![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
},
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "# 08. PyTorch Experiment Tracking Exercises\n",
- "\n",
- "Welcome to the 08. PyTorch Paper Replicating exercises.\n",
- "\n",
- "Your objective is to write code to satisify each of the exercises below.\n",
- "\n",
- "Some starter code has been provided to make sure you have all the resources you need.\n",
- "\n",
- "> **Note:** There may be more than one solution to each of the exercises.\n",
- "\n",
- "## Resources\n",
- "\n",
- "1. These exercises/solutions are based on [section 08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
- "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/tjpW_BY8y3g) (but try the exercises yourself first!).\n",
- "3. See [all solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions).\n",
- "\n",
- "> **Note:** The first section of this notebook is dedicated to getting various helper functions and datasets used for the exercises. The exercises start at the heading \"Exercise 1: ...\"."
- ],
- "metadata": {
- "id": "zNqPNlYylluR"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "### Get various imports and helper functions\n",
- "\n",
- "The code in the following cells prepares imports and data for the exercises below. They are taken from [08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/). "
- ],
- "metadata": {
- "id": "sf8ab9cyHTzU"
- }
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zNqPNlYylluR"
+ },
+ "source": [
+ "# 08. PyTorch Paper Replicating Exercises\n",
+ "\n",
+ "Welcome to the 08. PyTorch Paper Replicating exercises.\n",
+ "\n",
+ "Your objective is to write code to satisify each of the exercises below.\n",
+ "\n",
+ "Some starter code has been provided to make sure you have all the resources you need.\n",
+ "\n",
+ "> **Note:** There may be more than one solution to each of the exercises.\n",
+ "\n",
+ "## Resources\n",
+ "\n",
+ "1. These exercises/solutions are based on [section 08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
+ "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/tjpW_BY8y3g) (but try the exercises yourself first!).\n",
+ "3. See [all solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions).\n",
+ "\n",
+ "> **Note:** The first section of this notebook is dedicated to getting various helper functions and datasets used for the exercises. The exercises start at the heading \"Exercise 1: ...\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sf8ab9cyHTzU"
+ },
+ "source": [
+ "### Get various imports and helper functions\n",
+ "\n",
+ "The code in the following cells prepares imports and data for the exercises below. They are taken from [08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "ChRaHUSJ8DYZ",
+ "outputId": "8f1c0a98-c04a-4d4b-a748-e591a315f805"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
- "try:\n",
- " import torch\n",
- " import torchvision\n",
- " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
- " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n",
- "except:\n",
- " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
- " !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
- " import torch\n",
- " import torchvision\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ChRaHUSJ8DYZ",
- "outputId": "8f1c0a98-c04a-4d4b-a748-e591a315f805"
- },
- "execution_count": 2,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "torch version: 1.12.0+cu113\n",
- "torchvision version: 0.13.0+cu113\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch version: 1.12.0+cu113\n",
+ "torchvision version: 0.13.0+cu113\n"
+ ]
+ }
+ ],
+ "source": [
+ "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
+ "try:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
+ " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n",
+ "except:\n",
+ " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
+ " !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "Y5H5P8EjCNGK",
+ "outputId": "4214da9e-f3a8-43e6-a48c-1a33f44a9be9"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# Continue with regular imports\n",
- "import matplotlib.pyplot as plt\n",
- "import torch\n",
- "import torchvision\n",
- "\n",
- "from torch import nn\n",
- "from torchvision import transforms\n",
- "\n",
- "# Try to get torchinfo, install it if it doesn't work\n",
- "try:\n",
- " from torchinfo import summary\n",
- "except:\n",
- " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
- " !pip install -q torchinfo\n",
- " from torchinfo import summary\n",
- "\n",
- "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
- "try:\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
- "except:\n",
- " # Get the going_modular scripts\n",
- " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
- " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
- " !mv pytorch-deep-learning/going_modular .\n",
- " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
