def main(args):
############ init config ################
#################### init logger ###################################
log_dir = './search_exp/' + '/{}'.format(args.model) + \
'/{}'.format(args.dataset) + '/{}_{}'.format(time.strftime('%Y%m%d-%H%M%S'),args.note)
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Search'.format(args.model))
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.multi_gpu = args.gpus > 1 and torch.cuda.is_available()
args.device = torch.device('cuda:0' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.enabled = True
cudnn.benchmark = True
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
####################### init dataset ###########################################
logger.info("Dataset for search is {}".format(args.dataset))
train_dataset = datasets_dict[args.dataset](args,
args.dataset_root,
split='train')
val_dataset = datasets_dict[args.dataset](args,
args.dataset_root,
split='valid')
# train_dataset=datasets_dict[args.dataset](args,split='train')
# val_dataset=datasets_dict[args.dataset](args,split='valid')
num_train = len(train_dataset)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load criterion to gpu !")
criterion = criterion.to(args.device)
######################## init model ############################################
switches_normal = []
switches_down = []
switches_up = []
nums_mixop = sum([2 + i for i in range(args.meta_node_num)])
for i in range(nums_mixop):
switches_normal.append([True for j in range(len(CellPos))])
for i in range(nums_mixop):
switches_down.append([True for j in range(len(CellLinkDownPos))])
for i in range(nums_mixop):
switches_up.append([True for j in range(len(CellLinkUpPos))])
# 6-->3-->1
drop_op_down = [2, 3]
# 4-->2-->1
drop_op_up = [2, 1]
# 7-->4-->1
drop_op_normal = [3, 3]
# stage0 pruning stage 1 pruning, stage 2 (training)
original_train_batch = args.train_batch
original_val_batch = args.val_batch
for sp in range(2):
# build dataloader
# model ,numclass=1,im_ch=3,init_channel=16,intermediate_nodes=4,layers=9
if sp == 0:
args.model = "UnetLayer7"
args.layers = 7
sp_train_batch = original_train_batch
sp_val_batch = original_val_batch
sp_epoch = args.epochs
sp_lr = args.lr
else:
#args.model = "UnetLayer9"
# 在算力平台上面UnetLayer9就是UnetLayer9_v2
args.model = "UnetLayer9"
args.layers = 9
sp_train_batch = original_train_batch
sp_val_batch = original_val_batch
sp_lr = args.lr
sp_epoch = args.epochs
train_queue = data.DataLoader(
train_dataset,
batch_size=sp_train_batch,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[:split]),
pin_memory=True,
num_workers=args.num_workers)
val_queue = data.DataLoader(
train_dataset,
batch_size=sp_train_batch,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.num_workers)
test_dataloader = data.DataLoader(val_dataset,
batch_size=sp_val_batch,
pin_memory=True,
num_workers=args.num_workers)
logger.info(
"stage:{} model:{} epoch:{} lr:{} train_batch:{} val_batch:{}".
format(sp, args.model, sp_epoch, sp_lr, sp_train_batch,
sp_val_batch))
model = get_models(args, switches_normal, switches_down, switches_up)
save_model_path = os.path.join(args.save_path,
"stage_{}_model".format(sp))
if not os.path.exists(save_model_path):
os.mkdir(save_model_path)
if args.multi_gpu:
logger.info('use: %d gpus', args.gpus)
model = nn.DataParallel(model)
model = model.to(args.device)
logger.info('param size = %fMB', calc_parameters_count(model))
# init optimizer for arch parameters and weight parameters
# final stage, just train the network parameters
optimizer_arch = torch.optim.Adam(model.arch_parameters(),
lr=args.arch_lr,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
optimizer_weight = torch.optim.SGD(model.weight_parameters(),
lr=sp_lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_weight, sp_epoch, eta_min=args.lr_min)
#################################### train and val ########################
max_value = 0
for epoch in range(0, sp_epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# train
if epoch < args.arch_after:
weight_loss_avg, arch_loss_avg, mr, ms, mp, mf, mjc, md, macc = train(
args,
train_queue,
val_queue,
model,
criterion,
optimizer_weight,
optimizer_arch,
train_arch=False)
else:
weight_loss_avg, arch_loss_avg, mr, ms, mp, mf, mjc, md, macc = train(
args,
train_queue,
val_queue,
model,
criterion,
optimizer_weight,
optimizer_arch,
train_arch=True)
logger.info("Epoch:{} WeightLoss:{:.3f} ArchLoss:{:.3f}".format(
epoch, weight_loss_avg, arch_loss_avg))
logger.info(" Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(
macc, md, mjc))
# write
writer.add_scalar('Train/W_loss', weight_loss_avg, epoch)
writer.add_scalar('Train/A_loss', arch_loss_avg, epoch)
writer.add_scalar('Train/Dice', md, epoch)
# infer
if (epoch + 1) % args.infer_epoch == 0:
genotype = model.genotype()
logger.info('genotype = %s', genotype)
val_loss, (vmr, vms, vmp, vmf, vmjc, vmd,
vmacc) = infer(args, model, val_queue, criterion)
logger.info(
"ValLoss:{:.3f} ValAcc:{:.3f} ValDice:{:.3f} ValJc:{:.3f}"
.format(val_loss, vmacc, vmd, vmjc))
writer.add_scalar('Val/loss', val_loss, epoch)
is_best = True if (vmjc >= max_value) else False
max_value = max(max_value, vmjc)
state = {
'epoch': epoch,
'optimizer_arch': optimizer_arch.state_dict(),
'optimizer_weight': optimizer_weight.state_dict(),
'scheduler': scheduler.state_dict(),
'state_dict': model.state_dict(),
'alphas_dict': model.alphas_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(
epoch, is_best, max_value))
