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Description
I run the "train.py" and found the following error.
$ py train.py
0%| | 0/518 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/calebee/DL/Machine-Learning-Collection/ML/Pytorch/object_detection/YOLOv3/train.py", line 114, in <module>
main()
File "/home/calebee/DL/Machine-Learning-Collection/ML/Pytorch/object_detection/YOLOv3/train.py", line 83, in main
train_fn(train_loader, model, optimizer, loss_fn, scaler, scaled_anchors)
File "/home/calebee/DL/Machine-Learning-Collection/ML/Pytorch/object_detection/YOLOv3/train.py", line 31, in train_fn
for batch_idx, (x, y) in enumerate(loop):
File "/home/calebee/miniconda3/lib/python3.10/site-packages/tqdm/std.py", line 1178, in __iter__
for obj in iterable:
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1372, in _process_data
data.reraise()
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/_utils.py", line 722, in reraise
raise exception
cv2.error: Caught error in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/calebee/miniconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/calebee/DL/Machine-Learning-Collection/ML/Pytorch/object_detection/YOLOv3/dataset.py", line 56, in __getitem__
augmentations = self.transform(image=image, bboxes=bboxes)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/core/composition.py", line 210, in __call__
data = t(**data)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/core/composition.py", line 326, in __call__
data = t(force_apply=True, **data)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/core/transforms_interface.py", line 118, in __call__
return self.apply_with_params(params, **kwargs)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/core/transforms_interface.py", line 131, in apply_with_params
res[key] = target_function(arg, **dict(params, **target_dependencies))
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/augmentations/geometric/transforms.py", line 657, in apply
return F.warp_affine(
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/augmentations/utils.py", line 122, in wrapped_function
result = func(img, *args, **kwargs)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/augmentations/geometric/functional.py", line 560, in warp_affine
tmp = warp_fn(image)
File "/home/calebee/miniconda3/lib/python3.10/site-packages/albumentations/augmentations/utils.py", line 208, in __process_fn
img = process_fn(img, **kwargs)
cv2.error: OpenCV(4.9.0) :-1: error: (-5:Bad argument) in function 'warpAffine'
> Overload resolution failed:
> - Argument 'borderMode' is required to be an integer
> - Argument 'borderMode' is required to be an integer
It seems that "test transofrms = A.Compose(" in Line 68 of "config.py" has a problem, but I don't know how to solve the problem.
test_transforms = A.Compose(
[
A.LongestMaxSize(max_size=IMAGE_SIZE),
A.PadIfNeeded(
min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
),
A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
ToTensorV2(),
],
bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[]),
)
Could you help me to solve the error?
Thank you in advance.
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