nb/dataset/IntroToDSImages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e0ed3434",
   "metadata": {},
   "source": [
    "# Introduction to the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19d0f047",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from torch.utils.data import SubsetRandomSampler\n",
    "from torchvision.transforms.functional import to_pil_image\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f42bf158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pallets library from project root\n",
    "import sys, os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..', '..')))\n",
    "\n",
    "from pallets import (\n",
    "    images as I,\n",
    "    datasets as DS,\n",
    "    models as M,\n",
    "    logging as L\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "189cf27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Logger pallets (INFO)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logging\n",
    "L.init_logger(notebook=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573c049b",
   "metadata": {},
   "source": [
    "# The Colors and The Shapes\n",
    "\n",
    "The images are 24x24 with 4 color channels for RGB and transparency. We simplify access to both with functions found in the `pallets.images` module, which we use as `I`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70bb312e",
   "metadata": {},
   "source": [
    "## One Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e98eded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([4, 24, 24])\n",
      "\n",
      "total colors: 7\n",
      "\n",
      "image colors:\n",
      "tensor([[0.0000, 0.0000, 0.0000, 0.0000],\n",
      "        [0.0000, 0.0000, 0.0000, 1.0000],\n",
      "        [0.3137, 0.4863, 0.2000, 1.0000],\n",
      "        [0.3647, 0.5451, 0.2627, 1.0000],\n",
      "        [0.3725, 0.1137, 0.0353, 1.0000],\n",
      "        [0.6824, 0.5451, 0.3804, 1.0000],\n",
      "        [1.0000, 0.9647, 0.5569, 1.0000]])\n"
     ]
    }
   ],
   "source": [
    "# Load image for 0th punk\n",
    "image = I.get_punk_tensor(0)\n",
    "\n",
    "# Get list of colors found in image\n",
    "colors = I.one_image_colors(image)\n",
    "\n",
    "print(f\"image shape: {image.shape}\\n\")\n",
    "print(f\"total colors: {len(colors)}\\n\")\n",
    "print(f\"image colors:\\n{colors}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8726be8",
   "metadata": {},
   "source": [
    "## All Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "864aacb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colors found: 222\n"
     ]
    }
   ],
   "source": [
    "# Get list of unique colors for all punk images\n",
    "\n",
    "all_colors = I.get_punk_colors()\n",
    "\n",
    "print(f\"colors found: {len(all_colors)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4820e21c",
   "metadata": {},
   "source": [
    "## Visualize every color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e1ae026",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 222 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_imgs = [np.full((24, 24, 4), c) for c in all_colors]\n",
    "(x,y) = (15,15)\n",
    "fig = plt.figure(figsize=(x,y))\n",
    "i=1\n",
    "for c in random.sample(color_imgs, 222):\n",
    "    fig.add_subplot(x, y, i)\n",
    "    plt.imshow(c)\n",
    "    plt.axis('off')\n",
    "    i=i+1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a79bb4",
   "metadata": {},
   "source": [
    "# Two Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c3cb93",
   "metadata": {},
   "source": [
    "## 4 Channel RGBA Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ffa75de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO | loading punk images\n",
      "INFO | - image 0\n",
      "INFO | - image 1000\n",
      "INFO | - image 2000\n",
      "INFO | - image 3000\n",
      "INFO | - image 4000\n",
      "INFO | - image 5000\n",
      "INFO | - image 6000\n",
      "INFO | - image 7000\n",
      "INFO | - image 8000\n",
      "INFO | - image 9000\n",
      "INFO | punk images complete\n",
      "INFO | loading punk labels: None\n",
      "\n",
      "dataset size:   10000\n",
      "  - train size: 8000\n",
      "  - test size:  2000\n",
      "\n",
      "train sampler:  8000\n",
      "  - sample id:  2783\n",
      "\n",
      "test sampler:   2000\n",
      "  - sample id:  123\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = DS.CPunksDataset(test_size=2000)\n",
    "train_sampler = SubsetRandomSampler(dataset.train_idx)\n",
    "test_sampler = SubsetRandomSampler(dataset.test_idx)\n",
    "\n",
    "print(f\"\\ndataset size:   {len(dataset)}\")\n",
    "print(f\"  - train size: {len(dataset.train_idx)}\")\n",
    "print(f\"  - test size:  {len(dataset.test_idx)}\\n\")\n",
    "print(f\"train sampler:  {len(train_sampler)}\")\n",
    "print(f\"  - sample id:  {next(iter(train_sampler))}\\n\")\n",
    "print(f\"test sampler:   {len(test_sampler)}\")\n",
    "print(f\"  - sample id:  {next(iter(test_sampler))}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdea8683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n"
     ]
    }
   ],
   "source": [
    "# Load punk from data loader and plot it\n",
    "\n",
    "image, label = dataset[0]\n",
    "\n",
    "plt.imshow(to_pil_image(image))\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d36b887",
   "metadata": {},
   "source": [
    "## 222 Channel One Hot Dataset\n",
    "\n",
    "1. Instantiate a mapper to go from `all_colors` to its one-hot encoding.\n",
    "2. Use mapper to encode & decode an image.\n",
    "3. Create dataloader for punks that represents its color data as one hot vectors.\n",
    "4. Load one-hot encoded punk from dataloader, decode it, plot it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecf08ce",
   "metadata": {},
   "source": [
    "### Instantiate a mapper to go from `all_colors` to its one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d09b245",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color:\n",
