DFC.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMxy0wSpAUMQWbixBGSmQEN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/marcory-hub/hailo/blob/main/DFC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Prepair you dataset\n",
        "\n",
        "\n",
        "1. Dataset Structure:\n",
        "  \n",
        "  Make a annotated dataset with this folder and naming structure. If you do not have your own dataset, you can download the [hornet3000+ dataset](https://www.kaggle.com/datasets/marcoryvandijk/vespa-velutina-v-crabro-vespulina-vulgaris). If you want to annotate your own dataset take a look at CVAT or Roboflow to annotate images and save it in YOLO format.\n",
        "\n",
        "```\n",
        "data/\n",
        "  train/\n",
        "    images/\n",
        "      image_1.jpg  # Image file\n",
        "      image_2.png  # Image file\n",
        "      etc...\n",
        "    labels/\n",
        "      image_1.txt  # Label file\n",
        "      image_2.txt  # Label file\n",
        "      etc...\n",
        "  val/\n",
        "    images/\n",
        "      image_3.jpg  # Image for validation\n",
        "      image_4.png  # Image for validation\n",
        "      ...\n",
        "    labels/\n",
        "      image_3.txt  # Label file for validation\n",
        "      image_4.txt  # Label file for validation\n",
        "      ...\n",
        "```\n",
        "\n",
        "2. Data.yaml Configuration (Optional)\n",
        "\n",
        "  Add `data.yaml` (configuration file used by the training script to locate the data) to folder datasets. This contains:\n",
        "  - absolute path to train images (train)\n",
        "  - absolute path validation (val)\n",
        "  - the number of classes (nc)\n",
        "  - and the class names (names)\n",
        "\n",
        "  For example a dataset with 3 types of insects would look like this:\n",
        "```\n",
        "train: /content/data/train # path to train images\n",
        "val: /content/data/val # path to val images\n",
        "nc: 3\n",
        "names: ['Vespa_velutina', 'Vespa_crabro', 'Vespula_vulgaris']\n",
        "```\n",
        "Make sure to adjust the `nc` (number of classes) and `names` accordingly.\n",
        "\n",
        "3. Zip Your Dataset (Optional):\n",
        "\n",
        "  Only if you use own dataset: zip the data folder to a file names `dataset.zip` (or a custom name). On mac use `ditto -c -k --norsrc --keepParent images dataset.zip` to exclude finderfiles from the zipped file.\n",
        "\n",
        "4. Copy the zipped dataset:\n",
        "  \n",
        "  Copy the zipped file with dataset and yaml to your google drive.\n",
        "\n"
      ],
      "metadata": {
        "id": "rqDdKd2X_Mns"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Unzip dataset.zip and rename the folder on google drive\n",
        "\n",
        "Adjust the names of the `dataset_path` and `dataset_filename` in the boxes on the right."
      ],
      "metadata": {
        "id": "cuSaGAA8DyDq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## 2. Unzip dataset.zip and rename the folder on google drive\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "SUPnZCbI96Al"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Define Paths with Parameters\n",
        "dataset_path = \"/content/gdrive/MyDrive/vespA/100_1_dataset.zip\"  # @param {type:\"string\"}\n",
        "dataset_filename = \"100_1_sampleSize\"  # @param {type:\"string\"}\n",
        "\n",
        "# Unzip the Dataset (using the defined path)\n",
        "!unzip {dataset_path} -d '/content/'\n",
        "\n",
        "# Rename the Extracted Folder\n",
        "old_path = f'/content/{dataset_filename}'\n",
        "new_path = '/content/dataset'\n",
        "os.rename(old_path, new_path)"
      ],
      "metadata": {
        "id": "05bLQk36i-94"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Optional: Check is path is correct\n",
        "\n",
        "output should be\n",
        "- train valid\n",
        "- images labels\n",
        "- /content/gdrive/MyDrive/vespA/data.yaml\n"
      ],
      "metadata": {
        "id": "WGlnv5GXtaz5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional chech datapaths\n",
        "\n",
        "!ls '/content/dataset/'\n",
        "!ls '/content/dataset/train/'\n",
        "!ls '/content/dataset/valid/'\n",
        "!ls '/content/gdrive/MyDrive/vespA/data.yaml'"
      ],
      "metadata": {
        "id": "bbYyE7zctW_4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Train YOLO11 model"
      ],
      "metadata": {
        "id": "BZJitLHv6AGA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Install ultralytics"
      ],
      "metadata": {
        "id": "6pb6wMhVHd0Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Installing the python package\n",
        "!pip install ultralytics\n",
        "\n",
        "#Verifying the installation\n",
        "!pip show ultralytics\n",
        "import ultralytics\n",
        "ultralytics.checks()\n",
        "from ultralytics import YOLO\n",
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "0PqKpDtbHgKO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f38e53d5-33b8-4771-fc8d-d30bce18cc07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.49 🚀 Python-3.10.12 torch-2.5.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 33.2/107.7 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Retrain model\n",
        "\n",
        "TODO fix log W&B and project name with params"
      ],
      "metadata": {
        "id": "rZGcPMJIHlJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "model_name = \"yolo11s\" #@param {type:\"string\"}\n",
        "dataset_path = \"/content/gdrive/MyDrive/vespA/data.yaml\" #@param {type:\"string\"}\n",
        "\n",
        "# Get pre-trained model\n",
        "model = YOLO(f\"{model_name}.pt\")\n",
        "\n",
        "# Train/fine-tune model\n",
        "model.train(project=\"yolo11vespA\",  # wandb project name\n",
        "            name=\"train1\",          # run name wandb\n",
        "            data=dataset_path,\n",
        "            epochs=3,\n",
        "            imgsz=640,\n",
        "            fraction=0.1)"
      ],
      "metadata": {
        "id": "x6WpYNS4HkxN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "123a07b6-84d2-4b8c-f436-3048f8f5c20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt to 'yolo11s.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 18.4M/18.4M [00:00<00:00, 176MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.49 🚀 Python-3.10.12 torch-2.5.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11s.pt, data=/content/gdrive/MyDrive/vespA/data.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=yolo11vespA, name=train1, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=0.1, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=yolo11vespA/train1\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 58.6MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml nc=80 with nc=5\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n",
            "  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  2                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     \n",
            "  3                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            "  4                  -1  1    103360  ultralytics.nn.modules.block.C3k2            [128, 256, 1, False, 0.25]    \n",
            "  5                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
            "  6                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           \n",
            "  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
            "  8                  -1  1   1380352  ultralytics.nn.modules.block.C3k2            [512, 512, 1, True]           \n",
            "  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n",
            " 10                  -1  1    990976  ultralytics.nn.modules.block.C2PSA           [512, 512, 1]                 \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  1    443776  ultralytics.nn.modules.block.C3k2            [768, 256, 1, False]          \n",
            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 16                  -1  1    127680  ultralytics.nn.modules.block.C3k2            [512, 128, 1, False]          \n",
            " 17                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 19                  -1  1    345472  ultralytics.nn.modules.block.C3k2            [384, 256, 1, False]          \n",
            " 20                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 22                  -1  1   1511424  ultralytics.nn.modules.block.C3k2            [768, 512, 1, True]           \n",
            " 23        [16, 19, 22]  1    821343  ultralytics.nn.modules.head.Detect           [5, [128, 256, 512]]          \n",
            "YOLO11s summary: 319 layers, 9,429,727 parameters, 9,429,711 gradients, 21.6 GFLOPs\n",
            "\n",
            "Transferred 493/499 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolo11vespA/train1', view at http://localhost:6006/\n",
            "Freezing layer 'model.23.dfl.conv.weight'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/dataset/train/labels... 205 images, 0 backgrounds, 0 corrupt: 100%|██████████| 205/205 [00:00<00:00, 262.55it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/dataset/train/labels.cache\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 1.4.22 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "  check_for_updates()\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/dataset/valid/labels... 582 images, 0 backgrounds, 0 corrupt: 100%|██████████| 582/582 [00:01<00:00, 376.04it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/dataset/valid/labels.cache\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plotting labels to yolo11vespA/train1/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001111, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 0 dataloader workers\n",
            "Logging results to \u001b[1myolo11vespA/train1\u001b[0m\n",
            "Starting training for 3 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        1/3         0G      1.775      5.782       1.95         29        640:  15%|█▌        | 2/13 [02:14<12:20, 67.34s/it]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-c99a3904d5b5>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Train/fine-tune model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m model.train(project=\"yolo11vespA\",  # wandb project name\n\u001b[0m\u001b[1;32m     11\u001b[0m             \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train1\"\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0;31m# run name wandb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m    804\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    805\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m  \u001b[0;31m# attach optional HUB session\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 806\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    807\u001b[0m         \u001b[0;31m# Update model and cfg after training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mRANK\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_scheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36m_do_train\u001b[0;34m(self, world_size)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m                 \u001b[0;31m# Backward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m                 \u001b[0;31m# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             )\n\u001b[0;32m--> 581\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    582\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    345\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m     _engine_run_backward(\n\u001b[0m\u001b[1;32m    348\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    826\u001b[0m             \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m         )  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Validate model\n",
        "\n",
        "set to gdrive, adjust when workflow below is debugged"
      ],
      "metadata": {
        "id": "tuCAatGuHqKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=val \\\n",
        "model=\"/content/gdrive/MyDrive/best.pt\" \\\n",
        "    data=\"/content/gdrive/MyDrive/vespA/data.yaml\""
      ],
      "metadata": {
        "id": "MTZsycVfHupF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a4f1634-d212-4029-8576-ac987766bd96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.49 🚀 Python-3.10.12 torch-2.5.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLO11s summary (fused): 238 layers, 9,414,735 parameters, 0 gradients, 21.3 GFLOPs\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/dataset/valid/labels.cache... 582 images, 0 backgrounds, 0 corrupt: 100% 582/582 [00:00<?, ?it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):  41% 15/37 [00:39<00:57,  2.64s/it]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/yolo\", line 8, in <module>\n",
            "    sys.exit(entrypoint())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py\", line 972, in entrypoint\n",
            "    getattr(model, mode)(**overrides)  # default args from model\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py\", line 639, in val\n",
            "    validator(model=self.model)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/engine/validator.py\", line 180, in __call__\n",
            "    preds = model(batch[\"img\"], augment=augment)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/autobackend.py\", line 527, in forward\n",
            "    y = self.model(im, augment=augment, visualize=visualize, embed=embed)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py\", line 112, in forward\n",
            "    return self.predict(x, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py\", line 130, in predict\n",
            "    return self._predict_once(x, profile, visualize, embed)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py\", line 151, in _predict_once\n",
            "    x = m(x)  # run\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/upsampling.py\", line 172, in forward\n",
            "    return F.interpolate(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\", line 4536, in interpolate\n",
            "    return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)\n",
            "KeyboardInterrupt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Optional: Zip and download runs/train folder\n",
        "\n",
        "TODO: parameters voor folder path en file name\n",
        "\n"
      ],
      "metadata": {
        "id": "2mlnuN5uzWaq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "train_folder_path = \"/content/yolo11vespA/train1\"\n",
        "download_file_name = \"/content/train1.zip\"\n",
        "try:\n",
        "  # Zipping the folder\n",
        "  !zip -r {download_file_name} {train_folder_path}\n",
