During the debugging process, 'Data' and 'Target' are stored inX_trainandY_trainrespectively. However, I am unable to comprehend why the tensor values are not being accepted by the enumerate (dataloader) in the for loop. In the dataloader, there are values b...
for i, data in enumerate(train_loader): #从dataloader里面一步步取数据 optimizer.zero_grad() # 用 optimizer 将 model 参数的 gradient 清零,防止上一步训练出来的gradient影响这一步的训练 train_pred = model(data[0].cuda()) # 利用 model 得到预测的概率分步 这样实际上是在使用 model 的 forward ...
for epoch in range(250): # hidden = (torch.zeros(2, 13, 5), # torch.zeros(2, 13, 5)) # model.hidden = hidden for i, data in enumerate(train_loader): hidden = model.init_hidden(13) inputs = data[0] outputs = data[1] print('inputs', inputs.size()) # print('outputs',...
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 tokenized_text = tokenizer(text, max_length=100, add_speci...
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( ...
( 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]) ...
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...
当使用enumerate(dataloader)时,每次迭代都会从dataloader中获取一个元素(通常是一个批次的数据),并将其与当前迭代的索引一起赋值给idx和batch_x。 idx是当前的迭代索引(从0开始),而batch_x是当前批次的数据。 描述for idx, batch_x in enumerate(dataloader):这行代码的整体工作流程: 这行代码启动了一个循环,该...
for _, (images, _) in enumerate(dataloader): 这是一个循环,使用enumerate 函数遍历数据加载器dataloader 中的数据批次。_ 通常用于表示不需要使用的循环变量,images 是从数据批次中提取出的图像数据。images = images.to('cuda'):将图像数据images 转移到 GPU (如果有可用的 CUDA 设备),以加快计算速度。
for i, data in enumerate(data_loader): if init_: inputs = [t.cuda() for t in data['img_inputs'][0]] metas_ = model.get_bev_pool_input(inputs) if model.__class__.__name__ in ['FBOCCTRT', 'FBOCC2DTRT']: metas_ = model.get_bev_pool_input(inputs, img_metas=data['...