在您的代码中,您应该已经创建了一个这样的数据加载器,并假设它已经被命名为test_loader。 3. 使用for循环遍历test_loader 遍历test_loader是一个常见的操作,特别是在进行模型评估或测试时。test_loader会按照批次(batch)返回数据,每个批次包含一定数量的样本。 4. 在for循环中使用tqdm(test_loader)来显示进度条 ...
dataset train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True) model = BertForQA(config) model.to(config.device) # Train! logger.info("*** Running training ***") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num ...
# For each batch of training data... for batch in tqdm(dataloader, total=len(dataloader)): # Add original labels - use later for evaluation. true_labels += batch['labels'].numpy().flatten().tolist() # move batch to device batch = {k:v.type(torch.long).to(device_) for k,v in...
train() total_loss = 0.0 # 读取数据,对训练数据进行shuffle train_loader = torch.utils.data.DataLoader(train_data, num_workers=4, batch_size=model.batch_size, shuffle=True, pin_memory=True) for data in tqdm(train_loader): model.optimizer.zero_grad() # 重点在于forward函数 targets, scores ...
total_iter=len(train_loader)forbatchintrain_loader:#正向传播optim.zero_grad() input_ids= batch['input_ids'].to(device) attention_mask= batch['attention_mask'].to(device) label= batch['labels'].to(device) outputs= model(input_ids, attention_mask=attention_mask, labels=label) ...
from tqdm import tqdm import copy # The quantization algorithm requires calibration data. Below we show a rough example of how to # set up a calibration data loader with the desired calib_size data_loader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x[0][0])...
total_iter=len(train_loader)forbatchintrain_loader:#正向传播optim.zero_grad() input_ids= batch['input_ids'].to(device) attention_mask= batch['attention_mask'].to(device) label= batch['labels'].to(device) outputs= model(input_ids, attention_mask=attention_mask, labels=label) ...
Here, we will implement a custom collate_function to give to our PyTorch data loader. This function will take care of Padding for us: to make all the sequences the same length by adding PAD_IDX to the shorter ones in order to be able to build a batch with them. We are going to pa...
time_out=0time_in=0forepochintqdm(range(self.n_epoch)):running_loss=0running_error=0running_acc=0ifself.cuda:torch.cuda.synchronize()#time_out_start epst1=time.time()forstep,(batch_x,batch_y)inenumerate(self.normal_loader):ifself.cuda:torch.cuda.synchronize()#time_in_start ...
{AZURE_DATA_NAME}@latest"), # Get data from Data asset epoch=d['train']['epoch'], train_batch_size=d['train']['train_batch_size'], eval_batch_size=d['train']['eval_batch_size'], model_dir=d['train']['model_dir'] ), code="./src_train", # l...