{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRDyfbk2klCU"
      },
      "source": [
        "See code at https://github.com/google-research/vision_transformer/\n",
        "\n",
        "This Colab is about the paper\n",
        "\n",
        "- LiT: Zero-Shot Transfer with Locked-image text Tuning: https://arxiv.org/abs/2111.07991\n",
        "\n",
        "For ViT, MLP Mixer etc see the other Colab\n",
        "\n",
        "https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax.ipynb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vauyt3WMehFx"
      },
      "source": [
        "### Load model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "JHkfIOBXp-2J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76d3d483-eb71-4810-86be-eea459545ce4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 92 kB 583 kB/s \n",
            "\u001b[K     |████████████████████████████████| 197 kB 9.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 77 kB 4.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.6 MB 27.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 288 kB 48.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.3 MB 48.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 140 kB 61.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 216 kB 65.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 51 kB 6.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 98 kB 7.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 72 kB 593 kB/s \n",
            "\u001b[K     |████████████████████████████████| 511.7 MB 5.6 kB/s \n",
            "\u001b[K     |████████████████████████████████| 511.7 MB 4.4 kB/s \n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 48.1 MB/s \n",
            "\u001b[?25h  Building wheel for vit-jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for flaxformer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for ml-collections (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "# Installs the vit_jax package from Github.\n",
        "!pip install -q git+https://github.com/google-research/vision_transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4DS88TsHsli7"
      },
      "outputs": [],
      "source": [
        "import jax\n",
        "import jax.numpy as jnp\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import tqdm\n",
        "\n",
        "from vit_jax import models\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "9mj2ysEQkXXF",
        "outputId": "e15545d2-6bf7-4254-a323-5636307a5a22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LiT-B16B', 'LiT-L16L', 'LiT-L16S', 'LiT-L16Ti']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Currently available LiT models\n",
        "[name for name in models.model_configs.MODEL_CONFIGS if name.startswith('LiT')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "lv0dIxWckOJ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eba68d45-0b3a-45f0-bf99-246c4c8f4b16"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading params from cloud: gs://vit_models/lit/LiT-B16B.npz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 75/75 [00:53<00:00,  1.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "⚠️ Reusing local copy: LiT-B16B.npz\n"
          ]
        }
      ],
      "source": [
        "model_name = 'LiT-B16B'\n",
        "\n",
        "lit_model = models.get_model(model_name)\n",
        "# Loading the variables from cloud can take a while the first time...\n",
        "lit_variables = lit_model.load_variables()\n",
        "# Creating tokens from freeform text (see next section).\n",
        "tokenizer = lit_model.get_tokenizer()\n",
        "# Resizing images & converting value range to -1..1 (see next section).\n",
        "image_preprocessing = lit_model.get_image_preprocessing()\n",
        "# Preprocessing op for use in tfds pipeline (see last section).\n",
        "pp = lit_model.get_pp()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzQg_w-Cn8a2"
      },
      "source": [
        "### Use model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YBd39mOzmjPK",
        "outputId": "57e77985-37fe-4345-d11b-5ff97a5c244c",
        "colab": {
