notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "[![Roboflow Notebooks](https://media.roboflow.com/notebooks/template/bannertest2-2.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672932710194)](https://github.com/roboflow/notebooks)\n",
        "\n",
        "# Fine-tune PaliGemma on Object Detection Dataset\n",
        "\n",
        "---\n",
        "\n",
        "[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md)\n",
        "[![Roboflow](https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg)](https://blog.roboflow.com/how-to-fine-tune-paligemma/)\n",
        "[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=OMBmVInx68M)\n",
        "\n",
        "PaliGemma is an open vision-language model (VLM) inspired by PaLI-3, built with\n",
        "open components, such as\n",
        "the [SigLIP vision model](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/SigLIP_demo.ipynb)\n",
        "and\n",
        "the [Gemma language model](https://ai.google.dev/gemma).\n",
        "PaliGemma is designed as a versatile model for transfer to a wide range of\n",
        "vision-language tasks such as image and short video caption, visual question\n",
        "answering, text reading, object detection and object segmentation. Together with\n",
        "the pretrained and transfer checkpoints at multiple resolutions, we provide a\n",
        "checkpoint transferred to a mixture of tasks that can be used for off-the-shelf\n",
        "exploration.\n",
        "\n",
        "This notebook is an extension of the [official notebook](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/finetune_paligemma.ipynb) prepared by Google Research.\n",
        "\n",
        "![PaliGemma model](https://storage.cloud.google.com/com-roboflow-marketing/notebooks/examples/paligemma.png)\n",
        "\n",
        "This notebook shows how to fine-tune [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) on a vision-language task with [JAX](https://jax.readthedocs.io/en/latest/index.html). *Fine-tuning* is a process that can improve your model's performance on specific tasks or help the model adhere to specific output requirements when instructions aren't sufficient and you have a set of examples that demonstrate the outputs you want. Gemma-based models like PaliGemma require fine-tuning to produce expected results.\n",
        "\n",
        "To make it runnable on a T4 colab runtime with 16GB HBM and 12GB RAM, we opt to only finetune the attention layers of the language model and freeze the other parameters."
      ],
      "metadata": {
        "id": "4LqvmtZPzyY1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "lBp3Czz3GBmc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get access to PaliGemma\n",
        "\n",
        "Before using PaliGemma for the first time, you must request access to the model through Kaggle by completing the following steps:\n",
        "\n",
        "1. Log in to [`Kaggle`](https://www.kaggle.com), or create a new Kaggle account if you don't already have one.\n",
        "1. Go to the [`PaliGemma Model Card`](https://www.kaggle.com/models/google/paligemma/) and click `Request Access`.\n",
        "1. Complete the consent form and accept the terms and conditions."
      ],
      "metadata": {
        "id": "4ohXT9pQFjZs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Configure your API keys\n",
        "\n",
        "To use PaliGemma, you need to provide your Kaggle username, Kaggle API key, and Roboflow API key. Follow these steps:\n",
        "\n",
        "- Open your [`Kaggle Settings`](https://www.kaggle.com/settings) page. Click `Create New Token`. This will download a `kaggle.json` file containing your API credentials.\n",
        "- Go to your [`Roboflow Settings`](https://app.roboflow.com/settings/api) page. Click `Copy`. This will place your private key in the clipboard.\n",
        "- In Colab, go to the left pane and click on `Secrets` (🔑).\n",
        "    - Store Kaggle Username under the name `KAGGLE_USERNAME`.\n",
        "    - Store Kaggle API Key under the name `KAGGLE_KEY`.\n",
        "    - Store Roboflow API Key under the name `ROBOFLOW_API_KEY`."
      ],
      "metadata": {
        "id": "ADTkh-2y_9Yv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Select the runtime\n",
        "\n",
        "Let's make sure that we have access to GPU. We can use `nvidia-smi` command to do that. In case of any problems navigate to `Edit` -> `Notebook settings` -> `Hardware accelerator`, set it to `T4 GPU`, and then click `Save`."
      ],
      "metadata": {
        "id": "4wyojKiG_hX9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "O_8BLW6R_x-z",
        "outputId": "f2c36369-fd26-4b58-c93f-4ef2aebc1764",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May 30 08:04:49 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   67C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download dataset from Roboflow Universe\n",
        "\n",
        "To fine-tune PaliGemma, prepare your dataset in JSONL format. You can use Roboflow to easily convert any dataset into this format."
      ],
      "metadata": {
        "id": "FMlw3ru1YvLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q roboflow\n",
        "!pip install -q git+https://github.com/roboflow/supervision.git"
      ],
      "metadata": {
        "id": "Wtvz4QZ9YuG8",
        "outputId": "7ca5c1c8-4aa1-442c-c942-5cdc15fc277a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.5/75.5 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.3/158.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 MB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "from roboflow import Roboflow\n",
        "\n",
        "ROBOFLOW_API_KEY = userdata.get('ROBOFLOW_API_KEY')\n",
        "rf = Roboflow(api_key=ROBOFLOW_API_KEY)\n",
        "\n",
        "project = rf.workspace(\"roboflow-jvuqo\").project(\"number-ops-j1426\")\n",
        "version = project.version(1)\n",
        "dataset = version.download(\"paligemma\")"
      ],
      "metadata": {
        "id": "TGDFTYVnY4zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!head -n 5 {dataset.location}/dataset/_annotations.train.jsonl"
      ],
      "metadata": {
        "id": "WLhSenP5AtQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!head -n 5 {dataset.location}/dataset/_annotations.valid.jsonl"
      ],
      "metadata": {
        "id": "YwHY21ABA0WG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import json\n",
        "import supervision as sv\n",
        "from typing import List\n",
        "\n",
        "def read_n_lines(file_path: str, n: int) -> List[str]:\n",
        "    with open(file_path, 'r') as file:\n",
        "        lines = [next(file).strip() for _ in range(n)]\n",
        "    return lines\n",
        "\n",
        "images = []\n",
        "lines = read_n_lines(f\"{dataset.location}/dataset/_annotations.train.jsonl\", 25)\n",
        "first = json.loads(lines[0])\n",
        "\n",
        "CLASSES = first.get('prefix').replace(\"detect \", \"\").split(\" ; \")\n",
        "\n",
        "for line in lines:\n",
        "    data = json.loads(line)\n",
        "    image = cv2.imread(f\"{dataset.location}/dataset/{data.get('image')}\")\n",
        "    (h, w, _) = image.shape\n",
        "    detections = sv.Detections.from_lmm(\n",
        "        lmm='paligemma',\n",
        "        result=data.get('suffix'),\n",
        "        resolution_wh=(w, h),\n",
        "        classes=CLASSES)\n",
        "\n",
        "    image = sv.BoundingBoxAnnotator(thickness=4).annotate(image, detections)\n",
        "    image = sv.LabelAnnotator(text_scale=2, text_thickness=4).annotate(image, detections)\n",
        "    images.append(image)\n",
        "\n",
        "sv.plot_images_grid(images, (5, 5))"
      ],
      "metadata": {
        "id": "6ihTTuTd747l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fetch the `big_vision` repository and install related dependencies\n",
        "\n",
        "Download the `big_vision` repository to your Colab notebook from GitHub and install dependencies related to `big_vision` by running the following code."
      ],
      "metadata": {
        "id": "eg3sqaoPFS3W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DfxKb3F839Ks"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "# TPUs with\n",
        "if \"COLAB_TPU_ADDR\" in os.environ:\n",
        "  raise \"It seems you are using Colab with remote TPUs which is not supported.\"\n",
        "\n",
        "# Fetch big_vision repository if python doesn't know about it and install\n",
        "# dependencies needed for this notebook.\n",
        "if not os.path.exists(\"big_vision_repo\"):\n",
        "  !git clone --quiet --branch=main --depth=1 \\\n",
        "     https://github.com/google-research/big_vision big_vision_repo\n",
        "\n",
        "# Append big_vision code to python import path\n",
        "if \"big_vision_repo\" not in sys.path:\n",
        "  sys.path.append(\"big_vision_repo\")\n",
        "\n",
        "# Install missing dependencies. Assume jax~=0.4.25 with GPU available.\n",
        "!pip3 install -q \"overrides\" \"ml_collections\" \"einops~=0.7\" \"sentencepiece\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set environment variables\n",
        "\n",
        "Set the environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
      ],
      "metadata": {
        "id": "YU2fs7d0F1Fo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zGLIp1Cx3_CX"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
        "# vars as appropriate or make your credentials available in ~/.kaggle/kaggle.json\n",
        "\n",
        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import JAX and other dependencies\n",
        "\n",
        "Import JAX and other dependencies required for PaliGemma, like TensorFlow and NumPy."
      ],
      "metadata": {
        "id": "zx3dj5NzG93I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import base64\n",
        "import functools\n",
        "import html\n",
        "import io\n",
        "import os\n",
        "import warnings\n",
        "\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "import numpy as np\n",
        "import ml_collections\n",
        "\n",
        "import tensorflow as tf\n",
        "import sentencepiece\n",
        "\n",
        "from IPython.core.display import display, HTML\n",
        "from PIL import Image\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# Import model definition from big_vision\n",
        "from big_vision.models.proj.paligemma import paligemma\n",
        "from big_vision.trainers.proj.paligemma import predict_fns\n",
        "\n",
        "# Import big vision utilities\n",
        "import big_vision.datasets.jsonl\n",
        "import big_vision.utils\n",
        "import big_vision.sharding\n",
        "\n",
        "# Don't let TF use the GPU or TPUs\n",
        "tf.config.set_visible_devices([], \"GPU\")\n",
        "tf.config.set_visible_devices([], \"TPU\")\n",
        "\n",
        "backend = jax.lib.xla_bridge.get_backend()\n",
        "print(f\"JAX version:  {jax.__version__}\")\n",
        "print(f\"JAX platform: {backend.platform}\")\n",
        "print(f\"JAX devices:  {jax.device_count()}\")"
      ],
      "metadata": {
        "id": "OlWELn2FHB22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and configure the model\n",
        "\n",
        "In this step, you'll download the model checkpoint and configure it so that you can fine-tune it later on. This step shows you how to move model parameters into TPU memory, which is useful for fine-tuning models on devices with limited resources."
      ],
      "metadata": {
        "id": "_PHhkFGuHMFF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download the model checkpoint\n",
        "\n",
        "PaliGemma includes several model variations. For this tutorial, you'll use the base [JAX/FLAX PaliGemma 3B weight model](https://www.kaggle.com/models/google/paligemma/jax/paligemma-3b-pt-224).\n",
        "\n",
        "Download the `float16` version of the model checkpoint from Kaggle by running the following code. This process takes several minutes to complete."
      ],
      "metadata": {
        "id": "9wU_sHbGHQka"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gQNOTfF24AV4"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import kagglehub\n",
        "\n",
        "MODEL_PATH = \"./pt_224_128.params.f16.npz\"\n",
        "if not os.path.exists(MODEL_PATH):\n",
        "    print(\"Downloading the checkpoint from Kaggle, this could take a few minutes....\")\n",
        "    # Note: kaggle archive contains the same checkpoint in multiple formats.\n",
        "    # Download only the float16 model.\n",
        "    MODEL_PATH = kagglehub.model_download('google/paligemma/jax/paligemma-3b-pt-224', 'paligemma-3b-pt-224.f16.npz')\n",
        "    print(f\"Model path: {MODEL_PATH}\")\n",
        "\n",
        "TOKENIZER_PATH = \"./paligemma_tokenizer.model\"\n",
        "if not os.path.exists(TOKENIZER_PATH):\n",
        "    print(\"Downloading the model tokenizer...\")\n",
        "    !gsutil cp gs://big_vision/paligemma_tokenizer.model {TOKENIZER_PATH}\n",
        "    print(f\"Tokenizer path: {TOKENIZER_PATH}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Configure the model\n",
        "\n",
        "It's time to actually start configuring the model that you're going to use.\n",
        "\n",
        "For this notebook, you need to be able to fit your model onto a T4 GPU. Having a limited resource like space constraints means that you have to be mindful of how your model is configured.\n",
        "\n",
        "If you fine-tune every parameter, your model won't be able to run in the notebook environment. As a result, in this part of the notebook, you'll configure your model so that it has the ability to freeze some of the parameters, and only fine-tune the parameters that really need to be fine-tuned for the model to give you accurate results. In LLMs, parameters are said to be *frozen* when they are no longer actively being used to train the model.\n",
        "\n",
        "In order to configure your model, you need to:\n",
        "\n",
        "* Initialize the `model_config` as a [`FrozenConfigDict`](https://github.com/google/ml_collections/tree/master#frozenconfigdict) so that you can freeze some of the parameters and keep memory usage low\n",
        "* Initialize an instance of the PaliGemma `Model` class using the `model_config` as its configurations\n",
        "* Load the model parameters into RAM\n",
        "* Define a `decode` function to sample outputs from the model\n",
        "\n",
        "This code in this cell takes about a minute to run to completion."
