lib/losses/fpointnet_loss.py [111:135]:
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    size_pred = output_dict['size_residuals'] + torch.from_numpy(mean_size_arr).cuda().view(1, -1, 3)
    size_pred = torch.sum(size_pred * scls_onehot, 1)
    # true pred heading
    heading_bin_centers = torch.from_numpy(np.arange(0, 2 * np.pi, 2 * np.pi / num_heading_bin)).cuda().float()
    heading_pred = output_dict['heading_residuals'] + heading_bin_centers.view(1, -1)
    heading_pred = torch.sum(heading_pred * hcls_onehot, 1)

    box3d_pred = torch.cat([output_dict['center'], size_pred, heading_pred.view(-1, 1)], 1)
    corners_3d_pred = boxes3d_to_corners3d_torch(box3d_pred)

    # heading true label
    heading_bin_centers = torch.from_numpy(np.arange(0,2*np.pi,2*np.pi/num_heading_bin)).cuda().float()
    heading_label = heading_residual_label.view(-1, 1) + heading_bin_centers.view(1, -1)
    heading_label = torch.sum(hcls_onehot*heading_label, -1).float()

    # size true label
    size_label = torch.sum(torch.from_numpy(mean_size_arr).cuda() * scls_onehot, 1) + size_residual_label
    size_label = size_label.float()

    # corners_3d label
    box3d = torch.cat([center_label, size_label, heading_label.view(-1, 1)], 1)

    # true 3d corners
    corners_3d_gt = boxes3d_to_corners3d_torch(box3d)
    corners_3d_gt_flip = boxes3d_to_corners3d_torch(box3d, flip=True)
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lib/losses/patchnet_loss.py [117:141]:
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    size_pred = output_dict['size_residuals'] + torch.from_numpy(mean_size_arr).cuda().view(1, -1, 3)
    size_pred = torch.sum(size_pred * scls_onehot, 1)
    # true pred heading
    heading_bin_centers = torch.from_numpy(np.arange(0, 2 * np.pi, 2 * np.pi / num_heading_bin)).cuda().float()
    heading_pred = output_dict['heading_residuals'] + heading_bin_centers.view(1, -1)
    heading_pred = torch.sum(heading_pred * hcls_onehot, 1)

    box3d_pred = torch.cat([output_dict['center'], size_pred, heading_pred.view(-1, 1)], 1)
    corners_3d_pred = boxes3d_to_corners3d_torch(box3d_pred)

    # heading true label
    heading_bin_centers = torch.from_numpy(np.arange(0,2*np.pi,2*np.pi/num_heading_bin)).cuda().float()
    heading_label = heading_residual_label.view(-1, 1) + heading_bin_centers.view(1, -1)
    heading_label = torch.sum(hcls_onehot*heading_label, -1).float()

    # size true label
    size_label = torch.sum(torch.from_numpy(mean_size_arr).cuda() * scls_onehot, 1) + size_residual_label
    size_label = size_label.float()

    # corners_3d label
    box3d = torch.cat([center_label, size_label, heading_label.view(-1, 1)], 1)

    # true 3d corners
    corners_3d_gt = boxes3d_to_corners3d_torch(box3d)
    corners_3d_gt_flip = boxes3d_to_corners3d_torch(box3d, flip=True)
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