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          "height": 185,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 1.45 GiB (download: 1.45 GiB, generated: 1.46 GiB, total: 2.91 GiB) to ~/tensorflow_datasets/imagenette/full-size-v2/1.0.0...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bba3c25b79f44da81a2290c8924fd56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7e8e8ff782543418892daa3e6e413e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce1ec7da83074219b106e0ec4b77d3cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6925ea3fb5ac4e5c95a925ea082d91b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/9469 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a18db5be35b146cf842fdc7c9e436db1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/imagenette/full-size-v2/1.0.0.incomplete7T8YUJ/imagenette-train.tfrecord*...: …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "419905e4ea5b4c48a115f9ada28d8eca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/3925 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bb9288e80b5424bb145b7adce3f7d8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/imagenette/full-size-v2/1.0.0.incomplete7T8YUJ/imagenette-validation.tfrecord*…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9495efe798de4194a9f0dcd726e5cdcb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset imagenette downloaded and prepared to ~/tensorflow_datasets/imagenette/full-size-v2/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(101, 125, 3), (375, 500, 3), (335, 500, 3), (429, 500, 3), (359, 500, 3)]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Let's load some sample images from tfds.\n",
        "# Alternatively you can also load these images from the internet / your Drive.\n",
        "ds = tfds.load('imagenette', split='train')\n",
        "images_list = [\n",
        "    example['image'].numpy()\n",
        "    for _, example in zip(range(5), ds)\n",
        "]\n",
        "# Note that this is a list of images with different shapes, not a four\n",
        "# dimensional tensor.\n",
        "[image.shape for image in images_list]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VFBbuHtLmpGs",
        "outputId": "111ceca5-6a57-4f5f-a887-c3730308f4df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5, 224, 224, 3), -1.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Note that our preprocessing converts to floats ranging from -1..1 !\n",
        "images = image_preprocessing(images_list)\n",
        "images.shape, images.min(), images.max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "height": 198,
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDJkYZR6nHGh",
        "outputId": "6bc3efc6-cda1-4394-83e4-2629d03189ee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "[encoded data, 387684 characters]\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 4))\n",
        "plt.imshow(np.hstack(images) * .5 + .5)\n",
        "plt.axis('off');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "3hjjaxlHnWbb",
        "outputId": "ed5856ef-6483-4ac8-feca-1154c8b9baf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "texts = [\n",
        "    'itap of a cd player',\n",
        "    'a photo of a truck',\n",
        "    'gas station',\n",
        "    'chainsaw',\n",
        "    'a bad photo of colorful houses',\n",
        "]\n",
        "tokens = tokenizer(texts)\n",
        "tokens.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "GOe1Miplnf7Z"
      },
      "outputs": [],
      "source": [
        "# Embed both texts and images with a single model call.\n",
        "# See next section for embedding images/texts separately.\n",
        "zimg, ztxt, out = lit_model.apply(lit_variables, images=images, tokens=tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "height": 282,
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtATpe2Bn5I0",
        "outputId": "94cb78e5-95bb-495b-cf9d-28e73f645381"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7cc4dd5b90>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.imshow(ztxt @ zimg.T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "height": 206,
          "base_uri": "https://localhost:8080/"
        },
        "id": "bs9fr-1kCRQW",
        "outputId": "98a62940-213c-43cb-c1d4-75426d655ff9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f7cc4f952d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_e15d4_row0_col0, #T_e15d4_row2_col2, #T_e15d4_row3_col3 {\n",
              "  background-color: #00441b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_e15d4_row0_col1, #T_e15d4_row0_col2, #T_e15d4_row0_col3, #T_e15d4_row0_col4, #T_e15d4_row1_col0, #T_e15d4_row1_col2, #T_e15d4_row1_col3, #T_e15d4_row1_col4, #T_e15d4_row2_col0, #T_e15d4_row2_col1, #T_e15d4_row2_col3, #T_e15d4_row2_col4, #T_e15d4_row3_col0, #T_e15d4_row3_col1, #T_e15d4_row3_col2, #T_e15d4_row3_col4, #T_e15d4_row4_col0, #T_e15d4_row4_col3 {\n",
              "  background-color: #f7fcf5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e15d4_row1_col1 {\n",
