for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{}]tLoss: {}'.format( epoch, batch_idx...
( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model = torch.load(os.path.join(args.output_dir,"model.pt") ) model.cuda() model.eval() full_logits=[] full_label_ids=[]forstep, batchinenumerate(eval_dataloader): batch ...
train() total_loss = 0 t = tqdm(train_dataloader) for step, batch in enumerate(t): for k, v in batch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], token_type_ids=batch["token_type_ids"], attention_mask=batch["attention_mask"], labels=batch[...
model.train() total_loss =0t = tqdm(train_dataloader)forstep, batchinenumerate(t):fork, vinbatch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], token_type_ids=batch["token_type_ids"], attention_mask=batch["attention_mask"], ...
这可以在许多情况下用于生成设备不可知代码。以下是使用 dataloader 的例子: cuda0 = torch.device('cuda:0') # CUDA GPU 0 for i, x in enumerate(train_loader): x = x.to(cuda0) 1. 2. 3. 在系统上使用多个 GPU 时,您可以使用 CUDA_VISIBLE_DEVICES 环境标志来管理 PyTorch 可用的 GPU。如上所...
for step, batch in enumerate(train_dataloader): optimizer.zero_grad() loss = model(batch) loss.backward() optimizer.step() Note on using gradient scaler: If you are using a gradient scaler, you need to specifically call the unscale_ on the inner optimizer. scaler.unscale_(optimizer.inner...
batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=batch_size, shuffle=True) ...
train_loader = DataLoader(dataset = train_data, batch_size=BATCH_SIZE, shuffle=True) test_data = MyDataset(txt= root + 'test.txt', transform = torchvision.transforms.ToTensor()) test_loader = DataLoader(dataset = test_data, batch_size=BATCH_SIZE) ...
train() for step, (texts, labels) in enumerate(train_dataloader): labels = labels.to(model.device) optimizer.zero_grad() # Encode text and pass through classification head. inputs = model.tokenize(texts) input_ids = inputs['input_ids'].to(model.device) input_attention_mas...