For CIFAR-10 data augmentations using torchvision transforms. torchvision version: ‘0.15.2+cu117’ and torch version: 2.0.1+cu117
strength = 0.2
color_jitter = transforms.ColorJitter(
brightness = 0.8 * strength, contrast = 0.8 * strength,
saturation = 0.8 * strength, hue = 0.2 * strength
)
rand_color_jitter = transforms.RandomApply([color_jitter], p = 0.8)
rand_gray = transforms.RandomGrayscale(p = 0.2)
color_distortion = transforms.Compose(
[
rand_color_jitter,
rand_gray
]
)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_data_augmentation = transforms.Compose(
[
transforms.RandomResizedCrop(size = 32, scale = (0.14, 1.0)),
transforms.GaussianBlur(kernel_size = (3, 3), sigma = (0.1, 2.0)),
color_distortion,
# v2.ToImage(),
# v2.ToDtype(torch.float32, scale = True),
# v2.ToDtype(torch.float32),
# v2.Normalize(),
transforms.ToTensor(),
transforms.Normalize(mean = mean, std = std)
]
)
I use these augmentations inside a datasets class:
class Cifar10Dataset(torchvision.datasets.CIFAR10):
def __init__(
self, root = "~/data/cifar10",
train = True, download = True,
transform = None
):
super().__init__(
root = root, train = train,
download = download, transform = transform
)
def __getitem__(self, index):
image, label = self.data[index], self.targets[index]
if self.transform is not None:
image = self.transform(image)
# image = transformed["image"]
# Randomly select 0, 1, 2 or 3 for image rotation-
ang = np.random.randint(low = 0, high = 4, size = None)
image = TF.rotate(img = image, angle = ang * 90)
return image, ang
train_dataset = Cifar10Dataset(
root = some_path, train = True,
download = True, transform = train_data_augmentation
)
train_loader = torch.utils.data.DataLoader(
dataset = train_dataset, batch_size = 128,
shuffle = True
)
x, y = next(iter(training_loader))
Which throws the error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[124], line 1
----> 1 x, y = next(iter(train_loader))
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torch/utils/data/dataloader.py:633, in _BaseDataLoaderIter.__next__(self)
630 if self._sampler_iter is None:
631 # TODO(https://github.com/pytorch/pytorch/issues/76750)
632 self._reset() # type: ignore[call-arg]
--> 633 data = self._next_data()
634 self._num_yielded += 1
635 if self._dataset_kind == _DatasetKind.Iterable and \
636 self._IterableDataset_len_called is not None and \
637 self._num_yielded > self._IterableDataset_len_called:
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torch/utils/data/dataloader.py:677, in _SingleProcessDataLoaderIter._next_data(self)
675 def _next_data(self):
676 index = self._next_index() # may raise StopIteration
--> 677 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
678 if self._pin_memory:
679 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
Cell In[121], line 16, in Cifar10Dataset.__getitem__(self, index)
13 image, label = self.data[index], self.targets[index]
15 if self.transform is not None:
---> 16 image = self.transform(image)
17 # image = transformed["image"]
18
19 # Randomly select 0, 1, 2 or 3 for image rotation-
20 ang = np.random.randint(low = 0, high = 4, size = None)
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torchvision/transforms/transforms.py:95, in Compose.__call__(self, img)
93 def __call__(self, img):
94 for t in self.transforms:
---> 95 img = t(img)
96 return img
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torchvision/transforms/transforms.py:979, in RandomResizedCrop.forward(self, img)
971 def forward(self, img):
972 """
973 Args:
974 img (PIL Image or Tensor): Image to be cropped and resized.
(...)
977 PIL Image or Tensor: Randomly cropped and resized image.
978 """
--> 979 i, j, h, w = self.get_params(img, self.scale, self.ratio)
980 return F.resized_crop(img, i, j, h, w, self.size, self.interpolation, antialias=self.antialias)
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torchvision/transforms/transforms.py:940, in RandomResizedCrop.get_params(img, scale, ratio)
927 @staticmethod
928 def get_params(img: Tensor, scale: List[float], ratio: List[float]) -> Tuple[int, int, int, int]:
929 """Get parameters for ``crop`` for a random sized crop.
930
931 Args:
(...)
938 sized crop.
939 """
--> 940 _, height, width = F.get_dimensions(img)
941 area = height * width
943 log_ratio = torch.log(torch.tensor(ratio))
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torchvision/transforms/functional.py:78, in get_dimensions(img)
75 if isinstance(img, torch.Tensor):
76 return F_t.get_dimensions(img)
---> 78 return F_pil.get_dimensions(img)
File ~/anaconda3/envs/lightning_cuda/lib/python3.11/site-packages/torchvision/transforms/_functional_pil.py:31, in get_dimensions(img)
29 width, height = img.size
30 return [channels, height, width]
---> 31 raise TypeError(f"Unexpected type {type(img)}")
TypeError: Unexpected type <class 'numpy.ndarray'>
Shouldn’t RandomResizedCrop() be able to convert np array to torch tensor? What am I missing?