      ],
      "metadata": {
        "id": "fnCT0G9sHxsX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1aghcULcEdtv"
      },
      "outputs": [],
      "source": [
        "# Define model\n",
        "model_config = ml_collections.FrozenConfigDict({\n",
        "    \"llm\": {\"vocab_size\": 257_152},\n",
        "    \"img\": {\"variant\": \"So400m/14\", \"pool_type\": \"none\", \"scan\": True, \"dtype_mm\": \"float16\"}\n",
        "})\n",
        "model = paligemma.Model(**model_config)\n",
        "tokenizer = sentencepiece.SentencePieceProcessor(TOKENIZER_PATH)\n",
        "\n",
        "# Load params - this can take up to 1 minute in T4 colabs.\n",
        "params = paligemma.load(None, MODEL_PATH, model_config)\n",
        "\n",
        "# Define `decode` function to sample outputs from the model.\n",
        "decode_fn = predict_fns.get_all(model)['decode']\n",
        "decode = functools.partial(decode_fn, devices=jax.devices(), eos_token=tokenizer.eos_id())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Move model parameters into GPU/TPU memory\n",
        "\n",
        "Now you need to move the model parameters into GPU/TPU memory. First, shard the parameters across the available GPUs, then load the parameters. Here, you'll load the parameters sequentially. This process takes longer than loading them simultaneously, but it requires more RAM than you have available in this notebook.\n",
        "\n",
        "Finally, print out all of the parameters to see what type each individual parameter is cast to. Frozen parameters are kept as `float16`, while the trainable parameters are cast to `float32`. When you inspect the list, you'll see that most of the parameters have been frozen and are `float16`."
      ],
      "metadata": {
        "id": "jytrSroKIfLD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RWOdf_fw2SAO"
      },
      "outputs": [],
      "source": [
        "# Create a pytree mask of the trainable params.\n",
        "def is_trainable_param(name, param):  # pylint: disable=unused-argument\n",
        "  if name.startswith(\"llm/layers/attn/\"):  return True\n",
        "  if name.startswith(\"llm/\"):              return False\n",
        "  if name.startswith(\"img/\"):              return False\n",
        "  raise ValueError(f\"Unexpected param name {name}\")\n",
        "trainable_mask = big_vision.utils.tree_map_with_names(is_trainable_param, params)\n",
        "\n",
        "# If more than one device is available (e.g. multiple GPUs) the parameters can\n",
        "# be sharded across them to reduce HBM usage per device.\n",
        "mesh = jax.sharding.Mesh(jax.devices(), (\"data\"))\n",
        "\n",
        "data_sharding = jax.sharding.NamedSharding(\n",
        "    mesh, jax.sharding.PartitionSpec(\"data\"))\n",
        "\n",
        "params_sharding = big_vision.sharding.infer_sharding(\n",
        "    params, strategy=[('.*', 'fsdp(axis=\"data\")')], mesh=mesh)\n",
        "\n",
        "# Yes: Some donated buffers are not usable.\n",
        "warnings.filterwarnings(\n",
        "    \"ignore\", message=\"Some donated buffers were not usable\")\n",
        "\n",
        "@functools.partial(jax.jit, donate_argnums=(0,), static_argnums=(1,))\n",
        "def maybe_cast_to_f32(params, trainable):\n",
        "  return jax.tree.map(lambda p, m: p.astype(jnp.float32) if m else p,\n",
        "                      params, trainable)\n",
        "\n",
        "# Loading all params in simultaneous - albeit much faster and more succinct -\n",
        "# requires more RAM than the T4 colab runtimes have by default.\n",
        "# Instead we do it param by param.\n",
        "params, treedef = jax.tree.flatten(params)\n",
        "sharding_leaves = jax.tree.leaves(params_sharding)\n",
        "trainable_leaves = jax.tree.leaves(trainable_mask)\n",
        "for idx, (sharding, trainable) in enumerate(zip(sharding_leaves, trainable_leaves)):\n",
        "  params[idx] = big_vision.utils.reshard(params[idx], sharding)\n",
        "  params[idx] = maybe_cast_to_f32(params[idx], trainable)\n",
        "  params[idx].block_until_ready()\n",
        "params = jax.tree.unflatten(treedef, params)\n",
        "\n",
        "# Print params to show what the model is made of.\n",
        "def parameter_overview(params):\n",
        "  for path, arr in big_vision.utils.tree_flatten_with_names(params)[0]:\n",
        "    print(f\"{path:80s} {str(arr.shape):22s} {arr.dtype}\")\n",
        "\n",
        "print(\" == Model params == \")\n",
        "parameter_overview(params)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare to tune the model\n",
        "\n",
        "Now that your model is configured, you can tune it. In this step, you'll create your model's inputs as well as the training and validation iterators, view the training examples, and define the training and validation loops."
      ],
      "metadata": {
        "id": "Y1sfijh_Ix09"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create model inputs\n",
        "\n",
        "The model checkpoint you're using has already been trained on images of various aspect ratios that have been resized to 224x224 pixels, and to handle tokenized texts.\n",
        "\n",
        "The code below defines three functions that you'll use in the next step create the model's inputs:\n",
        "\n",
        "* **`preprocess_image`:** Normalizes the image data. In this case, pre-processing converts the passed-in image to greyscale, removes the alpha layer, and resizes the passed-in image to the size required by the model for image inputs (224x224 pixels).\n",
        "* **`preprocess_tokens`:** Splits the tokens up and adds flags to mark whether a token is a prefix or suffix token. These flags will be used later on in the code, during the training step and the evaluation loop.\n",
        "* **`postprocess_tokens`:** Removes any tokens left at and/or after the end-of-sequence (EOS) token and returns the remaining decoded tokens.\n"
      ],
      "metadata": {
        "id": "6hNkSJwJI138"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8SRW0NuU4UcW"
      },
      "outputs": [],
      "source": [
        "def preprocess_image(image, size=224):\n",
        "  # Model has been trained to handle images of different aspects ratios\n",
        "  # resized to 224x224 in the range [-1, 1]. Bilinear and antialias resize\n",
        "  # options are helpful to improve quality in some tasks.\n",
        "  image = np.asarray(image)\n",
        "  if image.ndim == 2:  # Convert image without last channel into greyscale.\n",
        "    image = np.stack((image,)*3, axis=-1)\n",
        "  image = image[..., :3]  # Remove alpha layer.\n",
        "  assert image.shape[-1] == 3\n",
        "\n",
        "  image = tf.constant(image)\n",
        "  image = tf.image.resize(image, (size, size), method='bilinear', antialias=True)\n",
        "  return image.numpy() / 127.5 - 1.0  # [0, 255]->[-1,1]\n",
        "\n",
        "def preprocess_tokens(prefix, suffix=None, seqlen=None):\n",
        "  # Model has been trained to handle tokenized text composed of a prefix with\n",
        "  # full attention and a suffix with causal attention.\n",
        "  separator = \"\\n\"\n",
        "  tokens = tokenizer.encode(prefix, add_bos=True) + tokenizer.encode(separator)\n",
        "  mask_ar = [0] * len(tokens)    # 0 to use full attention for prefix.\n",
        "  mask_loss = [0] * len(tokens)  # 0 to not use prefix tokens in the loss.\n",
        "\n",
        "  if suffix:\n",
        "    suffix = tokenizer.encode(suffix, add_eos=True)\n",
        "    tokens += suffix\n",
        "    mask_ar += [1] * len(suffix)    # 1 to use causal attention for suffix.\n",
        "    mask_loss += [1] * len(suffix)  # 1 to use suffix tokens in the loss.\n",
        "\n",
        "  mask_input = [1] * len(tokens)    # 1 if it's a token, 0 if padding.\n",
        "  if seqlen:\n",
        "    padding = [0] * max(0, seqlen - len(tokens))\n",
        "    tokens = tokens[:seqlen] + padding\n",
        "    mask_ar = mask_ar[:seqlen] + padding\n",
        "    mask_loss = mask_loss[:seqlen] + padding\n",
        "    mask_input = mask_input[:seqlen] + padding\n",
        "\n",
        "  return jax.tree.map(np.array, (tokens, mask_ar, mask_loss, mask_input))\n",
        "\n",
        "def postprocess_tokens(tokens):\n",
        "  tokens = tokens.tolist()  # np.array to list[int]\n",
        "  try:  # Remove tokens at and after EOS if any.\n",
        "    eos_pos = tokens.index(tokenizer.eos_id())\n",
        "    tokens = tokens[:eos_pos]\n",
        "  except ValueError:\n",
        "    pass\n",
        "  return tokenizer.decode(tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create the training and validation iterators\n",
        "\n",
        "Create two iterators:\n",
        "\n",
        "*   A **training iterator** to allow the training process to go through the data in chunks rather than processing it all at once. This allows you to do some data pre-processing before use.\n",
        "*   A **validation iterator** that allows the training process to iterate over the validation dataset to see how well the tuned model aligned with the provided results."
      ],
      "metadata": {
        "id": "h4Lul8c3JDBQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whzWOojGOtzi"
      },
      "outputs": [],
      "source": [
        "SEQLEN = 128\n",
        "\n",
        "train_dataset = big_vision.datasets.jsonl.DataSource(\n",
        "    os.path.join(dataset.location, \"dataset/_annotations.train.jsonl\"),\n",
        "    fopen_keys={\"image\": f\"{dataset.location}/dataset\"})\n",
        "\n",
        "val_dataset = big_vision.datasets.jsonl.DataSource(\n",
        "    os.path.join(dataset.location, \"dataset/_annotations.valid.jsonl\"),\n",
        "    fopen_keys={\"image\": f\"{dataset.location}/dataset\"})\n",
        "\n",
        "\n",
        "def train_data_iterator():\n",
        "  \"\"\"Never ending iterator over training examples.\"\"\"\n",
        "  # Shuffle examples and repeat so one can train for many epochs.\n",
        "  dataset = train_dataset.get_tfdata().shuffle(1_000).repeat()\n",
        "  for example in dataset.as_numpy_iterator():\n",
        "    image = Image.open(io.BytesIO(example[\"image\"]))\n",
        "    image = preprocess_image(image)\n",
        "\n",
        "    prefix = example[\"prefix\"].decode().lower()\n",
        "    suffix = example[\"suffix\"].decode().lower()\n",
        "    tokens, mask_ar, mask_loss, _ = preprocess_tokens(prefix, suffix, SEQLEN)\n",
        "    label, _, _, _ = preprocess_tokens(suffix, seqlen=SEQLEN)\n",
        "\n",
        "    yield {\n",
        "        \"image\": np.asarray(image),\n",
        "        \"text\": np.asarray(tokens),\n",
        "        \"label\": np.asarray(label),\n",
        "        \"mask_ar\": np.asarray(mask_ar),\n",
        "        \"mask_loss\": np.asarray(mask_loss),\n",
        "    }\n",
        "\n",
        "\n",
        "def validation_data_iterator():\n",
        "  \"\"\"Single iterator over validation examples.\"\"\"\n",
        "  for example in val_dataset.get_tfdata(ordered=True).as_numpy_iterator():\n",
        "    image = Image.open(io.BytesIO(example[\"image\"]))\n",
        "    image = preprocess_image(image)\n",
        "\n",
        "    prefix = example[\"prefix\"].decode().lower()\n",
        "    suffix = example[\"suffix\"].decode().lower()\n",
        "    tokens, mask_ar, _, mask_input = preprocess_tokens(prefix, seqlen=SEQLEN)\n",
        "    label, _, _, _ = preprocess_tokens(suffix, seqlen=SEQLEN)\n",
        "\n",
        "    yield {\n",
        "        \"image\": np.asarray(image),\n",
        "        \"text\": np.asarray(tokens),\n",
        "        \"label\": np.asarray(label),\n",
        "        \"mask_ar\": np.asarray(mask_ar),\n",
        "        \"mask_input\": np.asarray(mask_input),\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### View training examples\n",
        "\n",
        "In this notebook, the training data contains 90 images that are paired with long descriptions of what's depicted in the image.\n",
        "\n",
        "**Note:** Normal training data sets that are meant to be used for practical use cases should contain more images, but this notebook limits the number of data points so that you can train the model in a reasonable amount of time for an example.\n",
        "\n",
        "The code below prints a random selection of images with their descriptions from the training data set so that you can see what the images and descriptions your model is trained on looks like. Each image is displayed in as a 128x128 pixel JPEG, with the description printed next to the image to the right."
