每个元素为一个dict ,对应一张图片的元数据,self._dataset=datasetdef__getitem__(self,idx):idx_data=self._data[idx]data:dict=self.map_func(idx_data)returndata# 返回一个字典,如 {"image" : torch.Tensor
kwargs["collate_fn"]=default_data_collator else: # raise ValueError(f"Unknown batching strategy: {train_config.batching_strategy}") iftrain_config.enable_fsdportrain_config.enable_ddp: iftrain_config.enable_fsdportrain_config.enable_ddportrain_config.enable_deepspeed: ...
collate_fn=data_collator, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory, ) train_sampler = self._get_train_sampler() return DataLoader( train_dataset, batch_size=self.**_train_batch_size**, sampler=train_sampler, collate_fn=data_collator, drop_last...
数据集准备和预处理:这部分就是回顾上一集的内容:通过dataset包加载数据集加载预训练模型和tokenizer 定义Dataset.map要使用的预处理函数定义DataCollator来用于构造训练...使用Trainer来训练 Trainer是Huggingface transformers库的一个高级API,可以帮助我们快速搭建训练框架: from transformers import Trainer...的参数都有...
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