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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
# This is just to show an interface where one draws a number and gets prediction.
n_epochs = 10
batch_size_train = 128
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 10
random_seed = 1
TRAIN_CUTOFF = 10
MODEL_PATH = 'model'
METRIC_PATH = os.path.join(MODEL_PATH,'metrics.json')
MODEL_WEIGHTS_PATH = os.path.join(MODEL_PATH,'mnist_model.pth')
OPTIMIZER_PATH = os.path.join(MODEL_PATH,'optimizer.pth')
REPOSITORY_DIR = "data"
LOCAL_DIR = 'data_local'
HF_TOKEN = os.getenv("HF_TOKEN")
MODEL_REPO = 'mnist-adversarial-model'
HF_DATASET ="mnist-adversarial-dataset"
DATASET_REPO_URL = f"https://huggingface.co/datasets/chrisjay/{HF_DATASET}"
MODEL_REPO_URL = f"https://huggingface.co/model/chrisjay/{MODEL_REPO}"
torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)
TRAIN_TRANSFORM = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])
# 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)
random_seed = 1
torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)
network = MNIST_Model() #Initialize the model with random weights
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
momentum=momentum)
# Train
#train(n_epochs,network,optimizer)
def image_classifier(inp):
"""
It takes an image as input and returns a dictionary of class labels and their corresponding
confidence scores.
:param inp: the image to be classified
:return: A dictionary of the class index and the confidence value.
"""
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
def main():
block = gr.Blocks()
with block:
with gr.Row():
image_input =gr.inputs.Image(source="canvas",shape=(28,28),invert_colors=True,image_mode="L",type="pil")
label_output = gr.outputs.Label(num_top_classes=2)
image_input.change(image_classifier,inputs = [image_input],outputs=[label_output])
block.launch()
if __name__ == "__main__":
main() |