The problems about train.py

[Yxs-160]

https://github.com/FomalhautB/3D-RETR/blob/a359ec453a930cf507aab442fac57b7bae3029ce/train.py#L76
When i ran the raw codes, I have met a problem

python train.py --model_path SHAPENET_VOXEL --image_path SHAPENET_IMAGE --annot_path data/ShapeNet.json --transformer_config config/3d-retr-s.yaml --gpus 2
{ 'accelerator': None,
'accumulate_grad_batches': 1,
'amp_backend': 'native',
'amp_level': 'O2',
'annot_path': 'data/ShapeNet.json',
'auto_lr_find': False,
'auto_scale_batch_size': False,
'auto_select_gpus': False,
'background': (0, 0, 0),
'benchmark': False,
'check_val_every_n_epoch': 1,
'checkpoint_callback': True,
'continue_from': None,
'data_aug': False,
'default_root_dir': None,
'deterministic': False,
'distributed_backend': None,
'experiment_name': '3D-RETR',
'fast_dev_run': False,
'flush_logs_every_n_steps': 100,
'gpus': 2,
'gradient_clip_algorithm': 'norm',
'gradient_clip_val': 0.0,
'image_path': 'SHAPENET_IMAGE',
'limit_predict_batches': 1.0,
'limit_test_batches': 1.0,
'limit_train_batches': 1.0,
'limit_val_batches': 1.0,
'log_every_n_steps': 50,
'log_gpu_memory': None,
'logger': True,
'loss_type': 'dice',
'lr': 0.0001,
'max_epochs': None,
'max_steps': None,
'max_time': None,
'min_epochs': None,
'min_steps': None,
'model_path': 'SHAPENET_VOXEL',
'move_metrics_to_cpu': False,
'multiple_trainloader_mode': 'max_size_cycle',
'num_nodes': 1,
'num_processes': 1,
'num_sanity_val_steps': 2,
'num_workers': 8,
'overfit_batches': 0.0,
'plugins': None,
'precision': 32,
'prepare_data_per_node': True,
'process_position': 0,
'profiler': None,
'progress_bar_refresh_rate': None,
'reload_dataloaders_every_epoch': False,
'replace_sampler_ddp': True,
'resume_from_checkpoint': None,
'sample_batch_num': 0,
'sched_factor': 1,
'seed': 0,
'stochastic_weight_avg': False,
'sync_batchnorm': False,
'terminate_on_nan': False,
'threshold': 0.5,
'tpu_cores': None,
'track_grad_norm': -1,
'train_batch_size': 8,
'transformer_config': 'config/3d-retr-s.yaml',
'truncated_bptt_steps': None,
'val_batch_size': 8,
'val_check_interval': 1.0,
'view_num': 1,
'weights_save_path': None,
'weights_summary': 'top'}
Global seed set to 0
{ 'decoder_depth': 6,
'decoder_dim': 192,
'decoder_dropout': 0.4,
'decoder_heads': 3,
'decoder_model': 'cnn',
'encoder_dropout': 0.4,
'encoder_model': 'vit_deit_tiny_distilled_patch16_224',
'num_cnn_layers': 3,
'num_resnet_blocks': 2,
'patch_num': 4,
'voxel_size': 32}
Ignored parameter "head.weight" on loading
Ignored parameter "head.bias" on loading
Ignored parameter "head_dist.weight" on loading
Ignored parameter "head_dist.bias" on loading
/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: You requested multiple GPUs but did not specify a backend, e.g. Trainer(accelerator="dp"|"ddp"|"ddp2"). Setting accelerator="ddp_spawn" for you.
warnings.warn(*args, **kwargs)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
Traceback (most recent call last):
File "train.py", line 155, in
trainer.fit(model, train_loader, val_loader)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit
self._run(model)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 756, in _run
self.dispatch()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 797, in dispatch
self.accelerator.start_training(self)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 122, in start_training
mp.spawn(self.new_process, **self.mp_spawn_kwargs)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 179, in start_processes
process.start()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/process.py", line 105, in start
self._popen = self._Popen(self)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 32, in init
super().init(process_obj)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/popen_fork.py", line 19, in init
self._launch(process_obj)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function at 0x7f5b76c48510>: attribute lookup on main failed

I try to solve it by https://stackoverflow.com/a/25353243/1791279 and to change this line of code to pickle.dumps(lambda x: x[0]), but still can not run the codes successfully, another error occured :