if not is_best:
torch.save(
state,
os.path.join(save_model_path, "checkpoint.pth.tar"))
else:
torch.save(
state,
os.path.join(save_model_path, "checkpoint.pth.tar"))
torch.save(
state,
os.path.join(save_model_path, "model_best.pth.tar"))
# one stage end, we should change the operations num (divided 2)
weight_down = F.softmax(model.arch_parameters()[0],
dim=-1).data.cpu().numpy()
weight_up = F.softmax(model.arch_parameters()[1],
dim=-1).data.cpu().numpy()
weight_normal = F.softmax(model.arch_parameters()[2],
dim=-1).data.cpu().numpy()
weight_network = F.softmax(model.arch_parameters()[3],
dim=-1).data.cpu().numpy()
logger.info("alphas_down: \n{}".format(weight_down))
logger.info("alphas_up: \n{}".format(weight_up))
logger.info("alphas_normal: \n{}".format(weight_normal))
logger.info("alphas_network: \n{}".format(weight_network))
genotype = model.genotype()
logger.info('Stage:{} \n Genotype: {}'.format(sp, genotype))
logger.info(
'------Stage {} end ! Then Dropping Paths------'.format(sp))
# 6 4 7
# CellLinkDownPos CellLinkUpPos CellPos
# # 6-->3-->1
# drop_op_down = [3, 2]
# # 4-->2-->1
# drop_op_up = [2, 1]
# # 7-->4-->1
# drop_op_normal = [3, 3]
# update switches in 0 stage end
if sp == 0:
switches_down = update_switches(weight_down.copy(),
switches_down.copy(),
CellLinkDownPos, drop_op_down[sp])
switches_up = update_switches(weight_up.copy(), switches_up.copy(),
CellLinkUpPos, drop_op_up[sp])
switches_normal = update_switches(weight_normal.copy(),
switches_normal.copy(), CellPos,
drop_op_normal[sp])
logger.info('switches_down = %s', switches_down)
logger.info('switches_up = %s', switches_up)
logger.info('switches_normal = %s', switches_normal)
logging_switches(logger, switches_down, CellLinkDownPos)
logging_switches(logger, switches_up, CellLinkUpPos)
logging_switches(logger, switches_normal, CellPos)
else:
# sp==1 is the final stage, we don`t need the keep operations
# because we has the model.genotype
# show the final one op in 14 mixop
switches_down = update_switches(weight_down.copy(),
switches_down.copy(),
CellLinkDownPos, drop_op_down[sp])
switches_up = update_switches(weight_up.copy(), switches_up.copy(),
CellLinkUpPos, drop_op_up[sp])
switches_normal = update_switches_nozero(weight_normal.copy(),
switches_normal.copy(),
CellPos,
drop_op_normal[sp])
logger.info('switches_down = %s', switches_down)
logger.info('switches_up = %s', switches_up)
logger.info('switches_normal = %s', switches_normal)
logging_switches(logger, switches_down, CellLinkDownPos)
logging_switches(logger, switches_up, CellLinkUpPos)
logging_switches(logger, switches_normal, CellPos)
writer.close()
def main(args):
############ init config ################
model_name = args.model
assert model_name in models_dict.keys(),"The Usage model is not exist !"
print('Usage model :{}'.format(model_name))
#################### init logger ###################################
log_dir = './logs/'+ args.model+'_'+args.note + '/{}'.format(time.strftime('%Y%m%d-%H%M%S'))
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Train'.format(args.model))
# setting
setting={k: v for k, v in args._get_kwargs()}
logger.info(setting)
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda= args.gpus>0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
train_loader=get_dataloder(args,split_flag="train")
val_loader=get_dataloder(args,split_flag="valid")
######################## init model ############################################
# model
logger.info("Model Dict has keys: \n {}".format(models_dict.keys()))
model=get_models(args)
if torch.cuda.device_count() > 1 and args.use_cuda:
logger.info('use: %d gpus', torch.cuda.device_count())
model = nn.DataParallel(model)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss=='bce':
criterion=nn.BCELoss()
elif args.loss=='bcelog':
criterion=nn.BCEWithLogitsLoss()
elif args.loss=="dice":
criterion=DiceLoss()
elif args.loss=="softdice":
criterion=SoftDiceLoss()
elif args.loss=='bcedice':
criterion=BCEDiceLoss()
else:
criterion=nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model=model.to(args.device)
criterion=criterion.to(args.device)
# init optimizer
if args.model_optimizer=="sgd":
#torch.optim.SGD(parametetrs,lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
optimizer=torch.optim.SGD(model.parameters(),lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
else:
optimizer=torch.optim.Adam(model.parameters(),args.lr,[args.beta1, args.beta2],
weight_decay=args.weight_decay)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,args.epoch)
# scheduler=torch.optim.lr_scheduler.StepLR(optimizer=optimizer,step_size=30,gamma=0.1,last_epoch=-1)
############################### check resume #########################
start_epoch=0
if args.resume is not None:
if os.path.isfile(args.resume):
logger.info("Loading model and optimizer from checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
else:
raise FileNotFoundError("No checkpoint found at '{}'".format(args.resume))
#################################### train and val ########################
max_value=0
for epoch in range(start_epoch,args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# train
mr, ms, mp, mf, mjc, md, macc, mean_loss=train(args, model, criterion, train_loader,
optimizer, epoch, logger)
# write
writer.add_scalar('Train/Loss', mean_loss, epoch)
writer.add_scalar('Train/mAcc', macc, epoch)
writer.add_scalar('Train/Recall', mr, epoch)
writer.add_scalar('Train/Specifi', ms, epoch)
