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< h1 > Source code for torchreid.data.transforms< / h1 > < div class = "highlight" > < pre >
< span > < / span > < span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > absolute_import< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > division< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > print_function< / span >
< span class = "kn" > from< / span > < span class = "nn" > PIL< / span > < span class = "k" > import< / span > < span class = "n" > Image< / span >
< span class = "kn" > import< / span > < span class = "nn" > random< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "k" > as< / span > < span class = "nn" > np< / span >
< span class = "kn" > import< / span > < span class = "nn" > math< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > from< / span > < span class = "nn" > torchvision.transforms< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< div class = "viewcode-block" id = "Random2DTranslation" > < a class = "viewcode-back" href = "../../../pkg/data.html#torchreid.data.transforms.Random2DTranslation" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > Random2DTranslation< / span > < span class = "p" > (< / span > < span class = "nb" > object< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Randomly translates the input image with a probability.< / span >
< span class = "sd" > Specifically, given a predefined shape (height, width), the input is first< / span >
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< span class = "sd" > resized with a factor of 1.125, leading to (height*1.125, width*1.125), then< / span >
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< span class = "sd" > a random crop is performed. Such operation is done with a probability.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > height (int): target image height.< / span >
< span class = "sd" > width (int): target image width.< / span >
< span class = "sd" > p (float, optional): probability that this operation takes place.< / span >
< span class = "sd" > Default is 0.5.< / span >
< span class = "sd" > interpolation (int, optional): desired interpolation. Default is< / span >
< span class = "sd" > ``PIL.Image.BILINEAR``< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "mf" > 0.5< / span > < span class = "p" > ,< / span > < span class = "n" > interpolation< / span > < span class = "o" > =< / span > < span class = "n" > Image< / span > < span class = "o" > .< / span > < span class = "n" > BILINEAR< / span > < span class = "p" > ):< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > height< / span > < span class = "o" > =< / span > < span class = "n" > height< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > width< / span > < span class = "o" > =< / span > < span class = "n" > width< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "n" > p< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > interpolation< / span > < span class = "o" > =< / span > < span class = "n" > interpolation< / span >
< span class = "k" > def< / span > < span class = "nf" > __call__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > img< / span > < span class = "p" > ):< / span >
< span class = "k" > if< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span > < span class = "o" > > < / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > p< / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > resize< / span > < span class = "p" > ((< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > height< / span > < span class = "p" > ),< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > interpolation< / span > < span class = "p" > )< / span >
< span class = "n" > new_width< / span > < span class = "p" > ,< / span > < span class = "n" > new_height< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > width< / span > < span class = "o" > *< / span > < span class = "mf" > 1.125< / span > < span class = "p" > )),< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > height< / span > < span class = "o" > *< / span > < span class = "mf" > 1.125< / span > < span class = "p" > ))< / span >
< span class = "n" > resized_img< / span > < span class = "o" > =< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > resize< / span > < span class = "p" > ((< / span > < span class = "n" > new_width< / span > < span class = "p" > ,< / span > < span class = "n" > new_height< / span > < span class = "p" > ),< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > interpolation< / span > < span class = "p" > )< / span >
< span class = "n" > x_maxrange< / span > < span class = "o" > =< / span > < span class = "n" > new_width< / span > < span class = "o" > -< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > width< / span >
< span class = "n" > y_maxrange< / span > < span class = "o" > =< / span > < span class = "n" > new_height< / span > < span class = "o" > -< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > height< / span >
< span class = "n" > x1< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > x_maxrange< / span > < span class = "p" > )))< / span >
< span class = "n" > y1< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > y_maxrange< / span > < span class = "p" > )))< / span >
< span class = "n" > croped_img< / span > < span class = "o" > =< / span > < span class = "n" > resized_img< / span > < span class = "o" > .< / span > < span class = "n" > crop< / span > < span class = "p" > ((< / span > < span class = "n" > x1< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "p" > ,< / span > < span class = "n" > x1< / span > < span class = "o" > +< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "o" > +< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > height< / span > < span class = "p" > ))< / span >
