def main():
global args, best_prec1
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
torch.cuda.manual_seed(int(time.time()) % 1000)
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch.startswith('inception_v3'):
print('inception_v3 without aux_logits!')
image_size = 341
input_size = 299
model = models.__dict__[args.arch](aux_logits=True,
num_classes=1000,
pretrained=args.pretrained)
else:
image_size = 182
input_size = 160
student_model = models.__dict__[args.arch](
num_classes=args.num_classes,
pretrained=args.pretrained,
avgpool_size=input_size / 32)
student_model.cuda()
student_params = list(student_model.parameters())
student_optimizer = torch.optim.Adam(student_model.parameters(),
args.base_lr * 0.1)
args.save_path = "checkpoint/" + args.exp_name
if not osp.exists(args.save_path):
os.mkdir(args.save_path)
tb_logger = SummaryWriter(args.save_path)
logger = create_logger('global_logger', args.save_path + '/log.txt')
for key, val in vars(args).items():
logger.info("{:16} {}".format(key, val))
criterion = nn.CrossEntropyLoss()
print("Build network")
last_iter = -1
best_prec1 = 0
load_state(args.save_path + "/ckptmodel_best.pth.tar", student_model)
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
se_normalize = se_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
border_value = int(np.mean([0.485, 0.456, 0.406]) * 255 + 0.5)
test_aug = se_transforms.ImageAugmentation(True,
0,
rot_std=0.0,
scale_u_range=[0.75, 1.333],
affine_std=0,
scale_x_range=None,
scale_y_range=None)
val_dataset = NormalDataset(args.val_root,
"./data/visda/list/validation_list.txt",
transform=transforms.Compose([
se_transforms.ScaleAndCrop(
(input_size, input_size), args.padding,
False, np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]))
]),
is_train=False,
args=args)
val_loader = DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=args.workers)
val_multi_dataset = NormalDataset(
args.val_root,
"./data/visda/list/validation_list.txt",
transform=transforms.Compose([
se_transforms.ScaleCropAndAugmentAffineMultiple(
16, (input_size, input_size), args.padding, True, test_aug,
border_value, np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]))
]),
is_train=False,
args=args)
val_multi_loader = DataLoader(val_multi_dataset,
batch_size=1,
shuffle=False,
num_workers=args.workers)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
student_optimizer, args.lr_steps, args.lr_gamma)
#logger.info('{}'.format(args))
validate(val_loader, student_model, criterion)
validate_multi(val_multi_loader, student_model, criterion)
def main():
global args, best_prec1
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
torch.cuda.manual_seed(int(time.time()) % 1000)
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch.startswith('inception_v3'):
print('inception_v3 without aux_logits!')
image_size = 341
input_size = 299
model = models.__dict__[args.arch](aux_logits=True,
num_classes=1000,
pretrained=args.pretrained)
else:
image_size = 182
input_size = 160
student_model = models.__dict__[args.arch](
num_classes=args.num_classes,
pretrained=args.pretrained,
avgpool_size=input_size / 32)
student_model.cuda()
student_params = list(student_model.parameters())
args.save_path = "checkpoint/" + args.exp_name
if not osp.exists(args.save_path):
os.mkdir(args.save_path)
tb_logger = SummaryWriter(args.save_path)
logger = create_logger('global_logger', args.save_path + '/log.txt')
for key, val in vars(args).items():
logger.info("{:16} {}".format(key, val))
logger.info("filename {}".format(osp.basename(__file__)))
df_train = pd.read_csv(args.train_source_source, sep=" ", header=None)
weight = df_train[1].value_counts()
weight = weight.sort_index()
weight = len(df_train) / weight
weight = torch.from_numpy(weight.values).float().cuda()
weight = (weight - torch.min(weight)) / (torch.max(weight) -
torch.min(weight))
weight = (1 - torch.mean(weight)) + weight
# define loss function (criterion) and optimizer
if not args.use_weight:
weight = None
criterion = nn.CrossEntropyLoss(weight=weight)
ignored_params = list(map(id, student_model.classifier.parameters()))
base_params = filter(
lambda p: id(p) not in ignored_params and p.requires_grad == True,
student_model.parameters())
if args.pretrained == True:
student_optimizer = torch.optim.Adam(
[{
'params': base_params
}, {
'params': student_model.classifier.parameters(),
'lr': args.base_lr
}], args.base_lr * 0.1)
else:
student_optimizer = torch.optim.Adam(student_model.parameters(),
args.base_lr)
# optionally resume from a checkpoint
print("Build network")
last_iter = -1
best_prec1 = 0
if args.load_path:
print(args.load_path)
if args.resume_opt:
best_prec1, last_iter = load_state(args.load_path,
model,
optimizer=student_optimizer)
else:
load_state(args.load_path, model)
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
se_normalize = se_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
src_aug = se_transforms.ImageAugmentation(
args.src_hflip,
args.src_xlat_range,
args.src_affine_std,
rot_std=args.src_rot_std,
intens_scale_range_lower=args.src_intens_scale_range_lower,
intens_scale_range_upper=args.src_intens_scale_range_upper,
colour_rot_std=args.src_colour_rot_std,
colour_off_std=args.src_colour_off_std,
greyscale=args.src_greyscale,
scale_u_range=args.src_scale_u_range,
scale_x_range=(None, None),
scale_y_range=(None, None),
cutout_probability=args.src_cutout_prob,
cutout_size=args.src_cutout_size)
tgt_aug = se_transforms.ImageAugmentation(
args.tgt_hflip,
args.tgt_xlat_range,
args.tgt_affine_std,
rot_std=args.tgt_rot_std,
intens_scale_range_lower=args.tgt_intens_scale_range_lower,
intens_scale_range_upper=args.tgt_intens_scale_range_upper,
colour_rot_std=args.tgt_colour_rot_std,
colour_off_std=args.tgt_colour_off_std,
greyscale=args.tgt_greyscale,
scale_u_range=args.tgt_scale_u_range,
scale_x_range=[None, None],
scale_y_range=[None, None],
cutout_probability=args.tgt_cutout_prob,
cutout_size=args.tgt_cutout_size)
border_value = int(np.mean([0.485, 0.456, 0.406]) * 255 + 0.5)
test_aug = se_transforms.ImageAugmentation(
args.tgt_hflip,
args.tgt_xlat_range,
0.0,
rot_std=0.0,
scale_u_range=args.tgt_scale_u_range,
scale_x_range=[None, None],
scale_y_range=[None, None])
train_source_dataset = NormalDataset(
args.train_source_root,
args.train_source_source,
transform=transforms.Compose([
se_transforms.ScaleCropAndAugmentAffine(
(input_size, input_size), args.padding, True, src_aug,
border_value, np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]))
]),
args=args)
train_target_dataset = TeacherDataset(
args.train_target_root,
args.train_target_source,
transform=transforms.Compose([
se_transforms.ScaleCropAndAugmentAffinePair(
(input_size, input_size), args.padding, 0, True, tgt_aug,
border_value, np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]))
]),
args=args)
val_dataset = NormalDataset(args.val_root,
args.val_source,
transform=transforms.Compose([
se_transforms.ScaleAndCrop(
(input_size, input_size), args.padding,
False, np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]))
]),
is_train=False,
args=args)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
student_optimizer, args.lr_steps, args.lr_gamma)
#logger.info('{}'.format(args))
if args.evaluate:
validate(val_loader, student_model, criterion)
return
train(train_source_loader,
train_target_loader,
val_loader,
student_model,
criterion,
student_optimizer=student_optimizer,
lr_scheduler=lr_scheduler,
start_iter=last_iter + 1,
tb_logger=tb_logger)