      ],
      "metadata": {
        "id": "ml_wkTbJJj-N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "BzJfb5t0nsLq",
        "outputId": "65726c5b-2d7e-4fb8-b5f4-5183978f8118"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training examples\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0204&gt;&lt;loc0178&gt;&lt;loc0972&gt;&lt;loc0720&gt; mult</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0128&gt;&lt;loc0284&gt;&lt;loc0752&gt;&lt;loc0751&gt; 3</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0277&gt;&lt;loc0356&gt;&lt;loc0685&gt;&lt;loc0612&gt; 6</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+ikooAWiiigAopKWgBK8/wDHX/Ibh/69l/8AQmr0CvP/AB1/yG4f+vZf/Qmr0Ms/3j5M8vN/92fqjmKXa2zftO3OM44zUlrbvd3McEeNznAJOAPc10uq2EsXh/yYbfbDbz53ZGXG3lzz3Pb0xXvVK0YSUe583SoSqQlNbI5WvQfAv/IEm/6+W/8AQVrz6vQfAv8AyBJv+vlv/QVrlzP/AHd+qO3KP95Xozp6KKK+bPqwopKM0ALRSUtABRRRQAUUUUAFFFFABRRRQAUUUUAFFFJQAtFFFABRRRQAV5946/5DcP8A17L/AOhNXoFcF4ltrrWfFElraQb5LeFQRvAyOuecf3wK9DLWlW5nskzzM2TeH5Vq20crU32qT7H9lyPL8zzOnOcYrU/4RPW/+fL/AMip/jR/wiet/wDPl/5FT/GvdeIoPea+9HziwuJW0JfczFr0HwL/AMgSb/r5b/0Fa5f/AIRTW/8Any/8ip/jWvpkXiXRLN4IdPtzGXMhaWReDgDqHHHFcuNnTrUuSE1f1R2ZfTq0K3POErejO4orkYdZ8S3OfIstPlA4JjlVgPykqf7d4v8A+gXaf99D/wCLrxvqr/mj96Pe+uR/kl/4CzpW/wAmkJwOcVzRvfFx4Ol2n/fQ/wDi64zxj46utBureDWr3+zJCA6LBEXDDPcgNR9Vf80fvQfXI/yS/wDAWes9RxSivPPDPjbVPFFi8+ix2t7FEQjuyGM5+jFf5VuC98Xf9Au0/wC+h/8AF0fVX/NH70H1yP8AJL/wFnUUVzH23xf/ANAu0/76H/xdH23xf/0C7T/vof8AxdH1V/zR+9B9cj/JL/wFnT0VkeHdWl1nTWuJo0SRJDGdmcHgHPPTr+la9YVISpycJbo6aVSNWCnHZhRRRUFhRRRQAUUUUAJRS0lAC0UUUAJS0UUAJXM2P/JQdS/69h/KOumrmbL/AJKDqX/XsP5R11Ybap/h/VHHi/ipf4l+TOmopaSuU7BjnAOK+bvit8VdSudVudF0i4a3tYjseSNiGY9TzmvpCU/I2P7p5r4Q1CRpNSuXc5Yytkn60Aezfs+6pqEviG9smlkezeEyPluFfIx/M19G1458AfDr6b4bn1OZNr3rgoT12rkf4V7JQAh4+leCftIwRi00GcAeY8kqk+oAXH8697Iz1rwL9pKUNBocX915G/MCgDs/ghZQW/w3srmOMCW4dzK4HLYYgfpXpK9Oa4j4Qw+R8M9KQ+jn82JruBQAtFFFAHMeBf8AkCTf9fLf+grXT1zHgX/kCTf9fLf+grXT11Y3/eJ+px5f/u0PQKKKK5TsCiiigAooooASiiigApaKKAEoopaAErmbL/koOpf9ew/lHXTVzNl/yUHUv+vYfyjrqw21T/D+qOPF/FS/xL8mdNRRRXKdhBcSxwQySyuFRFJYnoBXxqdMj8T/ABHey0uJvIuLrCrjnb/F+ma92+OHjD+w/Dw0q1kH2u+DK2Dyqd/5isD4BeD2jhm8TXcfzSfJbluw7t+pFAHtWkadDpOk2un24xHBEsYx7DFaFMUAHj8D61JQAhPNfNf7RF4H8UafZc/u7cSY+pI/pX0m2MHJxXyd48uW8T/GVrQZdFu0tVxz8u//AOvQB9HeBLU2PgnSoTjPkK3HuM/1rpBVawthaafbWw6QxKg/AYqyOmaAFooooA5jwL/yBJv+vlv/AEFa6euY8C/8gSb/AK+W/wDQVrp66sb/ALxP1OPL/wDdoegUUUVynYFFFFABRRRQAlFFFAC0UlFABS0lFABXM2X/ACUHUv8Ar2H8o66auZsv+Sg6l/17D+UddWG2qf4f1Rx4v4qX+JfkzpicVVv76LT9PnvJ3CRwoXYk46VZbOOK8Y+O/i/+z9Gj0C1kK3F380hU8hAf8RXKdh5VqNxffFH4m7Iixjml2R9xHGDjP8q+rtG0u30jSrawto1jihTaFFeRfAXwaLLSpPEF1Fie5+WHI5Cdz+PFe1qB1HegBdoFLRRQBV1G4S1sJ53OFRCSfwr5X+Fdu/iD4sw3U43gO8zn3AJB/SvoL4n6j/Znw91eZTiQwFU5715H+znp3maxq1+45jRFQ47ndmgD6LxQBRS0AFFJRkUAcz4F/wCQJN/18t/6CtdPXMeBf+QJN/18t/6CtdPXVjf94n6nHl/+7Q9AoopK5TsFopKKAFopKWgApKWkoAWiiigAooooASuXs/8AkoGo/wDXuv8AKOuorl7M4+IOpf8AXuP5R11Ybap/h/VHHi/ipf4l+TNzUb6HTtPnu55NkMKF3Y9gK+T1N78UPigCB8s03I7JGvUfiAfzr1H49eLvsGkw+H7Z8TXPzSheoX0P1z+lO+AvhD+ztFk164T9/d/LF7Rj/wCuDXKdh6/p1jBpunwWdsu2KGNY0HoAMCrQooxQAtFFJnnpQB5J+0FqP2XwPDbKfmnuVBHttam/s+6f9m8Ez3bDDz3LflhcVy37R2ob9Q0ixVuPLd2Hvnj+dep/CywFh8OtHUjDSQiRvqaAO0opMignAoAD0qDzYjIUDrv9M8isDxb430fwlYPLf3CiYr+7hB+Zz7V8++B9T17xh8W4dUjlcEy+ZLjosWclPyzQB9BeBf8AkCTf9fLf+grXT1zHgX/kCTf9fLf+grXT11Y3/eJ+px5f/u0PQKKKK5TsCiiigAooooAKKKKACiiigAooooAK5OGZYPHOrTPjalrubPoBGa6yvJviJq76KPEt1E+yU2qRofdvLB/TNdWG2qf4f1Rx4v4qX+JfkzxzUnl+I/xaMaFmhnufKVhztiDYz+VfV9hZx6fYQWsACxRKFwO3FfP37PGhLcavqGtSoSbdBHHkZB3Zz/6CK+i8A9uK5TsHDpS03J7Y/Ogk49DQA6mjqaMmsTWPFuiaFG7ahqNvEy/8szIN5/DOaAPnD40XTal8UXtYzuWLy0UfVRmvpjToodJ0a3gJWOOGMKCxwAAK+PvEPiFb7x5da5FiZBcF0DcblBwP0rR1Lxh418cTiBJLqZScLHbRkYHuVFAH0V4g+KnhbQI3D6hHdSr1jt3VyD+deNeJfjvr2q7oNIjWwiOQHQBmYe4IOKh8PfAvxHrDrPqkgs4n53Mdz/iDivZfC/wi8M+HFWQ2ourpTzLLkg/8BJIoA+f9C8A+LvHF79qmSVIpGy1xcgqp+nGK+jvA/gLTvBemG3tcvcOR507D5nx29h1rrYreGCMRxRJGg6KqgAfhUm0Zz60Acz4F/wCQJN/18t/6CtdPXMeBf+QJN/18t/6CtdPXVjf94n6nHl/+7Q9AooorlOwKKKKACiiigAooooAKKKKACiiigArwb44pM8GomIEqskRkx/d2D+uK95riLzSbXXPFes6dexpJBNagMrDI6R811Ybap/h/VHHi/ipf4l+TPNvgp418O6H4dk0+/uhBeNKWOVOCM8c17LB4r0GdQ0erWeD/AHp1H8zXhWt/s+6hHcs+kXyS25+5G4+YfU8CuXm+CPjWJyI9N80eolQfzauU7D6XvfGvhzT0LTata4H9yQN/KuG1348eGtPRhp/mXsw42AMg/MivJrP4HeMrlwtxZLbD+8zq38mrs9F/Z1KyI+r6mrR/xRwgq35nNAHJa98avFPiB2trD/RIXPCxLmQf8CGDWNa+BvGPia3m1O6iuWhhjaR5buQ7sAZPDHNfSehfDXwt4eVVtdOilmH3ZbhFdx+OKuePbsad4E1aUYA+zPH/AN9Aj+tAHzH8L/C1p4o8brp96pkt40MkgU4yAQP619VaT4Y0fQoVTTtPgi2jG4RgN+eK8F/Z0sd/iTUL0j/V25jz9SD/AEr6S70ANxgZPSnLnFLRQAUUUUAcx4F/5Ak3/Xy3/oK109cx4F/5Ak3/AF8t/wCgrXT11Y3/AHifqceX/wC7Q9AooorlOwKKKKACiiigAooooAKKKKACiiigArl7MZ+IGpD/AKdx/KOuormLH/koWpf9ew/lHXVhtqn+H9UceL+Kl/iX5M6Tbnrg07FLRXKdgmKQrx0GadRQA3bgYHArzz42XX2b4ZaiAcM7RqP++xXoteRftBXHleCYos/66YDH0INAGR+zhbY0nWbkgZaZFX8jXuYBzXk37P1sIvAzzY5mmJ/IkV61QAUUUUAFFFFAHMeBf+QJN/18t/6CtdPXMeBf+QJN/wBfLf8AoK109dWN/wB4n6nHl/8Au0PQKKKK5TsCiiigAooooAKKKKACiiigAooooAK5ix/5KDqX/XsP5R109cxY/wDJQtS/69h/KOurDbVP8P6o48X8VL/EvyZ09FFFcp2BRRRQAV4r+0XkeGdMx089s/kK9qryb4+WLXPgX7Qqk/Z5Q3TpkgUAaHwLQL8MLBh1aSXP/fbV6TXlPwCvluPAItgRm3lYEem4k16rkUALRRRQAUUUUAcx4F/5Ak3/AF8t/wCgrXT1zHgX/kCTf9fLf+grXT11Y3/eJ+px5f8A7tD0CiiiuU7AooooAKKKKACiiigAooooAKKKKACuOOo2umeO9Qmu5fLjaFUB2k84Q9h7GuxqvPYWd04e4tIJnAwGkjDHHpyK3oVIwcudaNW0ObE0p1FFwdmnfX5mb/wlmif8/v8A5Cf/AAo/4SzRP+f3/wAhP/hV3+x9M/6B1n/34X/Cj+x9M/6B1n/34X/Crvhe0vvX+RFsZ3j9z/zKX/CWaJ/z+/8AkJ/8KP8AhLNE/wCf3/yE/wDhV3+x9M/6B1n/AN+F/wAKP7H0z/oHWf8A34X/AAovhe0vvX+QWxneP3P/ADKX/CWaJ/z+/wDkJ/8ACuf8Y3+i+IvCl/pgu90ksRKAxvy45Ucj1xXW/wBj6Z/0DrP/AL8L/hTP7F07/oH2fX/ngv8AhRfC9pfev8gtjO8fuf8AmeG/Bsar4Q1G9tdZtzBp843iUOrfOOAMKScYJ7V7MPFeid7zH/bJ/wDCoYvBumQ6tJfhZH3jAgdsxL7helaf9jabnP8AZ9p/34X/AAovhe0vvX+QWxneP3P/ADKn/CWaJ/z+/wDkJ/8ACj/hLNE/5/f/ACE/+FXf7H0z/oHWf/fhf8KP7H0z/oHWf/fhf8KL4XtL71/kFsZ3j9z/AMyl/wAJZon/AD+/+Qn/AMKP+Es0T/n9/wDIT/4Vd/sfTP8AoHWf/fhf8KP7H0z/AKB1n/34X/Ci+F7S+9f5BbGd4/c/8zF8C/8AIEm/6+W/9BWunpkUMVvEsUMaRxr0RFAA/AU+sq9T2tRzXU2w9L2NKNNu9gooorI2CiiigAooooAKKKKACiiigAooooAKKKKACiikoAWiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/9k=\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0128&gt;&lt;loc0271&gt;&lt;loc0765&gt;&lt;loc0736&gt; 4</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def split_and_keep_second_part(s):\n",
        "    parts = s.split('\\n', 1)\n",
        "    if len(parts) > 1:\n",
        "        return parts[1]\n",
        "    return s\n",
        "\n",
        "def render_inline(image, resize=(128, 128)):\n",
        "    \"\"\"Convert image into inline html.\"\"\"\n",
        "    image = Image.fromarray(image)\n",
        "    image.resize(resize)\n",
        "    with io.BytesIO() as buffer:\n",
        "        image.save(buffer, format='jpeg')\n",
        "        image_b64 = str(base64.b64encode(buffer.getvalue()), \"utf-8\")\n",
        "        return f\"data:image/jpeg;base64,{image_b64}\"\n",
        "\n",
        "def render_example(image, caption):\n",
        "    image = ((image + 1)/2 * 255).astype(np.uint8)  # [-1,1] -> [0, 255]\n",
        "    h, w, _ = image.shape\n",
        "    try:\n",
        "        detections = sv.Detections.from_lmm(\n",
        "            lmm='paligemma',\n",
        "            result=caption,\n",
        "            resolution_wh=(w, h),\n",
        "            classes=CLASSES)\n",
        "        image = sv.BoundingBoxAnnotator().annotate(image, detections)\n",
        "        image = sv.LabelAnnotator().annotate(image, detections)\n",
        "    except:\n",
        "        print(caption)\n",
        "    return f\"\"\"\n",
        "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
        "    <img style=\"width:128px; height:128px;\" src=\"{render_inline(image, resize=(64,64))}\" />\n",
        "    <p style=\"width:256px; margin:10px; font-size:small;\">{html.escape(caption)}</p>\n",
        "</div>\n",
        "\"\"\"\n",
        "\n",
        "html_out = \"\"\n",
        "for idx, example in zip(range(4), train_data_iterator()):\n",
        "    caption = postprocess_tokens(example[\"text\"])  # detokenize model input.\n",
        "    caption = split_and_keep_second_part(caption)\n",
        "    html_out += render_example(example[\"image\"], caption)\n",
        "\n",
        "print(\"Training examples\")\n",
        "display(HTML(html_out))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define the training and evaluation loops\n",
        "\n",
        "Define the training loop to train the model on the provided dataset, and the evaluation loop to look at all of the examples in the validation dataset and make its predictions.\n",
        "\n",
        "#### Defining the training loop\n",
        "\n",
        "The `update_fn` function defines the training step. During the training step, the loss per example is calculated and stochastic gradient descent (SGD) is applied to the trainable parameters.\n",
        "\n",
        "Recall that earlier in the notebook, you included flags in the `preprocess_tokens` function that included `mask_loss`. You'll use the `mask_loss` flag here to exclude prefix and padded tokens from the loss. Without it, the loss calculation will be skewed. You also need to normalize each example, since each of them has a different number of tokens. After the prefix and padded tokens have been excluded and the examples have been normalized, you can calculate the loss per example.\n",
        "\n",
        "The training step also includes a function to apply an SGD to optimize the training.\n",
        "\n",
        "#### Defining the evaluation loop\n",
        "\n",
        "The `make_predictions` function is your evaluation loop. The evaluation loop is fairly straight forward with one notable change. If you recall from the beginning of the notebook, you only have 90 examples in your training data set. This is a very small amount of training examples, and your model ends up not having enough examples for the batch size when you run the training. This means that in the evaluation loop, you need to pad the batch by repeating examples.\n",
        "\n",
        "To make sure that your evaluation loop only counts actual examples and not the padded examples, you have to apply a mask to the padded examples that excludes them from the output."