< span class = "k" > return< / span > < span class = "n" > croped_img< / span > < / div >
< div class = "viewcode-block" id = "RandomErasing" > < a class = "viewcode-back" href = "../../../pkg/data.html#torchreid.data.transforms.RandomErasing" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > RandomErasing< / span > < span class = "p" > (< / span > < span class = "nb" > object< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Randomly erases an image patch.< / span >
< span class = "sd" > Origin: `< https://github.com/zhunzhong07/Random-Erasing> `_< / span >
< span class = "sd" > Reference:< / span >
< span class = "sd" > Zhong et al. Random Erasing Data Augmentation.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > probability (float, optional): probability that this operation takes place.< / span >
< span class = "sd" > Default is 0.5.< / span >
< span class = "sd" > sl (float, optional): min erasing area.< / span >
< span class = "sd" > sh (float, optional): max erasing area.< / span >
< span class = "sd" > r1 (float, optional): min aspect ratio.< / span >
< span class = "sd" > mean (list, optional): erasing value.< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > probability< / span > < span class = "o" > =< / span > < span class = "mf" > 0.5< / span > < span class = "p" > ,< / span > < span class = "n" > sl< / span > < span class = "o" > =< / span > < span class = "mf" > 0.02< / span > < span class = "p" > ,< / span > < span class = "n" > sh< / span > < span class = "o" > =< / span > < span class = "mf" > 0.4< / span > < span class = "p" > ,< / span > < span class = "n" > r1< / span > < span class = "o" > =< / span > < span class = "mf" > 0.3< / span > < span class = "p" > ,< / span > < span class = "n" > mean< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mf" > 0.4914< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.4822< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.4465< / span > < span class = "p" > ]):< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > probability< / span > < span class = "o" > =< / span > < span class = "n" > probability< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "o" > =< / span > < span class = "n" > mean< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sl< / span > < span class = "o" > =< / span > < span class = "n" > sl< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sh< / span > < span class = "o" > =< / span > < span class = "n" > sh< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > r1< / span > < span class = "o" > =< / span > < span class = "n" > r1< / span >
< span class = "k" > def< / span > < span class = "nf" > __call__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > img< / span > < span class = "p" > ):< / span >
< span class = "k" > if< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span > < span class = "o" > > < / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > probability< / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > img< / span >
< span class = "k" > for< / span > < span class = "n" > attempt< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 100< / span > < span class = "p" > ):< / span >
< span class = "n" > area< / span > < span class = "o" > =< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span > < span class = "o" > *< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 2< / span > < span class = "p" > ]< / span >
< span class = "n" > target_area< / span > < span class = "o" > =< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sl< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sh< / span > < span class = "p" > )< / span > < span class = "o" > *< / span > < span class = "n" > area< / span >
< span class = "n" > aspect_ratio< / span > < span class = "o" > =< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > r1< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "o" > /< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > r1< / span > < span class = "p" > )< / span >
< span class = "n" > h< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > math< / span > < span class = "o" > .< / span > < span class = "n" > sqrt< / span > < span class = "p" > (< / span > < span class = "n" > target_area< / span > < span class = "o" > *< / span > < span class = "n" > aspect_ratio< / span > < span class = "p" > )))< / span >
< span class = "n" > w< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > math< / span > < span class = "o" > .< / span > < span class = "n" > sqrt< / span > < span class = "p" > (< / span > < span class = "n" > target_area< / span > < span class = "o" > /< / span > < span class = "n" > aspect_ratio< / span > < span class = "p" > )))< / span >
< span class = "k" > if< / span > < span class = "n" > w< / span > < span class = "o" > < < / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 2< / span > < span class = "p" > ]< / span > < span class = "ow" > and< / span > < span class = "n" > h< / span > < span class = "o" > < < / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 1< / span > < span class = "p" > ]:< / span >
< span class = "n" > x1< / span > < span class = "o" > =< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > randint< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span > < span class = "o" > -< / span > < span class = "n" > h< / span > < span class = "p" > )< / span >
< span class = "n" > y1< / span > < span class = "o" > =< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > randint< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 2< / span > < span class = "p" > ]< / span > < span class = "o" > -< / span > < span class = "n" > w< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > img< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > ==< / span > < span class = "mi" > 3< / span > < span class = "p" > :< / span >
