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abril 14, 2025 4:12 pm

In the current deep learning landscape, optimizing models for deployment in environments with limited resources is increasingly critical. Weight quantization fulfills this requirement by lowering the precision of model parameters, often converting 32-bit floating-point values to representations with fewer bits, resulting in smaller models that open up faster processing on constrained hardware. This tutorial presents the concept of weight quantization via PyTorch’s dynamic quantization method applied to a pre-trained ResNet18 model. It covers how to examine weight distributions, apply dynamic quantization to significant layers like fully connected layers, evaluate model size differences, and visualize the impact. By the end, you’ll gain both theoretical insights and practical skills for deploying deep learning models.

                import torch
import torch.nn as nn
import torch.quantization
import torchvision.models as models
import matplotlib.pyplot as plt
import numpy as np
import os

print("Torch version:", torch.__version__)

Here, we import essential libraries like PyTorch, torchvision, and matplotlib, alongside printing the PyTorch version to confirm all modules are set for model manipulation and visualization.

                model_fp32 = models.resnet18(pretrained=True)
model_fp32.eval()  

print("Pretrained ResNet18 (FP32) model loaded.")

A pre-trained ResNet18 model is loaded in FP32 precision and put in evaluation mode, setting it up for further processing and quantization.

                fc_weights_fp32 = model_fp32.fc.weight.data.cpu().numpy().flatten()

plt.figure(figsize=(8, 4))
plt.hist(fc_weights_fp32, bins=50, color='skyblue', edgecolor='black')
plt.title("FP32 - FC Layer Weight Distribution")
plt.xlabel("Weight values")
plt.ylabel("Frequency")
plt.grid(True)
plt.show()

This block extracts and flattens the weights from the final fully connected layer of the FP32 model, followed by plotting a histogram to visualize their distribution prior to quantization.

quantized_model = torch.quantization.quantize_dynamic(model_fp32, {nn.Linear}, dtype=torch.qint8)

quantized_model.eval()
print("Dynamic

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