Add files using upload-large-folder tool

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deformable/clothmove/ind_test/episode_00001.mp4

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deformable/clothmove/ind_test/episode_00002.mp4

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deformable/clothmove/ind_test/episode_00017.mp4

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deformable/clothmove/ind_test/episode_00018.mp4

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deformable/clothmove/ind_test/episode_00028.mp4

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deformable/transform_cloth_lessaction.py

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+import h5py
+import torch
+import os
+import shutil
+from tqdm import tqdm
+import numpy as np
+
+def transform_dataset(src_dir, dest_dir):
+    os.makedirs(dest_dir, exist_ok=True)
+    src_h5 = os.path.join(src_dir, 'dataset.h5')
+    src_episodes = os.path.join(src_dir, 'episodes')
+    
+    if not os.path.exists(src_h5):
+        print(f"Skipping {src_dir}, no dataset.h5 found.")
+        return
+
+    print(f"Transforming {src_dir} to {dest_dir}...")
+    
+    metadata = []
+    with h5py.File(src_h5, 'r') as f:
+        # Sort episode keys to maintain order
+        ep_keys = sorted(f.keys())
+        
+        for ep_key in tqdm(ep_keys):
+            ep = f[ep_key]
+            
+            # Extract actions
+            # Original actions: (64, 8) -> [dx1, dy1, dz1, pick1, dx2, dy2, dz2, pick2]
+            # New actions: (64, 3) -> [dx, dy, dz] (where both grippers have the same dx, dy, dz)
+            # We'll take the first 3 dimensions of the original actions
+            orig_actions = ep['actions'][:]
+            new_actions = orig_actions[:, :3] # Take dx1, dy1, dz1
+            
+            # Convert to torch tensor
+            actions_tensor = torch.from_numpy(new_actions).float()
+            
+            # Video path
+            video_filename = f"{ep_key}.mp4"
+            src_video = os.path.join(src_episodes, video_filename)
+            dest_video = os.path.join(dest_dir, video_filename)
+            
+            if os.path.exists(src_video):
+                # Use symlink instead of copy to save space
+                if os.path.lexists(dest_video):
+                    os.remove(dest_video)
+                os.symlink(src_video, dest_video)
+            else:
+                print(f"Warning: Video {src_video} not found.")
+            
+            # Metadata entry
+            metadata.append({
+                'video_path': video_filename,
+                'actions': actions_tensor,
+                'length': len(new_actions),
+                'cloth_dimx': 16, # Default values from clothmove if not in source
+                'cloth_dimy': 16
+            })
+            
+    # Save metadata.pt
+    torch.save(metadata, os.path.join(dest_dir, 'metadata.pt'))
+    print(f"Finished {dest_dir}. Total episodes: {len(metadata)}")
+
+if __name__ == "__main__":
+    base_src = "/storage/ice-shared/ae8803che/ychen3302/cloth_data/"
+    base_dest = "/storage/ice-shared/ae8803che/hxue/data/world_model/datasets/deformable/clothmove_lessaction/"
+    
+    mapping = [
+        ('cloth_push_sphere_synced', 'ind_train'),
+        ('cloth_push_sphere_synced_id_test', 'ind_test'),
+        ('cloth_push_sphere_synced_ood_test_larger', 'ood_test_larger'),
+        ('cloth_push_sphere_synced_ood_test_smaller', 'ood_test_smaller'),
+    ]
+    
+    for src_sub, dest_sub in mapping:
+        transform_dataset(os.path.join(base_src, src_sub), os.path.join(base_dest, dest_sub))

upload_to_hf.py

@@ -13,21 +13,19 @@ def upload():
     
     # Ensure the repo exists as a dataset
     try:
-        create_repo(REPO_ID, repo_type="dataset", exist_ok=True)
+        create_repo(REPO_ID, repo_type="dataset", exist_ok=True, private=False)
         print(f"Dataset repo '{REPO_ID}' is ready.")
     except Exception as e:
         print(f"Warning: could not ensure repo existence: {e}")
 
-    # Use upload_folder to efficiently sync the entire directory.
-    # We ignore temporary files or logs.
+    # Use upload_large_folder for reliable large-dataset sync.
     print(f"Uploading folder '{LOCAL_DIR}' to '{REPO_ID}'...")
     try:
-        api.upload_folder(
+        api.upload_large_folder(
             folder_path=LOCAL_DIR,
             repo_id=REPO_ID,
             repo_type="dataset",
             ignore_patterns=["**/_tmp_episodes/**", "**/launch_nohup.log", "**/upload_to_hf.py"],
-            commit_message="Initial dataset upload (standardized MP4 + metadata.pt)"
         )
         print(f"Successfully uploaded dataset to: https://huggingface.co/datasets/{REPO_ID}")
     except Exception as e: