def main(args):
# 0.762
# args.model='unet'
# model1=get_models(args)
# model1.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\unet_ep1600\cvc\20200312-143050\model_best.pth.tar',map_location='cpu')['state_dict'])
# # 0.766/0.773
# args.model='unet++'
# model2=get_models(args)
# model2.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\unet++_nodeep_ep800\cvc\no_deep\model_best.pth.tar',map_location='cpu')['state_dict'])
#
# # mutilres 0.695
# args.model='multires_unet'
# model3=get_models(args)
# model3.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\multires_unet_800\cvc\20200310-172036\checkpoint.pth.tar',map_location='cpu')['state_dict'])
#
#
# attention_unet 0.778
args.model = 'attention_unet_v1'
model4 = get_models(args)
model4.load_state_dict(
torch.load(
r'E:\segmentation\Image_Segmentation\logs\cvc_logs\attention_unet_v1_ep1600\cvc\20200312-143413\model_best.pth.tar',
map_location='cpu')['state_dict'])
genotype = eval('genotypes.%s' % 'layer7_double_deep')
#BuildNasUnetPrune
model5 = BuildNasUnetPrune(
genotype=genotype,
input_c=3,
c=16,
num_classes=1,
meta_node_num=4,
layers=9,
dp=0,
use_sharing=True,
double_down_channel=True,
aux=True,
)
model5.load_state_dict(
torch.load(
r'E:\segmentation\Image_Segmentation\nas_search_unet\logs\cvc\layer7_double_deep_ep1600_20200320-200539\model_best.pth.tar',
map_location='cpu')['state_dict'])
models_list = [model4, model5]
inference_isic(models_list, args.image, args.mask)
def main(args):
model1 = get_models(args)
ckpt1 = torch.load(args.model_weight1, map_location='cpu')
model1.load_state_dict(ckpt1['state_dict'])
# inference_isic(model,args.image,args.mask)
ckpt2 = torch.load(args.model_weight2, map_location='cpu')
genotype = eval('genotypes.%s' %
'stage1_layer9_110epoch_double_deep_final')
#BuildNasUnetPrune
model2 = BuildNasUnetPrune(
genotype=genotype,
input_c=3,
c=16,
num_classes=1,
meta_node_num=4,
layers=9,
dp=0,
use_sharing=True,
double_down_channel=True,
aux=True,
)
model2.load_state_dict(ckpt2['state_dict'])
inference_isic(model1, model2, args.image, args.mask)
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 ###################################
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):
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):
args.device = torch.device('cuda')
args.dataset = 'isic2018'
args.train_batch = 2
args.val_batch = 2
args.num_workers = 2
args.crop_size = 256
args.base_size = 256
train_loader = get_dataloder(args, split_flag="train")
#args.model = 'nodouble_deep'
######################## init model ############################################
# model
# get the network parameters
if args.model == '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 = 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
)
print('param size = %fMB', calc_parameters_count(model))
elif args.model == '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
)
print('param size = %fMB', calc_parameters_count(model))
elif args.model == '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
)
print('param size = %fMB', calc_parameters_count(model))
elif args.model == '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
)
print('param size = %fMB', calc_parameters_count(model))
else:
raise NotImplementedError()
time=test_time(args,model,train_loader)
print("Infrence time:{}".format(time))
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)