python train.py --model_path SHAPENET_VOXEL --image_path SHAPENET_IMAGE --annot_path data/ShapeNet.json --transformer_config config/3d-retr-s.yaml --gpus 2
{ 'accelerator': None,
'accumulate_grad_batches': 1,
'amp_backend': 'native',
'amp_level': 'O2',
'annot_path': 'data/ShapeNet.json',
'auto_lr_find': False,
'auto_scale_batch_size': False,
'auto_select_gpus': False,
'background': (0, 0, 0),
'benchmark': False,
'check_val_every_n_epoch': 1,
'checkpoint_callback': True,
'continue_from': None,
'data_aug': False,
'default_root_dir': None,
'deterministic': False,
'distributed_backend': None,
'experiment_name': '3D-RETR',
'fast_dev_run': False,
'flush_logs_every_n_steps': 100,
'gpus': 2,
'gradient_clip_algorithm': 'norm',
'gradient_clip_val': 0.0,
'image_path': 'SHAPENET_IMAGE',
'limit_predict_batches': 1.0,
'limit_test_batches': 1.0,
'limit_train_batches': 1.0,
'limit_val_batches': 1.0,
'log_every_n_steps': 50,
'log_gpu_memory': None,
'logger': True,
'loss_type': 'dice',
'lr': 0.0001,
'max_epochs': None,
'max_steps': None,
'max_time': None,
'min_epochs': None,
'min_steps': None,
'model_path': 'SHAPENET_VOXEL',
'move_metrics_to_cpu': False,
'multiple_trainloader_mode': 'max_size_cycle',
'num_nodes': 1,
'num_processes': 1,
'num_sanity_val_steps': 2,
'num_workers': 8,
'overfit_batches': 0.0,
'plugins': None,
'precision': 32,
'prepare_data_per_node': True,
'process_position': 0,
'profiler': None,
'progress_bar_refresh_rate': None,
'reload_dataloaders_every_epoch': False,
'replace_sampler_ddp': True,
'resume_from_checkpoint': None,
'sample_batch_num': 0,
'sched_factor': 1,
'seed': 0,
'stochastic_weight_avg': False,
'sync_batchnorm': False,
'terminate_on_nan': False,
'threshold': 0.5,
'tpu_cores': None,
'track_grad_norm': -1,
'train_batch_size': 8,
'transformer_config': 'config/3d-retr-s.yaml',
'truncated_bptt_steps': None,
'val_batch_size': 8,
'val_check_interval': 1.0,
'view_num': 1,
'weights_save_path': None,
'weights_summary': 'top'}
Global seed set to 0
{ 'decoder_depth': 6,
'decoder_dim': 192,
'decoder_dropout': 0.4,
'decoder_heads': 3,
'decoder_model': 'cnn',
'encoder_dropout': 0.4,
'encoder_model': 'vit_deit_tiny_distilled_patch16_224',
'num_cnn_layers': 3,
'num_resnet_blocks': 2,
'patch_num': 4,
'voxel_size': 32}
Ignored parameter "head.weight" on loading
Ignored parameter "head.bias" on loading
Ignored parameter "head_dist.weight" on loading
Ignored parameter "head_dist.bias" on loading
/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: You requested multiple GPUs but did not specify a backend, e.g. Trainer(accelerator="dp"|"ddp"|"ddp2"). Setting accelerator="ddp_spawn" for you.
warnings.warn(*args, **kwargs)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
Global seed set to 0
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/2
Global seed set to 0
initializing ddp: GLOBAL_RANK: 1, MEMBER: 2/2

| Name | Type | Params

0 | encoder | VisionTransformerEncoder | 5.5 M
1 | decoder | VoxelDecoderCNN | 4.9 M

10.4 M Trainable params
0 Non-trainable params
10.4 M Total params
41.761 Total estimated model params size (MB)
Validation sanity check: 0it [00:00, ?it/s]/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: num_workers>0, persistent_workers=False, and accelerator=ddp_spawn may result in data loading bottlenecks. Consider setting persistent_workers=True (this is a limitation of Python .spawn() and PyTorch)
warnings.warn(*args, **kwargs)
Validation sanity check: 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "train.py", line 155, in
trainer.fit(model, train_loader, val_loader)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit
self._run(model)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 756, in _run
self.dispatch()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 797, in dispatch
self.accelerator.start_training(self)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 122, in start_training
mp.spawn(self.new_process, **self.mp_spawn_kwargs)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
while not context.join():
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 150, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrap
fn(i, *args)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 172, in new_process
results = trainer.run_stage()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 807, in run_stage
return self.run_train()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 842, in run_train
self.run_sanity_check(self.lightning_module)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1107, in run_sanity_check
self.run_evaluation()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 949, in run_evaluation
for batch_idx, batch in enumerate(dataloader):
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 517, in next
data = self._next_data()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1199, in _next_data
return self._process_data(data)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data
data.reraise()
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/_utils.py", line 429, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
data = fetcher.fetch(index)
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/ubuntu/anaconda3/envs/3d-retr/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/ubuntu/YaoXinSheng/3D-RETR-main/src/data/datasets.py", line 25, in getitem
return self._dataset[self._indices[index].item()]
File "/home/ubuntu/YaoXinSheng/3D-RETR-main/src/data/datasets.py", line 109, in getitem
image = self._image_transforms(image)
File "/home/ubuntu/YaoXinSheng/3D-RETR-main/src/data/transforms.py", line 32, in call
rendering_images = t(rendering_images)
TypeError: 'bytes' object is not callable

And I do not know how to solve it, could you help me?