- " !rm -rf pytorch-deep-learning\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves"
- ],
- "metadata": {
- "id": "Y5H5P8EjCNGK",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "4214da9e-f3a8-43e6-a48c-1a33f44a9be9"
- },
- "execution_count": 3,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "[INFO] Couldn't find torchinfo... installing it.\n",
- "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
- "Cloning into 'pytorch-deep-learning'...\n",
- "remote: Enumerating objects: 2589, done.\u001b[K\n",
- "remote: Counting objects: 100% (28/28), done.\u001b[K\n",
- "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
- "remote: Total 2589 (delta 11), reused 28 (delta 11), pack-reused 2561\u001b[K\n",
- "Receiving objects: 100% (2589/2589), 446.45 MiB | 41.00 MiB/s, done.\n",
- "Resolving deltas: 100% (1455/1455), done.\n",
- "Checking out files: 100% (184/184), done.\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] Couldn't find torchinfo... installing it.\n",
+ "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
+ "Cloning into 'pytorch-deep-learning'...\n",
+ "remote: Enumerating objects: 2589, done.\u001b[K\n",
+ "remote: Counting objects: 100% (28/28), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
+ "remote: Total 2589 (delta 11), reused 28 (delta 11), pack-reused 2561\u001b[K\n",
+ "Receiving objects: 100% (2589/2589), 446.45 MiB | 41.00 MiB/s, done.\n",
+ "Resolving deltas: 100% (1455/1455), done.\n",
+ "Checking out files: 100% (184/184), done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Continue with regular imports\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch\n",
+ "import torchvision\n",
+ "\n",
+ "from torch import nn\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "# Try to get torchinfo, install it if it doesn't work\n",
+ "try:\n",
+ " from torchinfo import summary\n",
+ "except:\n",
+ " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
+ " !pip install -q torchinfo\n",
+ " from torchinfo import summary\n",
+ "\n",
+ "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
+ "try:\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
+ "except:\n",
+ " # Get the going_modular scripts\n",
+ " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
+ " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
+ " !mv pytorch-deep-learning/going_modular .\n",
+ " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
+ " !rm -rf pytorch-deep-learning\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
},
+ "id": "bE1AAH_uCjiP",
+ "outputId": "d4f0001d-3854-4845-f195-eeb08c503aa8"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "device"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "bE1AAH_uCjiP",
- "outputId": "d4f0001d-3854-4845-f195-eeb08c503aa8"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
},
- "execution_count": 4,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'cuda'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 4
- }
+ "text/plain": [
+ "'cuda'"
]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GmS5yuvxCpLp"
+ },
+ "source": [
+ "### Get data\n",
+ "\n",
+ "Want to download the data we've been using in PyTorch Paper Replicating: https://www.learnpytorch.io/08_pytorch_paper_replicating/#1-get-data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "dm772wqgCzN9",
+ "outputId": "ca6c646d-dd40-4453-c12c-a1f0ef5f86f4"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "source": [
- "### Get data\n",
- "\n",
- "Want to download the data we've been using in PyTorch Paper Replicating: https://www.learnpytorch.io/08_pytorch_paper_replicating/#1-get-data"
- ],
- "metadata": {
- "id": "GmS5yuvxCpLp"
- }
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] Did not find data/pizza_steak_sushi directory, creating one...\n",
+ "[INFO] Downloading pizza_steak_sushi.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip...\n",
+ "[INFO] Unzipping pizza_steak_sushi.zip data...\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "# Download pizza, steak, sushi images from GitHub\n",
- "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
- " destination=\"pizza_steak_sushi\")\n",
- "image_path"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dm772wqgCzN9",
- "outputId": "ca6c646d-dd40-4453-c12c-a1f0ef5f86f4"
- },
- "execution_count": 5,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "[INFO] Did not find data/pizza_steak_sushi directory, creating one...\n",
- "[INFO] Downloading pizza_steak_sushi.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip...\n",
- "[INFO] Unzipping pizza_steak_sushi.zip data...\n"
- ]
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "PosixPath('data/pizza_steak_sushi')"
- ]
- },
- "metadata": {},
- "execution_count": 5
- }
+ "data": {
+ "text/plain": [
+ "PosixPath('data/pizza_steak_sushi')"
]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Download pizza, steak, sushi images from GitHub\n",
+ "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
+ " destination=\"pizza_steak_sushi\")\n",
