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loss.py
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loss.py
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import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def _sigmoid(x):
y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
return y
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
feat = feat.view(-1, dim)
return feat
def _tranpose_and_gather_feat(feat, ind):
feat = feat.permute(0, 2, 3, 1).contiguous()
feat = feat.view(feat.size(0), -1, feat.size(3))
feat = _gather_feat(feat, ind)
return feat
class CtclsLoss(nn.Module):
def __init__(self, opt):
super(CtclsLoss, self).__init__()
self.crit = FocalLoss()
self.calc_cls = torch.nn.CrossEntropyLoss().cuda()
self.opt = opt
def forward(self, output, mask, batch):
mask_loss = self.crit(mask, batch['hm'])
cls_loss = self.calc_cls(output, batch['cls'])
return cls_loss + 0.1 * mask_loss, {'loss_cls': cls_loss, 'loss_mask': mask_loss}
class CtdetLoss(torch.nn.Module):
def __init__(self, opt):
super(CtdetLoss, self).__init__()
self.crit = FocalLoss()
self.crit_reg = RegL1Loss()
self.crit_wh = self.crit_reg
self.opt = opt
def forward(self, output, mask, batch):
opt = self.opt
hm_loss, wh_loss, off_loss, mask_loss = 0, 0, 0, 0
output['hm'] = _sigmoid(output['hm'])
hm_loss += self.crit(output['hm'], batch['hm'])
wh_loss += self.crit_reg(
output['wh'], batch['reg_mask'],
batch['ind'], batch['wh'])
off_loss += self.crit_reg(output['reg'], batch['reg_mask'],
batch['ind'], batch['reg'])
if mask is not None:
# g_mask = nn.functional.avg_pool2d(batch['mask'], (3, 3), stride=1, padding=1)
mask_loss += self.crit(mask, batch['mask'])
loss = opt.hm_weight * hm_loss + opt.wh_weight * wh_loss + \
opt.off_weight * off_loss + opt.mask_weight * mask_loss
return loss, {'loss_hm': hm_loss, 'loss_wh': wh_loss, 'loss_off': off_loss, 'loss_mask': mask_loss}
def _slow_neg_loss(pred, gt):
'''focal loss from CornerNet'''
pos_inds = gt.eq(1)
neg_inds = gt.lt(1)
neg_weights = torch.pow(1 - gt[neg_inds], 4)
loss = 0
pos_pred = pred[pos_inds]
neg_pred = pred[neg_inds]
pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, 2)
neg_loss = torch.log(1 - neg_pred) * torch.pow(neg_pred, 2) * neg_weights
num_pos = pos_inds.float().sum()
pos_loss = pos_loss.sum()
neg_loss = neg_loss.sum()
if pos_pred.nelement() == 0:
loss = loss - neg_loss
else:
loss = loss - (pos_loss + neg_loss) / num_pos
return loss
def _neg_loss(pred, gt):
''' Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
Arguments:
pred (batch x c x h x w)
gt_regr (batch x c x h x w)
'''
pos_inds = gt.eq(1).float()
neg_inds = gt.lt(1).float()
neg_weights = torch.pow(1 - gt, 4)
loss = 0
pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
num_pos = pos_inds.float().sum()
pos_loss = pos_loss.sum()
neg_loss = neg_loss.sum()
if num_pos == 0:
loss = loss - neg_loss
else:
loss = loss - (pos_loss + neg_loss) / num_pos
return loss
def _not_faster_neg_loss(pred, gt):
pos_inds = gt.eq(1).float()
neg_inds = gt.lt(1).float()
num_pos = pos_inds.float().sum()
neg_weights = torch.pow(1 - gt, 4)
loss = 0
trans_pred = pred * neg_inds + (1 - pred) * pos_inds
weight = neg_weights * neg_inds + pos_inds
all_loss = torch.log(1 - trans_pred) * torch.pow(trans_pred, 2) * weight
all_loss = all_loss.sum()
if num_pos > 0:
all_loss /= num_pos
loss -= all_loss
return loss
def _slow_reg_loss(regr, gt_regr, mask):
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr)
regr = regr[mask]
gt_regr = gt_regr[mask]
regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
regr_loss = regr_loss / (num + 1e-4)
return regr_loss
def _reg_loss(regr, gt_regr, mask):
''' L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects)
'''
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr).float()
regr = regr * mask
gt_regr = gt_regr * mask
regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
regr_loss = regr_loss / (num + 1e-4)
return regr_loss
class FocalLoss(nn.Module):
'''nn.Module warpper for focal loss'''
def __init__(self):
