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loss.py
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loss.py
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import torch
import torch.nn as nn
class JointsRMSELoss(nn.Module):
def __init__(self, use_target_weight=True):
super(JointsRMSELoss, self).__init__()
self.use_target_weight = use_target_weight
self.criterion = nn.MSELoss(reduction='none')
def forward(self, pred, target):
target_coord = target[:, :, :2]
target_weight = target[:, :, 2].unsqueeze(-1)
loss = self.criterion(pred, target_coord)
if self.use_target_weight:
loss *= target_weight
loss = torch.sqrt(torch.mean(torch.mean(loss, dim=0)))
return loss
class OffsetMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(OffsetMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_target_weight = use_target_weight
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_joints = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
loss_hm = 0
loss_offset = 0
num_joints = output.size(1) // 3
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx*3].squeeze()
heatmap_gt = heatmaps_gt[idx*3].squeeze()
offset_x_pred = heatmaps_pred[idx*3+1].squeeze()
offset_x_gt = heatmaps_gt[idx*3+1].squeeze()
offset_y_pred = heatmaps_pred[idx * 3 + 2].squeeze()
offset_y_gt = heatmaps_gt[idx * 3 + 2].squeeze()
if self.use_target_weight:
loss_hm += 0.5 * self.criterion(
heatmap_pred.mul(target_weight[:, idx]),
heatmap_gt.mul(target_weight[:, idx])
)
loss_offset += 0.5 * self.criterion(
heatmap_gt * offset_x_pred,
heatmap_gt * offset_x_gt
)
loss_offset += 0.5 * self.criterion(
heatmap_gt * offset_y_pred,
heatmap_gt * offset_y_gt
)
return loss_hm / num_joints, loss_offset/num_joints
class OffsetL1Loss(nn.Module):
def __init__(self, use_target_weight,reduction = 'mean'):
super(OffsetL1Loss, self).__init__()
self.criterion = nn.SmoothL1Loss(reduction=reduction)
self.use_target_weight = use_target_weight
self.reduction = reduction
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_joints = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
loss_hm = 0
loss_offset = 0
num_joints = output.size(1) // 3
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx*3].squeeze()
heatmap_gt = heatmaps_gt[idx*3].squeeze()
offset_x_pred = heatmaps_pred[idx*3+1].squeeze()
offset_x_gt = heatmaps_gt[idx*3+1].squeeze()
offset_y_pred = heatmaps_pred[idx * 3 + 2].squeeze()
offset_y_gt = heatmaps_gt[idx * 3 + 2].squeeze()
if self.use_target_weight:
loss_hm += 0.5 * self.criterion(
heatmap_pred.mul(target_weight[:, idx]),
heatmap_gt.mul(target_weight[:, idx])
)
loss_offset += 0.5 * self.criterion(
heatmap_gt * offset_x_pred,
heatmap_gt * offset_x_gt
)
loss_offset += 0.5 * self.criterion(
heatmap_gt * offset_y_pred,
heatmap_gt * offset_y_gt
)
if self.reduction == 'mean':
return loss_hm / num_joints, loss_offset/num_joints
else:
return loss_hm,loss_offset
class HeatmapMSELoss(nn.Module):
def __init__(self, use_target_weight=True):
super(HeatmapMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_target_weight = use_target_weight
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_joints = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
loss = 0
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx].squeeze()
heatmap_gt = heatmaps_gt[idx].squeeze()
if self.use_target_weight:
loss += 0.5 * self.criterion(
heatmap_pred.mul(target_weight[:, idx]),
heatmap_gt.mul(target_weight[:, idx])
)
else:
loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)
return loss / num_joints
class HeatmapOHKMMSELoss(nn.Module):
def __init__(self, use_target_weight=True, topk=8):
super(HeatmapOHKMMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='none')
self.use_target_weight = use_target_weight
self.topk = topk
def ohkm(self, loss):
ohkm_loss = 0.
for i in range(loss.size()[0]):
sub_loss = loss[i]
topk_val, topk_idx = torch.topk(
sub_loss, k=self.topk, dim=0, sorted=False
)
tmp_loss = torch.gather(sub_loss, 0, topk_idx)
ohkm_loss += torch.sum(tmp_loss) / self.topk
ohkm_loss /= loss.size()[0]
return ohkm_loss
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_joints = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
loss = []
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx].squeeze()
heatmap_gt = heatmaps_gt[idx].squeeze()
if self.use_target_weight:
loss.append(0.5 * self.criterion(
heatmap_pred.mul(target_weight[:, idx]),
heatmap_gt.mul(target_weight[:, idx])
))
else:
loss.append(
0.5 * self.criterion(heatmap_pred, heatmap_gt)
)
loss = [l.sum(dim=1).unsqueeze(dim=1) for l in loss]
loss = torch.cat(loss, dim=1)
return self.ohkm(loss)