transform_linear_transformation | R Documentation |
Given transformation_matrix
and mean_vector
, will flatten the
torch_tensor
and subtract mean_vector
from it which is then followed by
computing the dot product with the transformation matrix and then reshaping
the tensor to its original shape.
transform_linear_transformation(img, transformation_matrix, mean_vector)
img |
A |
transformation_matrix |
(Tensor): tensor |
mean_vector |
(Tensor): tensor D, D = C x H x W. |
whitening transformation: Suppose X is a column vector zero-centered data.
Then compute the data covariance matrix [D x D]
with torch.mm(X.t(), X),
perform SVD on this matrix and pass it as transformation_matrix
.
Other transforms:
transform_adjust_brightness()
,
transform_adjust_contrast()
,
transform_adjust_gamma()
,
transform_adjust_hue()
,
transform_adjust_saturation()
,
transform_affine()
,
transform_center_crop()
,
transform_color_jitter()
,
transform_convert_image_dtype()
,
transform_crop()
,
transform_five_crop()
,
transform_grayscale()
,
transform_hflip()
,
transform_normalize()
,
transform_pad()
,
transform_perspective()
,
transform_random_affine()
,
transform_random_apply()
,
transform_random_choice()
,
transform_random_crop()
,
transform_random_erasing()
,
transform_random_grayscale()
,
transform_random_horizontal_flip()
,
transform_random_order()
,
transform_random_perspective()
,
transform_random_resized_crop()
,
transform_random_rotation()
,
transform_random_vertical_flip()
,
transform_resize()
,
transform_resized_crop()
,
transform_rgb_to_grayscale()
,
transform_rotate()
,
transform_ten_crop()
,
transform_to_tensor()
,
transform_vflip()
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