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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()
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