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...
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False) 1. 2. 3. 代码解读 model_best.eval() #使用刚刚训练好的model_best来进行预测 prediction = [] with torch.no_grad(): for i, data in enumerate(test_loader): test_pred = model_best(data.cuda()) test_label = np....
for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['trai...
( 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]) ...
for i, (features, target) in enumerate(dataloader): # Forward pass output = model(features) loss = criterion(output, target) # Backward pass loss.backward() # Only update weights every other 2 iterations # Effective batch size is doubled if (i+1) % 2 == 0 or (i+1) == len(...
for i, data in enumerate(train_loader): optimizer.zero_grad() # reset hidden states model.hidden = model.init_hidden() # get inputs inputs, labels = data inputs = inputs.view(-1,28,28).to(device) labels = labels.to(device) # forward+backward+optimize outputs = model(inputs) loss...
fori, (inputs, labels)inenumerate(dataloaders['val']): inputs=inputs.to(device) labels=labels.to(device) outputs=model(inputs) _, preds=torch.max(outputs,1) forjinrange(inputs.size()[0]): images_so_far+=1 ax=plt.subplot(num_images//2,2, images_so_far) ...
def train(model, dataloader, optimizer, criterion, device): model.train() epoch_loss = 0 epoch_acc = 0 for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask...
fori, datainenumerate(data_lab_and_docs): file_name = pref_val +"_"+ str(i) +"_"+ suff_val tab_tensor = torch.tensor(data[1], dtype=torch.float32) input_t = torch.squeeze(torch.index_select(batch_encoding["input_ids"],dim=0, index=torch.tensor(i))) ...
n[i, i] = 1 kernel = scipy.ndimage.gaussian_filter(n, sigma) for name, param in self.named_parameters(): param.data.copy_(torch.from_numpy(kernel)) 损失函数计算: gt_semantic = F.interpolate(gt_matte,scale_factor=1/16, mode='bilinear') ...