              "  background-color: #00451c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_e15d4_row4_col1, #T_e15d4_row4_col2 {\n",
              "  background-color: #f6fcf4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e15d4_row4_col4 {\n",
              "  background-color: #00491d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e15d4_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >0</th>\n",
              "      <th class=\"col_heading level0 col1\" >1</th>\n",
              "      <th class=\"col_heading level0 col2\" >2</th>\n",
              "      <th class=\"col_heading level0 col3\" >3</th>\n",
              "      <th class=\"col_heading level0 col4\" >4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e15d4_level0_row0\" class=\"row_heading level0 row0\" >itap of a cd player</th>\n",
              "      <td id=\"T_e15d4_row0_col0\" class=\"data row0 col0\" >99.71%</td>\n",
              "      <td id=\"T_e15d4_row0_col1\" class=\"data row0 col1\" >0.04%</td>\n",
              "      <td id=\"T_e15d4_row0_col2\" class=\"data row0 col2\" >0.02%</td>\n",
              "      <td id=\"T_e15d4_row0_col3\" class=\"data row0 col3\" >0.23%</td>\n",
              "      <td id=\"T_e15d4_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e15d4_level0_row1\" class=\"row_heading level0 row1\" >a photo of a truck</th>\n",
              "      <td id=\"T_e15d4_row1_col0\" class=\"data row1 col0\" >0.01%</td>\n",
              "      <td id=\"T_e15d4_row1_col1\" class=\"data row1 col1\" >99.55%</td>\n",
              "      <td id=\"T_e15d4_row1_col2\" class=\"data row1 col2\" >0.37%</td>\n",
              "      <td id=\"T_e15d4_row1_col3\" class=\"data row1 col3\" >0.07%</td>\n",
              "      <td id=\"T_e15d4_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e15d4_level0_row2\" class=\"row_heading level0 row2\" >gas station</th>\n",
              "      <td id=\"T_e15d4_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
              "      <td id=\"T_e15d4_row2_col1\" class=\"data row2 col1\" >0.11%</td>\n",
              "      <td id=\"T_e15d4_row2_col2\" class=\"data row2 col2\" >99.77%</td>\n",
              "      <td id=\"T_e15d4_row2_col3\" class=\"data row2 col3\" >0.11%</td>\n",
              "      <td id=\"T_e15d4_row2_col4\" class=\"data row2 col4\" >0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e15d4_level0_row3\" class=\"row_heading level0 row3\" >chainsaw</th>\n",
              "      <td id=\"T_e15d4_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
              "      <td id=\"T_e15d4_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
              "      <td id=\"T_e15d4_row3_col2\" class=\"data row3 col2\" >0.01%</td>\n",
              "      <td id=\"T_e15d4_row3_col3\" class=\"data row3 col3\" >99.99%</td>\n",
              "      <td id=\"T_e15d4_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e15d4_level0_row4\" class=\"row_heading level0 row4\" >a bad photo of colorful houses</th>\n",
              "      <td id=\"T_e15d4_row4_col0\" class=\"data row4 col0\" >0.02%</td>\n",
              "      <td id=\"T_e15d4_row4_col1\" class=\"data row4 col1\" >0.80%</td>\n",
              "      <td id=\"T_e15d4_row4_col2\" class=\"data row4 col2\" >0.82%</td>\n",
              "      <td id=\"T_e15d4_row4_col3\" class=\"data row4 col3\" >0.03%</td>\n",
              "      <td id=\"T_e15d4_row4_col4\" class=\"data row4 col4\" >98.34%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "probs = np.array(jax.nn.softmax(out['t'] * ztxt @ zimg.T, axis=1))\n",
        "pd.DataFrame(probs, index=texts).style.background_gradient('Greens', vmin=0, vmax=1).format('{:.2%}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_w1fANZM0FZY"
      },
      "source": [
        "### `tfds` zero-shot evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown `imagenet_classnames`\n",
        "# From https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb\n",