writer.add_scalar('Train/Precision', mp, epoch)
writer.add_scalar('Train/F1', mf, epoch)
writer.add_scalar('Train/Jc', mjc, epoch)
writer.add_scalar('Train/Dice', md, epoch)
# val
vmr, vms, vmp, vmf, vmjc, vmd, vmacc, vmean_loss=val(args, model, criterion, val_loader, epoch, logger)
writer.add_scalar('Val/Loss', vmean_loss, epoch)
writer.add_scalar('Val/mAcc', vmacc, epoch)
writer.add_scalar('Val/Recall', vmr, epoch)
writer.add_scalar('Val/Specifi', vms, epoch)
writer.add_scalar('Val/Precision', vmp, epoch)
writer.add_scalar('Val/F1', vmf, epoch)
writer.add_scalar('Val/Jc', vmjc, epoch)
writer.add_scalar('Val/Dice', vmd, epoch)
is_best=True if (vmjc>=max_value) else False
max_value=max(max_value,vmjc)
state={
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(epoch,is_best,max_value))
if not is_best:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
else:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
torch.save(state,os.path.join(args.save_path,"model_best.pth.tar"))
writer.close()
def main(args):
#################### init logger ###################################
log_dir = './logs/' + '{}'.format(args.dataset) + '/{}_{}_{}'.format(args.model,args.note,time.strftime('%Y%m%d-%H%M%S'))
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Train'.format(args.model))
# setting
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
train_loader = get_dataloder(args, split_flag="train")
val_loader = get_dataloder(args, split_flag="valid")
############init model ###########################
if args.model == "layer7_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'layer7_double_deep'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=7,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "stage1_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_double_deep'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "stage1_nodouble_deep":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_deep'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "stage1_nodouble_deep_slim":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_deep'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPruneSlim(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "alpha1_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha1_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "alpha0_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
#isic trans
elif args.model == "stage1_layer9_110epoch_double_deep_final":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
# just normaL cell keep
elif args.model == "dd_normal":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPruneNormal(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
# normal+down
elif args.model == "dd_normaldown":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPruneNormalDown(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
# normal+up
elif args.model == "dd_normalup":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPruneNormalUp(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
# normal+up+down
elif args.model == "alpha0_5_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
# abliation study of channel doubling and deepsupervision
elif args.model == "alpha0_5_stage1_double_nodeep_ep80":
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_nodeep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "alpha0_5_stage1_nodouble_deep_ep80":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'alpha0_5_stage1_nodouble_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
elif args.model == "alpha0_5_stage1_nodouble_nodeep_ep80":
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'alpha0_5_stage1_nodouble_nodeep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux
)
if torch.cuda.device_count() > 1 and args.use_cuda:
logger.info('use: %d gpus', torch.cuda.device_count())
model = nn.DataParallel(model)
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
# init optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epoch)
# scheduler=torch.optim.lr_scheduler.StepLR(optimizer=optimizer,step_size=30,gamma=0.1,last_epoch=-1)
############################### check resume #########################
start_epoch = 0
if args.resume is not None:
if os.path.isfile(args.resume):
logger.info("Loading model and optimizer from checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
else:
raise FileNotFoundError("No checkpoint found at '{}'".format(args.resume))
#################################### train and val ########################
max_value = 0
for epoch in range(start_epoch, args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# train
if args.deepsupervision:
mean_loss, value1, value2 = train(args, model, criterion, train_loader, optimizer)
mr, ms, mp, mf, mjc, md, macc = value1
mmr, mms, mmp, mmf, mmjc, mmd, mmacc = value2
logger.info(
"Epoch:{} Train_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(epoch, mean_loss, macc, md, mjc))
logger.info(" dmAcc:{:.3f} dmDice:{:.3f} dmJc:{:.3f}".format(mmacc, mmd, mmjc))
writer.add_scalar('Train/dmAcc', mmacc, epoch)
writer.add_scalar('Train/dRecall', mmr, epoch)
writer.add_scalar('Train/dSpecifi', mms, epoch)
writer.add_scalar('Train/dPrecision', mmp, epoch)
writer.add_scalar('Train/dF1', mmf, epoch)
writer.add_scalar('Train/dJc', mmjc, epoch)
writer.add_scalar('Train/dDice', mmd, epoch)
else:
mean_loss, value1 = train(args, model, criterion, train_loader,
optimizer)
mr, ms, mp, mf, mjc, md, macc = value1
logger.info(
