Closed MilesTheProwler closed 1 year ago
Hi,
According to the error message:
raise ImportError("This is the kaolin placeholder wheel from https://pypi.org/project/kaolin/. Please follow https://kaolin.readthedocs.io/en/latest/notes/installation.html for installation.")
ImportError: This is the kaolin placeholder wheel from https://pypi.org/project/kaolin/. Please follow https://kaolin.readthedocs.io/en/latest/notes/installation.html for installation.
It appears that the installation of kaolin is problematic.
Best, Tianjian
@tijiang13 thank you for your reply. Can I get your social account? I have something to ask about my custom data. Could you please help me. Currently, I am doing my master degree research on this.
Can I also use mask rvm instead of using sam ?
HYDRA_FULL_ERROR=1 python fit.py
Global seed set to 42
Switch to /mnt/c/Users/ADMIN/Desktop/InstantAvatar/bash/outputs/neuman/test
[train] No optimized smpl found.
[val] No optimized smpl found.
[test] No optimized smpl found.
[2023-08-08 00:09:26,193][torch.distributed.nn.jit.instantiator][INFO] - Created a temporary directory at /tmp/tmpxjkg3mtq
[2023-08-08 00:09:26,194][torch.distributed.nn.jit.instantiator][INFO] - Writing /tmp/tmpxjkg3mtq/_remote_module_non_scriptable.py
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
Loading model from: /mnt/c/Users/ADMIN/Desktop/InstantAvatar/bash/third_parties/lpips/weights/v0.1/vgg.pth
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
Saving configs.
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
--------------------------------------------------
0 | net_coarse | NeRFNGPNet | 13.0 M
1 | SMPL_param | SMPLParamEmbedding | 1.5 K
2 | loss_fn | NGPLoss | 14.7 M
3 | renderer | Raymarcher | 0
--------------------------------------------------
13.0 M Trainable params
14.7 M Non-trainable params
27.8 M Total params
111.016 Total estimated model params size (MB)
Error executing job with overrides: ['dataset=neuman/test', 'experiment=neuman/test/exp', 'deformer=smpl', 'train.max_epochs=200']
Traceback (most recent call last):
File "fit.py", line 78, in <module>
main()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/main.py", line 48, in decorated_main
_run_hydra(
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "fit.py", line 50, in main
trainer.fit(model)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 738, in fit
self._call_and_handle_interrupt(
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 683, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 773, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1195, in _run
self._dispatch()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1275, in _dispatch
self.training_type_plugin.start_training(self)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
self._results = trainer.run_stage()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1285, in run_stage
return self._run_train()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1315, in _run_train
self.fit_loop.run()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 140, in run
self.on_run_start(*args, **kwargs)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 197, in on_run_start
self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py", line 569, in reset_train_val_dataloaders
self.reset_train_dataloader(model=model)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py", line 345, in reset_train_dataloader
self.train_dataloader = self.request_dataloader(RunningStage.TRAINING, model=model)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py", line 585, in request_dataloader
dataloader = source.dataloader()
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 296, in dataloader
return self.instance.trainer.call_hook(self.name, pl_module=self.instance)
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1497, in call_hook
output = model_fx(*args, **kwargs)
File "/mnt/c/Users/ADMIN/Desktop/InstantAvatar/bash/instant_avatar/models/DNeRF.py", line 246, in train_dataloader
return self.datamodule.train_dataloader()
File "/mnt/c/Users/ADMIN/Desktop/InstantAvatar/bash/instant_avatar/datasets/custom.py", line 164, in train_dataloader
return DataLoader(self.trainset,
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 351, in __init__
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "/home/thar/miniconda3/envs/TestAvatar/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 107, in __init__
raise ValueError("num_samples should be a positive integer "
ValueError: num_samples should be a positive integer value, but got num_samples=0
Thanks.
Hi,
Unfortunately I barely use any social media. However, feel free to leave your questions here, and I'll try to get back to you.
Regarding the error message, it looks like the dataloader is unable to locate any data at the specified location. Please ensure that the required data is placed correctly and that the file paths are accurate.
Best, Tianjian
Thank you for your reply. Can I know where should I put my custom dataset in order to match with your repo dataset location ?
In addition, I saw that your repo can output 3D obj by using marching cube, do you have any obj example from marching cube? If so could you please share with me , I want to see the result of obj file.
Could you also please check about my custom dataset is correct or not ?
You can run the demo code and refer https://github.com/tijiang13/InstantAvatar/issues/39 for details.
For the image you show, it appears that you didn't run the SAM.
Best, Tianjian
I cannot run SAM got erorr, so I run RVM, Is it same right ?
No it's not.
Btw if you are looking for the mesh this is probably the wrong repo. Generally the mesh produced by NeRF is very noisy.
Best, Tianjian
Could you give me some obj file that is obtained from NeRF and convert with marching cube ? I want to see the result of obj file.
Thanks.
I don't have that at hand. But you can try the snippet in quick-start and the script in #39 which do not require the installation of SAM and should get you some results in 10 minutes.
Best, Tianjian
Thank you !
Hi, can someone help me? I have errors when I run bash run-demo.sh.
My python version is 3.8.16, Torch : 2.0.0+cu118 Ubuntu : 22.04