("Dataset is empty, cannot retrieve items.") ValueError: Dataset is empty, canno...
🚨 错误信息
Traceback (most recent calls WITHOUT Sacred internals):
File "training.py", line 82, in main
tr_parent = SuperpixelDataset( # base dataset
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/dataloaders/GenericSuperDatasetv2.py", line 68, in __init__
self.scan_ids = self.get_scanids(mode, idx_split) # patient ids of the entire fold
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/dataloaders/GenericSuperDatasetv2.py", line 100, in get_scanids
val_ids = copy.deepcopy(self.img_pids[self.sep[idx_split]: self.sep[idx_split + 1] + self.nsup])
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/util/utils.py", line 101, in __getitem__
return [self[x] for x in self._rangeify(x)]
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/util/utils.py", line 101, in <listcomp>
return [self[x] for x in self._rangeify(x)]
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/util/utils.py", line 108, in __getitem__
raise ValueError("Dataset is empty, cannot retrieve items.")
ValueError: Dataset is empty, cannot retrieve items.
🤖 AI解决方案
错误分析与解决方案
1. 错误翻译
ValueError: Dataset is empty, cannot retrieve items.
# ValueError: 数据集为空,无法检索项目。
2. 原因分析
val_ids 的切片操作 (self.img_pids[self.sep[idx_split]: self.sep[idx_split + 1] + self.nsup]) 导致了一个空列表,进而使得 _rangeify 函数在尝试处理这个空列表时,最终抛出了“数据集为空”的 ValueError。这通常意味着 self.sep[idx_split + 1] + self.nsup 这个索引超出了 self.img_pids 的范围,或者 self.sep[idx_split] 本身就是无效的。
3. 解决方案
检查 GenericSuperDatasetv2.py 中 get_scanids 方法内的 self.sep 列表和 self.img_pids 列表的长度以及 idx_split 的值。
- 检查
self.sep的长度和值: 确保idx_split是一个有效的索引,并且idx_split + 1没有超出self.sep的范围。 - 检查
self.img_pids的长度: 确保self.img_pids列表有足够的数据,并且切片操作不会越界。 - 检查
self.nsup的值: 确保self.nsup的值是合理的,不会导致索引越界。
示例代码修改(假设问题出在 idx_split 超出 self.sep 范围):
在 get_scanids 方法中,添加边界检查:
# dataloaders/GenericSuperDatasetv2.py, in get_scanids
val_ids = []
if idx_split < len(self.sep) - 1:
start_idx = self.sep[idx_split]
end_idx = self.sep[idx_split + 1] + self.nsup
if start_idx < len(self.img_pids) and end_idx <= len(self.img_pids):
val_ids = copy.deepcopy(self.img_pids[start_idx: end_idx])
else:
# Handle out-of-bounds gracefully, perhaps by logging or raising a more specific error
print(f"Warning: Index out of bounds for img_pids. start_idx={start_idx}, end_idx={end_idx}, img_pids_len={len(self.img_pids)}")
elif idx_split == len(self.sep) - 1:
# Handle the last split if needed, adjust logic based on your dataset structure
start_idx = self.sep[idx_split]
if start_idx < len(self.img_pids):
val_ids = copy.deepcopy(self.img_pids[start_idx:])
else:
print(f"Warning: Index out of bounds for img_pids for the last split. start_idx={start_idx}, img_pids_len={len(self.img_pids)}")
else:
print(f"Error: Invalid idx_split value: {idx_split}")
self.scan_ids = val_ids # Assign potentially empty list if no valid data is found
4. 预防措施
idx_split 和 nsup 等可能影响索引的参数,应在函数入口处进行严格校验,防止传入非法值。