def main():
print(CP_R + "e-Lab Segmentation Training Script" + CP_C)
#################################################################
# Initialization step
torch.manual_seed(args.seed)
torch.set_default_tensor_type('torch.FloatTensor')
#################################################################
# Acquire dataset loader object
# Normalization factor based on ResNet stats
prep_data = transforms.Compose([
#transforms.RandomCrop(900),
transforms.Resize(args.img_size, 0),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
prep_target = transforms.Compose([
#transforms.RandomCrop(900),
transforms.Resize(args.img_size, 0),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
if args.dataset == 'cs':
import data.segmented_data as segmented_data
print("{}Cityscapes dataset in use{}!!!".format(CP_G, CP_C))
else:
print("{}Invalid data-loader{}".format(CP_R, CP_C))
# Training data loader
data_obj_train = segmented_data.SegmentedData(root=args.datapath,
mode='train',
transform=prep_data,
target_transform=prep_target)
data_loader_train = DataLoader(data_obj_train,
batch_size=args.bs,
shuffle=True,
num_workers=args.workers)
data_len_train = len(data_obj_train)
# Testing data loader
data_obj_test = segmented_data.SegmentedData(root=args.datapath,
mode='test',
transform=prep_data,
target_transform=prep_target)
data_loader_test = DataLoader(data_obj_test,
batch_size=args.bs,
shuffle=True,
num_workers=args.workers)
data_len_test = len(data_obj_test)
#################################################################
# Load model
print('{}{:=<80}{}'.format(CP_R, '', CP_C))
print('{}Models will be saved in: {}{}'.format(CP_Y, CP_C, str(args.save)))
if not os.path.exists(str(args.save)):
os.mkdir(str(args.save))
if args.saveAll:
if not os.path.exists(str(args.save) + '/all'):
os.mkdir(str(args.save) + '/all')
epoch = 0
if args.resume:
# Load previous model state
checkpoint = torch.load(args.save + '/model_resume.pt')
epoch = checkpoint['epoch']
model = checkpoint['model_def']
model.load_state_dict(checkpoint['state_dict'])
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.wd)
optimizer.load_stat_dict(checkpoint['optim_state'])
print('{}Loaded model from previous checkpoint epoch # {}()'.format(
CP_G, CP_C, epoch))
else:
# Load fresh model definition
if args.model == 'linknet':
# Save model definiton script
call(["cp", "./models/linknet.py", args.save])
from models.linknet import LinkNet
model = LinkNet(len(data_obj_train.class_name()))
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.wd)
# Criterion
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
model.cuda()
criterion = nn.NLLLoss2d()
# Save arguements used for training
args_log = open(args.save + '/args.log', 'w')
args_log.write(str(args))
args_log.close()
error_log = list()
prev_error = 1000
train = Train(model, data_loader_train, optimizer, criterion, args.lr,
args.wd, args.visdom)
test = Test(model, data_loader_test, criterion, args.visdom)
while epoch <= args.maxepoch:
train_error = train.forward()
test_error = test.forward()
print('{}{:-<80}{}'.format(CP_R, '', CP_C))
print('{}Epoch #: {}{:03}'.format(CP_B, CP_C, epoch))
print('{}Training Error: {}{:.6f} | {}Testing Error: {}{:.6f}'.format(
CP_B, CP_C, train_error, CP_B, CP_C, test_error))
error_log.append((train_error, test_error))
# Save weights and model definition
prev_error = save_model(
{
'epoch': epoch,
'model_def': LinkNet,
'state_dict': model.state_dict(),
'optim_state': optimizer.state_dict(),
}, test_error, prev_error, args.save, args.saveAll)
logger = open(args.save + '/error.log', 'w')
logger.write('{:10} {:10}'.format('Train Error', 'Test Error'))
logger.write('\n{:-<20}'.format(''))
for total_error in error_log:
logger.write('\n{:.6f} {:.6f}'.format(total_error[0], total_error[1]))
logger.close()
def main():
print(CP_R + "e-Lab Segmentation Training Script" + CP_C)
#################################################################
# Initialization step
torch.manual_seed(args.seed)
cudnn.benchmark = True
torch.set_default_tensor_type('torch.FloatTensor')
#################################################################
# Acquire dataset loader object
# Normalization factor based on ResNet stats
prep_data = transforms.Compose([
#transforms.Crop((512, 512)),
transforms.Resize((1024, 2048)),
transforms.ToTensor(),
transforms.Normalize([[0.406, 0.456, 0.485], [0.225, 0.224, 0.229]])
])
prep_target = transforms.Compose([
#transforms.Crop((512, 512)),