        "  # Downloading the zipped file\n",
        "  files.download(download_file_name)\n",
        "except Exception as e:\n",
        "  print(f\"An error occurred: {e}\")\n",
        "  print(\"Click 'Runtime' -> 'Restart session' and try running the code again.\")\n"
      ],
      "metadata": {
        "id": "EAK6jpLhzQLL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "60d1fa4b-25a3-40c7-a09a-9bb213d1870e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/yolo11vespA/train1/ (stored 0%)\n",
            "  adding: content/yolo11vespA/train1/train_batch1.jpg (deflated 1%)\n",
            "  adding: content/yolo11vespA/train1/weights/ (stored 0%)\n",
            "  adding: content/yolo11vespA/train1/events.out.tfevents.1733931970.8f797b4d64d2.2840.0 (deflated 94%)\n",
            "  adding: content/yolo11vespA/train1/args.yaml (deflated 53%)\n",
            "  adding: content/yolo11vespA/train1/train_batch0.jpg (deflated 3%)\n",
            "  adding: content/yolo11vespA/train1/labels.jpg (deflated 31%)\n",
            "  adding: content/yolo11vespA/train1/labels_correlogram.jpg (deflated 43%)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f90d5810-6872-44de-b645-19254c059943\", \"train1.zip\", 1354330)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Convert .pt file to ONNX via CLI\n",
        "\n",
        "TODO parameter locatie best\n",
        "now set path to gdrive for debugging"
      ],
      "metadata": {
        "id": "d33KrIaIHy2M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo export model=/content/gdrive/MyDrive/best.pt format=onnx opset=9 # export custom trained model"
      ],
      "metadata": {
        "id": "WnxbaG2NH3T-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49b2db97-838c-4ae2-be25-ba2f7056660c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.49 🚀 Python-3.10.12 torch-2.5.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLO11s summary (fused): 238 layers, 9,414,735 parameters, 0 gradients, 21.3 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/content/gdrive/MyDrive/best.pt' with input shape (1, 3, 320, 320) BCHW and output shape(s) (1, 9, 2100) (18.2 MB)\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirements ['onnx>=1.12.0', 'onnxslim', 'onnxruntime'] not found, attempting AutoUpdate...\n",
            "Collecting onnx>=1.12.0\n",
            "  Downloading onnx-1.17.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)\n",
            "Collecting onnxslim\n",
            "  Downloading onnxslim-0.1.43-py3-none-any.whl.metadata (4.2 kB)\n",
            "Collecting onnxruntime\n",
            "  Downloading onnxruntime-1.20.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (4.5 kB)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from onnx>=1.12.0) (1.26.4)\n",
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            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (24.3.25)\n",
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            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl.metadata (9.2 kB)\n",
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            "Installing collected packages: onnx, humanfriendly, onnxslim, coloredlogs, onnxruntime\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnx-1.17.0 onnxruntime-1.20.1 onnxslim-0.1.43\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 8.8s, installed 3 packages: ['onnx>=1.12.0', 'onnxslim', 'onnxruntime']\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.17.0 opset 9...\n",
            "\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.43...\n",
            "\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 11.9s, saved as '/content/gdrive/MyDrive/best.onnx' (36.0 MB)\n",
            "\n",
            "Export complete (13.1s)\n",
            "Results saved to \u001b[1m/content/gdrive/MyDrive\u001b[0m\n",
            "Predict:         yolo predict task=detect model=/content/gdrive/MyDrive/best.onnx imgsz=320  \n",
            "Validate:        yolo val task=detect model=/content/gdrive/MyDrive/best.onnx imgsz=320 data=/content/gdrive/MyDrive/vespA/data.yaml  \n",
            "Visualize:       https://netron.app\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/export\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OR\n",
        "\n",
        "convert .pt file to ONNX via python code\n",
        "\n",
        "TODO: parameters\n",
        "now checkpoint set to gdrive for debugging"
      ],
      "metadata": {
        "id": "27rMhIynIKOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Load our model into our environment\n",
        "checkpoint = torch.load('/content/gdrive/MyDrive/best.pt')\n",
        "model = checkpoint['model']\n",
        "\n",
        "model = model.float()\n",
        "model.eval()\n",
        "\n",
        "# Dummy input in FP32\n",
        "dummy_input = torch.randn(16, 3, 640, 640, dtype=torch.float)\n",
        "\n",
        "# Export to ONNX\n",
        "torch.onnx.export(\n",
        "    model,\n",
        "    dummy_input,\n",
        "    \"modified_run_3.onnx\",\n",
        "    export_params=True,\n",
        "    opset_version=9,  # Adjust opset version if needed, changed from 11 to 9\n",
        "    do_constant_folding=True,\n",
        "    input_names=['input'],\n",
        "    output_names=['output']\n",
        ")\n",
        "print(\"ONNX model exported successfully!\")"
      ],
      "metadata": {
        "id": "JcCiYhosIKDv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18ffb217-0df4-42f3-c4b6-65959a3388d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-8ea025138c3d>:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  checkpoint = torch.load('/content/gdrive/MyDrive/best.pt')\n",
            "/usr/local/lib/python3.10/dist-packages/ultralytics/nn/modules/head.py:105: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if self.format != \"imx\" and (self.dynamic or self.shape != shape):\n",
            "/usr/local/lib/python3.10/dist-packages/ultralytics/utils/tal.py:338: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).\n",
            "  for i, stride in enumerate(strides):\n",
            "/usr/local/lib/python3.10/dist-packages/torch/onnx/symbolic_helper.py:795: UserWarning: You are trying to export the model with onnx:Upsample for ONNX opset version 9. This operator might cause results to not match the expected results by PyTorch.\n",
            "ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. Attributes to determine how to transform the input were added in onnx:Resize in opset 11 to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n",
            "We recommend using opset 11 and above for models using this operator.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ONNX model exported successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Verify the\n",
        "\n",
        "TODO parameters\n",
        "\n",
        "onnx model\n",
        "input shape"
      ],
      "metadata": {
        "id": "nlgmtDFQIgpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import onnx\n",
        "import onnxruntime as ort\n",
        "import torch\n",
        "\n",
        "# Load the ONNX model\n",
        "onnx_model = onnx.load(\"/content/gdrive/MyDrive/best_opset9.onnx\")\n",
        "onnx.checker.check_model(onnx_model)\n",
        "print(\"ONNX model is valid!\")\n",
        "\n",
        "# Test the ONNX model with ONNX Runtime\n",
        "dummy_input = torch.randn(1, 3, 320, 320).numpy() # change to 640 again after debugging\n",
        "ort_session = ort.InferenceSession(\"/content/gdrive/MyDrive/best_opset9.onnx\")\n",
        "outputs = ort_session.run(None, {\"images\": dummy_input})\n",
        "print(outputs[0])"
      ],
      "metadata": {
        "id": "j8eatV0iIJey",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d9f0472-0677-4a40-b2d8-d5e6eb1faeea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ONNX model is valid!\n",
            "[[[     6.0062      10.285      21.618 ...      214.13      252.06       282.9]\n",
            "  [      6.665      5.3329      5.0171 ...      266.74      268.46      272.43]\n",
            "  [     12.015      18.834      21.744 ...      254.57      206.63      188.73]\n",
            "  ...\n",
            "  [  0.0016357   0.0014248  0.00082025 ...  0.00015348  0.00014409  0.00031254]\n",
            "  [  0.0002355  0.00030908  0.00029042 ...   0.0060495   0.0071386    0.007114]\n",
            "  [ 0.00030759  0.00025287  0.00017902 ...  0.00024694  0.00030869  0.00037241]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Hailo compilation\n",
        "\n",
        "The Hailo compilation process involves three main steps:\n",
        "\n",
        "### 1. Model Conversion:\n",
        "- input: yolo.onnx\n",
        "- output: .har\n",
        "\n",
        "The YOLO11 model, is parsed into a format that the Hailo device can understand. This involves converting the model's structure and operations into a Hailo-specific representation.\n",
        "\n",
        "### 2. Model Quantization:\n",
        "- input:\n",
        "- output:\n",
        "\n",
        "The model's precision is reduced from high-precision floating-point numbers to lower-precision integers (usually 8-bit). This significantly reduces the model's size and memory footprint, making it more efficient for the Hailo device.\n",
        "\n",
        "### 3. Model Compilation:\n",
        "\n",
        "- input:\n",
        "- output: .hef\n",
        "\n",
        "The quantized model is compiled into a specific binary format called HEF (Hailo Executable Format). This format is optimized for the Hailo device's architecture and allows for efficient execution of the model's operations.\n",
        "\n",
        "To make this work in Colab the DataFlowCompiler (DFC) should be downloaded from the site, copied to google drive and installed in a vitual  environment in colab"
      ],
      "metadata": {
        "id": "bqAGJvQUI1UQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Make virtual environment in Colab"
      ],
      "metadata": {
        "id": "jtouN6GbJjJd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get update\n",
        "!sudo apt-get install -y python3-dev python3-distutils python3-tk libfuse2 graphviz libgraphviz-dev\n",
        "\n",
        "# Will need a venv to install the DFC in\n",
        "!pip install --upgrade pip virtualenv\n",
        "!virtualenv my_env"
      ],
      "metadata": {
        "id": "XNtqRTnpJm2Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94946cb9-887e-4c95-bf33-16289c1f2810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libgdk-pixbuf-2.0-0:amd64 (2.42.8+dfsg-1ubuntu0.3) ...\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (24.1.2)\n",
            "Collecting pip\n",
            "  Downloading pip-24.3.1-py3-none-any.whl.metadata (3.7 kB)\n",
            "Collecting virtualenv\n",
            "  Downloading virtualenv-20.28.0-py3-none-any.whl.metadata (4.4 kB)\n",
            "Collecting distlib<1,>=0.3.7 (from virtualenv)\n",
            "  Downloading distlib-0.3.9-py2.py3-none-any.whl.metadata (5.2 kB)\n",
            "Requirement already satisfied: filelock<4,>=3.12.2 in /usr/local/lib/python3.10/dist-packages (from virtualenv) (3.16.1)\n",
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            "Downloading pip-24.3.1-py3-none-any.whl (1.8 MB)\n",
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            "\u001b[?25hDownloading virtualenv-20.28.0-py3-none-any.whl (4.3 MB)\n",
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            "\u001b[?25hDownloading distlib-0.3.9-py2.py3-none-any.whl (468 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: distlib, virtualenv, pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 24.1.2\n",
            "    Uninstalling pip-24.1.2:\n",
            "      Successfully uninstalled pip-24.1.2\n",
            "Successfully installed distlib-0.3.9 pip-24.3.1 virtualenv-20.28.0\n",
            "created virtual environment CPython3.10.12.final.0-64 in 975ms\n",
            "  creator CPython3Posix(dest=/content/my_env, clear=False, no_vcs_ignore=False, global=False)\n",
            "  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv)\n",
            "    added seed packages: pip==24.3.1, setuptools==75.6.0, wheel==0.45.1\n",
            "  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Download hailo dataflow compiler (python 3.10) from https://hailo.ai/developer-zone/software-downloads/ (you need to make an account)\n",
        "\n",
        "3. Then copy the .whl to google drive\n"
      ],
      "metadata": {
        "id": "LwsOA_V3I4zW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Installing the WHL file for Hailo DFC\n",
        "!my_env/bin/pip install /content/gdrive/MyDrive/hailo_dataflow_compiler-3.29.0-py3-none-linux_x86_64.whl\n",
        "\n",
        "# Making sure it's installed properly\n",
        "!my_env/bin/hailo --version\n",
        "!my_env/bin/hailo -h"
      ],
      "metadata": {
        "id": "p71hAN7HJf7Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb160b26-d308-490f-a03d-64d3045e44eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing ./gdrive/MyDrive/hailo_dataflow_compiler-3.29.0-py3-none-linux_x86_64.whl\n",
            "Collecting absl-py (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting annotated-types==0.4.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading annotated_types-0.4.0-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting argcomplete (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading argcomplete-3.5.2-py3-none-any.whl.metadata (16 kB)\n",
            "Collecting contextlib2 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading contextlib2-21.6.0-py2.py3-none-any.whl.metadata (4.1 kB)\n",
            "Collecting future (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading future-1.0.0-py3-none-any.whl.metadata (4.0 kB)\n",
            "Collecting jsonref (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jsonref-1.1.0-py3-none-any.whl.metadata (2.7 kB)\n",
            "Collecting jsonschema (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jsonschema-4.23.0-py3-none-any.whl.metadata (7.9 kB)\n",
            "Collecting matplotlib (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading matplotlib-3.9.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Collecting networkx==2.8.8 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB)\n",
            "Collecting jax==0.4.13 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jax-0.4.13.tar.gz (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m45.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting jaxlib==0.4.13 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jaxlib-0.4.13-cp310-cp310-manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting packaging (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading packaging-24.2-py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting pandas (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n",
            "Collecting Pillow (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pillow-11.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.1 kB)\n",
            "Collecting prompt-toolkit (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading prompt_toolkit-3.0.48-py3-none-any.whl.metadata (6.4 kB)\n",
            "Collecting pwlf (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pwlf-2.3.0.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting py (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading py-1.11.0-py2.py3-none-any.whl.metadata (2.8 kB)\n",
            "Collecting pydantic==2.0.2 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pydantic-2.0.2-py3-none-any.whl.metadata (119 kB)\n",
            "Collecting pydantic-core==2.1.2 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pydantic_core-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.1 kB)\n",
            "Collecting pygraphviz (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pygraphviz-1.14.tar.gz (106 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting PyYAML (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting scipy (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading scipy-1.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
            "Collecting setuptools==68.0.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading setuptools-68.0.0-py3-none-any.whl.metadata (6.4 kB)\n",
            "Collecting tabulate (from hailo-dataflow-compiler==3.29.0)\n",
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            "Collecting testresources (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading testresources-2.0.1-py2.py3-none-any.whl.metadata (11 kB)\n",
            "Collecting h5py (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading h5py-3.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\n",
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            "  Downloading disjoint_set-0.8.0-py3-none-any.whl.metadata (2.5 kB)\n",
            "Collecting importlib-metadata (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading importlib_metadata-8.5.0-py3-none-any.whl.metadata (4.8 kB)\n",
            "Collecting grpcio (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading grpcio-1.68.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)\n",
            "Collecting six (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)\n",
            "Collecting typing-extensions==4.12.2 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: wheel in ./my_env/lib/python3.10/site-packages (from hailo-dataflow-compiler==3.29.0) (0.45.1)\n",
            "Collecting onnx-tf (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading onnx_tf-1.10.0-py3-none-any.whl.metadata (510 bytes)\n",
            "Collecting pyparsing==2.4.7 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl.metadata (3.6 kB)\n",
            "Collecting tqdm (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n",
            "Collecting py-cpuinfo (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading py_cpuinfo-9.0.0-py3-none-any.whl.metadata (794 bytes)\n",
            "Collecting msgpack (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading msgpack-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.4 kB)\n",
            "Collecting prettytable==3.5.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading prettytable-3.5.0-py3-none-any.whl.metadata (24 kB)\n",
            "Collecting tensorflow-probability==0.20.1 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tensorflow_probability-0.20.1-py2.py3-none-any.whl.metadata (13 kB)\n",
            "Collecting tensorflow==2.12.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tensorflow-2.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
            "Collecting keras==2.12.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading keras-2.12.0-py2.py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting numpy==1.23.3 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.3 kB)\n",
            "Collecting flatbuffers==23.5.26 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)\n",
            "Collecting protobuf==3.20.3 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading protobuf-3.20.3-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (679 bytes)\n",
            "Collecting onnx==1.14.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading onnx-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (15 kB)\n",
            "Collecting onnxsim==0.4.36 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading onnxsim-0.4.36-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)\n",
            "Collecting onnxruntime==1.12.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading onnxruntime-1.12.0-cp310-cp310-manylinux_2_27_x86_64.whl.metadata (3.6 kB)\n",
            "Collecting tflite==2.10.0 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tflite-2.10.0-py2.py3-none-any.whl.metadata (5.6 kB)\n",
            "Collecting tensorboard==2.12.3 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tensorboard-2.12.3-py3-none-any.whl.metadata (1.8 kB)\n",
            "Collecting gast<=0.4.0,>=0.3.2 (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading gast-0.4.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Collecting ipython (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading ipython-8.30.0-py3-none-any.whl.metadata (4.9 kB)\n",
            "Collecting jedi (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jedi-0.19.2-py2.py3-none-any.whl.metadata (22 kB)\n",
            "Collecting jupyter (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading jupyter-1.1.1-py2.py3-none-any.whl.metadata (2.0 kB)\n",
            "Collecting parso (from hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading parso-0.8.4-py2.py3-none-any.whl.metadata (7.7 kB)\n",
            "Collecting ml_dtypes>=0.1.0 (from jax==0.4.13->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading ml_dtypes-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n",
            "Collecting opt_einsum (from jax==0.4.13->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading opt_einsum-3.4.0-py3-none-any.whl.metadata (6.3 kB)\n",
            "Collecting coloredlogs (from onnxruntime==1.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl.metadata (12 kB)\n",
            "Collecting sympy (from onnxruntime==1.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading sympy-1.13.3-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting rich (from onnxsim==0.4.36->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading rich-13.9.4-py3-none-any.whl.metadata (18 kB)\n",
            "Collecting wcwidth (from prettytable==3.5.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading wcwidth-0.2.13-py2.py3-none-any.whl.metadata (14 kB)\n",
            "Collecting google-auth<3,>=1.6.3 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading google_auth-2.36.0-py2.py3-none-any.whl.metadata (4.7 kB)\n",
            "Collecting google-auth-oauthlib<1.1,>=0.5 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl.metadata (2.7 kB)\n",
            "Collecting markdown>=2.6.8 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading Markdown-3.7-py3-none-any.whl.metadata (7.0 kB)\n",
            "Collecting requests<3,>=2.21.0 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)\n",
            "Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl.metadata (1.1 kB)\n",
            "Collecting werkzeug>=1.0.1 (from tensorboard==2.12.3->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading werkzeug-3.1.3-py3-none-any.whl.metadata (3.7 kB)\n",
            "Collecting astunparse>=1.6.0 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading astunparse-1.6.3-py2.py3-none-any.whl.metadata (4.4 kB)\n",
            "Collecting google-pasta>=0.1.1 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading google_pasta-0.2.0-py3-none-any.whl.metadata (814 bytes)\n",
            "Collecting libclang>=13.0.0 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading libclang-18.1.1-py2.py3-none-manylinux2010_x86_64.whl.metadata (5.2 kB)\n",
            "Collecting tensorflow-estimator<2.13,>=2.12.0 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading tensorflow_estimator-2.12.0-py2.py3-none-any.whl.metadata (1.3 kB)\n",
            "Collecting termcolor>=1.1.0 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading termcolor-2.5.0-py3-none-any.whl.metadata (6.1 kB)\n",
            "Collecting wrapt<1.15,>=1.11.0 (from tensorflow==2.12.0->hailo-dataflow-compiler==3.29.0)\n",
            "  Downloading wrapt-1.14.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
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            "  Created wheel for pyDOE: filename=pyDOE-0.3.8-py3-none-any.whl size=18168 sha256=0a46a6d86176a088b6e9d5f35c691ec08b077eb9f8538091118b675d4e381d37\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/b6/d7/c6b64746dba6433c593e471e0ac3acf4f36040456d1d160d17\n",
            "Successfully built jax pwlf pygraphviz pyDOE\n",
            "Installing collected packages: webencodings, wcwidth, verboselogs, pytz, py-cpuinfo, pure-eval, ptyprocess, mpmath, libclang, flatbuffers, fastjsonschema, dm-tree, disjoint-set, zipp, wrapt, widgetsnbextension, websocket-client, webcolors, urllib3, uri-template, tzdata, typing-extensions, types-python-dateutil, typeguard, traitlets, tqdm, tornado, tomli, tinycss2, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, tabulate, sympy, soupsieve, sniffio, six, setuptools, send2trash, rpds-py, rfc3986-validator, pyzmq, PyYAML, python-json-logger, pyparsing, pygraphviz, pygments, pycparser, pyasn1, py, psutil, protobuf, prompt-toolkit, prometheus-client, prettytable, platformdirs, Pillow, pexpect, pbr, parso, pandocfilters, packaging, overrides, opt_einsum, oauthlib, numpy, networkx, nest-asyncio, msgpack, mistune, mdurl, MarkupSafe, markdown, kiwisolver, keras, jupyterlab-widgets, jupyterlab-pygments, jsonref, jsonpointer, json5, idna, humanfriendly, h11, grpcio, gast, future, fqdn, fonttools, executing, exceptiongroup, defusedxml, decorator, debugpy, cycler, contextlib2, cloudpickle, charset-normalizer, certifi, cachetools, bleach, babel, attrs, asttokens, argcomplete, annotated-types, absl-py, werkzeug, tflite, testresources, terminado, tensorflow-probability, tensorflow-addons, stack_data, scipy, rsa, rfc3339-validator, requests, referencing, python-dateutil, pydantic-core, pyasn1-modules, onnx, ml_dtypes, matplotlib-inline, markdown-it-py, jupyter-core, jinja2, jedi, importlib-metadata, httpcore, h5py, google-pasta, contourpy, comm, coloredlogs, cffi, beautifulsoup4, async-lru, astunparse, anyio, rich, requests-oauthlib, pyDOE, pydantic, pandas, onnxruntime, onnx-tf, matplotlib, jupyter-server-terminals, jupyter-client, jsonschema-specifications, jaxlib, jax, ipython, httpx, google-auth, arrow, argon2-cffi-bindings, pwlf, onnxsim, jsonschema, isoduration, ipywidgets, ipykernel, google-auth-oauthlib, argon2-cffi, tensorboard, nbformat, jupyter-console, tensorflow, nbclient, jupyter-events, nbconvert, jupyter-server, notebook-shim, jupyterlab-server, jupyter-lsp, jupyterlab, notebook, jupyter, hailo-dataflow-compiler\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 75.6.0\n",
            "    Uninstalling setuptools-75.6.0:\n",
            "      Successfully uninstalled setuptools-75.6.0\n",
            "Successfully installed MarkupSafe-3.0.2 Pillow-11.0.0 PyYAML-6.0.2 absl-py-2.1.0 annotated-types-0.4.0 anyio-4.7.0 argcomplete-3.5.2 argon2-cffi-23.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 astunparse-1.6.3 async-lru-2.0.4 attrs-24.2.0 babel-2.16.0 beautifulsoup4-4.12.3 bleach-6.2.0 cachetools-5.5.0 certifi-2024.8.30 cffi-1.17.1 charset-normalizer-3.4.0 cloudpickle-3.1.0 coloredlogs-15.0.1 comm-0.2.2 contextlib2-21.6.0 contourpy-1.3.1 cycler-0.12.1 debugpy-1.8.9 decorator-5.1.1 defusedxml-0.7.1 disjoint-set-0.8.0 dm-tree-0.1.8 exceptiongroup-1.2.2 executing-2.1.0 fastjsonschema-2.21.1 flatbuffers-23.5.26 fonttools-4.55.3 fqdn-1.5.1 future-1.0.0 gast-0.4.0 google-auth-2.36.0 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.68.1 h11-0.14.0 h5py-3.12.1 hailo-dataflow-compiler-3.29.0 httpcore-1.0.7 httpx-0.28.1 humanfriendly-10.0 idna-3.10 importlib-metadata-8.5.0 ipykernel-6.29.5 ipython-8.30.0 ipywidgets-8.1.5 isoduration-20.11.0 jax-0.4.13 jaxlib-0.4.13 jedi-0.19.2 jinja2-3.1.4 json5-0.10.0 jsonpointer-3.0.0 jsonref-1.1.0 jsonschema-4.23.0 jsonschema-specifications-2024.10.1 jupyter-1.1.1 jupyter-client-8.6.3 jupyter-console-6.6.3 jupyter-core-5.7.2 jupyter-events-0.10.0 jupyter-lsp-2.2.5 jupyter-server-2.14.2 jupyter-server-terminals-0.5.3 jupyterlab-4.3.3 jupyterlab-pygments-0.3.0 jupyterlab-server-2.27.3 jupyterlab-widgets-3.0.13 keras-2.12.0 kiwisolver-1.4.7 libclang-18.1.1 markdown-3.7 markdown-it-py-3.0.0 matplotlib-3.9.3 matplotlib-inline-0.1.7 mdurl-0.1.2 mistune-3.0.2 ml_dtypes-0.5.0 mpmath-1.3.0 msgpack-1.1.0 nbclient-0.10.1 nbconvert-7.16.4 nbformat-5.10.4 nest-asyncio-1.6.0 networkx-2.8.8 notebook-7.3.1 notebook-shim-0.2.4 numpy-1.23.3 oauthlib-3.2.2 onnx-1.14.0 onnx-tf-1.10.0 onnxruntime-1.12.0 onnxsim-0.4.36 opt_einsum-3.4.0 overrides-7.7.0 packaging-24.2 pandas-2.2.3 pandocfilters-1.5.1 parso-0.8.4 pbr-6.1.0 pexpect-4.9.0 platformdirs-4.3.6 prettytable-3.5.0 prometheus-client-0.21.1 prompt-toolkit-3.0.48 protobuf-3.20.3 psutil-6.1.0 ptyprocess-0.7.0 pure-eval-0.2.3 pwlf-2.3.0 py-1.11.0 py-cpuinfo-9.0.0 pyDOE-0.3.8 pyasn1-0.6.1 pyasn1-modules-0.4.1 pycparser-2.22 pydantic-2.0.2 pydantic-core-2.1.2 pygments-2.18.0 pygraphviz-1.14 pyparsing-2.4.7 python-dateutil-2.9.0.post0 python-json-logger-2.0.7 pytz-2024.2 pyzmq-26.2.0 referencing-0.35.1 requests-2.32.3 requests-oauthlib-2.0.0 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rich-13.9.4 rpds-py-0.22.3 rsa-4.9 scipy-1.13.1 send2trash-1.8.3 setuptools-68.0.0 six-1.17.0 sniffio-1.3.1 soupsieve-2.6 stack_data-0.6.3 sympy-1.13.3 tabulate-0.9.0 tensorboard-2.12.3 tensorboard-data-server-0.7.2 tensorflow-2.12.0 tensorflow-addons-0.23.0 tensorflow-estimator-2.12.0 tensorflow-io-gcs-filesystem-0.37.1 tensorflow-probability-0.20.1 termcolor-2.5.0 terminado-0.18.1 testresources-2.0.1 tflite-2.10.0 tinycss2-1.4.0 tomli-2.2.1 tornado-6.4.2 tqdm-4.67.1 traitlets-5.14.3 typeguard-2.13.3 types-python-dateutil-2.9.0.20241206 typing-extensions-4.12.2 tzdata-2024.2 uri-template-1.3.0 urllib3-2.2.3 verboselogs-1.7 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0 werkzeug-3.1.3 widgetsnbextension-4.0.13 wrapt-1.14.1 zipp-3.21.0\n",
            "[\u001b[32minfo\u001b[0m] Current Time: 15:55:23, 12/11/24\n",
            "[\u001b[32minfo\u001b[0m] CPU: Architecture: x86_64, Model: Intel(R) Xeon(R) CPU @ 2.20GHz, Number Of Cores: 2, Utilization: 63.9%\n",
            "[\u001b[32minfo\u001b[0m] Memory: Total: 12GB, Available: 10GB\n",
            "[\u001b[32minfo\u001b[0m] System info: OS: Linux, Kernel: 6.1.85+\n",
            "[\u001b[32minfo\u001b[0m] Hailo DFC Version: 3.29.0\n",
            "[\u001b[32minfo\u001b[0m] HailoRT Version: Not Installed\n",
            "[\u001b[32minfo\u001b[0m] PCIe: No Hailo PCIe device was found\n",
            "[\u001b[32minfo\u001b[0m] Running `hailo --version`\n",
            "Hailo Dataflow Compiler v3.29.0\n",
            "[\u001b[32minfo\u001b[0m] Current Time: 15:55:32, 12/11/24\n",
            "[\u001b[32minfo\u001b[0m] CPU: Architecture: x86_64, Model: Intel(R) Xeon(R) CPU @ 2.20GHz, Number Of Cores: 2, Utilization: 2.5%\n",
            "[\u001b[32minfo\u001b[0m] Memory: Total: 12GB, Available: 10GB\n",
            "[\u001b[32minfo\u001b[0m] System info: OS: Linux, Kernel: 6.1.85+\n",
            "[\u001b[32minfo\u001b[0m] Hailo DFC Version: 3.29.0\n",
            "[\u001b[32minfo\u001b[0m] HailoRT Version: Not Installed\n",
            "[\u001b[32minfo\u001b[0m] PCIe: No Hailo PCIe device was found\n",
            "[\u001b[32minfo\u001b[0m] Running `hailo -h`\n",
            "usage: hailo [-h] [--version]\n",
            "             {analyze-noise,compiler,params-csv,parser,profiler,optimize,tb,visualizer,tutorial,har,join,har-onnx-rt,runtime-profiler,dfc-studio,help}\n",
            "             ...\n",
            "\n",
            "Hailo Command Line Utility\n",
            "\n",
            "positional arguments:\n",
            "  {analyze-noise,compiler,params-csv,parser,profiler,optimize,tb,visualizer,tutorial,har,join,har-onnx-rt,runtime-profiler,dfc-studio,help}\n",
            "                        Hailo utilities aimed to help with everything you need\n",
            "    analyze-noise       Analyze network quantization noise\n",
            "    compiler            Compile Hailo model to HEF binary files\n",
            "    params-csv          Convert translated params to csv\n",
            "    parser              Translate network to Hailo network\n",
            "    profiler            Hailo models Profiler\n",
            "    optimize            Optimize model\n",
            "    tb                  Create Tensorboard summary for Tensorflow model\n",
            "    visualizer          HAR visualization tool\n",
            "    tutorial            Runs the tutorials in jupyter notebook\n",
            "    har                 Query and extract information from Hailo Archive file\n",
            "    join                Join two Hailo models to a single model\n",
            "    har-onnx-rt         Generates ONNX-Runtime model including pre/post processing\n",
            "    runtime-profiler    Hailo Runtime Profiler\n",
            "    dfc-studio          Start DFC Studio\n",
            "    help                Show the list of commands\n",
            "\n",
            "options:\n",
            "  -h, --help            show this help message and exit\n",
            "  --version             show program's version number and exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1.  Model conversion onnx --> har\n",
        "\n",
        "HAR format = Hailo Archive\n",
        "a tar.gz archive file that contains the representation of the graph structure and the weights. These are deployed to Hailo's runtime.\n",
        "\n",
        "\n",
        "HAR compressed file, which includes HN and NPZ files). The HN model is a textual JSON output file. The weights are also returned as a NumPy NPZ file.\n",
        "\n",
        "\n",
        "chose_hw_arch =\"hailo8\" for the raspberry AI-kit\n",
        "\n",
        "Identifying the six end node names via www.netron.app\n",
        "\n",
        "To identify the Yolo’s end nodes, they are the nodes right before the post-processing operations at the very bottom of the model. Their are 2 end nodes per map. I used a search for `onnx::Reshape` to get to the two `conv` layers that pointed to the `onnx::Reshape`\n",
        "\n",
        "In yolov8 till yolo11 model this are the endpoints\n",
        "```\n",
        "\"/model.23/cv2.2/cv2.2.2/Conv\",\n",
        "\"/model.23/cv3.2/cv3.2.2/Conv\",\n",
        "\"/model.23/cv2.1/cv2.1.2/Conv\",\n",
        "\"/model.23/cv3.1/cv3.1.2/Conv\",\n",
        "\"/model.23/cv2.0/cv2.0.2/Conv\",\n",
        "\"/model.23/cv3.0/cv3.0.2/Conv\",\n",
        "```\n"
      ],
      "metadata": {
        "id": "euWouzZiKcEm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "d9iWYW7QLWh_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"translate_model.py\", \"w\") as f:\n",
        "    f.write(\"\"\"\n",
        "\n",
        "from hailo_sdk_client import ClientRunner\n",
        "\n",
        "print(\"Starting model translation...\")\n",
        "\n",
        "# Define the ONNX model path and configuration\n",
        "chosen_hw_arch = \"hailo8l\" # Replace with your hardware architecture\n",
        "onnx_model_name = \"best_opset9\"\n",
        "onnx_path = \"/content/gdrive/MyDrive/best_opset9.onnx\"  # Replace with your ONNX model path\n",
        "\n",
        "\n",
        "# Initialize the ClientRunner\n",
        "runner = ClientRunner(hw_arch=chosen_hw_arch)\n",
        "\n",
        "# Use the recommended end node names for translation\n",
        "end_node_names = [\n",
        "  \"/model.23/cv2.0/cv2.0.2/Conv\",\n",
        "  \"/model.23/cv3.0/cv3.0.2/Conv\",\n",
        "  \"/model.23/cv2.1/cv2.1.2/Conv\",\n",
        "  \"/model.23/cv3.1/cv3.1.2/Conv\",\n",
        "  \"/model.23/cv2.2/cv2.2.2/Conv\",\n",
        "  \"/model.23/cv3.2/cv3.2.2/Conv\",\n",
        "]\n",
        "\n",
        "try:\n",
        "    # Translate the ONNX model to Hailo's format\n",
        "    hn, npz = runner.translate_onnx_model(\n",
        "        onnx_path,\n",
        "        onnx_model_name,\n",
        "        end_node_names=end_node_names,\n",
        "        net_input_shapes={\"images\": [1, 3, 320, 320]},  # Adjust input shapes if needed\n",
        "    )\n",
        "    print(\"Model translation successful.\")\n",
        "except Exception as e:\n",
        "    print(f\"Error during model translation: {e}\")\n",
        "    raise\n",
        "\n",
        "# Save the Hailo model HAR file\n",
        "hailo_model_har_name = f\"{onnx_model_name}_hailo_model.har\"\n",
        "try:\n",
        "    runner.save_har(hailo_model_har_name)\n",
        "    print(f\"HAR file saved as: {hailo_model_har_name}\")\n",
        "except Exception as e:\n",
        "    print(f\"Error saving HAR file: {e}\")\n",
        "\n",
        "\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "8_VrxC1pIsv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run model in CLI\n",
        "!my_env/bin/python translate_model.py"
      ],
      "metadata": {
        "id": "gidGCc-wLf1K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a918a33b-4fab-43a5-c48c-30dfbf85a73a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting model translation...\n",
            "[\u001b[32minfo\u001b[0m] Translation started on ONNX model best_opset9\n",
            "[\u001b[32minfo\u001b[0m] Restored ONNX model best_opset9 (completion time: 00:00:00.35)\n",
            "[\u001b[32minfo\u001b[0m] Extracted ONNXRuntime meta-data for Hailo model (completion time: 00:00:01.12)\n",
            "[\u001b[32minfo\u001b[0m] NMS structure of yolov8 (or equivalent architecture) was detected.\n",
            "[\u001b[32minfo\u001b[0m] In order to use HailoRT post-processing capabilities, these end node names should be used: /model.23/cv3.0/cv3.0.2/Conv /model.23/cv2.0/cv2.0.2/Conv /model.23/cv3.1/cv3.1.2/Conv /model.23/cv2.1/cv2.1.2/Conv /model.23/cv2.2/cv2.2.2/Conv /model.23/cv3.2/cv3.2.2/Conv.\n",
            "[\u001b[32minfo\u001b[0m] Start nodes mapped from original model: 'images': 'best_opset9/input_layer1'.\n",
            "[\u001b[32minfo\u001b[0m] End nodes mapped from original model: '/model.23/cv2.0/cv2.0.2/Conv', '/model.23/cv3.0/cv3.0.2/Conv', '/model.23/cv2.1/cv2.1.2/Conv', '/model.23/cv3.1/cv3.1.2/Conv', '/model.23/cv2.2/cv2.2.2/Conv', '/model.23/cv3.2/cv3.2.2/Conv'.\n",
            "[\u001b[32minfo\u001b[0m] Translation completed on ONNX model best_opset9 (completion time: 00:00:02.71)\n",
            "Model translation successful.\n",
            "[\u001b[32minfo\u001b[0m] Saved HAR to: /content/best_opset9_hailo_model.har\n",
            "HAR file saved as: best_opset9_hailo_model.har\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Optimize model\n",
        "\n",
        "- input: HAR file in Hailo Model state (before optimization; with native weights)\n",
        "- output: quantized HAR file with quantized weights"
      ],
      "metadata": {
        "id": "CR5d4kdrLlJz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"har_model.py\", \"w\") as f:\n",
        "\n",
        "    f.write(\"\"\"\n",
        "\n",
        "from hailo_sdk_client import ClientRunner\n",
        "\n",
        "# Load the HAR file\n",
        "har_path = \"/content/best_opset9_hailo_model.har\"\n",
        "\n",
        "runner = ClientRunner(har=har_path)\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "try:\n",
        "    # Access the HailoNet as an OrderedDict\n",
        "    hn_dict = runner.get_hn()  # Or use runner._hn if get_hn() is unavailable\n",
        "    print(\"Inspecting layers from HailoNet (OrderedDict):\")\n",
        "\n",
        "    # Pretty-print each layer\n",
        "    for key, value in hn_dict.items():\n",
        "        print(f\"Key: {key}\")\n",
        "        pprint(value)\n",
        "        print(\"\\\\n\" + \"=\"*80 + \"\\\\n\", flush=True)\n",
        "\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"Error while inspecting hn_dict: {e}\")\n",
        "\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "Ruz-fk86L0Fu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run model in CLI\n",
        "!my_env/bin/python har_model.py\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLUOA_thaAUX",
        "outputId": "00ca08bb-c8d9-4d83-f7d8-4a94ea5f0311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inspecting layers from HailoNet (OrderedDict):\n",
            "Key: name\n",
            "'best_opset9'\n",
            "\n",
            "================================================================================\n",
            "\n",
            "Key: net_params\n",
            "OrderedDict([('version', '1.0'),\n",
            "             ('stage', 'HN'),\n",
            "             ('clusters_placement', [[]]),\n",
            "             ('clusters_to_skip', []),\n",
            "             ('output_layers_order',\n",
            "              ['best_opset9/conv51',\n",
            "               'best_opset9/conv54',\n",
            "               'best_opset9/conv62',\n",
            "               'best_opset9/conv65',\n",
            "               'best_opset9/conv77',\n",
            "               'best_opset9/conv80']),\n",
            "             ('transposed_net', False),\n",
            "             ('net_scopes', ['best_opset9'])])\n",
            "\n",
            "================================================================================\n",
            "\n",
            "Key: layers\n",
            "OrderedDict([('best_opset9/input_layer1',\n",
            "              OrderedDict([('type', 'input_layer'),\n",
            "                           ('input', []),\n",
            "                           ('output', ['best_opset9/conv1']),\n",
            "                           ('input_shapes', [[-1, 320, 320, 3]]),\n",
            "                           ('output_shapes', [[-1, 320, 320, 3]]),\n",
            "                           ('original_names', ['images']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('transposed', False),\n",
            "                           ('engine', 'nn_core'),\n",
            "                           ('io_type', 'standard')])),\n",
            "             ('best_opset9/conv1',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/input_layer1']),\n",
            "                           ('output', ['best_opset9/conv2']),\n",
            "                           ('input_shapes', [[-1, 320, 320, 3]]),\n",
            "                           ('output_shapes', [[-1, 160, 160, 32]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.0/conv/Conv',\n",
            "                             '/model.0/act/Sigmoid',\n",
            "                             '/model.0/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 3, 32]),\n",
            "                                         ('strides', [1, 2, 2, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('layer_disparity', 1),\n",
            "                                         ('input_disparity', 1),\n",
            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/conv2',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/conv1']),\n",
            "                           ('output', ['best_opset9/conv3']),\n",
            "                           ('input_shapes', [[-1, 160, 160, 32]]),\n",
            "                           ('output_shapes', [[-1, 80, 80, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.1/conv/Conv',\n",
            "                             '/model.1/act/Sigmoid',\n",
            "                             '/model.1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 32, 64]),\n",
            "                                         ('strides', [1, 2, 2, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/conv3',\n",
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            "                           ('input', ['best_opset9/conv2']),\n",
            "                           ('output', ['best_opset9/feature_splitter1']),\n",
            "                           ('input_shapes', [[-1, 80, 80, 64]]),\n",
            "                           ('output_shapes', [[-1, 80, 80, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.2/cv1/conv/Conv',\n",
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            "                             '/model.2/cv1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 64, 64]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "                                         ('batch_norm', False),\n",
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            "             ('best_opset9/feature_splitter1',\n",
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            "                           ('output',\n",
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            "                           ('output_shapes',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params', {'groups': 1})])),\n",
            "             ('best_opset9/shortcut1',\n",
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            "                           ('input', ['best_opset9/feature_splitter1']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/conv4',\n",
            "                             'best_opset9/ew_add1',\n",
