def main():
parser = argparse.ArgumentParser(description='Semantic Segmentation')
parser.add_argument('--train_cfg',
type=str,
default='./configs/train_config.yaml',
help='train config path')
args = parser.parse_args()
config_folder = Path(args.train_cfg.strip("/"))
config = load_yaml(config_folder)
init_seed(config['SEED'])
df, train_ids, valid_ids = split_dataset(config['DATA_TRAIN'])
train_dataset = getattribute(config=config,
name_package='TRAIN_DATASET',
df=df,
img_ids=train_ids)
valid_dataset = getattribute(config=config,
name_package='VALID_DATASET',
df=df,
img_ids=valid_ids)
train_dataloader = getattribute(config=config,
name_package='TRAIN_DATALOADER',
dataset=train_dataset)
valid_dataloader = getattribute(config=config,
name_package='VALID_DATALOADER',
dataset=valid_dataset)
model = getattribute(config=config, name_package='MODEL')
criterion = getattribute(config=config, name_package='CRITERION')
optimizer = getattribute(config=config,
name_package='OPTIMIZER',
params=model.parameters())
scheduler = getattribute(config=config,
name_package='SCHEDULER',
optimizer=optimizer)
device = config['DEVICE']
metric_ftns = [accuracy_dice_score]
num_epoch = config['NUM_EPOCH']
gradient_clipping = config['GRADIENT_CLIPPING']
gradient_accumulation_steps = config['GRADIENT_ACCUMULATION_STEPS']
early_stopping = config['EARLY_STOPPING']
validation_frequency = config['VALIDATION_FREQUENCY']
saved_period = config['SAVED_PERIOD']
checkpoint_dir = Path(config['CHECKPOINT_DIR'], type(model).__name__)
checkpoint_dir.mkdir(exist_ok=True, parents=True)
resume_path = config['RESUME_PATH']
learning = Learning(model=model,
optimizer=optimizer,
criterion=criterion,
device=device,
metric_ftns=metric_ftns,
num_epoch=num_epoch,
scheduler=scheduler,
grad_clipping=gradient_clipping,
grad_accumulation_steps=gradient_accumulation_steps,
early_stopping=early_stopping,
validation_frequency=validation_frequency,
save_period=saved_period,
checkpoint_dir=checkpoint_dir,
resume_path=resume_path)
learning.train(tqdm(train_dataloader), tqdm(valid_dataloader))
def main():
parser = argparse.ArgumentParser(description='Semantic Segmentation')
parser.add_argument('--train_cfg',
type=str,
default='./configs/train.yaml',
help='train config path')
args = parser.parse_args()
config_folder = Path(args.train_cfg.strip("/"))
config = load_yaml(config_folder)
init_seed(config['SEED'])
image_datasets = {
x: vinDataset(root_dir=config['ROOT_DIR'],
file_name=config['FILE_NAME'],
num_triplet=config['NUM_TRIPLET'],
phase=x)
for x in ['train', 'valid']
}
dataloaders = {
x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=config['BATCH_SIZE'],
shuffle=True,
num_workers=4,
pin_memory=True)
for x in ['train', 'valid']
}
model = getattribute(config=config, name_package='MODEL')
criterion = getattribute(config=config, name_package='CRITERION')
metric_ftns = [accuracy_score]
optimizer = getattribute(config=config,
name_package='OPTIMIZER',
params=model.parameters())
scheduler = getattribute(config=config,
name_package='SCHEDULER',
optimizer=optimizer)
device = config['DEVICE']
num_epoch = config['NUM_EPOCH']
gradient_clipping = config['GRADIENT_CLIPPING']
gradient_accumulation_steps = config['GRADIENT_ACCUMULATION_STEPS']
early_stopping = config['EARLY_STOPPING']
validation_frequency = config['VALIDATION_FREQUENCY']
saved_period = config['SAVED_PERIOD']
checkpoint_dir = Path(config['CHECKPOINT_DIR'], type(model).__name__)
checkpoint_dir.mkdir(exist_ok=True, parents=True)
resume_path = config['RESUME_PATH']
learning = Learning(model=model,
criterion=criterion,
metric_ftns=metric_ftns,
optimizer=optimizer,
device=device,
num_epoch=num_epoch,
scheduler=scheduler,
grad_clipping=gradient_clipping,
grad_accumulation_steps=gradient_accumulation_steps,
early_stopping=early_stopping,
validation_frequency=validation_frequency,
save_period=saved_period,
checkpoint_dir=checkpoint_dir,
resume_path=resume_path)
learning.train(tqdm(dataloaders['train']), tqdm(dataloaders['valid']))
def main():
parser = argparse.ArgumentParser(description='Pytorch parser')
parser.add_argument('--train_cfg',
type=str,
default='./configs/efficientdet-d0.yaml',
help='train config path')
parser.add_argument('-d',
'--device',
default=None,
type=str,
help='indices of GPUs to enable (default: all)')
parser.add_argument('-r',
'--resume',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
options = [
CustomArgs(['-lr', '--learning_rate'],
type=float,
target='OPTIMIZER,ARGS,lr'),
CustomArgs(
['-bs', '--batch_size'],
type=int,
target=
'TRAIN_DATALOADER,ARGS,batch_size;VALID_DATALOADER,ARGS,batch_size'
)
]
config = config_parser(parser, options)
init_seed(config['SEED'])
train_dataset = VOCDetection(root=VOC_ROOT,
transform=SSDAugmentation(
voc['min_dim'], MEANS))
train_dataloader = getattribute(config=config,
name_package='TRAIN_DATALOADER',
dataset=train_dataset,
collate_fn=detection_collate)
# valid_dataloader = getattribute(config = config, name_package = 'VALID_DATALOADER', dataset = valid_dataset)
model = getattribute(config=config, name_package='MODEL')
criterion = getattribute(config=config, name_package='CRITERION')
optimizer = getattribute(config=config,
name_package='OPTIMIZER',
params=model.parameters())
scheduler = getattribute(config=config,
name_package='SCHEDULER',
optimizer=optimizer)
device = config['DEVICE']
metric_ftns = []
num_epoch = config['NUM_EPOCH']
gradient_clipping = config['GRADIENT_CLIPPING']
gradient_accumulation_steps = config['GRADIENT_ACCUMULATION_STEPS']
early_stopping = config['EARLY_STOPPING']
validation_frequency = config['VALIDATION_FREQUENCY']
tensorboard = config['TENSORBOARD']
checkpoint_dir = Path(config['CHECKPOINT_DIR'], type(model).__name__)
checkpoint_dir.mkdir(exist_ok=True, parents=True)
resume_path = config['RESUME_PATH']
learning = Learning(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
metric_ftns=metric_ftns,
device=device,
num_epoch=num_epoch,
grad_clipping=gradient_clipping,
grad_accumulation_steps=gradient_accumulation_steps,
early_stopping=early_stopping,
validation_frequency=validation_frequency,
tensorboard=tensorboard,
checkpoint_dir=checkpoint_dir,
resume_path=resume_path)
learning.train(tqdm(train_dataloader))