Hi @qq456cvb,

I've had experience working with the ModelNet40 data which usually comes in the format of .OFF https://segeval.cs.princeton.edu/public/off_format.html with x, y, z listed in each file. However, the dataset provided on this repo is from the shapenet website and seems to be in a different format.

Digging through the files, I find that there are 3 extra values following the x, y, z. What do these 3 additional values represent? What are the differences between the two mentioned datsets?

Thanks!

batch_size: 32
epoch: 500
learning_rate: 0.0005
gpu: 0
num_point: 1024
optimizer: Adam
weight_decay: 0.0001
normal: true
model:
  nneighbor: 16
  nblocks: 4
  transformer_dim: 512
  name: Hengshuang

Best Instance Accuracy: 0.917067, Class Accuracy: 0.893971

I have installed Torch and CUDA，and my computer system also has GPU. However, this model still is successfully transformed to CUDA. As this How to solve this problem？

Hi, @qq456cvb,

I got the error RuntimeError: cuda runtime error (38) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:50 . The details is here (https://gist.github.com/amiltonwong/9feaaa7d91721ccf6ad8a0d9b5d27d56)

The version of pytorch in my system is v1.3. Which version should we use?

Thanks!

Thanks for your attention to PCT ! In our experiments, I find the PCT is sensitive to initial lr. 1e-3 is too large to make pct converge. I change the initial lr from 1e-3 to 1e-4 and get following results.

batch_size: 16 epoch: 200 gpu: 1 learning_rate: 0.0001 model: name: Menghao name: Menghao normal: true num_point: 1024 optimizer: Adam weight_decay: 0.0001

[2021-01-13 11:59:39,205][main][INFO] - Load dataset ... The size of train data is 9843 The size of test data is 2468 [2021-01-13 11:59:42,420][main][INFO] - No existing model, starting training from scratch... [2021-01-13 11:59:42,421][main][INFO] - Start training... [2021-01-13 11:59:42,421][main][INFO] - Epoch 1 (1/200): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 616/616 [03:09<00:00, 3.25it/s] [2021-01-13 12:02:51,857][main][INFO] - Train Instance Accuracy: 0.387277 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:48<00:00, 3.20it/s] [2021-01-13 12:03:40,472][main][INFO] - Test Instance Accuracy: 0.511694, Class Accuracy: 0.345986 [2021-01-13 12:03:40,472][main][INFO] - Best Instance Accuracy: 0.511694, Class Accuracy: 0.345986 [2021-01-13 12:03:40,472][main][INFO] - Save model... [2021-01-13 12:03:40,472][main][INFO] - Saving at best_model.pth [2021-01-13 12:03:40,559][main][INFO] - Epoch 2 (2/200): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 616/616 [03:13<00:00, 3.18it/s] [2021-01-13 12:06:54,418][main][INFO] - Train Instance Accuracy: 0.488636 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:48<00:00, 3.16it/s] [2021-01-13 12:07:43,537][main][INFO] - Test Instance Accuracy: 0.663710, Class Accuracy: 0.506854 [2021-01-13 12:07:43,538][main][INFO] - Best Instance Accuracy: 0.663710, Class Accuracy: 0.506854 [2021-01-13 12:07:43,538][main][INFO] - Save model... [2021-01-13 12:07:43,538][main][INFO] - Saving at best_model.pth [2021-01-13 12:07:43,680][main][INFO] - Epoch 3 (3/200): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 616/616 [03:09<00:00, 3.25it/s] [2021-01-13 12:10:53,202][main][INFO] - Train Instance Accuracy: 0.549682 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:49<00:00, 3.11it/s] [2021-01-13 12:11:43,145][main][INFO] - Test Instance Accuracy: 0.747581, Class Accuracy: 0.612702 [2021-01-13 12:11:43,146][main][INFO] - Best Instance Accuracy: 0.747581, Class Accuracy: 0.612702 [2021-01-13 12:11:43,146][main][INFO] - Save model... [2021-01-13 12:11:43,146][main][INFO] - Saving at best_model.pth [2021-01-13 12:11:43,239][main][INFO] - Epoch 4 (4/200): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 616/616 [03:11<00:00, 3.22it/s] [2021-01-13 12:14:54,499][main][INFO] - Train Instance Accuracy: 0.592549 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:49<00:00, 3.16it/s] [2021-01-13 12:15:43,646][main][INFO] - Test Instance Accuracy: 0.799597, Class Accuracy: 0.684245 [2021-01-13 12:15:43,647][main][INFO] - Best Instance Accuracy: 0.799597, Class Accuracy: 0.684245 [2021-01-13 12:15:43,647][main][INFO] - Save model... [2021-01-13 12:15:43,647][main][INFO] - Saving at best_model.pth [2021-01-13 12:15:43,773][main][INFO] - Epoch 5 (5/200): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 616/616 [03:10<00:00, 3.23it/s] [2021-01-13 12:18:54,845][main][INFO] - Train Instance Accuracy: 0.625095 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:48<00:00, 3.19it/s] [2021-01-13 12:19:43,585][main][INFO] - Test Instance Accuracy: 0.813306, Class Accuracy: 0.711771 [2021-01-13 12:19:43,586][main][INFO] - Best Instance Accuracy: 0.813306, Class Accuracy: 0.711771 [2021-01-13 12:19:43,586][main][INFO] - Save model... [2021-01-13 12:19:43,586][main][INFO] - Saving at best_model.pth [2021-01-13 12:19:43,692][main][INFO] - Epoch 6 (6/200): 7%|██████████▏ | 44/616 [00:15<02:27, 3.89it/s]