+ "image_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "r1ML2c-dCzCi"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup directory paths to train and test images\n",
+ "train_dir = image_path / \"train\"\n",
+ "test_dir = image_path / \"test\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nNBZ_2h_Cy86"
+ },
+ "source": [
+ "### Preprocess data\n",
+ "\n",
+ "Turn images into tensors using same code as PyTorch Paper Replicating section 2.1 and 2.2: https://www.learnpytorch.io/08_pytorch_paper_replicating/#21-prepare-transforms-for-images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "mU0T4gP3DJdF",
+ "outputId": "de32055d-b807-4245-e4a6-c10653502630"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# Setup directory paths to train and test images\n",
- "train_dir = image_path / \"train\"\n",
- "test_dir = image_path / \"test\""
- ],
- "metadata": {
- "id": "r1ML2c-dCzCi"
- },
- "execution_count": 6,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "### Preprocess data\n",
- "\n",
- "Turn images into tensors using same code as PyTorch Paper Replicating section 2.1 and 2.2: https://www.learnpytorch.io/08_pytorch_paper_replicating/#21-prepare-transforms-for-images"
- ],
- "metadata": {
- "id": "nNBZ_2h_Cy86"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Create image size (from Table 3 in the ViT paper) \n",
- "IMG_SIZE = 224\n",
- "\n",
- "# Create transform pipeline manually\n",
- "manual_transforms = transforms.Compose([\n",
- " transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
- " transforms.ToTensor(),\n",
- "]) \n",
- "print(f\"Manually created transforms: {manual_transforms}\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "mU0T4gP3DJdF",
- "outputId": "de32055d-b807-4245-e4a6-c10653502630"
- },
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Manually created transforms: Compose(\n",
- " Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
- " ToTensor()\n",
- ")\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Manually created transforms: Compose(\n",
+ " Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
+ " ToTensor()\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create image size (from Table 3 in the ViT paper) \n",
+ "IMG_SIZE = 224\n",
+ "\n",
+ "# Create transform pipeline manually\n",
+ "manual_transforms = transforms.Compose([\n",
+ " transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
+ " transforms.ToTensor(),\n",
+ "]) \n",
+ "print(f\"Manually created transforms: {manual_transforms}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "W4vWgIprDJau",
+ "outputId": "6de4b52b-1570-408a-8399-8f6465258a31"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# Set the batch size\n",
- "BATCH_SIZE = 32 # this is lower than the ViT paper but it's because we're starting small\n",
- "\n",
- "# Create data loaders\n",
- "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
- " train_dir=train_dir,\n",
- " test_dir=test_dir,\n",
- " transform=manual_transforms, # use manually created transforms\n",
- " batch_size=BATCH_SIZE\n",
- ")\n",
- "\n",
- "train_dataloader, test_dataloader, class_names"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "W4vWgIprDJau",
- "outputId": "6de4b52b-1570-408a-8399-8f6465258a31"
- },
- "execution_count": 8,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "("
+ ]
},
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zNqPNlYylluR"
+ },
+ "source": [
+ "# 08. PyTorch Paper Replicating Exercise Solutions\n",
+ "\n",
+ "Welcome to the 08. PyTorch Paper Replicating exercise solutions notebook.\n",
+ "\n",
+ "> **Note:** There may be more than one solution to each of the exercises. This notebook only shows one possible example.\n",
+ "\n",
+ "## Resources\n",
+ "\n",
+ "1. These exercises/solutions are based on [section 08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
+ "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/tjpW_BY8y3g).\n",
+ "3. See [other solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sf8ab9cyHTzU"
+ },
+ "source": [
+ "### Get various imports and helper functions\n",
+ "\n",
+ "The code in the following cells prepares imports and data for the exercises below. They are taken from [08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "ChRaHUSJ8DYZ",
+ "outputId": "1267a93a-69dc-47ba-fdc8-9a202fa1f627"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "source": [
- "# 08. PyTorch Experiment Tracking Exercise Solutions\n",
- "\n",
- "Welcome to the 08. PyTorch Paper Replicating exercise solutions notebook.\n",
- "\n",
- "> **Note:** There may be more than one solution to each of the exercises. This notebook only shows one possible example.\n",
- "\n",
- "## Resources\n",
- "\n",
- "1. These exercises/solutions are based on [section 08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
- "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/tjpW_BY8y3g).\n",
- "3. See [other solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions)."
