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Running
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A10G
| # ------------------------------------------------------------------------------ | |
| # The code is from GLPDepth (https://github.com/vinvino02/GLPDepth). | |
| # For non-commercial purpose only (research, evaluation etc). | |
| # ------------------------------------------------------------------------------ | |
| import torch | |
| def eval_depth(pred, target): | |
| assert pred.shape == target.shape | |
| thresh = torch.max((target / pred), (pred / target)) | |
| d1 = torch.sum(thresh < 1.25).float() / len(thresh) | |
| d2 = torch.sum(thresh < 1.25 ** 2).float() / len(thresh) | |
| d3 = torch.sum(thresh < 1.25 ** 3).float() / len(thresh) | |
| diff = pred - target | |
| diff_log = torch.log(pred) - torch.log(target) | |
| abs_rel = torch.mean(torch.abs(diff) / target) | |
| sq_rel = torch.mean(torch.pow(diff, 2) / target) | |
| rmse = torch.sqrt(torch.mean(torch.pow(diff, 2))) | |
| rmse_log = torch.sqrt(torch.mean(torch.pow(diff_log , 2))) | |
| log10 = torch.mean(torch.abs(torch.log10(pred) - torch.log10(target))) | |
| silog = torch.sqrt(torch.pow(diff_log, 2).mean() - 0.5 * torch.pow(diff_log.mean(), 2)) | |
| return {'d1': d1.item(), 'd2': d2.item(), 'd3': d3.item(), 'abs_rel': abs_rel.item(), | |
| 'sq_rel': sq_rel.item(), 'rmse': rmse.item(), 'rmse_log': rmse_log.item(), | |
| 'log10':log10.item(), 'silog':silog.item()} | |
| def cropping_img(args, pred, gt_depth): | |
| min_depth_eval = args.min_depth_eval | |
| max_depth_eval = args.max_depth_eval | |
| pred[torch.isinf(pred)] = max_depth_eval | |
| pred[torch.isnan(pred)] = min_depth_eval | |
| valid_mask = torch.logical_and( | |
| gt_depth > min_depth_eval, gt_depth < max_depth_eval) | |
| if args.dataset == 'kitti': | |
| if args.do_kb_crop: | |
| height, width = gt_depth.shape | |
| top_margin = int(height - 352) | |
| left_margin = int((width - 1216) / 2) | |
| gt_depth = gt_depth[top_margin:top_margin + | |
| 352, left_margin:left_margin + 1216] | |
| if args.kitti_crop: | |
| gt_height, gt_width = gt_depth.shape | |
| eval_mask = torch.zeros(valid_mask.shape).to( | |
| device=valid_mask.device) | |
| if args.kitti_crop == 'garg_crop': | |
| eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), | |
| int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1 | |
| elif args.kitti_crop == 'eigen_crop': | |
| eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), | |
| int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1 | |
| else: | |
| eval_mask = valid_mask | |
| elif args.dataset == 'nyudepthv2': | |
| eval_mask = torch.zeros(valid_mask.shape).to(device=valid_mask.device) | |
| eval_mask[45:471, 41:601] = 1 | |
| else: | |
| eval_mask = valid_mask | |
| valid_mask = torch.logical_and(valid_mask, eval_mask) | |
| return pred[valid_mask], gt_depth[valid_mask] | |