train_dataloader:一个 torch.utils.data.DataLoader,指定 training dataloader。 eval_dataloader:一个 torch.utils.data.DataLoader,指定 evaluation dataloader。 metrics:一个字典 Dict[str, float],指定由上一次 evaluation 阶段计算得到的指标。 它仅在 on_evaluate 事件中才能访问。 logs:一个字典 Dict[str, floa...
info(f" Will skip the first {epochs_trained} epochs") for epoch in range(epochs_trained): sampler = get_dataloader_sampler(train_dataloader) sampler_kinds = [RandomSampler] if version.parse(accelerate_version) > version.parse("0.23.0"): sampler_kinds.append(SeedableRandomSampler) is_random_...
trainer.train_dataset[0].keys() 输出:dict_keys(['premise', 'hypothesis', 'label', 'idx', 'input_ids', 'attention_mask']) data_collator = trainer.get_train_dataloader().collate_fn actual_train_set = trainer._remove_unused_columns(trainer.train_dataset) batch = data_collator([actual_tra...
以下のget_train_dataloader()と_get_train_sampler()はTrainerクラス内に定義されている。 train()時は,train_datasetが読み込まれるが,この際にget_train_dataloader()によってDataLoaderが読み込まれる。ここで,DataLoaderのsamplerとして,_get_train_sampler()内でtorch.utils.data.RandomSamplerが指定さ...
def get_train_dataloader(self): """get train dataloader of mindspore.""" return build_dataset_loader(self.config.train_dataset.data_loader) def get_eval_dataloader(self): """get eval dataloader of mindspore.""" return build_dataset_loader(self.config.eval_dataset.data_loader) def ...
def train_dataloader(self): return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=False, num_workers=0) def val_dataloader(self): return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=False) def test_dataloader(self): return DataLoader(self.test_dataset, ...
一般情况下教程会教你这样去写Pytorch 的train 代码: #准备好训练数据加载器train_loader=DataLoader(dataset=train_data,batch_size=64,shuffle=True)#准备好模型model=Net()#优化方法optimizer=torch.optim.Adam(model.parameters())#loss 函数loss_func=torch.nn.CrossEntropyLoss()##然后开始每一轮的迭代forepoch...
self.train_losses = [] self.val_losses = [] def on_train_end(self, trainer, model: "pl.LightningModule") -> None: #记录训练和验证的损失 self.train_losses.append(trainer.train_dataloader.dataset.get_loss() self.val_losses.append(trainer.val_dataloaders[0].dataset.get_loss() ...
train_args ''' TrainingArguments( _n_gpu=0, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_...
[`TrainingArguments`], *optional*): The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided. data_collator(`DataCollator`,可选): 用于将 `train_...