def test_model(args):
# create model
model = dla.__dict__[args.arch](pretrained=args.pretrained,
pool_size=args.crop_size // 32)
model = torch.nn.DataParallel(model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {} prec {:.03f}) "
.format(args.resume, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
data = dataset.get_data(args.data_name)
if data is None:
data = dataset.load_dataset_info(args.data, data_name=args.data_name)
if data is None:
raise ValueError('{} is not pre-defined in dataset.py and info.json '
'does not exist in {}', args.data_name, args.data)
# Data loading code
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=data.mean, std=data.std)
if args.crop_10:
t = transforms.Compose([
transforms.Resize(args.scale_size),
transforms.ToTensor(),
normalize])
else:
t = transforms.Compose([
transforms.Resize(args.scale_size),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
normalize])
val_loader = torch.utils.data.DataLoader(
ImageFolder(valdir, t, out_name=args.crop_10),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss()
if args.cuda:
model = model.cuda()
criterion = criterion.cuda()
if args.crop_10:
validate_10(args, val_loader, model,
'{}_i_{}_c_10.txt'.format(args.arch, args.start_epoch))
else:
validate(args, val_loader, model, criterion)
def test_seg(args):
batch_size = args.batch_size
num_workers = args.workers
phase = args.phase
for k, v in args.__dict__.items():
print(k, ':', v)
single_model = dla_up.__dict__.get(args.arch)(args.classes,
down_ratio=args.down)
model = torch.nn.DataParallel(single_model).cuda()
data_dir = args.data_dir
info = dataset.load_dataset_info(data_dir)
normalize = transforms.Normalize(mean=info.mean, std=info.std)
# scales = [0.5, 0.75, 1.25, 1.5, 1.75]
scales = [0.5, 0.75, 1.25, 1.5]
t = []
if args.crop_size > 0:
t.append(transforms.PadToSize(args.crop_size))
t.extend([transforms.ToTensor(), normalize])
if args.ms:
data = SegListMS(data_dir, phase, transforms.Compose(t), scales)
else:
data = SegList(data_dir,
phase,
transforms.Compose(t),
out_name=True,
out_size=True,
binary=args.classes == 2)
test_loader = torch.utils.data.DataLoader(data,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=False)
cudnn.benchmark = True
# optionally resume from a checkpoint
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
out_dir = '{}_{:03d}_{}'.format(args.arch, start_epoch, phase)
if len(args.test_suffix) > 0:
out_dir += '_' + args.test_suffix
if args.ms:
out_dir += '_ms'
if args.ms:
mAP = test_ms(test_loader,
model,
args.classes,
save_vis=True,
has_gt=phase != 'test' or args.with_gt,
output_dir=out_dir,
scales=scales)
else:
mAP = test(test_loader,
model,
args.classes,
save_vis=True,
has_gt=phase != 'test' or args.with_gt,
output_dir=out_dir)
print('mAP: ', mAP)
def train_seg(args):
batch_size = args.batch_size
num_workers = args.workers
crop_size = args.crop_size
print(' '.join(sys.argv))
for k, v in args.__dict__.items():
print(k, ':', v)
pretrained_base = args.pretrained_base
single_model = dla_up.__dict__.get(args.arch)(args.classes,
pretrained_base,
down_ratio=args.down)
model = torch.nn.DataParallel(single_model).cuda()
if args.edge_weight > 0:
weight = torch.from_numpy(
np.array([1, args.edge_weight], dtype=np.float32))
criterion = nn.NLLLoss2d(ignore_index=255, weight=weight)
else:
criterion = nn.NLLLoss2d(ignore_index=255)
criterion.cuda()
data_dir = args.data_dir
info = dataset.load_dataset_info(data_dir)
normalize = transforms.Normalize(mean=info.mean, std=info.std)
t = []
if args.random_rotate > 0:
t.append(transforms.RandomRotate(args.random_rotate))
if args.random_scale > 0:
t.append(transforms.RandomScale(args.random_scale))
t.append(transforms.RandomCrop(crop_size))
if args.random_color:
t.append(transforms.RandomJitter(0.4, 0.4, 0.4))
t.extend(
[transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize])
train_loader = torch.utils.data.DataLoader(SegList(
data_dir, 'train', transforms.Compose(t), binary=(args.classes == 2)),
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(
SegList(
data_dir,
'val',
transforms.Compose([
transforms.RandomCrop(crop_size),
# transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
binary=(args.classes == 2)),
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
optimizer = torch.optim.SGD(single_model.optim_parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
cudnn.benchmark = True
best_prec1 = 0
start_epoch = 0
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.evaluate:
validate(val_loader, model, criterion, eval_score=accuracy)
return
for epoch in range(start_epoch, args.epochs):
lr = adjust_learning_rate(args, optimizer, epoch)
print('Epoch: [{0}]\tlr {1:.06f}'.format(epoch, lr))
# train for one epoch
train(train_loader,
model,
criterion,
optimizer,
epoch,
eval_score=accuracy)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion, eval_score=accuracy)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
checkpoint_path = 'checkpoint_latest.pth.tar'
