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darts_segmentation.SMPSegmenter

An actor that keeps a model as its state and segments tiles.

Initialize the segmenter.

Parameters:

  • model_checkpoint (pathlib.Path) –

    The path to the model checkpoint.

  • device (torch.device, default: darts_segmentation.segment.DEFAULT_DEVICE ) –

    The device to run the model on. Defaults to torch.device("cuda") if cuda is available, else torch.device("cpu").

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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def __init__(self, model_checkpoint: Path | str, device: torch.device = DEFAULT_DEVICE):
    """Initialize the segmenter.

    Args:
        model_checkpoint (Path): The path to the model checkpoint.
        device (torch.device): The device to run the model on.
            Defaults to torch.device("cuda") if cuda is available, else torch.device("cpu").

    """
    model_checkpoint = model_checkpoint if isinstance(model_checkpoint, Path) else Path(model_checkpoint)
    self.device = device
    ckpt = torch.load(model_checkpoint, map_location=self.device)
    self.config = validate_config(ckpt["config"])
    # Overwrite the encoder weights with None, because we load our own
    self.config["model"] |= {"encoder_weights": None}
    self.model = smp.create_model(**self.config["model"])
    self.model.to(self.device)
    self.model.load_state_dict(ckpt["statedict"])
    self.model.eval()

    logger.debug(
        f"Successfully loaded model from {model_checkpoint.resolve()} with inputs: "
        f"{self.config['input_combination']}"
    )

config instance-attribute 

config: darts_segmentation.segment.SMPSegmenterConfig = (
    darts_segmentation.segment.validate_config(
        ckpt["config"]
    )
)

device instance-attribute 

device: torch.device = (
    darts_segmentation.segment.SMPSegmenter(device)
)

model instance-attribute 

model: torch.nn.Module = (
    segmentation_models_pytorch.create_model(
        **darts_segmentation.segment.SMPSegmenter(
            self
        ).config["model"]
    )
)

__call__ 

__call__(
    input: xarray.Dataset | list[xarray.Dataset],
    patch_size: int = 1024,
    overlap: int = 16,
    batch_size: int = 8,
    reflection: int = 0,
) -> xarray.Dataset | list[xarray.Dataset]

Run inference on a single tile or a list of tiles.

Parameters:

  • input (xarray.Dataset | list[xarray.Dataset]) –

    A single tile or a list of tiles.

  • patch_size (int, default: 1024 ) –

    The size of the patches. Defaults to 1024.

  • overlap (int, default: 16 ) –

    The size of the overlap. Defaults to 16.

  • batch_size (int, default: 8 ) –

    The batch size for the prediction, NOT the batch_size of input tiles. Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.

  • reflection (int, default: 0 ) –

    Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

Returns:

Raises:

  • ValueError –

    in case the input is not an xr.Dataset or a list of xr.Dataset

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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def __call__(
    self,
    input: xr.Dataset | list[xr.Dataset],
    patch_size: int = 1024,
    overlap: int = 16,
    batch_size: int = 8,
    reflection: int = 0,
) -> xr.Dataset | list[xr.Dataset]:
    """Run inference on a single tile or a list of tiles.

    Args:
        input (xr.Dataset | list[xr.Dataset]): A single tile or a list of tiles.
        patch_size (int): The size of the patches. Defaults to 1024.
        overlap (int): The size of the overlap. Defaults to 16.
        batch_size (int): The batch size for the prediction, NOT the batch_size of input tiles.
            Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.
        reflection (int): Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

    Returns:
        A single tile or a list of tiles augmented by a predicted `probabilities` layer, depending on the input.
        Each `probability` has type float32 and range [0, 1].

