PyTorch: how to use torchvision.transforms.AugMIx with torch.float32? - pytorch

Question

PyTorch: how to use torchvision.transforms.AugMIx with torch.float32?
I am trying to apply data augmentation in image dataset by using torchvision.transforms.AugMIx, but I have the following error: TypeError: Only torch.uint8 image tensors are supported, but found torch.float32.
I tried to convert it to int, but I have another error.
My code where I am trying to use the AugMix function:
transform = torchvision.transforms.Compose(
[
torchvision.transforms.Resize((224, 224)), # resize to 224*224
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), # normalization
torchvision.transforms.AugMix()
]
)
to_tensor = torchvision.transforms.ToTensor()
Image.MAX_IMAGE_PIXELS = None
class BreastDataset(torch.utils.data.Dataset):
def __init__(self, json_path, data_dir_path='./dataset', clinical_data_path=None, is_preloading=True):
self.data_dir_path = data_dir_path
self.is_preloading = is_preloading
with open(json_path) as f:
print(f"load data from {json_path}")
self.json_data = json.load(f)
def __len__(self):
return len(self.json_data)
def __getitem__(self, index):
label = int(self.json_data[index]["label"])
patient_id = self.json_data[index]["id"]
patch_paths = self.json_data[index]["patch_paths"]
data = {}
if self.is_preloading:
data["bag_tensor"] = self.bag_tensor_list[index]
else:
data["bag_tensor"] = self.load_bag_tensor([os.path.join(self.data_dir_path, p_path) for p_path in patch_paths])
data["label"] = label
data["patient_id"] = patient_id
data["patch_paths"] = patch_paths
return data
def load_bag_tensor(self, patch_paths):
"""Load a bag data as tensor with shape [N, C, H, W]"""
patch_tensor_list = []
for p_path in patch_paths:
patch = Image.open(p_path).convert("RGB")
patch_tensor = transform(patch) # [C, H, W]
patch_tensor = torch.unsqueeze(patch_tensor, dim=0) # [1, C, H, W]
patch_tensor_list.append(patch_tensor)
bag_tensor = torch.cat(patch_tensor_list, dim=0) # [N, C, H, W]
return bag_tensor
Any help is appreciated! Thank you in advance!

Answer 1

For me applying AugMix first and then ToTensor() worked
transformation = transforms.Compose([
transforms.AugMix(severity= 6,mixture_width=2),
transforms.ToTensor(),
transforms.RandomErasing(),
transforms.RandomGrayscale(p = 0.35)
])

Answer 2

torchvision.transforms.AugMix takes images at format uint8. It means that every pixels is 1 (gray) or 3 (rgb) numbers between 0 and 255 that is a classic format of image.
torch.Tensor.type(torch.float32) cast a uint8 tensor to float32 but it is not likely the single transformation that was applied in your image. The float32 images are often normalized to be in range [-1, 1] or [0, 1]. The common way to do so are:
img = img.type(torch.float32) / 128.0 - 1.0 # [-1, 1]
img = img.type(torch.float32) / 255.0 # [0, 1]
When you know in what cases you are you can recast to uint8:
img = (img + 1.0) * 128.0 # case [-1, 1]
img = img * 255.0 # case [0, 1]
img = torch.clip(img, 0.0, 255.0)
img = img.type(torch.uint8)