- ],
- "metadata": {
- "id": "zNqPNlYylluR"
- }
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch version: 1.12.0+cu113\n",
+ "torchvision version: 0.13.0+cu113\n"
+ ]
+ }
+ ],
+ "source": [
+ "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
+ "try:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
+ " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n",
+ "except:\n",
+ " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
+ " !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "Y5H5P8EjCNGK",
+ "outputId": "99778a15-76a4-4329-9b8e-5b34b39ff6c4"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "source": [
- "### Get various imports and helper functions\n",
- "\n",
- "The code in the following cells prepares imports and data for the exercises below. They are taken from [08. PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/). "
- ],
- "metadata": {
- "id": "sf8ab9cyHTzU"
- }
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] Couldn't find torchinfo... installing it.\n",
+ "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
+ "Cloning into 'pytorch-deep-learning'...\n",
+ "remote: Enumerating objects: 2579, done.\u001b[K\n",
+ "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
+ "remote: Total 2579 (delta 4), reused 6 (delta 0), pack-reused 2561\u001b[K\n",
+ "Receiving objects: 100% (2579/2579), 446.36 MiB | 14.08 MiB/s, done.\n",
+ "Resolving deltas: 100% (1448/1448), done.\n",
+ "Checking out files: 100% (184/184), done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Continue with regular imports\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch\n",
+ "import torchvision\n",
+ "\n",
+ "from torch import nn\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "# Try to get torchinfo, install it if it doesn't work\n",
+ "try:\n",
+ " from torchinfo import summary\n",
+ "except:\n",
+ " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
+ " !pip install -q torchinfo\n",
+ " from torchinfo import summary\n",
+ "\n",
+ "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
+ "try:\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
+ "except:\n",
+ " # Get the going_modular scripts\n",
+ " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
+ " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
+ " !mv pytorch-deep-learning/going_modular .\n",
+ " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
+ " !rm -rf pytorch-deep-learning\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
},
+ "id": "bE1AAH_uCjiP",
+ "outputId": "6a4f8e86-2157-4578-e295-cf2d72ee05d6"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
- "try:\n",
- " import torch\n",
- " import torchvision\n",
- " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
- " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n",
- "except:\n",
- " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
- " !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
- " import torch\n",
- " import torchvision\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ChRaHUSJ8DYZ",
- "outputId": "1267a93a-69dc-47ba-fdc8-9a202fa1f627"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
},
- "execution_count": 1,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "torch version: 1.12.0+cu113\n",
- "torchvision version: 0.13.0+cu113\n"
- ]
- }
+ "text/plain": [
+ "'cuda'"
]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GmS5yuvxCpLp"
+ },
+ "source": [
+ "### Get data\n",
+ "\n",
+ "Want to download the data we've been using in PyTorch Paper Replicating: https://www.learnpytorch.io/08_pytorch_paper_replicating/#1-get-data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "dm772wqgCzN9",
+ "outputId": "9cb1072e-1a32-45bc-ee1d-e92cef63a074"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# Continue with regular imports\n",
- "import matplotlib.pyplot as plt\n",
- "import torch\n",
- "import torchvision\n",
- "\n",
- "from torch import nn\n",
- "from torchvision import transforms\n",
- "\n",
- "# Try to get torchinfo, install it if it doesn't work\n",
- "try:\n",
- " from torchinfo import summary\n",
- "except:\n",
- " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
- " !pip install -q torchinfo\n",
- " from torchinfo import summary\n",
- "\n",
- "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
- "try:\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
- "except:\n",
- " # Get the going_modular scripts\n",
- " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
- " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
- " !mv pytorch-deep-learning/going_modular .\n",
- " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
- " !rm -rf pytorch-deep-learning\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves"
- ],
- "metadata": {
- "id": "Y5H5P8EjCNGK",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "99778a15-76a4-4329-9b8e-5b34b39ff6c4"
- },
- "execution_count": 2,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "[INFO] Couldn't find torchinfo... installing it.\n",
