Reputation: 119
I have a list called wordImages
. It contains images in np.array format with different width & height.
How Do I convert this into a tensor and use this instead of my_dataset
in the below code?
Currently i am using this. But I need to save/read images
demo_data = RawDataset(root="output_craft/", opt=opt)
demo_loader = torch.utils.data.DataLoader(
demo_data , batch_size=opt.batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_demo, pin_memory=True)
Upvotes: 6
Views: 11179
Reputation: 2368
You can use transforms
from the torchvision
library to do so. You can pass whatever transformation(s) you declare as an argument into whatever class
you use to create my_dataset
, like so:
from torchvision import transforms as transforms
class MyDataset(data.Dataset):
def __init__(self, transform=transforms.ToTensor()):
self.transform = transform
...
def __getitem__(self, idx):
...
img_tensor = self.transform(img)
return (img_tensor, label)
Upvotes: 3