Revert "Update model loading and add new features"

.gitignore

@@ -1,7 +1 @@
 *.pyc
-
-# checkpoint directories
-checkpoint/*
-
-# data
-data/*

geort/dataset.py

@@ -47,7 +47,7 @@ def __init__(self, qpos_keypoint_file, keypoint_names):
         self.qpos = np_array["qpos"]
         self.keypoints = np_array["keypoint"].item()
         self.keypoint_names = keypoint_names
-        # print("Keypoint Names", self.keypoint_names)  # Commented out to reduce verbose output
+        print("Keypoint Names", self.keypoint_names)
         self.n = len(self.qpos)
         return
 

geort/export.py

@@ -7,7 +7,6 @@
 import torch
 import os 
 from pathlib import Path
-import json
 from geort.formatter import HandFormatter
 from geort.model import IKModel
 from geort.utils.path import to_package_root, get_checkpoint_root
@@ -22,7 +21,7 @@ def __init__(self, model_path, config_path):
         config = load_json(config_path)
         keypoint_info = parse_config_keypoint_info(config)
         joint_lower_limit, joint_upper_limit = parse_config_joint_limit(config)
-        # print(keypoint_info["joint"])  # Commented out to reduce verbose output
+        print(keypoint_info["joint"])
         self.human_ids = keypoint_info["human_id"]
         self.model = IKModel(keypoint_joints=keypoint_info["joint"]).cuda()
         self.model.load_state_dict(torch.load(model_path))
@@ -37,14 +36,9 @@ def forward(self, keypoints):
         return joint_raw[0]
 
 
-def load_model(tag='', epoch=0, use_best=True):
+def load_model(tag='', epoch=0):
     '''
-        Loading API with best model preference.
-
-        Args:
-            tag: checkpoint tag to search for
-            epoch: specific epoch to load (0 means latest)
-            use_best: if True, prefer best.pth over last.pth when epoch=0
+        Loading API.
     '''
     checkpoint_root = get_checkpoint_root()
     all_checkpoints = os.listdir(checkpoint_root)
@@ -56,21 +50,10 @@ def load_model(tag='', epoch=0, use_best=True):
             break 
 
     checkpoint_root = Path(checkpoint_root) / checkpoint_name
-
     if epoch > 0:
         model_path = checkpoint_root / f"epoch_{epoch}.pth"
     else:
-        # Try to load best model first if use_best is True
-        if use_best and (checkpoint_root / "best.pth").exists():
-            model_path = checkpoint_root / "best.pth"
-            # Load training history to show best model info
-            history_path = checkpoint_root / "training_history.json"
-            if history_path.exists():
-                with open(history_path, 'r') as f:
-                    history = json.load(f)
-                    print(f"Loading best model from epoch {history['best_epoch']} with validation loss: {history['best_val_loss']:.4f}")
-        else:
-            model_path = checkpoint_root / "last.pth"
+        model_path = checkpoint_root / f"last.pth"
 
     config_path = checkpoint_root / "config.json"
     return GeoRTRetargetingModel(model_path=model_path, config_path=config_path)