- "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
- "Cloning into 'pytorch-deep-learning'...\n",
- "remote: Enumerating objects: 2579, done.\u001b[K\n",
- "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
- "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
- "remote: Total 2579 (delta 4), reused 6 (delta 0), pack-reused 2561\u001b[K\n",
- "Receiving objects: 100% (2579/2579), 446.36 MiB | 14.08 MiB/s, done.\n",
- "Resolving deltas: 100% (1448/1448), done.\n",
- "Checking out files: 100% (184/184), done.\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] Did not find data/pizza_steak_sushi directory, creating one...\n",
+ "[INFO] Downloading pizza_steak_sushi.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip...\n",
+ "[INFO] Unzipping pizza_steak_sushi.zip data...\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "device"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "bE1AAH_uCjiP",
- "outputId": "6a4f8e86-2157-4578-e295-cf2d72ee05d6"
- },
- "execution_count": 3,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'cuda'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 3
- }
+ "data": {
+ "text/plain": [
+ "PosixPath('data/pizza_steak_sushi')"
]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Download pizza, steak, sushi images from GitHub\n",
+ "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
+ " destination=\"pizza_steak_sushi\")\n",
+ "image_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "r1ML2c-dCzCi"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup directory paths to train and test images\n",
+ "train_dir = image_path / \"train\"\n",
+ "test_dir = image_path / \"test\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nNBZ_2h_Cy86"
+ },
+ "source": [
+ "### Preprocess data\n",
+ "\n",
+ "Turn images into tensors using same code as PyTorch Paper Replicating section 2.1 and 2.2: https://www.learnpytorch.io/08_pytorch_paper_replicating/#21-prepare-transforms-for-images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "mU0T4gP3DJdF",
+ "outputId": "7d354ca6-d0de-4680-c356-23b372a74a43"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "source": [
- "### Get data\n",
- "\n",
- "Want to download the data we've been using in PyTorch Paper Replicating: https://www.learnpytorch.io/08_pytorch_paper_replicating/#1-get-data"
- ],
- "metadata": {
- "id": "GmS5yuvxCpLp"
- }
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Manually created transforms: Compose(\n",
+ " Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
+ " ToTensor()\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create image size (from Table 3 in the ViT paper) \n",
+ "IMG_SIZE = 224\n",
+ "\n",
+ "# Create transform pipeline manually\n",
+ "manual_transforms = transforms.Compose([\n",
+ " transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
+ " transforms.ToTensor(),\n",
+ "]) \n",
+ "print(f\"Manually created transforms: {manual_transforms}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "W4vWgIprDJau",
+ "outputId": "15d080c5-bb82-413c-b69f-46a0f6621f78"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "# Download pizza, steak, sushi images from GitHub\n",
- "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
- " destination=\"pizza_steak_sushi\")\n",
- "image_path"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dm772wqgCzN9",
- "outputId": "9cb1072e-1a32-45bc-ee1d-e92cef63a074"
- },
- "execution_count": 4,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "[INFO] Did not find data/pizza_steak_sushi directory, creating one...\n",
- "[INFO] Downloading pizza_steak_sushi.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip...\n",
- "[INFO] Unzipping pizza_steak_sushi.zip data...\n"
- ]
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "PosixPath('data/pizza_steak_sushi')"
- ]
- },
- "metadata": {},
- "execution_count": 4
- }
+ "data": {
+ "text/plain": [
+ "(| \n", + " | image_path | \n", + "class_name | \n", + "pred_prob | \n", + "pred_class | \n", + "correct | \n", + "
|---|---|---|---|---|---|
| 25 | \n", + "data/pizza_steak_sushi/test/pizza/2508636.jpg | \n", + "pizza | \n", + "0.801234 | \n", + "steak | \n", + "False | \n", + "
| 22 | \n", + "data/pizza_steak_sushi/test/pizza/1687143.jpg | \n", + "pizza | \n", + "0.999948 | \n", + "pizza | \n", + "True | \n", + "
| 43 | \n", + "data/pizza_steak_sushi/test/pizza/2111981.jpg | \n", + "pizza | \n", + "0.999943 | \n", + "pizza | \n", + "True | \n", + "
| 24 | \n", + "data/pizza_steak_sushi/test/pizza/714866.jpg | \n", + "pizza | \n", + "0.999935 | \n", + "pizza | \n", + "True | \n", + "
| 8 | \n", + "data/pizza_steak_sushi/test/steak/3424937.jpg | \n", + "steak | \n", + "0.999927 | \n", + "steak | \n", + "True | \n", + "
| \n", - " | image_path | \n", - "class_name | \n", - "pred_prob | \n", - "pred_class | \n", - "correct | \n", - "
|---|---|---|---|---|---|
