From 30522ecec03eef9c9c5d8946ebf319feadb64b97 Mon Sep 17 00:00:00 2001
From: zineb15022001 <zineb.kabbaj@etu.ec-lyon.fr>
Date: Tue, 28 Nov 2023 17:47:48 +0100
Subject: [PATCH] Update TD2 Deep Learning.ipynb
---
TD2 Deep Learning.ipynb | 161 +++++++++++++++++++++++++++++++++++++---
1 file changed, 150 insertions(+), 11 deletions(-)
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 63d0b6b..c8dad87 100644
--- a/TD2 Deep Learning.ipynb
+++ b/TD2 Deep Learning.ipynb
@@ -1002,7 +1002,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Le nouveau modèle est beaucoup plus performant, on a pas de problème d'overfitting ."
+ "Le nouveau modèle est beaucoup plus performant, on a pas de problème d'overfitting contrairement à l'encien modèle."
]
},
{
@@ -1022,23 +1022,41 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 46,
"id": "ef623c26",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "model: fp32 \t Size (KB): 365.058\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "365058"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import os\n",
"\n",
"\n",
- "def print_size_of_model(model, label=\"\"):\n",
- " torch.save(model.state_dict(), \"temp.p\")\n",
+ "def print_size_of_model(model2, label=\"\"):\n",
+ " torch.save(model2.state_dict(), \"temp.p\")\n",
" size = os.path.getsize(\"temp.p\")\n",
" print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
" os.remove(\"temp.p\")\n",
" return size\n",
"\n",
"\n",
- "print_size_of_model(model, \"fp32\")"
+ "print_size_of_model(model2, \"fp32\")"
]
},
{
@@ -1051,16 +1069,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 51,
"id": "c4c65d4b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "model: int8 \t Size (KB): 168.478\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "168478"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import torch.quantization\n",
"\n",
- "\n",
- "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
- "print_size_of_model(quantized_model, \"int8\")"
+ "quantized_model2 = torch.quantization.quantize_dynamic(model2, dtype=torch.qint8)\n",
+ "print_size_of_model(quantized_model2, \"int8\")"
]
},
{
@@ -1071,6 +1106,20 @@
"For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torchvision import datasets, transforms\n",
+ "from torch.utils.data.sampler import SubsetRandomSampler\n",
+ "import os\n",
+ "import numpy as np"
+ ]
+ },
{
"cell_type": "markdown",
"id": "a0a34b90",
@@ -1079,6 +1128,96 @@
"Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# function to evaluate the accuracy of the model\n",
+ "def evaluate_model(model, dataloader):\n",
+ " model.eval()\n",
+ " correct = {cls: 0 for cls in classes}\n",
+ " total = {cls: 0 for cls in classes}\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for data in dataloader:\n",
+ " images, labels = data\n",
+ " outputs = model(images)\n",
+ " _, predicted = torch.max(outputs.data, 1)\n",
+ " \n",
+ " for i in range(len(labels)):\n",
+ " total[classes[labels[i]]] += 1\n",
+ " correct[classes[labels[i]]] += int(predicted[i] == labels[i])\n",
+ "\n",
+ " accuracy = {cls: correct[cls] / total[cls] for cls in classes}\n",
+ " return accuracy\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy before quantization:\n",
+ "airplane: 0.6300\n",
+ "automobile: 0.8050\n",
+ "bird: 0.3740\n",
+ "cat: 0.3750\n",
+ "deer: 0.7130\n",
+ "dog: 0.5410\n",
+ "frog: 0.7490\n",
+ "horse: 0.7570\n",
+ "ship: 0.8580\n",
+ "truck: 0.8130\n",
+ "Overall Accuracy: 0.6615\n",
+ "model: int8 \t Size (KB): 168.478\n",
+ "Accuracy after quantization:\n",
+ "airplane: 0.6270\n",
+ "automobile: 0.8040\n",
+ "bird: 0.3770\n",
+ "cat: 0.3790\n",
+ "deer: 0.7100\n",
+ "dog: 0.5370\n",
+ "frog: 0.7490\n",
+ "horse: 0.7560\n",
+ "ship: 0.8580\n",
+ "truck: 0.8140\n",
+ "Overall Accuracy: 0.6611\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Evaluate the accuracy of the original model on the test set\n",
+ "accuracy_before_quantization = evaluate_model(model2, test_loader)\n",
+ "overall_accuracy_before_quantization = sum(accuracy_before_quantization.values()) / len(classes)\n",
+ "\n",
+ "print(\"Accuracy before quantization:\")\n",
+ "for cls, acc in accuracy_before_quantization.items():\n",
+ " print(f\"{cls}: {acc:.4f}\")\n",
+ "print(f\"Overall Accuracy: {overall_accuracy_before_quantization:.4f}\")\n",
+ "\n",
+ "# Post-training quantization\n",
+ "quantized_model = torch.quantization.quantize_dynamic(model2, dtype=torch.qint8)\n",
+ "\n",
+ "# Print the size of the quantized model\n",
+ "print_size_of_model(quantized_model, \"int8\")\n",
+ "\n",
+ "# Evaluate the accuracy of the quantized model on the test set\n",
+ "accuracy_after_quantization = evaluate_model(quantized_model, test_loader)\n",
+ "overall_accuracy_after_quantization = sum(accuracy_after_quantization.values()) / len(classes)\n",
+ "\n",
+ "print(\"Accuracy after quantization:\")\n",
+ "for cls, acc in accuracy_after_quantization.items():\n",
+ " print(f\"{cls}: {acc:.4f}\")\n",
+ "print(f\"Overall Accuracy: {overall_accuracy_after_quantization:.4f}\")\n"
+ ]
+ },
{
"cell_type": "markdown",
"id": "201470f9",
--
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