      ],
      "metadata": {
        "id": "hKFJ9rbLKoTa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dwUV_imW3WQJ"
      },
      "outputs": [],
      "source": [
        "# The main update_fn using a simple stochastic gradient descent (SGD).\n",
        "@functools.partial(jax.jit, donate_argnums=(0,))\n",
        "def update_fn(params, batch, learning_rate):\n",
        "  imgs, txts, mask_ar = batch[\"image\"], batch[\"text\"], batch[\"mask_ar\"]\n",
        "\n",
        "  def loss_fn(params):\n",
        "    text_logits, _ = model.apply({\"params\": params}, imgs, txts[:, :-1], mask_ar[:, :-1], train=True)\n",
        "    logp = jax.nn.log_softmax(text_logits, axis=-1)\n",
        "\n",
        "    # The model takes as input txts[:, :-1] but the loss is defined as predicting\n",
        "    # next tokens txts[:, 1:]. Additionally, mask_loss[:, 1:] indicates which tokens\n",
        "    # are part of the loss (e.g. prefix and padded tokens are not included).\n",
        "    mask_loss = batch[\"mask_loss\"][:, 1:]\n",
        "    targets = jax.nn.one_hot(txts[:, 1:], text_logits.shape[-1])\n",
        "\n",
        "    # Compute the loss per example. i.e. the mean of per token pplx.\n",
        "    # Since each example has a different number of tokens we normalize it.\n",
        "    token_pplx = jnp.sum(logp * targets, axis=-1)  # sum across vocab_size.\n",
        "    example_loss = -jnp.sum(token_pplx * mask_loss, axis=-1)  # sum across seq_len.\n",
        "    example_loss /= jnp.clip(jnp.sum(mask_loss, -1), 1)  # weight by num of tokens.\n",
        "\n",
        "    # batch_loss: mean of per example loss.\n",
        "    return jnp.mean(example_loss)\n",
        "\n",
        "  loss, grads = jax.value_and_grad(loss_fn)(params)\n",
        "\n",
        "  # Apply gradients to trainable params using SGD.\n",
        "  def apply_grad(param, gradient, trainable):\n",
        "    if not trainable: return param\n",
        "    return param - learning_rate * gradient\n",
        "\n",
        "  params = jax.tree_util.tree_map(apply_grad, params, grads, trainable_mask)\n",
        "\n",
        "  return params, loss\n",
        "\n",
        "# Evaluation/inference loop.\n",
        "def make_predictions(data_iterator, *, num_examples=None,\n",
        "                     batch_size=4, seqlen=SEQLEN, sampler=\"greedy\"):\n",
        "  outputs = []\n",
        "  while True:\n",
        "    # Construct a list of examples in the batch.\n",
        "    examples = []\n",
        "    try:\n",
        "      for _ in range(batch_size):\n",
        "        examples.append(next(data_iterator))\n",
        "        examples[-1][\"_mask\"] = np.array(True)  # Indicates true example.\n",
        "    except StopIteration:\n",
        "      if len(examples) == 0:\n",
        "        return outputs\n",
        "\n",
        "    # Not enough examples to complete a batch. Pad by repeating last example.\n",
        "    while len(examples) % batch_size:\n",
        "      examples.append(dict(examples[-1]))\n",
        "      examples[-1][\"_mask\"] = np.array(False)  # Indicates padding example.\n",
        "\n",
        "    # Convert list of examples into a dict of np.arrays and load onto devices.\n",
        "    batch = jax.tree.map(lambda *x: np.stack(x), *examples)\n",
        "    batch = big_vision.utils.reshard(batch, data_sharding)\n",
        "\n",
        "    # Make model predictions\n",
        "    tokens = decode({\"params\": params}, batch=batch,\n",
        "                    max_decode_len=seqlen, sampler=sampler)\n",
        "\n",
        "    # Fetch model predictions to device and detokenize.\n",
        "    tokens, mask = jax.device_get((tokens, batch[\"_mask\"]))\n",
        "    tokens = tokens[mask]  # remove padding examples.\n",
        "    labels = [postprocess_tokens(e[\"label\"]) for e in examples]\n",
        "    responses = [postprocess_tokens(t) for t in tokens]\n",
        "\n",
        "    # Append to html output.\n",
        "    for example, label, response in zip(examples, labels, responses):\n",
        "      outputs.append((example[\"image\"], label, response))\n",
        "      if num_examples and len(outputs) >= num_examples:\n",
        "        return outputs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "html_out = \"\"\n",
        "for image, _, caption in make_predictions(validation_data_iterator(), num_examples=4, batch_size=4):\n",
        "  html_out += render_example(image, caption)\n",
        "display(HTML(html_out))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "GCXYnIdm4ILQ",
        "outputId": "51ed1a0f-6580-4728-dfea-606310bc6056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0921&gt; 1 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0921&gt; 3 ; &lt;loc0046&gt;&lt;loc0505&gt;&lt;loc0896&gt;&lt;loc0696&gt; 7 ; &lt;loc0046&gt;&lt;loc0603&gt;&lt;loc0432&gt;&lt;loc0687&gt; 5 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0536&gt;&lt;loc0912&gt; 6 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0921&gt; 4 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0533&gt;&lt;loc0912&gt; 9 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0912&gt; 2 ; &lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0531&gt;&lt;loc0912&gt; 8 ; &lt;loc0045&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0912&gt; 1 ; &lt;loc0045&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1013&gt; mult ; &lt;loc0045&gt;&lt;loc0541&gt;&lt;loc0896&gt;&lt;loc0696&gt; 7 ; &lt;loc0045&gt;&lt;loc0567&gt;&lt;loc0456&gt;&lt;loc0690&gt; minus ; &lt;loc0413&gt;&lt;loc0233&gt;&lt;loc0538&gt;&lt;loc0912&gt; div ; &lt;loc0036&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1015&gt; 1 ; &lt;loc0036&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 1 ; &lt;loc0036&gt;&lt;loc0000&gt;&lt;loc1023&gt;</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0217&gt;&lt;loc0261&gt;&lt;loc0957&gt;&lt;loc0819&gt; 4 ; &lt;loc0207&gt;&lt;loc0252&gt;&lt;loc0954&gt;&lt;loc0826&gt; 7 ; &lt;loc0207&gt;&lt;loc0246&gt;&lt;loc0949&gt;&lt;loc0826&gt; 5 ; &lt;loc0207&gt;&lt;loc0246&gt;&lt;loc0949&gt;&lt;loc0826&gt; 1 ; &lt;loc0207&gt;&lt;loc0421&gt;&lt;loc0778&gt;&lt;loc0703&gt; minus ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1017&gt; mult ; &lt;loc0207&gt;&lt;loc0246&gt;&lt;loc0949&gt;&lt;loc0818&gt; 3 ; &lt;loc0207&gt;&lt;loc0246&gt;&lt;loc0944&gt;&lt;loc0809&gt; 6 ; &lt;loc0754&gt;&lt;loc0264&gt;&lt;loc0936&gt;&lt;loc0513&gt; 8 ; &lt;loc0754&gt;&lt;loc0261&gt;&lt;loc0944&gt;&lt;loc0813&gt; 4 ; &lt;loc0754&gt;&lt;loc0261&gt;&lt;loc0936&gt;&lt;loc0513&gt; 5 ; &lt;loc0754&gt;&lt;loc0261&gt;&lt;loc0944&gt;&lt;loc0813&gt; 7 ; &lt;loc0754&gt;&lt;loc0261&gt;&lt;loc0936&gt;&lt;loc0813&gt; 9 ; &lt;loc0747&gt;&lt;loc0496&gt;&lt;loc0956&gt;&lt;loc0817&gt; div ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1017&gt; 1 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 2 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0920&gt;&lt;loc0634&gt; 1 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0531&gt;&lt;loc0549&gt; 7 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0920&gt;&lt;loc0629&gt; 3 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0531&gt;&lt;loc0438&gt; 6 ; &lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0925&gt;&lt;loc0629&gt; 4 ; &lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0925&gt;&lt;loc0629&gt; 5 ; &lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0541&gt;&lt;loc0553&gt; 8 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1018&gt;&lt;loc1018&gt; mult ; &lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0925&gt;&lt;loc0629&gt; 2 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0541&gt;&lt;loc0557&gt; minus ; &lt;loc0866&gt;&lt;loc0404&gt;&lt;loc0926&gt;&lt;loc0658&gt; plus ; &lt;loc0168&gt;&lt;loc0505&gt;&lt;loc0906&gt;&lt;loc0580&gt; 4 ; &lt;loc0168&gt;&lt;loc0505&gt;&lt;loc0902&gt;&lt;loc0575&gt; 9 ; &lt;loc0866&gt;&lt;loc0403&gt;&lt;loc0922&gt;&lt;loc0658&gt; div ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0925&gt;&lt;loc0635&gt; 1 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0925&gt;&lt;loc0626&gt; 4 ; &lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0541&gt;&lt;loc0549&gt;</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0143&gt;&lt;loc0374&gt;&lt;loc0658&gt;&lt;loc0442&gt; 4 ; &lt;loc0143&gt;&lt;loc0374&gt;&lt;loc0658&gt;&lt;loc0441&gt; 5 ; &lt;loc0140&gt;&lt;loc0366&gt;&lt;loc0667&gt;&lt;loc0442&gt; 6 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1017&gt; mult ; &lt;loc0140&gt;&lt;loc0366&gt;&lt;loc0667&gt;&lt;loc0442&gt; 7 ; &lt;loc0134&gt;&lt;loc0365&gt;&lt;loc0667&gt;&lt;loc0442&gt; 8 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1017&gt; 3 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 1 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; eq ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 2 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc0693&gt;&lt;loc1023&gt; 4 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 9 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; minus ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 10 ; &lt;loc0000&gt;&lt;loc0356&gt;&lt;loc0703&gt;&lt;loc0456&gt; 1 ; &lt;loc0000&gt;&lt;loc0000&gt;&lt;loc1023&gt;&lt;loc1023&gt; 3 ; &lt;loc0000&gt;&lt;loc0000&gt;</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tune the model\n",
        "\n",
        "Now that you've set everything up and taken a look at the training data, it's time to finally tune the model. The code below runs the training loop for the model for 64 steps and prints the learning rate (`lr` in the printed output) and loss rate for each step.\n",
        "\n",
        "Every 16 steps, the model prints what its predictions are at that step in the training. This code prints out predictions for the same set of images so that you can see the model's ability to predict descriptions improve over time.\n",
        "\n",
        "At earlier steps in the training, there's likely issues with the descriptions, such as repeated sentences as the model gets stuck in its predictive loop or unfinished sentences. The model's predictions become steadily more accurate as training progresses. By step 64, the model's predictions should closely resemble the descriptions provided by the training data.\n",
        "\n",
        "This process takes around 15 minutes to complete on T4 TPUs."
