File size: 7,040 Bytes
14ce5a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
# this file is taken from https://github.com/autonomousvision/stylegan-t/blob/36ab80ce76237fefe03e65e9b3161c040ae888e3/training/diffaug.py
import math
import torch
import torch.nn.functional as F
def load_png(file_name: str):
from torchvision.io import read_image
return (
read_image(file_name).float().div_(255).mul_(2).sub_(1).unsqueeze(0)
) # to [-1, 1]
def show(tensor): # from [-1, 1]
from torchvision.utils import make_grid
from torchvision.transforms.functional import to_pil_image
if tensor.shape[0] == 1:
tensor = tensor[0]
if tensor.ndim == 3:
to_pil_image(tensor.add(1).div_(2).clamp_(0, 1).detach().cpu()).convert(
"RGB"
).show()
else:
to_pil_image(
make_grid(tensor.add(1).div_(2).clamp_(0, 1).detach().cpu())
).convert("RGB").show()
class DiffAug(object):
def __init__(self, prob=1.0, cutout=0.2): # todo: swin ratio = 0.5, T&XL = 0.2
self.grids = {}
self.prob = abs(prob)
self.using_cutout = prob > 0
self.cutout = cutout
self.img_channels = -1
self.last_blur_radius = -1
self.last_blur_kernel_h = self.last_blur_kernel_w = None
def get_grids(self, B, x, y, dev):
if (B, x, y) in self.grids:
return self.grids[(B, x, y)]
self.grids[(B, x, y)] = ret = torch.meshgrid(
torch.arange(B, dtype=torch.long, device=dev),
torch.arange(x, dtype=torch.long, device=dev),
torch.arange(y, dtype=torch.long, device=dev),
indexing="ij",
)
return ret
def aug(self, BCHW: torch.Tensor, warmup_blur_schedule: float = 0) -> torch.Tensor:
# warmup blurring
if BCHW.dtype != torch.float32:
BCHW = BCHW.float()
if warmup_blur_schedule > 0:
self.img_channels = BCHW.shape[1]
sigma0 = (BCHW.shape[-2] * 0.5) ** 0.5
sigma = sigma0 * warmup_blur_schedule
blur_radius = math.floor(sigma * 3) # 3-sigma is enough for Gaussian
if blur_radius >= 1:
if self.last_blur_radius != blur_radius:
self.last_blur_radius = blur_radius
gaussian = torch.arange(
-blur_radius,
blur_radius + 1,
dtype=torch.float32,
device=BCHW.device,
)
gaussian = gaussian.mul_(1 / sigma).square_().neg_().exp2_()
gaussian.div_(gaussian.sum()) # normalize
self.last_blur_kernel_h = (
gaussian.view(1, 1, 2 * blur_radius + 1, 1)
.repeat(self.img_channels, 1, 1, 1)
.contiguous()
)
self.last_blur_kernel_w = (
gaussian.view(1, 1, 1, 2 * blur_radius + 1)
.repeat(self.img_channels, 1, 1, 1)
.contiguous()
)
BCHW = F.pad(
BCHW,
[blur_radius, blur_radius, blur_radius, blur_radius],
mode="reflect",
)
BCHW = F.conv2d(
input=BCHW,
weight=self.last_blur_kernel_h,
bias=None,
groups=self.img_channels,
)
BCHW = F.conv2d(
input=BCHW,
weight=self.last_blur_kernel_w,
bias=None,
groups=self.img_channels,
)
# BCHW = filter2d(BCHW, f.div_(f.sum())) # no need to specify padding (filter2d will add padding in itself based on filter size)
if self.prob < 1e-6:
return BCHW
trans, color, cut = torch.rand(3) <= self.prob
trans, color, cut = trans.item(), color.item(), cut.item()
B, dev = BCHW.shape[0], BCHW.device
rand01 = torch.rand(7, B, 1, 1, device=dev) if (trans or color or cut) else None
raw_h, raw_w = BCHW.shape[-2:]
if trans:
ratio = 0.125
delta_h = round(raw_h * ratio)
delta_w = round(raw_w * ratio)
translation_h = (
rand01[0].mul(delta_h + delta_h + 1).floor().long() - delta_h
)
translation_w = (
rand01[1].mul(delta_w + delta_w + 1).floor().long() - delta_w
)
# translation_h = torch.randint(-delta_h, delta_h+1, size=(B, 1, 1), device=dev)
# translation_w = torch.randint(-delta_w, delta_w+1, size=(B, 1, 1), device=dev)
grid_B, grid_h, grid_w = self.get_grids(B, raw_h, raw_w, dev)
grid_h = (grid_h + translation_h).add_(1).clamp_(0, raw_h + 1)
grid_w = (grid_w + translation_w).add_(1).clamp_(0, raw_w + 1)
bchw_pad = F.pad(BCHW, [1, 1, 1, 1, 0, 0, 0, 0])
BCHW = (
bchw_pad.permute(0, 2, 3, 1)
.contiguous()[grid_B, grid_h, grid_w]
.permute(0, 3, 1, 2)
.contiguous()
)
if color:
BCHW = BCHW.add(rand01[2].unsqueeze(-1).sub(0.5))
# BCHW.add_(torch.rand(B, 1, 1, 1, dtype=BCHW.dtype, device=dev).sub_(0.5))
bchw_mean = BCHW.mean(dim=1, keepdim=True)
BCHW = (
BCHW.sub(bchw_mean).mul(rand01[3].unsqueeze(-1).mul(2)).add_(bchw_mean)
)
# BCHW.sub_(bchw_mean).mul_(torch.rand(B, 1, 1, 1, dtype=BCHW.dtype, device=dev).mul_(2)).add_(bchw_mean)
bchw_mean = BCHW.mean(dim=(1, 2, 3), keepdim=True)
BCHW = (
BCHW.sub(bchw_mean)
.mul(rand01[4].unsqueeze(-1).add(0.5))
.add_(bchw_mean)
)
# BCHW.sub_(bchw_mean).mul_(torch.rand(B, 1, 1, 1, dtype=BCHW.dtype, device=dev).add_(0.5)).add_(bchw_mean)
if self.using_cutout and cut:
ratio = self.cutout # todo: styleswin ratio = 0.5, T&XL = 0.2
cutout_h = round(raw_h * ratio)
cutout_w = round(raw_w * ratio)
offset_h = rand01[5].mul(raw_h + (1 - cutout_h % 2)).floor().long()
offset_w = rand01[6].mul(raw_w + (1 - cutout_w % 2)).floor().long()
# offset_h = torch.randint(0, raw_h + (1 - cutout_h % 2), size=(B, 1, 1), device=dev)
# offset_w = torch.randint(0, raw_w + (1 - cutout_w % 2), size=(B, 1, 1), device=dev)
grid_B, grid_h, grid_w = self.get_grids(B, cutout_h, cutout_w, dev)
grid_h = (grid_h + offset_h).sub_(cutout_h // 2).clamp(min=0, max=raw_h - 1)
grid_w = (grid_w + offset_w).sub_(cutout_w // 2).clamp(min=0, max=raw_w - 1)
mask = torch.ones(B, raw_h, raw_w, dtype=BCHW.dtype, device=dev)
mask[grid_B, grid_h, grid_w] = 0
BCHW = BCHW.mul(mask.unsqueeze(1))
return BCHW
|