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            "             ('best_opset9/conv4',\n",
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            "                           ('output', ['best_opset9/conv5']),\n",
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            "                           ('original_names',\n",
            "                            ['/model.2/m.0/cv1/conv/Conv',\n",
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            "                             '/model.2/m.0/cv1/act/Mul']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 32, 16]),\n",
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            "             ('best_opset9/conv5',\n",
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            "                           ('output', ['best_opset9/ew_add1']),\n",
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            "                           ('output_shapes', [[-1, 80, 80, 32]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.2/m.0/cv2/conv/Conv',\n",
            "                             '/model.2/m.0/cv2/act/Sigmoid',\n",
            "                             '/model.2/m.0/cv2/act/Mul']),\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 16, 32]),\n",
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            "             ('best_opset9/ew_add1',\n",
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            "                            ['best_opset9/shortcut1', 'best_opset9/conv5']),\n",
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            "             ('best_opset9/concat1',\n",
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            "                           ('input',\n",
            "                            ['best_opset9/feature_splitter1',\n",
            "                             'best_opset9/shortcut1',\n",
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            "                           ('input_shapes',\n",
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            "                           ('output_shapes', [[-1, 80, 80, 96]]),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('concat_axis', 'features')]))])),\n",
            "             ('best_opset9/conv6',\n",
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            "                           ('output', ['best_opset9/conv7']),\n",
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            "                           ('original_names',\n",
            "                            ['/model.2/cv2/conv/Conv',\n",
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            "                           ('params',\n",
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            "             ('best_opset9/conv7',\n",
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            "                           ('output', ['best_opset9/conv8']),\n",
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            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.3/conv/Conv',\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 128, 128]),\n",
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            "             ('best_opset9/conv8',\n",
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            "                           ('output', ['best_opset9/feature_splitter2']),\n",
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            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.4/cv1/conv/Conv',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 128, 128]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/feature_splitter2',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/concat2', 'best_opset9/shortcut2']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes',\n",
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            "                           ('original_names', ['/model.4/Split']),\n",
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            "                           ('quantization_params', {}),\n",
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            "             ('best_opset9/shortcut2',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/conv9',\n",
            "                             'best_opset9/ew_add2',\n",
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            "                           ('input_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('output_shapes',\n",
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            "             ('best_opset9/conv9',\n",
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            "                           ('output', ['best_opset9/conv10']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 32]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.4/m.0/cv1/conv/Conv',\n",
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            "                           ('params',\n",
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            "             ('best_opset9/conv10',\n",
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            "                           ('output', ['best_opset9/ew_add2']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 32]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.4/m.0/cv2/conv/Conv',\n",
            "                             '/model.4/m.0/cv2/act/Sigmoid',\n",
            "                             '/model.4/m.0/cv2/act/Mul']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 32, 64]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME_TENSORFLOW'),\n",
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            "             ('best_opset9/ew_add2',\n",
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            "                            ['best_opset9/shortcut2', 'best_opset9/conv10']),\n",
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            "                           ('input_shapes',\n",
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            "                           ('output_shapes', [[-1, 40, 40, 64]]),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/concat2',\n",
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            "                           ('input',\n",
            "                            ['best_opset9/feature_splitter2',\n",
            "                             'best_opset9/shortcut2',\n",
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            "                           ('input_shapes',\n",
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            "                             [-1, 40, 40, 64]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 192]]),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv11',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/concat2']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/conv12', 'best_opset9/concat11']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 192]]),\n",
            "                           ('output_shapes',\n",
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            "                           ('original_names',\n",
            "                            ['/model.4/cv2/conv/Conv',\n",
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            "                            OrderedDict([('kernel_shape', [1, 1, 192, 256]),\n",
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            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv12',\n",
            "              OrderedDict([('type', 'conv'),\n",
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            "                           ('output', ['best_opset9/conv13']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 256]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 256, 256]),\n",
            "                                         ('strides', [1, 2, 2, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv13',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/conv12']),\n",
            "                           ('output', ['best_opset9/feature_splitter3']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.6/cv1/conv/Conv',\n",
            "                             '/model.6/cv1/act/Sigmoid',\n",
            "                             '/model.6/cv1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 256, 256]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'VALID'),\n",
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            "             ('best_opset9/feature_splitter3',\n",
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            "                           ('output',\n",
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            "                           ('output_shapes',\n",
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            "                           ('params', {'groups': 1})])),\n",
            "             ('best_opset9/shortcut3',\n",
            "              OrderedDict([('type', 'shortcut'),\n",
            "                           ('input', ['best_opset9/feature_splitter3']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/conv14',\n",
            "                             'best_opset9/conv15',\n",
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            "                           ('input_shapes', [[-1, 20, 20, 128]]),\n",
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            "                             [-1, 20, 20, 128]]),\n",
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            "             ('best_opset9/conv14',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/shortcut3']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/conv16', 'best_opset9/ew_add3']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('output_shapes',\n",
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            "                           ('original_names',\n",
            "                            ['/model.6/m.0/cv1/conv/Conv',\n",
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            "                            ['/model.6/m.0/cv2/conv/Conv',\n",
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            "             ('best_opset9/conv22',\n",
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            "                           ('original_names',\n",
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            "             ('best_opset9/conv23',\n",
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            "                           ('output_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('original_names',\n",
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            "                            OrderedDict([('kernel_shape', [1, 1, 512, 512]),\n",
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            "             ('best_opset9/feature_splitter4',\n",
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            "                           ('output',\n",
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            "             ('best_opset9/shortcut4',\n",
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            "             ('best_opset9/conv24',\n",
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            "                            ['/model.8/m.0/cv1/conv/Conv',\n",
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            "             ('best_opset9/conv25',\n",
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            "             ('best_opset9/conv26',\n",
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            "                           ('original_names',\n",
            "                            ['/model.8/m.0/m/m.0/cv1/conv/Conv',\n",
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            "             ('best_opset9/conv27',\n",
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            "             ('best_opset9/ew_add5',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv28',\n",
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            "                           ('input', ['best_opset9/ew_add5']),\n",
            "                           ('output', ['best_opset9/conv29']),\n",
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            "                           ('output_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.8/m.0/m/m.1/cv1/conv/Conv',\n",
            "                             '/model.8/m.0/m/m.1/cv1/act/Sigmoid',\n",
            "                             '/model.8/m.0/m/m.1/cv1/act/Mul']),\n",
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            "                                         ('strides', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv29',\n",
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            "                            ['/model.8/m.0/m/m.1/cv2/conv/Conv',\n",
            "                             '/model.8/m.0/m/m.1/cv2/act/Sigmoid',\n",
            "                             '/model.8/m.0/m/m.1/cv2/act/Mul']),\n",
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            "             ('best_opset9/ew_add6',\n",
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            "             ('best_opset9/conv30',\n",
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            "                           ('original_names',\n",
            "                            ['/model.8/m.0/cv3/conv/Conv',\n",
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            "                            OrderedDict([('kernel_shape', [1, 1, 256, 256]),\n",
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            "             ('best_opset9/concat6',\n",
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            "                           ('input',\n",
            "                            ['best_opset9/feature_splitter4',\n",
            "                             'best_opset9/shortcut4',\n",
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            "                           ('output', ['best_opset9/conv31']),\n",
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            "                           ('quantization_params', {}),\n",
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            "             ('best_opset9/conv31',\n",
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            "                           ('original_names',\n",
            "                            ['/model.8/cv2/conv/Conv',\n",
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            "                            OrderedDict([('kernel_shape', [1, 1, 768, 512]),\n",
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            "             ('best_opset9/conv32',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/maxpool1', 'best_opset9/concat7']),\n",
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            "                           ('original_names',\n",
