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View Code? Open in Web Editor NEWSimple and Effective All-In-One Automotive Perception Model with Multi Task Learning
Simple and Effective All-In-One Automotive Perception Model with Multi Task Learning
Traceback (most recent call last):
File "inference/run.py", line 19, in
from dataset.utils.transforms import Preproc
ModuleNotFoundError: No module named 'dataset'
Hi, looking for a quantized model if you can make avaialble
Thank you for the author's contribution. In this framework, It was very easy and effective to use the BDD100K open dataset for training. Therefore, I would like to further fine-tune the model using actual experimental scene data. The annotation work for object detection and scene classification can be done easily in the format of BDD100K.
However, I would like to ask for the author's suggestion on how to annotate road lines for lane detection in a proper way?
Hi cant find the pth files in the link,kindly fix
Traceback (most recent call last):
File "compressor.py", line 260, in
trainer_run()
File "compressor.py", line 211, in trainer_run
trainer.fit(model, train_loader, val_loader)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1234, in _run
results = self._run_stage()
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1321, in _run_stage
return self._run_train()
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1343, in _run_train
self._run_sanity_check()
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1411, in _run_sanity_check
val_loop.run()
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 154, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 127, in advance
output = self._evaluation_step(**kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 222, in _evaluation_step
output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1763, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 344, in validation_step
return self.model.validation_step(*args, **kwargs)
File "compressor.py", line 118, in validation_step
preds = self.forward(img)
File "compressor.py", line 92, in forward
pred = self.backbone(img)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/inferno/Downloads/loconav/CERBERUS/models/cerberus.py", line 78, in forward
scn_out = self.head_scn(small, argmax=inference)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/inferno/Downloads/loconav/CERBERUS/models/heads.py", line 96, in forward
w_pred, s_pred, t_pred = torch.split(x, self.cls_splits, 1)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/torch/functional.py", line 189, in split
return tensor.split(split_size_or_sections, dim)
File "/home/inferno/anaconda3/envs/cerberus/lib/python3.8/site-packages/torch/_tensor.py", line 611, in split
return super(Tensor, self).split_with_sizes(split_size, dim)
RuntimeError: start (14) + length (4) exceeds dimension size (16).
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