Initial commit

.gitignore

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+__pycache__/
+data/

README.md

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+# PyTorch with Flask Demo
+
+A simple, bare-bones example on how to serve an ML model through a web application, using
+[PyTorch](https://pytorch.org/) and [Flask](https://flask.palletsprojects.com/en/2.2.x/).
+Based off the official [classifier tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).
+
+Images for testing can be found in the `images/` directory.
+
+**Installing dependencies**
+
+```
+$ pip install -r requirements.txt
+```
+
+**Training the model**
+
+```
+$ python ./model.py train
+```
+
+**Testing the model**
+
+```
+$ python ./model.py test
+```
+
+**Running the server**
+
+```
+$ python ./app.py
+```

app.py

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+#! /usr/bin/env python
+
+import io
+from flask import Flask, render_template, request
+from model import load_saved_model, predict
+from PIL import Image
+
+app = Flask(__name__)
+net = load_saved_model()
+
+
+@app.route("/", methods=["GET"])
+def index():
+    return render_template("index.html")
+
+
+@app.route("/eval", methods=["POST"])
+def eval():
+    image = Image.open(io.BytesIO(request.data)).convert("L")
+    result = predict(net, image)
+
+    # Dictionary return values are implicitly converted to JSON,
+    # which the client can parse to get the result.
+    return {"result": result}
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0")

model.py

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+#! /usr/bin/env python
+#
+# Heavily based off of
+# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
+
+import sys
+import pathlib
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.utils.data
+import torchvision
+import torchvision.transforms as transforms
+from PIL import Image
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+PATH = pathlib.Path(__file__).parent.joinpath("weight.pt")
+CLASSES = [str(x) for x in range(0, 10)]
+BATCH_SIZE = 4
+
+transform = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,)),
+    ]
+)
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 4 * 4, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+def train() -> Net:
+    torch.manual_seed(0)
+
+    net = Net()
+    net.train()
+    net.to(DEVICE)
+
+    trainset = torchvision.datasets.MNIST(
+        root="./data", train=True, download=True, transform=transform
+    )
+    trainloader = torch.utils.data.DataLoader(
+        trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4
+    )
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.AdamW(net.parameters(), lr=0.001)
+
+    for epoch in range(1, 3):
+
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 1):
+
+            # get the inputs; data is a list of [inputs, labels]
+            inputs, labels = data[0].to(DEVICE), data[1].to(DEVICE)
+
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            running_loss += loss.item()
+
+            if i % 2000 == 0:
+                print(f"[{epoch}, {i:5d}] loss: {running_loss / 2000:.3f}")
+                running_loss = 0.0
+
+    return net
+
+
+def test(net: Net):
+    net.eval()
+
+    testset = torchvision.datasets.MNIST(
+        root="./data", train=False, download=True, transform=transform
+    )
+    testloader = torch.utils.data.DataLoader(
+        testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4
+    )
+
+    correct_pred = {classname: 0 for classname in CLASSES}
+    total_pred = {classname: 0 for classname in CLASSES}
+
+    with torch.no_grad():
+        for data in testloader:
+            images, labels = data[0].to(DEVICE), data[1].to(DEVICE)
+            outputs = net(images)
+            _, predictions = torch.max(outputs, 1)
+
+            for label, prediction in zip(labels, predictions):
+                if label == prediction:
+                    correct_pred[CLASSES[label]] += 1
+                total_pred[CLASSES[label]] += 1
+
+    for classname, correct_count in correct_pred.items():
+        accuracy = 100 * float(correct_count) / total_pred[classname]
+        print(f"Accuracy for class: {classname:5s} is {accuracy:.1f} %")
+
+
+def predict(net: Net, image: Image) -> str:
+    with torch.no_grad():
+        image = transform(image).to(DEVICE).unsqueeze(0)
+        outputs = net(image)
+        _, predictions = torch.max(outputs, 1)
+        return CLASSES[predictions[0]]
+
+
+def load_saved_model() -> Net:
+    net = Net()
+    net.load_state_dict(torch.load(PATH))
+    net.to(DEVICE)
+    net.eval()
+
+    return net
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Invalid arguments, must be one of 'train', 'test'")
+    elif sys.argv[1] == "train":
+        net = train()
+        torch.save(net.state_dict(), PATH)
+        print(f"Finished training model, saved to {PATH}")
+    elif sys.argv[1] == "test":
+        net = load_saved_model()
+        test(net)
+    else:
+        print("Invalid argument, must be one of 'train', 'test'")

requirements.txt

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+flask
+torch
+torchvision
+Pillow

templates/index.html

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+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>My Model</title>
+
+    <script>
+      async function submit() {
+        // Get the HTML elements.
+        let prediction = document.getElementById("prediction");
+        let file = document.getElementById("file");
+
+        // Ensure that there is a file uploaded, otherwise do nothing.
+        if (file.files.length === 0) {
+          return;
+        }
+
+        // Call the server's "/eval" endpoint with the file.
+        let response = await fetch("/eval", {
+          method: "POST",
+          body: file.files[0],
+        });
+
+        // Parse the JSON response.
+        let json = await response.json();
+
+        // Set the prediction and clear the input.
+        prediction.innerText = `Prediction: ${json.result}`;
+        file.value = "";
+      }
+    </script>
+  </head>
+  <body>
+    <input id="file" type="file" />
+    <button onclick="submit();">Submit</button>
+    <p id="prediction"></p>
+  </body>
+</html>