commit 58e344ef137c0837085420f8859fe353379394ed
Author: TaurusXin <i@taurusxin.com>
Date:   Fri Dec 29 19:53:46 2023 +0800

    first commit

diff --git a/.gitignore b/.gitignore
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+__pycache__
+
+data/*
+
+!data/.gitkeep
+
+*.pt
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diff --git a/README.md b/README.md
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+# MNIST 数据集
+
+使用 PyTorch 训练
+
+## 依赖安装
+
+首先根据 [PyTorch 官网](https://pytorch.org/get-started/locally/) 安装 PyTorch，以便启用 GPU 加速
+
+然后安装其他依赖
+
+```shell
+pip install fastapi uvicorn pillow tqdm
+```
+
+## 训练模型
+
+首先你当然要先训练模型啦
+
+```shell
+python mnist_train.py --save-model
+```
+
+## 使用方法
+
+训练完模型后，本仓库包含两种使用方法，即本地文件和 Web UI
+
+### 本地文件
+
+将图片文件放到 input 文件夹内，必须是 28*28 的灰度png格式，然后执行
+
+```shell
+python mnist.test.py
+```
+
+### Web UI
+
+执行以下命令启动 Web UI
+
+```shell
+uvicorn webui:app
+```
+
+然后打开 <http://127.0.0.1:8000> 即可看到网页
diff --git a/data/.gitkeep b/data/.gitkeep
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+# ignore data but keep folder
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diff --git a/mnist_net.py b/mnist_net.py
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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class MnistNet(nn.Module):
+    def __init__(self):
+        super(MnistNet, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        output = F.log_softmax(x, dim=1)
+        return output
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diff --git a/mnist_test.py b/mnist_test.py
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+import torch
+from mnist_net import MnistNet
+from torchvision import transforms
+from PIL import Image
+
+import os
+
+# find device
+device = None
+
+use_cuda = torch.cuda.is_available()
+use_mps = torch.backends.mps.is_available()
+
+if use_cuda:
+    device = torch.device("cuda")
+elif use_mps:
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+
+print(f"Using device: {device}")
+
+# load image
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+)
+
+def predict(device, model, img):
+    # transform image
+    img = transform(img)
+    img = img.to(device)
+    img = img.unsqueeze(0)
+
+    # predict
+    output = model(img)
+    pred = output.argmax(dim=1, keepdim=True)
+    return pred.item()
+
+if __name__ == '__main__':
+    # load model
+    model = MnistNet()
+    model.load_state_dict(torch.load('mnist_cnn.pt', map_location=device))
+    model.to(device)
+
+    # set model to eval mode
+    model.eval()
+
+    # load image
+    folder = './input'
+    predictions = []
+
+    for image_name in sorted(os.listdir(folder)):
+        img = Image.open(os.path.join(folder, image_name))
+        predictions.append(predict(device, model, img))
+
+    print(predictions)
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diff --git a/mnist_train.py b/mnist_train.py
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+import argparse
+
+from mnist_net import MnistNet
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+from torch.optim.lr_scheduler import StepLR
+from tqdm import tqdm
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    with tqdm(train_loader, desc=f"Train Epoch {epoch}", unit="batch") as t:
+        for batch_idx, (data, target) in enumerate(t):
+            data, target = data.to(device), target.to(device)
+            optimizer.zero_grad()
+            output = model(data)
+            loss = F.nll_loss(output, target)
+            loss.backward()
+            optimizer.step()
+
+            if batch_idx % args.log_interval == 0:
+                t.set_postfix(loss=round(loss.item(), 3))
+
+            if args.dry_run:
+                break
+
+
+def test(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(
+                output, target, reduction="sum"
+            ).item()  # sum up batch loss
+            pred = output.argmax(
+                dim=1, keepdim=True
+            )  # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    print(
+        "Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+            test_loss,
+            correct,
+            len(test_loader.dataset),
+            100.0 * correct / len(test_loader.dataset),
+        )
+    )
+
+
+def main():
+    # Training settings
+    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=64,
+        metavar="N",
+        help="input batch size for training (default: 64)",
+    )
+    parser.add_argument(
+        "--test-batch-size",
+        type=int,
+        default=1000,
+        metavar="N",
+        help="input batch size for testing (default: 1000)",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=14,
+        metavar="N",
+        help="number of epochs to train (default: 14)",
+    )
+    parser.add_argument(
+        "--lr",
+        type=float,
+        default=1.0,
+        metavar="LR",
+        help="learning rate (default: 1.0)",
+    )
+    parser.add_argument(
+        "--gamma",
+        type=float,
+        default=0.7,
+        metavar="M",
+        help="Learning rate step gamma (default: 0.7)",
+    )
+    parser.add_argument(
+        "--no-cuda", action="store_true", default=False, help="disables CUDA training"
+    )
+    parser.add_argument(
+        "--no-mps",
+        action="store_true",
+        default=False,
+        help="disables macOS GPU training",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        default=False,
+        help="quickly check a single pass",
+    )
+    parser.add_argument(
+        "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
+    )
+    parser.add_argument(
+        "--log-interval",
+        type=int,
+        default=10,
+        metavar="N",
