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| import numpy as np | |
| import scipy.linalg | |
| from . import metric_utils | |
| import math | |
| import cv2 | |
| def compute_psnr(opts, max_real): | |
| # stats: numpy, [N, 3] | |
| stats = metric_utils.compute_image_stats_for_generator(opts=opts, capture_all=True, max_items=max_real).get_all() | |
| if opts.rank != 0: | |
| return float('nan'), float('nan'), float('nan') | |
| print('Number of samples: %d' % stats.shape[0]) | |
| avg_psnr = stats[:, 0].sum() / stats.shape[0] | |
| avg_ssim = stats[:, 1].sum() / stats.shape[0] | |
| avg_l1 = stats[:, 2].sum() / stats.shape[0] | |
| return avg_psnr, avg_ssim, avg_l1 |