test_auto.py

import torchvision
import torch
import torchvision.transforms as transforms
from DataSet  import  MyDataset
from torch.utils.data import DataLoader
import torch.nn as nn

test_root="./data/test_data/0_9/"
test_data  = MyDataset(root=test_root, datatxt="test.txt" ,transform=transforms.ToTensor())
test_loader    = DataLoader(dataset=test_data, batch_size=1 )
# restore entire net1 to net2
net = torch.load('net.pkl')

#for name, param in net.named_parameters():
#	print(name, param)
# 构造测试的dataloader
#dataiter = iter(testloader)
# 预测正确的数量和总数量
correct = 0
total = 0
# 使用torch.no_grad的话在前向传播中不记录梯度，节省内存
with torch.no_grad():
    for i,data in enumerate(test_loader):
        images,labels = data
        outputs = net(images)
        print("outputs=",outputs.data)
        # 我们的网络输出的实际上是个概率分布，去最大概率的哪一项作为预测分类
        _, predicted = torch.max(outputs.data, 1)
        print("i=",i)
        print("predicted",predicted)
        print("label",labels)
        total += labels.size(0)
        correct +=(predicted==labels).sum().item()
print('Accuracy of the network on the 10000  test images: %d %%' % (100*correct/total))