def add_eval_scores()

in fairnr/models/fairnr_model.py [0:0]
45 lines of code
15 McCabe index (conditional complexity)

    def add_eval_scores(self, logging_output, sample, output, criterion, scores=['ssim', 'psnr', 'lpips'], outdir=None):
        predicts, targets = output['colors'], sample['colors']
        ssims, psnrs, lpips, rmses = [], [], [], []
        
        for s in range(predicts.size(0)):
            for v in range(predicts.size(1)):
                width = int(sample['size'][s, v][1])
                p = recover_image(predicts[s, v], width=width, min_val=float(self.args.min_color))
                t = recover_image(targets[s, v],  width=width, min_val=float(self.args.min_color))
                pn, tn = p.numpy(), t.numpy()
                p, t = p.to(predicts.device), t.to(targets.device)

                if 'ssim' in scores:
                    ssims += [skimage.metrics.structural_similarity(pn, tn, multichannel=True, data_range=1)]
                if 'psnr' in scores:
                    psnrs += [skimage.metrics.peak_signal_noise_ratio(pn, tn, data_range=1)]
                if 'lpips' in scores and hasattr(criterion, 'lpips'):
                    with torch.no_grad():
                        lpips += [criterion.lpips(
                            2 * p.unsqueeze(-1).permute(3,2,0,1) - 1,
                            2 * t.unsqueeze(-1).permute(3,2,0,1) - 1).item()]
                if 'depths' in sample:
                    td = sample['depths'][sample['depths'] > 0]
                    pd = output['depths'][sample['depths'] > 0]
                    rmses += [torch.sqrt(((td - pd) ** 2).mean()).item()]

                if outdir is not None:
                    def imsave(filename, image):
                        imageio.imsave(os.path.join(outdir, filename), (image * 255).astype('uint8'))
                    
                    figname = '-{:03d}_{:03d}.png'.format(sample['id'][s], sample['view'][s, v])
                    imsave('output' + figname, pn)
                    imsave('target' + figname, tn)
                    imsave('normal' + figname, recover_image(compute_normal_map(
                        sample['ray_start'][s, v].float(), sample['ray_dir'][s, v].float(),
                        output['depths'][s, v].float(), sample['extrinsics'][s, v].float().inverse(), width=width),
                        min_val=-1, max_val=1, width=width).numpy())
                    if 'featn2' in output:
                        imsave('featn2' + figname, output['featn2'][s, v].cpu().numpy())
                    if 'voxel' in output:
                        imsave('voxel' + figname, output['voxel'][s, v].cpu().numpy())

        if len(ssims) > 0:
            logging_output['ssim_loss'] = np.mean(ssims)
        if len(psnrs) > 0:
            logging_output['psnr_loss'] = np.mean(psnrs)
        if len(lpips) > 0:
            logging_output['lpips_loss'] = np.mean(lpips)
        if len(rmses) > 0:
            logging_output['rmses_loss'] = np.mean(rmses)