def main(args):
dataset = load_config(args.dataset)
path = dataset["common"]["dataset"]
num_classes = len(dataset["common"]["classes"])
train_transform = Compose([ConvertImageMode(mode="P"), MaskToTensor()])
train_dataset = SlippyMapTiles(os.path.join(path, "training", "labels"), transform=train_transform)
n = 0
counts = np.zeros(num_classes, dtype=np.int64)
loader = DataLoader(train_dataset, batch_size=1)
for images, tile in tqdm(loader, desc="Loading", unit="image", ascii=True):
image = torch.squeeze(images)
image = np.array(image, dtype=np.uint8)
n += image.shape[0] * image.shape[1]
counts += np.bincount(image.ravel(), minlength=num_classes)
# Class weighting scheme `w = 1 / ln(c + p)` see:
# - https://arxiv.org/abs/1707.03718
# LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
# - https://arxiv.org/abs/1606.02147
# ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
probs = counts / n
weights = 1 / np.log(1.02 + probs)
weights.round(6, out=weights)
print(weights.tolist())
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):
inputs = ["tests/fixtures/images/"]
input_transforms = [ToTensor()]
target = "tests/fixtures/labels/"
target_transform = MaskToTensor()
dataset = SlippyMapTilesConcatenation(inputs, input_transforms, target, target_transform)
self.assertEqual(len(dataset), 3)
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):
inputs = ["tests/fixtures/images/"]
input_transforms = [ToTensor()]
target = "tests/fixtures/labels/"
target_transform = MaskToTensor()
dataset = SlippyMapTilesConcatenation(inputs, input_transforms, target, target_transform)
images, mask, tiles = dataset[0]
self.assertEqual(tiles[0], mercantile.Tile(69105, 105093, 18))
self.assertEqual(type(images), torch.Tensor)
self.assertEqual(type(mask), torch.Tensor)
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(model, dataset):
target_size = (model["common"]["image_size"], ) * 2
batch_size = model["common"]["batch_size"]
path = dataset["common"]["dataset"]
mean, std = dataset["stats"]["mean"], dataset["stats"]["std"]
image_transform = Compose([
ConvertImageMode("RGB"),
Resize(target_size, Image.BILINEAR),
CenterCrop(target_size),
ImageToTensor(),
Normalize(mean=mean, std=std),
])
target_transform = Compose([
ConvertImageMode("P"),
Resize(target_size, Image.NEAREST),
CenterCrop(target_size),
MaskToTensor()
])
train_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, "training", "images")],
[image_transform],
os.path.join(path, "training", "labels"),
target_transform,
)
val_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, "validation", "images")],
[image_transform],
os.path.join(path, "validation", "labels"),
target_transform,
)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, drop_last=True)
return train_loader, val_loader
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
def get_dataset_loaders(model, dataset):
target_size = (model['common']['image_size'], ) * 2
batch_size = model['common']['batch_size']
path = dataset['common']['dataset']
mean, std = dataset['stats']['mean'], dataset['stats']['std']
image_transform = Compose([
ConvertImageMode('RGB'),
Resize(target_size, Image.BILINEAR),
CenterCrop(target_size),
ImageToTensor(),
Normalize(mean=mean, std=std)])
target_transform = Compose([
ConvertImageMode('P'),
Resize(target_size, Image.NEAREST),
CenterCrop(target_size),
MaskToTensor()])
train_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, 'training', 'images')],
[image_transform],
os.path.join(path, 'training', 'labels'),
target_transform)
val_dataset = SlippyMapTilesConcatenation(
[os.path.join(path, 'validation', 'images')],
[image_transform],
os.path.join(path, 'validation', 'labels'),
target_transform)
train_sampler = RandomSubsetSampler(train_dataset, dataset['samples']['training'])
val_sampler = RandomSubsetSampler(val_dataset, dataset['samples']['validation'])
train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, sampler=val_sampler, drop_last=True)
return train_loader, val_loader
