pytorch_mixed_precision_example.py

"""
Example code of how to use mixed precision training with PyTorch. In this
case with a (very) small and simple CNN training on MNIST dataset. This
example is based on the official PyTorch documentation on mixed precision 
training.

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-10 Initial programming
*    2022-12-19 Updated comments, made sure it works with latest PyTorch

"""

# Imports
import torch
import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F  # All functions that don't have any parameters
from torch.utils.data import (
    DataLoader,
)  # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets  # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms  # Transformations we can perform on our dataset


# Simple CNN
class CNN(nn.Module):
    def __init__(self, in_channels=1, num_classes=10):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(
            in_channels=1,
            out_channels=420,
            kernel_size=(3, 3),
            stride=(1, 1),
            padding=(1, 1),
        )
        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
        self.conv2 = nn.Conv2d(
            in_channels=420,
            out_channels=1000,
            kernel_size=(3, 3),
            stride=(1, 1),
            padding=(1, 1),
        )
        self.fc1 = nn.Linear(1000 * 7 * 7, num_classes)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc1(x)

        return x


# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
assert device == "cuda", "GPU not available"

# Hyperparameters
in_channel = 1
num_classes = 10
learning_rate = 3e-4
batch_size = 100
num_epochs = 5

# Load Data
train_dataset = datasets.MNIST(
    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(
    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

# Initialize network
model = CNN().to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Necessary for FP16
scaler = torch.cuda.amp.GradScaler()

# Train Network
for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(train_loader):
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # forward
        with torch.cuda.amp.autocast():
            scores = model(data)
            loss = criterion(scores, targets)

        # backward
        optimizer.zero_grad()
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()


# Check accuracy on training & test to see how good our model
def check_accuracy(loader, model):
    num_correct = 0
    num_samples = 0
    model.eval()

    with torch.no_grad():
        for x, y in loader:
            x = x.to(device=device)
            y = y.to(device=device)

            scores = model(x)
            _, predictions = scores.max(1)
            num_correct += (predictions == y).sum()
            num_samples += predictions.size(0)

        print(
            f"Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}"
        )

    model.train()


check_accuracy(train_loader, model)
check_accuracy(test_loader, model)