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fix c_transforms comments

This commit is contained in:
xiefangqi 2021-07-07 22:43:26 +08:00
parent 2d0fdf3904
commit d730ad9a73
1 changed files with 15 additions and 17 deletions
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32
mindspore/dataset/vision/c_transforms.py
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@ -332,7 +332,7 @@ class Equalize(ImageTensorOperation):
class GaussianBlur(ImageTensorOperation):
"""
BLur input image with the specified Gaussian kernel.
Blur input image with the specified Gaussian kernel.
Args:
kernel_size (Union[int, sequence]): Size of the Gaussian kernel to use. The value must be positive and odd. If
@ -415,6 +415,8 @@ class MixUpBatch(ImageTensorOperation):
Apply MixUp transformation on input batch of images and labels. Each image is
multiplied by a random weight (lambda) and then added to a randomly selected image from the batch
multiplied by (1 - lambda). The same formula is also applied to the one-hot labels.
The lambda is generated based on the specified alpha value. Two coefficients x1, x2 are randomly generated
in the range [alpha, 1], and lambda = (x1 / (x1 + x2)).
Note that you need to make labels into one-hot format and batched before calling this operator.
Args:
@ -441,7 +443,7 @@ class MixUpBatch(ImageTensorOperation):
class Normalize(ImageTensorOperation):
"""
Normalize the input image with respect to mean and standard deviation. This operator will normalize
the input image with: output = (input - mean) / std.
the input image with: output[channel] = (input[channel] - mean[channel]) / std[channel], where channel >= 1.
Args:
mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
@ -572,7 +574,6 @@ class RandomAffine(ImageTensorOperation):
and a shear parallel to Y axis in the range of (shear[2], shear[3]) is applied.
If None, no shear is applied.
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.
@ -583,7 +584,7 @@ class RandomAffine(ImageTensorOperation):
fill_value (tuple or int, optional): Optional fill_value to fill the area outside the transform
in the output image. There must be three elements in tuple and the value of single element is [0, 255].
Used only in Pillow versions > 5.0.0 (default=0, filling is performed).
(default=0, filling is performed).
Raises:
ValueError: If degrees is negative.
@ -1164,14 +1165,13 @@ class RandomResizeWithBBox(ImageTensorOperation):
class RandomRotation(ImageTensorOperation):
"""
Rotate the input image by a random angle.
Rotate the input image randomly within a specified range of degrees.
Args:
degrees (Union[int, float, sequence]): Range of random rotation degrees.
If degrees is a number, the range will be converted to (-degrees, degrees).
If degrees is a sequence, it should be (min, max).
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.
@ -1230,12 +1230,13 @@ class RandomRotation(ImageTensorOperation):
class RandomSelectSubpolicy(ImageTensorOperation):
"""
Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
(op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability.
Choose a random sub-policy from a policy list to be applied on the input image.
Args:
policy (list(list(tuple(TensorOp, float))): List of sub-policies to choose from.
policy (list(list(tuple(TensorOp, prob (float)))): List of sub-policies to choose from.
A sub-policy is a list of tuples (op, prob), where op is a TensorOp operation and prob is the probability
that this op will be applied, and the prob values must be in range [0, 1]. Once a sub-policy is selected,
each op within the sub-policy with be applied in sequence according to its probability.
Examples:
>>> policy = [[(c_vision.RandomRotation((45, 45)), 0.5),
@ -1252,9 +1253,6 @@ class RandomSelectSubpolicy(ImageTensorOperation):
self.policy = policy
def parse(self):
"""
Return a C++ representation of the operator for execution
"""
policy = []
for list_one in self.policy:
policy_one = []
@ -1297,12 +1295,13 @@ class RandomSharpness(ImageTensorOperation):
class RandomSolarize(ImageTensorOperation):
"""
Randomly invert the pixel values of input image within given range.
Randomly selects a subrange within the specified threshold range and sets the pixel value within
the subrange to (255 - pixel).
Args:
threshold (tuple, optional): Range of random solarize threshold (default=(0, 255)).
Threshold values should always be in (min, max) format,
where min <= max, min and max are integers in the range (0, 255).
where min and max are integers in the range (0, 255), and min <= max.
If min=max, then invert all pixel values above min(max).
Examples:
@ -1364,7 +1363,7 @@ class RandomVerticalFlipWithBBox(ImageTensorOperation):
class Rescale(ImageTensorOperation):
"""
Rescale the input image with the given rescale and shift. This operator will rescale the input image
with: output = (image + rescale) / shift.
with: output = image * rescale + shift.
Args:
rescale (float): Rescale factor.
@ -1492,7 +1491,6 @@ class Rotate(ImageTensorOperation):
degrees (Union[int, float]): Rotation degrees.
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.