        "imagenet_classnames = \"tench;goldfish;great white shark;tiger shark;hammerhead shark;electric ray;stingray;rooster;hen;ostrich;brambling;goldfinch;house finch;junco;indigo bunting;American robin;bulbul;jay;magpie;chickadee;American dipper;kite (bird of prey);bald eagle;vulture;great grey owl;fire salamander;smooth newt;newt;spotted salamander;axolotl;American bullfrog;tree frog;tailed frog;loggerhead sea turtle;leatherback sea turtle;mud turtle;terrapin;box turtle;banded gecko;green iguana;Carolina anole;desert grassland whiptail lizard;agama;frilled-necked lizard;alligator lizard;Gila monster;European green lizard;chameleon;Komodo dragon;Nile crocodile;American alligator;triceratops;worm snake;ring-necked snake;eastern hog-nosed snake;smooth green snake;kingsnake;garter snake;water snake;vine snake;night snake;boa constrictor;African rock python;Indian cobra;green mamba;sea snake;Saharan horned viper;eastern diamondback rattlesnake;sidewinder rattlesnake;trilobite;harvestman;scorpion;yellow garden spider;barn spider;European garden spider;southern black widow;tarantula;wolf spider;tick;centipede;black grouse;ptarmigan;ruffed grouse;prairie grouse;peafowl;quail;partridge;african grey parrot;macaw;sulphur-crested cockatoo;lorikeet;coucal;bee eater;hornbill;hummingbird;jacamar;toucan;duck;red-breasted merganser;goose;black swan;tusker;echidna;platypus;wallaby;koala;wombat;jellyfish;sea anemone;brain coral;flatworm;nematode;conch;snail;slug;sea slug;chiton;chambered nautilus;Dungeness crab;rock crab;fiddler crab;red king crab;American lobster;spiny lobster;crayfish;hermit crab;isopod;white stork;black stork;spoonbill;flamingo;little blue heron;great egret;bittern bird;crane bird;limpkin;common gallinule;American coot;bustard;ruddy turnstone;dunlin;common redshank;dowitcher;oystercatcher;pelican;king penguin;albatross;grey whale;killer whale;dugong;sea lion;Chihuahua;Japanese Chin;Maltese;Pekingese;Shih Tzu;King Charles Spaniel;Papillon;toy terrier;Rhodesian Ridgeback;Afghan Hound;Basset Hound;Beagle;Bloodhound;Bluetick Coonhound;Black and Tan Coonhound;Treeing Walker Coonhound;English foxhound;Redbone Coonhound;borzoi;Irish Wolfhound;Italian Greyhound;Whippet;Ibizan Hound;Norwegian Elkhound;Otterhound;Saluki;Scottish Deerhound;Weimaraner;Staffordshire Bull Terrier;American Staffordshire Terrier;Bedlington Terrier;Border Terrier;Kerry Blue Terrier;Irish Terrier;Norfolk Terrier;Norwich Terrier;Yorkshire Terrier;Wire Fox Terrier;Lakeland Terrier;Sealyham Terrier;Airedale Terrier;Cairn Terrier;Australian Terrier;Dandie Dinmont Terrier;Boston Terrier;Miniature Schnauzer;Giant Schnauzer;Standard Schnauzer;Scottish Terrier;Tibetan Terrier;Australian Silky Terrier;Soft-coated Wheaten Terrier;West Highland White Terrier;Lhasa Apso;Flat-Coated Retriever;Curly-coated Retriever;Golden Retriever;Labrador Retriever;Chesapeake Bay Retriever;German Shorthaired Pointer;Vizsla;English Setter;Irish Setter;Gordon Setter;Brittany dog;Clumber Spaniel;English Springer Spaniel;Welsh Springer Spaniel;Cocker Spaniel;Sussex Spaniel;Irish Water Spaniel;Kuvasz;Schipperke;Groenendael dog;Malinois;Briard;Australian Kelpie;Komondor;Old English Sheepdog;Shetland Sheepdog;collie;Border Collie;Bouvier des Flandres dog;Rottweiler;German Shepherd Dog;Dobermann;Miniature Pinscher;Greater Swiss Mountain Dog;Bernese Mountain Dog;Appenzeller Sennenhund;Entlebucher Sennenhund;Boxer;Bullmastiff;Tibetan Mastiff;French Bulldog;Great Dane;St. Bernard;husky;Alaskan Malamute;Siberian Husky;Dalmatian;Affenpinscher;Basenji;pug;Leonberger;Newfoundland dog;Great Pyrenees dog;Samoyed;Pomeranian;Chow Chow;Keeshond;brussels griffon;Pembroke Welsh Corgi;Cardigan Welsh Corgi;Toy Poodle;Miniature Poodle;Standard Poodle;Mexican hairless dog (xoloitzcuintli);grey wolf;Alaskan tundra wolf;red wolf or maned wolf;coyote;dingo;dhole;African wild dog;hyena;red fox;kit fox;Arctic fox;grey fox;tabby cat;tiger cat;Persian cat;Siamese cat;Egyptian Mau;cougar;lynx;leopard;snow leopard;jaguar;lion;tiger;cheetah;brown bear;American black bear;polar bear;sloth bear;mongoose;meerkat;tiger beetle;ladybug;ground beetle;longhorn beetle;leaf beetle;dung beetle;rhinoceros beetle;weevil;fly;bee;ant;grasshopper;cricket insect;stick insect;cockroach;praying mantis;cicada;leafhopper;lacewing;dragonfly;damselfly;red admiral butterfly;ringlet butterfly;monarch butterfly;small white butterfly;sulphur butterfly;gossamer-winged butterfly;starfish;sea urchin;sea cucumber;cottontail rabbit;hare;Angora rabbit;hamster;porcupine;fox squirrel;marmot;beaver;guinea pig;common sorrel horse;zebra;pig;wild boar;warthog;hippopotamus;ox;water buffalo;bison;ram (adult male sheep);bighorn sheep;Alpine ibex;hartebeest;impala (antelope);gazelle;arabian camel;llama;weasel;mink;European polecat;black-footed ferret;otter;skunk;badger;armadillo;three-toed sloth;orangutan;gorilla;chimpanzee;gibbon;siamang;guenon;patas monkey;baboon;macaque;langur;black-and-white colobus;proboscis monkey;marmoset;white-headed capuchin;howler monkey;titi monkey;Geoffroy's spider monkey;common squirrel monkey;ring-tailed lemur;indri;Asian elephant;African bush elephant;red panda;giant panda;snoek fish;eel;silver salmon;rock beauty fish;clownfish;sturgeon;gar fish;lionfish;pufferfish;abacus;abaya;academic gown;accordion;acoustic guitar;aircraft