| 25 | \n", - "data/pizza_steak_sushi/test/pizza/2508636.jpg | \n", - "pizza | \n", - "0.801234 | \n", - "steak | \n", - "False | \n", - "
| 22 | \n", - "data/pizza_steak_sushi/test/pizza/1687143.jpg | \n", - "pizza | \n", - "0.999948 | \n", - "pizza | \n", - "True | \n", - "
| 43 | \n", - "data/pizza_steak_sushi/test/pizza/2111981.jpg | \n", - "pizza | \n", - "0.999943 | \n", - "pizza | \n", - "True | \n", - "
| 24 | \n", - "data/pizza_steak_sushi/test/pizza/714866.jpg | \n", - "pizza | \n", - "0.999935 | \n", - "pizza | \n", - "True | \n", - "
| 8 | \n", - "data/pizza_steak_sushi/test/steak/3424937.jpg | \n", - "steak | \n", - "0.999927 | \n", - "steak | \n", - "True | \n", - "
"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zNqPNlYylluR"
+ },
+ "source": [
+ "# 09. PyTorch Model Deployment Exercise Solutions\n",
+ "\n",
+ "Welcome to the 09. PyTorch Model Deployment exercise solutions.\n",
+ "\n",
+ "Your objective is to write code to satisify each of the exercises below.\n",
+ "\n",
+ "Some starter code has been provided to make sure you have all the resources you need.\n",
+ "\n",
+ "> **Note:** There may be more than one solution to each of the exercises.\n",
+ "\n",
+ "## Resources\n",
+ "\n",
+ "1. These exercises/solutions are based on [section 09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
+ "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/jOX5ZCkWO-0) (but try the exercises yourself first!).\n",
+ "3. See [all solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions).\n",
+ "\n",
+ "> **Note:** The first section of this notebook is dedicated to getting various helper functions and datasets used for the exercises. The exercises start at the heading \"Exercise 1: ...\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sf8ab9cyHTzU"
+ },
+ "source": [
+ "### Get various imports and helper functions\n",
+ "\n",
+ "The code in the following cells prepares imports and data for the exercises below. They are taken from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "ChRaHUSJ8DYZ",
+ "outputId": "0a27f03f-33ea-4721-e4ec-858b8dc255b1"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "zNqPNlYylluR"
- },
- "source": [
- "# 09. PyTorch Model Deployment Exercise Solutions\n",
- "\n",
- "Welcome to the 09. PyTorch Model Deployment exercise solutions.\n",
- "\n",
- "Your objective is to write code to satisify each of the exercises below.\n",
- "\n",
- "Some starter code has been provided to make sure you have all the resources you need.\n",
- "\n",
- "> **Note:** There may be more than one solution to each of the exercises.\n",
- "\n",
- "## Resources\n",
- "\n",
- "1. These exercises/solutions are based on [section 09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
- "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/jOX5ZCkWO-0) (but try the exercises yourself first!).\n",
- "3. See [all solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions).\n",
- "\n",
- "> **Note:** The first section of this notebook is dedicated to getting various helper functions and datasets used for the exercises. The exercises start at the heading \"Exercise 1: ...\"."
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch version: 1.12.1+cu113\n",
+ "torchvision version: 0.13.1+cu113\n"
+ ]
+ }
+ ],
+ "source": [
+ "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
+ "try:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
+ " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n",
+ "except:\n",
+ " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
+ " !pip3 install -U torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(f\"torch version: {torch.__version__}\")\n",
+ " print(f\"torchvision version: {torchvision.__version__}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "Y5H5P8EjCNGK"
+ },
+ "outputs": [],
+ "source": [
+ "# Continue with regular imports\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch\n",
+ "import torchvision\n",
+ "\n",
+ "from torch import nn\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "# Try to get torchinfo, install it if it doesn't work\n",
+ "try:\n",
+ " from torchinfo import summary\n",
+ "except:\n",
+ " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
+ " !pip install -q torchinfo\n",
+ " from torchinfo import summary\n",
+ "\n",
+ "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
+ "try:\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
+ "except:\n",
+ " # Get the going_modular scripts\n",
+ " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
+ " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
+ " !mv pytorch-deep-learning/going_modular .\n",
+ " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
+ " !rm -rf pytorch-deep-learning\n",
+ " from going_modular.going_modular import data_setup, engine\n",
+ " from helper_functions import download_data, set_seeds, plot_loss_curves"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
},
+ "id": "bE1AAH_uCjiP",