      "tensor([0.0000, 0.2510, 1.0000, 1.0000])\n",
      "\n",
      "one-hot encoded:\n",
      "tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.])\n",
      "\n",
      "back as color:\n",
      "tensor([0.0000, 0.2510, 1.0000, 1.0000])\n"
     ]
    }
   ],
   "source": [
    "mapper = DS.ColorOneHotMapper(all_colors)\n",
    "\n",
    "# Start with a test color\n",
    "test_color = all_colors[2]\n",
    "print(f\"color:\\n{test_color}\\n\")\n",
    "\n",
    "# Convert test color to one hot\n",
    "test_one_hot = mapper.to_one_hot(test_color)\n",
    "print(f\"one-hot encoded:\\n{test_one_hot}\\n\")\n",
    "\n",
    "# Convert one hot back to test color\n",
    "retrieved_color = mapper.to_color(test_one_hot)\n",
    "print(f\"back as color:\\n{retrieved_color}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aed7173",
   "metadata": {},
   "source": [
    "### Use mapper to encode & decode an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f7ccbe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one hot encoded: torch.Size([222, 24, 24])\n",
      "rgba encoded:    torch.Size([4, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAGa0lEQVR4nO3dPWpcZxSA4ZkwjaqsRNlEFhDkKk0CrgUGFd5BOhWCgGpDqlRWvJA00UpSqfzchBdBfnDmjvTdmXme/nKPx6CX05y7HWOMDQBsNpuvZg8AwHqIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAyG72AJygp7vZE5yXi5vZE3BCbAoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYBsxxhj9hC8AOer+VJOb/OMTQGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACC72QMAky359oZvMZwcmwIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACDbMcaYPQT/YMk5Y47Ow/3j3s9eXV8ecJJX5Oz2KtkUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAsps9AOuz5Lb/ZnPE9/0XWPqbzXz3Of5/8e9sCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgDidzcHNPCO95Az0zLlhLWwKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIA8T2Fl/R0N3uCs+ObCLCMTQGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABCns0/UzBPSv/z5x7R3//j1N9PevcTM3+xqcznt3ayPTQGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABCnszm43376fe9nf7j99oCTHA+/GWthUwAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAMh2jDFmD3Gynu5mT7CXh/vH2SPwiq6uL+e8+OJmznv5TzYFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBAdrMHAM7U0tPyTm+/CJsCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBAfE8B/vLm/Ye9n/14+/aAk8A8NgUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEC2Y4wxe4hVe7qbPcHRebh/nD0C/8PV9eXsEV7fxc3sCVbLpgBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIjT2Wt1pie7l57d/vTzrwea5HV99+77vZ+devraCeqTY1MAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACI7ynwN9vtdtHzH2/fHmgSvsSb9x8WPe9PAM/ZFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCANnNHgCeW3oG+ljPdi/5dzt9zSHZFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFAOJ7CqzK0u8hLP0ewyy+icBa2BQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgBxOpuTsuT09pKz205fcypsCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAbMcYY/YQAKyDTQGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgHwGqxl9dfASug0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use mapper to encode & decode a whole punk image.\n",
    "\n",
    "one_hot_encoded_image = DS.rgba_to_one_hot(image, mapper)\n",
    "print(f\"one hot encoded: {one_hot_encoded_image.shape}\")\n",
    "\n",
    "restored = DS.one_hot_to_rgba(one_hot_encoded_image, mapper)\n",
    "print(f\"rgba encoded:    {restored.shape}\")\n",
    "\n",
    "# Show an image after going to one hot encoding and back\n",
    "plt.imshow(to_pil_image(restored))\n",
    "plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd02f353",
   "metadata": {},
   "source": [
    "### Create dataloader for punks that represents its color data as one hot vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35fef1c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO | loading punk images\n",
      "INFO | - image 0\n",
      "INFO | - image 1000\n",
      "INFO | - image 2000\n",
      "INFO | - image 3000\n",
      "INFO | - image 4000\n",
      "INFO | - image 5000\n",
      "INFO | - image 6000\n",
      "INFO | - image 7000\n",
      "INFO | - image 8000\n",
      "INFO | - image 9000\n",
      "INFO | punk images complete\n",
      "INFO | loading punk labels: None\n",
      "dataset size:   10000\n",
      "  - train size: 8000\n",
      "  - test size:  2000\n",
      "\n",
      "train sampler:  8000\n",
      "  - sample id:  2230\n",
      "\n",
      "test sampler:   2000\n",
      "  - sample id:  6671\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = DS.OneHotCPunksDataset(mapper, test_size=2000)\n",
    "train_sampler = SubsetRandomSampler(dataset.train_idx)\n",
    "test_sampler = SubsetRandomSampler(dataset.test_idx)\n",
    "\n",
    "print(f\"dataset size:   {len(dataset)}\")\n",
    "print(f\"  - train size: {len(dataset.train_idx)}\")\n",
    "print(f\"  - test size:  {len(dataset.test_idx)}\\n\")\n",
    "print(f\"train sampler:  {len(train_sampler)}\")\n",
    "print(f\"  - sample id:  {next(iter(train_sampler))}\\n\")\n",
    "print(f\"test sampler:   {len(test_sampler)}\")\n",
    "print(f\"  - sample id:  {next(iter(test_sampler))}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22d782f",
   "metadata": {},
   "source": [
    "### Load one-hot encoded punk from dataloader, decode it, plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ac9c638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rgba encoded:    torch.Size([4, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Associated label: tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n"
     ]
    }
   ],
   "source": [
    "one_hot_encoded_image, label = dataset[0]\n",
    "\n",
    "restored = DS.one_hot_to_rgba(one_hot_encoded_image, mapper)\n",
    "print(f\"rgba encoded:    {restored.shape}\")\n",
    "\n",
    "plt.imshow(to_pil_image(restored))\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Associated label: {label}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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