def __init__(self,
system_config: configuration.SystemConfig = configuration.
SystemConfig(),
dataset_config: configuration.DatasetConfig = configuration.
DatasetConfig(),
dataloader_config: configuration.
DataloaderConfig = configuration.DataloaderConfig(),
optimizer_config: configuration.
OptimizerConfig = configuration.OptimizerConfig()):
self.loader_train, self.loader_test = get_data(
batch_size=dataloader_config.batch_size,
num_workers=dataloader_config.num_workers,
data_root=dataset_config.root_dir)
setup_system(system_config)
self.model = LeNet5()
self.loss_fn = nn.CrossEntropyLoss()
self.metric_fn = AccuracyEstimator(topk=(1, ))
self.optimizer = optim.SGD(self.model.parameters(),
lr=optimizer_config.learning_rate,
weight_decay=optimizer_config.weight_decay,
momentum=optimizer_config.momentum)
self.lr_scheduler = MultiStepLR(
self.optimizer,
milestones=optimizer_config.lr_step_milestones,
gamma=optimizer_config.lr_gamma)
self.visualizer = TensorBoardVisualizer()
def run(self, trainer_config: configuration.TrainerConfig):
setup_system(self.system_config)
device = torch.device(trainer_config.device)
self.model = self.model.to(device)
self.loss_fn = self.loss_fn.to(device)
model_trainer = Trainer(
model=self.model,
loader_train=self.loader_train,
loader_test=self.loader_test,
loss_fn=self.loss_fn,
metric_fn=self.metric_fn,
optimizer=self.optimizer,
lr_scheduler=self.lr_scheduler,
device=device,
data_getter=itemgetter("image"),
target_getter=itemgetter("target"),
stage_progress=trainer_config.progress_bar,
get_key_metric=itemgetter("mAP"),
visualizer=self.visualizer,
model_save_best=trainer_config.model_save_best,
model_saving_frequency=trainer_config.model_saving_frequency,
save_dir=trainer_config.model_dir
)
model_trainer.register_hook("train", hooks.train_hook_detection)
model_trainer.register_hook("test", hooks.test_hook_detection)
model_trainer.register_hook("end_epoch", hooks.end_epoch_hook_detection)
self.metrics = model_trainer.fit(trainer_config.epoch_num)
return self.metrics
def __init__(
self,
system_config: configuration.SystemConfig = configuration.SystemConfig(),
dataset_config: configuration.DatasetConfig = configuration.DatasetConfig(), # pylint: disable=redefined-outer-name
dataloader_config: configuration.DataloaderConfig = configuration.DataloaderConfig(), # pylint: disable=redefined-outer-name
optimizer_config: configuration.OptimizerConfig = configuration.OptimizerConfig(), # pylint: disable=redefined-outer-name
):
self.system_config = system_config
setup_system(system_config)
self.dataset_train = ListDataset(
root_dir=dataset_config.root_dir,
list_file='../train_anno.txt',
classes=["__background__", "person"],
mode='train',
transform=Compose(dataset_config.train_transforms),
input_size=300
)
self.loader_train = DataLoader(
dataset=self.dataset_train,
batch_size=dataloader_config.batch_size,
shuffle=True,
collate_fn=self.dataset_train.collate_fn,
num_workers=dataloader_config.num_workers,
pin_memory=True
)
self.dataset_test = ListDataset(
root_dir=dataset_config.root_dir,
list_file='../test_anno.txt',
classes=["__background__", "person"],
mode='val',
transform=Compose([Normalize(), ToTensorV2()]),
input_size=300
)
self.loader_test = DataLoader(
dataset=self.dataset_test,
batch_size=dataloader_config.batch_size,
shuffle=False,
collate_fn=self.dataset_test.collate_fn,
num_workers=dataloader_config.num_workers,
pin_memory=True
)
self.model = Detector(len(self.dataset_train.classes))
self.loss_fn = DetectionLoss(len(self.dataset_train.classes))
self.metric_fn = APEstimator(classes=self.dataset_test.classes)
self.optimizer = optim.SGD(
self.model.parameters(),
lr=optimizer_config.learning_rate,
weight_decay=optimizer_config.weight_decay,
momentum=optimizer_config.momentum
)
self.lr_scheduler = MultiStepLR(
self.optimizer, milestones=optimizer_config.lr_step_milestones, gamma=optimizer_config.lr_gamma
)
self.visualizer = MatplotlibVisualizer()
def __init__(self,
system_config: configuration.SystemConfig = configuration.