< span class = "n" > img< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > x1< / span > < span class = "p" > :< / span > < span class = "n" > x1< / span > < span class = "o" > +< / span > < span class = "n" > h< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "p" > :< / span > < span class = "n" > y1< / span > < span class = "o" > +< / span > < span class = "n" > w< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "n" > img< / span > < span class = "p" > [< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > x1< / span > < span class = "p" > :< / span > < span class = "n" > x1< / span > < span class = "o" > +< / span > < span class = "n" > h< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "p" > :< / span > < span class = "n" > y1< / span > < span class = "o" > +< / span > < span class = "n" > w< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > [< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span >
< span class = "n" > img< / span > < span class = "p" > [< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > x1< / span > < span class = "p" > :< / span > < span class = "n" > x1< / span > < span class = "o" > +< / span > < span class = "n" > h< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "p" > :< / span > < span class = "n" > y1< / span > < span class = "o" > +< / span > < span class = "n" > w< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > [< / span > < span class = "mi" > 2< / span > < span class = "p" > ]< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "n" > img< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > x1< / span > < span class = "p" > :< / span > < span class = "n" > x1< / span > < span class = "o" > +< / span > < span class = "n" > h< / span > < span class = "p" > ,< / span > < span class = "n" > y1< / span > < span class = "p" > :< / span > < span class = "n" > y1< / span > < span class = "o" > +< / span > < span class = "n" > w< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > return< / span > < span class = "n" > img< / span >
< span class = "k" > return< / span > < span class = "n" > img< / span > < / div >
< div class = "viewcode-block" id = "ColorAugmentation" > < a class = "viewcode-back" href = "../../../pkg/data.html#torchreid.data.transforms.ColorAugmentation" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > ColorAugmentation< / span > < span class = "p" > (< / span > < span class = "nb" > object< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Randomly alters the intensities of RGB channels.< / span >
< span class = "sd" > Reference:< / span >
< span class = "sd" > Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural< / span >
< span class = "sd" > Networks. NIPS 2012.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > p (float, optional): probability that this operation takes place.< / span >
< span class = "sd" > Default is 0.5.< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "mf" > 0.5< / span > < span class = "p" > ):< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "n" > p< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > eig_vec< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > ([< / span >
< span class = "p" > [< / span > < span class = "mf" > 0.4009< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.7192< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.5675< / span > < span class = "p" > ],< / span >
< span class = "p" > [< / span > < span class = "o" > -< / span > < span class = "mf" > 0.8140< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.0045< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.5808< / span > < span class = "p" > ],< / span >
< span class = "p" > [< / span > < span class = "mf" > 0.4203< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.6948< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.5836< / span > < span class = "p" > ],< / span >
< span class = "p" > ])< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > eig_val< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > ([[< / span > < span class = "mf" > 0.2175< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.0188< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.0045< / span > < span class = "p" > ]])< / span >
< span class = "k" > def< / span > < span class = "nf" > _check_input< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > tensor< / span > < span class = "p" > ):< / span >
< span class = "k" > assert< / span > < span class = "n" > tensor< / span > < span class = "o" > .< / span > < span class = "n" > dim< / span > < span class = "p" > ()< / span > < span class = "o" > ==< / span > < span class = "mi" > 3< / span > < span class = "ow" > and< / span > < span class = "n" > tensor< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "o" > ==< / span > < span class = "mi" > 3< / span >
< span class = "k" > def< / span > < span class = "nf" > __call__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > tensor< / span > < span class = "p" > ):< / span >
< span class = "k" > if< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > uniform< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span > < span class = "o" > > < / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > p< / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > tensor< / span >
< span class = "n" > alpha< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > normal< / span > < span class = "p" > (< / span > < span class = "n" > mean< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > zeros_like< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > eig_val< / span > < span class = "p" > ))< / span > < span class = "o" > *< / span > < span class = "mf" > 0.1< / span >