transforms.Resize((512, 1024)),
transforms.ToTensor(basic=True),
])
if args.dataset == 'cs':
import data.segmented_data as segmented_data
print ("{}Cityscapes dataset in use{}!!!".format(CP_G, CP_C))
else:
print ("{}Invalid data-loader{}".format(CP_R, CP_C))
# Training data loader
data_obj_train = segmented_data.SegmentedData(root=args.datapath, mode='train',
transform=prep_data, target_transform=prep_target)
data_loader_train = DataLoader(data_obj_train, batch_size=args.bs, shuffle=True,
num_workers=args.workers, pin_memory=True)
data_len_train = len(data_obj_train)
# Testing data loader
data_obj_test = segmented_data.SegmentedData(root=args.datapath, mode='val',
transform=prep_data, target_transform=prep_target)
data_loader_test = DataLoader(data_obj_test, batch_size=args.bs, shuffle=False,
num_workers=args.workers, pin_memory=True)
data_len_test = len(data_obj_test)
class_names = data_obj_train.class_name()
n_classes = len(class_names)
#################################################################
# Load model
epoch = 0
prev_iou = 0.0001
# Load fresh model definition
print('{}{:=<80}{}'.format(CP_R, '', CP_C))
print('{}Models will be saved in: {}{}'.format(CP_Y, CP_C, str(args.save)))
if not os.path.exists(str(args.save)):
os.mkdir(str(args.save))
if args.saveAll:
if not os.path.exists(str(args.save)+'/all'):
os.mkdir(str(args.save)+'/all')
if args.model == 'linknet':
# Save model definiton script
call(["cp", "./models/linknet.py", args.save])
from models.linknet import LinkNet
from torchvision.models import resnet18
model = LinkNet(n_classes)
# Copy weights of resnet18 into encoder
pretrained_model = resnet18(pretrained=True)
for i, j in zip(model.modules(), pretrained_model.modules()):
if not list(i.children()):
if not isinstance(i, nn.Linear) and len(i.state_dict()) > 0:
i.weight.data = j.weight.data
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), args.lr)#,
#momentum=args.momentum, weight_decay=args.wd)
if args.resume:
# Load previous model state
checkpoint = torch.load(args.save + '/model_resume.pth')
epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optim_state'])
prev_iou = checkpoint['min_error']
print('{}Loaded model from previous checkpoint epoch # {}({})'.format(CP_G, CP_C, epoch))
# Criterion
print("Model initialized for training...")
hist_path = os.path.join(args.save, 'hist')
if os.path.isfile(hist_path + '.npy'):
hist = np.load(hist_path + '.npy')
print('{}Loaded cached dataset stats{}!!!'.format(CP_Y, CP_C))
else:
# Get class weights based on training data
hist = np.zeros((n_classes), dtype=np.float)
for batch_idx, (x, yt) in enumerate(data_loader_train):
h, bins = np.histogram(yt.numpy(), list(range(n_classes + 1)))
hist += h
hist = hist/(max(hist)) # Normalize histogram
print('{}Saving dataset stats{}...'.format(CP_Y, CP_C))
np.save(hist_path, hist)
criterion_weight = 1/np.log(1.02 + hist)
criterion_weight[0] = 0
criterion = nn.NLLLoss(Variable(torch.from_numpy(criterion_weight).float().cuda()))
print('{}Using weighted criterion{}!!!'.format(CP_Y, CP_C))
#criterion = cross_entropy2d
# Save arguements used for training
args_log = open(args.save + '/args.log', 'w')
for k in args.__dict__:
args_log.write(k + ' : ' + str(args.__dict__[k]) + '\n')
args_log.close()
# Setup Metrics
metrics = ConfusionMatrix(n_classes, class_names)
train = Train(model, data_loader_train, optimizer, criterion, args.lr, args.wd, args.bs, args.visdom)
test = Test(model, data_loader_test, criterion, metrics, args.bs, args.visdom)
# Save error values in log file
logger = open(args.save + '/error.log', 'w')
logger.write('{:10} {:10}'.format('Train Error', 'Test Error'))
logger.write('\n{:-<20}'.format(''))
while epoch <= args.maxepoch:
train_error = 0
print('{}{:-<80}{}'.format(CP_R, '', CP_C))
print('{}Epoch #: {}{:03}'.format(CP_B, CP_C, epoch))
train_error = train.forward()
test_error, accuracy, avg_accuracy, iou, miou, conf_mat= test.forward()
logger.write('\n{:.6f} {:.6f} {:.6f}'.format(train_error, test_error, miou))
print('{}Training Error: {}{:.6f} | {}Testing Error: {}{:.6f} |{}Mean IoU: {}{:.6f}'.format(
CP_B, CP_C, train_error, CP_B, CP_C, test_error, CP_G, CP_C, miou))
# Save weights and model definition
prev_iou = save_model({
'epoch': epoch,
'model_def': model,
'state_dict': model.state_dict(),
'optim_state': optimizer.state_dict(),
'min_error': prev_iou
}, class_names, conf_mat, miou, prev_iou, avg_accuracy, iou, args.save, args.saveAll)
epoch += 1
logger.close()