+ "outputId": "8337aa8d-9a46-41ad-9f96-658e857fcf57"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "sf8ab9cyHTzU"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
},
- "source": [
- "### Get various imports and helper functions\n",
- "\n",
- "The code in the following cells prepares imports and data for the exercises below. They are taken from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/). "
+ "text/plain": [
+ "'cuda'"
]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GmS5yuvxCpLp"
+ },
+ "source": [
+ "### Get data\n",
+ "\n",
+ "Want to download the data we've been using in PyTorch Model Deployment: https://www.learnpytorch.io/09_pytorch_model_deployment/#1-getting-data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "dm772wqgCzN9",
+ "outputId": "ca47901f-5786-4d76-d768-58ad8349704c"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ChRaHUSJ8DYZ",
- "outputId": "0a27f03f-33ea-4721-e4ec-858b8dc255b1"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "torch version: 1.12.1+cu113\n",
- "torchvision version: 0.13.1+cu113\n"
- ]
- }
- ],
- "source": [
- "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
- "try:\n",
- " import torch\n",
- " import torchvision\n",
- " assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
- " assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n",
- "except:\n",
- " print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
- " !pip3 install -U torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
- " import torch\n",
- " import torchvision\n",
- " print(f\"torch version: {torch.__version__}\")\n",
- " print(f\"torchvision version: {torchvision.__version__}\")\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[INFO] data/pizza_steak_sushi_20_percent directory exists, skipping download.\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "Y5H5P8EjCNGK"
- },
- "outputs": [],
- "source": [
- "# Continue with regular imports\n",
- "import matplotlib.pyplot as plt\n",
- "import torch\n",
- "import torchvision\n",
- "\n",
- "from torch import nn\n",
- "from torchvision import transforms\n",
- "\n",
- "# Try to get torchinfo, install it if it doesn't work\n",
- "try:\n",
- " from torchinfo import summary\n",
- "except:\n",
- " print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
- " !pip install -q torchinfo\n",
- " from torchinfo import summary\n",
- "\n",
- "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
- "try:\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves\n",
- "except:\n",
- " # Get the going_modular scripts\n",
- " print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
- " !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
- " !mv pytorch-deep-learning/going_modular .\n",
- " !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
- " !rm -rf pytorch-deep-learning\n",
- " from going_modular.going_modular import data_setup, engine\n",
- " from helper_functions import download_data, set_seeds, plot_loss_curves"
+ "data": {
+ "text/plain": [
+ "PosixPath('data/pizza_steak_sushi_20_percent')"
]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Download pizza, steak, sushi images from GitHub\n",
+ "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
+ " destination=\"pizza_steak_sushi_20_percent\")\n",
+ "image_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "r1ML2c-dCzCi"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup directory paths to train and test images\n",
+ "train_dir = image_path / \"train\"\n",
+ "test_dir = image_path / \"test\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nNBZ_2h_Cy86"
+ },
+ "source": [
+ "### Preprocess data\n",
+ "\n",
+ "Turn images into tensors using same code as PyTorch Paper Replicating section 2.1 and 2.2: https://www.learnpytorch.io/08_pytorch_paper_replicating/#21-prepare-transforms-for-images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "mU0T4gP3DJdF",
+ "outputId": "00d58b26-a6cb-4c3f-b774-2414c36bbce9"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "bE1AAH_uCjiP",
- "outputId": "8337aa8d-9a46-41ad-9f96-658e857fcf57"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'cuda'"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "device"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Manually created transforms: Compose(\n",
+ " Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
+ " ToTensor()\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create image size (from Table 3 in the ViT paper) \n",
+ "IMG_SIZE = 224\n",
+ "\n",
+ "# Create transform pipeline manually\n",
+ "manual_transforms = transforms.Compose([\n",
+ " transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
+ " transforms.ToTensor(),\n",
+ "]) \n",
+ "print(f\"Manually created transforms: {manual_transforms}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "W4vWgIprDJau",
+ "outputId": "10423c3f-dc63-4e76-cd80-db5c6915662a"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "GmS5yuvxCpLp"
- },
- "source": [
- "### Get data\n",
- "\n",
