Spaces:
Runtime error
Runtime error
File size: 5,966 Bytes
193c713 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
import torch
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import numpy as np
from math import exp
import torch.nn as nn
class ImgMerger:
def __init__(self, eval_fn):
self.eval_fn = eval_fn
self.loc2imgs = {}
self.max_x = 0
self.max_y = 0
self.clear()
def clear(self):
self.loc2imgs = {}
self.max_x = 0
self.max_y = 0
def push(self, imgs, loc, loc_bdr):
"""
Args:
imgs: each of img is [C, H, W] np.array, range: [0, 255]
loc: string, e.g., 0_0, 0_1 ...
"""
self.max_x, self.max_y = loc_bdr
x, y = loc
self.loc2imgs[f'{x},{y}'] = imgs
if len(self.loc2imgs) == self.max_x * self.max_y:
return self.compute()
def compute(self):
img_inputs = []
for i in range(len(self.loc2imgs['0,0'])):
img_full = []
for x in range(self.max_x):
imgx = []
for y in range(self.max_y):
imgx.append(self.loc2imgs[f'{x},{y}'][i])
img_full.append(np.concatenate(imgx, 2))
img_inputs.append(np.concatenate(img_full, 1))
self.clear()
return self.eval_fn(*img_inputs)
##########
# SSIM
##########
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def _ssim(img1, img2, window, window_size, channel, size_average=True):
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
C1 = 0.01 ** 2
C2 = 0.03 ** 2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
class SSIM(torch.nn.Module):
def __init__(self, window_size=11, size_average=True):
super(SSIM, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.window = create_window(window_size, self.channel)
def forward(self, img1, img2):
img1 = img1 * 0.5 + 0.5
img2 = img2 * 0.5 + 0.5
(_, channel, _, _) = img1.size()
if channel == self.channel and self.window.data.type() == img1.data.type():
window = self.window
else:
window = create_window(self.window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
self.window = window
self.channel = channel
return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
def ssim(img1, img2, window_size=11, size_average=True):
(_, channel, _, _) = img1.size()
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, size_average)
class VGGFeatureExtractor(nn.Module):
def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True):
super(VGGFeatureExtractor, self).__init__()
self.use_input_norm = use_input_norm
if use_bn:
model = torchvision.models.vgg19_bn(pretrained=True)
else:
model = torchvision.models.vgg19(pretrained=True)
if self.use_input_norm:
mean = torch.Tensor([0.485 - 1, 0.456 - 1, 0.406 - 1]).view(1, 3, 1, 1)
# mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
std = torch.Tensor([0.229 * 2, 0.224 * 2, 0.225 * 2]).view(1, 3, 1, 1)
# std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
self.register_buffer('mean', mean)
self.register_buffer('std', std)
self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
# No need to BP to variable
for k, v in self.features.named_parameters():
v.requires_grad = False
def forward(self, x):
# Assume input range is [0, 1]
if self.use_input_norm:
x = (x - self.mean) / self.std
output = self.features(x)
return output
class PerceptualLoss(nn.Module):
def __init__(self):
super(PerceptualLoss, self).__init__()
loss_network = VGGFeatureExtractor()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.l1_loss = nn.L1Loss()
def forward(self, high_resolution, fake_high_resolution):
if next(self.loss_network.parameters()).device != high_resolution.device:
self.loss_network.to(high_resolution.device)
self.loss_network.eval()
perception_loss = self.l1_loss(self.loss_network(high_resolution), self.loss_network(fake_high_resolution))
return perception_loss
|