    Raises:
        ValueError: in case the input is not an xr.Dataset or a list of xr.Dataset

    """
    if isinstance(input, xr.Dataset):
        return self.segment_tile(
            input, patch_size=patch_size, overlap=overlap, batch_size=batch_size, reflection=reflection
        )
    elif isinstance(input, list):
        return self.segment_tile_batched(
            input, patch_size=patch_size, overlap=overlap, batch_size=batch_size, reflection=reflection
        )
    else:
        raise ValueError(f"Expected xr.Dataset or list of xr.Dataset, got {type(input)}")

segment_tile 

segment_tile(
    tile: xarray.Dataset,
    patch_size: int = 1024,
    overlap: int = 16,
    batch_size: int = 8,
    reflection: int = 0,
) -> xarray.Dataset

Run inference on a tile.

Parameters:

  • tile (xarray.Dataset) –

    The input tile, containing preprocessed, harmonized data.

  • patch_size (int, default: 1024 ) –

    The size of the patches. Defaults to 1024.

  • overlap (int, default: 16 ) –

    The size of the overlap. Defaults to 16.

  • batch_size (int, default: 8 ) –

    The batch size for the prediction, NOT the batch_size of input tiles. Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.

  • reflection (int, default: 0 ) –

    Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

Returns:

  • xarray.Dataset –

    Input tile augmented by a predicted probabilities layer with type float32 and range [0, 1].

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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@stopuhr.funkuhr(
    "Segmenting tile",
    logger.debug,
    print_kwargs=["patch_size", "overlap", "batch_size", "reflection"],
)
def segment_tile(
    self, tile: xr.Dataset, patch_size: int = 1024, overlap: int = 16, batch_size: int = 8, reflection: int = 0
) -> xr.Dataset:
    """Run inference on a tile.

    Args:
        tile: The input tile, containing preprocessed, harmonized data.
        patch_size (int): The size of the patches. Defaults to 1024.
        overlap (int): The size of the overlap. Defaults to 16.
        batch_size (int): The batch size for the prediction, NOT the batch_size of input tiles.
            Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.
        reflection (int): Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

    Returns:
        Input tile augmented by a predicted `probabilities` layer with type float32 and range [0, 1].

    """
    # Convert the tile to a tensor
    tensor_tile = self.tile2tensor(tile)

    # Create a batch dimension, because predict expects it
    tensor_tile = tensor_tile.unsqueeze(0)

    probabilities = predict_in_patches(
        self.model, tensor_tile, patch_size, overlap, batch_size, reflection, self.device
    ).squeeze(0)

    # Highly sophisticated DL-based predictor
    # TODO: is there a better way to pass metadata?
    tile["probabilities"] = tile["red"].copy(data=probabilities.cpu().numpy())
    tile["probabilities"].attrs = {"long_name": "Probabilities"}
    tile["probabilities"] = tile["probabilities"].fillna(float("nan")).rio.write_nodata(float("nan"))

    # Cleanup cuda memory
    del tensor_tile, probabilities
    free_torch()

    return tile

segment_tile_batched 

segment_tile_batched(
    tiles: list[xarray.Dataset],
    patch_size: int = 1024,
    overlap: int = 16,
    batch_size: int = 8,
    reflection: int = 0,
) -> list[xarray.Dataset]

Run inference on a list of tiles.

Parameters:

  • tiles (list[xarray.Dataset]) –

    The input tiles, containing preprocessed, harmonized data.

  • patch_size (int, default: 1024 ) –

    The size of the patches. Defaults to 1024.

  • overlap (int, default: 16 ) –

    The size of the overlap. Defaults to 16.

  • batch_size (int, default: 8 ) –

    The batch size for the prediction, NOT the batch_size of input tiles. Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.

  • reflection (int, default: 0 ) –

    Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

Returns:

  • list[xarray.Dataset] –

    A list of input tiles augmented by a predicted probabilities layer with type float32 and range [0, 1].

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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@stopuhr.funkuhr(
    "Segmenting tiles",
    logger.debug,
    print_kwargs=["patch_size", "overlap", "batch_size", "reflection"],
)
def segment_tile_batched(
    self,
    tiles: list[xr.Dataset],
    patch_size: int = 1024,
    overlap: int = 16,
    batch_size: int = 8,
    reflection: int = 0,
) -> list[xr.Dataset]:
    """Run inference on a list of tiles.