      ],
      "metadata": {
        "id": "fNigSP99MJFe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "067wj_6bZAG3",
        "outputId": "8671008b-9a90-4a36-f297-0facd27185b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step:  1/64   lr: 0.00083   loss: 5.6622\n",
            "step:  2/64   lr: 0.00167   loss: 3.8568\n",
            "step:  3/64   lr: 0.00250   loss: 2.8670\n",
            "step:  4/64   lr: 0.00333   loss: 2.9897\n",
            "step:  5/64   lr: 0.00417   loss: 4.1907\n",
            "step:  6/64   lr: 0.00500   loss: 3.3373\n",
            "step:  7/64   lr: 0.00500   loss: 2.8551\n",
            "step:  8/64   lr: 0.00499   loss: 2.9553\n",
            "Model predictions at step 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0039&gt;&lt;loc0238&gt;&lt;loc0912&gt;&lt;loc0921&gt; mult</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0264&gt;&lt;loc0968&gt;&lt;loc0826&gt; mult</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0936&gt;&lt;loc0646&gt; mult</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0137&gt;&lt;loc0377&gt;&lt;loc0694&gt;&lt;loc0452&gt; 9</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step:  9/64   lr: 0.00497   loss: 2.9513\n",
            "step: 10/64   lr: 0.00494   loss: 3.1961\n",
            "step: 11/64   lr: 0.00491   loss: 3.0491\n",
            "step: 12/64   lr: 0.00487   loss: 2.8612\n",
            "step: 13/64   lr: 0.00483   loss: 2.5327\n",
            "step: 14/64   lr: 0.00478   loss: 2.4965\n",
            "step: 15/64   lr: 0.00472   loss: 2.9966\n",
            "step: 16/64   lr: 0.00465   loss: 2.9704\n",
            "Model predictions at step 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0216&gt;&lt;loc0887&gt;&lt;loc0887&gt; div</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0236&gt;&lt;loc0235&gt;&lt;loc0949&gt;&lt;loc0789&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0123&gt;&lt;loc0216&gt;&lt;loc0912&gt;&lt;loc0617&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0144&gt;&lt;loc0363&gt;&lt;loc0653&gt;&lt;loc0432&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 17/64   lr: 0.00458   loss: 2.8967\n",
            "step: 18/64   lr: 0.00451   loss: 2.5452\n",
            "step: 19/64   lr: 0.00442   loss: 2.5070\n",
            "step: 20/64   lr: 0.00434   loss: 2.5249\n",
            "step: 21/64   lr: 0.00424   loss: 2.3752\n",
            "step: 22/64   lr: 0.00415   loss: 2.6018\n",
            "step: 23/64   lr: 0.00404   loss: 2.5676\n",
            "step: 24/64   lr: 0.00394   loss: 2.3139\n",
            "Model predictions at step 24\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0223&gt;&lt;loc0906&gt;&lt;loc0919&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0246&gt;&lt;loc0961&gt;&lt;loc0826&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0216&gt;&lt;loc0928&gt;&lt;loc0647&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0140&gt;&lt;loc0366&gt;&lt;loc0667&gt;&lt;loc0452&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 25/64   lr: 0.00383   loss: 2.2908\n",
            "step: 26/64   lr: 0.00371   loss: 2.1218\n",
            "step: 27/64   lr: 0.00359   loss: 2.4826\n",
            "step: 28/64   lr: 0.00347   loss: 2.5923\n",
            "step: 29/64   lr: 0.00335   loss: 2.7243\n",
            "step: 30/64   lr: 0.00322   loss: 2.4624\n",
            "step: 31/64   lr: 0.00309   loss: 2.4676\n",
            "step: 32/64   lr: 0.00296   loss: 2.2581\n",
            "Model predictions at step 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0036&gt;&lt;loc0230&gt;&lt;loc0888&gt;&lt;loc0912&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0211&gt;&lt;loc0251&gt;&lt;loc0961&gt;&lt;loc0826&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0112&gt;&lt;loc0222&gt;&lt;loc0919&gt;&lt;loc0662&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0132&gt;&lt;loc0366&gt;&lt;loc0662&gt;&lt;loc0447&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 33/64   lr: 0.00283   loss: 2.2747\n",
            "step: 34/64   lr: 0.00270   loss: 2.4034\n",
            "step: 35/64   lr: 0.00257   loss: 2.1800\n",
            "step: 36/64   lr: 0.00243   loss: 2.3051\n",
            "step: 37/64   lr: 0.00230   loss: 2.2851\n",
            "step: 38/64   lr: 0.00217   loss: 2.1452\n",
            "step: 39/64   lr: 0.00204   loss: 2.3099\n",
            "step: 40/64   lr: 0.00191   loss: 2.1190\n",
            "Model predictions at step 40\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0238&gt;&lt;loc0896&gt;&lt;loc0912&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+ikooAWiiigAopKWgBKWkqs2o2KOyPeW6spwQZVBB/Omot7IC1SVV/tPT/8An+tv+/y/40f2np//AD/W3/f1f8ar2c+wXLdFVP7T0/8A5/rb/v8AL/jVlWV0V0YMrDIIOQRUuLW6AdRRRSAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiikoAWiiigAooooAK811P8A5C15/wBd3/8AQjXpNeban/yFrz/ru/8A6Ea9LLfjkRMq0VNbfZw7NcbiqjIReN59M9qmvIYfs8NzEjRCUkeUW3dO4Pp/hXquVnYzKdek6Z/yCbP/AK4J/wCgivNq9J0z/kE2f/XBP/QRXn5l8ES4FuiiivINAooooAKKKKACiiigAooooAKKKKACiiigAooooASilpKAFooooASloooASvNtT/5C15/13f8A9CNek15rqLK+p3bowZWmcgg5BG416WW/FIiY7TorWScm7mCRqM7Tn5z6ZA4FO1E+ZKJPtUMueAsQICAdAARwKpUV6vJ73NczCvSdM/5BNn/1wT/0EV5tXb2Ov6ZDp1tFJc4dIlVh5bcEAZ7VxZhCU4x5VcuDNzil4rG/4SLSun2rgnPEbf4Up8RaVz/pf5Rv/hXlewq/yv7mXdGxRWOPEelY/wCPvn3jb/Cn/wDCSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWiq1nqFrqCM1rMJApw3BBH4GrNZuLi7NDCiiikAUUUUAJRRRQAUtFFACUUUtACVzXhaztbjTJHntoZWExALoGOML610tYHhD/kEy/8AXc/+grXTTbVGbXl+onuav9maf/z423/fpf8ACj+zNP8A+fG2/wC/S/4VaorH2k+47FU6Zp4/5cbb/vyv+FZ2tTaLoWkXGo3dpapDAu4kxL17D862W6j25rkviXpVzrnw/wBU0+zXdcSIrKo6nDAn9BR7SfcLHgPib4y6lqU5XR7S106JT8rJCjMw9wwNc+nxO8Wb1J1FGA6r9li5/wDHa5iS0uIro2zQSJcA7fKZDuB9MetXrrw1rllbLcXWj30EPXfJAyr+ZFHtJ9wseu+FPjfYmSO28RaPbBSQPPhhUn6nOBXtuj3Xh7XbMXempY3MR6mONTg+h96+IMZyRxW74b8W6x4V1FLrTrpoiOChwVYfQ8fjR7SfcLH2mNN08/8ALhbf9+V/wo/s7Ts4Fjakjr+6X/CuM+HnxH0/xtp4j/1GoxLmaDPX3HcjpXdLktyenNHtJ9wsQ/2Zp/8Az423/flf8KP7M0//AJ8bb/vyv+FW6KPaT7hYqf2Zp/8Az423/fpf8KP7MsP+fG2/78r/AIVboo9pPuFjnvDqqmp6wiKFVZgAAMADc9dDXP6B/wAhbWv+u/8A7M9dBWuK/iv5fkhR2CiiiucYUUUUAJRRRQAtFJRQAUtJRQAVgeEP+QTL/wBdz/6Ctb9YHhD/AJBMv/Xc/wDoK10U/wCDP5fqJ7m/RRRXOMCMjFJsHcdaWigDNfw7osk5nfSbFpid3mG3Qtn1zirNxYWl1B5FxbRTRYxskQMPyNWqaeucUAeP+NfgdpurLLd6Hi0umyfL/hY+mMgCvnbWtE1DQNQey1G2eKWNip3Dg49D3H0r7nOcHmuH+IngO18ZaFIEiUahEpMEmBu3Y6E+hOKAPlLQ9avvD2rxX1jO0U8RyNp4Yeh9RX2B4I8X2XjHw9DqFuwWXhZos8xv3H05618bX9jNYXs1pcxmOeJijKeOa7b4U+NpfCfieKKVt9hdsI5kJxgngEenJFAH11n3paiikSeJJUYFGGQR0IqUd6AFopKWgDn9A/5C2tf9d/8A2Z66Cuf0D/kLa1/13/8AZnrfroxX8V/L8kKOwtFJRXOMWikpaACkpaSgBaKKKACiiigBKwPCH/IJl/67n/0Fa36wPCH/ACCZf+u5/wDQVrop/wAGfy/UT3OgpKWkrnGLRRRQAUUUUAIelMwM/WnmmhaAPBPjx4IAjHieyhG7IS6AHtw36AV4EWPynuP85r7o13SoNa0a6065TfFcIUYfr/MV8T67pU2iazd6Zc4822kKEjvj0oA+ovg54o/4SDwRDDI+66sgInX/AGRwv6CvR1ORXyp8C9fOl+N1s5HbybxPL2ju+QF/ma+q19KAHUUUUAc/oH/IW1r/AK7/APsz10Fc/oH/ACFta/67/wDsz10FdGK/iv5fkhR2CiiiucYUUUUAFFFFABRRRQAUUUUAFc/4Q/5BMv8A13P/AKCtdBXP+EP+QTL/ANdz/wCgrXRT/gz+X6ie50FFFFc4wooooAKKKKACiiigBre3Wvlr4+6Wll47S4iTatxbq7H1fc2f6V9TGvn39pC3VX0i4A+Ztyk+w/8A10AeO+E9Q/srxXpd7/zwuUc/QGvuC2k862il/voG/MV8EwsUmRv7pBr7v0r/AJBFmfWBP/QRQBcooooA5/QP+QtrX/Xf/wBmeugrn9A/5C2tf9d//ZnroK6MV/Ffy/JCjsFFFFc4wooooAKKKKACiiigAooooAK5/wAIf8gmX/ruf/QVroK5/wAIf8gmX/ruf/QVrop/wZ/L9RPc6CiiiucYUUUUAFFFFABRRRQA0kV85ftF3+/W9N08H/Vw+b+ZI/pX0W+AQx4A6mvkD4ua2da+IF++8NHan7PER3UEn+tAHF2MBur+CAAkyOFwPevu3TV2aZaL6QoP0FfHPw00yTVfH+kxJGHSO4SaQH+4rDNfZsahVCjgDoPSgB9FFFAHP6B/yFta/wCu/wD7M9dBXP6B/wAhbWv+u/8A7M9dBXRiv4r+X5IUdgooornGFFFFABRRRQAUUUUAFFFFABXP+EP+QTL/ANdz/wCgrXQVz/hD/kEy/wDXc/8AoK10U/4M/l+onudBRRRXOMKKKKACiiigApN1LTDgAnt3oA53xx4ih8NeEr7UZGUSIm2NW/iY9vyzXxXLK0sjyOdzMSSTXsXx48Y/2lq8Xh+1l3W9qQ0pHd8cfoa8fhgluruO2jG6SRwigdyTxQB7b+zz4fL39/rcqkCNPKiP94HOcfkK+iE6VyXw68ODwv4MsdPx+9K+ZL6hm5I/AmutX09KAHUUUUAc/oH/ACFta/67/wDsz10Fc/oH/IW1r/rv/wCzPXQV0Yr+K/l+SFHYKKKK5xhRRRQAUUUUAFFFFABRRRQAVz/hD/kEy/8AXc/+grXQVz/hA/8AEql/67n/ANBWuin/AAZ/L9RPc6CikzRkYrnGLRRRQAUhpaQnigANcf8AEXxhF4P8L3F35gF3IPLt0GCdxHBweoBxXSanqdrpOnzXt3KkcEKlmZmwBXyL8R/HMvjXxC06llsYiUgTPb+99TgcUAcje3U17ezXE7F5ZWLMxPc16b8EPBn9v+Jf7Uuoy1lYncMggM/OOfY4NecaTplxrGp29jaoXlnfYABk19k+CvDFt4R8OW2mwoN6gGVwOXbHWgDpAoFKBilooAKKKKAOf0D/AJC2tf8AXf8A9meugrn9A/5C2tf9d/8A2Z66CujFfxX8vyQo7BRRRXOMKKKKACiiigAooooAKKKKACud8JZ/sqTBA/fn/wBBWug3cZ7VzvhZ0j0eZ5CFVZyST/urXRT/AIM/l+onub+4DAzwelZuqeIdK0W3ea/voYlTlhuyfyHNeJ/ET44SiabTPDLgJ91rvnJP+z0IrxDUNTvtVumuNQuZbmdurytuJ/GucZ9YN8avAaMQ2stn0+zyf/E0n/C7fAP/AEGW/wDAaT/4mvkUrnpQVx3+lAH2BB8YfBN1II4tY+Y9mgcfzFb8vi3QotMfUG1K3Nui7mwwJx9OtfD+acjMAcdMYNAHo3xK+Jt34xu3s7V5IdKQ8IGI83nqw79uorzzDM21VySOABmmnJ6H6V2Hw2ufDdl4miufEhIgjIZGIyob/aGCTQB678FPhzJpMQ8Q6nCPtMy4gRwCYxn7w9Dx+te1ArnA6is3SNa0vWLSOfTbyOaHGFKccfQ81pgYOe9AD6K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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0261&gt;&lt;loc0965&gt;&lt;loc0814&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0919&gt;&lt;loc0653&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0140&gt;&lt;loc0369&gt;&lt;loc0662&gt;&lt;loc0447&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 41/64   lr: 0.00178   loss: 2.3048\n",
