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augs.py
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import sys
import torch.nn as nn
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from torch.nn import functional as F
sys.path.append("ResizeRight")
from resize_right import resize
class MakeCutouts(nn.Module):
def __init__(self, cut_size, cutn, skip_augs=False):
super().__init__()
self.cut_size = cut_size
self.cutn = cutn
self.skip_augs = skip_augs
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomPerspective(distortion_scale=0.4, p=0.7),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.15),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
def forward(self, input):
input = T.Pad(input.shape[2]//4, fill=0)(input)
sideY, sideX = input.shape[2:4]
max_size = min(sideX, sideY)
cutouts = []
for ch in range(self.cutn):
if ch > self.cutn - self.cutn//4:
cutout = input.clone()
else:
size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))
offsetx = torch.randint(0, abs(sideX - size + 1), ())
offsety = torch.randint(0, abs(sideY - size + 1), ())
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
if not self.skip_augs:
cutout = self.augs(cutout)
cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
del cutout
cutouts = torch.cat(cutouts, dim=0)
return cutouts
class MakeCutoutsDango(nn.Module):
def __init__(self, cut_size,
Overview=4,
InnerCrop = 0,
IC_Size_Pow=0.5,
IC_Grey_P = 0.2,
animation_mode='None',
):
super().__init__()
self.cut_size = cut_size
self.Overview = Overview
self.InnerCrop = InnerCrop
self.IC_Size_Pow = IC_Size_Pow
self.IC_Grey_P = IC_Grey_P
if animation_mode == 'None':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.1),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
elif animation_mode == 'Video Input':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomPerspective(distortion_scale=0.4, p=0.7),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.15),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
elif animation_mode == '2D':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.4),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.1),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),
])
def forward(self, input):
cutouts = []
gray = T.Grayscale(3)
sideY, sideX = input.shape[2:4]
max_size = min(sideX, sideY)
min_size = min(sideX, sideY, self.cut_size)
l_size = max(sideX, sideY)
output_shape = [1,3,self.cut_size,self.cut_size]
output_shape_2 = [1,3,self.cut_size+2,self.cut_size+2]
pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2))
cutout = resize(pad_input, out_shape=output_shape)
if self.Overview>0:
if self.Overview <= 4:
if self.Overview >= 1:
cutouts.append(cutout)
if self.Overview >= 2:
cutouts.append(gray(cutout))
if self.Overview >= 3:
cutouts.append(TF.hflip(cutout))
if self.Overview == 4:
cutouts.append(gray(TF.hflip(cutout)))
else:
cutout = resize(pad_input, out_shape=output_shape)
for _ in range(self.Overview):
cutouts.append(cutout)
#if cutout_debug:
# TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/cutout_overview0.jpg",quality=99)
if self.InnerCrop >0:
for i in range(self.InnerCrop):
size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)
offsetx = torch.randint(0, sideX - size + 1, ())
offsety = torch.randint(0, sideY - size + 1, ())
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
if i <= int(self.IC_Grey_P * self.InnerCrop):
cutout = gray(cutout)
cutout = resize(cutout, out_shape=output_shape)
cutouts.append(cutout)
#if cutout_debug:
# TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/cutout_InnerCrop.jpg",quality=99)
cutouts = torch.cat(cutouts)
#if skip_augs is not True: cutouts=self.augs(cutouts)
return cutouts
def sinc(x):
return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
def lanczos(x, a):
cond = torch.logical_and(-a < x, x < a)
out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
return out / out.sum()
def ramp(ratio, width):
n = math.ceil(width / ratio + 1)
out = torch.empty([n])
cur = 0
for i in range(out.shape[0]):
out[i] = cur
cur += ratio
return torch.cat([-out[1:].flip([0]), out])[1:-1]
def resample(input, size, align_corners=True):
n, c, h, w = input.shape
dh, dw = size
input = input.reshape([n * c, 1, h, w])
if dh < h:
kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
pad_h = (kernel_h.shape[0] - 1) // 2
input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
input = F.conv2d(input, kernel_h[None, None, :, None])
if dw < w:
kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
pad_w = (kernel_w.shape[0] - 1) // 2
input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
input = F.conv2d(input, kernel_w[None, None, None, :])
input = input.reshape([n, c, h, w])
return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)