解决 torch.cat(): input types can‘t be cast to the desired output type Byte

最近使用 U2Net 训练模型的时候，遇到了下面的错误：

RuntimeError: torch.cat(): input types can't be cast to the desired output type Byte

错误堆栈信息如下

Original Traceback (most recent call last): File "/datapython3.11/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) ^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch return self.collate_fn(data) ^^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/collate.py", line 268, in default_collate return collate(batch, collate_fn_map=default_collate_fn_map) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/collate.py", line 127, in collate return elem_type({key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map) for key in elem}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/collate.py", line 127, in return elem_type({key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map) for key in elem}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/collate.py", line 119, in collate return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
File "/datapython3.11/site-packages/torch/utils/data/_utils/collate.py", line 165, in collate_tensor_fn return torch.stack(batch, 0, out=out) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
RuntimeError: torch.cat(): input types can't be cast to the desired output type Byte

原因：个人猜想是多个 worker? 在一起工作引起的并发问题。

解决方法一：

在构建 DataLoader 实例的时候，把 workers 设置为 0 即可。

缺点：会导致训练速度变慢
?

train_dataset = SalObjDataset(
    img_name_list=train_img_name_list,
    lbl_name_list=train_label_name_list,
    transform=transforms.Compose([
        RescaleT(320),
        # RandomCrop(288),
        ToTensorLab(flag=0)]))
train_dataset.auto_collation = True
train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, num_workers=0)

解决方法二：

修改 pytorch 里面的代码。

在上面的堆栈中，显示?/datapython3.11/site-packages/torch/utils/data/_utils/collate.py:165 报错了

我们打开?collate.py 文件，找到 collate_tensor_fn 这个函数，把 if 语句的内容注释掉就可以了

缺点：需要修改pytorch的代码，会增加多一次内存拷贝?

def collate_tensor_fn(batch, *, collate_fn_map: Optional[Dict[Union[Type, Tuple[Type, ...]], Callable]] = None):
    elem = batch[0]
    out = None
    # if torch.utils.data.get_worker_info() is not None:
    #     # If we're in a background process, concatenate directly into a
    #     # shared memory tensor to avoid an extra copy
    #     numel = sum(x.numel() for x in batch)
    #     storage = elem._typed_storage()._new_shared(numel, device=elem.device)
    #     out = elem.new(storage).resize_(len(batch), *list(elem.size()))
    return torch.stack(batch, 0, out=out)

.

.