fix wilor_home

_wilor_helper.py

@@ -6,14 +6,12 @@
 import numpy as np
 from dataclasses import dataclass
 from torch import Tensor
-from typing import Any, Generator, Literal, TypedDict
-
-from wilor.models import load_wilor
-from wilor.utils import recursive_to
-from wilor.datasets.vitdet_dataset import ViTDetDataset
-from wilor.utils.renderer import Renderer, cam_crop_to_full
+from typing import Literal, TypedDict
 from ultralytics import YOLO 
 from jaxtyping import Float, Int
+
+import sys
+
 LIGHT_PURPLE=(0.25098039,  0.274117647,  0.65882353)
 
 
@@ -67,16 +65,32 @@ class _RawHamerOutputs:
 
 class WiLoRHelper:
     def __init__(self, wilor_home: str="./"):
-            model, model_cfg = load_wilor(wilor_home, checkpoint_path = './pretrained_models/wilor_final.ckpt' ,
-                cfg_path= './pretrained_models/model_config.yaml')
-            detector = YOLO(os.path.join(wilor_home, 'pretrained_models/detector.pt'))
-            # Setup the renderer
-            self.renderer = Renderer(model_cfg, faces=model.mano.faces)
-
-            self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-            self._model = model.to(self.device)
-            self._model_cfg = model_cfg
-            self._detector = detector.to(self.device)
+        sys.path.append(wilor_home)
+        global load_wilor
+        global recursive_to
+        global ViTDetDataset
+        global Renderer
+        global cam_crop_to_full
+
+        from wilor.models import load_wilor
+        from wilor.utils import recursive_to
+        from wilor.datasets.vitdet_dataset import ViTDetDataset
+        from wilor.utils.renderer import Renderer, cam_crop_to_full
+        checkpoint_path = os.path.join(wilor_home, 'pretrained_models/wilor_final.ckpt')
+        cfg_path = os.path.join(wilor_home, 'pretrained_models/model_config.yaml')
+        original_dir = os.getcwd()
+        os.chdir(wilor_home)
+        model, model_cfg = load_wilor(checkpoint_path = checkpoint_path ,
+            cfg_path= cfg_path)
+        os.chdir(original_dir)
+        detector = YOLO(os.path.join(wilor_home, 'pretrained_models/detector.pt'))
+        # Setup the renderer
+        self.renderer = Renderer(model_cfg, faces=model.mano.faces)
+
+        self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+        self._model = model.to(self.device)
+        self._model_cfg = model_cfg
+        self._detector = detector.to(self.device)
 
     def look_for_hands(
         self,