carrier;airliner;airship;altar;ambulance;amphibious vehicle;analog clock;apiary;apron;trash can;assault rifle;backpack;bakery;balance beam;balloon;ballpoint pen;Band-Aid;banjo;baluster / handrail;barbell;barber chair;barbershop;barn;barometer;barrel;wheelbarrow;baseball;basketball;bassinet;bassoon;swimming cap;bath towel;bathtub;station wagon;lighthouse;beaker;military hat (bearskin or shako);beer bottle;beer glass;bell tower;baby bib;tandem bicycle;bikini;ring binder;binoculars;birdhouse;boathouse;bobsleigh;bolo tie;poke bonnet;bookcase;bookstore;bottle cap;hunting bow;bow tie;brass memorial plaque;bra;breakwater;breastplate;broom;bucket;buckle;bulletproof vest;high-speed train;butcher shop;taxicab;cauldron;candle;cannon;canoe;can opener;cardigan;car mirror;carousel;tool kit;cardboard box / carton;car wheel;automated teller machine;cassette;cassette player;castle;catamaran;CD player;cello;mobile phone;chain;chain-link fence;chain mail;chainsaw;storage chest;chiffonier;bell or wind chime;china cabinet;Christmas stocking;church;movie theater;cleaver;cliff dwelling;cloak;clogs;cocktail shaker;coffee mug;coffeemaker;spiral or coil;combination lock;computer keyboard;candy store;container ship;convertible;corkscrew;cornet;cowboy boot;cowboy hat;cradle;construction crane;crash helmet;crate;infant bed;Crock Pot;croquet ball;crutch;cuirass;dam;desk;desktop computer;rotary dial telephone;diaper;digital clock;digital watch;dining table;dishcloth;dishwasher;disc brake;dock;dog sled;dome;doormat;drilling rig;drum;drumstick;dumbbell;Dutch oven;electric fan;electric guitar;electric locomotive;entertainment center;envelope;espresso machine;face powder;feather boa;filing cabinet;fireboat;fire truck;fire screen;flagpole;flute;folding chair;football helmet;forklift;fountain;fountain pen;four-poster bed;freight car;French horn;frying pan;fur coat;garbage truck;gas mask or respirator;gas pump;goblet;go-kart;golf ball;golf cart;gondola;gong;gown;grand piano;greenhouse;radiator grille;grocery store;guillotine;hair clip;hair spray;half-track;hammer;hamper;hair dryer;hand-held computer;handkerchief;hard disk drive;harmonica;harp;combine harvester;hatchet;holster;home theater;honeycomb;hook;hoop skirt;gymnastic horizontal bar;horse-drawn vehicle;hourglass;iPod;clothes iron;carved pumpkin;jeans;jeep;T-shirt;jigsaw puzzle;rickshaw;joystick;kimono;knee pad;knot;lab coat;ladle;lampshade;laptop computer;lawn mower;lens cap;letter opener;library;lifeboat;lighter;limousine;ocean liner;lipstick;slip-on shoe;lotion;music speaker;loupe magnifying glass;sawmill;magnetic compass;messenger bag;mailbox;tights;one-piece bathing suit;manhole cover;maraca;marimba;mask;matchstick;maypole;maze;measuring cup;medicine cabinet;megalith;microphone;microwave oven;military uniform;milk can;minibus;miniskirt;minivan;missile;mitten;mixing bowl;mobile home;ford model t;modem;monastery;monitor;moped;mortar and pestle;graduation cap;mosque;mosquito net;vespa;mountain bike;tent;computer mouse;mousetrap;moving van;muzzle;metal nail;neck brace;necklace;baby pacifier;notebook computer;obelisk;oboe;ocarina;odometer;oil filter;pipe organ;oscilloscope;overskirt;bullock cart;oxygen mask;product packet / packaging;paddle;paddle wheel;padlock;paintbrush;pajamas;palace;pan flute;paper towel;parachute;parallel bars;park bench;parking meter;railroad car;patio;payphone;pedestal;pencil case;pencil sharpener;perfume;Petri dish;photocopier;plectrum;Pickelhaube;picket fence;pickup truck;pier;piggy bank;pill bottle;pillow;ping-pong ball;pinwheel;pirate ship;drink pitcher;block plane;planetarium;plastic bag;plate rack;farm plow;plunger;Polaroid camera;pole;police van;poncho;pool table;soda bottle;plant pot;potter's wheel;power drill;prayer rug;printer;prison;missile;projector;hockey puck;punching bag;purse;quill;quilt;race car;racket;radiator;radio;radio telescope;rain barrel;recreational vehicle;fishing casting reel;reflex camera;refrigerator;remote control;restaurant;revolver;rifle;rocking chair;rotisserie;eraser;rugby ball;ruler measuring stick;sneaker;safe;safety pin;salt shaker;sandal;sarong;saxophone;scabbard;weighing scale;school bus;schooner;scoreboard;CRT monitor;screw;screwdriver;seat belt;sewing machine;shield;shoe store;shoji screen / room divider;shopping basket;shopping cart;shovel;shower cap;shower curtain;ski;balaclava ski mask;sleeping bag;slide rule;sliding door;slot machine;snorkel;snowmobile;snowplow;soap dispenser;soccer ball;sock;solar thermal collector;sombrero;soup bowl;keyboard space bar;space heater;space shuttle;spatula;motorboat;spider web;spindle;sports car;spotlight;stage;steam locomotive;through arch bridge;steel