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loss.py
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import torch
import torch.nn as nn
import numpy as np
eps = 1e-10
class loss_ce(nn.Module):
def __init__(self):
super(loss_ce, self).__init__()
def forward(self, pred, labels):
'''
pred.size: [8, 2, 256, 256]
labels.size: [8, 2, 256, 256]
# '''
# print(pred.size())
# print(labels.size())
pred = torch.squeeze(nn.functional.softmax(pred, dim=1))
pred = torch.clamp(pred, 1e-5, 1-1e-5) #将pred限制在1e-5到1-1e-5之间
# pos_num = torch.sum(labels)*1.0
# pos_ratio = pos_num/labels.numel()
# neg_ratio = 1.- pos_ratio
# print(pos_ratio)
# print(pre, labels)
# loss = nn.CrossEntropyLoss(weight = weight)
# print(pred.size(), labels.size())
# print(labels.size(), pred.size())
# print(pred.size(), labels.size())
# loss = torch.sum(-1.*(neg_ratio*labels*torch.log(pred[:,0,:,:]) + pos_ratio*(1.-labels)*torch.log(pred[:,1,:,:])))/labels.numel()
temp1 = torch.log(pred[:,0,:,:])
temp2 = torch.log(pred[:,1,:,:])
# print(labels.size())
# print(temp1.size())
# print(temp2.size())
loss = torch.mean(-1.*(labels*temp1 + (1.-labels)*temp2))
# print(loss.size())
# image_level_scores = torch.clamp(pred, min=0.0, max=1.0)
# loss = nn.functional.binary_cross_entropy(pred, labels, reduction="sum")
return loss
class loss_T(nn.Module):
def __init__(self):
super(loss_T, self).__init__()
def forward(self, pred, labels, weight):
'''
pred.size: [8, 2, 256, 256]
labels.size: [8, 2, 256, 256]
# '''
# print(pred.size())
# print(labels.size())
pred = torch.squeeze(nn.functional.softmax(pred, dim=1))
pred = torch.clamp(pred, 1e-5, 1-1e-5) #将pred限制在1e-5到1-1e-5之间
loss = torch.mean(-1.*weight*(labels*torch.log(pred[:,0,:,:]) + (1.-labels)*torch.log(pred[:,1,:,:])))
return loss
class loss_ce_T(nn.Module):
def __init__(self):
super(loss_ce_T, self).__init__()
def forward(self, pred, labels):
'''
pred.size: [8, 2, 256, 256]
labels.size: [8, 2, 256, 256]
# '''
# print(pred.size())
# print(labels.size())
# pred = torch.squeeze(nn.functional.softmax(pred, dim=1))
pred = torch.clamp(pred, 1e-5, 1-1e-5)
pos_num = torch.sum(labels)*1.0 #计算labels的和
pos_ratio = pos_num/labels.numel() #numel()返回lables的个数
neg_ratio = 1.- pos_ratio
# print(pos_ratio)
# print(pre, labels)
# loss = nn.CrossEntropyLoss(weight = weight)
# print(pred.size(), labels.size())
# print(labels.size(), pred.size())
# print(pred.size(), labels.size())
# loss = torch.sum(-1.*(neg_ratio*labels*torch.log(pred[:,0,:,:]) + pos_ratio*(1.-labels)*torch.log(pred[:,1,:,:])))/labels.numel()
loss = torch.mean(-1.*(labels*torch.log(pred[:,0,:,:]) + (1.-labels)*torch.log(pred[:,1,:,:])))
# print(loss.size())
# image_level_scores = torch.clamp(pred, min=0.0, max=1.0)
# loss = nn.functional.binary_cross_entropy(pred, labels, reduction="sum")
return loss
class loss_R(nn.Module):
def __init__(self):
super(loss_R, self).__init__()
def forward(self, pred, labels, weight):
'''
pred.size: [8, 2, 256, 256]
labels.size: [8, 2, 256, 256]
# '''
pred = torch.squeeze(nn.functional.softmax(pred, dim=1))
pred = torch.clamp(pred, 1e-5, 1-1e-5)
loss = torch.mean(-1.*weight*(labels*torch.log(pred[:,0,:,:]) + (1.-labels)*torch.log(pred[:,1,:,:])))
return loss
class loss_similarity(nn.Module):
def __init__(self):
super(loss_similarity, self).__init__()
def forward(self, ssw, seg, score):
ssw_block = torch.squeeze(ssw, dim = 0)[:, 1:]
ssw_block = ssw_block.cpu().numpy()
ssw_block = ssw_block.astype(np.int16)
seg = nn.functional.softmax(seg, dim=1)
score_map = torch.squeeze(seg[0,1,:,:])
roi_score = torch.squeeze(score)
roi_num = ssw_block.shape[0]
iou = torch.empty(roi_num).cuda()
for i in range(roi_num):
x1, y1, x2, y2 = ssw_block[i,:]
iou_region = score_map[y1:y2,x1:x2]
iou[i] = iou_region.mean()
iou = iou.expand(roi_num,roi_num).cuda()
iouT = iou.T
dis_iou = iou - iou.T
sim_iou = torch.sqrt(dis_iou*dis_iou + eps)
score = score.expand(roi_num,roi_num)
scoreT = score.T
dis_score = score - scoreT
sim_score = torch.sqrt(dis_score*dis_score+eps)
if torch.sum(torch.isnan(sim_iou))>0:
print(iou, roi_num)
print('sim_iou')
if torch.sum(torch.isnan(sim_score))>0:
print('sim_score')
# print('sim_iou', torch.sum(torch.isnan(sim_iou)))
# print('sim_score', torch.sum(torch.isnan(sim_score)))
loss_fn = nn.MSELoss()
loss = loss_fn(sim_iou, sim_score)
# print(loss)
return loss
class loss_adv(nn.Module):
def __init__(self):
super(loss_adv, self).__init__()
def forward(self, ssw, seg, score):
ssw_block = torch.squeeze(ssw, dim = 0)[:, 1:]
ssw_block = ssw_block.cpu().numpy()
ssw_block = ssw_block.astype(np.int16)
seg = nn.functional.softmax(seg, dim=1)
score_map = torch.squeeze(seg[0,1,:,:])
roi_score = torch.squeeze(score)
roi_num = ssw_block.shape[0]
iou = torch.empty(roi_num).cuda()
for i in range(roi_num):
x1, y1, x2, y2 = ssw_block[i,:]
iou_region = score_map[y1:y2,x1:x2]
iou[i] = iou_region.mean()
iou = iou.expand(roi_num,roi_num).cuda()
iouT = iou.T
dis_iou = iou - iou.T
sim_iou = torch.sqrt(dis_iou*dis_iou + eps)
score = score.expand(roi_num,roi_num)
scoreT = score.T
dis_score = score - scoreT
sim_score = torch.sqrt(dis_score*dis_score+eps)
if torch.sum(torch.isnan(sim_iou))>0:
print(iou, roi_num)
print('sim_iou')
if torch.sum(torch.isnan(sim_score))>0:
print('sim_score')
# print('sim_iou', torch.sum(torch.isnan(sim_iou)))
# print('sim_score', torch.sum(torch.isnan(sim_score)))
loss_fn = nn.MSELoss()
loss = loss_fn(sim_iou, sim_score)
# print(loss)
return loss