- mySSL - Failed after 0:00:16! Traceback (most recent calls WITHOUT Sacred inte...

ERROR - mySSL - Failed after 0:00:16!
Traceback (most recent calls WITHOUT Sacred internals):
  File "training.py", line 146, in main
    for _, sample_batched in enumerate(trainloader):
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/root/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/miniconda3/lib/python3.8/site-applications/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/autodl-tmp/DSPNet-main/dataloaders/GenericSuperDatasetv2.py", line 313, in __getitem__
    label_t = self.supcls_pick_binarize(new_label, sup_max_cls)  # 使用过滤后的标签
  File "/root/autodl-tmp/DSPNet-main/dataloaders/GenericSuperDatasetv2.py", line 256, in supcls_pick_binarize
    bi_val = int(torch.randint(low = 1, high = int(sup_max_cls), size = (1,)))
RuntimeError: random_ expects 'from' to be less than 'to', but got from=1 >= to=1

错误翻译

原因分析

解决方案

示例修改（假设当 sup_max_cls 为1时，应选择类别0）：

# 在 GenericSuperDatasetv2.py 的 supcls_pick_binarize 函数中
# 找到这行：
# bi_val = int(torch.randint(low = 1, high = int(sup_max_cls), size = (1,)))

# 修改为：
if int(sup_max_cls) <= 1:
    bi_val = 0 # 或者其他合适的默认值，取决于你的业务逻辑
else:
    bi_val = int(torch.randint(low = 1, high = int(sup_max_cls), size = (1,)))

# 在 GenericSuperDatasetv2.py 的 supcls_pick_binarize 函数中
# 找到这行：
# bi_val = int(torch.randint(low = 1, high = int(sup_max_cls), size = (1,)))

# 修改为：
if int(sup_max_cls) < 2:
    # 如果最大类别数小于2，则无法生成1到sup_max_cls之间的随机数
    # 这里可以根据需求调整，例如设置为0，或者抛出更明确的错误
    bi_val = 0
else:
    bi_val = int(torch.randint(low=1, high=int(sup_max_cls), size=(1,)))

预防措施

• 在调用可能产生边界问题的函数（如 torch.randint）前，对输入参数进行校验，确保其符合函数的约束条件。
• 在数据加载或预处理阶段，增加对类别数量等关键参数的检查，防止出现不符合预期的值。