Initial commit

metric_depth/util/metric.py

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+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()}