chore: Abandon of option to get circular patches since it was never u…

…sed.

.github/workflows/cicd.yaml

@@ -61,7 +61,7 @@ jobs:
         myria3d
         python run.py
         --config-path /inputs/
-        --config-name proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.4.0
+        --config-name proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0
         predict.ckpt_path=/inputs/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt
         predict.src_las=/inputs/792000_6272000_subset_buildings.las
         predict.output_dir=/outputs/

CHANGELOG.md

@@ -1,5 +1,8 @@
 # main
 
+### 3.5.0
+- Abandon of option to get circular patches since it was never used.
+
 ### 3.4.9
 - Support edge-case where source LAZ has no valid subtile (i.e. pre_filter=False for all candidate subtiles) during hdf5 creation
 

configs/datamodule/hdf5_datamodule.yaml

@@ -19,7 +19,6 @@ pre_filter:
 
 tile_width: 1000
 subtile_width: 50
-subtile_shape: "square"  # "square" or "disk"
 subtile_overlap_train: 0
 subtile_overlap_predict: "${predict.subtile_overlap}"
 

docs/source/apidoc/default_config.yml

@@ -119,7 +119,6 @@ datamodule:
     min_num_nodes: 50
   tile_width: 1000
   subtile_width: 50
-  subtile_shape: square
   subtile_overlap_train: 0
   subtile_overlap_predict: ${predict.subtile_overlap}
   batch_size: 2

myria3d/pctl/datamodule/hdf5.py

@@ -11,7 +11,6 @@
 from myria3d.pctl.dataset.hdf5 import HDF5Dataset
 from myria3d.pctl.dataset.iterable import InferenceDataset
 from myria3d.pctl.dataset.utils import (
-    SHAPE_TYPE,
     get_las_paths_by_split_dict,
     pre_filter_below_n_points,
 )
@@ -34,13 +33,13 @@ def __init__(
         pre_filter: Optional[Callable[[Data], bool]] = pre_filter_below_n_points,
         tile_width: Number = 1000,
         subtile_width: Number = 50,
-        subtile_shape: SHAPE_TYPE = "square",
         subtile_overlap_train: Number = 0,
         subtile_overlap_predict: Number = 0,
         batch_size: int = 12,
         num_workers: int = 1,
         prefetch_factor: int = 2,
         transforms: Optional[Dict[str, TRANSFORMS_LIST]] = None,
+        **kwargs
     ):
         self.split_csv_path = split_csv_path
         self.data_dir = data_dir
@@ -53,7 +52,6 @@ def __init__(
 
         self.tile_width = tile_width
         self.subtile_width = subtile_width
-        self.subtile_shape = subtile_shape
         self.subtile_overlap_train = subtile_overlap_train
         self.subtile_overlap_predict = subtile_overlap_predict
 
@@ -62,32 +60,50 @@ def __init__(
         self.prefetch_factor = prefetch_factor
 
         t = transforms
-        self.preparation_train_transform: TRANSFORMS_LIST = t.get("preparations_train_list", [])
-        self.preparation_eval_transform: TRANSFORMS_LIST = t.get("preparations_eval_list", [])
-        self.preparation_predict_transform: TRANSFORMS_LIST = t.get("preparations_predict_list", [])
+        self.preparation_train_transform: TRANSFORMS_LIST = t.get(
+            "preparations_train_list", []
+        )
+        self.preparation_eval_transform: TRANSFORMS_LIST = t.get(
+            "preparations_eval_list", []
+        )
+        self.preparation_predict_transform: TRANSFORMS_LIST = t.get(
+            "preparations_predict_list", []
+        )
         self.augmentation_transform: TRANSFORMS_LIST = t.get("augmentations_list", [])
         self.normalization_transform: TRANSFORMS_LIST = t.get("normalizations_list", [])
 
     @property
     def train_transform(self) -> CustomCompose:
-        return CustomCompose(self.preparation_train_transform + self.normalization_transform + self.augmentation_transform)
+        return CustomCompose(
+            self.preparation_train_transform
+            + self.normalization_transform
+            + self.augmentation_transform
+        )
 
