main.py", line 572, in entry_test test_perf = validate(cfg.EXP.TASK, loader_test, model, cfg.EXP.DATASET, cfg.ADAPT, confusion) File "main.py", line 228, in validate model = adapt_tent(inp,model,adapt) File "main.py", line 45, in adapt_tent tent_helper.forward_and_adapt(data,model,optimizer_tent) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 96, in decorate_enable_grad return func(*args, **kwargs) File "~/ModelNet40-C/third_party/tent_helper.py", line 52, in forward_and_adapt outputs = model(**x) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 152, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 162, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 85, in parallel_apply output.reraise() File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/_utils.py", line 394, in reraise raise self.exc_type(msg) ValueError: Caught ValueError in replica 0 on device 0. Original Traceback (most recent call last): File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 60, in _worker output = module(*input, **kwargs) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "~/ModelNet40-C/models/pct.py", line 29, in forward logit = self.model(pc) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "~/ModelNet40-C/PCT_Pytorch/model.py", line 69, in forward x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/modules/batchnorm.py", line 107, in forward exponential_average_factor, self.eps) File "~/miniconda3/envs/modelnetc/lib/python3.7/site-packages/torch/nn/functional.py", line 1666, in batch_norm raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size)) ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512])

Hi, thank you for your excellent paper and code release. I tried to generate ModelNet40-C but when I run generate_c.py, an error occured.

Traceback (most recent call last):
  File "data/generate_c.py", line 401, in <module>
    save_data(data[:,index,:], cor, sev)
  File "data/generate_c.py", line 360, in save_data
    new_data.append(MAP[corruption](data[i],severity))
TypeError: occlusion() takes 1 positional argument but 2 were given

I saw you comment some lines in generate_c.py, then can you give me some guides for 'occlusion' and 'lidar' corruptions?

# if cor in ['occlusion', 'lidar','original']:
        #     continue

And, if I want to use this dataset for benchmark, should I download ModelNet40-C directly using ./download.sh modelnet40_c rather than generate myself?

Thank you!

Hi authors,

We are the authors of GDANet.

Since you use our code in your repo, it is necessary for you to cite us following the other works, as shown at the end of your README.

Thanks, Mino

• Static data (e.g., dataset, pretrained models) move to a folder like data.
• Dynamic data (e.g., saved execution results) move to a folder like result.
• Executable code (e.g., python with main) move to a folder like src.
• Libraries and modules (e.g., third-party tools/repos) move to sub-directories in src.
• Configuration files (looks good now).
• Script tools (*.sh files) move to scripts or in the root directory.