fix: 修复 load_h5 丢失文件的问题

khaosz/data/dataset.py

@@ -13,11 +13,13 @@ class BaseSegmentFetcher:
     def __init__(self, segments: List[Tensor]):
         self.segments = segments
         self.cum_lengths = []
+        
         total = 0
         for seg in segments:
-            total += len(seg)
+            total += torch.numel(seg)
             self.cum_lengths.append(total)
-        self.total_length = total if segments else 0
+            
+        self.total_length = total
         
     def __len__(self) -> int:
         return self.total_length

khaosz/data/file.py

@@ -1,11 +1,10 @@
 import os
 import h5py
-import numpy as np
 import torch
 
 from pathlib import Path
 from torch import Tensor
-from typing import Dict, List, Tuple
+from typing import Dict, List
 
 
 def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
@@ -14,15 +13,12 @@ def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]
     with h5py.File(full_file_path, 'w') as f:
         for key, tensors in tensor_group.items():
             grp = f.create_group(key)
-            grp.attrs['num_tensors'] = len(tensors)
-            
             for idx, tensor in enumerate(tensors):
                 arr = tensor.cpu().numpy()
                 grp.create_dataset(f'data_{idx}', data=arr)
 
-def load_h5(file_path: str) -> Tuple[Dict[str, List[Tensor]], int]:
+def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
     tensor_group: Dict[str, List[Tensor]] = {}
-    total_samples = 0
 
     root_path = Path(file_path)
     h5_files = list(root_path.rglob("*.h5")) + list(root_path.rglob("*.hdf5"))
@@ -34,7 +30,13 @@ def load_h5(file_path: str) -> Tuple[Dict[str, List[Tensor]], int]:
                 dsets = []
                 for dset_name in grp.keys():
                     dset = grp[dset_name]
-                    dsets.append(torch.from_numpy(dset[:]).share_memory_())
-                tensor_group[key] = dsets
+                    tensor = torch.from_numpy(dset[:])
+                    if share_memory:
+                        tensor = tensor.share_memory_()
+                    dsets.append(tensor)
+            
+                if tensor_group.get(key) is None:
+                    tensor_group[key] = []
+                tensor_group[key].extend(dsets)
 
     return tensor_group