enumerate用于给可迭代对象标出索引 即batch size = len(dataloader.dataset) 参数为dataloader.dataset 而不是dataloader dataloader为num_batches 循环中先两步 pred=model(x) loss=loss_fn(pred,y) 循环中后三步 optimizer.zero_grad() loss.backward() optimizer.step() def test_loop(dataloader,model,loss_f...
( 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 step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader b_x = Variable(x) # batch x b_y = Variable(y) # batch y output = cnn(b_x)[0] # cnn output loss = loss_func(output, b_y) # cross entropy loss optimizer.zero_grad() ...
x = torch.empty((8, 42), device = args.device) net = Network().to(device = args.device) 1. 2. 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. ...
print(x.grad) def create_nn(batch_size=200, learning_rate=0.01, epochs=10, log_interval=10): train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), ...
for(k, batch)inenumerate(dataloader): # compute current value of loss function via forward pass output =gnn_model(batch) loss_function_value =loss_function(output[:,0], torch.tensor(batch.y, dtype = torch.float32)) # set past gradient to zero ...
for batch in tqdm(dataloader, desc='Running the model inference'): images = batch['image'].to(device) labels += batch['label'] image_paths += batch['image_path'] output = model.forward(images) current_outputs = output.cpu().numpy() features = np.concatenate((outputs, current_outputs...
" for batch_idx, (x, y) in enumerate(train_loader):\n", " \n", " print('Epoch:', epoch+1, end='')\n", " print(' | Batch index:', batch_idx, end='')\n", " print(' | Batch size:', y.size()[0])\n", " \n", " x = x.to(device)\n", " y = y.to(devi...
train_loader = DataLoader(train_t, batch_size=data_yaml['batch_size'], shuffle=True) test_t = dataset_tensor(X_test, data_yaml['embed_val']) test_loader = DataLoader(test_t, batch_size=data_yaml['batch_size'], shuffle=True)
epst1=time.time()forstep,(batch_x,batch_y)inenumerate(self.normal_loader):ifself.cuda:torch.cuda.synchronize()#time_in_start t1=time.time()batch_x,batch_y=batch_x.to(self.device),batch_y.to(self.device)b_x=Variable(batch_x)b_y=Variable(batch_y)pred_y=self.model(b_x)#print(pr...