            "step: 42/64   lr: 0.00165   loss: 2.2890\n",
            "step: 43/64   lr: 0.00153   loss: 2.4525\n",
            "step: 44/64   lr: 0.00141   loss: 2.3838\n",
            "step: 45/64   lr: 0.00129   loss: 2.2427\n",
            "step: 46/64   lr: 0.00117   loss: 2.0810\n",
            "step: 47/64   lr: 0.00106   loss: 2.2332\n",
            "step: 48/64   lr: 0.00096   loss: 2.1453\n",
            "Model predictions at step 48\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0238&gt;&lt;loc0896&gt;&lt;loc0919&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+ikooAWiiigAopKWgBKWkqs2o2KOyPeW6spwQZVBB/Omot7IC1SVV/tPT/8An+tv+/y/40f2np//AD/W3/f1f8ar2c+wXLdFVP7T0/8A5/rb/v8AL/jVlWV0V0YMrDIIOQRUuLW6AdRRRSAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiikoAWiiigAooooAK811P8A5C15/wBd3/8AQjXpNeban/yFrz/ru/8A6Ea9LLfjkRMq0VNbfZw7NcbiqjIReN59M9qmvIYfs8NzEjRCUkeUW3dO4Pp/hXquVnYzKdek6Z/yCbP/AK4J/wCgivNq9J0z/kE2f/XBP/QRXn5l8ES4FuiiivINAooooAKKKKACiiigAooooAKKKKACiiigAooooASilpKAFooooASloooASvNtT/5C15/13f8A9CNek15rqLK+p3bowZWmcgg5BG416WW/FIiY7TorWScm7mCRqM7Tn5z6ZA4FO1E+ZKJPtUMueAsQICAdAARwKpUV6vJ73NczCvSdM/5BNn/1wT/0EV5tXb2Ov6ZDp1tFJc4dIlVh5bcEAZ7VxZhCU4x5VcuDNzil4rG/4SLSun2rgnPEbf4Up8RaVz/pf5Rv/hXlewq/yv7mXdGxRWOPEelY/wCPvn3jb/Cn/wDCSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWisr/hJNJ/5+/8AyG/+FH/CSaT/AM/f/kN/8KPYVf5X9zC6NWiqFrrOn3s3k29wGkIJClSufpkVfqJRlF2krDCiiipAKKKKAEooooAKWiigBKKKWgBK5rwtZ2txpkjz20MrCYgF0DHGF9a6WsDwh/yCZf8Aruf/AEFa6abaoza8v1E9zV/szT/+fG2/79L/AIUf2Zp//Pjbf9+l/wAKtUVj7SfcdiqdM08f8uNt/wB+V/wrO1qbRdC0i41G7tLVIYF3EmJevYfnWy3Ue3Ncl8S9Kudc+H+qafZruuJEVlUdThgT+go9pPuFjwHxN8ZdS1Kcro9pa6dEp+VkhRmYe4YGufT4neLN6k6ijAdV+yxc/wDjtcxJaXEV0bZoJEuAdvlMh3A+mPWr114a1yytluLrR76CHrvkgZV/Mij2k+4WPXfCnxvsTJHbeItHtgpIHnwwqT9TnAr23R7rw9rtmLvTUsbmI9THGpwfQ+9fEGM5I4rd8N+LdY8K6il1p100RHBQ4KsPoePxo9pPuFj7TGm6ef8Alwtv+/K/4Uf2dp2cCxtSR1/dL/hXGfDz4j6f4208R/6jUYlzNBnr7juR0rulyW5PTmj2k+4WIf7M0/8A58bb/vyv+FH9maf/AM+Nt/35X/CrdFHtJ9wsVP7M0/8A58bb/v0v+FH9mWH/AD423/flf8Kt0Ue0n3CxzcsENv4ysUgijiUwkkIoUZw/pXSVz93/AMjrY/8AXA/yeugrXEO6hft+rEgooornGFFFFACUUUUALRSUUAFLSUUAFYHhD/kEy/8AXc/+grW/WB4Q/wCQTL/13P8A6CtdFP8Agz+X6ie5v0UUVzjAjIxSbB3HWlooAzX8O6LJOZ30mxaYnd5ht0LZ9c4qzcWFpdQeRcW0U0WMbJEDD8jVqmnrnFAHj/jX4Habqyy3eh4tLpsny/4WPpjIAr521rRNQ0DUHstRtniljYqdw4OPQ9x9K+5znB5rh/iJ4DtfGWhSBIlGoRKTBJgbt2OhPoTigD5S0PWr7w9q8V9YztFPEcjaeGHofUV9geCPF9l4x8PQ6hbsFl4WaLPMb9x9OetfG1/YzWF7NaXMZjniYoynjmu2+FPjaXwn4niilbfYXbCOZCcYJ4BHpyRQB9dZ96WoopEniSVGBRhkEdCKlHegBaKSloA5+7/5HWx/64H+T10Fc/d/8jrY/wDXA/yet+uivtD0/ViXUWikornGLRSUtABSUtJQAtFFFABRRRQAlYHhD/kEy/8AXc/+grW/WB4Q/wCQTL/13P8A6CtdFP8Agz+X6ie50FJS0lc4xaKKKACiiigBD0pmBn60800LQB4J8ePBAEY8T2UI3ZCXQA9uG/QCvAix+U9x/nNfdGu6VBrWjXWnXKb4rhCjD9f5ivifXdKm0TWbvTLnHm20hQkd8elAH1F8HPFH/CQeCIYZH3XVkBE6/wCyOF/QV6OpyK+VPgXr50vxutnI7eTeJ5e0d3yAv8zX1WvpQA6iiigDn7v/AJHWx/64H+T10Fc/d/8AI62P/XA/yeugror7Q9P1Yl1CiiiucYUUUUAFFFFABRRRQAUUUUAFc/4Q/wCQTL/13P8A6CtdBXP+EP8AkEy/9dz/AOgrXRT/AIM/l+onudBRRRXOMKKKKACiiigAooooAa3t1r5a+PulpZeO0uIk2rcW6ux9X3Nn+lfUxr59/aQt1V9IuAPmbcpPsP8A9dAHjvhPUP7K8V6Xe/8APC5Rz9Aa+4LaTzraKX++gb8xXwTCxSZG/ukGvu/Sv+QRZn1gT/0EUAXKKKKAOfu/+R1sf+uB/k9dBXP3f/I62P8A1wP8nroK6K+0PT9WJdQooornGFFFFABRRRQAUUUUAFFFFABXP+EP+QTL/wBdz/6CtdBXP+EP+QTL/wBdz/6CtdFP+DP5fqJ7nQUUUVzjCiiigAooooAKKKKAGkivnL9ou/363pung/6uHzfzJH9K+i3wCGPAHU18gfFzWzrXxAv33ho7U/Z4iO6gk/1oA4uxgN1fwQAEmRwuB71926auzTLRfSFB+gr45+GmmSar4/0mJIw6R3CTSA/3FYZr7NjUKoUcAdB6UAPooooA5+7/AOR1sf8Argf5PXQVz93/AMjrY/8AXA/yeugror7Q9P1Yl1CiiiucYUUUUAFFFFABRRRQAUUUUAFc/wCEP+QTL/13P/oK10Fc/wCEP+QTL/13P/oK10U/4M/l+onudBRRRXOMKKKKACiiigApN1LTDgAnt3oA53xx4ih8NeEr7UZGUSIm2NW/iY9vyzXxXLK0sjyOdzMSSTXsXx48Y/2lq8Xh+1l3W9qQ0pHd8cfoa8fhgluruO2jG6SRwigdyTxQB7b+zz4fL39/rcqkCNPKiP8AeBznH5CvohOlcl8OvDg8L+DLHT8fvSvmS+oZuSPwJrrV9PSgB1FFFAHP3f8AyOtj/wBcD/J66Cufu/8AkdbH/rgf5PXQV0V9oen6sS6hRRRXOMKKKKACiiigAooooAKKKKACuf8ACH/IJl/67n/0Fa6Cuf8ACB/4lUv/AF3P/oK10U/4M/l+onudBRSZoyMVzjFooooAKQ0tITxQAGuP+IvjCLwf4XuLvzALuQeXboME7iODg9QDiuk1PU7XSdPmvbuVI4IVLMzNgCvkX4j+OZfGviFp1LLYxEpAme3976nA4oA5G9upr29muJ2LyysWZie5r034IeDP7f8AEv8Aal1GWsrE7hkEBn5xz7HBrzjSdMuNY1O3sbVC8s77AAMmvsnwV4YtvCPhy202FBvUAyuBy7Y60AdIFApQMUtFABRRRQBz93/yOtj/ANcD/J66Cufu/wDkdbH/AK4H+T10FdFfaHp+rEuoUUUVzjCiiigAooooAKKKKACiiigArnfCWf7KkwQP35/9BWug3cZ7VzvhZ0j0eZ5CFVZyST/urXRT/gz+X6ie5v7gMDPB6Vm6p4h0rRbd5r++hiVOWG7J/Ic14n8RPjhKJptM8MuAn3Wu+ck/7PQivENQ1O+1W6a41C5luZ26vK24n8a5xn1g3xq8BoxDay2fT7PJ/wDE0n/C7fAP/QZb/wABpP8A4mvkUrnpQVx3+lAH2BB8YfBN1II4tY+Y9mgcfzFb8vi3QotMfUG1K3Nui7mwwJx9OtfD+acjMAcdMYNAHo3xK+Jt34xu3s7V5IdKQ8IGI83nqw79uorzzDM21VySOABmmnJ6H6V2Hw2ufDdl4miufEhIgjIZGIyob/aGCTQB678FPhzJpMQ8Q6nCPtMy4gRwCYxn7w9Dx+te1ArnA6is3SNa0vWLSOfTbyOaHGFKccfQ81pgYOe9AD6KTNAOaAFooooA5+7/AOR1sf8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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0261&gt;&lt;loc0961&gt;&lt;loc0824&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0230&gt;&lt;loc0919&gt;&lt;loc0653&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0141&gt;&lt;loc0374&gt;&lt;loc0662&gt;&lt;loc0450&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 49/64   lr: 0.00085   loss: 2.1180\n",
            "step: 50/64   lr: 0.00076   loss: 2.5212\n",
            "step: 51/64   lr: 0.00066   loss: 2.3260\n",
            "step: 52/64   lr: 0.00058   loss: 2.3060\n",
            "step: 53/64   lr: 0.00049   loss: 2.3717\n",
            "step: 54/64   lr: 0.00042   loss: 2.3142\n",
            "step: 55/64   lr: 0.00035   loss: 1.9866\n",
            "step: 56/64   lr: 0.00028   loss: 2.1863\n",
            "Model predictions at step 56\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0919&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0261&gt;&lt;loc0957&gt;&lt;loc0824&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+ikooAWiiigAopKWgDxenIjSOqIpZ2OAoGSTTa1dHks7dZppbrybrG2FvLLbM9W4744Hp1r7KpJxjdK58HTgpySbsUbu0lsrp7eYASJjcAc9s1BWx4ljiTWJDHN5jMF3DaRt+UfnWPSpT54KT6odaChUlFdGe0UUlLXxx92FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlAC0UUUAFFFVdSle30q7mibbJHC7qcZwQpIpxXM0kKUuVNvoeQ0V0+k+EP7U0yG8+3eV5m75PJ3YwxHXcPSrv8AwgH/AFE//IH/ANlX1Esfh4ycXLVeT/yPj4ZbipxUox0fmv8AM4xnZ2LOxZj3JzSV2n/CAf8AUT/8gf8A2VMk8CLFGzvqmFUEk+R0H/fVT/aOG/m/B/5Ff2Xi/wCT8V/mdvRXzJe/Gq6t9Tmt7f7dPbJIVWc3rKWUHG7bg4+ma9r07Rb/AFLTba+h8SXojuIllUENnDDP9+vC9jR/5+L7n/kfR+3r/wDPp/ev8zsKK5j/AIRjU/8AoZLz8m/+Lo/4RjU/+hkvPyb/AOLo9jR/5+L7n/kHt6//AD6f3r/M6eiuY/4RjU/+hkvPyb/4uj/hGNT/AOhkvPyb/wCLo9jR/wCfi+5/5B7ev/z6f3r/ADOnormP+EY1P/oZLz8m/wDi6zdastU0G1ivBrt1OTKFCsWA6E5OWIPTpiqhhqc5KMaiu/J/5EVMXVpxc50mkvNf5nc0UUVxneFFFFABRRRQAUUUUAFFFFACUUtJQAtFFFACVT1j/kCX/wD17Sf+gmrtUtY/5Al//wBe0n/oJq6Xxr1M6v8ADl6MpeE/+RZs/wDgf/obVs1jeE/+RZs/+B/+htW1WmJ/jT9X+ZnhP93h6L8hK5/xvq0Wh+C9Wv5iNsds4A9WYbQPzIroK8d/aG1tbTwda6Uj4lvbgMy/7Ccn9dtYHQfN2m2cuparbWUCl5biVY1A7knFfddhbiz062th0hiWP8gBXyl8DdGGqfEm1mdd0dlG1wc+o4X9T+lfWo6UALRRVe8kkitJ5IVDyrGzIp7kDgUAT5GetLXxI3jfxGniJtY/tW6F6Jd5PmHHX7uOmO2K+ytB1A6roGn6gy7WubeOUr6FlBoA0a5jx1/yBIf+vlf/AEFq6euY8df8gSH/AK+V/wDQWrqwX+8Q9TjzD/dp+h09FFFcp2BRRRQAUUUUAFFFFABRRRQAlFFFABS0UUAJVPWP+QJf/wDXtJ/6CauVT1j/AJAl/wD9e0n/AKCaul8a9TOr/Dl6MpeE/wDkWbP/AIH/AOhtWzWN4