"Epoch:{} Train_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(epoch, mean_loss, macc, md, mjc))
# write
writer.add_scalar('Train/Loss', mean_loss, epoch)
writer.add_scalar('Train/mAcc', macc, epoch)
writer.add_scalar('Train/Recall', mr, epoch)
writer.add_scalar('Train/Specifi', ms, epoch)
writer.add_scalar('Train/Precision', mp, epoch)
writer.add_scalar('Train/F1', mf, epoch)
writer.add_scalar('Train/Jc', mjc, epoch)
writer.add_scalar('Train/Dice', md, epoch)
# val
if args.deepsupervision:
vmean_loss, valuev1, valuev2 = infer(args, model, criterion, val_loader)
vmr, vms, vmp, vmf, vmjc, vmd, vmacc = valuev1
mvmr, mvms, mvmp, mvmf, mvmjc, mvmd, mvmacc = valuev2
logger.info(
"Epoch:{} Val_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(epoch, vmean_loss, vmacc, vmd, vmjc))
logger.info(" dmAcc:{:.3f} dmDice:{:.3f} dmJc:{:.3f}".format(mvmacc, mvmd, mvmjc))
writer.add_scalar('Val/mAcc', mvmacc, epoch)
writer.add_scalar('Val/Recall', mvmr, epoch)
writer.add_scalar('Val/Specifi', mvms, epoch)
writer.add_scalar('Val/Precision', mvmp, epoch)
writer.add_scalar('Val/F1', mvmf, epoch)
writer.add_scalar('Val/Jc', mvmjc, epoch)
writer.add_scalar('Val/Dice', mvmd, epoch)
else:
vmean_loss, valuev1 = infer(args, model, criterion, val_loader)
vmr, vms, vmp, vmf, vmjc, vmd, vmacc = valuev1
logger.info(
"Epoch:{} Val_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(epoch, vmean_loss, vmacc, vmd, vmjc))
is_best = True if (vmjc >=max_value) else False
max_value = max(max_value, vmjc)
writer.add_scalar('Val/Loss', vmean_loss, epoch)
writer.add_scalar('Val/mAcc', vmacc, epoch)
writer.add_scalar('Val/Recall', vmr, epoch)
writer.add_scalar('Val/Specifi', vms, epoch)
writer.add_scalar('Val/Precision', vmp, epoch)
writer.add_scalar('Val/F1', vmf, epoch)
writer.add_scalar('Val/Jc', vmjc, epoch)
writer.add_scalar('Val/Dice', vmd, epoch)
state={
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(epoch,is_best,max_value))
if not is_best:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
else:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
torch.save(state,os.path.join(args.save_path,"model_best.pth.tar"))
writer.close()
def main(args):
#################### init logger ###################################
args.model='unet'
model_weight_path='../logs/isic2018/unet_ep300/20200402-135108/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
log_dir = './models/' + args.model+'_prune_'+args.note
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-L1Prune'.format(args.model))
# setting
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
train_loader=get_dataloder(args,split_flag="train")
val_loader=get_dataloder(args,split_flag="valid")
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
logger.info("Original trained model performance test: ")
infer(args, model, criterion, val_loader,logger)
# Pruning
# Pruning Configuration, in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
configure_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d'],
'op_names': ['Conv1.conv.0','Conv1.conv.3','Conv2.conv.0','Conv2.conv.3','Conv3.conv.0','Conv3.conv.3',
'Conv4.conv.0','Conv4.conv.3','Conv5.conv.0','Conv5.conv.3',
'Up5.up.1','Up_conv5.conv.0','Up_conv5.conv.3',
'Up4.up.1','Up_conv4.conv.0','Up_conv4.conv.3',
'Up3.up.1','Up_conv3.conv.0','Up_conv3.conv.3',
'Up2.up.1','Up_conv2.conv.0','Up_conv2.conv.3',
]}
]
# Prune model and test accuracy without fine tuning.
logger.info('=' * 10 + 'Test on the pruned model before fine tune' + '=' * 10)
pruner = L1FilterPruner(model, configure_list)
# change the forward func (mul pruning mask )
model = pruner.compress()
# test performance without finetuning
logger.info("Pruning trained model performance test: ")
infer(args, model, criterion, val_loader,logger)
# Fine tune the pruned model for 40 epochs and test accuracy
logger.info('=' * 10 + 'Fine tuning' + '=' * 10)
#torch.optim.SGD(parametetrs,lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
optimizer=torch.optim.SGD(model.parameters(),lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,args.epoch)
max_value = 0
for epoch in range(0, args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# update mask
pruner.update_epoch(epoch)
# train
train(args, model, criterion, train_loader,optimizer, epoch, logger)
# val
vmr, vms, vmp, vmf, vmjc, vmd, vmacc,vloss = infer(args, model, criterion, val_loader,logger)
writer.add_scalar('Val/Loss', vloss, epoch)
writer.add_scalar('Val/mAcc', vmacc, epoch)
writer.add_scalar('Val/Recall', vmr, epoch)
writer.add_scalar('Val/Specifi', vms, epoch)
writer.add_scalar('Val/Precision', vmp, epoch)
writer.add_scalar('Val/F1', vmf, epoch)
writer.add_scalar('Val/Jc', vmjc, epoch)
writer.add_scalar('Val/Dice', vmd, epoch)
is_best = True if (vmjc >= max_value) else False
max_value = max(max_value, vmjc)
if is_best:
pruner.export_model(model_path=os.path.join(args.save_path,"best_prune_unet.pth"), mask_path=os.path.join(args.save_path,'mask_prune_indexs.pth'))
state = {
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(epoch, is_best, max_value))
torch.save(state, os.path.join(args.save_path, "checkpoint.pth.tar"))
writer.close()
# test the best_prune_unet.pth
args.model='unet'
model_weight_path=os.path.join(args.save_path,"best_prune_unet.pth")
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu'))
model = model.to(args.device)
logger.info("Final saved pruned model performance test: ")
infer(args, model, criterion, val_loader,logger)
print('Performing data augmentation...')
