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A10G
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# ------------------------------------------------------------------------------
# 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]
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