def _build_trainer(config_file: Dict):
train = DataLoader(
PairedDataset.from_config(config_file["train"]),
config_file["batch_size"],
shuffle=True,
drop_last=False,
)
val = DataLoader(
PairedDataset.from_config(config_file["val"]),
config_file["batch_size"],
shuffle=True,
drop_last=False,
)
trainer = Trainer(config_file, train=train, val=val)
trainer.train()
return trainer
train_transform = get_transforms(config["train"]["transform"])
val_transform = get_transforms(config["val"]["transform"])
train_ds = PascalCRFSegmentationDataset(
config["train"]["path"],
transform=train_transform,
image_set="train",
masks_folder=config["train"]["masks"],
scale_factor=config["crf"]["scale_factor"],
)
val_ds = PascalCRFSegmentationDataset(
config["val"]["path"],
transform=val_transform,
image_set="validation",
masks_folder=config["val"]["masks"],
scale_factor=config["crf"]["scale_factor"],
)
train_dl = torch.utils.data.DataLoader(train_ds,
batch_size=config["batch_size"],
shuffle=True,
num_workers=12)
val_dl = torch.utils.data.DataLoader(val_ds,
batch_size=config["batch_size"],
shuffle=True,
num_workers=12)
trainer = Trainer(config, train_dl, val_dl)
trainer.train()