            "                            ['/model.9/cv1/conv/Conv',\n",
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            "                            OrderedDict([('kernel_shape', [1, 1, 512, 256]),\n",
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            "             ('best_opset9/maxpool1',\n",
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            "                           ('output',\n",
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            "                           ('input_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('output_shapes',\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 5, 5, 1]),\n",
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            "             ('best_opset9/maxpool2',\n",
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            "                           ('output_shapes',\n",
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            "             ('best_opset9/maxpool3',\n",
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            "                            OrderedDict([('kernel_shape', [1, 5, 5, 1]),\n",
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            "             ('best_opset9/concat7',\n",
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            "                             'best_opset9/maxpool2',\n",
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            "                           ('output_shapes', [[-1, 10, 10, 1024]]),\n",
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            "                           ('quantization_params', {}),\n",
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            "             ('best_opset9/conv33',\n",
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            "                           ('original_names',\n",
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            "             ('best_opset9/conv34',\n",
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            "                           ('output_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('original_names',\n",
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            "                           ('params',\n",
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            "             ('best_opset9/feature_splitter5',\n",
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            "                           ('output',\n",
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            "             ('best_opset9/shortcut5',\n",
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            "                           ('output',\n",
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            "                           ('output_shapes',\n",
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            "             ('best_opset9/conv35',\n",
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            "                           ('output', ['best_opset9/format_conversion1']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 512]]),\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 256, 512]),\n",
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            "             ('best_opset9/format_conversion1',\n",
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            "                           ('output', ['best_opset9/feature_splitter6']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 512]]),\n",
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            "                           ('compilation_params',\n",
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            "                            OrderedDict([('conversion_type', 'spatial_reshape'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('spatial_reshape_sizes',\n",
            "                                          [1, 100])]))])),\n",
            "             ('best_opset9/feature_splitter6',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/shortcut6',\n",
            "                             'best_opset9/matmul1',\n",
            "                             'best_opset9/shortcut7']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 512]]),\n",
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            "                           ('params', {'groups': 4})])),\n",
            "             ('best_opset9/shortcut6',\n",
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            "                           ('input', ['best_opset9/feature_splitter6']),\n",
            "                           ('output', ['best_opset9/matmul1']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 128]]),\n",
            "                           ('output_shapes', [[-1, 1, 100, 128]]),\n",
            "                           ('original_names',\n",
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            "             ('best_opset9/matmul1',\n",
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            "                            ['best_opset9/shortcut6',\n",
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            "                           ('output', ['best_opset9/mul_and_add1']),\n",
            "                           ('input_shapes',\n",
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            "                           ('output_shapes', [[-1, 1, 100, 400]]),\n",
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            "                           ('compilation_params', {'mixed_mem': 'disabled'}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('dynamic_weights', True),\n",
            "                                         ('transpose_matmul_input', True),\n",
            "                                         ('kernel_shape', [1, 1, 128, 400]),\n",
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            "                                         ('zp_correction_type', 'zp_comp_none'),\n",
            "                                         ('zp_comp_rank', 0),\n",
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            "             ('best_opset9/mul_and_add1',\n",
            "              OrderedDict([('type', 'normalization'),\n",
            "                           ('input', ['best_opset9/matmul1']),\n",
            "                           ('output', ['best_opset9/softmax1']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 400]]),\n",
            "                           ('output_shapes', [[-1, 1, 100, 400]]),\n",
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            "                           ('params',\n",
            "                            OrderedDict([('elementwise_add', False),\n",
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            "             ('best_opset9/shortcut7',\n",
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            "                           ('input', ['best_opset9/feature_splitter6']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/activation1',\n",
            "                             'best_opset9/format_conversion2']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('output_shapes',\n",
            "                            [[-1, 1, 100, 256], [-1, 1, 100, 256]]),\n",
            "                           ('original_names', ['/model.10/m/m.0/attn/Split']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {})])),\n",
            "             ('best_opset9/activation1',\n",
            "              OrderedDict([('type', 'activation'),\n",
            "                           ('input', ['best_opset9/shortcut7']),\n",
            "                           ('output', ['best_opset9/matmul2']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('output_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/attn/MatMul_1']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('activation', 'linear')]))])),\n",
            "             ('best_opset9/format_conversion2',\n",
            "              OrderedDict([('type', 'format_conversion'),\n",
            "                           ('input', ['best_opset9/shortcut7']),\n",
            "                           ('output', ['best_opset9/dw1']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/attn/Reshape_2']),\n",
            "                           ('compilation_params',\n",
            "                            {'hw_layer_type_list': ['lcu']}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('conversion_type', 'spatial_reshape'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('spatial_reshape_sizes',\n",
            "                                          [10, 10])]))])),\n",
            "             ('best_opset9/dw1',\n",
            "              OrderedDict([('type', 'dw'),\n",
            "                           ('input', ['best_opset9/format_conversion2']),\n",
            "                           ('output', ['best_opset9/ew_add7']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/attn/pe/conv/Conv']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 256, 1]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME_TENSORFLOW'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('layer_disparity', 1),\n",
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            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'linear')]))])),\n",
            "             ('best_opset9/softmax1',\n",
            "              OrderedDict([('type', 'softmax'),\n",
            "                           ('input', ['best_opset9/mul_and_add1']),\n",
            "                           ('output', ['best_opset9/matmul2']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 400]]),\n",
            "                           ('output_shapes', [[-1, 1, 100, 400]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/attn/Softmax',\n",
            "                             '/model.10/m/m.0/attn/Transpose_1']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('groups', 4),\n",
            "                                         ('logits_axis', 3)]))])),\n",
            "             ('best_opset9/matmul2',\n",
            "              OrderedDict([('type', 'matmul'),\n",
            "                           ('input',\n",
            "                            ['best_opset9/softmax1',\n",
            "                             'best_opset9/activation1']),\n",
            "                           ('output', ['best_opset9/format_conversion3']),\n",
            "                           ('input_shapes',\n",
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            "                           ('output_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('original_names',\n",
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            "                           ('compilation_params', {'mixed_mem': 'disabled'}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('dynamic_weights', True),\n",
            "                                         ('transpose_matmul_input', False),\n",
            "                                         ('kernel_shape', [1, 1, 400, 256]),\n",
            "                                         ('groups', 4),\n",
            "                                         ('zp_correction_type', 'zp_comp_none'),\n",
            "                                         ('zp_comp_rank', 0),\n",
            "                                         ('activation', 'linear')]))])),\n",
            "             ('best_opset9/format_conversion3',\n",
            "              OrderedDict([('type', 'format_conversion'),\n",
            "                           ('input', ['best_opset9/matmul2']),\n",
            "                           ('output', ['best_opset9/ew_add7']),\n",
            "                           ('input_shapes', [[-1, 1, 100, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
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            "                            ['/model.10/m/m.0/attn/Reshape_1']),\n",
            "                           ('compilation_params',\n",
            "                            {'hw_layer_type_list': ['lcu']}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('conversion_type', 'spatial_reshape'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('spatial_reshape_sizes',\n",
            "                                          [10, 10])]))])),\n",
            "             ('best_opset9/ew_add7',\n",
            "              OrderedDict([('type', 'ew_add'),\n",
            "                           ('input',\n",
            "                            ['best_opset9/format_conversion3',\n",
            "                             'best_opset9/dw1']),\n",
            "                           ('output', ['best_opset9/conv36']),\n",
            "                           ('input_shapes',\n",
            "                            [[-1, 10, 10, 256], [-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('activation', 'linear')]))])),\n",
            "             ('best_opset9/conv36',\n",
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            "                           ('input', ['best_opset9/ew_add7']),\n",
            "                           ('output', ['best_opset9/ew_add8']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/attn/proj/conv/Conv']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 256, 256]),\n",
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            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/ew_add8',\n",
            "              OrderedDict([('type', 'ew_add'),\n",
            "                           ('input',\n",
            "                            ['best_opset9/shortcut5', 'best_opset9/conv36']),\n",
            "                           ('output',\n",
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            "                           ('input_shapes',\n",
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            "                           ('output_shapes',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv37',\n",
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            "                           ('input', ['best_opset9/ew_add8']),\n",
            "                           ('output', ['best_opset9/conv38']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.10/m/m.0/ffn/ffn.0/conv/Conv',\n",
            "                             '/model.10/m/m.0/ffn/ffn.0/act/Sigmoid',\n",
            "                             '/model.10/m/m.0/ffn/ffn.0/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 256, 512]),\n",
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            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv38',\n",
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            "                           ('output', ['best_opset9/ew_add9']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('original_names',\n",
