def get_model(model_path, model_type):
"""
:param model_path:
:param model_type: 'UNet', 'UNet11', 'UNet16', 'AlbuNet34'
:return:
"""
num_classes = 1
if model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
else:
model = UNet(num_classes=num_classes)
state = torch.load(str(model_path))
state = {
key.replace('module.', ''): value
for key, value in state['model'].items()
}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def get_model(model_path, model_type):
"""
:param model_path:
:param model_type: 'UNet', 'UNet11', 'UNet16', 'AlbuNet34'
:return:
"""
num_classes = 1
if model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes)
elif model_type == 'MDeNet':
print('Mine MDeNet..................')
model = MDeNet(num_classes=num_classes)
elif model_type == 'EncDec':
print('Mine EncDec..................')
model = EncDec(num_classes=num_classes)
elif model_type == 'hourglass':
model = hourglass(num_classes=num_classes)
elif model_type == 'MDeNetplus':
print('load MDeNetplus..................')
model = MDeNetplus(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
else:
print('I am here')
model = UNet(num_classes=num_classes)
state = torch.load(str(model_path))
state = {
key.replace('module.', ''): value
for key, value in state['model'].items()
}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def unlabel_prediction(PATH_model, unlabel_name_file):
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
num_classes = 1
model = UNet11(num_classes=num_classes)
model.to('cuda:1')
model.load_state_dict(torch.load(PATH_model))
model.eval()
######################### setting all data paths#######
outfile_path = 'predictions_VHR/unlabel_test/'
data_path = 'data_VHR'
test_path = "data_VHR/unlabel/" + unlabel_name_file
get_files_path = test_path + "/*.npy"
test_file_names = np.array(sorted(glob.glob(get_files_path)))
###################################
test_transform = DualCompose([CenterCrop(512), ImageOnly(Normalize())])
test_loader = make_loader(test_file_names,
shuffle=False,
transform=test_transform)
metrics = defaultdict(float)
count_img = 0
input_vec = []
pred_vec = []
for inputs, name in test_loader:
inputs = inputs.to(device)
with torch.set_grad_enabled(False):
input_vec.append(inputs.data.cpu().numpy())
pred = model(inputs)
pred = torch.sigmoid(pred)
pred_vec.append(pred.data.cpu().numpy())
count_img += 1
print(count_img)
name_imgs = outfile_path + unlabel_name_file + "_inputs_unlab_" + str(
count_img) + ".npy"
name_preds = outfile_path + unlabel_name_file + "pred_unlab_" + str(
count_img) + ".npy"
np.save(name_imgs, np.array(input_vec))
np.save(name_preds, np.array(pred_vec))
return name_imgs, name_preds
def get_model(model_path, model_type='UNet11', problem_type='binary'):
"""
:param model_path:
:param model_type: 'UNet', 'UNet16', 'UNet11', 'LinkNet34', 'AlbuNet'
:param problem_type: 'binary', 'parts', 'instruments'
:return:
"""
if problem_type == 'binary':
num_classes = 1
elif problem_type == 'parts':
num_classes = 4
elif problem_type == 'instruments':
num_classes = 8
if model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'LinkNet34':
model = LinkNet34(num_classes=num_classes)
elif model_type == 'AlbuNet':
model = AlbuNet(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
state = None
if torch.cuda.is_available():
state = torch.load(str(model_path))
else:
state = torch.load(str(model_path), map_location='cpu')
state = {
key.replace('module.', ''): value
for key, value in state['model'].items()
}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--device-ids', type=str, default='0', help='For example 0,1 to run on two GPUs')
arg('--fold-out', type=int, help='fold train test', default=0)
arg('--fold-in', type=int, help='fold train val', default=0)
arg('--percent', type=float, help='percent of data', default=1)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=4)
arg('--limit', type=int, default=10000, help='number of images in epoch')
arg('--n-epochs', type=int, default=40)
arg('--n-steps', type=int, default=200)
arg('--lr', type=float, default=0.003)
arg('--modelVHR', type=str, default='UNet11', choices=['UNet11','UNet','AlbuNet34','SegNet'])
arg('--dataset-path-HR', type=str, default='data_HR', help='ain path of the HR dataset')
arg('--model-path-HR', type=str, default='logs_HR/mapping/model_40epoch_HR_UNet11.pth', help='path of the model of HR')
arg('--dataset-path-VHR', type=str, default='data_VHR', help='ain path of the VHR dataset')
arg('--name-file-HR', type=str, default='_HR', help='name file of HR dataset')
arg('--dataset-file', type=str, default='VHR', help='main dataset resolution,depend of this correspond a specific crop' )
arg('--out-file', type=str, default='seq', help='the file in which save the outputs')
arg('--train-val-file-HR', type=str, default='train_val_HR', help='name of the train-val file' )
arg('--test-file-HR', type=str, default='test_HR', help='name of the test file' )
arg('--train-val-file-VHR', type=str, default='train_val_850', help='name of the train-val file' )
arg('--test-file-VHR', type=str, default='test_850', help='name of the test file' )
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 1
input_channels=4
if args.modelVHR == 'UNet11':
model_VHR = UNet11(num_classes=num_classes, input_channels=input_channels)
elif args.modelVHR == 'UNet':
model_VHR = UNet(num_classes=num_classes, input_channels=input_channels)
elif args.modelVHR == 'AlbuNet34':
model_VHR =AlbuNet34(num_classes=num_classes, num_input_channels=input_channels, pretrained=False)
elif args.modelVHR == 'SegNet':
model_VHR = SegNet(num_classes=num_classes, num_input_channels=input_channels, pretrained=False)
else:
model_VHR = UNet11(num_classes=num_classes, input_channels=4)
if torch.cuda.is_available():
if args.device_ids:#
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model_VHR = nn.DataParallel(model_VHR, device_ids=device_ids).cuda()
cudnn.benchmark = True
out_path = Path(('logs_{}/mapping/').format(args.out_file))
#Data-paths:--------------------------VHr-------------------------------------
data_path_VHR = Path(args.dataset_path_VHR)
print("data_path:",data_path_VHR)
name_file_VHR = '_'+ str(int(args.percent*100))+'_percent_'+args.out_file
data_all='data'
##--------------------------------------
############################
# NEstes cross validation K-fold train test
##train_val_file_names, test_file_names_HR = get_split_out(data_path_HR,data_all,args.fold_out)
