Setting up the dataset

04_pytorch_custom_datasets.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyOj1u8Pwdu+m8kZy1VQC8bj",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/KRollins26/learning_pytorch/blob/main/04_pytorch_custom_datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Importing PyTorch and setting up device-agnostic code"
+      ],
+      "metadata": {
+        "id": "9OSpxyEwSfAI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from torch import nn\n",
+        "\n",
+        "# Note: this notebook requires torch >= 1.10.0\n",
+        "torch.__version__"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "XHVVGzhSS3XX",
+        "outputId": "39b8c0c0-6403-430a-b2a4-5766337ff800"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'2.1.0+cu121'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Setup device-agnostic code\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "device"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "mrpx2f3JS3U6",
+        "outputId": "c15cb9ae-35de-41a8-8c0b-3c0ba381746a"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'cuda'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Returning Dataset\n",
+        "\n",
+        "* Original Food101 dataset and paper website.\n",
+        "* `torchvision.datasets.Food101` - the version of the data I downloaded for this notebook.\n",
+        "* `extras/04_custom_data_creation.ipynb` - a notebook I used to format the Food101 dataset to use for this notebook.\n",
+        "* `data/pizza_steak_sushi.zip` - the zip archive of pizza, steak and sushi images from Food101, created with the notebook linked above.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "vVurMo0LUHC2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import requests\n",
+        "import zipfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# Setup path to data folder\n",
+        "data_path = Path(\"data/\")\n",
+        "image_path = data_path / \"pizza_steak_sushi\"\n",
+        "\n",
+        "# If the image folder doesn't exist, download it and prepare it...\n",
+        "if image_path.is_dir():\n",
+        "    print(f\"{image_path} directory exists.\")\n",
+        "else:\n",
+        "    print(f\"Did not find {image_path} directory, creating one...\")\n",
+        "    image_path.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "    # Download pizza, steak, sushi data\n",
+        "    with open(data_path / \"pizza_steak_sushi.zip\", \"wb\") as f:\n",
+        "        request = requests.get(\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\")\n",
+        "        print(\"Downloading pizza, steak, sushi data...\")\n",
+        "        f.write(request.content)\n",
+        "\n",
+        "    # Unzip pizza, steak, sushi data\n",
+        "    with zipfile.ZipFile(data_path / \"pizza_steak_sushi.zip\", \"r\") as zip_ref:\n",
+        "        print(\"Unzipping pizza, steak, sushi data...\")\n",
+        "        zip_ref.extractall(image_path)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xW1aHd0_S3Sr",
+        "outputId": "7ac0cd74-e9ee-4176-a51b-5d7c2cb6bf38"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Did not find data/pizza_steak_sushi directory, creating one...\n",
+            "Downloading pizza, steak, sushi data...\n",
+            "Unzipping pizza, steak, sushi data...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "RxuGixriS3Qk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "sd3BoVdBS3OT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "5Y1cD1FoS3L7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
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+        "id": "TFlZtM0kS3Jq"
+      },
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+    {
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+      },
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+    },
+    {
+      "cell_type": "code",
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+        "id": "r4JRci5uS3Dp"
+      },
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+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F7uTEDrgS3Bj"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}