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            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 512, 256]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'VALID'),\n",
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            "                                         ('batch_norm', False),\n",
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            "             ('best_opset9/ew_add9',\n",
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            "                            ['best_opset9/ew_add8', 'best_opset9/conv38']),\n",
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            "                           ('input_shapes',\n",
            "                            [[-1, 10, 10, 256], [-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 256]]),\n",
            "                           ('original_names', ['/model.10/m/m.0/Add_1']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('activation', 'linear')]))])),\n",
            "             ('best_opset9/concat8',\n",
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            "                            ['best_opset9/feature_splitter5',\n",
            "                             'best_opset9/ew_add9']),\n",
            "                           ('output', ['best_opset9/conv39']),\n",
            "                           ('input_shapes',\n",
            "                            [[-1, 10, 10, 256], [-1, 10, 10, 256]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('original_names', ['/model.10/Concat']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('concat_axis', 'features')]))])),\n",
            "             ('best_opset9/conv39',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/resize1', 'best_opset9/concat15']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('output_shapes',\n",
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            "                           ('original_names',\n",
            "                            ['/model.10/cv2/conv/Conv',\n",
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            "                             '/model.10/cv2/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 512, 512]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/resize1',\n",
            "              OrderedDict([('type', 'resize'),\n",
            "                           ('input', ['best_opset9/conv39']),\n",
            "                           ('output', ['best_opset9/concat9']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 512]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 512]]),\n",
            "                           ('original_names', ['/model.11/Upsample']),\n",
            "                           ('compilation_params',\n",
            "                            {'hw_layer_type_list': ['lcu'],\n",
            "                             'resize_bilinear_streaming': 'disabled'}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('resize_h_ratio_list', [2.0]),\n",
            "                                         ('resize_w_ratio_list', [2.0]),\n",
            "                                         ('resize_f_ratio_list', [1.0]),\n",
            "                                         ('method', 'nearest_neighbor'),\n",
            "                                         ('resize_bilinear_pixels_mode',\n",
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            "             ('best_opset9/concat9',\n",
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            "                           ('input',\n",
            "                            ['best_opset9/resize1', 'best_opset9/conv21']),\n",
            "                           ('output', ['best_opset9/conv40']),\n",
            "                           ('input_shapes',\n",
            "                            [[-1, 20, 20, 512], [-1, 20, 20, 256]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 768]]),\n",
            "                           ('original_names', ['/model.12/Concat']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('concat_axis', 'features')]))])),\n",
            "             ('best_opset9/conv40',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/concat9']),\n",
            "                           ('output', ['best_opset9/feature_splitter7']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 768]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.13/cv1/conv/Conv',\n",
            "                             '/model.13/cv1/act/Sigmoid',\n",
            "                             '/model.13/cv1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 768, 256]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'VALID'),\n",
            "                                         ('groups', 1),\n",
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            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/feature_splitter7',\n",
            "              OrderedDict([('type', 'feature_splitter'),\n",
            "                           ('input', ['best_opset9/conv40']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/concat10', 'best_opset9/shortcut8']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('output_shapes',\n",
            "                            [[-1, 20, 20, 128], [-1, 20, 20, 128]]),\n",
            "                           ('original_names', ['/model.13/Split']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params', {'groups': 1})])),\n",
            "             ('best_opset9/shortcut8',\n",
            "              OrderedDict([('type', 'shortcut'),\n",
            "                           ('input', ['best_opset9/feature_splitter7']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/conv41',\n",
            "                             'best_opset9/ew_add10',\n",
            "                             'best_opset9/concat10']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('output_shapes',\n",
            "                            [[-1, 20, 20, 128],\n",
            "                             [-1, 20, 20, 128],\n",
            "                             [-1, 20, 20, 128]]),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {})])),\n",
            "             ('best_opset9/conv41',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/shortcut8']),\n",
            "                           ('output', ['best_opset9/conv42']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.13/m.0/cv1/conv/Conv',\n",
            "                             '/model.13/m.0/cv1/act/Sigmoid',\n",
            "                             '/model.13/m.0/cv1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 128, 64]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME_TENSORFLOW'),\n",
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            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/conv42',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/conv41']),\n",
            "                           ('output', ['best_opset9/ew_add10']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 64]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.13/m.0/cv2/conv/Conv',\n",
            "                             '/model.13/m.0/cv2/act/Sigmoid',\n",
            "                             '/model.13/m.0/cv2/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 64, 128]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME_TENSORFLOW'),\n",
            "                                         ('groups', 1),\n",
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            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/ew_add10',\n",
            "              OrderedDict([('type', 'ew_add'),\n",
            "                           ('input',\n",
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            "                           ('output', ['best_opset9/concat10']),\n",
            "                           ('input_shapes',\n",
            "                            [[-1, 20, 20, 128], [-1, 20, 20, 128]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('original_names', ['/model.13/m.0/Add']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('activation', 'linear')]))])),\n",
            "             ('best_opset9/concat10',\n",
            "              OrderedDict([('type', 'concat'),\n",
            "                           ('input',\n",
            "                            ['best_opset9/feature_splitter7',\n",
            "                             'best_opset9/shortcut8',\n",
            "                             'best_opset9/ew_add10']),\n",
            "                           ('output', ['best_opset9/conv43']),\n",
            "                           ('input_shapes',\n",
            "                            [[-1, 20, 20, 128],\n",
            "                             [-1, 20, 20, 128],\n",
            "                             [-1, 20, 20, 128]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 384]]),\n",
            "                           ('original_names', ['/model.13/Concat']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('concat_axis', 'features')]))])),\n",
            "             ('best_opset9/conv43',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/concat10']),\n",
            "                           ('output',\n",
            "                            ['best_opset9/resize2', 'best_opset9/concat13']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 384]]),\n",
            "                           ('output_shapes',\n",
            "                            [[-1, 20, 20, 256], [-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.13/cv2/conv/Conv',\n",
            "                             '/model.13/cv2/act/Sigmoid',\n",
            "                             '/model.13/cv2/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 384, 256]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/resize2',\n",
            "              OrderedDict([('type', 'resize'),\n",
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            "                            OrderedDict([('resize_h_ratio_list', [2.0]),\n",
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            "                                         ('resize_f_ratio_list', [1.0]),\n",
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            "             ('best_opset9/concat11',\n",
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            "             ('best_opset9/conv44',\n",
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            "                           ('output',\n",
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            "             ('best_opset9/conv45',\n",
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            "                           ('original_names',\n",
            "                            ['/model.16/m.0/cv1/conv/Conv',\n",
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            "                           ('params',\n",
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            "             ('best_opset9/conv46',\n",
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            "                           ('original_names',\n",
            "                            ['/model.16/m.0/cv2/conv/Conv',\n",
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            "                            OrderedDict([('kernel_shape', [3, 3, 32, 64]),\n",
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            "             ('best_opset9/ew_add11',\n",
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            "             ('best_opset9/concat12',\n",
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            "                           ('input',\n",
            "                            ['best_opset9/feature_splitter8',\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv47',\n",
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            "                           ('output',\n",
            "                            ['best_opset9/conv48',\n",
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            "                           ('original_names',\n",
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            "             ('best_opset9/conv48',\n",
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            "                           ('original_names',\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 128, 128]),\n",
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            "             ('best_opset9/concat13',\n",
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            "                           ('input_shapes',\n",
            "                            [[-1, 20, 20, 128], [-1, 20, 20, 256]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 384]]),\n",
            "                           ('original_names', ['/model.18/Concat']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv49',\n",
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            "                           ('output', ['best_opset9/conv50']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv2.0/cv2.0.0/conv/Conv',\n",
            "                             '/model.23/cv2.0/cv2.0.0/act/Sigmoid',\n",
            "                             '/model.23/cv2.0/cv2.0.0/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
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            "             ('best_opset9/conv50',\n",
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            "                           ('input_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv2.0/cv2.0.1/conv/Conv',\n",
            "                             '/model.23/cv2.0/cv2.0.1/act/Sigmoid',\n",
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            "                           ('quantization_params', {}),\n",
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            "                            OrderedDict([('kernel_shape', [3, 3, 64, 64]),\n",
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            "             ('best_opset9/conv51',\n",
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            "                           ('output', ['best_opset9/output_layer1']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 64]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 64]]),\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 64, 64]),\n",
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            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv55',\n",
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            "                           ('output', ['best_opset9/feature_splitter9']),\n",
            "                           ('input_shapes', [[-1, 20, 20, 384]]),\n",
            "                           ('output_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.19/cv1/conv/Conv',\n",
