437 lines
26 KiB
Python
437 lines
26 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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"""Augmentation pipeline from the paper
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"Training Generative Adversarial Networks with Limited Data".
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Matches the original implementation by Karras et al. at
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https://github.com/NVlabs/stylegan2-ada/blob/main/training/augment.py"""
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import numpy as np
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import scipy.signal
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import torch
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from torch_utils import persistence
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from torch_utils import misc
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from torch_utils.ops import upfirdn2d
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from torch_utils.ops import grid_sample_gradfix
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from torch_utils.ops import conv2d_gradfix
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#----------------------------------------------------------------------------
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# Coefficients of various wavelet decomposition low-pass filters.
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wavelets = {
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'haar': [0.7071067811865476, 0.7071067811865476],
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'db1': [0.7071067811865476, 0.7071067811865476],
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'db2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
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'db3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
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'db4': [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
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'db5': [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
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'db6': [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
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'db7': [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
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'db8': [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
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'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
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'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
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'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
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'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
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'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
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'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
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'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
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}
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#----------------------------------------------------------------------------
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# Helpers for constructing transformation matrices.
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def matrix(*rows, device=None):
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assert all(len(row) == len(rows[0]) for row in rows)
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elems = [x for row in rows for x in row]
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ref = [x for x in elems if isinstance(x, torch.Tensor)]
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if len(ref) == 0:
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return misc.constant(np.asarray(rows), device=device)
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assert device is None or device == ref[0].device
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elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
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return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
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def translate2d(tx, ty, **kwargs):
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return matrix(
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[1, 0, tx],
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[0, 1, ty],
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[0, 0, 1],
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**kwargs)
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def translate3d(tx, ty, tz, **kwargs):
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return matrix(
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[1, 0, 0, tx],
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[0, 1, 0, ty],
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[0, 0, 1, tz],
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[0, 0, 0, 1],
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**kwargs)
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def scale2d(sx, sy, **kwargs):
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return matrix(
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[sx, 0, 0],
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[0, sy, 0],
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[0, 0, 1],
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**kwargs)
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def scale3d(sx, sy, sz, **kwargs):
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return matrix(
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[sx, 0, 0, 0],
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[0, sy, 0, 0],
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[0, 0, sz, 0],
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[0, 0, 0, 1],
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**kwargs)
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def rotate2d(theta, **kwargs):
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return matrix(
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[torch.cos(theta), torch.sin(-theta), 0],
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[torch.sin(theta), torch.cos(theta), 0],
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[0, 0, 1],
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**kwargs)
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def rotate3d(v, theta, **kwargs):
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vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
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s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
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return matrix(
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[vx*vx*cc+c, vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
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[vy*vx*cc+vz*s, vy*vy*cc+c, vy*vz*cc-vx*s, 0],
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[vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c, 0],
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[0, 0, 0, 1],
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**kwargs)
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def translate2d_inv(tx, ty, **kwargs):
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return translate2d(-tx, -ty, **kwargs)
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def scale2d_inv(sx, sy, **kwargs):
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return scale2d(1 / sx, 1 / sy, **kwargs)
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def rotate2d_inv(theta, **kwargs):
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return rotate2d(-theta, **kwargs)
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#----------------------------------------------------------------------------
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# Versatile image augmentation pipeline from the paper
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# "Training Generative Adversarial Networks with Limited Data".
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#
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# All augmentations are disabled by default; individual augmentations can
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# be enabled by setting their probability multipliers to 1.
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@persistence.persistent_class
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class AugmentPipe(torch.nn.Module):
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def __init__(self,
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xflip=0, rotate90=0, xint=0, xint_max=0.125,
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scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
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brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
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imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
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noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
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):
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super().__init__()
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self.register_buffer('p', torch.ones([])) # Overall multiplier for augmentation probability.
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# Pixel blitting.
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self.xflip = float(xflip) # Probability multiplier for x-flip.
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self.rotate90 = float(rotate90) # Probability multiplier for 90 degree rotations.
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self.xint = float(xint) # Probability multiplier for integer translation.
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self.xint_max = float(xint_max) # Range of integer translation, relative to image dimensions.
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# General geometric transformations.
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self.scale = float(scale) # Probability multiplier for isotropic scaling.
