From fe4bfa53e68c52f01e73cc282a3eb9ceb0174ed3 Mon Sep 17 00:00:00 2001
From: Omar El Rifai <omar.void@gmail.com>
Date: Thu, 12 Sep 2024 11:12:34 +0200
Subject: [PATCH] Add notebook to repo
---
draft.ipynb | 291 ++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 291 insertions(+)
create mode 100644 draft.ipynb
diff --git a/draft.ipynb b/draft.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c1649e65-6fb0-4af7-8ecd-d94f44511d9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tarfile\n",
+ "import os \n",
+ "\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from torch.utils.data import random_split\n",
+ "import torchvision\n",
+ "from torchvision.datasets.utils import download_url\n",
+ "from torchvision.datasets import ImageFolder\n",
+ "from torchvision.transforms import ToTensor\n",
+ "import torchvision.transforms as transforms\n",
+ "\n",
+ "\n",
+ "from src.utils_results import plot_img\n",
+ "from src.fedclass import Client\n",
+ "from src.utils_training import train_central, test_model\n",
+ "from src.utils_data import get_clients_data, data_preparation\n",
+ "from src.models import GenericConvModel\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59c7fe59-a1ce-4925-bdca-8ecb777902e8",
+ "metadata": {},
+ "source": [
+ "## Three Methods to load the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "60951c57-4f25-4e62-9255-a57d120c6370",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using downloaded and verified file: ./cifar10.tgz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_6044/2990241823.py:6: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n",
+ " tar.extractall(path='./data')\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ }
+ ],
+ "source": [
+ "### 1- Using raw image folder\n",
+ "\n",
+ "dataset_url = \"https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz\"\n",
+ "download_url(dataset_url, '.')\n",
+ "with tarfile.open('./cifar10.tgz', 'r:gz') as tar:\n",
+ " tar.extractall(path='./data')\n",
+ "data_dir = './data/cifar10'\n",
+ "\n",
+ "classes = os.listdir(data_dir + \"/train\")\n",
+ "dataset1 = ImageFolder(data_dir+'/train', transform=ToTensor())\n",
+ "\n",
+ "\n",
+ "\n",
+ "### 2- Using project functions\n",
+ "\n",
+ "dict_clients = get_clients_data(num_clients = 1, num_samples_by_label = 600, dataset = 'cifar10', nn_model = 'convolutional')\n",
+ "x_data, y_data = dict_clients[0]['x'], dict_clients[0]['y']\n",
+ "x_data = np.transpose(x_data, (0, 3, 1, 2))\n",
+ "\n",
+ "dataset2 = TensorDataset(torch.tensor(x_data, dtype=torch.float32), torch.tensor(y_data, dtype=torch.long))\n",
+ "\n",
+ "\n",
+ "\n",
+ "### 3 - Using CIFAR10 dataset from Pytorch\n",
+ "\n",
+ "cifar10 = torchvision.datasets.CIFAR10(\"datasets\", download=True, transform=ToTensor())\n",
+ "(x_data, y_data) = cifar10.data, cifar10.targets\n",
+ "x_data = np.transpose(x_data, (0, 3, 1, 2))\n",
+ "dataset3 = TensorDataset(torch.tensor(x_data, dtype=torch.float32), torch.tensor(y_data, dtype=torch.long))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "2ff13653-be89-4b0b-97f0-ffe7ee9c23ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = GenericConvModel(32,3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "148d883c-a667-49a2-87c1-5962f1c859eb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4f0dc1d-4cdc-47cb-b200-2c58984ac171",
+ "metadata": {},
+ "source": [
+ "## Conversion to dataloaders"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "bcefeb34-f9f4-4086-8af9-73469c3fd375",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "random_seed = 42\n",
+ "torch.manual_seed(random_seed);\n",
+ "val_size = 5000\n",
+ "train_size = len(dataset1) - val_size\n",
+ "\n",
+ "train_ds, val_ds = random_split(dataset1, [train_size, val_size])\n",
+ "\n",
+ "batch_size=128\n",
+ "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
+ "val_dl = DataLoader(val_ds, batch_size*2, num_workers=4, pin_memory=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "91bec335-be85-4ef8-a27f-298ed08b80fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_epochs = 10\n",
+ "opt_func = torch.optim.Adam\n",
+ "lr = 0.001\n",
+ "\n",
+ "@torch.no_grad()\n",
+ "def evaluate(model, val_loader):\n",
+ " model.eval()\n",
+ " outputs = [model.validation_step(batch) for batch in val_loader]\n",
+ " return model.validation_epoch_end(outputs)\n",
+ "\n",
+ "def fit(epochs, lr, model, train_loader, val_loader, opt_func=opt_func):\n",
+ " history = []\n",
+ " optimizer = opt_func(model.parameters(), lr)\n",
+ " for epoch in range(epochs):\n",
+ " # Training Phase \n",
+ " model.train()\n",
+ " train_losses = []\n",
+ " for batch in train_loader:\n",
+ " loss = model.training_step(batch)\n",
+ " train_losses.append(loss)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " optimizer.zero_grad()\n",
+ " # Validation phase\n",
+ " result = evaluate(model, val_loader)\n",
+ " result['train_loss'] = torch.stack(train_losses).mean().item()\n",
+ " model.epoch_end(epoch, result)\n",
+ " history.append(result)\n",
+ " return history\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56aa9198-0a07-4ec8-802b-792352667795",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch [0], train_loss: 1.7809, val_loss: 1.4422, val_acc: 0.4745\n",
+ "Epoch [1], train_loss: 1.2344, val_loss: 1.0952, val_acc: 0.6092\n",
+ "Epoch [2], train_loss: 0.9971, val_loss: 0.9526, val_acc: 0.6552\n",
+ "Epoch [3], train_loss: 0.8338, val_loss: 0.8339, val_acc: 0.7085\n",
+ "Epoch [4], train_loss: 0.7093, val_loss: 0.7892, val_acc: 0.7239\n",
+ "Epoch [5], train_loss: 0.6082, val_loss: 0.7572, val_acc: 0.7490\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = fit(num_epochs, lr, model, train_dl, val_dl, opt_func)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "a106673e-a9a9-4525-bc94-98d3b64f2a7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = evaluate(model, test_loader)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "24941b20-3aed-4336-9f79-87e4fcf0bba7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'val_loss': 2.3049447536468506, 'val_acc': 0.10572139918804169}"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "218e7550-2b48-4a8e-8547-e3afe81d34fe",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15f616e4-e565-4396-9450-03c891530640",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3fbea674-afff-495a-ac28-c34b44561d47",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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