############################
############################ Cross validation
train_val_file_names=np.array(sorted(glob.glob(str((data_path_VHR/args.train_val_file_VHR/'images'))+ "/*.npy")))
test_file_names_VHR = np.array(sorted(glob.glob(str((data_path_VHR/args.test_file_VHR/'images')) + "/*.npy")))
if args.percent !=1:
extra, train_val_file_names= percent_split(train_val_file_names, args.percent)
train_file_VHR_lab,val_file_VHR_lab = get_split_in(train_val_file_names,args.fold_in)
np.save(str(os.path.join(out_path,"train_files{}_{}_fold{}_{}.npy".format(name_file_VHR, args.modelVHR, args.fold_out, args.fold_in))), train_file_VHR_lab)
np.save(str(os.path.join(out_path,"val_files{}_{}_fold{}_{}.npy". format(name_file_VHR, args.modelVHR, args.fold_out, args.fold_in))), val_file_VHR_lab)
#Data-paths:--------------------------unlabeled VHR-------------------------------------
train_path_VHR_unlab= data_path_VHR/'unlabel'/'train'/'images'
val_path_VHR_unlab = data_path_VHR/'unlabel'/'val'/'images'
train_file_VHR_unlab = np.array(sorted(list(train_path_VHR_unlab.glob('*.npy'))))
val_file_VHR_unlab = np.array(sorted(list(val_path_VHR_unlab.glob('*.npy'))))
print('num train_lab = {}, num_val_lab = {}'.format(len(train_file_VHR_lab), len(val_file_VHR_lab)))
print('num train_unlab = {}, num_val_unlab = {}'.format(len(train_file_VHR_unlab), len(val_file_VHR_unlab)))
max_values_VHR, mean_values_VHR, std_values_VHR=meanstd(train_file_VHR_lab, val_file_VHR_lab,test_file_names_VHR,str(data_path_VHR),input_channels)
def make_loader(file_names, shuffle=False, transform=None,mode='train',batch_size=4, limit=None):
return DataLoader(
dataset=WaterDataset(file_names, transform=transform,mode=mode, limit=limit),
shuffle=shuffle,
batch_size=batch_size,
pin_memory=torch.cuda.is_available()
)
#transformations ---------------------------------------------------------------------------
train_transform_VHR = DualCompose([
CenterCrop(512),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(Normalize(mean=mean_values_VHR,std= std_values_VHR))
])
val_transform_VHR = DualCompose([
CenterCrop(512),
ImageOnly(Normalize(mean=mean_values_VHR, std=std_values_VHR))
])
#-------------------------------------------------------------------
mean_values_HR=(0.11952524, 0.1264638 , 0.13479991, 0.15017026)
std_values_HR=(0.08844988, 0.07304429, 0.06740904, 0.11003125)
train_transform_VHR_unlab = DualCompose([
CenterCrop(512),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(Normalize(mean=mean_values_HR,std= std_values_HR))
])
val_transform_VHR_unlab = DualCompose([
CenterCrop(512),
ImageOnly(Normalize(mean=mean_values_HR, std=std_values_HR))
])
######################## DATA-LOADERS ###########################################################49
train_loader_VHR_lab = make_loader(train_file_VHR_lab, shuffle=True, transform=train_transform_VHR , batch_size = 2, mode = "train")
valid_loader_VHR_lab = make_loader(val_file_VHR_lab, transform=val_transform_VHR, batch_size = 4, mode = "train")
train_loader_VHR_unlab = make_loader(train_file_VHR_unlab, shuffle=True, transform=train_transform_VHR, batch_size = 4, mode = "unlb_train")
valid_loader_VHR_unlab = make_loader(val_file_VHR_unlab, transform=val_transform_VHR, batch_size = 2, mode = "unlb_val")
dataloaders_VHR_lab= {
'train': train_loader_VHR_lab, 'val': valid_loader_VHR_lab
}
dataloaders_VHR_unlab= {
'train': train_loader_VHR_unlab, 'val': valid_loader_VHR_unlab
}
#----------------------------------------------
root.joinpath(('params_{}.json').format(args.out_file)).write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
# Observe that all parameters are being optimized
optimizer_ft = optim.Adam(model_VHR.parameters(), lr= args.lr)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=20, gamma=0.1)
#--------------------------model HR-------------------------------------
PATH_HR= args.model_path_HR
#Initialise the model
model_HR = UNet11(num_classes=num_classes)
model_HR.cuda()
model_HR.load_state_dict(torch.load(PATH_HR))
#---------------------------------------------------------------
model_VHR= utilsTrain_seq.train_model(
out_file=args.out_file,
name_file_VHR=name_file_VHR,
model_HR=model_HR,
model_VHR=model_VHR,
optimizer=optimizer_ft,
scheduler=exp_lr_scheduler,
dataloaders_VHR_lab=dataloaders_VHR_lab,
dataloaders_VHR_unlab=dataloaders_VHR_unlab,
fold_out=args.fold_out,
fold_in=args.fold_in,
name_model_VHR=args.modelVHR,
n_steps=args.n_steps,
num_epochs=args.n_epochs
)
torch.save(model_VHR.module.state_dict(), (str(out_path)+'/model{}_{}_foldout{}_foldin{}_{}epochs.pth').format(args.n_epochs,name_file_VHR,args.modelVHR, args.fold_out,args.fold_in,args.n_epochs))
print(args.modelVHR)
max_values_all_VHR=3521
find_metrics(train_file_names=train_file_VHR_lab,
val_file_names=val_file_VHR_lab,
test_file_names=test_file_names_VHR,
max_values=max_values_all_VHR,
mean_values=mean_values_VHR,
std_values=std_values_VHR,
model=model_VHR,
fold_out=args.fold_out,
fold_in=args.fold_in,
name_model=args.modelVHR,
epochs=args.n_epochs,
out_file=args.out_file,
dataset_file=args.dataset_file,
name_file=name_file_VHR)
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', default=0.3, type=float)
arg('--device-ids',
type=str,
default='0',
help='For example 0,1 to run on two GPUs')
arg('--fold', type=int, help='fold', default=0)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=1)
arg('--limit', type=int, default=10000, help='number of images in epoch')
arg('--n-epochs', type=int, default=100)
arg('--lr', type=float, default=0.0001)
arg('--workers', type=int, default=12)
arg('--model',
type=str,
default='UNet',
choices=['UNet', 'UNet11', 'UNet16', 'AlbuNet34'])
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 1
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained=True)
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained=True)
elif args.model == 'LinkNet34':
model = LinkNet34(num_classes=num_classes, pretrained=True)
elif args.model == 'AlbuNet':
model = AlbuNet34(num_classes=num_classes, pretrained=True)
else:
model = UNet(num_classes=num_classes, input_channels=3)
if torch.cuda.is_available():
if args.device_ids:
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model, device_ids=device_ids).cuda()
loss = LossBinary(jaccard_weight=args.jaccard_weight)
