hmr2/models/losses.py

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()