T/5Fmz/AOB/+htWzWmJ/jT9X+ZnhP8Ad4ei/IK+TPjlrh1b4i3FujlobCNYFGeA3Vv1P6V9R65qkOiaJe6ncMBFbQtK2e+B0r4fvLm41rWZrlwZLm7nLn1LM3T9awOg+if2ePDws/DN5rcqfvb2Xy42P/PNP8Tn8q9nrD8HaKPD/hHS9LwA1vbqr4/vYy36k1uUALUc3+ok/wB0/wAqkpk3+ok/3T/KgD4Iuf8Aj6m/32/nX254K/5EfQu3+gQ/+gCviS6BF3MGGDvbI/GvtnwPIsngXQmVgwNjDyP9wUAdBXMeOv8AkCQ/9fK/+gtXT1zHjr/kCQ/9fK/+gtXVgv8AeIepx5h/u0/Q6eiiiuU7AooooAKKKKACiiigAooooASiiigBaKSigAqnrH/IEv8A/r2k/wDQTVyqesf8gS//AOvaT/0E1dL416mdX+HL0ZS8J/8AIs2f/A//AENq2axfCf8AyLNn/wAD/wDQ2rWuZ47a2lnlYLHEhdiewAya0xP8afq/zM8J/u8PRfkeP/tB+JlsPDFvoUMn7+/ffIAeREvP6nH5GvKPgx4b/wCEg+INq8ibrawH2mTPTI+6Pzx+Vc7418T3Xi/xTd6rcE7XbbDHnhIx90Cvo/4K+CW8MeFft12u2/1ELK6kcon8K/rk/WsDoPTh0ooooAWiikoA+FvE6hPFWrKowBeSgD/gZr68+GAA+Gnh/HT7ItfLHxJtDZfEbXoSmz/THcD2b5h/OvpD4Jap/aXwy09CfntGe3b8DkfoRQB6LXMeOv8AkCQ/9fK/+gtXT1zHjr/kCQ/9fK/+gtXVgv8AeIepx5h/u0/Q6eikpa5TsCikpaACiikoAWikooAWikpaACkpaSgBaKKKACqWsf8AIEv/APr2k/8AQTV2qWsf8gS//wCvaT/0E1dL416mdX+HL0ZS8J/8izZ/8D/9DauE+Oni9/D3hIabbHF1qm6LcD92MfeP45A/Gu68KHHhi0P+/wD+htXy98Y/FyeKfG0q27brOxBt4j/eIPzN+f8AKtMT/Gn6v8zPCf7vD0X5GR8OfDU3inxtp9kkZeBJBNcHHCxqcnP16fjX2iqhVCgAADAArxf9nfw8bPw5fa1NEVkvJRHExHWNfT2yT+Ve01gdAtFFFABRSFgOtczrHxD8J6FI8V/rlpHKn3o1few/Bc0AfJPjrVJ9Y8b6xeXJzI106AeiqdoH5AV9Cfs+NaH4eyCAYnF2/n8/xYGP0xXzVr91FfeIdRuoG3RTXMkiNjGQWJFe7/s1s39ma8pJ2+dEQO33WoA93rmPHX/IEh/6+V/9BaunrmPHX/IEh/6+V/8AQWrqwX+8Q9TjzD/dp+h09FFFcp2BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVLWP+QJf/wDXtJ/6Cau1S1j/AJAl/wD9e0n/AKCaul8a9TOr/Dl6M4jV/Eo8KfCOXUlZRcBHjgDd5GdgP8fwr5U0vTrrXtbtrC2QyXN1KEUDuSeteifF/XvtFvomhxSNstYnmmTtvZ2x+g/WvOdJ1i+0O/W+02dre6QELKoGVyMHGehxWmJ/jT9X+ZnhP93h6L8j7g0uztdD0W1so/LhgtoljHOAMCsXVviP4R0XcLzXrMOvVI38xvyXNfHd7ruram5N9qV3ckn/AJazM38zU+m+F9e1lgNO0m9uc9DHCxH59KwOg+htW/aI8NWmV06zvb5x0O0RqfxPP6Vw2rftFeIrrcum6fZ2SnozAysPzwP0rJ0j4D+MtSCvcw22nof+fiXLfkua7rSf2b7KPa2ra3NKe6W0YQfmc/yoA8d1j4heLNd3C/1y7eNusaN5a/kuBWbpegazr0pXTdOurx88mKMt+Zr6u0j4OeCdIYOukLcyD+K6cyfoeP0rtbSxtbCAQWlvFbxDokSBVH4CgD4W1bSL7Q9Qew1K2e2uowC0T9RkZFfTnwBs7GH4eC5tgftE9w/2gk/xDgD6Yx+deR/HpFT4nTlRgtbRE+5xj+lep/s7/wDIg3J/6fn/APQVoA9ermPHX/IEh/6+V/8AQWrp65jx1/yBIf8Ar5X/ANBaurBf7xD1OPMP92n6HT0UUVynYFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFUtY/5Al/8A9e0n/oJq7VLWP+QJf/8AXtJ/6Caul8a9TOr/AA5ejPC/Efwb1Dxf9k1vSb23jkmQpPHcEgDaxAIIB7AcVraF+zro9qyy6zqU962OYoh5aZ+vJP6V6h4TH/FM2f8AwP8A9DatutMT/Gn6v8zPCf7vD0X5HOaX4D8LaNGqWOhWSEDG9og7H8Tk10CRrGoVFCgdABin0VgdAUUUUAFFFFAHyf8AHs5+J03taxD9DXqX7Oxz4Cuh6Xz/APoK15Z8e/8Akp0/Of8ARovw4r1L9nY/8UHd85/05uPT5VoA9frmPHX/ACBIf+vlf/QWrp65jx1/yBIf+vlf/QWrqwX+8Q9TjzD/AHafodPRRRXKdgUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVS1j/kCX//AF7Sf+gmrtUtY/5Al/8A9e0n/oJq6Xxr1M6v8OXoyl4T/wCRZs/+B/8AobVtVi+E/wDkWbP/AIH/AOhtW1WmJ/jT9X+ZnhP93h6L8gooorA6AooooAKKKKAPk349En4n3GQOLaLH5V6l+zqT/wAIJeDjAvmx/wB8rXlvx5/5Kfc/9e8X8q9S/Z1/5EW8/wCv5v8A0FaAPYK5jx1/yBIf+vlf/QWrp65jx1/yBIf+vlf/AEFq6sF/vEPU48w/3afodPRRRXKdgUUUUAFFFFA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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0121&gt;&lt;loc0222&gt;&lt;loc0919&gt;&lt;loc0653&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0141&gt;&lt;loc0374&gt;&lt;loc0655&gt;&lt;loc0450&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step: 57/64   lr: 0.00022   loss: 2.1201\n",
            "step: 58/64   lr: 0.00017   loss: 2.2197\n",
            "step: 59/64   lr: 0.00013   loss: 2.1977\n",
            "step: 60/64   lr: 0.00009   loss: 2.2539\n",
            "step: 61/64   lr: 0.00006   loss: 2.0506\n",
            "step: 62/64   lr: 0.00003   loss: 2.4619\n",
            "step: 63/64   lr: 0.00001   loss: 2.2024\n",
            "step: 64/64   lr: 0.00000   loss: 2.3148\n",
            "Model predictions at step 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0912&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0261&gt;&lt;loc0957&gt;&lt;loc0824&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0919&gt;&lt;loc0653&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0141&gt;&lt;loc0374&gt;&lt;loc0658&gt;&lt;loc0450&gt; 1</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 11min 12s, sys: 463 ms, total: 11min 12s\n",
            "Wall time: 11min 14s\n"
          ]
        }
      ],
      "source": [
        "# Run a short training loop with cosine learning rate schedule.\n",
        "#\n",
        "# Note: the first step can be quite slow on some machines (up to several minutes)\n",
        "# due to XLA compilation of the jax.jit'd function.\n",
        "#\n",
        "%%time\n",
        "\n",
        "BATCH_SIZE = 8\n",
        "TRAIN_EXAMPLES = 512\n",
        "LEARNING_RATE = 0.005\n",
        "\n",
        "TRAIN_STEPS = TRAIN_EXAMPLES // BATCH_SIZE\n",
        "EVAL_STEPS = TRAIN_STEPS // 8\n",
        "\n",
        "train_data_it = train_data_iterator()\n",
        "\n",
        "sched_fn = big_vision.utils.create_learning_rate_schedule(\n",
        "    total_steps=TRAIN_STEPS+1, base=LEARNING_RATE,\n",
        "    decay_type=\"cosine\", warmup_percent=0.10)\n",
        "\n",
        "for step in range(1, TRAIN_STEPS+1):\n",
        "  # Make list of N training examples.\n",
        "  examples = [next(train_data_it) for _ in range(BATCH_SIZE)]\n",
        "\n",
        "  # Convert list of examples into a dict of np.arrays and load onto devices.\n",
        "  batch = jax.tree.map(lambda *x: np.stack(x), *examples)\n",
        "  batch = big_vision.utils.reshard(batch, data_sharding)\n",
        "\n",
        "  # Training step and report training loss\n",
        "  learning_rate = sched_fn(step)\n",
        "  params, loss = update_fn(params, batch, learning_rate)\n",
        "\n",
        "  loss = jax.device_get(loss)\n",
        "  print(f\"step: {step:2d}/{TRAIN_STEPS:2d}   lr: {learning_rate:.5f}   loss: {loss:.4f}\")\n",
        "\n",
        "  if (step % EVAL_STEPS) == 0:\n",
        "    print(f\"Model predictions at step {step}\")\n",
        "    html_out = \"\"\n",
        "    for image, _, caption in make_predictions(\n",
        "        validation_data_iterator(), num_examples=4, batch_size=4):\n",
        "      html_out += render_example(image, caption)\n",
        "    display(HTML(html_out))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate fine-tuned model"
      ],
      "metadata": {
        "id": "rGjB_4Mo8RA_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "hgUhEKjzPdMQ",
        "outputId": "59b2fe9a-5ba4-48e1-a08d-fcc7c91049b9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0046&gt;&lt;loc0233&gt;&lt;loc0896&gt;&lt;loc0912&gt; plus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0219&gt;&lt;loc0261&gt;&lt;loc0957&gt;&lt;loc0824&gt; 2</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0116&gt;&lt;loc0222&gt;&lt;loc0919&gt;&lt;loc0653&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0141&gt;&lt;loc0374&gt;&lt;loc0658&gt;&lt;loc0450&gt; 1</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0112&gt;&lt;loc0155&gt;&lt;loc0927&gt;&lt;loc0639&gt; 3</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0127&gt;&lt;loc0297&gt;&lt;loc0785&gt;&lt;loc0673&gt; 4</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0058&gt;&lt;loc0261&gt;&lt;loc0831&gt;&lt;loc0662&gt; 8</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0222&gt;&lt;loc0453&gt;&lt;loc0735&gt;&lt;loc0819&gt; 7</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0449&gt;&lt;loc0180&gt;&lt;loc0533&gt;&lt;loc0797&gt; minus</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
