75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
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# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple
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import torch
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def _image_gradients_validate(img: torch.Tensor) -> torch.Tensor:
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""" Validates whether img is a 4D torch Tensor """
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if not isinstance(img, torch.Tensor):
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raise TypeError(f"The `img` expects a value of <torch.Tensor> type but got {type(img)}")
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if img.ndim != 4:
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raise RuntimeError(f"The `img` expects a 4D tensor but got {img.ndim}D tensor")
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def _compute_image_gradients(img: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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""" Computes image gradients (dy/dx) for a given image """
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batch_size, channels, height, width = img.shape
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dy = img[..., 1:, :] - img[..., :-1, :]
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dx = img[..., :, 1:] - img[..., :, :-1]
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shapey = [batch_size, channels, 1, width]
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dy = torch.cat([dy, torch.zeros(shapey, device=img.device, dtype=img.dtype)], dim=2)
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dy = dy.view(img.shape)
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shapex = [batch_size, channels, height, 1]
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dx = torch.cat([dx, torch.zeros(shapex, device=img.device, dtype=img.dtype)], dim=3)
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dx = dx.view(img.shape)
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return dy, dx
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def image_gradients(img: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Computes the `gradients <https://en.wikipedia.org/wiki/Image_gradient>`_ of a given image using finite difference
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Args:
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img: An ``(N, C, H, W)`` input tensor where C is the number of image channels
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Return:
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Tuple of (dy, dx) with each gradient of shape ``[N, C, H, W]``
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Example:
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>>> image = torch.arange(0, 1*1*5*5, dtype=torch.float32)
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>>> image = torch.reshape(image, (1, 1, 5, 5))
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>>> dy, dx = image_gradients(image)
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>>> dy[0, 0, :, :]
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tensor([[5., 5., 5., 5., 5.],
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[5., 5., 5., 5., 5.],
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[5., 5., 5., 5., 5.],
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[5., 5., 5., 5., 5.],
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[0., 0., 0., 0., 0.]])
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.. note:: The implementation follows the 1-step finite difference method as followed
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by the TF implementation. The values are organized such that the gradient of
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[I(x+1, y)-[I(x, y)]] are at the (x, y) location
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"""
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_image_gradients_validate(img)
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return _compute_image_gradients(img)
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