def get_dataset_loaders(model, dataset, workers):
target_size = (model["common"]["image_size"],) * 2
batch_size = model["common"]["batch_size"]
path = dataset["common"]["dataset"]
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
transform = JointCompose(
[
JointTransform(ConvertImageMode("RGB"), ConvertImageMode("P")),
JointTransform(Resize(target_size, Image.BILINEAR), Resize(target_size, Image.NEAREST)),
JointTransform(CenterCrop(target_size), CenterCrop(target_size)),
JointRandomHorizontalFlip(0.5),
JointRandomRotation(0.5, 90),
JointRandomRotation(0.5, 90),
JointRandomRotation(0.5, 90),
JointTransform(ImageToTensor(), MaskToTensor()),
JointTransform(Normalize(mean=mean, std=std), None),
]
)
train_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, "training", "images")], os.path.join(path, "training", "labels"), transform
)
val_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, "validation", "images")], os.path.join(path, "validation", "labels"), transform
)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=workers)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, drop_last=True, num_workers=workers)
return train_loader, val_loader
def test_len(self):
path = "tests/fixtures"
target = "tests/fixtures/labels/"
channels = [{"sub": "images", "bands": [1, 2, 3]}]
transform = JointCompose(
[JointTransform(ImageToTensor(), MaskToTensor())])
dataset = SlippyMapTilesConcatenation(path, channels, target,
transform)
self.assertEqual(len(dataset), 3)
def test_getitem(self):
path = "tests/fixtures"
target = "tests/fixtures/labels/"
channels = [{"sub": "images", "bands": [1, 2, 3]}]
transform = JointCompose(
[JointTransform(ImageToTensor(), MaskToTensor())])
dataset = SlippyMapTilesConcatenation(path, channels, target,
transform)
images, mask, tiles = dataset[0]
self.assertEqual(tiles, mercantile.Tile(69105, 105093, 18))
self.assertEqual(type(images), torch.Tensor)
self.assertEqual(type(mask), torch.Tensor)
def get_dataset_loaders(path, config, workers):
# Values computed on ImageNet DataSet
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
transform = JointCompose([
JointResize(config["model"]["image_size"]),
JointRandomFlipOrRotate(config["model"]["data_augmentation"]),
JointTransform(ImageToTensor(), MaskToTensor()),
JointTransform(Normalize(mean=mean, std=std), None),
])
train_dataset = SlippyMapTilesConcatenation(
os.path.join(path, "training"),
config["channels"],
os.path.join(path, "training", "labels"),
joint_transform=transform,
)
val_dataset = SlippyMapTilesConcatenation(
os.path.join(path, "validation"),
config["channels"],
os.path.join(path, "validation", "labels"),
joint_transform=transform,
)
batch_size = config["model"]["batch_size"]
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=workers)
val_loader = DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=True,
num_workers=workers)
return train_loader, val_loader