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              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0266&gt;&lt;loc0502&gt;&lt;loc0936&gt;&lt;loc0626&gt; 1</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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ZnaFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFU9V/5A97/ANe8n/oJq5VPVf8AkD3v/XvJ/wCgmnHdGdb+HL0ZU8Mf8i9a/wDA/wD0Nq16yPDH/IvWv/A//Q2rTlmjgjaSV1RFGSzHAFVU+N+plg/92p/4V+RJRVaz1Cz1CIy2d1DcRg43xSBx+YqxkZxnmoOkaW5wOtcX8RPHlr4L0NpdyS30q4gh3DJPTP0/wq3438bad4M0mS6unV52GIoA2Gc/0HvXhnhXQNZ+LPi1ta1lpBp0TgnIIXA6Ko4/SgDpPg94Uu9Y1m58Ya7ExeRy1uJAeSTnP6cfWvegBVW1tIbG2htbeJUiiUKiAcACrdACAAUYFKTikJFAHiP7RWmQf8I9p+p7B54uRDu77SrH+grrvhJ/yK1tn/nytf8A0A157+0Rriyvp2hRsGIbz3AOcHkD9Gr0T4TxtF4ahjdSrpZ2ysD1BCGtIfDL+upxYj/eKPq//SWd/RRRWZ2hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVPVf+QPe/wDXvJ/6CauVT1X/AJA97/17yf8AoJpx3RnW/hy9GVPDH/IvWv8AwP8A9Das34hadd6r4H1WzspGiuHiyrqcHggkfkDWl4Y/5F21/wCB/wDobVpsofKtyCORVVPjfqZYP/dqf+FfkfNHwO8Wto/iGTQ7yVlgvDiNXJwkgPAx75/SvaPHfjuw8E6SbiciS7kGIYh1Y/0FfPfxS0hvB/xIkuLMbVeQXUZ9GJyR+FVJbrU/ir47tYm3/vNqbc8RoBk/1NQdJqaJo+v/ABf8VveX0sgs0fMjEnbGpPRQT6enpX03oWjWOg6XFp+nwrFDEMYAxk+pqHw54csfDWjwafYxKkcagEgcsfU1r9OtADqKTNJvB6c0AI/UZPFYfinxDaeGNBudTu3AEakopPLt2H51a13XtO0HTnvdQnWKJOcnqTXyz478b6l8R9fhs7SN1tRJsggHUknqecelADdIttQ+KHxHWeVXZJJN8hPIRB0H8q+l/C8SwahrESABElCqB2ALisj4YeBofBvh5RIqnUJwHnfHI9v5VteHP+Qvrf8A13/9metIfDL+upxYj/eKPq//AElnR0UUVmdoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVT1X/kD3v/XvJ/6CauVT1X/kD3v/AF7yf+gmnHdGdb+HL0ZU8Mf8i7a/8D/9DatXvWV4Y/5F61/4H/6G1apFVU+N+plg/wDdqf8AhX5Hzv8AtHKi6norAfOUk3H/AL5xXTfAnwrBp3hv+3ZUBurzOxvROmPzBrm/2kIdt5oU3d0lH5ba7n4Pa7YN8OLCCS5iR7RSr7mAxyT/AFqDpPTh0oJwK5HUfiV4T0xWMmtWkjDrHHKpb8s1wHiH9oXTrbMei2T3L45ab5V/DBNAHtEsqRJvdlVfVq8y8afGXRfDsTwWMi316B8qpyqn/a5B/KvDta8eeLvHN0bYPM8bn5beBQcfQgZrrPCHwL1XVJEu9ekNrbkgmPnzG+oIoA4++1PxV8Ttb8s+bOzNlIk+5GK98+G/wssvB0K3l4Fn1Vl/1nUJxzt4Fdh4d8K6R4ZsxbabapEAAGbklj681tBQOKAG4BU+nrWB4c/5C+t/9d//AGZ66LArnvDv/IY1z/r4/wDZnrSHwy/rqcWI/wB4o+r/APSWdFRRRWZ2hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVPVf+QPe/8AXvJ/6CauUU07O5M480XHucvoWu6bZ6Nb29xc7JU3bl2McZYnsPetA+JtH/5+/wDyG/8AhVz+ytO/6B9r/wB+V/wo/srTv+gfa/8Aflf8K0cqbd9ThpUsZTgoJxslbZ9PmeSfGTSIvGmm2T6TOkt3aFtsbBkyGxnkjHYV4xF4G8aQIUis5o0/updxgH/x+vsE6Vp3/Pha/wDflf8ACm/2Vp+f+PC2P/bJf8KX7vzLtje8fuf+Z8l2Xwu8QXkge9eC2BPPmS72/wDHc/zruNA+D/hyFxLrWtGcZ5hSFwD+Ir38aVp2P+PC1/78r/hR/ZWnf9A+1/78r/hR+78wtje8fuf+ZymiWXgrw7GE06KOMAYBaJ2P5kVu/wDCSaPjH2z/AMhP/hV7+ytO/wCfC1/78r/hR/ZWnf8APha/9+V/wo/d+YWxveP3P/MpL4l0df8Al84/65P/AIU7/hJ9H/5/P/IT/wCFW/7K07/nwtf+/K/4Uf2Vp3/Pha/9+V/wo/d+YWxveP3P/Mqf8JPo/wDz+f8AkJ/8Ko+GJUn1LWZYzuR5gynGMglyK2f7K07/AJ8LX/vyv+FWIbeG2QpBDHEpOSqKFGfXinzRUWo9SVQxE6sJ1WrRvsn1VurJKKKKyO8KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBCM0mKdRQAlLRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH/9k=\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0172&gt;&lt;loc0222&gt;&lt;loc0912&gt;&lt;loc0634&gt; 5</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0230&gt;&lt;loc0363&gt;&lt;loc0894&gt;&lt;loc0738&gt; 7</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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\" />\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0266&gt;&lt;loc0572&gt;&lt;loc0827&gt;&lt;loc0766&gt; 1</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0127&gt;&lt;loc0230&gt;&lt;loc0901&gt;&lt;loc0703&gt; 6</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" 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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0317&gt;&lt;loc0140&gt;&lt;loc0936&gt;&lt;loc0812&gt; mult</p>\n",
              "</div>\n",
              "\n",
              "<div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
              "    <img style=\"width:128px; height:128px;\" src=\"data:image/jpeg;base64,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/>\n",
              "    <p style=\"width:256px; margin:10px; font-size:small;\">&lt;loc0161&gt;&lt;loc0261&gt;&lt;loc0838&gt;&lt;loc0831&gt; 6</p>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title Visualize results\n",
        "html_out = \"\"\n",
        "for image, _, caption in make_predictions(validation_data_iterator(), num_examples=16, batch_size=8):\n",
        "  html_out += render_example(image, caption)\n",
        "display(HTML(html_out))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Collect predictions\n",
        "targets = []\n",
        "predictions = []\n",
        "\n",
        "for image, label, prediction in make_predictions(validation_data_iterator(), num_examples=512, batch_size=8):\n",
        "    h, w, _ = image.shape\n",
        "    target = sv.Detections.from_lmm(\n",
        "        lmm='paligemma',\n",
        "        result=label,\n",
        "        resolution_wh=(w, h),\n",
        "        classes=CLASSES)\n",
        "    targets.append(target)\n",
        "    prediction = sv.Detections.from_lmm(\n",
        "        lmm='paligemma',\n",
        "        result=prediction,\n",
        "        resolution_wh=(w, h),\n",
        "        classes=CLASSES)\n",
        "    prediction.confidence = np.ones(len(prediction))\n",
        "    predictions.append(prediction)"
      ],
      "metadata": {
        "id": "JD9l94a8pYRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Calculate mAP\n",
        "mean_average_precision = sv.MeanAveragePrecision.from_detections(\n",
        "    predictions=predictions,\n",
        "    targets=targets,\n",
        ")\n",
        "\n",
        "print(f\"map50_95: {mean_average_precision.map50_95:.2f}\")\n",
        "print(f\"map50: {mean_average_precision.map50:.2f}\")\n",
        "print(f\"map75: {mean_average_precision.map75:.2f}\")"
      ],
      "metadata": {
        "id": "d62AueMC7Yp3",
        "outputId": "487bb72c-c023-47c9-a33b-acdbf6ac1be0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "map50_95: 0.83\n",
            "map50: 0.94\n",
            "map75: 0.90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Calculate Confusion Matrix\n",
        "confusion_matrix = sv.ConfusionMatrix.from_detections(\n",
        "    predictions=predictions,\n",
        "    targets=targets,\n",
        "    classes=CLASSES\n",
        ")\n",
        "\n",
        "_ = confusion_matrix.plot()"
      ],
      "metadata": {
        "id": "lE7je-hL8Bj3",
        "outputId": "86db3d53-0caa-4186-ae8f-0e0460081c61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save fine-tuned model locally"
      ],
      "metadata": {
        "id": "Hr1gTKP8trRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "TARGET_MODEL_DIR = f\"{dataset.location}/model\"\n",
        "TARGET_MODEL_PATH = f\"{TARGET_MODEL_DIR}/paligemma-3b-pt-224.f16.npz\"\n",
        "\n",
        "os.makedirs(TARGET_MODEL_DIR, exist_ok=True)"
      ],
      "metadata": {
        "id": "N4Y43q4jKj7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flat, _ = big_vision.utils.tree_flatten_with_names(params)\n",
        "with open(TARGET_MODEL_PATH, \"wb\") as f:\n",
        "  np.savez(f, **{k: v for k, v in flat})"
      ],
      "metadata": {
        "id": "zyVxKr2FOxPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deploy model on Roboflow"
      ],
      "metadata": {
        "id": "7gj7L3BkMZrZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "version.deploy(model_type=\"paligemma-3b-pt-224\", model_path=TARGET_MODEL_DIR)"
      ],
      "metadata": {
        "id": "_YfNYT7qMMY2",
        "outputId": "2cbcf204-22d0-45d3-fe29-87cdd663278e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model files found in /content/number-ops-1/model: ['paligemma-3b-pt-224.f16.npz']\n",
            "Found .npz file paligemma-3b-pt-224.f16.npz in model path. Deploying JAX PaliGemma model.\n",
            "Zipping files for deploy: ['paligemma-3b-pt-224.f16.npz']\n",
            "Uploading to Roboflow... May take several minutes.\n",
            "View the status of your deployment at: https://app.roboflow.com/roboflow-jvuqo/number-ops-j1426/1\n",
            "Share your model with the world at: https://universe.roboflow.com/roboflow-jvuqo/number-ops-j1426/model/1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Congratulations\n",
        "\n",
        "⭐️ If you enjoyed this notebook, [**star the Roboflow Notebooks repo**](https://https://github.com/roboflow/notebooks) (and [**supervision**](https://github.com/roboflow/supervision) while you're at it) and let us know what tutorials you'd like to see us do next. ⭐️"
      ],
      "metadata": {
        "id": "kR8llI4Qv0pR"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}