    Args:
        tiles: The input tiles, containing preprocessed, harmonized data.
        patch_size (int): The size of the patches. Defaults to 1024.
        overlap (int): The size of the overlap. Defaults to 16.
        batch_size (int): The batch size for the prediction, NOT the batch_size of input tiles.
            Tensor will be sliced into patches and these again will be infered in batches. Defaults to 8.
        reflection (int): Reflection-Padding which will be applied to the edges of the tensor. Defaults to 0.

    Returns:
        A list of input tiles augmented by a predicted `probabilities` layer with type float32 and range [0, 1].

    """
    # Convert the tiles to tensors
    # TODO: maybe create a batched tile2tensor function?
    # tensor_tiles = [self.tile2tensor(tile).to(self.dev) for tile in tiles]
    tensor_tiles = self.tile2tensor_batched(tiles)

    # Create a batch dimension, because predict expects it
    tensor_tiles = torch.stack(tensor_tiles, dim=0)

    probabilities = predict_in_patches(
        self.model, tensor_tiles, patch_size, overlap, batch_size, reflection, self.device
    )

    # Highly sophisticated DL-based predictor
    for tile, probs in zip(tiles, probabilities):
        # TODO: is there a better way to pass metadata?
        tile["probabilities"] = tile["red"].copy(data=probs.cpu().numpy())
        tile["probabilities"].attrs = {"long_name": "Probabilities"}
        tile["probabilities"] = tile["probabilities"].fillna(float("nan")).rio.write_nodata(float("nan"))

    # Cleanup cuda memory
    del tensor_tiles, probabilities
    free_torch()

    return tiles

tile2tensor 

tile2tensor(tile: xarray.Dataset) -> torch.Tensor

Take a tile and convert it to a pytorch tensor.

Respects the input combination from the config.

Returns:

  • torch.Tensor –

    A torch tensor for the full tile consisting of the bands specified in self.band_combination.

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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def tile2tensor(self, tile: xr.Dataset) -> torch.Tensor:
    """Take a tile and convert it to a pytorch tensor.

    Respects the input combination from the config.

    Returns:
        A torch tensor for the full tile consisting of the bands specified in `self.band_combination`.

    """
    bands = []
    # e.g. input_combination: ["red", "green", "blue", "relative_elevation", ...]
    # tile.data_vars: ["red", "green", "blue", "relative_elevation", ...]

    for feature_name in self.config["input_combination"]:
        norm = self.config["norm_factors"][feature_name]
        band_data = tile[feature_name]
        # Normalize the band data
        band_data = band_data * norm
        bands.append(torch.from_numpy(band_data.to_numpy().astype("float32")))

    return torch.stack(bands, dim=0)

tile2tensor_batched 

tile2tensor_batched(
    tiles: list[xarray.Dataset],
) -> torch.Tensor

Take a list of tiles and convert them to a pytorch tensor.

Respects the the input combination from the config.

Returns:

  • torch.Tensor –

    A torch tensor for the full tile consisting of the bands specified in self.band_combination.

Source code in darts-segmentation/src/darts_segmentation/segment.py 
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def tile2tensor_batched(self, tiles: list[xr.Dataset]) -> torch.Tensor:
    """Take a list of tiles and convert them to a pytorch tensor.

    Respects the the input combination from the config.

    Returns:
        A torch tensor for the full tile consisting of the bands specified in `self.band_combination`.

    """
    bands = []
    for feature_name in self.config["input_combination"]:
        norm = self.config["norm_factors"][feature_name]
        for tile in tiles:
            band_data = tile[feature_name]
            # Normalize the band data
            band_data = band_data * norm
            bands.append(torch.from_numpy(band_data.to_numpy().astype("float32")))
    # TODO: Test this
    return torch.stack(bands, dim=0).reshape(len(tiles), len(self.config["input_combination"]), *bands[0].shape)