Best Regards, Menghao

Hi. Thanks for sharing the code.

When I run

python train.py

there's an error

Traceback (most recent call last):
  File "train.py", line 44, in <module>
    @hydra.main(config_path='config', config_name='config')
AttributeError: module 'hydra' has no attribute 'main'

How can I solve this?

Hi, @qq456cvb ,

According to the Fig. 3 in Hengshuang's Point Transformer model, the output feature dimension of each transformer block should be different . e.g. [32, 64, 128, 256, 512]. But your implementation uses a unqiue one, e.g. 512. Any comment on this?

Thanks!

1. In the paper, the output of the encoder would be processed by a MA-Pool layer, instead of a single max pooling.
2. The initial learning rate in paper is 0.01, with a cosine annealing schedule.

https://github.com/qq456cvb/Point-Transformers/blob/5caf2caede06b452592a16e2b342df48d24fbdd3/pointnet_util.py#L261

In this class, there is one line code as following:

interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)

I think points2 should be replaced by points1.

The variable "idx" refers to the top3 index of points1 whose distance is short to points2, and the “index_points” function is to find k(k=3) nearest neighbor of each point in points2 , which is found in points1.

Are there any questions in my understanding? I really sincerely want to get your answer, thanks !

Hello. I am getting this error everytime I run the code

/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'cls': Defaults list is missing _self_. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information warnings.warn(msg, UserWarning) /home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/core/default_element.py:126: UserWarning: In 'model/Menghao': Usage of deprecated keyword in package header '# @package group'. See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/changes_to_package_header for more information See {url} for more information""" {'model': {'name': 'Menghao'}, 'batch_size': 16, 'epoch': 200, 'learning_rate': 0.001, 'gpu': 1, 'num_point': 1024, 'optimizer': 'Adam', 'weight_decay': 0.0001, 'normal': True} [2022-09-17 13:35:42,215][main][INFO] - Load dataset ... The size of train data is 9843 The size of test data is 2468 Error executing job with overrides: [] Traceback (most recent call last): File "train_cls.py", line 162, in main() File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/main.py", line 52, in decorated_main config_name=config_name, File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/utils.py", line 378, in _run_hydra lambda: hydra.run( File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/utils.py", line 214, in run_and_report raise ex File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/utils.py", line 211, in run_and_report return func() File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/utils.py", line 381, in overrides=args.overrides, File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/_internal/hydra.py", line 111, in run _ = ret.return_value File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/core/utils.py", line 233, in return_value raise self._return_value File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/hydra/core/utils.py", line 160, in run_job ret.return_value = task_function(task_cfg) File "train_cls.py", line 68, in main classifier = getattr(importlib.import_module('models.{}.model'.format(args.model.name)), 'PointTransformerCls')(args).cuda() File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 689, in cuda return self._apply(lambda t: t.cuda(device)) File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 602, in _apply param_applied = fn(param) File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 689, in return self._apply(lambda t: t.cuda(device)) File "/home/myuser/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/cuda/init.py", line 217, in _lazy_init torch._C._cuda_init() RuntimeError: No CUDA GPUs are available

If I dont run it through hydra, it compiles easily. Any specific versions of Hydra that you are using?

In my experiment based on shapenet, thebest inctance avg mIOU is only 84.4%, which is 2% lower than that in the paper. Do you know the reason？Thank you very much

Hello,

When I used ModelNet 40 and other dataset to train Point Transformer(Hengshuang), I found after the 3rd epoch, the test accuracy would be higher than train accuracy. It makes no sense a model could perform better when processing unseen data, right? Do you also meet this phenomenon?

Thank you for your great work ! I wonder the hardware requirements for the all three versions of Point Transformer. It seems that Hengshuang's method needs 4 24GB GPUs (TITAN RTX),how about the another two versions? Can several RTX 2080 Ti GPUs work? Looking forward to your reply!