cudnn.benchmark = True
def make_loader(file_names, shuffle=False, transform=None, limit=None):
return DataLoader(dataset=AngyodysplasiaDataset(file_names,
transform=transform,
limit=limit),
shuffle=shuffle,
num_workers=args.workers,
batch_size=args.batch_size,
pin_memory=torch.cuda.is_available())
train_file_names, val_file_names = get_split(args.fold)
print('num train = {}, num_val = {}'.format(len(train_file_names),
len(val_file_names)))
train_transform = DualCompose([
SquarePaddingTraining(),
CenterCrop([574, 574]),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(RandomHueSaturationValue()),
ImageOnly(Normalize())
])
val_transform = DualCompose([
SquarePaddingTraining(),
CenterCrop([574, 574]),
ImageOnly(Normalize())
])
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform,
limit=args.limit)
valid_loader = make_loader(val_file_names, transform=val_transform)
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
utils.train(init_optimizer=lambda lr: Adam(model.parameters(), lr=lr),
args=args,
model=model,
criterion=loss,
train_loader=train_loader,
valid_loader=valid_loader,
validation=validation_binary,
fold=args.fold)
import os
import glob
import torch
from tqdm import tqdm
import torch.nn as nn
from models import UNet11
import torch.optim as optim
from dataset import PersonDataset
import torchvision.models as models
lr = 0.001
batch_size = 30
num_epochs = 20
generator = UNet11()
generator.load_state_dict(torch.load('generator_early_trained.pth'))
generator.cuda()
discriminator = torch.load('discriminator_early_trained.pth')
discriminator.cuda()
persontraindataset = PersonDataset('../datasets/celeb_dataset',
mode='train',
transforms=transforms)
persontraindataloader = DataLoader(persontraindataset,
batch_size=batch_size,
shuffle=True)
personvaldataset = PersonDataset('../datasets/celeb_dataset',
mode='test',
transforms=None)
scheduler.step(val_bce)
net.train()
print('Validation Dice Coeff: {}, bce: {}'.format(val_dice, val_bce))
if cp and epoch_num % 5 == 0:
torch.save(
net.state_dict(), CHECKPOINT_DIR +
'linknet_{}_loss{}.pth'.format(epoch_num + 1, loss.data[0]))
print('Checkpoint {} saved !'.format(epoch_num + 1))
if __name__ == '__main__':
print(NUM_CLASSES)
net = UNet11(NUM_CLASSES).cuda().double()
# net = LinkNet34(NUM_CLASSES).cuda()
cudnn.benchmark = True
# if os.path.exists(RESTORE_INTERRUPTED) and RESTORE_INTERRUPTED is not None:
# net.load_state_dict(torch.load(RESTORE_INTERRUPTED))
# print('Model loaded from {}'.format('interrupted.pth'))
try:
train_net(net, EPOCH_NUM, BATCH_SIZE, LEARNING_RATE, gpu=True)
except KeyboardInterrupt:
torch.save(net.state_dict(), RESTORE_INTERRUPTED)
print('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)
def get_model(path):
model = UNet11().cuda()
model.load_state_dict(torch.load(path))
model = model.eval()
return model
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', default=1, type=float)
arg('--device-ids',
type=str,
default='0',
help='For example 0,1 to run on two GPUs')
arg('--fold', type=int, help='fold', default=0)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=1)
arg('--n-epochs', type=int, default=10)
arg('--lr', type=float, default=0.0002)
arg('--workers', type=int, default=10)
arg('--type',
type=str,
default='binary',
choices=['binary', 'parts', 'instruments'])
arg('--model',
type=str,
default='DLinkNet',
choices=['UNet', 'UNet11', 'LinkNet34', 'DLinkNet'])
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
if args.type == 'parts':
num_classes = 4
elif args.type == 'instruments':
num_classes = 8
else:
num_classes = 1
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained='vgg')
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained='vgg')
elif args.model == 'LinkNet34':
model = LinkNet34(num_classes=num_classes, pretrained=True)
elif args.model == 'DLinkNet':
model = D_LinkNet34(num_classes=num_classes, pretrained=True)
else:
model = UNet(num_classes=num_classes, input_channels=3)
if torch.cuda.is_available():
if args.device_ids:
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model, device_ids=device_ids).cuda()
if args.type == 'binary':
# loss = LossBinary(jaccard_weight=args.jaccard_weight)
loss = LossBCE_DICE()
else:
loss = LossMulti(num_classes=num_classes,
jaccard_weight=args.jaccard_weight)
cudnn.benchmark = True
def make_loader(file_names,
shuffle=False,
transform=None,
problem_type='binary'):
return DataLoader(dataset=RoboticsDataset(file_names,
transform=transform,
problem_type=problem_type),
shuffle=shuffle,
num_workers=args.workers,
batch_size=args.batch_size,
pin_memory=torch.cuda.is_available())
# train_file_names, val_file_names = get_split(args.fold)
train_file_names, val_file_names = get_train_val_files()
print('num train = {}, num_val = {}'.format(len(train_file_names),
len(val_file_names)))
train_transform = DualCompose(
[HorizontalFlip(),
VerticalFlip(),
ImageOnly(Normalize())])
val_transform = DualCompose([ImageOnly(Normalize())])
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform,
problem_type=args.type)
valid_loader = make_loader(val_file_names,
transform=val_transform,
problem_type=args.type)
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
if args.type == 'binary':
valid = validation_binary
else:
valid = validation_multi
utils.train(init_optimizer=lambda lr: Adam(model.parameters(), lr=lr),
args=args,
model=model,
criterion=loss,
train_loader=train_loader,
valid_loader=valid_loader,
validation=valid,
fold=args.fold,
num_classes=num_classes)
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', default=0.3, type=float)
arg('--device-ids', type=str, default='0', help='For example 0,1 to run on two GPUs')
arg('--fold', type=int, help='fold', default=0)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=1)
arg('--limit', type=int, default=10000, help='number of images in epoch')
arg('--n-epochs', type=int, default=100)
arg('--lr', type=float, default=0.001)
arg('--workers', type=int, default=12)
arg('--model', type=str, default='UNet', choices=['UNet', 'UNet11', 'LinkNet34', 'UNet16', 'AlbuNet34', 'MDeNet', 'EncDec', 'hourglass', 'MDeNetplus'])
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 1
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained=True)
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained=True)
elif args.model == 'MDeNet':
print('Mine MDeNet..................')