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self.rotate = float(rotate) # Probability multiplier for arbitrary rotation.
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self.aniso = float(aniso) # Probability multiplier for anisotropic scaling.
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self.xfrac = float(xfrac) # Probability multiplier for fractional translation.
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self.scale_std = float(scale_std) # Log2 standard deviation of isotropic scaling.
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self.rotate_max = float(rotate_max) # Range of arbitrary rotation, 1 = full circle.
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self.aniso_std = float(aniso_std) # Log2 standard deviation of anisotropic scaling.
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self.xfrac_std = float(xfrac_std) # Standard deviation of frational translation, relative to image dimensions.
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# Color transformations.
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self.brightness = float(brightness) # Probability multiplier for brightness.
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self.contrast = float(contrast) # Probability multiplier for contrast.
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self.lumaflip = float(lumaflip) # Probability multiplier for luma flip.
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self.hue = float(hue) # Probability multiplier for hue rotation.
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self.saturation = float(saturation) # Probability multiplier for saturation.
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self.brightness_std = float(brightness_std) # Standard deviation of brightness.
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self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast.
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self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle.
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self.saturation_std = float(saturation_std) # Log2 standard deviation of saturation.
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# Image-space filtering.
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self.imgfilter = float(imgfilter) # Probability multiplier for image-space filtering.
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self.imgfilter_bands = list(imgfilter_bands) # Probability multipliers for individual frequency bands.
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self.imgfilter_std = float(imgfilter_std) # Log2 standard deviation of image-space filter amplification.
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# Image-space corruptions.
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self.noise = float(noise) # Probability multiplier for additive RGB noise.
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self.cutout = float(cutout) # Probability multiplier for cutout.
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self.noise_std = float(noise_std) # Standard deviation of additive RGB noise.
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self.cutout_size = float(cutout_size) # Size of the cutout rectangle, relative to image dimensions.
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# Setup orthogonal lowpass filter for geometric augmentations.
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self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
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# Construct filter bank for image-space filtering.
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Hz_lo = np.asarray(wavelets['sym2']) # H(z)
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Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
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Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2
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Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2
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Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i)
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for i in range(1, Hz_fbank.shape[0]):
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Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
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Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
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Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
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self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
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def forward(self, images, debug_percentile=None):
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assert isinstance(images, torch.Tensor) and images.ndim == 4
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batch_size, num_channels, height, width = images.shape
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device = images.device
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if debug_percentile is not None:
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debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
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# -------------------------------------
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# Select parameters for pixel blitting.
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# -------------------------------------
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# Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
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I_3 = torch.eye(3, device=device)
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G_inv = I_3
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# Apply x-flip with probability (xflip * strength).
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if self.xflip > 0:
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i = torch.floor(torch.rand([batch_size], device=device) * 2)
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i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
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if debug_percentile is not None:
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i = torch.full_like(i, torch.floor(debug_percentile * 2))
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G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
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# Apply 90 degree rotations with probability (rotate90 * strength).
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if self.rotate90 > 0:
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i = torch.floor(torch.rand([batch_size], device=device) * 4)
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i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
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if debug_percentile is not None:
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i = torch.full_like(i, torch.floor(debug_percentile * 4))
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G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
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# Apply integer translation with probability (xint * strength).
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if self.xint > 0:
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t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
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t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
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if debug_percentile is not None:
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t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
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G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
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# --------------------------------------------------------
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# Select parameters for general geometric transformations.
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# --------------------------------------------------------
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# Apply isotropic scaling with probability (scale * strength).
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if self.scale > 0:
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s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
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s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
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if debug_percentile is not None:
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s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
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G_inv = G_inv @ scale2d_inv(s, s)
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# Apply pre-rotation with probability p_rot.
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p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
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if self.rotate > 0:
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theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
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theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
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if debug_percentile is not None:
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theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
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G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
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# Apply anisotropic scaling with probability (aniso * strength).
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if self.aniso > 0:
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s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
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s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
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if debug_percentile is not None:
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s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
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G_inv = G_inv @ scale2d_inv(s, 1 / s)
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# Apply post-rotation with probability p_rot.
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if self.rotate > 0:
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theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
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theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
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if debug_percentile is not None:
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theta = torch.zeros_like(theta)
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G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
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# Apply fractional translation with probability (xfrac * strength).
