tensor_to_image(tensor: torch.Tensor) → numpy.array[source]

Converts a PyTorch tensor image to a numpy image. In case the tensor is in the GPU, it will be copied back to CPU.

Parameters:tensor (torch.Tensor) – image of the form \((C, H, W)\).
Returns:image of the form \((H, W, C)\).
Return type:numpy.ndarray
image_to_tensor(image: numpy.array) → torch.Tensor[source]

Converts a numpy image to a PyTorch tensor image.

Parameters:image (numpy.ndarray) – image of the form \((H, W, C)\).
Returns:tensor of the form \((C, H, W)\).
Return type:torch.Tensor
create_meshgrid(height: int, width: int, normalized_coordinates: Optional[bool] = True) → torch.Tensor[source]

Generates a coordinate grid for an image.

When the flag normalized_coordinates is set to True, the grid is normalized to be in the range [-1,1] to be consistent with the pytorch function grid_sample. http://pytorch.org/docs/master/nn.html#torch.nn.functional.grid_sample

  • height (int) – the image height (rows).
  • width (int) – the image width (cols).
  • normalized_coordinates (Optional[bool]) – wether to normalize coordinates in the range [-1, 1] in order to be consistent with the PyTorch function grid_sample.

returns a grid tensor with shape \((1, H, W, 2)\).

Return type:


one_hot(labels: torch.Tensor, num_classes: int, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, eps: Optional[float] = 1e-06) → torch.Tensor[source]

Converts an integer label 2D tensor to a one-hot 3D tensor.

  • labels (torch.Tensor) – tensor with labels of shape \((N, H, W)\), where N is batch siz. Each value is an integer representing correct classification.
  • num_classes (int) – number of classes in labels.
  • device (Optional[torch.device]) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
  • dtype (Optional[torch.dpython:type]) – the desired data type of returned tensor. Default: if None, infers data type from values.

the labels in one hot tensor.

Return type:


>>> labels = torch.LongTensor([[[0, 1], [2, 0]]])
>>> kornia.losses.one_hot(labels, num_classes=3)
tensor([[[[1., 0.],
          [0., 1.]],
         [[0., 1.],
          [0., 0.]],
         [[0., 0.],
          [1., 0.]]]]