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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",
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- "id": "15f616e4-e565-4396-9450-03c891530640",
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- "cell_type": "code",
- "execution_count": null,
- "id": "3fbea674-afff-495a-ac28-c34b44561d47",
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- }
- ],
- "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"
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