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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch import Tensor
def _image_gradients_validate(img: Tensor) -> None:
"""Validate whether img is a 4D torch Tensor."""
if not isinstance(img, Tensor):
raise TypeError(f"The `img` expects a value of <Tensor> type but got {type(img)}")
if img.ndim != 4:
raise RuntimeError(f"The `img` expects a 4D tensor but got {img.ndim}D tensor")
def _compute_image_gradients(img: Tensor) -> tuple[Tensor, Tensor]:
"""Compute image gradients (dy/dx) for a given image."""
batch_size, channels, height, width = img.shape
dy = img[..., 1:, :] - img[..., :-1, :]
dx = img[..., :, 1:] - img[..., :, :-1]
shapey = [batch_size, channels, 1, width]
dy = torch.cat([dy, torch.zeros(shapey, device=img.device, dtype=img.dtype)], dim=2)
dy = dy.view(img.shape)
shapex = [batch_size, channels, height, 1]
dx = torch.cat([dx, torch.zeros(shapex, device=img.device, dtype=img.dtype)], dim=3)
dx = dx.view(img.shape)
return dy, dx
def image_gradients(img: Tensor) -> tuple[Tensor, Tensor]:
"""Compute `Gradient Computation of Image`_ of a given image using finite difference.
Args:
img: An ``(N, C, H, W)`` input tensor where ``C`` is the number of image channels
Return:
Tuple of ``(dy, dx)`` with each gradient of shape ``[N, C, H, W]``
Raises:
TypeError:
If ``img`` is not of the type :class:`~torch.Tensor`.
RuntimeError:
If ``img`` is not a 4D tensor.
Example:
>>> from torchmetrics.functional.image import image_gradients
>>> image = torch.arange(0, 1*1*5*5, dtype=torch.float32)
>>> image = torch.reshape(image, (1, 1, 5, 5))
>>> dy, dx = image_gradients(image)
>>> dy[0, 0, :, :]
tensor([[5., 5., 5., 5., 5.],
[5., 5., 5., 5., 5.],
[5., 5., 5., 5., 5.],
[5., 5., 5., 5., 5.],
[0., 0., 0., 0., 0.]])
.. note::
The implementation follows the 1-step finite difference method as followed
by the TF implementation. The values are organized such that the gradient of
[I(x+1, y)-[I(x, y)]] are at the (x, y) location
"""
_image_gradients_validate(img)
return _compute_image_gradients(img)
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