Comments (5)
edgarriba
commented on June 26, 2024
the augmentations in the end are nn.Module 's, this same behaviour i believe you face when you forward a tensor to a regular model in pytorch that the tensor should match with the params device and not the other way around @johnnv1 @shijianjian
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ditwoo
commented on June 26, 2024
the augmentations in the end are nn.Module 's, this same behaviour i believe you face when you forward a tensor to a regular model in pytorch that the tensor should match with the params device and not the other way around @johnnv1 @shijianjian
Yeah, It should work like with nn.Module. When I'm applying the same logic to a RandomJPEG object - create an object, then move it to a CUDA device, then create an input tensor on the same CUDA device, then call the RandomJPEG object with the previously created tensor I still have an error. Here is an example:
import torch
from kornia.augmentation import RandomJPEG
device = "cuda"
jpegq = (1.0, 50.0)
aug = RandomJPEG(jpeg_quality=jpegq, p=1.0).to(device)
example_input = torch.randn((3, 224, 224)).to(device)
res = aug(example_input)And here is an error about the wrong devices:
/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Traceback (most recent call last):
File "/home/dmdr/Documents/Code/Python/aaa/ptrainer/tmp.py", line 27, in <module>
res = aug(example_input)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/augmentation/base.py", line 210, in forward
output = self.apply_func(in_tensor, params, flags)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/augmentation/_2d/base.py", line 129, in apply_func
output = self.transform_inputs(in_tensor, params, flags, trans_matrix)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/augmentation/base.py", line 261, in transform_inputs
output = self.apply_transform(in_tensor, params, flags, transform=transform)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/jpeg.py", line 56, in apply_transform
jpeg_output: Tensor = jpeg_codec_differentiable(input, params["jpeg_quality"])
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/utils/image.py", line 231, in _wrapper
output = f(input, *args, **kwargs)
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/enhance/jpeg.py", line 484, in jpeg_codec_differentiable
y_encoded, cb_encoded, cr_encoded = _jpeg_encode(
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/enhance/jpeg.py", line 281, in _jpeg_encode
y_encoded: Tensor = _quantize(
File "/home/dmdr/miniconda3/envs/ptrain/lib/python3.10/site-packages/kornia/enhance/jpeg.py", line 177, in _quantize
quantization_table[:, None] * _jpeg_quality_to_scale(jpeg_quality)[:, None, None, None]
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!from kornia.
edgarriba
commented on June 26, 2024
@ditwoo thanks ! we'll try to fix unless you want to give it a shot
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ditwoo
commented on June 26, 2024
@edgarriba I can write a PR with a fix.
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edgarriba
commented on June 26, 2024
@ditwoo thanks ! very appreciated
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Related Issues (20)
- `AugmentationSequential` does not support instance masks shape (N, H, W) HOT 1
- Improve Image Matching docs HOT 2
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- AttributeError: 'list' object has no attribute 'ndim' with RandomTransplantation and batch with keys HOT 2
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