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Models (Beta)
Discover, publish, and reuse pre-trained models
class
torchvision.transforms.
GaussianBlur
(
kernel_size
,
sigma
=
(0.1,
2.0)
)
[source]
Blurs image with randomly chosen Gaussian blur.
If the image is torch Tensor, it is expected
to have […, C, H, W] shape, where … means an arbitrary number of leading dimensions.
Parameters
:
kernel_size
(
int
or
sequence
) – Size of the Gaussian kernel.
sigma
(
float
or
tuple of python:float
(
min
,
max
)
) – Standard deviation to be used for
creating kernel to perform blurring. If float, sigma is fixed. If it is tuple
of float (min, max), sigma is chosen uniformly at random to lie in the
given range.
Returns
:
Gaussian blurred version of the input image.
Return type
:
PIL Image or Tensor
Examples using
GaussianBlur
:
Illustration of transforms
Illustration of transforms
forward
(
img
:
Tensor
)
→
Tensor
[source]
Parameters
:
img
(
PIL Image
or
Tensor
) – image to be blurred.
Returns
:
Gaussian blurred image
Return type
:
PIL Image or Tensor
static
get_params
(
sigma_min
:
float
,
sigma_max
:
float
)
→
float
[source]
Choose sigma for random gaussian blurring.
Parameters
:
sigma_min
(
float
) – Minimum standard deviation that can be chosen for blurring kernel.
sigma_max
(
float
) – Maximum standard deviation that can be chosen for blurring kernel.
Returns
:
Standard deviation to be passed to calculate kernel for gaussian blurring.
Return type
:
float
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