< span class = "n" > quatity< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > mm< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > eig_val< / span > < span class = "o" > *< / span > < span class = "n" > alpha< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > eig_vec< / span > < span class = "p" > )< / span >
< span class = "n" > tensor< / span > < span class = "o" > =< / span > < span class = "n" > tensor< / span > < span class = "o" > +< / span > < span class = "n" > quatity< / span > < span class = "o" > .< / span > < span class = "n" > view< / span > < span class = "p" > (< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > tensor< / span > < / div >
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< div class = "viewcode-block" id = "build_transforms" > < a class = "viewcode-back" href = "../../../pkg/data.html#torchreid.data.transforms.build_transforms" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > build_transforms< / span > < span class = "p" > (< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > transforms< / span > < span class = "o" > =< / span > < span class = "s1" > ' random_flip' < / span > < span class = "p" > ,< / span > < span class = "n" > norm_mean< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mf" > 0.485< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.456< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.406< / span > < span class = "p" > ],< / span >
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< span class = "n" > norm_std< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mf" > 0.229< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.224< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.225< / span > < span class = "p" > ],< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
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< span class = "sd" > " " " Builds train and test transform functions.< / span >
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< span class = "sd" > Args:< / span >
< span class = "sd" > height (int): target image height.< / span >
< span class = "sd" > width (int): target image width.< / span >
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< span class = "sd" > transforms (str or list of str, optional): transformations applied to model training.< / span >
< span class = "sd" > Default is ' random_flip' .< / span >
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< span class = "sd" > norm_mean (list): normalization mean values. Default is ImageNet means.< / span >
< span class = "sd" > norm_std (list): normalization standard deviation values. Default is< / span >
< span class = "sd" > ImageNet standard deviation values.< / span >
< span class = "sd" > " " " < / span >
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< span class = "k" > if< / span > < span class = "n" > transforms< / span > < span class = "ow" > is< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "n" > transforms< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
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< span class = "k" > if< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > transforms< / span > < span class = "p" > ,< / span > < span class = "nb" > str< / span > < span class = "p" > ):< / span >
< span class = "n" > transforms< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > transforms< / span > < span class = "p" > ]< / span >
< span class = "k" > if< / span > < span class = "ow" > not< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > transforms< / span > < span class = "p" > ,< / span > < span class = "nb" > list< / span > < span class = "p" > ):< / span >
< span class = "k" > raise< / span > < span class = "ne" > ValueError< / span > < span class = "p" > (< / span > < span class = "s1" > ' transforms must be a list of strings, but found to be < / span > < span class = "si" > {}< / span > < span class = "s1" > ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > type< / span > < span class = "p" > (< / span > < span class = "n" > transforms< / span > < span class = "p" > )))< / span >
2019-07-24 05:16:15 +08:00
< span class = "k" > if< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > transforms< / span > < span class = "p" > )< / span > < span class = "o" > > < / span > < span class = "mi" > 0< / span > < span class = "p" > :< / span >
< span class = "n" > transforms< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > t< / span > < span class = "o" > .< / span > < span class = "n" > lower< / span > < span class = "p" > ()< / span > < span class = "k" > for< / span > < span class = "n" > t< / span > < span class = "ow" > in< / span > < span class = "n" > transforms< / span > < span class = "p" > ]< / span >
2019-07-03 20:46:28 +08:00
2019-03-25 01:22:43 +08:00
< span class = "n" > normalize< / span > < span class = "o" > =< / span > < span class = "n" > Normalize< / span > < span class = "p" > (< / span > < span class = "n" > mean< / span > < span class = "o" > =< / span > < span class = "n" > norm_mean< / span > < span class = "p" > ,< / span > < span class = "n" > std< / span > < span class = "o" > =< / span > < span class = "n" > norm_std< / span > < span class = "p" > )< / span >
2019-07-03 20:46:28 +08:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' Building train transforms ...' < / span > < span class = "p" > )< / span >
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< span class = "n" > transform_tr< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
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< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > Resize< / span > < span class = "p" > ((< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ))]< / span >
2019-07-08 22:49:21 +08:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + resize to < / span > < span class = "si" > {}< / span > < span class = "s1" > x< / span > < span class = "si" > {}< / span > < span class = "s1" > ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ))< / span >
