66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
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import torch
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import torch.nn.functional as F
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from utils.experiment import make_nograd_func
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from torch.autograd import Variable
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from torch import Tensor
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# Update D1 from >3px to >=3px & >5%
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# matlab code:
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# E = abs(D_gt - D_est);
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# n_err = length(find(D_gt > 0 & E > tau(1) & E. / abs(D_gt) > tau(2)));
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# n_total = length(find(D_gt > 0));
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# d_err = n_err / n_total;
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def check_shape_for_metric_computation(*vars):
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assert isinstance(vars, tuple)
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for var in vars:
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assert len(var.size()) == 3
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assert var.size() == vars[0].size()
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# a wrapper to compute metrics for each image individually
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def compute_metric_for_each_image(metric_func):
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def wrapper(D_ests, D_gts, masks, *nargs):
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check_shape_for_metric_computation(D_ests, D_gts, masks)
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bn = D_gts.shape[0] # batch size
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results = [] # a list to store results for each image
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# compute result one by one
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for idx in range(bn):
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# if tensor, then pick idx, else pass the same value
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cur_nargs = [x[idx] if isinstance(x, (Tensor, Variable)) else x for x in nargs]
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if masks[idx].float().mean() / (D_gts[idx] > 0).float().mean() < 0.1:
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print("masks[idx].float().mean() too small, skip")
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else:
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ret = metric_func(D_ests[idx], D_gts[idx], masks[idx], *cur_nargs)
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results.append(ret)
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if len(results) == 0:
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print("masks[idx].float().mean() too small for all images in this batch, return 0")
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return torch.tensor(0, dtype=torch.float32, device=D_gts.device)
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else:
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return torch.stack(results).mean()
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return wrapper
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@make_nograd_func
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@compute_metric_for_each_image
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def D1_metric(D_est, D_gt, mask):
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D_est, D_gt = D_est[mask], D_gt[mask]
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E = torch.abs(D_gt - D_est)
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err_mask = (E > 3) & (E / D_gt.abs() > 0.05)
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return torch.mean(err_mask.float())
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@make_nograd_func
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@compute_metric_for_each_image
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def Thres_metric(D_est, D_gt, mask, thres):
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assert isinstance(thres, (int, float))
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D_est, D_gt = D_est[mask], D_gt[mask]
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E = torch.abs(D_gt - D_est)
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err_mask = E > thres
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return torch.mean(err_mask.float())
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# NOTE: please do not use this to build up training loss
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@make_nograd_func
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@compute_metric_for_each_image
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def EPE_metric(D_est, D_gt, mask):
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D_est, D_gt = D_est[mask], D_gt[mask]
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return F.l1_loss(D_est, D_gt, size_average=True)
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