Gradio - Pytorch MNIST Digit Recognizer

Question

I watched the following video on YouTube https://www.youtube.com/watch?v=jx9iyQZhSwI where it was shown that it is possible to use Gradio and the learned model of MNIST dataset in Tensorflow. I have read and written that it is possible to use Pytorch in Gradio, but I have problems with its implementation. Does anyone have an idea how to do this? My Pytorch code of cnn

import torch.nn as nn
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(         
            nn.Conv2d(
                in_channels=1,              
                out_channels=16,            
                kernel_size=5,              
                stride=1,                   
                padding=2,                  
            ),                              
            nn.ReLU(),                      
            nn.MaxPool2d(kernel_size=2),    
        )
        self.conv2 = nn.Sequential(         
            nn.Conv2d(16, 32, 5, 1, 2),     
            nn.ReLU(),                      
            nn.MaxPool2d(2),                
        )
        # fully connected layer, output 10 classes
        self.out = nn.Linear(32 * 7 * 7, 10)
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        x = x.view(x.size(0), -1)       
        output = self.out(x)
        return output, x    # return x for visualization

By watching I find that I need to change function that Gradio use

def predict_image(img):
  img_3d=img.reshape(-1,28,28)
  im_resize=img_3d/255.0
  prediction=CNN(im_resize)
  pred=np.argmax(prediction)
  return pred

Gradio's idea, after training the neural network on the MNIST dataset, is to draw a digit yourself and see how the network works. In the example on YouTube, that network was learned through Tensorflow, and I wonder if there is a way to do it through Pytorch. — josf, Mar 27 '22 at 06:50
can you upload the full version of your code? and you said you had problems with implementation, can you clarify what are those problems? — CuCaRot, Apr 01 '22 at 04:45

score 0 · Accepted Answer · answered Apr 05 '22 at 11:52

Im sorry if I got your question wrong, but from what I understand you are getting an error when trying to predict the digit using your function predict image.

So here are two possible hints. Maybe you have implemented them already, but I don't know because of the very small code snippet.

First of all. Have you set your model into evaluation mode using

CNN.eval()

Do after you finished training your model and want to evaluate inputs without training the model.

Second of all, maybe you need to add a fourth dimension to your input tensor "im_resize". Normally your model expects a dimension for the number of channels, the batch size, the height and the width of your input. In addition I can not tell if your input is a of the datatype torch.tensor . If not transform your array into a tensor first.

You can add a batch dimension to your input tensor by using

im_resize = im_resize.unsqueeze(0)

I hope that I understand your question correctly and was able to help you.

Gradio - Pytorch MNIST Digit Recognizer

1 Answers1