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import torch
def eval_pred(pred_dict, gt_dict,accuracy_tresh=[0.001,0.01,0.02,0.05,0.1,0.5]):
pointmaps_pred = pred_dict['pointmaps']
pointmaps_gt = gt_dict['pointmaps']
mask = gt_dict['valid_masks'].unsqueeze(-1).repeat(1,1,1,3)
points_pred = pointmaps_pred[mask].reshape(-1,3)
points_gt = pointmaps_gt[mask].reshape(-1,3)
dists = torch.norm(points_pred - points_gt, dim=1)
results = {'dist':dists.mean().detach().item()}
if 'classifier' in pred_dict:
classifier_pred = (torch.sigmoid(pred_dict['classifier']) > 0.5).bool()
classifier_gt = gt_dict['valid_masks']
results['classifier_acc'] = (classifier_pred == classifier_gt).float().mean().detach().item()
for tresh in accuracy_tresh:
acc = (dists < tresh).float().mean()
results[f'acc_{tresh}'] = acc.detach().item()
return results
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