| 18 | |
| 19 | |
| 20 | class ImageMetricTracker(nn.Module): |
| 21 | def __init__(self): |
| 22 | super().__init__() |
| 23 | |
| 24 | self.ssim = SSIM(data_range=1.) |
| 25 | self.ssims = [] |
| 26 | |
| 27 | self.psnrs = [] |
| 28 | |
| 29 | self.fid = FrechetInceptionDistance( |
| 30 | feature=2048, |
| 31 | reset_real_features=True, |
| 32 | normalize=False, |
| 33 | sync_on_compute=True |
| 34 | ) |
| 35 | |
| 36 | def __call__(self, target, pred): |
| 37 | """ Assumes target and pred in [-1, 1] range """ |
| 38 | real_ims = un_normalize_ims(target) |
| 39 | fake_ims = un_normalize_ims(pred) |
| 40 | |
| 41 | # update FID |
| 42 | self.fid.update(real_ims, real=True) |
| 43 | self.fid.update(fake_ims, real=False) |
| 44 | |
| 45 | # SSIM and PSNR |
| 46 | self.ssims.append(self.ssim(pred/2+0.5, target/2+0.5)) |
| 47 | self.psnrs.append(calculate_PSNR(pred/2+0.5, target/2+0.5)) |
| 48 | |
| 49 | def reset(self): |
| 50 | self.ssims = [] |
| 51 | self.psnrs = [] |
| 52 | self.fid.reset() |
| 53 | |
| 54 | def aggregate(self): |
| 55 | fid = self.fid.compute() |
| 56 | ssim = torch.stack(self.ssims).mean() |
| 57 | psnr = torch.stack(self.psnrs).mean() |
| 58 | out = dict(fid=fid, ssim=ssim, psnr=psnr) |
| 59 | return out |
| 60 | |
| 61 | |
| 62 | class DepthMetricTracker: |
nothing calls this directly
no outgoing calls
no test coverage detected