File size: 5,752 Bytes
9123ba9 |
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 |
import torch
import torch.nn.functional as F
import os
import sys
import math
import lpips as lpips_metric
import piq
import numpy as np
from numpy import cov
from numpy import trace
from numpy import iscomplexobj
from numpy.random import random
from scipy.linalg import sqrtm
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import peak_signal_noise_ratio
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
def blockPrint():
sys.stdout = open(os.devnull, 'w')
def enablePrint():
sys.stdout = sys.__stdout__
def normalize_tensor(outmap):
flattened_outmap = outmap.view(outmap.shape[0], -1, 1, 1) # Use 1's to preserve the number of dimensions for broadcasting later, as explained
outmap_min, _ = torch.min(flattened_outmap, dim=1, keepdim=True)
outmap_max, _ = torch.max(flattened_outmap, dim=1, keepdim=True)
outmap = (outmap - outmap_min) / (outmap_max - outmap_min)
return outmap
class ImageMetrics:
def __init__(self, grid_true, grid_pred, device='cpu'):
self.grid_true = grid_true.to(device) # [N, H, W]
self.grid_pred = grid_pred.to(device) # [N, H, W]
self.num_sequence = grid_true.shape[0]
self.loss_fn_vgg = lpips_metric.LPIPS(net='vgg', verbose=False).to(device) # closer to "traditional" perceptual loss, when used for optimization
self.device = device
def ssim(self):
"""Structured Similarity Index Metric"""
a = normalize_tensor(self.grid_true.unsqueeze(0))
b = normalize_tensor(self.grid_pred.unsqueeze(0))
ssim = piq.ssim(a, b, data_range=1., reduction='none').squeeze().item()
return ssim
def mssim(self):
"""Mean Structured Similarity Index Metric"""
mssim = 0
for idx in range(self.num_sequence):
max_value = max([self.grid_true[idx].max(), self.grid_pred[idx].max()])
min_value = min([self.grid_true[idx].min(), self.grid_pred[idx].min()])
data_range = abs(max_value - min_value)
a = self.grid_true[idx].detach().cpu().numpy()
b = self.grid_pred[idx].detach().cpu().numpy()
mssim += ssim(a, b, data_range=data_range.item())
return mssim / self.num_sequence
def multiscale_ssim(self):
"""Multi-Scale SSIM"""
a = normalize_tensor(self.grid_true.unsqueeze(0))
b = normalize_tensor(self.grid_pred.unsqueeze(0))
ms_ssim_index = piq.multi_scale_ssim(a, b, data_range=1., kernel_size=7).item()
return ms_ssim_index
def psnr(self):
"""Peak Signal-to-Noise Ratio"""
psnr = 0
for idx in range(self.num_sequence):
max_value = max([self.grid_true[idx].max(), self.grid_pred[idx].max()])
min_value = min([self.grid_true[idx].min(), self.grid_pred[idx].min()])
data_range = abs(max_value - min_value)
a = self.grid_true[idx].detach().cpu().numpy()
b = self.grid_pred[idx].detach().cpu().numpy()
psnr += peak_signal_noise_ratio(a, b, data_range=data_range.item())
return psnr / self.num_sequence
def _calculate_fid(self, img1, img2):
img1 = img1.detach().cpu().numpy()
img2 = img2.detach().cpu().numpy()
# calculate mean and covariance statistics
mu1, sigma1 = img1.mean(axis=0), cov(img1, rowvar=False)
mu2, sigma2 = img2.mean(axis=0), cov(img2, rowvar=False)
# calculate sum squared difference between means
ssdiff = np.sum((mu1 - mu2)**2.0)
# calculate sqrt of product between cov
covmean = sqrtm(sigma1.dot(sigma2))
# check and correct imaginary numbers from sqrt
if iscomplexobj(covmean):
covmean = covmean.real
# calculate score
fid = ssdiff + trace(sigma1 + sigma2 - 2.0 * covmean)
return fid
def fid(self):
"""Frechet Inception Distance"""
fid = 0
for idx in range(self.num_sequence):
fid += self._calculate_fid(self.grid_true[idx], self.grid_pred[idx])
return fid / self.num_sequence
def _calculate_lpips(self, img1, img2):
img1 = (2 * img1 / img1.max() - 1) # normalize between -1 to 1
img2 = (2 * img2 / img2.max() - 1) # normalize between -1 to 1
perceptual_loss = self.loss_fn_vgg(img1, img2).squeeze()
return perceptual_loss.item()
def lpips(self):
"""Learned Perceptual Image Patch Similarity"""
perceptual_loss = 0
for idx in range(self.num_sequence):
perceptual_loss += self._calculate_lpips(self.grid_true[idx], self.grid_pred[idx])
return perceptual_loss / self.num_sequence
def reconstruction(self):
return F.l1_loss(self.grid_true, self.grid_pred).item()
def IS(self):
"""Inception Score"""
is_score = 0
for idx in range(self.num_sequence):
is_score += piq.IS(distance='l1')(self.grid_true[idx], self.grid_pred[idx])
return (is_score / self.num_sequence).item()
def kid(self):
"""Kernel Inception Distance"""
kid_score = 0
for idx in range(self.num_sequence):
kid_score += piq.KID()(self.grid_true[idx], self.grid_pred[idx])
return (kid_score / self.num_sequence).item()
def get_metrics(self):
blockPrint()
metrics = dict(
SSIM=self.ssim(),
MSSIM=self.mssim(),
MS_SSIM=self.multiscale_ssim(),
PSNR=self.psnr(),
IS=self.IS(),
FID=self.fid(),
KID=self.kid(),
LPIPS=self.lpips(),
Reconstruction=self.reconstruction(),
)
enablePrint()
return metrics |