# load CUDA
cuda = torch.cuda.is_available()
torch.manual_seed(1)
if cuda:
torch.cuda.manual_seed(1)
model = model.cuda()
# load trainer
trainer = torchsrc.Trainer(
cuda=cuda,
model=model,
optimizer=optim,
#train_loader=train_loader,
test_loader=test_loader,
out=out,
outmodel=outmodel,
max_epoch=epoch_num,
batch_size=batch_size,
lmk_num=lmk_num,
finetune=finetune,
fineepoch=fineepoch)
print("==start training==")
start_epoch = 0
start_iteration = 1
trainer.epoch = start_epoch
trainer.iteration = start_iteration
trainer.test_epoch()
# optim = torch.optim.SGD(model.parameters(), lr=learning_curve() _rate, momentum=0.9)
# load CUDA
cuda = torch.cuda.is_available()
torch.manual_seed(1)
if cuda:
torch.cuda.manual_seed(1)
model = model.cuda()
# load trainer
trainer = torchsrc.Trainer(
cuda=cuda,
model=model,
optimizer=optim,
train_loader=train_loader,
# val_loader=val_loader,
test_loader=test_loader,
out=out,
max_epoch=epoch_num,
batch_size=batch_size,
lmk_num=lmk_num,
)
print("==start training==")
start_epoch = 0
start_iteration = 1
trainer.epoch = start_epoch
trainer.iteration = start_iteration
trainer.train_epoch()
cuda = torch.cuda.is_available()
#cuda = False
torch.manual_seed(1)
if cuda:
torch.cuda.manual_seed(1)
model = model.cuda()
# load trainer
trainer = torchsrc.Trainer(
cuda=cuda,
model=model,
optimizer=optim,
train_loader=train_loader,
test_loader=test_loader,
out=out,
max_epoch=epoch_num,
batch_size=batch_size,
lmk_num=clss_num,
dual_network=dual_network,
add_calcium_mask=add_calcium_mask,
use_siamese=use_siamese,
siamese_coeiff=siamese_coeiff,
)
print("==start training==")
start_iteration = 1
trainer.epoch = start_epoch
if ValidateAttention:
trainer.epoch = 84
trainer.max_epoch = trainer.epoch + 1
#
# load optimizor
# optim = torch.optim.SGD(model.parameters(), lr=learning_curve() _rate, momentum=0.9)
# load CUDA
cuda = torch.cuda.is_available()
torch.manual_seed(1)
if cuda:
torch.cuda.manual_seed(1)
model = model.cuda()
# load trainer
trainer = torchsrc.Trainer(
cuda=cuda,
model=model,
test_loader=test_loader,
train_root_dir=train_root_dir,
out=out,
max_epoch=epoch_num,
batch_size=batch_size,
lmk_num=lmk_num,
)
print("==start testing==")
start_epoch = 0
start_iteration = 1
trainer.epoch = start_epoch
trainer.iteration = start_iteration
trainer.test_epoch()
start_epoch = 0
start_iteration = 1
optim = torch.optim.Adam(model.parameters(),
lr=learning_rate,
betas=(0.9, 0.999))
trainer = torchsrc.Trainer(
cuda=cuda,
model=model,
optimizer=optim,
train_loader=train_loader,
test_loader=test_loader,
out=out,
network_num=network_num,
max_epoch=epoch_num,
compete=compete,
GAN=GAN,
batch_size=lmk_batch_size,
lmk_num=lmk_num,
onlyEval=onlyEval,
view=viewName,
loss_fun=loss_fun,
noLSGAN=noLSGAN,
)
print("==start training==")
print("==view is == %s " % viewName)
start_epoch = 0
start_iteration = 1
trainer.epoch = start_epoch