drum;stethoscope;scarf;stone wall;stopwatch;stove;strainer;tram;stretcher;couch;stupa;submarine;suit;sundial;sunglasses;sunglasses;sunscreen;suspension bridge;mop;sweatshirt;swim trunks / shorts;swing;electrical switch;syringe;table lamp;tank;tape player;teapot;teddy bear;television;tennis ball;thatched roof;front curtain;thimble;threshing machine;throne;tile roof;toaster;tobacco shop;toilet seat;torch;totem pole;tow truck;toy store;tractor;semi-trailer truck;tray;trench coat;tricycle;trimaran;tripod;triumphal arch;trolleybus;trombone;hot tub;turnstile;typewriter keyboard;umbrella;unicycle;upright piano;vacuum cleaner;vase;vaulted or arched ceiling;velvet fabric;vending machine;vestment;viaduct;violin;volleyball;waffle iron;wall clock;wallet;wardrobe;military aircraft;sink;washing machine;water bottle;water jug;water tower;whiskey jug;whistle;hair wig;window screen;window shade;Windsor tie;wine bottle;airplane wing;wok;wooden spoon;wool;split-rail fence;shipwreck;sailboat;yurt;website;comic book;crossword;traffic or street sign;traffic light;dust jacket;menu;plate;guacamole;consomme;hot pot;trifle;ice cream;popsicle;baguette;bagel;pretzel;cheeseburger;hot dog;mashed potatoes;cabbage;broccoli;cauliflower;zucchini;spaghetti squash;acorn squash;butternut squash;cucumber;artichoke;bell pepper;cardoon;mushroom;Granny Smith apple;strawberry;orange;lemon;fig;pineapple;banana;jackfruit;cherimoya (custard apple);pomegranate;hay;carbonara;chocolate syrup;dough;meatloaf;pizza;pot pie;burrito;red wine;espresso;tea cup;eggnog;mountain;bubble;cliff;coral reef;geyser;lakeshore;promontory;sandbar;beach;valley;volcano;baseball player;bridegroom;scuba diver;rapeseed;daisy;yellow lady's slipper;corn;acorn;rose hip;horse chestnut seed;coral fungus;agaric;gyromitra;stinkhorn mushroom;earth star fungus;hen of the woods mushroom;bolete;corn cob;toilet paper\".split(';')\n"
      ],
      "metadata": {
        "id": "tF-FzIC0PmHU",
        "cellView": "form"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # For imagenet evaluation, first prepare the dataset on a GCS bucket as\n",
        "# # described in\n",
        "# # https://www.tensorflow.org/datasets/catalog/imagenet2012\n",
        "# # and then replace `data_dir` below with that GCS bucket.\n",
        "# # If you get a `PermissionDeniedError`, try restarting the kernel.\n",
        "# from google.colab import auth\n",
        "# auth.authenticate_user()  # Required to access access protected GCS buckets.\n",
        "# builder = tfds.builder('imagenet2012', data_dir='gs://tensorflow-datasets/datasets')\n",
        "# ds_test = builder.as_dataset('validation')\n",
        "# info = builder.info\n",
        "# classnames = imagenet_classnames"
      ],
      "metadata": {
        "id": "vHOhCKIVOcm4"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Ojt_6bny09Nt",
        "outputId": "739f32de-a801-431c-afbd-6b3c7b27f52b",
        "colab": {
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          "height": 341,
          "referenced_widgets": [
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 160.71 MiB (download: 160.71 MiB, generated: 132.03 MiB, total: 292.74 MiB) to ~/tensorflow_datasets/cifar100/3.0.2...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e81b1f88fd9d4218bd47b9e4ea174da7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "085dd73a510b4f999c82ceddda4b1944"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "faaa91d10b814cc087dba74e4b2730d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62d6145be50443c98a478e07d4f5a6b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d835cfb8a79405bb326409fa41fe395"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/cifar100/3.0.2.incomplete5GRC1Y/cifar100-train.tfrecord*...:   0%|          | …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "807835fabc9c4a8987a6d4eed77b57fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "676dc582a0a84806abde3093a9cc260b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/cifar100/3.0.2.incomplete5GRC1Y/cifar100-test.tfrecord*...:   0%|          | 0…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbea0266da3f4ad58d8e5ef9fc76cfd8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset cifar100 downloaded and prepared to ~/tensorflow_datasets/cifar100/3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple',\n",
              " 'aquarium_fish',\n",
              " 'baby',\n",
              " 'bear',\n",
              " 'beaver',\n",
              " 'bed',\n",
              " 'bee',\n",
              " 'beetle',\n",
              " 'bicycle',\n",
