I’m using resnet-18 to infer test set. When I use one GPU, the time is only 10s. But when I want to use multiple GPUs and only add this code
if torch.cuda.device_count() > 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  model = torch.nn.DataParallel(model)

The result look like this

which cost 24s.
My whole code is below.
#coding=gbk
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
import time
model = torch.load('resnet_18-cifar_10.pth')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
#if torch.cuda.device_count() > 1:
#  print("Let's use", torch.cuda.device_count(), "GPUs!")
#  model = torch.nn.DataParallel(model)
model.to(device)
model.eval()
lossSum = 0.0
preLoss = 0.0
curLoss = 0.0
accuracy = 0.0
correctNum = 0
data_transforms = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
image_datasets = datasets.CIFAR10(root='../data', train=False,
                                  download=True, transform=data_transforms)
test_loader = torch.utils.data.DataLoader(image_datasets, batch_size=500,
                                          shuffle=True, num_workers=4, prefetch_factor=2)
since = time.time()
for data, target in test_loader:
    data = data.to(device)
    target = target.to(device)
    output = model(data)
    lossSum = lossSum + F.cross_entropy(output, target, reduction='sum').item()
    pred = output.data.max(1, keepdim=True)[1]  
    correctNum += pred.eq(target.data.view_as(pred)).cpu().sum() 
curLoss = lossSum / len(test_loader.dataset
accuracy = correctNum / len(test_loader.dataset)
time_elapsed = time.time() - since
print('Test complete in {:.0f}m {:.0f}s'.format(
    time_elapsed // 60, time_elapsed % 60))