def validation(self, epoch):
self.net.eval()
acc = 0. # Accuracy
SE = 0. # Sensit ivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length = 0
for i, (imgs, probs, gts) in enumerate(self.val_loader):
imgs = imgs.to(self.device)
probs = probs.to(self.device)
gts = gts.round().long().to(self.device)
outputs = self.net(imgs,probs)
# weight = np.array(
# [0., 100., 100., 100., 50., 50., 80., 80., 50., 80., 80., 80., 50., 50., 70., 70., 70., 70.,
# 60., 60., 100., 100., 100., ])
# weight = torch.tensor(
# [1., 100., 100., 50., 80., 50., 80., 80., 50., 70., 70.,
# 60., 100., 100., ]).to(self.device)
ious = MulticlassJaccardLoss(classes=list(range(23)))(outputs, gts.reshape(-1, 256, 256))
# ious = jaccard_similarity_score(gts.detach().cpu().squeeze().numpy().reshape(-1)
# , outputs.detach().cpu().squeeze().argmax(dim=1).numpy().reshape(-1))*imgs.size(0)
DC += ious
length += imgs.size(0)
DC = DC / length
score = DC
print('[Validation] DC: %.4f' % (
DC))
# save the best net model
if score < self.best_score: # 算的其实是loss 保小的
self.best_score = score
self.best_epoch = epoch
print('Best %s model score: %.4f'%(self.model_type, self.best_score))
torch.save(self.net.state_dict(), self.net_path)
def test(self):
del self.net
self.build_model()
self.net.load_state_dict(torch.load(self.net_path))
self.net.eval()
DC = 0. # Dice Coefficient
length = 0
for i, (imgs, probs, gts) in enumerate(self.test_loader):
imgs = imgs.to(self.device)
probs = probs.to(self.device)
gts = gts.round().long().to(self.device)
outputs = self.net(imgs, probs)
ious = MulticlassJaccardLoss(classes=list(range(23)))(outputs,
gts.reshape(
-1, 256,
256))
# ious = jaccard_similarity_score(gts.detach().cpu().squeeze().numpy().reshape(-1)
# , outputs.detach().cpu().squeeze().argmax(dim=1).numpy().reshape(-1))*imgs.size(0)
DC += ious
length += imgs.size(0)
# weight = np.array(
# [0., 100., 100., 100., 50., 50., 80., 80., 50., 80., 80., 80., 50., 50., 70., 70., 70., 70.,
# 60., 60., 100., 100., 100., ])
# ious = IoU(gts.detach().cpu().squeeze().numpy().reshape(-1),
# outputs.detach().cpu().squeeze().argmax(dim=0).numpy().reshape(-1), num_classes=14) * imgs.size(
# 0)
# DC += np.array(ious[1:]).mean()
length += imgs.size(0)
DC = DC / length
score = DC
f = open(os.path.join(self.csv_path, 'result.csv'),
'a',
encoding='utf8',
newline='')
wr = csv.writer(f)
wr.writerow([
self.model_type, DC, self.lr, self.best_epoch, self.num_epochs,
self.num_epochs_decay, self.augmentation_prob, self.batch_size,
self.comment
])
f.close()
def train(self, epoch):
self.net.train(True)
# Decay learning rate
if (epoch + 1) > (self.num_epochs - self.num_epochs_decay):
self.decayed_lr -= (self.lr / float(self.num_epochs_decay))
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.decayed_lr
print('epoch{}: Decay learning rate to lr: {}.'.format(epoch, self.decayed_lr))
epoch_loss = 0
acc = 0. # Accuracy
SE = 0. # Sensitivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length = 0
for i, (img, prob , gt) in enumerate(tqdm(self.train_loader)):
img = img.to(self.device)
prob = prob.to(self.device)
gt = gt.round().long().to(self.device)
self.optimizer.zero_grad()
outputs = self.net(img, prob)
# make sure shapes are the same by flattening them
# weight = torch.tensor([1.,100.,100.,100.,50.,50.,80.,80.,50.,80.,80.,80.,50.,50.,70.,70.,70.,70.,
# 60.,60.,100.,100.,100.,]).to(self.device)
#
# weight = torch.tensor(
# [1., 100., 130., 1000., 700., 900., 30., 1000., 60., 200., 100., 300., 100., 55.]).to(self.device)
weight = torch.tensor([
1., 100., 130., 130., 1000., 1000., 700.,700.,900., 30.,30.,1000.,60.,60.,200.,200.,
100.,100.,300.,300.,100.,55.,55.
]).to(self.device)
# weight = torch.tensor(
# [1., 100., 100., 50., 80., 50., 80., 80., 50., 70., 70.,
# 60., 100., 100., ]).to(self.device)
ce_loss = nn.CrossEntropyLoss(weight=weight,reduction='mean')(outputs, gt.reshape(-1,256,256))
#dice_loss = DiceLoss(sigmoid_normalization=False)(outputs, expand_as_one_hot(gt.reshape(-1,128,128),14))
dice_loss = MulticlassJaccardLoss(classes=list(range(14)))(outputs, gt.reshape(-1,256,256))
# bce_loss = torch.nn.BCEWithLogitsLoss()(outputs, gts)
# focal_loss = FocalLoss(alpha=0.8,gamma=0.5)(outputs, gts)
# focal_loss = FocalLoss2d(gamma=0.5)(outputs, gt.reshape(-1,256,256))
loss = ce_loss +dice_loss
#loss = focal_loss + dice_loss
epoch_loss += loss.item() * img.size(0) # because reduction = 'mean'
loss.backward()
self.optimizer.step()
# DC += iou(outputs.detach().cpu().squeeze().argmax(dim=1),gts.detach().cpu(),n_classes=14)*imgs.size(0)
length += img.size(0)
# DC = DC / length
# epoch_loss = epoch_loss/length
# # Print the log info
# print(
# 'Epoch [%d/%d], Loss: %.4f, \n[Training] DC: %.4f' % (
# epoch + 1, self.num_epochs,
# epoch_loss,
# DC))
print('EPOCH{}, Loss{}'.format(epoch,epoch_loss/length))