              " 'bottle']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Load dataset and create array of class names.\n",
        "builder = tfds.builder('cifar100')\n",
        "builder.download_and_prepare()\n",
        "ds_test = builder.as_dataset('test')\n",
        "info = builder.info\n",
        "classnames = [\n",
        "    info.features['label'].int2str(id_)\n",
        "    for id_ in range(info.features['label'].num_classes)\n",
        "]\n",
        "classnames[:10]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown `PROMPTS80`\n",
        "PROMPTS80 = [\n",
        "    'a bad photo of a {}.',\n",
        "    'a photo of many {}.',\n",
        "    'a sculpture of a {}.',\n",
        "    'a photo of the hard to see {}.',\n",
        "    'a low resolution photo of the {}.',\n",
        "    'a rendering of a {}.',\n",
        "    'graffiti of a {}.',\n",
        "    'a bad photo of the {}.',\n",
        "    'a cropped photo of the {}.',\n",
        "    'a tattoo of a {}.',\n",
        "    'the embroidered {}.',\n",
        "    'a photo of a hard to see {}.',\n",
        "    'a bright photo of a {}.',\n",
        "    'a photo of a clean {}.',\n",
        "    'a photo of a dirty {}.',\n",
        "    'a dark photo of the {}.',\n",
        "    'a drawing of a {}.',\n",
        "    'a photo of my {}.',\n",
        "    'the plastic {}.',\n",
        "    'a photo of the cool {}.',\n",
        "    'a close-up photo of a {}.',\n",
        "    'a black and white photo of the {}.',\n",
        "    'a painting of the {}.',\n",
        "    'a painting of a {}.',\n",
        "    'a pixelated photo of the {}.',\n",
        "    'a sculpture of the {}.',\n",
        "    'a bright photo of the {}.',\n",
        "    'a cropped photo of a {}.',\n",
        "    'a plastic {}.',\n",
        "    'a photo of the dirty {}.',\n",
        "    'a jpeg corrupted photo of a {}.',\n",
        "    'a blurry photo of the {}.',\n",
        "    'a photo of the {}.',\n",
        "    'a good photo of the {}.',\n",
        "    'a rendering of the {}.',\n",
        "    'a {} in a video game.',\n",
        "    'a photo of one {}.',\n",
        "    'a doodle of a {}.',\n",
        "    'a close-up photo of the {}.',\n",
        "    'a photo of a {}.',\n",
        "    'the origami {}.',\n",
        "    'the {} in a video game.',\n",
        "    'a sketch of a {}.',\n",
        "    'a doodle of the {}.',\n",
        "    'a origami {}.',\n",
        "    'a low resolution photo of a {}.',\n",
        "    'the toy {}.',\n",
        "    'a rendition of the {}.',\n",
        "    'a photo of the clean {}.',\n",
        "    'a photo of a large {}.',\n",
        "    'a rendition of a {}.',\n",
        "    'a photo of a nice {}.',\n",
        "    'a photo of a weird {}.',\n",
        "    'a blurry photo of a {}.',\n",
        "    'a cartoon {}.',\n",
        "    'art of a {}.',\n",
        "    'a sketch of the {}.',\n",
        "    'a embroidered {}.',\n",
        "    'a pixelated photo of a {}.',\n",
        "    'itap of the {}.',\n",
        "    'a jpeg corrupted photo of the {}.',\n",
        "    'a good photo of a {}.',\n",
        "    'a plushie {}.',\n",
        "    'a photo of the nice {}.',\n",
        "    'a photo of the small {}.',\n",
        "    'a photo of the weird {}.',\n",
        "    'the cartoon {}.',\n",
        "    'art of the {}.',\n",
        "    'a drawing of the {}.',\n",
        "    'a photo of the large {}.',\n",
        "    'a black and white photo of a {}.',\n",
        "    'the plushie {}.',\n",
        "    'a dark photo of a {}.',\n",
        "    'itap of a {}.',\n",
        "    'graffiti of the {}.',\n",
        "    'a toy {}.',\n",
        "    'itap of my {}.',\n",
        "    'a photo of a cool {}.',\n",
        "    'a photo of a small {}.',\n",
        "    'a tattoo of the {}.',\n",
        "]\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "q8mT-9QKScxl"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8BQtRYhd0F94",
        "outputId": "04d31d65-1651-40f7-c9e4-9a08f2603196",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "800"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# The official CLIP Colab\n",
        "# https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb\n",
        "# lists two sets of prompts: the 80 prompts mentioned in the CLIP paper\n",
        "# (https://arxiv.org/abs/2103.00020), as well as a shortlist of 7 prompts that\n",
        "# we'll be using by default below.\n",
        "\n",
        "PROMPTS = [\n",
        "    'itap of a {}.',\n",
        "    'a bad photo of the {}.',\n",
