Transform the image, add batch dimension and put image on target device transformed_image = transform(img).unsqueeze(0).to(device) # 9. Prepare model for inference by sending it to target device and turning on
Tensor+device+__init__(device)+to(device)Device+type+__init__(type)+is_available() 序列图展示 随后的序列图展示了在 CPU 和 GPU 之间转移 Tensor 的过程: TensorDeviceUserTensorDeviceUserCheck device availabilityReturns available deviceCreate tensorMove tensor to deviceTensor on specified device 总结 ...
to(device) ##优化器 optimizer = optim.SGD(model.parameters(), lr= learnrate, momentum= momentnum) ##训练函数 def train(epoch): model.train() for batch_idx, data in enumerate(train_mydataloader, 0): inputs, target = data ##加入CPU和GPU选择inputs, target = inputs.to(device), ...
for i, data in enumerate(trainloader):inputs, labels = data[0].to(device), data[1].to(device) # move data to GPU if possible (not necessary for CPU)optimizer.zero_grad() # clear gradients for this training step only (not necessary in the first epoch)outputs = model(inputs) # for...
# if gpu is to be used device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 记忆回放(Replay Memory) 为了训练DQN,我们将使用经验回放池(experience replay memory)来存储智能体所观测到的环境状态转移情况,在之后的训练中我们可以充分利用这些数据。通过对经验回放池中的数据进行随机采样...
# 创建模型并移动到GPUmodel= SimpleModel().to(rank) # 包装模型为DDP模型ddp_model = DDP(model, device_ids=[rank]) if__name__ =="__main__":importosimporttorch.multiprocessing as mp # 世界大小:总共的进程数world_size =4 # 使用mp.spawn启动多个进程mp.sp...
(x) # 将输入张量展开成一维张量 logits = self.linear_relu_stack(x) # 将展开后的向量经过神经网络的所有层,得到最终的输出向量 return logits # 返回输出向量 # Move model to the selected device model = NeuralNetwork().to(device) # 创建一个神经网络的实例,并将其移动到指定的设备上执行 print(...
@torch.no_grad()def forecastModel(model, device, dataloader, node):model.eval()model.to(device)for i, batch in enumerate(dataloader):batch = batch.to(device)withtorch.no_grad():pred =model(batch, device)truth = batch.y.view(...
(0).to(device) # Add a batch dimension and move to GPU# Set the model to evaluation modemodel.eval()with torch.no_grad():outputs = model(input_tensor)_, predicted = torch.max(outputs, 1)predicted_label = predicted.item()label=['猫','狗']print(label[predicted_label])plt.axis('...
()# Since the Pipe is only within a single host and process the ``RRef``# returned by forward method is local to this node and can simply# retrieved via ``RRef.local_value()``.output = model(data).local_value()# Need to move targets to the device where the output of the# ...