Even when the label passes through transform function (line 44), it still has large values.
It causes error when trainer try to compute loss (Cross entropy), because the model has 21 classes (including background) not over 200 classes.
The dataset have to convert mask pixel intensity to class values.
(np_trainer) junho.shin@3090e:~/netspresso-trainer-dev$ python ./train.py --data config/data/voc12.yaml --augmentation config/augmentation/segmentation.yaml --model config/model/segformer/segformer-segmentation.yaml --training config/training/segmentation.yaml --logging config/logging.yaml --environment config/environment.yaml
2023-09-12_14:53:39 KST | INFO | trainer:<trainer_common.py>:107 >>> Task: segmentation | Model: segformer | Training with torch.fx model? False
2023-09-12_14:53:39 KST | INFO | build_dataset:<builder.py>:16 >>> ----------------------------------------
2023-09-12_14:53:39 KST | INFO | build_dataset:<builder.py>:17 >>> Loading data...
2023-09-12_14:53:39 KST | INFO | build_dataset:<builder.py>:88 >>> Summary | Dataset: <voc2012> (with local format)
2023-09-12_14:53:39 KST | INFO | build_dataset:<builder.py>:89 >>> Summary | Training dataset: 1464 sample(s)
2023-09-12_14:53:39 KST | INFO | build_dataset:<builder.py>:91 >>> Summary | Validation dataset: 1449 sample(s)
2023-09-12_14:53:41 KST | INFO | train:<base.py>:150 >>> ----------------------------------------
0%| | 0/183 [00:00<?, ?it/s]../aten/src/ATen/native/cuda/NLLLoss2d.cu:104: nll_loss2d_forward_kernel: block: [10,0,0], thread: [416,0,0] Assertion `t >= 0 && t < n_classes` failed.
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2023-09-12_14:53:43 KST | ERROR | train:<base.py>:201 >>> CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Traceback (most recent call last):
File "./train.py", line 5, in <module>
trainer(is_graphmodule_training=False)
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/trainer_common.py", line 146, in trainer
raise e
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/trainer_common.py", line 139, in trainer
trainer.train()
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/pipelines/base.py", line 202, in train
raise e
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/pipelines/base.py", line 162, in train
self.train_one_epoch()
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/pipelines/base.py", line 206, in train_one_epoch
self.train_step(batch)
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/pipelines/segmentation.py", line 39, in train_step
self.loss_factory.backward()
File "/ssd1/junho.shin/netspresso-trainer-dev/src/netspresso_trainer/losses/builder.py", line 54, in backward
self.total_loss_for_backward.mean().backward()
File "/ssd1/junho.shin/anaconda3/envs/np_trainer/lib/python3.8/site-packages/torch/_tensor.py", line 487, in backward
torch.autograd.backward(
File "/ssd1/junho.shin/anaconda3/envs/np_trainer/lib/python3.8/site-packages/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.