model = MDeNet(num_classes=num_classes, pretrained=True)
elif args.model == 'MDeNetplus':
print('load MDeNetplus..................')
model = MDeNetplus(num_classes=num_classes, pretrained=True)
elif args.model == 'EncDec':
print('Mine EncDec..................')
model = EncDec(num_classes=num_classes, pretrained=True)
elif args.model == 'GAN':
model = GAN(num_classes=num_classes, pretrained=True)
elif args.model == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes, pretrained=False)
elif args.model == 'hourglass':
model = hourglass(num_classes=num_classes, pretrained=True)
else:
model = UNet(num_classes=num_classes, input_channels=3)
if torch.cuda.is_available():
if args.device_ids:
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model).cuda() # nn.DataParallel(model, device_ids=device_ids).cuda()
cudnn.benchmark = True
def make_loader(file_names, shuffle=False, transform=None, limit=None):
return DataLoader(
dataset=Polyp(file_names, transform=transform, limit=limit),
shuffle=shuffle,
num_workers=args.workers,
batch_size=args.batch_size,
pin_memory=torch.cuda.is_available()
)
train_file_names, val_file_names = get_split(args.fold)
print('num train = {}, num_val = {}'.format(len(train_file_names), len(val_file_names)))
train_transform = DualCompose([
CropCVC612(),
img_resize(512),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
Rescale(),
Zoomin(),
ImageOnly(RandomHueSaturationValue()),
ImageOnly(Normalize())
])
train_loader = make_loader(train_file_names, shuffle=True, transform=train_transform, limit=args.limit)
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
utils.train(
args=args,
model=model,
train_loader=train_loader,
fold=args.fold
)
arg('--test-file', type=str, default='test_512', help='name of the test file test_512 or test_160' )
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 3
channels=list(map(int, args.channels.split(','))) #5
input_channels=len(channels)
print('channels:',channels,'len',input_channels)
if args.model == 'UNet11':
model = UNet11(num_classes=num_classes, input_channels=input_channels)
elif args.model == 'UNet':
model = UNet(num_classes=num_classes, input_channels=input_channels)
elif args.model == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes, num_input_channels=input_channels, pretrained=False)
elif args.model == 'SegNet':
model = SegNet(num_classes=num_classes, num_input_channels=input_channels, pretrained=False)
elif args.model == 'DeepLabV3':
model = deeplabv3_resnet101(pretrained=False, progress=True, num_classes=num_classes)
#model = models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=num_classes)
elif args.model == 'FCN':
model = fcn_resnet101(pretrained=False, progress=True, num_classes=num_classes)
else:
model = UNet11(num_classes=num_classes, input_channels=input_channels)
persontraindataset = PersonDataset('../datasets/celeb_dataset',
mode='train',
transforms=transforms)
persontraindataloader = DataLoader(persontraindataset,
batch_size=batch_size,
shuffle=True)
personvaldataset = PersonDataset('../datasets/celeb_dataset',
mode='test',
transforms=None)
personvaldataloader = DataLoader(personvaldataset,
batch_size=batch_size,
shuffle=True)
generator = UNet11(pretrained='vgg')
generator.cuda()
image_loss = nn.MSELoss()
optim_generator = optim.SGD(generator.parameters(), lr=lr, momentum=0.9)
losses_generator_train = []
losses_generator_val = []
for epoch in range(num_epochs):
loss_batch_train = 0.0
loss_batch_val = 0.0
generator.train()
print('\n Epoch:{}'.format(epoch + 1))
for i, (correct_img, degraded_img, _,
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', type=float, default=1)
arg('--root', type=str, default='runs/debug', help='checkpoint root')
arg('--image-path', type=str, default='data', help='image path')
arg('--batch-size', type=int, default=2)
arg('--n-epochs', type=int, default=100)
arg('--optimizer', type=str, default='Adam', help='Adam or SGD')
arg('--lr', type=float, default=0.001)
arg('--workers', type=int, default=10)
arg('--model',
type=str,
default='UNet16',
choices=[
'UNet', 'UNet11', 'UNet16', 'LinkNet34', 'FCDenseNet57',
'FCDenseNet67', 'FCDenseNet103'
])
arg('--model-weight', type=str, default=None)
arg('--resume-path', type=str, default=None)
arg('--attribute',
type=str,
default='all',
choices=[
'pigment_network', 'negative_network', 'streaks',
'milia_like_cyst', 'globules', 'all'
])
args = parser.parse_args()
## folder for checkpoint
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
image_path = args.image_path
#print(args)
if args.attribute == 'all':
num_classes = 5
else:
num_classes = 1
args.num_classes = num_classes
### save initial parameters
print('--' * 10)
print(args)
print('--' * 10)
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
## load pretrained model
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained='vgg')
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained='vgg')
elif args.model == 'LinkNet34':
model = LinkNet34(num_classes=num_classes, pretrained=True)