     @property
     def eval_transform(self) -> CustomCompose:
-        return CustomCompose(self.preparation_eval_transform + self.normalization_transform)
+        return CustomCompose(
+            self.preparation_eval_transform + self.normalization_transform
+        )
 
     @property
     def predict_transform(self) -> CustomCompose:
-        return CustomCompose(self.preparation_predict_transform + self.normalization_transform)
+        return CustomCompose(
+            self.preparation_predict_transform + self.normalization_transform
+        )
 
     def prepare_data(self, stage: Optional[str] = None):
         """Prepare dataset containing train, val, test data."""
 
         if stage in ["fit", "test"] or stage is None:
             if self.split_csv_path and self.data_dir:
-                las_paths_by_split_dict = get_las_paths_by_split_dict(self.data_dir, self.split_csv_path)
+                las_paths_by_split_dict = get_las_paths_by_split_dict(
+                    self.data_dir, self.split_csv_path
+                )
             else:
-                log.warning("cfg.data_dir and cfg.split_csv_path are both null. Precomputed HDF5 dataset is used.")
+                log.warning(
+                    "cfg.data_dir and cfg.split_csv_path are both null. Precomputed HDF5 dataset is used."
+                )
                 las_paths_by_split_dict = None
         # Create the dataset in prepare_data, so that it is done one a single GPU.
         self.las_paths_by_split_dict = las_paths_by_split_dict
@@ -124,7 +140,6 @@ def dataset(self) -> HDF5Dataset:
             tile_width=self.tile_width,
             subtile_width=self.subtile_width,
             subtile_overlap_train=self.subtile_overlap_train,
-            subtile_shape=self.subtile_shape,
             pre_filter=self.pre_filter,
             train_transform=self.train_transform,
             eval_transform=self.eval_transform,
@@ -164,7 +179,6 @@ def _set_predict_data(self, las_file_to_predict):
             transform=self.predict_transform,
             tile_width=self.tile_width,
             subtile_width=self.subtile_width,
-            subtile_shape=self.subtile_shape,
             subtile_overlap=self.subtile_overlap_predict,
         )
 

myria3d/pctl/dataset/hdf5.py

@@ -34,7 +34,6 @@ def __init__(
         tile_width: Number = 1000,
         subtile_width: Number = 50,
         subtile_overlap_train: Number = 0,
-        subtile_shape: SHAPE_TYPE = "square",
         pre_filter=pre_filter_below_n_points,
         train_transform: List[Callable] = None,
         eval_transform: List[Callable] = None,
@@ -48,7 +47,6 @@ def __init__(
             points_pre_transform (Callable): Function to turn pdal points into a pyg Data object.
             tile_width (Number, optional): width of a LAS tile. Defaults to 1000.
             subtile_width (Number, optional): effective width of a subtile (i.e. receptive field). Defaults to 50.
-            subtile_shape (SHAPE_TYPE, optional): Shape of subtile could be either "square" or "disk". Defaults to "square".
             subtile_overlap_train (Number, optional): Overlap for data augmentation of train set. Defaults to 0.
             pre_filter (_type_, optional): Function to filter out specific subtiles. Defaults to None.
             train_transform (List[Callable], optional): Transforms to apply to a sample for training. Defaults to None.
@@ -64,7 +62,6 @@ def __init__(
         self.tile_width = tile_width
         self.subtile_width = subtile_width
         self.subtile_overlap_train = subtile_overlap_train
-        self.subtile_shape = subtile_shape
 
