fix hardcoded ycbv, some issues with panda batch and cosy eval doc

Author: kzorina

docs/book/cosypose/evaluate.md

@@ -1,11 +1,17 @@
-# Evaluating CosyPose
+# CosyPose single view evaluation
 
 Please make sure you followed the steps relative to the evaluation in the main readme.
 
-Please run the following command to evaluate on YCBV dataset
+To evaluate on YCBV dataset:
 
 ```
-python -m happypose.pose_estimators.cosypose.cosypose.scripts.run_full_cosypose_eval_new detector_run_id=bop_pbr coarse_run_id=coarse-bop-ycbv-pbr--724183 refiner_run_id=refiner-bop-ycbv-pbr--604090 ds_names=["ycbv.bop19"] result_id=ycbv-debug detection_coarse_types=["detector"]
+python -m happypose.pose_estimators.cosypose.cosypose.scripts.run_full_cosypose_eval_new detector_run_id=bop_pbr coarse_run_id=coarse-bop-ycbv-pbr--724183 refiner_run_id=refiner-bop-ycbv-pbr--604090 ds_names=["ycbv.bop19"] result_id=ycbv-debug detection_coarse_types=["detector"] inference.renderer=bullet inference.n_workers=0
 ```
 
-The other BOP datasets are supported as long as you download the correspond models.
+To change the renderer from bullet (originally used by cosypose) to panda3d:
+```
+python -m happypose.pose_estimators.cosypose.cosypose.scripts.run_full_cosypose_eval_new detector_run_id=bop_pbr coarse_run_id=coarse-bop-ycbv-pbr--724183 refiner_run_id=refiner-bop-ycbv-pbr--604090 ds_names=["ycbv.bop19"] result_id=ycbv-debug detection_coarse_types=["detector"] inference.renderer=panda3d inference.n_workers=1
+```
+
+To evaluate on other datasets, change ["ycbv.bop19"] to e.g. ["tless.bop19"].
+To evaluate on a collection of datasets, change "ycbv.bop19" to e.g. ["ycbv.bop19", "lmo.bop19", "tless.bop19"].

happypose/pose_estimators/cosypose/cosypose/evaluation/evaluation.py

@@ -34,7 +34,6 @@
 
 # Pose estimator
 from happypose.toolbox.datasets.datasets_cfg import make_object_dataset, make_scene_dataset, get_obj_ds_info
-from happypose.toolbox.inference.utils import load_detector
 from happypose.toolbox.lib3d.rigid_mesh_database import MeshDataBase
 from happypose.toolbox.utils.distributed import get_rank, get_tmp_dir
 from happypose.toolbox.utils.logging import get_logger
@@ -69,7 +68,7 @@ def load_detector(run_id, ds_name):
     return model
 
 
-def load_pose_models(object_dataset, coarse_run_id, refiner_run_id, n_workers, renderer_type="panda3d"):
+def load_pose_models_cosypose(object_dataset, coarse_run_id, refiner_run_id, n_workers, renderer_type="panda3d"):
     run_dir = EXP_DIR / coarse_run_id
     cfg = yaml.load((run_dir / "config.yaml").read_text(), Loader=yaml.UnsafeLoader)
     cfg = check_update_config_pose(cfg)
@@ -161,6 +160,8 @@ def run_eval(
     cfg.save_dir = str(save_dir)
 
     logger.info(f"Running eval on ds_name={cfg.ds_name} with setting={save_key}")
+    # e.g. "ycbv.bop19" -> "ycbv"
+    ds_name_short = cfg.ds_name.split('.')[0]
 
     # Load the dataset
     ds_kwargs = {"load_depth": False}
@@ -181,8 +182,7 @@ def run_eval(
     # Load detector model
     if cfg.inference.detection_type == "detector":
         assert cfg.detector_run_id is not None
-        detector_model = load_detector(cfg.detector_run_id, cfg.ds_name)
-        # detector_model = load_detector(cfg.detector_run_id, device=device)
+        detector_model = load_detector(cfg.detector_run_id, ds_name_short)
     elif cfg.inference.detection_type == "gt":
         detector_model = None
     else:
@@ -195,14 +195,15 @@ def run_eval(
     assert cfg.coarse_run_id is not None
     assert cfg.refiner_run_id is not None
 
-    object_ds = make_object_dataset('ycbv')
 
-    coarse_model, refiner_model, mesh_db = load_pose_models(
+    object_ds = make_object_dataset(ds_name_short)
+
+    coarse_model, refiner_model, mesh_db = load_pose_models_cosypose(
         object_ds,
         coarse_run_id=cfg.coarse_run_id,
         refiner_run_id=cfg.refiner_run_id,
-        n_workers=0,
-        renderer_type="bullet"
+        n_workers=cfg.inference.n_workers,
+        renderer_type=cfg.inference.renderer
     )
 
     renderer = refiner_model.renderer

happypose/pose_estimators/megapose/inference/types.py

@@ -97,6 +97,8 @@ class InferenceConfig:
     depth_refiner: Optional[str] = None  # ['icp', 'teaserpp']
     bsz_objects: int = 16  # How many parallel refiners to run
     bsz_images: int = 288  # How many images to push through coarse model
+    renderer: str = "panda3d"  # ['panda3d', 'pybullet']
+    n_workers: int = 1  # How many workers to use in the batch renderer
 
 
 @dataclass

happypose/toolbox/renderer/panda3d_batch_renderer.py

@@ -133,13 +133,16 @@ def __init__(
         preload_cache: bool = True,
         split_objects: bool = False,
     ):
-        assert n_workers >= 1
+        self._is_closed = False
         self._object_dataset = asset_dataset
         self._n_workers = n_workers
         self._split_objects = split_objects
+        self._renderers = []
+        self._in_queues = []
+        self._out_queue = None
+        assert n_workers >= 1
 
         self._init_renderers(preload_cache)
-        self._is_closed = False
 
     def make_scene_data(
         self,
@@ -340,7 +343,8 @@ def stop(self) -> None:
             renderer_process.terminate()
         for queue in self._in_queues:
             queue.close()
-        self._out_queue.close()
+        if self._out_queue is not None:
+            self._out_queue.close()
         self._is_closed = True
         logger.debug("Batch renderer is closed.")