- "Want to download the data we've been using in PyTorch Model Deployment: https://www.learnpytorch.io/09_pytorch_model_deployment/#1-getting-data"
+ "data": {
+ "text/plain": [
+ "(| \n", + " | effnetb2_cpu | \n", + "vit_cpu | \n", + "effnetb2_gpu | \n", + "vit_gpu | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.232874 | \n", + "0.538953 | \n", + "0.036263 | \n", + "0.019143 | \n", + "
| \n", + " | pred_prob | \n", + "pred_label | \n", + "label | \n", + "is_correct | \n", + "pred_class | \n", + "label_class | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "0.7948435 | \n", + "72 | \n", + "72 | \n", + "True | \n", + "pancakes | \n", + "pancakes | \n", + "
| 1 | \n", + "0.6136912 | \n", + "57 | \n", + "57 | \n", + "True | \n", + "hummus | \n", + "hummus | \n", + "
| 2 | \n", + "0.19282873 | \n", + "80 | \n", + "80 | \n", + "True | \n", + "pulled_pork_sandwich | \n", + "pulled_pork_sandwich | \n", + "
| 3 | \n", + "0.58169764 | \n", + "51 | \n", + "51 | \n", + "True | \n", + "guacamole | \n", + "guacamole | \n", + "
| 4 | \n", + "0.82304615 | \n", + "1 | \n", + "1 | \n", + "True | \n", + "baby_back_ribs | \n", + "baby_back_ribs | \n", + "
| \n", + " | pred_prob | \n", + "pred_label | \n", + "label | \n", + "is_correct | \n", + "pred_class | \n", + "label_class | \n", + "
|---|---|---|---|---|---|---|
| 3107 | \n", + "0.9835638 | \n", + "52 | \n", + "32 | \n", + "False | \n", + "gyoza | \n", + "dumplings | \n", + "
| 1306 | \n", + "0.9427046 | \n", + "71 | \n", + "44 | \n", + "False | \n", + "paella | \n", + "fried_rice | \n", + "
| 2848 | \n", + "0.9298615 | \n", + "41 | \n", + "8 | \n", + "False | \n", + "french_onion_soup | \n", + "bread_pudding | \n", + "
| 708 | \n", + "0.90341496 | \n", + "9 | \n", + "12 | \n", + "False | \n", + "breakfast_burrito | \n", + "cannoli | \n", + "
| 1726 | \n", + "0.884879 | \n", + "68 | \n", + "43 | \n", + "False | \n", + "onion_rings | \n", + "fried_calamari | \n", + "
| 3436 | \n", + "0.8786958 | \n", + "45 | \n", + "58 | \n", + "False | \n", + "frozen_yogurt | \n", + "ice_cream | \n", + "
| 2287 | \n", + "0.85854447 | \n", + "38 | \n", + "28 | \n", + "False | \n", + "fish_and_chips | \n", + "croque_madame | \n", + "
| 3209 | \n", + "0.84964484 | \n", + "9 | \n", + "36 | \n", + "False | \n", + "breakfast_burrito | \n", + "falafel | \n", + "
| 1872 | \n", + "0.84939903 | \n", + "79 | \n", + "37 | \n", + "False | \n", + "prime_rib | \n", + "filet_mignon | \n", + "
| 1338 | \n", + "0.8450013 | \n", + "15 | \n", + "51 | \n", + "False | \n", + "ceviche | \n", + "guacamole | \n", + "
| \n", - " | effnetb2_cpu | \n", - "vit_cpu | \n", - "effnetb2_gpu | \n", - "vit_gpu | \n", - "
|---|---|---|---|---|
| 0 | \n", - "0.232874 | \n", - "0.538953 | \n", - "0.036263 | \n", - "0.019143 | \n", - "
| \n", - " | pred_prob | \n", - "pred_label | \n", - "label | \n", - "is_correct | \n", - "pred_class | \n", - "label_class | \n", - "
|---|---|---|---|---|---|---|
| 0 | \n", - "0.7948435 | \n", - "72 | \n", - "72 | \n", - "True | \n", - "pancakes | \n", - "pancakes | \n", - "
| 1 | \n", - "0.6136912 | \n", - "57 | \n", - "57 | \n", - "True | \n", - "hummus | \n", - "hummus | \n", - "
| 2 | \n", - "0.19282873 | \n", - "80 | \n", - "80 | \n", - "True | \n", - "pulled_pork_sandwich | \n", - "pulled_pork_sandwich | \n", - "
| 3 | \n", - "0.58169764 | \n", - "51 | \n", - "51 | \n", - "True | \n", - "guacamole | \n", - "guacamole | \n", - "
| 4 | \n", - "0.82304615 | \n", - "1 | \n", - "1 | \n", - "True | \n", - "baby_back_ribs | \n", - "baby_back_ribs | \n", - "
| \n", - " | pred_prob | \n", - "pred_label | \n", - "label | \n", - "is_correct | \n", - "pred_class | \n", - "label_class | \n", - "
|---|---|---|---|---|---|---|
| 3107 | \n", - "0.9835638 | \n", - "52 | \n", - "32 | \n", - "False | \n", - "gyoza | \n", - "dumplings | \n", - "
| 1306 | \n", - "0.9427046 | \n", - "71 | \n", - "44 | \n", - "False | \n", - "paella | \n", - "fried_rice | \n", - "
| 2848 | \n", - "0.9298615 | \n", - "41 | \n", - "8 | \n", - "False | \n", - "french_onion_soup | \n", - "bread_pudding | \n", - "
| 708 | \n", - "0.90341496 | \n", - "9 | \n", - "12 | \n", - "False | \n", - "breakfast_burrito | \n", - "cannoli | \n", - "
| 1726 | \n", - "0.884879 | \n", - "68 | \n", - "43 | \n", - "False | \n", - "onion_rings | \n", - "fried_calamari | \n", - "
| 3436 | \n", - "0.8786958 | \n", - "45 | \n", - "58 | \n", - "False | \n", - "frozen_yogurt | \n", - "ice_cream | \n", - "
| 2287 | \n", - "0.85854447 | \n", - "38 | \n", - "28 | \n", - "False | \n", - "fish_and_chips | \n", - "croque_madame | \n", - "
| 3209 | \n", - "0.84964484 | \n", - "9 | \n", - "36 | \n", - "False | \n", - "breakfast_burrito | \n", - "falafel | \n", - "
| 1872 | \n", - "0.84939903 | \n", - "79 | \n", - "37 | \n", - "False | \n", - "prime_rib | \n", - "filet_mignon | \n", - "
| 1338 | \n", - "0.8450013 | \n", - "15 | \n", - "51 | \n", - "False | \n", - "ceviche | \n", - "guacamole | \n", - "