drop_last默认是False如果设置为True:这个是对最后的未完成的batch来说的,比如你的batch_size设置为64,而一个epoch只有100个样本,那么训练的时候后面的36个就被扔掉了… 如果为False(默认),那么会继续正常执行,只是最后的batch_size会小一点。 ——— 版权声明:本文为CSDN博主「hxxjxw」的原创文章,遵循CC4.0BY-...
Dataset只负责数据的抽象,一次调用__getitem__只返回一个样本。 DataLoader的函数定义如下:DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False) dataset:加载的数据集(Dataset对象) batch_size:batch size shuffle::是...
torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, multiprocessing_context=None) Data loader. Combines a dataset and a sampler, and provides ...
File "/mnt/workspace/workgroup/chenghao/video_analysis/internvl_chat_interleaved/internvl/train/intern_vl_chat_finetune_block_pp.py", line 830, in main with torch.cuda.amp.autocast(dtype=torch.bfloat16, cache_enabled=False): File "/mnt/workspace/workgroup/miniconda/envs/internvl/lib/python3....
The problem is that this issue is hard to detect, because only the last batch causes a conflict with the network structure. The suggestion is to raise a warning when encountered the combinationdrop_last == Falseandlen(dataset) % batch_size == 1 ...
Please tell me about the dataloader of pytorch. The drop_last in it defaults to False. Then after it gets the last batch, it is smaller than the normal batch. So will there be any problem if it is directly passed to the neural network for training?