在 pytorch 中显示错误分类的图像

Question

我是 pytorch 和 numpy 的新手，所以这可能是一个愚蠢的问题。我希望看到一些被我的网络错误分类的图像，带有正确的标签和预测的标签。这是我的代码

valid_and_test_set = torchvision.datasets.MNIST("./mnist", train=False, download=True)
dataset_valid, dataset_test = torch.utils.data.random_split(valid_and_test_set,[5000, 5000])
dataset_test.dataset.transform = transform #transform is composed by unsqueeze, normalize, view and gaussian noise with randn
dataset_test.dataset.target_transform = OneHot() #OneHot return the label
dataloader_test = torch.utils.data.DataLoader(dataset_test.dataset, batch_size=5000, num_workers=num_workers, pin_memory=True)

def test(dataset, dataloader):
    net.eval()  
    with torch.no_grad():
        for batch in dataloader:
            inputs = batch[0]
            inputs = inputs.to(device, non_blocking=True)
            outputs = net(inputs)
            predictions = torch.argmax(outputs, dim=1)
            return predictions

提前谢谢你

Answer 1

至少有两种方法可以做到这一点。

一个是存储在评估期间被错误分类的图像（运行测试数据）并绘制它们。这显示here

另一种方法是使用 TensorBoard。这在我看来是相当优雅的，你可以找到它的综合指南here

Answer 2

def test(dataset, dataloader):
    net.eval()
    with torch.no_grad():
        for batch in dataloader:
            inputs = batch[0]
            label=batch[1]
            inputs = inputs.to(device, non_blocking=True)
            outputs = net(inputs)
            predictions = torch.argmax(outputs, dim=1)
            for sampleno in range(batch[0].shape[0]):
                if(label[sampleno]!=predictions[sampleno]):
                    print("Actual Lable")
                    print(label[sampleno])
                    print("Predicted Label")
                    print(predictions[sampleno])
                    showimg(inputs[sampleno].cpu())
            return predictions

你可以这样写showing()函数

def showimg(model):
    model=np.reshape(model.numpy(),[28,28]) # For 1D Vector
    
    #If you normalize the image then use Next three-line
    #Otherwise skip that
    mean=np.array([0.485, 0.456, 0.406] )
    std=np.array([0.229, 0.224, 0.225])
    model=(model*std+mean)
    


    #print(model)

    cv2.imshow("ABC", model)
    
    #waits for user to press any key
    #(this is necessary to avoid Python kernel form crashing)
    cv2.waitKey(0)

    #closing all open windows
    cv2.destroyAllWindows()

Answer 3

我收到这个错误，不知道这是什么意思

ValueError                                Traceback (most recent call last)
 in 
    288 
    289         # test on validation
--> 290         predictions = test(dataset_valid, dataloader_valid)
    291         accuracy_valid = 100. * predictions.eq(dataset_valid.dataset.targets[dataset_valid.indices].to(device)).sum().float() / len(dataset_valid)
    292 

 in test(dataset, dataloader)
    236                     print("Predicted Label")
    237                     print(predictions[sampleno])
--> 238                     showimages(inputs[sampleno].cpu())
    239             return predictions
    240 

 in showimages(model)
    240 
    241 def showimages(model):
--> 242     model=np.transpose(model.numpy(),(1,2,0))
    243 
    244     

<__array_function__ internals> in transpose(*args, **kwargs)

~/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py in transpose(a, axes)
    649 
    650     """
--> 651     return _wrapfunc(a, 'transpose', axes)
    652 
    653 

~/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     59 
     60     try:
---> 61         return bound(*args, **kwds)
     62     except TypeError:
     63         # A TypeError occurs if the object does have such a method in its

ValueError: axes don't match array