Tutorial_1_to_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learning from : https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial 1 — Quickstart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets import some libraries \n",
    "import torch \n",
    "from torch import nn \n",
    "from torch.utils.data import dataloader\n",
    "from torchvision import datasets \n",
    "from torchvision.transforms import ToTensor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial 2 — Tensors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data : [[11, 22], [44, 55]]\n",
      "data in tensor : tensor([[11, 22],\n",
      "        [44, 55]])\n",
      "np to tensor : tensor([[11, 22],\n",
      "        [44, 55]], dtype=torch.int32)\n",
      "Ones tensor : tensor([[1, 1],\n",
      "        [1, 1]])\n",
      "Random tensor : tensor([[0.3985, 0.0555],\n",
      "        [0.4538, 0.0686]])\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "import numpy as np\n",
    "\n",
    "#  Directly from data\n",
    "data = [[11,22],[44,55]]\n",
    "print(\"Original data : {}\".format(data))\n",
    "data_tensor = torch.tensor(data)\n",
    "print(\"data in tensor : {}\".format(data_tensor))\n",
    "\n",
    "#From a NumPy array\n",
    "np_array = np.array(data)\n",
    "n_array_tensor = torch.from_numpy(np_array)\n",
    "print(\"np to tensor : {}\".format(n_array_tensor))\n",
    "\n",
    "# From another tensor\n",
    "data_ones = torch.ones_like(data_tensor)\n",
    "print(\"Ones tensor : {}\".format(data_ones))\n",
    "data_rand = torch.rand_like(data_ones, dtype=torch.float)\n",
    "print(\"Random tensor : {}\".format(data_rand))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial 3 -- Datasets and Dataloaders \n",
    "\n",
    "Learn in 5 steps:\n",
    "1) Loading\n",
    "2) Iterating & visualizing\n",
    "3) Creating a custom dataset\n",
    "4) Preparing your data for training with DataLoaders\n",
    "5) Iterating through the DataLoader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\LegacyApp\\Python\\Python39\\lib\\site-packages\\torchvision\\datasets\\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "figure = plt.figure(figsize=(8, 8))\n",
    "cols, rows = 3, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
    "    img, label = training_data[sample_idx]\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(labels_map[label])\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Custom Dataset for your files\n",
    "\n",
    "A custom Dataset class must implement three functions: __init__, __len__, and __getitem__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from torchvision.io import read_image\n",
    "\n",
    "class CustomImageDataset(Dataset):\n",
    "    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):\n",
    "        self.img_labels = pd.read_csv(annotations_file)\n",
    "        self.img_dir = img_dir\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.img_labels)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])\n",
    "        image = read_image(img_path)\n",
    "        label = self.img_labels.iloc[idx, 1]\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        if self.target_transform:\n",
    "            label = self.target_transform(label)\n",
    "        return image, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing your data for training with DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature batch shape: torch.Size([64, 1, 28, 28])\n",
      "Labels batch shape: torch.Size([64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 2\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)\n",
    "test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)\n",
    "\n",
    "# Display image and label.\n",
    "train_features, train_labels = next(iter(train_dataloader))\n",
    "print(f\"Feature batch shape: {train_features.size()}\")\n",
    "print(f\"Labels batch shape: {train_labels.size()}\")\n",
    "img = train_features[0].squeeze()\n",
    "label = train_labels[0]\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()\n",
    "print(f\"Label: {label}\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f5e714d59357b68dc9bbedf81c6c43d4200dbbef63b7b1420345dd5e39847a38"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}