Sweep: refactor the training loop from a script to a class in main.py

[wwzeng1]

Details

No response

Checklist

• Modify src/main.py ✓ 35f37cd
• Ran sandbox for src/main.py. ✓ 52cb808
• Modify src/api.py ✓ c1333ec
• Ran sandbox for src/api.py. ✓ b31fc72

[sweep-nightly]

Here's the PR! #163.

💎 Sweep Pro: I'm using GPT-4. You have unlimited GPT-4 tickets. (tracking ID: c863ad18ba)

---

Actions (click)

• ↻ Restart Sweep

Sandbox Execution ✓

Here are the sandbox execution logs prior to making any changes:

Sandbox logs for 7284908

trunk fmt src/main.py || exit 0 1/2 ✓

  NOTICES  
 src/main.py  ignored empty file [black]
 Hint: use --force to check ignored files
Checked 1 file
✔ No issues

trunk check --fix --filter=-ruff --print-failures src/main.py 2/2 ✓

  NOTICES  
 src/main.py  ignored empty file [black]
 Hint: use --force to check ignored files
Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter

Sandbox passed on the latest main, so sandbox checks will be enabled for this issue.

---

Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

evals/src/main.py

Lines 1 to 46 in 7284908

	from PIL import Image
	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,))
	])
	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):
	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()

evals/src/api.py

Lines 1 to 27 in 7284908

	from fastapi import FastAPI, UploadFile, File
	from PIL import Image
	import torch
	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()
	# Transform used for preprocessing the image
	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")
	image = transform(image)
	image = image.unsqueeze(0) # Add batch dimension
	with torch.no_grad():
	output = model(image)
	_, predicted = torch.max(output.data, 1)

evals/requirements.txt

Lines 1 to 15 in 7284908

	annotated-types==0.6.0
	anyio==3.7.1
	astroid==3.0.1
	certifi==2022.12.7
	charset-normalizer==2.1.1
	click==8.1.7
	dill==0.3.7
	exceptiongroup==1.1.3
	fastapi==0.104.0
	filelock==3.9.0
	fsspec==2023.4.0
	h11==0.14.0
	idna==3.4
	iniconfig==2.0.0
	isort==5.12.0

---

Step 2: ⌨️ Coding

• Modify src/main.py ✓ 35f37cd

Modify src/main.py with contents:
• Create a new class named `Trainer` at the top of the file.
• Move the model, optimizer, and loss function initialization from the global scope into the `__init__` method of the `Trainer` class. The `__init__` method should take the learning rate and the path to save the model as parameters, and use them to initialize the optimizer and save the model respectively.
• Move the training loop from the global scope into a new method of the `Trainer` class named `train`. This method should take the number of epochs as a parameter.
• Move the model saving code from the global scope into a new method of the `Trainer` class named `save_model`.
• Remove the global code for model initialization, training, and saving, as it is now encapsulated in the `Trainer` class.
• At the end of the file, create an instance of the `Trainer` class, and call its `train` and `save_model` methods to train and save the model.

• Ran sandbox for src/main.py. ✓ 52cb808

Sandbox logs for https://github.com/sweepai/evals/commit/52cb808b1b8bc91787d586dd565b782319ec01c4

trunk fmt src/main.py || exit 0 1/2 ✓

 ✔ Formatted src/main.py
Re-checking autofixed files...


Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter

trunk check --fix --filter=-ruff --print-failures src/main.py 2/2 ✓

Checked 1 file
✔ No issues

• Modify src/api.py ✓ c1333ec

Modify src/api.py with contents:
• Import the `Trainer` class from main.py at the top of the file.
• Replace the global model loading code with the creation of a `Trainer` instance and a call to its `load_model` method.
• Replace the global model usage in the `predict` function with a call to a method of the `Trainer` instance that returns the model.

• Ran sandbox for src/api.py. ✓ b31fc72

Sandbox logs for https://github.com/sweepai/evals/commit/b31fc724747dc0cd6fdae265be435aa36b06ab4a

trunk fmt src/api.py || exit 0 1/2 ✓

 ✔ Formatted src/api.py
Re-checking autofixed files...

 ✔ Formatted src/api.py
Re-checking autofixed files...


Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter

trunk check --fix --filter=-ruff --print-failures src/api.py 2/2 ✓

Checked 1 file
✔ No issues

---

Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/refactor-training-loop_27.

---

🎉 Latest improvements to Sweep:

• Sweep now uses the rope library to refactor Python! Check out Large Language Models are Bad at Refactoring Code. To have Sweep refactor your code, try sweep: Refactor <your_file>.py!
• Sweep finds and fixes bugs in your code by writing unit tests! Check out Having GPT-4 Iterate on Unit Tests like a Human.

---

💡 To recreate the pull request edit the issue title or description. To tweak the pull request, leave a comment on the pull request.
^{Join Our Discord}