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import time
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import numpy as np
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from skimage.metrics import peak_signal_noise_ratio, structural_similarity
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from skvideo.measure import niqe
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class AverageMeter():
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""" Computes and stores the average and current value """
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def __init__(self):
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self.reset()
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def reset(self):
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""" Reset all statistics """
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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""" Update statistics """
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def accuracy(output, target, topk=(1,)):
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""" Computes the precision@k for the specified values of k """
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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if target.ndimension() > 1:
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target = target.max(1)[1]
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0)
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res.append(correct_k.mul_(1.0 / batch_size))
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return res
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def compute_psnr_ssim(recoverd, clean):
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assert recoverd.shape == clean.shape
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recoverd = np.clip(recoverd.detach().cpu().numpy(), 0, 1)
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clean = np.clip(clean.detach().cpu().numpy(), 0, 1)
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recoverd = recoverd.transpose(0, 2, 3, 1)
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clean = clean.transpose(0, 2, 3, 1)
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psnr = 0
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ssim = 0
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for i in range(recoverd.shape[0]):
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psnr += peak_signal_noise_ratio(clean[i], recoverd[i], data_range=1)
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ssim += structural_similarity(clean[i], recoverd[i], data_range=1, multichannel=True)
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return psnr / recoverd.shape[0], ssim / recoverd.shape[0], recoverd.shape[0]
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def compute_niqe(image):
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image = np.clip(image.detach().cpu().numpy(), 0, 1)
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image = image.transpose(0, 2, 3, 1)
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niqe_val = niqe(image)
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return niqe_val.mean()
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class timer():
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def __init__(self):
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self.acc = 0
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self.tic()
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def tic(self):
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self.t0 = time.time()
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def toc(self):
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return time.time() - self.t0
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def hold(self):
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self.acc += self.toc()
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def release(self):
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ret = self.acc
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self.acc = 0
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return ret
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def reset(self):
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self.acc = 0 |
