[FIXED] PyTorch augmentation

Issue

i'm new to machine learning and pytorch. I'm using imgaug library for images augmentation (https://github.com/aleju/imgaug)

I have this code:

class ImgAugTransform:
    def __init__(self):
        self.aug = seq = iaa.Sequential(
            [
                # Apply the following augmenters to most images
                iaa.Fliplr(0.5), # horizontally flip 50% of all images
                iaa.Flipud(0.2),  # vertically flip 20% of all images
                random_aug_use(iaa.CropAndPad( # crop images by -5% to 10% of their height/width
                    percent=(-0.1, 0.2),
                    pad_mode=ia.ALL,
                    pad_cval=(0.,255)
                )),
                random_aug_use(iaa.Affine(
                    scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
                    translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
                    rotate=(-45, 45), # rotate by -45 to +45 degrees
                    shear=(-16, 16), # shear by -16 to +16 degrees
                    order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
                    cval=(0, 255), # if mode is constant, use a cval between 0 and 255
                    mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
                ))
            ], 
            random_order=True)
    
    def __call__(self, img):
        img = np.array(img)
        return self.aug.augment_image(img)

train_transforms = ImgAugTransform()

train_dataset = torchvision.datasets.ImageFolder(train_dir, train_transforms)

train_dataloader = torch.utils.data.DataLoader(
    train_dataset, batch_size=batch_size, shuffle=True, num_workers=batch_size)

So now i cant do this:

X_batch, y_batch = next(iter(train_dataloader))

I get error:

ValueError: some of the strides of a given numpy array are negative. This is currently not supported, but will be added in future releases.

Solution

You should make your augmented numpy arrays contiguous again. try modifying your augmenter code to:

    def __call__(self, img):
        img = np.array(img)
        return np.ascontiguousarray(self.aug.augment_image(img))

Answered By - Shai