Implementation of codes in the Introduction Introduction Weight: An important factor in developing efficiency in depth learning and Wellms

Today's deep reading, model models in the delivery of services areas are more important than ever. Weights refers to this need by reducing the accuracy of the model parameters, usually from 32-bit floating prices to reduce the width, thus giving small models to accelerate. This lesson introduces weight losses using the Pytorch rating study This lesson will equip theory on theory and active skills required to include deeper 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__)

We imported the required libraries, topostvision, and matplotlib, and prints the pytrot version, ensures that all required modules are ready for proper deception and monitoring.

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


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

The minimum model made in the FP32 (Floint-Point) accuracy and set in the test mode, preparing them to process and continue to process skill.

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()

In this Block, units from the final part of the FP32 model are discharged and tendiled, then histogram is organized to recognize its distribution before its use before its use.

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


print("Dynamic quantization applied to the model.")

We use dynamic energy, by directing straight layers – to convert them into low-minded formats, indicate an important process of reducing the model size and reducing.

def get_model_size(model, filename="temp.p"):
    torch.save(model.state_dict(), filename)
    size = os.path.getsize(filename) / 1e6
    os.remove(filename)
    return size


fp32_size = get_model_size(model_fp32, "fp32_model.p")
quant_size = get_model_size(quantized_model, "quant_model.p")


print(f"FP32 Model Size: {fp32_size:.2f} MB")
print(f"Quantized Model Size: {quant_size:.2f} MB")

The relief work is meant to save and check the model size from the disk; Then, it is used to measure and comparing the Original Fik32 model sizes and model made, indicates the impact of oppression.

dummy_input = torch.randn(1, 3, 224, 224)


with torch.no_grad():
    output_fp32 = model_fp32(dummy_input)
    output_quant = quantized_model(dummy_input)


print("Output from FP32 model (first 5 elements):", output_fp32[0][:5])
print("Output from Quantized model (first 5 elements):", output_quant[0][:5])

The dummy of tensor is designed to imitate the image, and both FP3 models and separate models are conducted in the entry to compare their results and ensure that their size is very changed.

if hasattr(quantized_model.fc, 'weight'):
    fc_weights_quant = quantized_model.fc.weight().dequantize().cpu().numpy().flatten()
else:
    fc_weights_quant = quantized_model.fc._packed_params._packed_weight.dequantize().cpu().numpy().flatten()


plt.figure(figsize=(14, 5))


plt.subplot(1, 2, 1)
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.subplot(1, 2, 2)
plt.hist(fc_weights_quant, bins=50, color="salmon", edgecolor="black")
plt.title("Quantized - FC Layer Weight Distribution")
plt.xlabel("Weight values")
plt.ylabel("Frequency")
plt.grid(True)


plt.tight_layout()
plt.show()

In this block, limited instruments) are released the fully-linked layer and hintograms against the original FP32 instruments to show changes in weight losses due to quantity.

In conclusion, the lesson granted a step guide to the step to understand and use weight, highlight their impact on the model and performance. By reducing the previously trained RESNET18 financial model, we saw shifts in mass distribution, material benefits from modeless, and the wrong speed. This assessment sets the additional stage of assessment, such as using the Notiation of the Knowledge of the Knowledge (Qat), which can also improve performance in divided models.

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