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import os
import sys
sys.path.append(os.getcwd())
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class KeypointLoss(nn.Module):
def __init__(self):
super(KeypointLoss, self).__init__()
def forward(self, pred_seq, gt_seq, gt_conf=None):
#pred_seq: (B, C, T)
if gt_conf is not None:
gt_conf = gt_conf >= 0.01
return F.mse_loss(pred_seq[gt_conf], gt_seq[gt_conf], reduction='mean')
else:
return F.mse_loss(pred_seq, gt_seq)
class KLLoss(nn.Module):
def __init__(self, kl_tolerance):
super(KLLoss, self).__init__()
self.kl_tolerance = kl_tolerance
def forward(self, mu, var, mul=1):
kl_tolerance = self.kl_tolerance * mul * var.shape[1] / 64
kld_loss = -0.5 * torch.sum(1 + var - mu**2 - var.exp(), dim=1)
# kld_loss = -0.5 * torch.sum(1 + (var-1) - (mu) ** 2 - (var-1).exp(), dim=1)
if self.kl_tolerance is not None:
# above_line = kld_loss[kld_loss > self.kl_tolerance]
# if len(above_line) > 0:
# kld_loss = torch.mean(kld_loss)
# else:
# kld_loss = 0
kld_loss = torch.where(kld_loss > kl_tolerance, kld_loss, torch.tensor(kl_tolerance, device='cuda'))
# else:
kld_loss = torch.mean(kld_loss)
return kld_loss
class L2KLLoss(nn.Module):
def __init__(self, kl_tolerance):
super(L2KLLoss, self).__init__()
self.kl_tolerance = kl_tolerance
def forward(self, x):
# TODO: check
kld_loss = torch.sum(x ** 2, dim=1)
if self.kl_tolerance is not None:
above_line = kld_loss[kld_loss > self.kl_tolerance]
if len(above_line) > 0:
kld_loss = torch.mean(kld_loss)
else:
kld_loss = 0
else:
kld_loss = torch.mean(kld_loss)
return kld_loss
class L2RegLoss(nn.Module):
def __init__(self):
super(L2RegLoss, self).__init__()
def forward(self, x):
#TODO: check
return torch.sum(x**2)
class L2Loss(nn.Module):
def __init__(self):
super(L2Loss, self).__init__()
def forward(self, x):
# TODO: check
return torch.sum(x ** 2)
class AudioLoss(nn.Module):
def __init__(self):
super(AudioLoss, self).__init__()
def forward(self, dynamics, gt_poses):
#pay attention, normalized
mean = torch.mean(gt_poses, dim=-1).unsqueeze(-1)
gt = gt_poses - mean
return F.mse_loss(dynamics, gt)
L1Loss = nn.L1Loss |