         self.hdf5_file_path = hdf5_file_path
 
@@ -83,7 +80,6 @@ def __init__(
             hdf5_file_path,
             tile_width,
             subtile_width,
-            subtile_shape,
             pre_filter,
             subtile_overlap_train,
             points_pre_transform,
@@ -198,7 +194,6 @@ def create_hdf5(
     hdf5_file_path: str,
     tile_width: Number = 1000,
     subtile_width: Number = 50,
-    subtile_shape: SHAPE_TYPE = "square",
     pre_filter: Optional[Callable[[Data], bool]] = pre_filter_below_n_points,
     subtile_overlap_train: Number = 0,
     points_pre_transform: Callable = lidar_hd_pre_transform,
@@ -215,7 +210,6 @@ def create_hdf5(
         hdf5_file_path (str): path to HDF5 dataset,
         tile_width (Number, optional): width of a LAS tile. 1000 by default,
         subtile_width: (Number, optional): effective width of a subtile (i.e. receptive field). 50 by default,
-        subtile_shape (SHAPE_TYPE, optional): Shape of subtile could be either "square" or "disk". "square" by default ,
         pre_filter: Function to filter out specific subtiles. "pre_filter_below_n_points" by default,
         subtile_overlap_train (Number, optional): Overlap for data augmentation of train set. 0 by default,
         points_pre_transform (Callable): Function to turn pdal points into a pyg Data object.
@@ -246,7 +240,6 @@ def create_hdf5(
                         las_path,
                         tile_width,
                         subtile_width,
-                        subtile_shape,
                         subtile_overlap,
                     )
                 ):

myria3d/pctl/dataset/iterable.py

@@ -7,7 +7,6 @@
 from torch_geometric.data import Data
 
 from myria3d.pctl.dataset.utils import (
-    SHAPE_TYPE,
     pre_filter_below_n_points,
     split_cloud_into_samples,
 )
@@ -26,7 +25,6 @@ def __init__(
         tile_width: Number = 1000,
         subtile_width: Number = 50,
         subtile_overlap: Number = 0,
-        subtile_shape: SHAPE_TYPE = "square",
     ):
         self.las_file = las_file
 
@@ -36,7 +34,6 @@ def __init__(
 
         self.tile_width = tile_width
         self.subtile_width = subtile_width
-        self.subtile_shape = subtile_shape
         self.subtile_overlap = subtile_overlap
 
     def __iter__(self):
@@ -48,7 +45,6 @@ def get_iterator(self):
             self.las_file,
             self.tile_width,
             self.subtile_width,
-            self.subtile_shape,
             self.subtile_overlap,
         ):
             sample_data = self.points_pre_transform(sample_points)

myria3d/pctl/dataset/utils.py

@@ -13,7 +13,6 @@
 from shapely.geometry import Point
 
 SPLIT_TYPE = Union[Literal["train"], Literal["val"], Literal["test"]]
-SHAPE_TYPE = Union[Literal["disk"], Literal["square"]]
 LAS_PATHS_BY_SPLIT_DICT_TYPE = Dict[SPLIT_TYPE, List[str]]
 
 # commons
@@ -31,7 +30,9 @@ def find_file_in_dir(data_dir: str, basename: str) -> str:
     return files[0]
 
 
-def get_mosaic_of_centers(tile_width: Number, subtile_width: Number, subtile_overlap: Number = 0):
+def get_mosaic_of_centers(
+    tile_width: Number, subtile_width: Number, subtile_overlap: Number = 0
+):
     if subtile_overlap < 0:
         raise ValueError("datamodule.subtile_overlap must be positive.")
 
@@ -61,7 +62,9 @@ def pdal_read_las_array(las_path: str):
 def pdal_read_las_array_as_float32(las_path: str):
     """Read LAS as a a named array, casted to floats."""
     arr = pdal_read_las_array(las_path)
-    all_floats = np.dtype({"names": arr.dtype.names, "formats": ["f4"] * len(arr.dtype.names)})
+    all_floats = np.dtype(
+        {"names": arr.dtype.names, "formats": ["f4"] * len(arr.dtype.names)}
+    )
     return arr.astype(all_floats)
 
 
@@ -105,34 +108,31 @@ def split_cloud_into_samples(
     las_path: str,
     tile_width: Number,
     subtile_width: Number,
-    shape: SHAPE_TYPE,
     subtile_overlap: Number = 0,
 ):
     """Split LAS point cloud into samples.
 
     Args:
         las_path (str): path to raw LAS file
         tile_width (Number): width of input LAS file
-        subtile_width (Number): width of receptive field ; may be increased for coverage in case of disk shape.
-        shape: "disk" or "square"
+        subtile_width (Number): width of receptive field.
         subtile_overlap (Number, optional): overlap between adjacent tiles. Defaults to 0.
 
