def __init__(self, args, cuda=None, train_id="None", logger=None):
self.args = args
os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu
self.cuda = cuda and torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
self.train_id = train_id
self.logger = logger
self.current_MIoU = 0
self.best_MIou = 0
self.best_source_MIou = 0
self.current_epoch = 0
self.current_iter = 0
self.second_best_MIou = 0
# set TensorboardX
self.writer = SummaryWriter(self.args.checkpoint_dir)
# Metric definition
self.Eval = Eval(self.args.num_classes)
# loss definition
self.loss = nn.CrossEntropyLoss(weight=None, ignore_index= -1)
self.loss.to(self.device)
# model
self.model, params = get_model(self.args)
self.model = nn.DataParallel(self.model, device_ids=[0])
self.model.to(self.device)
if self.args.optim == "SGD":
self.optimizer = torch.optim.SGD(
params=params,
momentum=self.args.momentum,
weight_decay=self.args.weight_decay
)
elif self.args.optim == "Adam":
self.optimizer = torch.optim.Adam(params, betas=(0.9, 0.99), weight_decay=self.args.weight_decay)
# dataloader
if self.args.dataset=="cityscapes":
self.dataloader = City_DataLoader(self.args)
elif self.args.dataset=="gta5":
self.dataloader = GTA5_DataLoader(self.args)
else:
self.dataloader = SYNTHIA_DataLoader(self.args)
self.dataloader.num_iterations = min(self.dataloader.num_iterations, ITER_MAX)
print(self.args.iter_max, self.dataloader.num_iterations)
self.epoch_num = ceil(self.args.iter_max / self.dataloader.num_iterations) if self.args.iter_stop is None else \
ceil(self.args.iter_stop / self.dataloader.num_iterations)
def __init__(self, args, cuda=None, train_id=None, logger=None):
self.args = args
self.cuda = cuda and torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
self.current_MIoU = 0
self.best_MIou = 0
self.current_epoch = 0
self.current_iter = 0
self.train_id = train_id
self.logger = logger
# set TensorboardX
self.writer = SummaryWriter(self.args.checkpoint_dir)
# Metric definition
self.Eval = Eval(self.args.num_classes)
# loss definition
self.loss = nn.CrossEntropyLoss(ignore_index= -1)
self.loss.to(self.device)
# model
self.model, params = get_model(self.args)
self.model = nn.DataParallel(self.model, device_ids=[0])
self.model.to(self.device)
# load pretrained checkpoint
if self.args.pretrained_ckpt_file is not None:
path1 = os.path.join(*self.args.checkpoint_dir.split('/')[:-1], self.train_id + 'best.pth')
path2 = self.args.pretrained_ckpt_file
if os.path.exists(path1):
pretrained_ckpt_file = path1
elif os.path.exists(path2):
pretrained_ckpt_file = path2
else:
raise AssertionError("no pretrained_ckpt_file")
self.load_checkpoint(pretrained_ckpt_file)
# dataloader
self.dataloader = City_DataLoader(self.args) if self.args.dataset=="cityscapes" else GTA5_DataLoader(self.args)
self.dataloader.val_loader = self.dataloader.data_loader
self.dataloader.valid_iterations = min(self.dataloader.num_iterations, 500)
self.epoch_num = ceil(self.args.iter_max / self.dataloader.num_iterations)
def __init__(self, args, cuda=None, train_id=None, logger=None):
self.args = args
os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu
self.cuda = cuda and torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
self.current_MIoU = 0
self.best_MIou = 0
self.current_epoch = 0
self.current_iter = 0
self.train_id = train_id
self.logger = logger
# set TensorboardX
self.writer = SummaryWriter(self.args.checkpoint_dir)
# Metric definition
self.Eval = Eval(self.args.num_classes)
# loss definition
self.loss = nn.CrossEntropyLoss(ignore_index=-1)
self.loss.to(self.device)
# model
self.model, params = get_model(self.args)
self.model = nn.DataParallel(self.model, device_ids=[0])
self.model.to(self.device)
# load pretrained checkpoint
if self.args.pretrained_ckpt_file is not None:
path1 = os.path.join(*self.args.checkpoint_dir.split('/')[:-1],
self.train_id + 'best.pth')
path2 = self.args.pretrained_ckpt_file
if os.path.exists(path1):
pretrained_ckpt_file = path1
elif os.path.exists(path2):
pretrained_ckpt_file = path2
else:
raise AssertionError("no pretrained_ckpt_file")
self.load_checkpoint(pretrained_ckpt_file)
# dataloader
self.dataloader = City_DataLoader(
self.args
) if self.args.dataset == "cityscapes" else GTA5_DataLoader(self.args)
if self.args.city_name != "None":
target_data_set = CrossCity_Dataset(
self.args,
data_root_path=self.args.data_root_path,
list_path=self.args.list_path,
split='val',
base_size=self.args.target_base_size,
crop_size=self.args.target_crop_size,
class_13=self.args.class_13)
self.target_val_dataloader = data.DataLoader(
target_data_set,
batch_size=self.args.batch_size,
shuffle=False,
num_workers=self.args.data_loader_workers,
pin_memory=self.args.pin_memory,
drop_last=True)
self.dataloader.val_loader = self.target_val_dataloader
self.dataloader.valid_iterations = (
len(target_data_set) +
self.args.batch_size) // self.args.batch_size
else:
self.dataloader.val_loader = self.dataloader.data_loader
self.dataloader.valid_iterations = min(
self.dataloader.num_iterations, 500)
self.epoch_num = ceil(self.args.iter_max /
self.dataloader.num_iterations)
def __init__(self, args, cuda=None, train_id="None", logger=None):
self.args = args
self.cuda = cuda and torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
self.train_id = train_id
self.logger = logger
self.current_MIoU = 0
self.best_MIou = 0
self.best_source_MIou = 0
self.current_epoch = 0
self.current_iter = 0
self.second_best_MIou = 0
# set TensorboardX
self.writer = SummaryWriter(self.args.checkpoint_dir)
# Metric definition
self.Eval = Eval(self.args.num_classes)
# loss definition
self.loss = nn.CrossEntropyLoss(weight=None, ignore_index= -1)
self.loss.to(self.device)
# model
self.model, params = get_model(self.args)
self.model = nn.DataParallel(self.model)
self.model.to(self.device)
# support for FCN8s
if self.args.backbone == "fcn8s_vgg" and self.args.optim == "SGD":
self.args.optim = "Adam"
print('WARNING: FCN8s requires Adam optimizer, but SGD was set. Switching to Adam.')
