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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def compute_tri_normal(geometry, tris): | |
| tri_1 = tris[:, 0] | |
| tri_2 = tris[:, 1] | |
| tri_3 = tris[:, 2] | |
| vert_1 = torch.index_select(geometry, 1, tri_1) | |
| vert_2 = torch.index_select(geometry, 1, tri_2) | |
| vert_3 = torch.index_select(geometry, 1, tri_3) | |
| nnorm = torch.cross(vert_2 - vert_1, vert_3 - vert_1, 2) | |
| normal = nn.functional.normalize(nnorm) | |
| return normal | |
| def euler2rot(euler_angle): | |
| batch_size = euler_angle.shape[0] | |
| theta = euler_angle[:, 0].reshape(-1, 1, 1) | |
| phi = euler_angle[:, 1].reshape(-1, 1, 1) | |
| psi = euler_angle[:, 2].reshape(-1, 1, 1) | |
| one = torch.ones(batch_size, 1, 1).to(euler_angle.device) | |
| zero = torch.zeros(batch_size, 1, 1).to(euler_angle.device) | |
| rot_x = torch.cat( | |
| ( | |
| torch.cat((one, zero, zero), 1), | |
| torch.cat((zero, theta.cos(), theta.sin()), 1), | |
| torch.cat((zero, -theta.sin(), theta.cos()), 1), | |
| ), | |
| 2, | |
| ) | |
| rot_y = torch.cat( | |
| ( | |
| torch.cat((phi.cos(), zero, -phi.sin()), 1), | |
| torch.cat((zero, one, zero), 1), | |
| torch.cat((phi.sin(), zero, phi.cos()), 1), | |
| ), | |
| 2, | |
| ) | |
| rot_z = torch.cat( | |
| ( | |
| torch.cat((psi.cos(), -psi.sin(), zero), 1), | |
| torch.cat((psi.sin(), psi.cos(), zero), 1), | |
| torch.cat((zero, zero, one), 1), | |
| ), | |
| 2, | |
| ) | |
| return torch.bmm(rot_x, torch.bmm(rot_y, rot_z)) | |
| def rot_trans_pts(geometry, rot, trans): | |
| rott_geo = torch.bmm(rot, geometry.permute(0, 2, 1)) + trans[:, :, None] | |
| return rott_geo.permute(0, 2, 1) | |
| def cal_lap_loss(tensor_list, weight_list): | |
| lap_kernel = ( | |
| torch.Tensor((-0.5, 1.0, -0.5)) | |
| .unsqueeze(0) | |
| .unsqueeze(0) | |
| .float() | |
| .to(tensor_list[0].device) | |
| ) | |
| loss_lap = 0 | |
| for i in range(len(tensor_list)): | |
| in_tensor = tensor_list[i] | |
| in_tensor = in_tensor.view(-1, 1, in_tensor.shape[-1]) | |
| out_tensor = F.conv1d(in_tensor, lap_kernel) | |
| loss_lap += torch.mean(out_tensor ** 2) * weight_list[i] | |
| return loss_lap | |
| def proj_pts(rott_geo, focal_length, cxy): | |
| cx, cy = cxy[0], cxy[1] | |
| X = rott_geo[:, :, 0] | |
| Y = rott_geo[:, :, 1] | |
| Z = rott_geo[:, :, 2] | |
| fxX = focal_length * X | |
| fyY = focal_length * Y | |
| proj_x = -fxX / Z + cx | |
| proj_y = fyY / Z + cy | |
| return torch.cat((proj_x[:, :, None], proj_y[:, :, None], Z[:, :, None]), 2) | |
| def forward_rott(geometry, euler_angle, trans): | |
| rot = euler2rot(euler_angle) | |
| rott_geo = rot_trans_pts(geometry, rot, trans) | |
| return rott_geo | |
| def forward_transform(geometry, euler_angle, trans, focal_length, cxy): | |
| rot = euler2rot(euler_angle) | |
| rott_geo = rot_trans_pts(geometry, rot, trans) | |
| proj_geo = proj_pts(rott_geo, focal_length, cxy) | |
| return proj_geo | |
| def cal_lan_loss(proj_lan, gt_lan): | |
| return torch.mean((proj_lan - gt_lan) ** 2) | |
| def cal_col_loss(pred_img, gt_img, img_mask): | |
| pred_img = pred_img.float() | |
| # loss = torch.sqrt(torch.sum(torch.square(pred_img - gt_img), 3))*img_mask/255 | |
| loss = (torch.sum(torch.square(pred_img - gt_img), 3)) * img_mask / 255 | |
| loss = torch.sum(loss, dim=(1, 2)) / torch.sum(img_mask, dim=(1, 2)) | |
| loss = torch.mean(loss) | |
| return loss | |