- mySSL - Failed after 0:00:05! Traceback (most recent calls WITHOUT Sacred inte...
🚨 错误信息
train_SABS_Superpix_lbgroup0_scale_MIDDLE_vfold0
WARNING - root - Changed type of config entry "min_fg_data" from str to int
INFO - mySSL - Running command 'main'
INFO - mySSL - Started run with ID "6"
INFO - main - ###### Create model ######
###### NETWORK: Using ms-coco initialization ######
INFO - main - ###### Load data ######
INFO - main - ###### Labels excluded in training : [2, 3] ######
INFO - main - ###### Unseen labels evaluated in testing: [2, 3] ######
###### Dataset: the following classes has been excluded [2, 3]######
ERROR - mySSL - Failed after 0:00:05!
Traceback (most recent calls WITHOUT Sacred internals):
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/util/utils.py", line 105, in __getitem__
return super().__getitem__(index % len(self))
ZeroDivisionError: integer division or modulo by zero
During handling of the above exception, another exception occurred:
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 67, 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 98, 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 107, in __getitem__
raise IndexError('list index out of range')
IndexError: list index out of range
root@autodl-container-008d42af78-93b315fe:~/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master# bash train_ssl_abdominal_ct.sh===================================
train_SABS_Superpix_lbgroup0_scale_MIDDLE_vfold0
WARNING - root - Changed type of config entry "min_fg_data" from str to int
INFO - mySSL - Running command 'main'
INFO - mySSL - Started run with ID "7"
INFO - main - ###### Create model ######
###### NETWORK: Using ms-coco initialization ######
INFO - main - ###### Load data ######
INFO - main - ###### Labels excluded in training : [2, 3] ######
INFO - main - ###### Unseen labels evaluated in testing: [2, 3] ######
###### Dataset: the following classes has been excluded [2, 3]######
ERROR - mySSL - Failed after 0:00:05!
Traceback (most recent calls WITHOUT Sacred internals):
File "/root/autodl-tmp/Self-supervised-Fewshot-Medical-Image-Segmentation-master/util/utils.py", line 105, in __getitem__
return super().__getitem__(index % len(self))
ZeroDivisionError: integer division or modulo by zero
During handling of the above exception, another exception occurred:
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 67, 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 98, 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 107, in __getitem__
raise IndexError('list index out of range')
IndexError: list index out of range
🤖 AI解决方案
ZeroDivisionError: integer division or modulo by zero
ZeroDivisionError: 整数除法或模运算数为零
IndexError: list index out of range
IndexError: 列表索引越界原因分析：
ZeroDivisionError 是由 index % len(self) 引起的，当 len(self) 为 0 时，会导致除零错误。这表明数据集为空或在某些处理环节中导致其长度变为零。紧接着的 IndexError 则是在尝试访问空列表或索引超出范围的列表时发生，这与前一个错误是连锁反应，共同指向了数据加载或处理过程中出现的异常。
解决方案：
- 检查数据集加载逻辑： * 定位到
dataloaders/GenericSuperDatasetv2.py 文件中的 __init__ 方法。
* 在 self.scan_ids = self.get_scanids(mode, idx_split) 这一行之前，添加对 self.img_pids 的长度检查。如果 self.img_pids 为空，则需要向上追溯其来源，确保数据文件存在且已被正确加载。
* 在 get_scanids 方法中，检查 self.sep 数组的边界以及 self.sep[idx_split + 1] 是否超出范围。
* 重要：请仔细检查 self.img_pids 的初始化和填充逻辑，确保它包含了预期的数据。如果数据集文件（如图像、标签文件）缺失或格式不正确，可能会导致 self.img_pids 为空。
示例代码片段（仅为示意，具体修改需根据实际代码结构调整）：
# 在 dataloaders/GenericSuperDatasetv2.py 的 __init__ 方法中
# ...
self.scan_ids = self.get_scanids(mode, idx_split) # patient ids of the entire fold
if not self.img_pids: # 检查 img_pids 是否为空
raise ValueError("img_pids is empty. Please check data loading and file paths.")
if not self.sep:
raise ValueError("self.sep is empty. Please check split configuration.")
# ...
# 在 dataloaders/GenericSuperDatasetv2.py 的 get_scanids 方法中
# ...
if idx_split + 1 >= len(self.sep):
raise ValueError(f"idx_split ({idx_split}) + 1 is out of bounds for self.sep with length {len(self.sep)}.")
val_ids = copy.deepcopy(self.img_pids[self.sep[idx_split]: self.sep[idx_split + 1] + self.nsup])
# ...
util/utils.py 中的 __getitem__ 方法：* 在
return super().__getitem__(index % len(self)) 之前，添加一个检查 len(self) 是否大于 0 的条件。* 如果
len(self) 为 0，应该直接返回一个空列表或抛出一个更具描述性的错误。
示例代码片段（仅为示意）：
# 在 util/utils.py 的 __getitem__ 方法中
# ...
if len(self) == 0:
# 或者 return None, [] 等，根据实际需求
raise ValueError("Dataset is empty, cannot retrieve items.")
return super().__getitem__(index % len(self))
# ...
预防措施：