def build_dataloader(batch_size, dataset_dir, cfg):
if cfg.dataset == "VOC2012":
train_dataset = dataset.PascalVOC(dataset_dir,
cfg.data_type,
order=["image", "mask"])
elif cfg.dataset == "Cityscapes":
train_dataset = dataset.Cityscapes(dataset_dir,
"train",
mode='gtFine',
order=["image", "mask"])
else:
raise ValueError("Unsupported dataset {}".format(cfg.dataset))
train_sampler = Infinite(
RandomSampler(train_dataset, batch_size, drop_last=True))
train_dataloader = DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose(
transforms=[
T.RandomHorizontalFlip(0.5),
T.RandomResize(scale_range=(0.5, 2)),
T.RandomCrop(
output_size=(cfg.img_height, cfg.img_width),
padding_value=[0, 0, 0],
padding_maskvalue=255,
),
T.Normalize(mean=cfg.img_mean, std=cfg.img_std),
T.ToMode(),
],
order=["image", "mask"],
),
num_workers=2,
)
return train_dataloader
def build_dataloader(dataset_dir, cfg):
if cfg.dataset == "VOC2012":
val_dataset = EvalPascalVOC(
dataset_dir,
"val",
order=["image", "mask", "info"]
)
elif cfg.dataset == "Cityscapes":
val_dataset = dataset.Cityscapes(
dataset_dir,
"val",
mode="gtFine",
order=["image", "mask", "info"]
)
else:
raise ValueError("Unsupported dataset {}".format(cfg.dataset))
val_sampler = InferenceSampler(val_dataset, 1)
val_dataloader = DataLoader(
val_dataset,
sampler=val_sampler,
transform=T.Normalize(
mean=cfg.img_mean, std=cfg.img_std, order=["image", "mask"]
),
num_workers=2,
)
return val_dataloader
def build_dataloader(dataset_dir, cfg):
if cfg.DATASET == "VOC2012":
val_dataset = EvalPascalVOC(dataset_dir,
"val",
order=["image", "mask", "info"])
elif cfg.DATASET == "Cityscapes":
val_dataset = dataset.Cityscapes(dataset_dir,
"val",
mode='gtFine',
order=["image", "mask", "info"])
else:
raise ValueError("Unsupported dataset {}".format(cfg.DATASET))
val_sampler = data.SequentialSampler(val_dataset, cfg.VAL_BATCHES)
val_dataloader = data.DataLoader(
val_dataset,
sampler=val_sampler,
transform=T.Normalize(mean=cfg.IMG_MEAN,
std=cfg.IMG_STD,
order=["image", "mask"]),
num_workers=cfg.DATA_WORKERS,
)
return val_dataloader, val_dataset.__len__()
def build_dataloader(batch_size, dataset_dir, cfg):
if cfg.DATASET == "VOC2012":
train_dataset = dataset.PascalVOC(
dataset_dir,
cfg.DATA_TYPE,
order=["image", "mask"]
)
elif cfg.DATASET == "Cityscapes":
train_dataset = dataset.Cityscapes(
dataset_dir,
"train",
mode='gtFine',
order=["image", "mask"]
)
else:
raise ValueError("Unsupported dataset {}".format(cfg.DATASET))
train_sampler = data.RandomSampler(train_dataset, batch_size, drop_last=True)
train_dataloader = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose(
transforms=[
T.RandomHorizontalFlip(0.5),
T.RandomResize(scale_range=(0.5, 2)),
T.RandomCrop(
output_size=(cfg.IMG_HEIGHT, cfg.IMG_WIDTH),
padding_value=[0, 0, 0],
padding_maskvalue=255,
),
T.Normalize(mean=cfg.IMG_MEAN, std=cfg.IMG_STD),
T.ToMode(),
],
order=["image", "mask"],
),
num_workers=0,
)
return train_dataloader, train_dataset.__len__()