Merge pull request #88 from IGNF/65-2-remove-circular-patches

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

.github/workflows/cicd.yaml

@@ -40,8 +40,8 @@ jobs:
     - name: Example inference run via Docker with default config and checkpoint
       run: >
         docker run
-        -v /var/data/cicd/CICD_github_assets/myria3d_V3.4.0/inputs/:/inputs/
-        -v /var/data/cicd/CICD_github_assets/myria3d_V3.4.0/outputs/:/outputs/
+        -v /var/data/cicd/CICD_github_assets/myria3d_V3.5.0/inputs/:/inputs/
+        -v /var/data/cicd/CICD_github_assets/myria3d_V3.5.0/outputs/:/outputs/
         --ipc=host
         --shm-size=2gb
         myria3d
@@ -54,14 +54,14 @@ jobs:
     - name: Example inference run via Docker with inference-time subtiles overlap to smooth-out results.
       run: >
         docker run
-        -v /var/data/cicd/CICD_github_assets/myria3d_V3.4.0/inputs/:/inputs/
-        -v /var/data/cicd/CICD_github_assets/myria3d_V3.4.0/outputs/:/outputs/
+        -v /var/data/cicd/CICD_github_assets/myria3d_V3.5.0/inputs/:/inputs/
+        -v /var/data/cicd/CICD_github_assets/myria3d_V3.5.0/outputs/:/outputs/
         --ipc=host
         --shm-size=2gb
         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 @@
 # CHANGELOG
 
+## 3.5.0
+- Abandon of option to get circular patches since it was never used.
+
 ### 3.4.12
 - Remove COPC datasets and dataloaders since they were abandonned and never used.
 

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
 
@@ -134,7 +132,6 @@ 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,
@@ -174,7 +171,6 @@ 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

@@ -12,7 +12,6 @@
 
 from myria3d.pctl.dataset.utils import (
     LAS_PATHS_BY_SPLIT_DICT_TYPE,
-    SHAPE_TYPE,
     SPLIT_TYPE,
     pre_filter_below_n_points,
     split_cloud_into_samples,
@@ -34,7 +33,6 @@ 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 +46,6 @@ 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 +61,6 @@ 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
 
@@ -85,7 +81,6 @@ def __init__(
             hdf5_file_path,
             tile_width,
             subtile_width,
-            subtile_shape,
             pre_filter,
             subtile_overlap_train,
             points_pre_transform,
@@ -202,7 +197,6 @@ 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,
@@ -218,7 +212,6 @@ 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.
@@ -253,7 +246,6 @@ 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

@@ -1,6 +1,5 @@
 import glob
 import json
-import math
 from pathlib import Path
 import subprocess as sp
 from numbers import Number
@@ -12,7 +11,6 @@
 from scipy.spatial import cKDTree
 
 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
@@ -104,16 +102,14 @@ 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:
@@ -127,11 +123,6 @@ def split_cloud_into_samples(
     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

package_metadata.yaml

@@ -1,4 +1,4 @@
-__version__: "3.4.11"
+__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(

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