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=checkpoint_path)
if (epoch + 1) % args.save_freq == 0:
history_path = 'checkpoint_{:03d}.pth.tar'.format(epoch + 1)
shutil.copyfile(checkpoint_path, history_path)
def train_net(net, paras):
# parameters
img_dir = paras.image_dir
anno_path = paras.anno_path
checkpoint_dir = paras.model_save_dir
val_percent = 0.1
epochs = paras.epochs
batch_size = paras.batch_size
lr = paras.learning_rate
num_workers = 2
# torch model saver
saver = ModelSaver(max_save_num=5)
# load dataset info
dataset = load_dataset_info(img_dir, anno_path)
train_set_info, valid_set_info = split_dataset_info(dataset, val_percent)
# build dataloader
building_trainset = Building_Dataset(train_set_info)
building_validset = Building_Dataset(valid_set_info)
train_dataloader = torch.utils.data.DataLoader(building_trainset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
valid_dataloader = torch.utils.data.DataLoader(building_validset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
# optimizer
optimizer = optim.SGD(net.parameters(),
lr=lr,
momentum=0.9,
weight_decay=0.0005)
# loss function
#criterion = nn.L1Loss(reduce=True, size_average=True)
criterion = nn.BCELoss()
train_num = len(building_trainset)
valid_num = len(building_validset)
print('''
Starting training:
Total Epochs: {}
Batch size: {}
Learning rate: {}
Training size: {}
Validation size: {}
Checkpoints save dir: {}
'''.format(epochs, batch_size, lr, train_num, valid_num, checkpoint_dir))
# ------------------------
# start training...
# ------------------------
best_valid_loss = 1000
for epoch in range(1, epochs + 1):
print('Starting epoch {}/{}.'.format(epoch, epochs))
# training
net.train()
epoch_loss = 0
for idx, data in enumerate(train_dataloader):
imgs, true_masks = data
imgs = imgs.cuda()
true_masks = true_masks.cuda()
pred_masks = net(imgs)
# compute loss
loss = criterion(pred_masks, true_masks)
epoch_loss += loss.item()
if idx % 10 == 0:
print(f'{idx}/{len(train_dataloader)}, loss: {loss.item()}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss = epoch_loss / len(train_dataloader)
print('Epoch finished ! Loss: {}\n'.format(epoch_loss))
# validation
net.eval()
valid_loss = 0
with torch.no_grad():
for idx, data in enumerate(valid_dataloader):
if idx % 10 == 0:
print(idx, '/', len(valid_dataloader))
imgs, true_masks = data
imgs = imgs.cuda()
true_masks = true_masks.cuda()
# inference
pred_masks = net(imgs)
# compute loss
loss = criterion(pred_masks, true_masks)
valid_loss += loss.item()
valid_loss = valid_loss / len(valid_dataloader)
print('Validation finished ! Loss:{} Best Loss before:{}\n'.format(
valid_loss, best_valid_loss))
# save check_point
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
print('New best model find, Checkpoint {} saving...'.format(epoch))
model_save_path = os.path.join(
checkpoint_dir, '{}_CP{}.pth'.format(best_valid_loss, epoch))
#torch.save(net.state_dict(), model_save_path)
saver.save_new_model(net, model_save_path)
def run_training(args):
model = dla.__dict__[args.arch](
pretrained=args.pretrained, num_classes=args.classes,
pool_size=args.crop_size // 32)
model = torch.nn.DataParallel(model)
best_prec1 = 0
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
data = dataset.get_data(args.data_name)
if data is None:
data = dataset.load_dataset_info(args.data, data_name=args.data_name)
if data is None:
raise ValueError('{} is not pre-defined in dataset.py and info.json '
'does not exist in {}', args.data_name, args.data)
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = data_transforms.Normalize(mean=data.mean, std=data.std)
tt = [data_transforms.RandomResizedCrop(
args.crop_size, min_area_ratio=args.min_area_ratio,
aspect_ratio=args.aspect_ratio)]
if data.eigval is not None and data.eigvec is not None \
and args.random_color:
ligiting = data_transforms.Lighting(0.1, data.eigval, data.eigvec)
jitter = data_transforms.RandomJitter(0.4, 0.4, 0.4)
tt.extend([jitter, ligiting])
tt.extend([data_transforms.RandomHorizontalFlip(),
data_transforms.ToTensor(),
normalize])
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, data_transforms.Compose(tt)),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(args.scale_size),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
normalize
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.cuda:
model = model.cuda()
criterion = criterion.cuda()
if args.evaluate:
validate(args, val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(args, optimizer, epoch)
# train for one epoch
train(args, train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(args, val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
checkpoint_path = 'checkpoint_latest.pth.tar'
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best, filename=checkpoint_path)
if (epoch + 1) % args.check_freq == 0:
history_path = 'checkpoint_{:03d}.pth.tar'.format(epoch + 1)
shutil.copyfile(checkpoint_path, history_path)