     Yields:
         _type_: idx_in_original_cloud, and points of sample in pdal input format casted as floats.
 
     """
     points = pdal_read_las_array_as_float32(las_path)
-    pos = np.asarray([points["X"], points["Y"], points["Z"]], dtype=np.float32).transpose()
+    pos = np.asarray(
+        [points["X"], points["Y"], points["Z"]], dtype=np.float32
+    ).transpose()
     kd_tree = cKDTree(pos[:, :2] - pos[:, :2].min(axis=0))
-    XYs = get_mosaic_of_centers(tile_width, subtile_width, subtile_overlap=subtile_overlap)
+    XYs = get_mosaic_of_centers(
+        tile_width, subtile_width, subtile_overlap=subtile_overlap
+    )
     for center in XYs:
         radius = subtile_width // 2  # Square receptive field.
         minkowski_p = np.inf
-        if shape == "disk":
-            # Disk receptive field.
-            # Adapt radius to have complete coverage of the data, with a slight overlap between samples.
-            minkowski_p = 2
-            radius = radius * math.sqrt(2)
         sample_idx = np.array(kd_tree.query_ball_point(center, r=radius, p=minkowski_p))
         if not len(sample_idx):
             # no points in this receptive fields
@@ -170,7 +170,9 @@ def get_las_paths_by_split_dict(
     for phase in ["train", "val", "test"]:
         basenames = split_df[split_df.split == phase].basename.tolist()
         # Reminder: an explicit data structure with ./val, ./train, ./test subfolder is required.
-        las_paths_by_split_dict[phase] = [str(Path(data_dir) / phase / b) for b in basenames]
+        las_paths_by_split_dict[phase] = [
+            str(Path(data_dir) / phase / b) for b in basenames
+        ]
 
     if not las_paths_by_split_dict:
         raise FileNotFoundError(

package_metadata.yaml

@@ -1,4 +1,4 @@
-__version__: "3.4.9"
+__version__: "3.5.0"
 __name__: "myria3d"
 __url__: "https://github.com/IGNF/myria3d"
 __description__: "Deep Learning for the Semantic Segmentation of Aerial Lidar Point Clouds"

run.py

@@ -22,7 +22,7 @@
 
 TASK_NAME_DETECTION_STRING = "task.task_name="
 DEFAULT_DIRECTORY = "trained_model_assets/"
-DEFAULT_CONFIG_FILE = "proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.4.0.yaml"
+DEFAULT_CONFIG_FILE = "proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0.yaml"
 DEFAULT_CHECKPOINT = "proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt"
 DEFAULT_ENV = "placeholder.env"
 
@@ -96,7 +96,6 @@ def launch_hdf5(config: DictConfig):
         hdf5_file_path=config.datamodule.get("hdf5_file_path"),
         tile_width=config.datamodule.get("tile_width"),
         subtile_width=config.datamodule.get("subtile_width"),
-        subtile_shape=config.datamodule.get("subtile_shape"),
         pre_filter=hydra.utils.instantiate(config.datamodule.get("pre_filter")),
         subtile_overlap_train=config.datamodule.get("subtile_overlap_train"),
         points_pre_transform=hydra.utils.instantiate(
@@ -114,7 +113,11 @@ def launch_hdf5(config: DictConfig):
 
     log.info(f"Task: {task_name}")
 
-    if task_name in [TASK_NAMES.FIT.value, TASK_NAMES.TEST.value, TASK_NAMES.FINETUNE.value]:
+    if task_name in [
+        TASK_NAMES.FIT.value,
+        TASK_NAMES.TEST.value,
+        TASK_NAMES.FINETUNE.value,
+    ]:
         # load environment variables from `.env` file if it exists
         # recursively searches for `.env` in all folders starting from work dir
         dotenv.load_dotenv(override=True)

trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.4.0.yaml → trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0.yaml

@@ -132,7 +132,6 @@ datamodule:
     min_num_nodes: 1
   tile_width: 1000
   subtile_width: 50
-  subtile_shape: square
   subtile_overlap_train: 0
   subtile_overlap_predict: ${predict.subtile_overlap}
   batch_size: 10