augmentation(args.root_path, mode='train')
augmentation(args.root_path, mode='val')
augmentation(args.root_path, mode='test')
exit()
# Configure loss function type and distance option
if args.loss == 'bce' and args.option == 'binary':
loss_fn = ShapeBCELoss()
elif args.loss == 'bce' and args.option == 'multi':
# loss_fn = ShapeBCELoss() if args.option == 'multi' else FPNLoss()
loss_fn = ShapeBCELoss()
elif args.loss == 'jaccard':
loss_fn = IoULoss()
elif args.loss == 'dice':
loss_fn = SoftDiceLoss()
elif args.loss == 'boundary':
alpha = 1.0 # a: (a * Region-based loss + (1-a) * boundary loss)
loss_fn = SurfaceLoss(alpha=alpha, dice=args.region_option)
else:
raise NotImplementedError(
'Loss function {0} not recognized'.format(loss))
# Configure network architecture option
c_in = 1
if args.option == 'binary':
c_out = 1
elif args.option == 'multi':
c_out = 3
else:
raise NotImplementedError(
def main(args):
#################### init logger ###################################
log_dir = './eval' + '/{}'.format(args.dataset) + '/{}'.format(args.model)
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Eval'.format(args.model))
# setting
args.save_path = log_dir
args.save_images = os.path.join(args.save_path, "images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
val_loader = get_dataloder(args, split_flag="valid")
######################## init model ############################################
if args.model == "layer7_double_deep_ep1600_8lr4e-3":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'layer7_double_deep'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=9,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/cvc/layer7_double_deep_ep1600_8lr4e-3/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif args.model == "alpha0_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_stage1_double_deep_ep200'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/cvc/alpha0_8lr4e-3/model_best.pth.tar'
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
elif args.model == "alpha0_5_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/cvc/alpha0_5_8lr4e-3/model_best.pth.tar'
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
elif args.model == "alpha1_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/cvc/alpha1_8lr4e-3/model_best.pth.tar'
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
else:
raise NotImplementedError()
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader, logger, args.save_images)
def main(args):
#################### init logger ###################################
# args.model_list=["unet","unet++_deep","unet++_nodeep",'attention_unet_v1','multires_unet','r2unet_t3',
# 'unet_ep800dice','unet++_deep_ep800dice','unet++_nodeep_ep800dice','attention_unet_v1_ep800dice','multires_unet_ep800dice'
# ]
args.model_list = ['unet', 'unet++', "attention_unet", "multires_unet"]
for model_name in args.model_list:
if model_name == 'unet':
args.model = 'unet'
model_weight_path = './logs/chaos/unet_ep150_v2/20200403-134703/checkpoint.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'unet++':
args.model = 'unet++'
args.deepsupervision = False
model_weight_path = './logs/chaos/unet++_ep150_v2/20200403-135401/checkpoint.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
# elif model_name == 'unet++_deep':
# args.model = 'unet++'
# args.deepsupervision = True
# model_weight_path = './logs/unet++_deep_ep1600/cvc/20200312-143345/model_best.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'attention_unet':
args.model = 'attention_unet_v1'
args.deepsupervision = False
model_weight_path = './logs/chaos/attention_unet_v1_ep150_v2/20200403-135445/checkpoint.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'multires_unet':
args.model = 'multires_unet'
args.deepsupervision = False
model_weight_path = './logs/chaos/multires_unet_ep150_v2/20200403-135549/checkpoint.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
else:
raise NotImplementedError()
assert os.path.exists(args.save)
args.model_save_path = os.path.join(args.save, model_name)
logger = get_logger(args.model_save_path)
args.save_images = os.path.join(args.model_save_path, "images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader, logger, args.save_images)
def main(args):
#################### init logger ###################################
args.model_list=["unet","unet++",'attention_unet_v1','multires_unet','r2unet_t3']
for model_name in args.model_list:
if model_name=='unet':
args.model='unet'
model_weight_path='./logs/unet_ep1600/cvc/20200312-143050/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name=='unet++':
args.model='unet++'
args.deepsupervision=False
model_weight_path='./logs/unet++_ep1600/cvc/20200312-143358/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'attention_unet_v1':
args.model = 'attention_unet_v1'
model_weight_path = './logs/attention_unet_v1_ep1600/cvc/20200312-143413/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'multires_unet':
args.model = 'multires_unet'
model_weight_path = './logs/multires_unet_ep1600_t2/20200322-194117/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# change bn relu order
elif model_name == 'multires_unet_align':
args.model = 'multires_unet'
model_weight_path = './logs/multires_unet_ep1600_chbnrelu/20200327-184457/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'r2unet_t3':
args.model = 'r2unet'
args.time_step=3
model_weight_path = './logs/r2unet_ep1600_t2/20200324-032815/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'unet_ep800dice':
args.model = 'unet'
model_weight_path = './logs/unet_ep800_bcedice/cvc/20200315-043021/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name=='unet++_nodeep_ep800dice':
args.model='unet++'
args.deepsupervision=False
model_weight_path='./logs/unet++_ep800_bcedice/cvc/20200315-043214/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'unet++_deep_ep800dice':
args.model = 'unet++'
args.deepsupervision = True
model_weight_path = './logs/unet++_deep_ep800_bcedice/cvc/20200315-043134/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'attention_unet_v1_ep800dice':
args.model = 'attention_unet_v1'
args.deepsupervision=False
model_weight_path = './logs/attention_unet_v1_ep800_bcedice/cvc/20200315-043300/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'multires_unet_ep800dice':
args.model = 'multires_unet'
args.deepsupervision=False
model_weight_path = './logs/multires_unet_ep800_bcedice/cvc/20200312-173031/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
else:
raise NotImplementedError()
assert os.path.exists(args.save)
args.model_save_path=os.path.join(args.save,model_name)
logger = get_logger(args.model_save_path)
args.save_images= os.path.join(args.model_save_path,"images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader,logger,args.save_images)
def main(args):
#################### init logger ###################################
#args.model_list=["unet","unet++_deep",'attention_unet_v1','multires_unet', 'r2unet_t3']
args.model_list = [
"unet", "unet++_deep", 'unet++_nodeep', "attention_unet_v1",
"multires_unet", "r2unet"
]
for model_name in args.model_list:
# if model_name=='unet':
# args.model='unet'
# model_weight_path='./logs/isic/logs_coslr/unet/isic2018/20200229-035150/checkpoint.pth.tar'
# model=get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# elif model_name=='unet++_deep':
# args.model='unet++'
# args.deepsupervision=True
# model_weight_path='./logs/isic/logs_coslr/unet++/isic2018/20200229-035514/checkpoint.pth.tar'
# model=get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# elif model_name == 'unet++_nodeep':
# args.model = 'unet++'
# args.deepsupervision = False
# model_weight_path = '/checkpoint.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# elif model_name == 'attention_unet_v1':
# args.model = 'attention_unet_v1'
# model_weight_path = './logs/isic/logs_coslr/attention_unet_v1/isic2018/20200302-190718/checkpoint.pth.tar'
# args.deepsupervision=False
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
