fixes in scripts

README.md

@@ -290,7 +290,7 @@ outputs one csv file with predictions for each npz file, the scenes are used for
 
 ```
 python scripts/evaluate_semantic_segmentation.py \
-  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # change this with your predictions dir
+  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \
   data/test_set_annotated_only/semantic_segprop8/ \
   -o results.csv \
   --classes land forest residential road little-objects water sky hill \

dronescapes_reader/dronescapes_representations.py

@@ -182,10 +182,9 @@ mapillary_color_map = [[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 1
                        [0, 60, 100], [0, 0, 142], [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110],
                        [0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10, 10]]
 
-class SemanticMask2FormerMapillaryConvertedPaper(TaskMapper):
-    def __init__(self, dep: NpzRepresentation):
-        super().__init__("semantic_mask2former_swin_mapillary_converted",
-                         dependencies=[dep], merge_fn=self._merge_fn, n_channels=8)
+class SemanticMask2FormerMapillaryMapped(TaskMapper):
+    def __init__(self, name: str, dep: NpzRepresentation):
+        super().__init__(name, dependencies=[dep], merge_fn=self._merge_fn, n_channels=8)
         self.mapping = {
             "land": ["Terrain", "Sand", "Snow"],
             "forest": ["Vegetation"],
@@ -236,7 +235,8 @@ _tasks: list[NpzRepresentation] = [ # some pre-baked representations
     SemanticRepresentation("semantic_mask2former_coco_47429163_0", classes=coco_classes, color_map=coco_color_map),
     m2f_mapillary := SemanticRepresentation("semantic_mask2former_mapillary_49189528_0", classes=mapillary_classes,
                                             color_map=mapillary_color_map),
-    SemanticMask2FormerMapillaryConvertedPaper(m2f_mapillary),
+    SemanticRepresentation("semantic_mask2former_swin_mapillary_converted", classes=8, color_map=color_map_8classes),
+    SemanticMask2FormerMapillaryMapped("semantic_mask2former_swin_mapillary_mapped", m2f_mapillary),
     NpzRepresentation("softseg_gb", 3),
 ]
 dronescapes_task_types: dict[str, NpzRepresentation] = {task.name: task for task in _tasks}

dronescapes_reader/multitask_dataset.py

@@ -240,7 +240,7 @@ class MultiTaskDataset(Dataset):
 
         if self.handle_missing_data == "drop":
             b4 = len(names_to_tasks)
-            names_to_tasks = {k: v for k, v in names_to_tasks if len(v) == len(relevant_tasks_for_files)}
+            names_to_tasks = {k: v for k, v in names_to_tasks.items() if len(v) == len(relevant_tasks_for_files)}
             logger.debug(f"Dropped {b4 - len(names_to_tasks)} files not in all tasks")
         all_names: list[str] = natsorted(names_to_tasks.keys())
         logger.info(f"Total files: {len(names_to_tasks)} per task across {len(task_names)} tasks")

scripts/evaluate_semantic_segmentation.py

@@ -18,7 +18,8 @@ import numpy as np
 import pandas as pd
 
 sys.path.append(Path(__file__).parents[1].__str__())
-from dronescapes_reader import MultiTaskDataset, SemanticRepresentation
+from dronescapes_reader import MultiTaskDataset
+from dronescapes_reader.dronescapes_representations import SemanticRepresentation
 
 def compute_metrics(tp: np.ndarray, fp: np.ndarray, tn: np.ndarray, fn: np.ndarray) -> pd.DataFrame:
     precision = tp / (tp + fp)
@@ -66,7 +67,7 @@ def compute_final_per_scene(res: pd.DataFrame, scene: str, classes: list[str],
 
 def _check_and_symlink_dirs(y_dir: Path, gt_dir: Path) -> Path:
     """checks whether the two provided paths are actual full of npz directories and links them together in a tmp dir"""
-    assert (l := [x.name for x in y_dir.iterdir()]) == (r := [x.name for x in gt_dir.iterdir()]), f"{l} \n vs \n {r}"
+    assert (l := {x.name for x in y_dir.iterdir()}) == (r := {x.name for x in gt_dir.iterdir()}), f"{l} \n vs \n {r}"
     assert all(x.endswith(".npz") for x in [*l, *r]), f"Not dirs of only .npz files: {l} \n {r}"
     (temp_dir := Path(TemporaryDirectory().name)).mkdir(exist_ok=False)
     os.symlink(y_dir, temp_dir / "pred")
@@ -100,9 +101,11 @@ def get_args() -> Namespace:
 def main(args: Namespace):
     # setup to put both directories in the same parent directory for the reader to work.
     temp_dir = _check_and_symlink_dirs(args.y_dir, args.gt_dir)
-    sema_repr = partial(SemanticRepresentation, classes=args.classes, color_map=[[0, 0, 0]] * len(args.classes))
-    reader = MultiTaskDataset(temp_dir, handle_missing_data="drop", task_types={"pred": sema_repr, "gt": sema_repr})
-    assert (a := len(reader.all_files_per_repr["gt"])) == (b := len(reader.all_files_per_repr["pred"])), f"{a} vs {b}"
+    pred_repr = SemanticRepresentation("pred", classes=args.classes, color_map=[[0, 0, 0]] * len(args.classes))
+    gt_repr = SemanticRepresentation("gt", classes=args.classes, color_map=[[0, 0, 0]] * len(args.classes))
+    reader = MultiTaskDataset(temp_dir, task_names=["pred", "gt"], task_types={"pred": pred_repr, "gt": gt_repr},
+                              handle_missing_data="drop", normalization=None)
+    assert (a := len(reader.files_per_repr["gt"])) == (b := len(reader.files_per_repr["pred"])), f"{a} vs {b}"
 
     # Compute TP, FP, TN, FN for each frame
     raw_stats = compute_raw_stats_per_frame(reader, args.classes, args.n_workers)