elif args.model == 'FCDenseNet103':
model = FCDenseNet103(num_classes=num_classes)
else:
model = UNet(num_classes=num_classes, input_channels=3)
## multiple GPUs
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to(device)
## load pretrained model
if args.model_weight is not None:
state = torch.load(args.model_weight)
#epoch = state['epoch']
#step = state['step']
model.load_state_dict(state['model'])
print('--' * 10)
print('Load pretrained model', args.model_weight)
#print('Restored model, epoch {}, step {:,}'.format(epoch, step))
print('--' * 10)
## replace the last layer
## although the model and pre-trained weight have differernt size (the last layer is different)
## pytorch can still load the weight
## I found that the weight for one layer just duplicated for all layers
## therefore, the following code is not necessary
# if args.attribute == 'all':
# model = list(model.children())[0]
# num_filters = 32
# model.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
# print('--' * 10)
# print('Load pretrained model and replace the last layer', args.model_weight, num_classes)
# print('--' * 10)
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
# model.to(device)
## model summary
print_model_summay(model)
## define loss
loss_fn = LossBinary(jaccard_weight=args.jaccard_weight)
## It enables benchmark mode in cudnn.
## benchmark mode is good whenever your input sizes for your network do not vary. This way, cudnn will look for the
## optimal set of algorithms for that particular configuration (which takes some time). This usually leads to faster runtime.
## But if your input sizes changes at each iteration, then cudnn will benchmark every time a new size appears,
## possibly leading to worse runtime performances.
cudnn.benchmark = True
## get train_test_id
train_test_id = get_split()
## train vs. val
print('--' * 10)
print('num train = {}, num_val = {}'.format(
(train_test_id['Split'] == 'train').sum(),
(train_test_id['Split'] != 'train').sum()))
print('--' * 10)
train_transform = DualCompose(
[HorizontalFlip(),
VerticalFlip(),
ImageOnly(Normalize())])
val_transform = DualCompose([ImageOnly(Normalize())])
## define data loader
train_loader = make_loader(train_test_id,
image_path,
args,
train=True,
shuffle=True,
transform=train_transform)
valid_loader = make_loader(train_test_id,
image_path,
args,
train=False,
shuffle=True,
transform=val_transform)
if True:
print('--' * 10)
print('check data')
train_image, train_mask, train_mask_ind = next(iter(train_loader))
print('train_image.shape', train_image.shape)
print('train_mask.shape', train_mask.shape)
print('train_mask_ind.shape', train_mask_ind.shape)
print('train_image.min', train_image.min().item())
print('train_image.max', train_image.max().item())
print('train_mask.min', train_mask.min().item())
print('train_mask.max', train_mask.max().item())
print('train_mask_ind.min', train_mask_ind.min().item())
print('train_mask_ind.max', train_mask_ind.max().item())
print('--' * 10)
valid_fn = validation_binary
###########
## optimizer
if args.optimizer == 'Adam':
optimizer = Adam(model.parameters(), lr=args.lr)
elif args.optimizer == 'SGD':
optimizer = SGD(model.parameters(), lr=args.lr, momentum=0.9)
## loss
criterion = loss_fn
## change LR
scheduler = ReduceLROnPlateau(optimizer,
'min',
factor=0.8,
patience=5,
verbose=True)
##########
## load previous model status
previous_valid_loss = 10
model_path = root / 'model.pt'
if args.resume_path is not None and model_path.exists():
state = torch.load(str(model_path))
epoch = state['epoch']
step = state['step']
model.load_state_dict(state['model'])
epoch = 1
step = 0
try:
previous_valid_loss = state['valid_loss']
except:
previous_valid_loss = 10
print('--' * 10)
print('Restored previous model, epoch {}, step {:,}'.format(
epoch, step))
print('--' * 10)
else:
epoch = 1
step = 0
#########
## start training
log = root.joinpath('train.log').open('at', encoding='utf8')
writer = SummaryWriter()
meter = AllInOneMeter()
#if previous_valid_loss = 10000
print('Start training')
print_model_summay(model)
previous_valid_jaccard = 0
for epoch in range(epoch, args.n_epochs + 1):
model.train()
random.seed()
#jaccard = []
start_time = time.time()
meter.reset()
w1 = 1.0
w2 = 0.5
w3 = 0.5
try:
train_loss = 0
valid_loss = 0
# if epoch == 1:
# freeze_layer_names = get_freeze_layer_names(part='encoder')
# set_freeze_layers(model, freeze_layer_names=freeze_layer_names)
# #set_train_layers(model, train_layer_names=['module.final.weight','module.final.bias'])
# print_model_summay(model)
# elif epoch == 5:
# w1 = 1.0
# w2 = 0.0
# w3 = 0.5
# freeze_layer_names = get_freeze_layer_names(part='encoder')
# set_freeze_layers(model, freeze_layer_names=freeze_layer_names)
# # set_train_layers(model, train_layer_names=['module.final.weight','module.final.bias'])
# print_model_summay(model)
#elif epoch == 3:
# set_train_layers(model, train_layer_names=['module.dec5.block.0.conv.weight','module.dec5.block.0.conv.bias',
# 'module.dec5.block.1.weight','module.dec5.block.1.bias',
# 'module.dec4.block.0.conv.weight','module.dec4.block.0.conv.bias',
# 'module.dec4.block.1.weight','module.dec4.block.1.bias',
# 'module.dec3.block.0.conv.weight','module.dec3.block.0.conv.bias',
# 'module.dec3.block.1.weight','module.dec3.block.1.bias',
# 'module.dec2.block.0.conv.weight','module.dec2.block.0.conv.bias',
# 'module.dec2.block.1.weight','module.dec2.block.1.bias',
# 'module.dec1.conv.weight','module.dec1.conv.bias',
# 'module.final.weight','module.final.bias'])
# print_model_summa zvgf t5y(model)
# elif epoch == 50:
# set_freeze_layers(model, freeze_layer_names=None)
# print_model_summay(model)
for i, (train_image, train_mask,
train_mask_ind) in enumerate(train_loader):
# inputs, targets = variable(inputs), variable(targets)
train_image = train_image.permute(0, 3, 1, 2)
train_mask = train_mask.permute(0, 3, 1, 2)
train_image = train_image.to(device)
train_mask = train_mask.to(device).type(torch.cuda.FloatTensor)
train_mask_ind = train_mask_ind.to(device).type(
torch.cuda.FloatTensor)
# if args.problem_type == 'binary':
# train_mask = train_mask.to(device).type(torch.cuda.FloatTensor)
# else:
# #train_mask = train_mask.to(device).type(torch.cuda.LongTensor)
# train_mask = train_mask.to(device).type(torch.cuda.FloatTensor)
outputs, outputs_mask_ind1, outputs_mask_ind2 = model(
train_image)
#print(outputs.size())
#print(outputs_mask_ind1.size())
#print(outputs_mask_ind2.size())
### note that the last layer in the model is defined differently
# if args.problem_type == 'binary':
# train_prob = F.sigmoid(outputs)
# loss = criterion(outputs, train_mask)
# else:
# #train_prob = outputs
# train_prob = F.sigmoid(outputs)
# loss = torch.tensor(0).type(train_mask.type())
# for feat_inx in range(train_mask.shape[1]):
# loss += criterion(outputs, train_mask)
train_prob = F.sigmoid(outputs)
train_mask_ind_prob1 = F.sigmoid(outputs_mask_ind1)
train_mask_ind_prob2 = F.sigmoid(outputs_mask_ind2)
loss1 = criterion(outputs, train_mask)
#loss1 = F.binary_cross_entropy_with_logits(outputs, train_mask)
#loss2 = nn.BCEWithLogitsLoss()(outputs_mask_ind1, train_mask_ind)
#print(train_mask_ind.size())
#weight = torch.ones_like(train_mask_ind)
#weight[:, 0] = weight[:, 0] * 1
#weight[:, 1] = weight[:, 1] * 14
#weight[:, 2] = weight[:, 2] * 14
#weight[:, 3] = weight[:, 3] * 4
#weight[:, 4] = weight[:, 4] * 4
#weight = weight * train_mask_ind + 1
#weight = weight.to(device).type(torch.cuda.FloatTensor)
loss2 = F.binary_cross_entropy_with_logits(
outputs_mask_ind1, train_mask_ind)
loss3 = F.binary_cross_entropy_with_logits(
outputs_mask_ind2, train_mask_ind)
#loss3 = criterion(outputs_mask_ind2, train_mask_ind)
loss = loss1 * w1 + loss2 * w2 + loss3 * w3
#print(loss1.item(), loss2.item(), loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
#jaccard += [get_jaccard(train_mask, (train_prob > 0).float()).item()]
meter.add(train_prob, train_mask, train_mask_ind_prob1,
train_mask_ind_prob2, train_mask_ind, loss1.item(),
loss2.item(), loss3.item(), loss.item())
# print(train_mask.data.shape)
# print(train_mask.data.sum(dim=-2).shape)
# print(train_mask.data.sum(dim=-2).sum(dim=-1).shape)
# print(train_mask.data.sum(dim=-2).sum(dim=-1).sum(dim=0).shape)
# intersection = train_mask.data.sum(dim=-2).sum(dim=-1)
# print(intersection.shape)
# print(intersection.dtype)
# print(train_mask.data.shape[0])
#torch.zeros([2, 4], dtype=torch.float32)
#########################
## at the end of each epoch, evualte the metrics
epoch_time = time.time() - start_time
train_metrics = meter.value()
train_metrics['epoch_time'] = epoch_time
train_metrics['image'] = train_image.data
train_metrics['mask'] = train_mask.data
train_metrics['prob'] = train_prob.data
#train_jaccard = np.mean(jaccard)
#train_auc = str(round(mtr1.value()[0],2))+' '+str(round(mtr2.value()[0],2))+' '+str(round(mtr3.value()[0],2))+' '+str(round(mtr4.value()[0],2))+' '+str(round(mtr5.value()[0],2))
valid_metrics = valid_fn(model, criterion, valid_loader, device,
num_classes)
##############
## write events
write_event(log,
step,
epoch=epoch,
train_metrics=train_metrics,
valid_metrics=valid_metrics)
#save_weights(model, model_path, epoch + 1, step)
#########################
## tensorboard
write_tensorboard(writer,
model,
epoch,
train_metrics=train_metrics,
valid_metrics=valid_metrics)
#########################
## save the best model
valid_loss = valid_metrics['loss1']
valid_jaccard = valid_metrics['jaccard']
if valid_loss < previous_valid_loss:
save_weights(model, model_path, epoch + 1, step, train_metrics,
valid_metrics)
previous_valid_loss = valid_loss
print('Save best model by loss')
if valid_jaccard > previous_valid_jaccard:
save_weights(model, model_path, epoch + 1, step, train_metrics,
valid_metrics)
previous_valid_jaccard = valid_jaccard
print('Save best model by jaccard')
#########################
## change learning rate
scheduler.step(valid_metrics['loss1'])
except KeyboardInterrupt:
# print('--' * 10)
# print('Ctrl+C, saving snapshot')
# save_weights(model, model_path, epoch, step)
# print('done.')
# print('--' * 10)
writer.close()
#return
writer.close()
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--device-ids',
type=str,
default='0',
help='For example 0,1 to run on two GPUs')
arg('--fold-out', type=int, default='0', help='fold train-val test')
arg('--fold-in', type=int, default='0', help='fold train val')
arg('--percent', type=float, default=1, help='percent of data')
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=4, help='HR:4,VHR:8')
arg('--limit', type=int, default=10000, help='number of images in epoch')
arg('--n-epochs', type=int, default=40)
arg('--lr', type=float, default=1e-3)
arg('--model',
type=str,
default='UNet11',
choices=['UNet11', 'UNet', 'AlbuNet34', 'SegNet'])
arg('--dataset-path',
type=str,
default='data_VHR',
help='main file,in which the dataset is: data_VHR or data_HR')
arg('--dataset-file',
type=str,
default='VHR',
help='resolution of the dataset VHR,HR')
#arg('--out-file', type=str, default='VHR', help='the file in which save the outputs')
arg('--train-val-file',
type=str,
default='train_val_850',
help='name of the train-val file VHR:train_val_850 or train_val_HR')
arg('--test-file',
type=str,
default='test_850',
help='name of the test file VHR:test_850 or HR:test_HR')
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 1
input_channels = 4
if args.model == 'UNet11':
model = UNet11(num_classes=num_classes, input_channels=input_channels)
elif args.model == 'UNet':
model = UNet(num_classes=num_classes, input_channels=input_channels)
elif args.model == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes,
num_input_channels=input_channels,
pretrained=False)
elif args.model == 'SegNet':
model = SegNet(num_classes=num_classes,
num_input_channels=input_channels,
pretrained=False)
else:
model = UNet11(num_classes=num_classes, input_channels=input_channels)
if torch.cuda.is_available():
if args.device_ids: #
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model, device_ids=device_ids).cuda()
cudnn.benchmark = True
####################Change the files_names ######################################
out_path = Path(('logs_{}/mapping/').format(args.dataset_file))
name_file = '_' + str(int(
args.percent * 100)) + '_percent_' + args.dataset_file
data_all = 'data' ##file with all the data
data_path = Path(args.dataset_path)
print("data_path:", data_path)
#################################################################################
# Nested cross validation K-fold train test
#train_val_file_names, test_file_names = get_split_out(data_path,data_all,args.fold_out)
#################################################################################
#eWe are consider the same test in all the cases
train_val_file_names = np.array(
sorted(
glob.glob(
str(data_path / args.train_val_file / 'images') + "/*.npy")))
test_file_names = np.array(
sorted(
glob.glob(str(data_path / args.test_file / 'images') + "/*.npy")))
if args.percent != 1:
extra, train_val_file_names = percent_split(train_val_file_names,
args.percent)
#################################################################################
train_file_names, val_file_names = get_split_in(train_val_file_names,
args.fold_in)
np.save(
str(
os.path.join(
out_path, "train_files{}_{}_fold{}_{}.npy".format(
name_file, args.model, args.fold_out, args.fold_in))),
train_file_names)
np.save(
str(
os.path.join(
out_path,
"val_files{}_{}_fold{}_{}.npy".format(name_file, args.model,
args.fold_out,
args.fold_in))),
val_file_names)
print('num train = {}, num_val = {}'.format(len(train_file_names),
len(val_file_names)))
def make_loader(file_names,
shuffle=False,
transform=None,
mode='train',
batch_size=4,
limit=None):
return DataLoader(dataset=WaterDataset(file_names,
transform=transform,
mode=mode,
limit=limit),
shuffle=shuffle,
batch_size=batch_size,
pin_memory=torch.cuda.is_available())
max_values, mean_values, std_values = meanstd(train_file_names,
val_file_names,
test_file_names,
str(data_path),
input_channels) #_60
print(max_values, mean_values, std_values)
if (args.dataset_file == 'VHR'):
train_transform = DualCompose([
CenterCrop(512),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(Normalize(mean=mean_values, std=std_values))
])
val_transform = DualCompose([
CenterCrop(512),
ImageOnly(Normalize(mean=mean_values, std=std_values))
])
max_values = 3521
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform,
mode='train',
batch_size=args.batch_size) #4 batch_size
valid_loader = make_loader(val_file_names,
transform=val_transform,
batch_size=args.batch_size,
mode="train")
if (args.dataset_file == 'HR'):
train_transform = DualCompose([
CenterCrop(64),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(Normalize2(mean=mean_values, std=std_values))
])
val_transform = DualCompose([
CenterCrop(64),
ImageOnly(Normalize2(mean=mean_values, std=std_values))
])
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform,
mode='train',
batch_size=args.batch_size) #8 batch_size
valid_loader = make_loader(val_file_names,
transform=val_transform,
mode="train",
batch_size=args.batch_size // 2)
#albunet 34 with only 3 batch_size
dataloaders = {'train': train_loader, 'val': valid_loader}
dataloaders_sizes = {x: len(dataloaders[x]) for x in dataloaders.keys()}
root.joinpath(('params_{}.json').format(args.dataset_file)).write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
optimizer_ft = optim.Adam(model.parameters(), lr=args.lr) #
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=20,
gamma=0.1)
utilsTrain.train_model(dataset_file=args.dataset_file,
name_file=name_file,
model=model,
optimizer=optimizer_ft,
scheduler=exp_lr_scheduler,
