app.py

import os
import torch
import gradio as gr
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


n_epochs = 3
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 10

random_seed = 1
torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)

train_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST('files/', train=True, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
  batch_size=batch_size_train, shuffle=True)

test_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST('files/', train=False, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
  batch_size=batch_size_test, shuffle=True)


# Source: https://nextjournal.com/gkoehler/pytorch-mnist
class MNIST_Model(nn.Module):
    def __init__(self):
        super(MNIST_Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)


def train(epochs,network,optimizer):
    
    train_losses=[]
    network.train()
    for epoch in range(epochs):
        for batch_idx, (data, target) in enumerate(train_loader):
            optimizer.zero_grad()
            output = network(data)
            loss = F.nll_loss(output, target)
            loss.backward()
            optimizer.step()
            if batch_idx % log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
                train_losses.append(loss.item())
        
                torch.save(network.state_dict(), 'model.pth')
                torch.save(optimizer.state_dict(), 'optimizer.pth')

def test():
    test_losses=[]
    network.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = network(data)
            test_loss += F.nll_loss(output, target, size_average=False).item()
            pred = output.data.max(1, keepdim=True)[1]
            correct += pred.eq(target.data.view_as(pred)).sum()
            test_loss /= len(test_loader.dataset)
        test_losses.append(test_loss)
        print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))



random_seed = 1
torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)

network = MNIST_Model()
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
                      momentum=momentum)        


model_state_dict = 'model.pth'
optimizer_state_dict = 'optmizer.pth'

if os.path.exists(model_state_dict):
    network_state_dict = torch.load(model_state_dict)
    network.load_state_dict(network_state_dict)

if os.path.exists(optimizer_state_dict):
    optimizer_state_dict = torch.load(optimizer_state_dict)
    optimizer.load_state_dict(optimizer_state_dict)                      



# Train
#train(n_epochs,network,optimizer)


def image_classifier(inp):
    input_image = torchvision.transforms.ToTensor()(inp).unsqueeze(0)
    with torch.no_grad():

        prediction = torch.nn.functional.softmax(network(input_image)[0], dim=0)
        #pred_number = prediction.data.max(1, keepdim=True)[1]
        sorted_prediction = torch.sort(prediction,descending=True)
        confidences={}
        for s,v in zip(sorted_prediction.indices.numpy().tolist(),sorted_prediction.values.numpy().tolist()):
            confidences.update({s:v})
        return confidences

TITLE = "MNIST Adversarial: Try to fool the MNIST model"
description = """This project is about dynamic adversarial data collection (DADC). 
The basic idea is to do data collection, but specifically collect “adversarial data”, the kind of data that is difficult for a model to predict correctly. 
This kind of data is presumably the most valuable for a model, so this can be helpful in low-resource settings where data is hard to collect and label.

### What to do:
- Draw a number from 0-9.
- Click `Submit` and see the model's prediciton.
- If the model misclassifies it, Flag that example.
- This will add your (adversarial) example to a dataset on which the model will be trained later.
"""
gr.Interface(fn=image_classifier, 
            inputs=gr.Image(source="canvas",shape=(28,28),invert_colors=True,image_mode="L",type="pil"),
            outputs=gr.outputs.Label(num_top_classes=10),
            allow_flagging="manual",
            title = TITLE,
            description=description).launch()