+        help="how many batches to wait before logging training status",
+    )
+    parser.add_argument(
+        "--save-model",
+        action="store_true",
+        default=False,
+        help="For Saving the current Model",
+    )
+    args = parser.parse_args()
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+    use_mps = not args.no_mps and torch.backends.mps.is_available()
+
+    torch.manual_seed(args.seed)
+
+    if use_cuda:
+        device = torch.device("cuda")
+    elif use_mps:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+    
+    print(f"Using device: {device}\n")
+
+    train_kwargs = {"batch_size": args.batch_size}
+    test_kwargs = {"batch_size": args.test_batch_size}
+    if use_cuda:
+        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
+        train_kwargs.update(cuda_kwargs)
+        test_kwargs.update(cuda_kwargs)
+
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
+    dataset1 = datasets.MNIST("./data", train=True, download=True, transform=transform)
+    dataset2 = datasets.MNIST("./data", train=False, transform=transform)
+    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+    model = MnistNet().to(device)
+    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
+
+    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
+    for epoch in range(1, args.epochs + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test(model, device, test_loader)
+        scheduler.step()
+
+    if args.save_model:
+        torch.save(model.state_dict(), "mnist_cnn.pt")
+        print("Model saved to mnist_cnn.pt")
+
+if __name__ == "__main__":
+    main()
diff --git a/ui/index.html b/ui/index.html
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+++ b/ui/index.html
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>绘图应用</title>
+    <style>
+        #canvas, #canvas2 {
+            background-color: black;
+            cursor: crosshair;
+        }
+
+        #result {
+            margin-top: 10px;
+            font-weight: bold;
+        }
+    </style>
+</head>
+<body>
+    <canvas id="canvas" width="400" height="400"></canvas>
+    <button onclick="submitDrawing()">提交</button>
+    <button onclick="clearCanvas()">清除</button>
+    <div>结果: <span id="result"></span></div>
+
+    <script>
+        const canvas = document.getElementById("canvas");
+        const context = canvas.getContext("2d");
+
+        let isDrawing = false;
+
+        canvas.addEventListener("mousedown", startDrawing);
+        canvas.addEventListener("mousemove", draw);
+        canvas.addEventListener("mouseup", stopDrawing);
+        canvas.addEventListener("mouseout", stopDrawing);
+
+        function startDrawing(e) {
+            isDrawing = true;
+            draw(e);
+        }
+
+        function draw(e) {
+            if (!isDrawing) return;
+
+            const rect = canvas.getBoundingClientRect();
+            const mouseX = e.clientX - rect.left;
+            const mouseY = e.clientY - rect.top;
+
+            context.lineWidth = 50;
+            context.lineCap = "round";
+            context.strokeStyle = "white";
+
+            context.lineTo(mouseX, mouseY);
+            context.stroke();
+            context.beginPath();
+            context.moveTo(mouseX, mouseY);
+        }
+
+        function stopDrawing() {
+            isDrawing = false;
+            context.beginPath();
+        }
+
+        function submitDrawing() {
+            const imageDataUrl = canvas.toDataURL("image/png");
+
+            const image = new Image();
+            image.src = imageDataUrl;
+
+            image.onload = function() {
+                const tempCanvas = document.createElement("canvas");
+                const tempContext = tempCanvas.getContext("2d");
+                tempCanvas.width = 28;
+                tempCanvas.height = 28;
+                tempContext.drawImage(image, 0, 0, 28, 28);
+
+                const croppedImageDataUrl = tempCanvas.toDataURL("image/png");
+
+                submitToAPI(croppedImageDataUrl);
+            };
+        }
+
+        function clearCanvas() {
+            // 清除Canvas上的绘图
+            context.clearRect(0, 0, canvas.width, canvas.height);
+        }
+
+        function submitToAPI(imageDataUrl) {
+            fetch('/api/predict', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ image: imageDataUrl }),
+            })
+            .then(response => response.json())
+            .then(data => {
+                console.log('API返回的结果:', data.result);
+                document.querySelector("#result").innerText = data.result.toString();
+            })
+            .catch(error => {
+                console.error('提交失败', error);
+            });
+        }
+    </script>
+</body>
+</html>
diff --git a/webui.py b/webui.py
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+++ b/webui.py
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+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+
+from PIL import Image
+
+import torch
+
+from mnist_net import MnistNet
+from mnist_test import device
+from mnist_test import predict
+
+import base64
+import io
+
+class Request(BaseModel):
+    image: str
+
+app = FastAPI()
+
+model = MnistNet()
+model.load_state_dict(torch.load('mnist_cnn.pt', map_location=device))
+model.to(device)
+model.eval()
+
+@app.get('/')
+async def root():
+    html_file = open("./ui/index.html", "r", encoding="utf-8")
+    content = html_file.read()
+    return HTMLResponse(content=content)
+
+@app.post("/api/predict")
+async def root(req: Request):
+    img = base64.b64decode(req.image.replace('data:image/png;base64,', ''))
+    img = Image.open(io.BytesIO(img)).convert('L')
+    prediction = predict(device, model, img)
+    return {
+        'result': prediction
+    }