#
# elif model_name == 'multires_unet':
# args.model = 'multires_unet'
# model_weight_path = './logs/isic/logs_coslr/multires_unet/isic2018/20200229-035734/checkpoint.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
#
# elif model_name == 'r2unet_t3':
# args.model = 'r2unet'
# args.time_step=3
# model_weight_path = './logs/isic/logs_coslr/r2unet/isic2018/20200302-190808/checkpoint.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# ep300 baseline
if model_name == 'unet':
args.model = 'unet'
model_weight_path = './logs/isic2018/unet_ep300/20200402-135108/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'unet++_deep':
args.model = 'unet++'
args.deepsupervision = True
model_weight_path = './logs/isic2018/unet++_ep300_deep/20200402-135243/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'unet++_nodeep':
args.model = 'unet++'
args.deepsupervision = False
model_weight_path = './logs/isic2018/unet++_ep300/20200402-135317/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'attention_unet_v1':
args.model = 'attention_unet_v1'
args.deepsupervision = False
model_weight_path = './logs/isic2018/attention_unet_v1_ep300/20200413-160808//model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'multires_unet':
args.model = 'multires_unet'
args.deepsupervision = False
model_weight_path = './logs/isic2018/attention_unet_v1_ep300/20200413-160808//model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
elif model_name == 'r2unet':
args.model = 'r2unet'
args.deepsupervision = False
model_weight_path = './logs/isic2018/attention_unet_v1_ep300/20200413-160808//model_best.pth.tar'
model = get_models(args)
model.load_state_dict(
torch.load(model_weight_path,
map_location='cpu')['state_dict'])
# elif model_name == 'attention_unet_v1':
# args.model = 'attention_unet_v1'
# model_weight_path = './logs/isic/logs_coslr/attention_unet_v1/isic2018/20200302-190718/checkpoint.pth.tar'
# args.deepsupervision=False
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
#
# elif model_name == 'multires_unet':
# args.model = 'multires_unet'
# model_weight_path = './logs/isic/logs_coslr/multires_unet/isic2018/20200229-035734/checkpoint.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
#
# elif model_name == 'r2unet_t3':
# args.model = 'r2unet'
# args.time_step=3
# model_weight_path = './logs/isic/logs_coslr/r2unet/isic2018/20200302-190808/checkpoint.pth.tar'
# model = get_models(args)
# model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
else:
raise NotImplementedError()
assert os.path.exists(args.save)
args.model_save_path = os.path.join(args.save, model_name)
logger = get_logger(args.model_save_path)
args.save_images = os.path.join(args.model_save_path, "images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader, logger, args.save_images)
def main(args):
args.model_list = [
'double_deep', 'double', 'nodouble', 'nodouble_deep', 'slim_dd',
'slim_double', 'slim_nodouble', 'slim_nodouble_deep'
]
#args.model_list=["slim_nodouble_deep_init32"]
for model_name in args.model_list:
print(model_name)
if model_name == "double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/prune_20200313-063406_32_32_ep300_double_deep/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'double':
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/prune_20200313-063428_32_32_ep300_double/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'nodouble':
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/prune_20200316-141125_nodouble_32_ep300/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'nodouble_deep':
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/prune_20200316-141242_nodouble_32_ep300_deep/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
if model_name == "slim_dd":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = net_dd(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/dd_20200319-170442_ep300/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'slim_double':
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = net_double(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/double_20200319-170621_ep300/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'slim_nodouble':
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = net_nodouble(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/nodouble_20200319-210910_ep300/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'slim_nodouble_deep':
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = net_nodouble_deep(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/nodouble_deep_20200319-210600_ep300/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == 'slim_nodouble_deep_init32':
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = net_nodouble_deep(
genotype=genotype,
input_c=args.in_channels,
c=32,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/nodouble_deep_ep300_init32/model_best.pth.tar'
model.load_state_dict(
torch.load(args.model_path, map_location='cpu')['state_dict'])
#################### init logger ###################################
log_dir = './eval' + '/{}'.format(
args.dataset) + '/{}'.format(model_name)
##################### init model ########################################
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Eval'.format(model_name))
# setting
args.save_path = log_dir
args.save_images = os.path.join(args.save_path, "images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
# sorted vaild datasets
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
def main(args):
#################### init logger ###################################
log_dir = './logs/' + '{}'.format(args.dataset) + '/{}_{}_{}'.format(
args.model, args.note, time.strftime('%Y%m%d-%H%M%S'))
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Train'.format(args.model))
# setting
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
train_loader = get_dataloder(args, split_flag="train")
val_loader = get_dataloder(args, split_flag="valid")
######################## init model ############################################
# model
############init model ###########################
if args.model == "nodouble_deep_init32_ep100":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'nodouble_deep_init32_ep100'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=32,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=9,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "nodouble_deep_isic":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "nodouble_deep_drop02_layer7end":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'nodouble_deep_drop02_layer7end'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "stage1_nodouble_deep_ep36":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_nodouble_deep_ep36'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "stage1_nodouble_deep_ep63":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_nodouble_deep_ep63'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "stage1_nodouble_deep_ep83":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_nodouble_deep_ep83'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha1_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha1_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_5_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
# isic trans
elif args.model == "stage1_layer9_110epoch_double_deep_final":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
#chaos