if self.args.optim == "SGD":
self.optimizer = torch.optim.SGD(
params=params,
momentum=self.args.momentum,
weight_decay=self.args.weight_decay
)
elif self.args.optim == "Adam":
self.optimizer = torch.optim.Adam(params, betas=(0.9, 0.99), weight_decay=self.args.weight_decay)
# dataloader
if DEBUG: print('DEBUG: Loading training and validation datasets (for UDA only val one is used, but it is overwritten)')
if self.args.dataset=="cityscapes":
self.dataloader = City_DataLoader(self.args)
elif self.args.dataset=="gta5":
self.dataloader = GTA5_DataLoader(self.args)
else:
self.dataloader = SYNTHIA_DataLoader(self.args)
###
use_target_val = True
if use_target_val:
if DEBUG: print('DEBUG: Overwriting validation set, using target set instead of source one')
target_data_set = City_Dataset(args,
data_root_path=datasets_path['cityscapes']['data_root_path'],
list_path=datasets_path['cityscapes']['list_path'],
split='test',
base_size=args.target_base_size,
crop_size=args.target_crop_size,
class_16=args.class_16)
self.target_val_dataloader = data.DataLoader(target_data_set,
batch_size=self.args.batch_size,
shuffle=False,
num_workers=self.args.data_loader_workers,
pin_memory=self.args.pin_memory,
drop_last=True)
self.dataloader.val_loader = self.target_val_dataloader
###
self.dataloader.num_iterations = min(self.dataloader.num_iterations, ITER_MAX)
self.epoch_num = ceil(self.args.iter_max / self.dataloader.num_iterations) if self.args.iter_stop is None else \
ceil(self.args.iter_stop / self.dataloader.num_iterations)
def __init__(self, args, config, cuda=None):
self.args = args
os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu
self.config = config
self.cuda = cuda and torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
self.best_MIou = 0
self.current_epoch = 0
self.epoch_num = self.config.epoch_num
self.current_iter = 0
self.writer = SummaryWriter()
# Metric definition
self.Eval = Eval(self.config.num_classes)
# loss definition
if args.loss_weight:
classes_weights_path = os.path.join(
self.config.classes_weight,
self.args.dataset + 'classes_weights_log.npy')
print(classes_weights_path)
if not os.path.isfile(classes_weights_path):
logger.info('calculating class weights...')
calculate_weigths_labels(self.config)
class_weights = np.load(classes_weights_path)
pprint.pprint(class_weights)
weight = torch.from_numpy(class_weights.astype(np.float32))
logger.info('loading class weights successfully!')
else:
weight = None
self.loss = nn.CrossEntropyLoss(weight=weight, ignore_index=255)
self.loss.to(self.device)
# model
self.model = DeepLab(output_stride=self.args.output_stride,
class_num=self.config.num_classes,
pretrained=self.args.imagenet_pretrained,
bn_momentum=self.args.bn_momentum,
freeze_bn=self.args.freeze_bn)
self.model = nn.DataParallel(self.model, device_ids=range(4))
patch_replication_callback(self.model)
self.model.to(self.device)
self.optimizer = torch.optim.SGD(
params=[
{
"params": self.get_params(self.model.module, key="1x"),
"lr": self.args.lr,
},
{
"params": self.get_params(self.model.module, key="10x"),
"lr": 10 * self.args.lr,
},
],
momentum=self.config.momentum,
# dampening=self.config.dampening,
weight_decay=self.config.weight_decay,
# nesterov=self.config.nesterov
)
# dataloader
self.dataloader = City_DataLoader(self.args, self.config)
if self.args.validation == True:
self.val_data = City_DataLoader(self.args, 'val')