dataloaders=dataloaders,
fold_out=args.fold_out,
fold_in=args.fold_in,
name_model=args.model,
num_epochs=args.n_epochs)
torch.save(
model.module.state_dict(),
(str(out_path) + '/model{}_{}_foldout{}_foldin{}_{}epochs').format(
name_file, args.model, args.fold_out, args.fold_in, args.n_epochs))
print(args.model)
find_metrics(train_file_names=train_file_names,
val_file_names=val_file_names,
test_file_names=test_file_names,
max_values=max_values,
mean_values=mean_values,
std_values=std_values,
model=model,
fold_out=args.fold_out,
fold_in=args.fold_in,
name_model=args.model,
epochs=args.n_epochs,
out_file=args.dataset_file,
dataset_file=args.dataset_file,
name_file=name_file)
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', default=0.5, type=float)
arg('--device-ids',
type=str,
default='0',
help='For example 0,1 to run on two GPUs')
arg('--fold', type=int, help='fold', default=0)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=1)
arg('--n-epochs', type=int, default=100)
arg('--lr', type=float, default=0.0001)
arg('--workers', type=int, default=12)
arg('--type',
type=str,
default='binary',
choices=['binary', 'parts', 'instruments'])
arg('--model',
type=str,
default='UNet',
choices=['UNet', 'UNet11', 'LinkNet34', 'AlbuNet'])
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
if args.type == 'parts':
num_classes = 4
elif args.type == 'instruments':
num_classes = 8
else:
num_classes = 1
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained=True)
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained=True)
elif args.model == 'LinkNet34':
model = LinkNet34(num_classes=num_classes, pretrained=True)
elif args.model == 'AlbuNet':
model = AlbuNet(num_classes=num_classes, pretrained=True)
else:
model = UNet(num_classes=num_classes, input_channels=3)
if torch.cuda.is_available():
if args.device_ids:
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model, device_ids=device_ids).cuda()
if args.type == 'binary':
loss = LossBinary(jaccard_weight=args.jaccard_weight)
else:
loss = LossMulti(num_classes=num_classes,
jaccard_weight=args.jaccard_weight)
cudnn.benchmark = True
def make_loader(file_names,
shuffle=False,
transform=None,
problem_type='binary',
batch_size=1):
return DataLoader(dataset=CustomDataset(file_names,
transform=transform),
shuffle=shuffle,
num_workers=args.workers,
batch_size=batch_size,
pin_memory=torch.cuda.is_available())
train_file_names, val_file_names = get_split()
print('num train = {}, num_val = {}'.format(len(train_file_names),
len(val_file_names)))
def train_transform(p=1):
return Compose(
[
# Rescale(SIZE),
RandomCrop(SIZE),
RandomBrightness(0.2),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.15),
# OneOf([
# OpticalDistortion(p=0.3),
# GridDistortion(p=.1),
# IAAPiecewiseAffine(p=0.3),
# ], p=0.1),
# OneOf([
# IAASharpen(),
# IAAEmboss(),
# RandomContrast(),
# RandomBrightness(),
# ], p=0.15),
HueSaturationValue(p=0.15),
HorizontalFlip(p=0.5),
Normalize(p=1),
],
p=p)
def val_transform(p=1):
return Compose(
[
# Rescale(256),
RandomCrop(SIZE),
Normalize(p=1)
],
p=p)
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform(p=1),
problem_type=args.type,
batch_size=args.batch_size)
valid_loader = make_loader(val_file_names,
transform=val_transform(p=1),
problem_type=args.type,
batch_size=len(device_ids))
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
if args.type == 'binary':
valid = validation_binary
else:
valid = validation_multi
utils.train(init_optimizer=lambda lr: Adam(model.parameters(), lr=lr),
args=args,
model=model,
criterion=loss,
train_loader=train_loader,
valid_loader=valid_loader,
validation=valid,
fold=args.fold,
num_classes=num_classes)
def train(
epochs: int,
models_dir: Path,
x_cities: List[CityData],
y_city: List[CityData],
mask_dir: Path,
):
model = UNet11().cuda()
optimizer = Adam(model.parameters(), lr=3e-4)
scheduler = ReduceLROnPlateau(optimizer, patience=4, factor=0.25)
min_loss = sys.maxsize
criterion = nn.BCEWithLogitsLoss()
train_data = DataLoader(TrainDataset(x_cities, mask_dir),
batch_size=4,
num_workers=4,
shuffle=True)
test_data = DataLoader(TestDataset(y_city, mask_dir),
batch_size=6,
num_workers=4)
for epoch in range(epochs):
print(f'Epoch {epoch}, lr {optimizer.param_groups[0]["lr"]}')
print(f" Training")
losses = []
ious = []
jaccs = []
batch_iterator = enumerate(train_data)
model = model.train().cuda()
for i, (x, y) in tqdm(batch_iterator):
optimizer.zero_grad()
x = x.cuda()
y = y.cuda()
y_real = y.view(-1).float()
y_pred = model(x)
y_pred_probs = torch.sigmoid(y_pred).view(-1)
loss = 0.75 * criterion(y_pred.view(
-1), y_real) + 0.25 * dice_loss(y_pred_probs, y_real)
iou_ = iou(y_pred_probs.float(), y_real.byte())
jacc_ = jaccard(y_pred_probs.float(), y_real)
ious.append(iou_.item())
losses.append(loss.item())
jaccs.append(jacc_.item())
loss.backward()
optimizer.step()
print(
f"Loss: {np.mean(losses)}, IOU: {np.mean(ious)}, jacc: {np.mean(jaccs)}"
)
model = model.eval().cuda()
losses = []
ious = []
jaccs = []
with torch.no_grad():
batch_iterator = enumerate(test_data)
for i, (x, y) in tqdm(batch_iterator):
x = x.cuda()
y = y.cuda()
y_real = y.view(-1).float()
y_pred = model(x)
y_pred_probs = torch.sigmoid(y_pred).view(-1)
loss = 0.75 * criterion(y_pred.view(
-1), y_real) + 0.25 * dice_loss(y_pred_probs, y_real)
iou_ = iou(y_pred_probs.float(), y_real.byte())
jacc_ = jaccard(y_pred_probs.float(), y_real)
ious.append(iou_.item())
losses.append(loss.item())
jaccs.append(jacc_.item())
test_loss = np.mean(losses)
print(
f"Loss: {np.mean(losses)}, IOU: {np.mean(ious)}, jacc: {np.mean(jaccs)}"
)
scheduler.step(test_loss)
if test_loss < min_loss:
min_loss = test_loss
save_model(model, epoch, models_dir / y_city[0].name)