SystemConfig(),
dataset_config: configuration.DatasetConfig = configuration.
DatasetConfig(),
dataloader_config: configuration.
DataloaderConfig = configuration.DataloaderConfig(),
optimizer_config: configuration.
OptimizerConfig = configuration.OptimizerConfig()):
# train dataloader
train_dataset = KenyanFood13Dataset(
dataset_config.root_dir,
flag=0,
split=dataset_config.split,
transform=dataset_config.train_transforms,
random_state=system_config.seed)
class_weight = train_dataset.get_class_weight()
self.loader_train = torch.utils.data.DataLoader(
train_dataset,
batch_size=dataloader_config.batch_size,
shuffle=True,
num_workers=dataloader_config.num_workers)
# validation dataloader
val_dataset = KenyanFood13Dataset(
dataset_config.root_dir,
flag=1,
split=dataset_config.split,
transform=dataset_config.test_transforms,
random_state=system_config.seed)
self.loader_test = torch.utils.data.DataLoader(
val_dataset,
batch_size=dataloader_config.batch_size,
shuffle=False,
num_workers=dataloader_config.num_workers)
setup_system(system_config)
self.model = pretrained_resnext50(pretrained=True, fine_tune_start=4)
self.loss_fn = nn.CrossEntropyLoss(
weight=torch.FloatTensor(class_weight))
self.metric_fn = AccuracyEstimator(topk=(1, ))
#self.optimizer = optim.SGD(
# self.model.parameters(),
# lr=optimizer_config.learning_rate,
# weight_decay=optimizer_config.weight_decay,
# momentum=optimizer_config.momentum
#)
#self.lr_scheduler = MultiStepLR(
# self.optimizer, milestones=optimizer_config.lr_step_milestones, gamma=optimizer_config.lr_gamma
#)
self.optimizer = optim.Adam(self.model.parameters())
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer)
self.visualizer = TensorBoardVisualizer()
def __init__(self,
system_config: configuration.SystemConfig = configuration.
SystemConfig(),
dataset_config: configuration.DatasetConfig = configuration.
DatasetConfig(),
dataloader_config: configuration.
DataloaderConfig = configuration.DataloaderConfig(),
optimizer_config: configuration.
OptimizerConfig = configuration.OptimizerConfig()):
# train dataloader
train_dataset = KenyanFood13Dataset(
dataset_config.root_dir,
flag=0,
split=1.0,
transform=dataset_config.train_transforms,
random_state=system_config.seed)
class_weight = train_dataset.get_class_weight()
self.loader_train = torch.utils.data.DataLoader(
train_dataset,
batch_size=dataloader_config.batch_size,
shuffle=True,
num_workers=dataloader_config.num_workers)
setup_system(system_config)
self.model = pretrained_resnext50(pretrained=True, fine_tune_start=4)
self.model.load_state_dict(torch.load('test/model_39_0.917'))
self.loss_fn = nn.CrossEntropyLoss(
weight=torch.FloatTensor(class_weight))
self.metric_fn = AccuracyEstimator(topk=(1, ))
#self.optimizer = optim.SGD(
# self.model.parameters(),
# lr=optimizer_config.learning_rate,
# weight_decay=optimizer_config.weight_decay,
# momentum=optimizer_config.momentum
#)
#self.lr_scheduler = MultiStepLR(
# self.optimizer, milestones=optimizer_config.lr_step_milestones, gamma=optimizer_config.lr_gamma
#)
self.optimizer = optim.AdamW(self.model.parameters())
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode='min',
factor=0.1,
patience=10,
threshold=0.0001,
threshold_mode='rel',
cooldown=0,
min_lr=0,
eps=1e-08,
verbose=False)
self.visualizer = TensorBoardVisualizer()