small fix for semantic mapper

scripts/semantic_mapper/semantic_mapper.py

@@ -16,10 +16,13 @@ from vre.representations.cv_representations import DepthRepresentation, NormalsR
 def plot_one(data: MultiTaskItem, title: str, order: list[str] | None,
              name_to_task: dict[str, Representation]) -> np.ndarray:
     """simple plot function: plot_one(reader[0][0], reader[0][1], None, reader.name_to_task)"""
-    def vre_plot_fn(rgb: tr.Tensor, x: tr.Tensor, node: Representation) -> np.ndarray:
-        node.data = ReprOut(rgb.cpu().detach().numpy()[None], MemoryData(x.cpu().detach().numpy()[None]), [0])
-        return node.make_images()[0]
-    img_data = {k: vre_plot_fn(data["rgb"], v, name_to_task[k]) for k, v in data.items()}
+    def vre_plot_fn(rgb_img: np.ndarray, x: tr.Tensor, node: Representation) -> np.ndarray:
+        node.data = ReprOut(frames=rgb_img, output=MemoryData(x.cpu().detach().numpy()[None]), key=[0])
+        res = node.make_images()[0]
+        return res
+    name_to_task["rgb"].data = ReprOut(frames=None, output=MemoryData(data["rgb"].detach().numpy())[None], key=[0])
+    rgb_img = name_to_task["rgb"].make_images()
+    img_data = {k: vre_plot_fn(rgb_img, v, name_to_task[k]) for k, v in data.items()}
     img_data = reorder_dict(img_data, order) if order is not None else img_data
     titles = [title if len(title) < 40 else f"{title[0:19]}..{title[-19:]}" for title in img_data]
     collage = collage_fn(list(img_data.values()), titles=titles, size_px=40)
@@ -456,27 +459,30 @@ def get_new_semantic_mapped_tasks(tasks_subset: list[str] | None = None) -> dict
     return {t.name: t for t in available_tasks if t.name in tasks_subset}
 
 if __name__ == "__main__":
-    cfg_path = Path.cwd() / "cfg.yaml"
-    data_path = Path.cwd() / "data"
+    cfg_path = Path.cwd() / "../../vre_dronescapes/cfg.yaml"
+    data_path = Path.cwd() / "../../vre_dronescapes/norway_210821_DJI_0015_full/"
     vre_dir = data_path
 
     task_names = ["rgb", "depth_marigold", "normals_svd(depth_marigold)", "opticalflow_rife",
-                  "semantic_mask2former_coco_47429163_0", "semantic_mask2former_mapillary_49189528_0"]
+                  "semantic_mask2former_coco_47429163_0", "semantic_mask2former_mapillary_49189528_0",
+                  "semantic_mask2former_mapillary_49189528_1"]
     order = ["rgb", "semantic_mask2former_mapillary_49189528_0", "semantic_mask2former_coco_47429163_0",
              "depth_marigold", "normals_svd(depth_marigold)"]
 
     representations = build_representations_from_cfg(cfg_path)
+    statistics = np.load(Path.cwd() / "../../data/train_set/.task_statistics.npz", allow_pickle=True)["arr_0"].item()
     reader = MultiTaskDataset(vre_dir, task_names=task_names,
                             task_types=representations, handle_missing_data="fill_nan",
-                            normalization="min_max", cache_task_stats=True, batch_size_stats=100)
+                            normalization="min_max", cache_task_stats=True, batch_size_stats=100,
+                            statistics=statistics)
     orig_task_names = list(reader.task_types.keys())
 
-    new_tasks = get_new_semantic_mapped_tasks()
-    for task_name in reader.task_names:
-        if task_name not in orig_task_names:
-            reader.remove_task(task_name)
-    for new_task in new_tasks.values():
-        reader.add_task(new_task, overwrite=True)
+    # new_tasks = get_new_semantic_mapped_tasks()
+    # for task_name in reader.task_names:
+    #     if task_name not in orig_task_names:
+    #         reader.remove_task(task_name)
+    # for new_task in new_tasks.values():
+    #     reader.add_task(new_task, overwrite=True)
 
     print("== Random loaded item ==")
     ixs = np.random.permutation(range(len(reader))).tolist()