Refactor training loop from script to class

src/api.py

@@ -1,22 +1,25 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
 from torchvision import transforms
-from main import Net # Importing Net class from main.py
 
-# Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
-model.eval()
+from main import MNISTTrainer # Importing MNISTTrainer class from main.py
+
+trainer = MNISTTrainer()
+trainloader = trainer.load_data()
+model = trainer.define_model()
+trainer.train_model(trainloader, model)
+trainer.save_model(model)
+model.eval() # Set the model to evaluation mode
 
 # Transform used for preprocessing the image
-transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
 app = FastAPI()
 
+
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
  image = Image.open(file.file).convert("L")

src/main.py

@@ -1,48 +1,61 @@
-from PIL import Image
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
-
-# Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.5,), (0.5,))
-])
+from torchvision import datasets, transforms
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
-trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
-# Step 2: Define the PyTorch Model
-class Net(nn.Module):
+class MNISTTrainer:
  def __init__(self):
- super().__init__()
- self.fc1 = nn.Linear(28 * 28, 128)
- self.fc2 = nn.Linear(128, 64)
- self.fc3 = nn.Linear(64, 10)
-
- def forward(self, x):
- x = x.view(-1, 28 * 28)
- x = nn.functional.relu(self.fc1(x))
- x = nn.functional.relu(self.fc2(x))
- x = self.fc3(x)
- return nn.functional.log_softmax(x, dim=1)
-
-# Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
-
-# Training loop
-epochs = 3
-for epoch in range(epochs):
- for images, labels in trainloader:
- optimizer.zero_grad()
- output = model(images)
- loss = criterion(output, labels)
- loss.backward()
- optimizer.step()
-
-torch.save(model.state_dict(), "mnist_model.pth")
+ self.transform = transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+ )
+ self.optimizer = None
+ self.criterion = nn.NLLLoss()
+ self.epochs = 3
+
+ def load_data(self):
+ trainset = datasets.MNIST(
+ ".", download=True, train=True, transform=self.transform
+ )
+ trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+ return trainloader
+
+ class Net(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.fc1 = nn.Linear(28 * 28, 128)
+ self.fc2 = nn.Linear(128, 64)
+ self.fc3 = nn.Linear(64, 10)
+
+ def forward(self, x):
+ x = x.view(-1, 28 * 28)
+ x = nn.functional.relu(self.fc1(x))
+ x = nn.functional.relu(self.fc2(x))
+ x = self.fc3(x)
+ return nn.functional.log_softmax(x, dim=1)
+
+ def define_model(self):
+ model = self.Net()
+ self.optimizer = optim.SGD(model.parameters(), lr=0.01)
+ return model
+
+ def train_model(self, trainloader, model):
+ for epoch in range(self.epochs):
+ for images, labels in trainloader:
+ self.optimizer.zero_grad()
+ output = model(images)
+ loss = self.criterion(output, labels)
+ loss.backward()
+ self.optimizer.step()
+
+ def save_model(self, model):
+ torch.save(model.state_dict(), "mnist_model.pth")
+
+
+trainer = MNISTTrainer()
+trainloader = trainer.load_data()
+model = trainer.define_model()
+trainer.train_model(trainloader, model)
+trainer.save_model(model)