        "    'a origami {}.',\n",
        "    'a photo of the large {}.',\n",
        "    'a {} in a video game.',\n",
        "    'art of the {}.',\n",
        "    'a photo of the small {}.',\n",
        "    '{}',\n",
        "]\n",
        "# PROMPTS = PROMPTS80\n",
        "\n",
        "texts = [\n",
        "    prompt.format(classname)\n",
        "    for classname in classnames\n",
        "    for prompt in PROMPTS\n",
        "]\n",
        "len(texts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Ye2MkSEVBcuF",
        "outputId": "26c6102f-215e-414d-bf65-b6d4bcccad8b",
        "colab": {
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        }
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(800, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Tokenize the texts using numpy like before.\n",
        "tokens = tokenizer(texts)\n",
        "tokens.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "5mECphbt1xvr",
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          "output_type": "execute_result",
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            "text/plain": [
              "(800, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "_, ztxt, _ = lit_model.apply(lit_variables, tokens=tokens)\n",
        "ztxt.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "height": 240,
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTe1MqMICNYO",
        "outputId": "1ca175e8-3927-4654-a5ea-9a3ae77662a1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "[encoded data, 252020 characters]\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# `pp` from above (section \"Load model\") is a TensorFlow graph that can\n",
        "# efficiently be added to the input pre-processing.\n",
        "imgs = next(iter(ds_test.map(pp).batch(4)))['image']\n",
        "# Note that `pp` would also tokenize \"texts\" to \"tokens\", if such a feature was\n",
        "# present in the dataset (which is not the case for cifar).\n",
        "\n",
        "plt.figure(figsize=(15, 4))\n",
        "plt.imshow(np.hstack(imgs) * .5 + .5)\n",
        "plt.axis('off');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "iO85T44aEmMs"
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      "source": [
        "# JIT-compile image embedding function because there are lots of images.\n",
        "@jax.jit\n",
        "def embed_images(variables, images):\n",
        "  zimg, _, _ = lit_model.apply(variables, images=images)\n",
        "  return zimg"
      ]
    },
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      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "8Drfz7TACX2P",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 20/20 [02:19<00:00,  6.99s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
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            ]
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        }
      ],
      "source": [
        "# Compute all images embeddings & collect correct labels.\n",
        "zimgs = []\n",
        "labels = []\n",
        "\n",
        "for batch in tqdm.tqdm(ds_test.map(lit_model.get_pp()).batch(500)):\n",
        "  labels += list(batch['label'].numpy())\n",
        "  zimg = embed_images(lit_variables, batch['image'].numpy())\n",
        "  zimgs.append(np.array(zimg))\n",
        "zimgs = np.concatenate(zimgs)\n",
        "zimgs.shape"
      ]
    },
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      "cell_type": "code",
      "execution_count": 22,
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            "text/plain": [
              "(10000, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Compute similarities ...\n",
        "sims = zimgs @ ztxt.reshape([len(classnames), len(PROMPTS), -1]).mean(axis=1).T\n",
        "sims.shape"
      ]
    },
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      "cell_type": "code",
      "execution_count": 23,
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        "id": "0mKZW4gNFl_6",
        "outputId": "ac7a85ab-eb12-4eea-c884-f8984f248c5c",
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        }
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeviceArray(0.79219997, dtype=float32)"
            ]
          },
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        }
      ],
      "source": [
        "# ... and use most similar embedding to predict label.\n",
        "(sims.argmax(axis=1) == np.array(labels)).mean()\n",
        "# Expected accuracy for model \"LiT-B16B\" : 79.19"
      ]
    }
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