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Zero
Running
on
Zero
| # A reimplemented version in public environments by Xiao Fu and Mu Hu | |
| import numpy as np | |
| import torch | |
| def ensemble_normals(input_images:torch.Tensor): | |
| normal_preds = input_images | |
| bsz, d, h, w = normal_preds.shape | |
| normal_preds = normal_preds / (torch.norm(normal_preds, p=2, dim=1).unsqueeze(1)+1e-5) | |
| phi = torch.atan2(normal_preds[:,1,:,:], normal_preds[:,0,:,:]).mean(dim=0) | |
| theta = torch.atan2(torch.norm(normal_preds[:,:2,:,:], p=2, dim=1), normal_preds[:,2,:,:]).mean(dim=0) | |
| normal_pred = torch.zeros((d,h,w)).to(normal_preds) | |
| normal_pred[0,:,:] = torch.sin(theta) * torch.cos(phi) | |
| normal_pred[1,:,:] = torch.sin(theta) * torch.sin(phi) | |
| normal_pred[2,:,:] = torch.cos(theta) | |
| angle_error = torch.acos(torch.cosine_similarity(normal_pred[None], normal_preds, dim=1)) | |
| normal_idx = torch.argmin(angle_error.reshape(bsz,-1).sum(-1)) | |
| return normal_preds[normal_idx] |