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| import torch | |
| import torch.nn as nn | |
| class Keypoint2DLoss(nn.Module): | |
| def __init__(self, loss_type: str = 'l1'): | |
| """ | |
| 2D keypoint loss module. | |
| Args: | |
| loss_type (str): Choose between l1 and l2 losses. | |
| """ | |
| super(Keypoint2DLoss, self).__init__() | |
| if loss_type == 'l1': | |
| self.loss_fn = nn.L1Loss(reduction='none') | |
| elif loss_type == 'l2': | |
| self.loss_fn = nn.MSELoss(reduction='none') | |
| else: | |
| raise NotImplementedError('Unsupported loss function') | |
| def forward(self, pred_keypoints_2d: torch.Tensor, gt_keypoints_2d: torch.Tensor) -> torch.Tensor: | |
| """ | |
| Compute 2D reprojection loss on the keypoints. | |
| Args: | |
| pred_keypoints_2d (torch.Tensor): Tensor of shape [B, S, N, 2] containing projected 2D keypoints (B: batch_size, S: num_samples, N: num_keypoints) | |
| gt_keypoints_2d (torch.Tensor): Tensor of shape [B, S, N, 3] containing the ground truth 2D keypoints and confidence. | |
| Returns: | |
| torch.Tensor: 2D keypoint loss. | |
| """ | |
| conf = gt_keypoints_2d[:, :, -1].unsqueeze(-1).clone() | |
| batch_size = conf.shape[0] | |
| loss = (conf * self.loss_fn(pred_keypoints_2d, gt_keypoints_2d[:, :, :-1])).sum(dim=(1,2)) | |
| return loss.sum() | |
| class Keypoint3DLoss(nn.Module): | |
| def __init__(self, loss_type: str = 'l1'): | |
| """ | |
| 3D keypoint loss module. | |
| Args: | |
| loss_type (str): Choose between l1 and l2 losses. | |
| """ | |
| super(Keypoint3DLoss, self).__init__() | |
| if loss_type == 'l1': | |
| self.loss_fn = nn.L1Loss(reduction='none') | |
| elif loss_type == 'l2': | |
| self.loss_fn = nn.MSELoss(reduction='none') | |
| else: | |
| raise NotImplementedError('Unsupported loss function') | |
| def forward(self, pred_keypoints_3d: torch.Tensor, gt_keypoints_3d: torch.Tensor, pelvis_id: int = 39): | |
| """ | |
| Compute 3D keypoint loss. | |
| Args: | |
| pred_keypoints_3d (torch.Tensor): Tensor of shape [B, S, N, 3] containing the predicted 3D keypoints (B: batch_size, S: num_samples, N: num_keypoints) | |
| gt_keypoints_3d (torch.Tensor): Tensor of shape [B, S, N, 4] containing the ground truth 3D keypoints and confidence. | |
| Returns: | |
| torch.Tensor: 3D keypoint loss. | |
| """ | |
| batch_size = pred_keypoints_3d.shape[0] | |
| gt_keypoints_3d = gt_keypoints_3d.clone() | |
| pred_keypoints_3d = pred_keypoints_3d - pred_keypoints_3d[:, pelvis_id, :].unsqueeze(dim=1) | |
| gt_keypoints_3d[:, :, :-1] = gt_keypoints_3d[:, :, :-1] - gt_keypoints_3d[:, pelvis_id, :-1].unsqueeze(dim=1) | |
| conf = gt_keypoints_3d[:, :, -1].unsqueeze(-1).clone() | |
| gt_keypoints_3d = gt_keypoints_3d[:, :, :-1] | |
| loss = (conf * self.loss_fn(pred_keypoints_3d, gt_keypoints_3d)).sum(dim=(1,2)) | |
| return loss.sum() | |
| class ParameterLoss(nn.Module): | |
| def __init__(self): | |
| """ | |
| SMPL parameter loss module. | |
| """ | |
| super(ParameterLoss, self).__init__() | |
| self.loss_fn = nn.MSELoss(reduction='none') | |
| def forward(self, pred_param: torch.Tensor, gt_param: torch.Tensor, has_param: torch.Tensor): | |
| """ | |
| Compute SMPL parameter loss. | |
| Args: | |
| pred_param (torch.Tensor): Tensor of shape [B, S, ...] containing the predicted parameters (body pose / global orientation / betas) | |
| gt_param (torch.Tensor): Tensor of shape [B, S, ...] containing the ground truth SMPL parameters. | |
| Returns: | |
| torch.Tensor: L2 parameter loss loss. | |
| """ | |
| batch_size = pred_param.shape[0] | |
| num_dims = len(pred_param.shape) | |
| mask_dimension = [batch_size] + [1] * (num_dims-1) | |
| has_param = has_param.type(pred_param.type()).view(*mask_dimension) | |
| loss_param = (has_param * self.loss_fn(pred_param, gt_param)) | |
| return loss_param.sum() | |