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import torch
import torch.nn as nn
class L1Loss(nn.Module):
"""
Compute L1 loss.
"""
def __init__(self, loss_weight=1, data_type=['lidar', 'denselidar', 'stereo', 'denselidar_syn'], **kwargs):
super(L1Loss, self).__init__()
self.loss_weight = loss_weight
self.data_type = data_type
self.eps = 1e-6
def forward(self, prediction, target, mask=None, **kwargs):
diff = torch.abs(prediction - target)* mask
loss = torch.sum(diff) / (torch.sum(mask) + self.eps)
if torch.isnan(loss).item() | torch.isinf(loss).item():
loss = 0 * torch.sum(prediction)
print(f'L1 NAN error, {loss}')
#raise RuntimeError(f'Silog error, {loss}, d_square_mean: {d_square_mean}, d_mean: {d_mean}')
return loss * self.loss_weight
class L1DispLoss(nn.Module):
"""
Compute L1 disparity loss of disparity.
"""
def __init__(self, loss_weight=1, data_type=['lidar', 'denselidar', 'stereo', 'denselidar_syn'], **kwargs):
super(L1DispLoss, self).__init__()
self.loss_weight = loss_weight
self.data_type = data_type
self.eps = 1e-6
def forward(self, prediction_disp, inv_depth, mask=None, **kwargs):
# gt_disp_mask = ~torch.all(inv_depth == 0, dim=1, keepdim=True)
# if mask is None:
# mask = gt_disp_mask
diff = torch.abs(prediction_disp - inv_depth)* mask
loss = torch.sum(diff) / (torch.sum(mask) + self.eps)
if torch.isnan(loss).item() | torch.isinf(loss).item():
loss = 0 * torch.sum(prediction_disp)
#raise RuntimeError(f'Silog error, {loss}, d_square_mean: {d_square_mean}, d_mean: {d_mean}')
return loss * self.loss_weight
class L1InverseLoss(nn.Module):
"""
Compute L1 disparity loss of disparity.
"""
def __init__(self, loss_weight=1, data_type=['lidar', 'denselidar', 'stereo'], **kwargs):
super(L1InverseLoss, self).__init__()
self.loss_weight = loss_weight
self.data_type = data_type
self.eps = 1e-6
def forward(self, prediction, inv_depth, mask=None, **kwargs):
mask = torch.logical_and(mask, inv_depth>0)
inv_pred = 1.0 / prediction * 10.0
inv_pred[~mask] = -1
diff = torch.abs(inv_pred - inv_depth)* mask
loss = torch.sum(diff) / (torch.sum(mask) + self.eps)
if torch.isnan(loss).item() | torch.isinf(loss).item():
loss = 0 * torch.sum(inv_pred)
#raise RuntimeError(f'Silog error, {loss}, d_square_mean: {d_square_mean}, d_mean: {d_mean}')
return loss * self.loss_weight |