ValueError: The provided lr scheduler "<torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7b985d13d730>" is invalid

[Family-Liao]

我在训练时遇到的报错，因为报错提示只有一行代码，trainer.fit(model, datamodule)，我不知道如何修改，请问该如何解决

这是我使用的包的版本

[youngzhou1999]

您好。

方便的话，您可以试一下降低torch的版本，参考这个链接有人遇到了类似的问题：Lightning-AI/pytorch-lightning#17476

[Family-Liao]

您好。

方便的话，您可以试一下降低torch的版本，参考这个链接有人遇到了类似的问题：Lightning-AI/pytorch-lightning#17476

好的，谢谢你的回答 @youngzhou1999

[Lukas88664]

您好。

方便的话，您可以试一下降低torch的版本，参考这个链接有人遇到了类似的问题：Lightning-AI/pytorch-lightning#17476

您好 我在复现代码的时候遇到了如下的问题 ：
/home/seivl/anaconda3/envs/smart/bin/python /home/seivl/桌面/SmartRefine-main/train.py --data_root /home/seivl/桌面/pkl_data/ori --p1_root /home/seivl/桌面/pkl_data/pkl --exp smartref_hivt_argo1 --gpus 1 --embed_dim 64 --refine_num 5 --seg_num 2 --refine_radius -1 --r_lo 2 --r_hi 10
Global seed set to 2024
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

| Name | Type | Params

0 | target_encoder | TargetRegion | 216 K
1 | reg_loss | LaplaceNLLLoss | 0
2 | cls_loss | SoftTargetCrossEntropyLoss | 0
3 | score_loss | ScoreRegL1Loss | 0
4 | minADE | ADE | 0
5 | minFDE | FDE | 0
6 | minMR | MR | 0

216 K Trainable params
0 Non-trainable params
216 K Total params
0.865 Total estimated model params size (MB)
/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch_geometric/deprecation.py:26: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead
warnings.warn(out)
Validation sanity check: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
File "/home/seivl/桌面/SmartRefine-main/train.py", line 45, in
trainer.fit(model, datamodule)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
self._dispatch()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
self.training_type_plugin.start_training(self)
File "/home/seivl/anaconda3/envs/smart/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/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
return self._run_train()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1311, in _run_train
self._run_sanity_check(self.lightning_module)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1375, in _run_sanity_check
self._evaluation_loop.run()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 140, in run
self.on_run_start(*args, **kwargs)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 86, in on_run_start
self._dataloader_iter = _update_dataloader_iter(data_fetcher, self.batch_progress.current.ready)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py", line 121, in _update_dataloader_iter
dataloader_iter = enumerate(data_fetcher, batch_idx)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 199, in iter
self.prefetching(self.prefetch_batches)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 258, in prefetching
self._fetch_next_batch()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 300, in _fetch_next_batch
batch = next(self.dataloader_iter)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 628, in next
data = self._next_data()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
return self._process_data(data)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
data.reraise()
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/_utils.py", line 543, in reraise
raise exception
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 58, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 58, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/seivl/anaconda3/envs/smart/lib/python3.8/site-packages/torch_geometric/data/dataset.py", line 289, in getitem
data = self.get(self.indices()[idx])
File "/home/seivl/桌面/SmartRefine-main/datasets/argoverse_v1_dataset.py", line 79, in get
return pickle.load(self.processed_paths[idx]), pickle.load(self._p1_paths[idx])
TypeError: file must have 'read' and 'readline' attributes

您方便帮我看看吗

[youngzhou1999]

您好。

已经在您issue下回复。