super(FocalLoss, self).__init__()
self.neg_loss = _neg_loss
def forward(self, out, target):
return self.neg_loss(out, target)
class RegLoss(nn.Module):
'''Regression loss for an output tensor
Arguments:
output (batch x dim x h x w)
mask (batch x max_objects)
ind (batch x max_objects)
target (batch x max_objects x dim)
'''
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, output, mask, ind, target):
pred = _tranpose_and_gather_feat(output, ind)
loss = _reg_loss(pred, target, mask)
return loss
class RegL1Loss(nn.Module):
def __init__(self):
super(RegL1Loss, self).__init__()
def forward(self, output, mask, ind, target):
pred = _tranpose_and_gather_feat(output, ind)
mask = mask.unsqueeze(2).expand_as(pred).float()
# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
loss = F.l1_loss(pred * mask, target * mask, size_average=False)
loss = loss / (mask.sum() + 1e-4)
return loss
class NormRegL1Loss(nn.Module):
def __init__(self):
super(NormRegL1Loss, self).__init__()
def forward(self, output, mask, ind, target):
pred = _tranpose_and_gather_feat(output, ind)
mask = mask.unsqueeze(2).expand_as(pred).float()
# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
pred = pred / (target + 1e-4)
target = target * 0 + 1
loss = F.l1_loss(pred * mask, target * mask, size_average=False)
loss = loss / (mask.sum() + 1e-4)
return loss
class RegWeightedL1Loss(nn.Module):
def __init__(self):
super(RegWeightedL1Loss, self).__init__()
def forward(self, output, mask, ind, target):
pred = _tranpose_and_gather_feat(output, ind)
mask = mask.float()
# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
loss = F.l1_loss(pred * mask, target * mask, size_average=False)
loss = loss / (mask.sum() + 1e-4)
return loss
class L1Loss(nn.Module):
def __init__(self):
super(L1Loss, self).__init__()
def forward(self, output, mask, ind, target):
pred = _tranpose_and_gather_feat(output, ind)
mask = mask.unsqueeze(2).expand_as(pred).float()
loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
return loss
class BinRotLoss(nn.Module):
def __init__(self):
super(BinRotLoss, self).__init__()
def forward(self, output, mask, ind, rotbin, rotres):
pred = _tranpose_and_gather_feat(output, ind)
loss = compute_rot_loss(pred, rotbin, rotres, mask)
return loss
def compute_res_loss(output, target):
return F.smooth_l1_loss(output, target, reduction='elementwise_mean')
# TODO: weight
def compute_bin_loss(output, target, mask):
mask = mask.expand_as(output)
output = output * mask.float()
return F.cross_entropy(output, target, reduction='elementwise_mean')
def compute_rot_loss(output, target_bin, target_res, mask):
# output: (B, 128, 8) [bin1_cls[0], bin1_cls[1], bin1_sin, bin1_cos,
# bin2_cls[0], bin2_cls[1], bin2_sin, bin2_cos]
# target_bin: (B, 128, 2) [bin1_cls, bin2_cls]
# target_res: (B, 128, 2) [bin1_res, bin2_res]
# mask: (B, 128, 1)
# import pdb; pdb.set_trace()
output = output.view(-1, 8)
target_bin = target_bin.view(-1, 2)
target_res = target_res.view(-1, 2)
mask = mask.view(-1, 1)
loss_bin1 = compute_bin_loss(output[:, 0:2], target_bin[:, 0], mask)
loss_bin2 = compute_bin_loss(output[:, 4:6], target_bin[:, 1], mask)
loss_res = torch.zeros_like(loss_bin1)
if target_bin[:, 0].nonzero().shape[0] > 0:
idx1 = target_bin[:, 0].nonzero()[:, 0]
valid_output1 = torch.index_select(output, 0, idx1.long())
valid_target_res1 = torch.index_select(target_res, 0, idx1.long())
loss_sin1 = compute_res_loss(
valid_output1[:, 2], torch.sin(valid_target_res1[:, 0]))
loss_cos1 = compute_res_loss(
valid_output1[:, 3], torch.cos(valid_target_res1[:, 0]))
loss_res += loss_sin1 + loss_cos1
if target_bin[:, 1].nonzero().shape[0] > 0:
idx2 = target_bin[:, 1].nonzero()[:, 0]
valid_output2 = torch.index_select(output, 0, idx2.long())
valid_target_res2 = torch.index_select(target_res, 0, idx2.long())
loss_sin2 = compute_res_loss(
valid_output2[:, 6], torch.sin(valid_target_res2[:, 1]))
loss_cos2 = compute_res_loss(
valid_output2[:, 7], torch.cos(valid_target_res2[:, 1]))
loss_res += loss_sin2 + loss_cos2
return loss_bin1 + loss_bin2 + loss_res