ab9c4f8474ca744c7e67822fa21d408d0b0ae2d0,kornia/augmentation/functional.py,,apply_affine,#Any#Any#Any#,412

Before Change

    x_data: torch.Tensor = input.view(-1, *input.shape[-3:])

    height, width = x_data.shape[-2:]
    transform: torch.Tensor = params["transform"].to(device, dtype)
    out_data: torch.Tensor = warp_affine(x_data, transform[:, :2, :], (height, width))

    if return_transform:
        return out_data.view_as(input), transform

After Change

    

    input = _transform_input(input)
    _validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])

    // arrange input data
    x_data: torch.Tensor = input.view(-1, *input.shape[-3:])

Instances