elif args.model == "stage0_double_deep_ep80_newim":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage0_double_deep_ep80_newim'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "stage1_double_deep_ep80_ts":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_double_deep_ep80_ts'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
# cvc trans
elif args.model == "layer7_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'layer7_double_deep'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
if torch.cuda.device_count() > 1 and args.use_cuda:
logger.info('use: %d gpus', torch.cuda.device_count())
model = nn.DataParallel(model)
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
elif args.loss == 'multibcedice':
criterion = MultiClassEntropyDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
# init optimizer
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, args.epoch)
# scheduler=torch.optim.lr_scheduler.StepLR(optimizer=optimizer,step_size=30,gamma=0.1,last_epoch=-1)
############################### check resume #########################
start_epoch = 0
if args.resume is not None:
if os.path.isfile(args.resume):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
else:
raise FileNotFoundError("No checkpoint found at '{}'".format(
args.resume))
#################################### train and val ########################
max_value = 0
for epoch in range(start_epoch, args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
# logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# train
total_loss = train(args, model, criterion, train_loader, optimizer,
epoch, logger)
# write
writer.add_scalar('Train/total_loss', total_loss, epoch)
# val
tloss, md = val(args, model, criterion, val_loader, epoch, logger)
writer.add_scalar('Val/total_loss', tloss, epoch)
is_best = True if (md >= max_value) else False
max_value = max(max_value, md)
state = {
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(
epoch, is_best, max_value))
if not is_best:
torch.save(state, os.path.join(args.save_path,
"checkpoint.pth.tar"))
else:
torch.save(state, os.path.join(args.save_path,
"checkpoint.pth.tar"))
torch.save(state, os.path.join(args.save_path,
"model_best.pth.tar"))
writer.close()
def main(args):
#################### init logger ###################################
log_dir = './logs/' + '{}'.format(args.dataset) + '/{}_{}_{}'.format(
args.model, time.strftime('%Y%m%d-%H%M%S'), args.note)
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Train'.format(args.model))
# setting
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
train_loader = get_dataloder(args, split_flag="train")
val_loader = get_dataloder(args, split_flag="valid")
######################## init model ############################################
# model
# get the network parameters
if args.model == "alpha_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
args.alphas_model = './search_exp/Nas_Search_Unet/isic2018/deepsupervision/stage_1_model/checkpoint.pth.tar'
model_alphas = torch.load(
args.alphas_model,
map_location=args.device)['alphas_dict']['alphas_network']
model_alphas.requires_grad = False
model_alphas = F.softmax(model_alphas, dim=-1)
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnet(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha_double":
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_final'
args.alphas_model = './search_exp/Nas_Search_Unet/isic2018/nodeepsupervision/stage_1_model/checkpoint.pth.tar'
model_alphas = torch.load(
args.alphas_model,
map_location=args.device)['alphas_dict']['alphas_network']
model_alphas.requires_grad = False
model_alphas = F.softmax(model_alphas, dim=-1)
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnet(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha_nodouble":
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_final'
args.alphas_model = './search_exp/Nas_Search_Unet/isic2018/nodouble/stage_1_model/checkpoint.pth.tar'
model_alphas = torch.load(
args.alphas_model,
map_location=args.device)['alphas_dict']['alphas_network']
model_alphas.requires_grad = False
model_alphas = F.softmax(model_alphas, dim=-1)
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnet(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha_nodouble_deep":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
args.alphas_model = './search_exp/Nas_Search_Unet/isic2018/nodouble_deep/stage_1_model/checkpoint.pth.tar'
model_alphas = torch.load(
args.alphas_model,
map_location=args.device)['alphas_dict']['alphas_network']
model_alphas.requires_grad = False
model_alphas = F.softmax(model_alphas, dim=-1)
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnet(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_deep_final'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "double":
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'stage1_layer9_110epoch_double_final'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "nodouble":
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_final'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "nodouble_deep":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'stage1_layer9_110epoch_deep_final'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha1_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha1_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_5_stage1_double_deep_ep80":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_5_stage1_double_nodeep_ep80":
args.deepsupervision = False
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_nodeep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_5_stage1_nodouble_deep_ep80":
args.deepsupervision = True
args.double_down_channel = False
args.genotype_name = 'alpha0_5_stage1_nodouble_deep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
elif args.model == "alpha0_5_stage1_nodouble_nodeep_ep80":
args.deepsupervision = False
args.double_down_channel = False
args.genotype_name = 'alpha0_5_stage1_nodouble_nodeep_ep80'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
# cvc trans
elif args.model == "layer7_double_deep":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'layer7_double_deep'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
# chaos trans
elif args.model == "stage0_double_deep_ep80_newim":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'stage0_double_deep_ep80_newim'
model_alphas = None
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
if torch.cuda.device_count() > 1 and args.use_cuda:
logger.info('use: %d gpus', torch.cuda.device_count())
model = nn.DataParallel(model)
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info(model_alphas)
flop, param = get_model_complexity_info(model, (3, 256, 256),
as_strings=True,
print_per_layer_stat=False)
print("GFLOPs: {}".format(flop))
print("Params: {}".format(param))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
# init optimizer
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, args.epoch)
#scheduler=torch.optim.lr_scheduler.StepLR(optimizer=optimizer,step_size=30,gamma=0.1,last_epoch=-1)
############################### check resume #########################
start_epoch = 0
if args.resume is not None:
if os.path.isfile(args.resume):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
else:
raise FileNotFoundError("No checkpoint found at '{}'".format(
args.resume))
#################################### train and val ########################
max_value = 0
for epoch in range(start_epoch, args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
# train
if args.deepsupervision:
mean_loss, value1, value2 = train(args, model_alphas, model,
criterion, train_loader,
optimizer)
mr, ms, mp, mf, mjc, md, macc = value1
logger.info(
"Epoch:{} Train_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".
