PyTorch Model Quantization Guide

In PyTorch, you can use the torch.quantization module to quantize models. The specific steps are as follows:

1. Define the model and load pre-trained model parameters.

import torch
import torchvision.models as models

model = models.resnet18(pretrained=True)
model.eval()

1. Develop a quantifiable model.

import torch.quantization

quantized_model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8
)

1. Evaluate the performance of quantitive models.

from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

dataset = datasets.ImageNet(root='path_to_ImageNet', split='val', transform=transform)
loader = DataLoader(dataset, batch_size=1)

def evaluate(model):
    model.eval()
    model = model.to('cuda')
    
    total_correct = 0
    total_samples = 0
    
    with torch.no_grad():
        for images, labels in loader:
            images = images.to('cuda')
            labels = labels.to('cuda')
            
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            
            total_samples += labels.size(0)
            total_correct += (predicted == labels).sum().item()
    
    accuracy = total_correct / total_samples
    print(f'Accuracy: {accuracy}')

evaluate(quantized_model)

By following the above steps, you can quantize your model using PyTorch’s quantization functionality and evaluate the performance of the quantized model.