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if self.xfrac > 0:
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t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
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t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
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if debug_percentile is not None:
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t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
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G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
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# ----------------------------------
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# Execute geometric transformations.
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# ----------------------------------
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# Execute if the transform is not identity.
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if G_inv is not I_3:
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# Calculate padding.
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cx = (width - 1) / 2
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cy = (height - 1) / 2
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cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
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cp = G_inv @ cp.t() # [batch, xyz, idx]
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Hz_pad = self.Hz_geom.shape[0] // 4
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margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
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margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
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margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
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margin = margin.max(misc.constant([0, 0] * 2, device=device))
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margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
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mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
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# Pad image and adjust origin.
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images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
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G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
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# Upsample.
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images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
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G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
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G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
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# Execute transformation.
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shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
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G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
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grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
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images = grid_sample_gradfix.grid_sample(images, grid)
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# Downsample and crop.
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images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
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# --------------------------------------------
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# Select parameters for color transformations.
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# --------------------------------------------
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# Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
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I_4 = torch.eye(4, device=device)
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C = I_4
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# Apply brightness with probability (brightness * strength).
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if self.brightness > 0:
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b = torch.randn([batch_size], device=device) * self.brightness_std
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b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
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if debug_percentile is not None:
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b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
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C = translate3d(b, b, b) @ C
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# Apply contrast with probability (contrast * strength).
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if self.contrast > 0:
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c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
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c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
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if debug_percentile is not None:
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c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
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C = scale3d(c, c, c) @ C
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# Apply luma flip with probability (lumaflip * strength).
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v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
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if self.lumaflip > 0:
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i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
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i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
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if debug_percentile is not None:
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i = torch.full_like(i, torch.floor(debug_percentile * 2))
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C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
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# Apply hue rotation with probability (hue * strength).
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if self.hue > 0 and num_channels > 1:
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theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
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theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
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if debug_percentile is not None:
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theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
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C = rotate3d(v, theta) @ C # Rotate around v.
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# Apply saturation with probability (saturation * strength).
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if self.saturation > 0 and num_channels > 1:
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s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
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s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
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if debug_percentile is not None:
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s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
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C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
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# ------------------------------
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# Execute color transformations.
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# ------------------------------
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# Execute if the transform is not identity.
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if C is not I_4:
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images = images.reshape([batch_size, num_channels, height * width])
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if num_channels == 3:
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images = C[:, :3, :3] @ images + C[:, :3, 3:]
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elif num_channels == 1:
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C = C[:, :3, :].mean(dim=1, keepdims=True)
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images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
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else:
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raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
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images = images.reshape([batch_size, num_channels, height, width])
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# ----------------------
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# Image-space filtering.
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# ----------------------
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if self.imgfilter > 0:
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num_bands = self.Hz_fbank.shape[0]
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assert len(self.imgfilter_bands) == num_bands
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expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
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# Apply amplification for each band with probability (imgfilter * strength * band_strength).
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g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
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for i, band_strength in enumerate(self.imgfilter_bands):
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t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
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t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
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if debug_percentile is not None:
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t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
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t = torch.ones([batch_size, num_bands], device=device) # Temporary gain vector.
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t[:, i] = t_i # Replace i'th element.
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t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
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g = g * t # Accumulate into global gain.
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# Construct combined amplification filter.
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Hz_prime = g @ self.Hz_fbank # [batch, tap]
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Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1]) # [batch, channels, tap]
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Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
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# Apply filter.
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p = self.Hz_fbank.shape[1] // 2
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images = images.reshape([1, batch_size * num_channels, height, width])
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images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
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images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
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images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
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images = images.reshape([batch_size, num_channels, height, width])
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# ------------------------
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# Image-space corruptions.
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# ------------------------
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# Apply additive RGB noise with probability (noise * strength).
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if self.noise > 0:
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sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
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sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
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if debug_percentile is not None:
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sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
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images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
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# Apply cutout with probability (cutout * strength).
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if self.cutout > 0:
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size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
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size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
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center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
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if debug_percentile is not None:
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size = torch.full_like(size, self.cutout_size)
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center = torch.full_like(center, debug_percentile)
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coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
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coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
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mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
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mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
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mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
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images = images * mask
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return images
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#----------------------------------------------------------------------------
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