format(epoch, mean_loss, macc, md, mjc))
writer.add_scalar('Train/dDice', mmd, epoch)
else:
mean_loss, value1 = train(args, model_alphas, model, criterion,
train_loader, optimizer)
mr, ms, mp, mf, mjc, md, macc = value1
logger.info(
"Epoch:{} Train_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".
format(epoch, mean_loss, macc, md, mjc))
# write
writer.add_scalar('Train/Loss', mean_loss, epoch)
# val
if args.deepsupervision:
vmean_loss, valuev1, valuev2 = infer(args, model_alphas, model,
criterion, val_loader)
vmr, vms, vmp, vmf, vmjc, vmd, vmacc = valuev1
logger.info(
"Epoch:{} Val_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".
format(epoch, vmean_loss, vmacc, vmd, vmjc))
else:
vmean_loss, valuev1 = infer(args, model_alphas, model, criterion,
val_loader)
vmr, vms, vmp, vmf, vmjc, vmd, vmacc = valuev1
logger.info(
"Epoch:{} Val_Loss:{:.3f} Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".
format(epoch, vmean_loss, vmacc, vmd, vmjc))
is_best = True if vmjc >= max_value else False
max_value = max(max_value, vmjc)
writer.add_scalar('Val/Loss', vmean_loss, epoch)
state = {
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(
epoch, is_best, max_value))
if not is_best:
torch.save(state, os.path.join(args.save_path,
"checkpoint.pth.tar"))
else:
torch.save(state, os.path.join(args.save_path,
"checkpoint.pth.tar"))
torch.save(state, os.path.join(args.save_path,
"model_best.pth.tar"))
writer.close()
def main(args):
#args.model_list=['alpha0_double_deep_0.01','alpha0_5_double_deep_0.01','alpha1_double_deep_0.01','nodouble_deep','slim_dd','slim_double','slim_nodouble','slim_nodouble_deep']
#args.model_list=["double_deep","nodouble_deep","slim_nodouble"]
#args.model_list=["slim_nodouble_deep_init32"]
#args.model_list=["slim_nodouble_deep_init48"]
args.model_list = [
'alpha0_double_deep_0.01', 'alpha0_5_double_deep_0.01',
'alpha1_double_deep_0.01'
]
for model_name in args.model_list:
if model_name == "alpha0_double_deep_0.01":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_stage1_double_deep_ep200'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/alpha0_double_deep_0.01/model_best.pth.tar'
# kwargs = {'map_location': lambda storage, loc: storage.cuda(0)}
# state_dict = torch.load(args.model_path, **kwargs)
# # create new OrderedDict that does not contain `module.`
# model.load_state_dict(state_dict)
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
elif model_name == "alpha0_5_double_deep_0.01":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha0_5_stage1_double_deep_ep80'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/alpha0_5_double_deep_0.01/model_best.pth.tar'
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
#model.load_state_dict(torch.load(args.model_path, map_location='cpu')['state_dict'])
elif model_name == "alpha1_double_deep_0.01":
args.deepsupervision = True
args.double_down_channel = True
args.genotype_name = 'alpha1_stage1_double_deep_ep200'
genotype = eval('genotypes.%s' % args.genotype_name)
model = BuildNasUnetPrune(
genotype=genotype,
input_c=args.in_channels,
c=args.init_channels,
num_classes=args.nclass,
meta_node_num=args.middle_nodes,
layers=args.layers,
dp=args.dropout_prob,
use_sharing=args.use_sharing,
double_down_channel=args.double_down_channel,
aux=args.aux)
args.model_path = './logs/isic2018/alpha1_double_deep_0.01/model_best.pth.tar'
state_dict = torch.load(args.model_path,
map_location='cpu')['state_dict']
state_dict = remove_module(state_dict)
model.load_state_dict(state_dict)
#model.load_state_dict(torch.load(args.model_path, map_location='cpu')['state_dict'])
#################### init logger ###################################
log_dir = './eval' + '/{}'.format(
args.dataset) + '/{}'.format(model_name)
##################### init model ########################################
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Eval'.format(model_name))
# setting
args.save_path = log_dir
args.save_images = os.path.join(args.save_path, "images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
# sorted vaild datasets
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info(genotype)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader, logger, args.save_images)