2019-07-03 20:46:28 +08:00
< span class = "k" > if< / span > < span class = "s1" > ' random_flip' < / span > < span class = "ow" > in< / span > < span class = "n" > transforms< / span > < span class = "p" > :< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + random flip' < / span > < span class = "p" > )< / span >
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > RandomHorizontalFlip< / span > < span class = "p" > ()]< / span >
< span class = "k" > if< / span > < span class = "s1" > ' random_crop' < / span > < span class = "ow" > in< / span > < span class = "n" > transforms< / span > < span class = "p" > :< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + random crop (enlarge to < / span > < span class = "si" > {}< / span > < span class = "s1" > x< / span > < span class = "si" > {}< / span > < span class = "s1" > and ' < / span > \
< span class = "s1" > ' crop < / span > < span class = "si" > {}< / span > < span class = "s1" > x< / span > < span class = "si" > {}< / span > < span class = "s1" > )' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > height< / span > < span class = "o" > *< / span > < span class = "mf" > 1.125< / span > < span class = "p" > )),< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "nb" > round< / span > < span class = "p" > (< / span > < span class = "n" > width< / span > < span class = "o" > *< / span > < span class = "mf" > 1.125< / span > < span class = "p" > )),< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ))< / span >
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > Random2DTranslation< / span > < span class = "p" > (< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > )]< / span >
< span class = "k" > if< / span > < span class = "s1" > ' color_jitter' < / span > < span class = "ow" > in< / span > < span class = "n" > transforms< / span > < span class = "p" > :< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + color jitter' < / span > < span class = "p" > )< / span >
2019-03-25 01:22:43 +08:00
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > ColorJitter< / span > < span class = "p" > (< / span > < span class = "n" > brightness< / span > < span class = "o" > =< / span > < span class = "mf" > 0.2< / span > < span class = "p" > ,< / span > < span class = "n" > contrast< / span > < span class = "o" > =< / span > < span class = "mf" > 0.15< / span > < span class = "p" > ,< / span > < span class = "n" > saturation< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > hue< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > )]< / span >
2019-07-03 20:46:28 +08:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + to torch tensor of range [0, 1]' < / span > < span class = "p" > )< / span >
2019-03-25 01:22:43 +08:00
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > ToTensor< / span > < span class = "p" > ()]< / span >
2019-07-03 20:46:28 +08:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + normalization (mean=< / span > < span class = "si" > {}< / span > < span class = "s1" > , std=< / span > < span class = "si" > {}< / span > < span class = "s1" > )' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > norm_mean< / span > < span class = "p" > ,< / span > < span class = "n" > norm_std< / span > < span class = "p" > ))< / span >
2019-03-25 01:22:43 +08:00
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > normalize< / span > < span class = "p" > ]< / span >
2019-07-03 20:46:28 +08:00
< span class = "k" > if< / span > < span class = "s1" > ' random_erase' < / span > < span class = "ow" > in< / span > < span class = "n" > transforms< / span > < span class = "p" > :< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + random erase' < / span > < span class = "p" > )< / span >
2019-03-25 01:22:43 +08:00
< span class = "n" > transform_tr< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > RandomErasing< / span > < span class = "p" > ()]< / span >
< span class = "n" > transform_tr< / span > < span class = "o" > =< / span > < span class = "n" > Compose< / span > < span class = "p" > (< / span > < span class = "n" > transform_tr< / span > < span class = "p" > )< / span >
2019-07-03 20:46:28 +08:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' Building test transforms ...' < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + resize to < / span > < span class = "si" > {}< / span > < span class = "s1" > x< / span > < span class = "si" > {}< / span > < span class = "s1" > ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ))< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + to torch tensor of range [0, 1]' < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' + normalization (mean=< / span > < span class = "si" > {}< / span > < span class = "s1" > , std=< / span > < span class = "si" > {}< / span > < span class = "s1" > )' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > norm_mean< / span > < span class = "p" > ,< / span > < span class = "n" > norm_std< / span > < span class = "p" > ))< / span >
2019-03-25 01:22:43 +08:00
< span class = "n" > transform_te< / span > < span class = "o" > =< / span > < span class = "n" > Compose< / span > < span class = "p" > ([< / span >
< span class = "n" > Resize< / span > < span class = "p" > ((< / span > < span class = "n" > height< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > )),< / span >
< span class = "n" > ToTensor< / span > < span class = "p" > (),< / span >
< span class = "n" > normalize< / span > < span class = "p" > ,< / span >
< span class = "p" > ])< / span >
< span class = "k" > return< / span > < span class = "n" > transform_tr< / span > < span class = "p" > ,< / span > < span class = "n" > transform_te< / span > < / div >
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