            "                             '/model.19/cv1/act/Sigmoid',\n",
            "                             '/model.19/cv1/act/Mul']),\n",
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            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 384, 256]),\n",
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            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/dw2',\n",
            "              OrderedDict([('type', 'dw'),\n",
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            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv',\n",
            "                             '/model.23/cv3.0/cv3.0.0/cv3.0.0.0/act/Sigmoid',\n",
            "                             '/model.23/cv3.0/cv3.0.0/cv3.0.0.0/act/Mul']),\n",
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            "             ('best_opset9/conv52',\n",
            "              OrderedDict([('type', 'conv'),\n",
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            "                           ('output', ['best_opset9/dw3']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv',\n",
            "                             '/model.23/cv3.0/cv3.0.0/cv3.0.0.1/act/Sigmoid',\n",
            "                             '/model.23/cv3.0/cv3.0.0/cv3.0.0.1/act/Mul']),\n",
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            "             ('best_opset9/dw3',\n",
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            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv',\n",
            "                             '/model.23/cv3.0/cv3.0.1/cv3.0.1.0/act/Sigmoid',\n",
            "                             '/model.23/cv3.0/cv3.0.1/cv3.0.1.0/act/Mul']),\n",
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            "                           ('params',\n",
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            "             ('best_opset9/conv53',\n",
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            "                           ('output', ['best_opset9/conv54']),\n",
            "                           ('input_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('output_shapes', [[-1, 40, 40, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv',\n",
            "                             '/model.23/cv3.0/cv3.0.1/cv3.0.1.1/act/Sigmoid',\n",
            "                             '/model.23/cv3.0/cv3.0.1/cv3.0.1.1/act/Mul']),\n",
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            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 128, 128]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
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            "             ('best_opset9/conv54',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/conv53']),\n",
            "                           ('output', ['best_opset9/output_layer2']),\n",
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            "             ('best_opset9/feature_splitter9',\n",
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            "                            ['/model.19/m.0/cv2/conv/Conv',\n",
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            "             ('best_opset9/conv60',\n",
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            "                           ('original_names',\n",
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            "             ('best_opset9/conv66',\n",
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            "                           ('output', ['best_opset9/feature_splitter10']),\n",
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            "                           ('original_names',\n",
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            "             ('best_opset9/dw4',\n",
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            "                           ('output_shapes', [[-1, 20, 20, 256]]),\n",
            "                           ('original_names',\n",
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            "                             '/model.23/cv3.1/cv3.1.0/cv3.1.0.0/act/Sigmoid',\n",
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            "             ('best_opset9/conv63',\n",
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            "                            ['/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv',\n",
            "                             '/model.23/cv3.1/cv3.1.0/cv3.1.0.1/act/Sigmoid',\n",
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            "             ('best_opset9/dw5',\n",
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            "                           ('output_shapes', [[-1, 20, 20, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv',\n",
            "                             '/model.23/cv3.1/cv3.1.1/cv3.1.1.0/act/Sigmoid',\n",
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            "             ('best_opset9/conv64',\n",
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            "                           ('output_shapes', [[-1, 20, 20, 128]]),\n",
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            "             ('best_opset9/conv65',\n",
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            "             ('best_opset9/feature_splitter10',\n",
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            "                           ('output',\n",
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            "             ('best_opset9/conv74',\n",
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            "                           ('input', ['best_opset9/conv78']),\n",
            "                           ('output', ['best_opset9/conv79']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv',\n",
            "                             '/model.23/cv3.2/cv3.2.1/cv3.2.1.0/act/Sigmoid',\n",
            "                             '/model.23/cv3.2/cv3.2.1/cv3.2.1.0/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [3, 3, 128, 1]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'SAME_TENSORFLOW'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('layer_disparity', 1),\n",
            "                                         ('input_disparity', 1),\n",
            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/conv79',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/dw7']),\n",
            "                           ('output', ['best_opset9/conv80']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('original_names',\n",
            "                            ['/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv',\n",
            "                             '/model.23/cv3.2/cv3.2.1/cv3.2.1.1/act/Sigmoid',\n",
            "                             '/model.23/cv3.2/cv3.2.1/cv3.2.1.1/act/Mul']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 128, 128]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'VALID'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('layer_disparity', 1),\n",
            "                                         ('input_disparity', 1),\n",
            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'silu')]))])),\n",
            "             ('best_opset9/conv80',\n",
            "              OrderedDict([('type', 'conv'),\n",
            "                           ('input', ['best_opset9/conv79']),\n",
            "                           ('output', ['best_opset9/output_layer6']),\n",
            "                           ('input_shapes', [[-1, 10, 10, 128]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 5]]),\n",
            "                           ('original_names', ['/model.23/cv3.2/cv3.2.2/Conv']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('params',\n",
            "                            OrderedDict([('kernel_shape', [1, 1, 128, 5]),\n",
            "                                         ('strides', [1, 1, 1, 1]),\n",
            "                                         ('dilations', [1, 1, 1, 1]),\n",
            "                                         ('padding', 'VALID'),\n",
            "                                         ('groups', 1),\n",
            "                                         ('layer_disparity', 1),\n",
            "                                         ('input_disparity', 1),\n",
            "                                         ('batch_norm', False),\n",
            "                                         ('elementwise_add', False),\n",
            "                                         ('activation', 'linear')]))])),\n",
            "             ('best_opset9/output_layer5',\n",
            "              OrderedDict([('type', 'output_layer'),\n",
            "                           ('input', ['best_opset9/conv77']),\n",
            "                           ('output', []),\n",
            "                           ('input_shapes', [[-1, 10, 10, 64]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 64]]),\n",
            "                           ('original_names', ['out']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('transposed', False),\n",
            "                           ('engine', 'nn_core'),\n",
            "                           ('io_type', 'standard')])),\n",
            "             ('best_opset9/output_layer6',\n",
            "              OrderedDict([('type', 'output_layer'),\n",
            "                           ('input', ['best_opset9/conv80']),\n",
            "                           ('output', []),\n",
            "                           ('input_shapes', [[-1, 10, 10, 5]]),\n",
            "                           ('output_shapes', [[-1, 10, 10, 5]]),\n",
            "                           ('original_names', ['out']),\n",
            "                           ('compilation_params', {}),\n",
            "                           ('quantization_params', {}),\n",
            "                           ('transposed', False),\n",
            "                           ('engine', 'nn_core'),\n",
            "                           ('io_type', 'standard')]))])\n",
            "\n",
            "================================================================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, you can scroll through the output to verify which layers correspond to which end node in your ONNX model. In this dict, each layer is stored under a new name, and it’s original name is a key within the layer under ‘original_names’. You will need this when generating a NMS file for your model, you can find examples NMS configs here."
      ],
      "metadata": {
        "id": "gzARsQW6L9ow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/gdrive/', force_remount=True)\n",
        "\n",
        "# Updated NMS layer configuration dictionary\n",
        "nms_layer_config = {\n",
        "    \"nms_scores_th\": 0.3,\n",
        "    \"nms_iou_th\": 0.7,\n",
        "    \"image_dims\": [640, 640],\n",
        "    \"max_proposals_per_class\": 25,\n",
        "    \"classes\": 1,\n",
        "    \"regression_length\": 16,\n",
        "    \"background_removal\": False,\n",
        "    \"background_removal_index\": 0,\n",
        "    \"bbox_decoders\": [\n",
        "        {\n",
        "            \"name\": \"best_opset9/bbox_decoder51\",\n",
        "            \"stride\": 8,\n",
        "            \"reg_layer\": \"conv41\",\n",
        "            \"cls_layer\": \"conv42\"\n",
        "        },\n",
        "        {\n",
        "            \"name\": \"best_opset9/bbox_decoder62\",\n",
        "            \"stride\": 16,\n",
        "            \"reg_layer\": \"conv62\",\n",
        "            \"cls_layer\": \"conv65\"\n",
        "        },\n",
        "        {\n",
        "            \"name\": \"best_opset9/bbox_decoder77\",\n",
        "            \"stride\": 32,\n",
        "            \"reg_layer\": \"best_opset9/conv77\",\n",
        "            \"cls_layer\": \"best_opset9/conv80\"\n",
        "        }\n",
        "    ]\n",
        "}\n",
        "\n",
        "# Path to save the updated JSON configuration\n",
        "output_dir = \"/content/\"\n",
        "os.makedirs(output_dir, exist_ok=True)  # Create the directory if it doesn't exist\n",
        "output_path = os.path.join(\"nms_layer_config.json\")\n",
        "\n",
        "# Save the updated configuration as a JSON file\n",
        "with open(output_path, \"w\") as json_file:\n",
        "    json.dump(nms_layer_config, json_file, indent=4)\n",
        "\n",
        "print(f\"NMS layer configuration saved to {output_path}\")"
      ],
      "metadata": {
        "id": "ggWqQem7MARq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e79a4e3-134a-4748-940f-06b4c283aa0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n",
            "NMS layer configuration saved to nms_layer_config.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After this, I made calibration data for the optimization step.\n",
        "\n",
        "Model Optimization for Hailo Hardware\n",
        "\n",
        "This step prepares the model for deployment on Hailo hardware. A key part of this process is quantization, which involves converting the model's parameters from floating-point numbers to integer numbers. This is essential for efficient execution on Hailo hardware.\n",
        "\n",
        "Timing and Resource Considerations:\n",
        "\n",
        "Model Translation: This step occurs after converting the model from its original framework (e.g., TensorFlow, PyTorch).\n",
        "Hardware Requirements: For optimal performance during model optimization, it's recommended to use a machine equipped with a GPU.\n",
        "Calibration Data: To achieve the best results, prepare a calibration dataset containing at least 1024 data samples.\n",
        "By following these steps and guidelines, you can effectively optimize your model for deployment on Hailo hardware.\n",
        "\n",
        "Callibrationset:\n",
        "- at leat 1024 images\n",
        "- annotated\n",
        "- representative\n",
        "\n",
        "Some confliction information:\n",
        "\n",
        "not more than 64: https://community.hailo.ai/t/should-i-use-as-many-images-as-possible-for-calibration/155\n",
        "\n",
        "or at least 1024 (https://hailo.ai/developer-zone/documentation/dataflow-compiler-v3-29-0/?sp_referrer=sdk/model_optimization.html)\n",
        "\n",
        "\n",
        "No labels are needed, the calibration is a way to collect statistics on the data that the network is going to process, thus allowing for a smarter choise of the quanlized bins. The statistics are collected per layer.\n",
        "\n",
        "Both would probably be good. but it should be represantative, so if you have a real video from a scene, taking 100 consective images would not be a good choice. (https://community.hailo.ai/t/calibration-dataset-details/6967)\n",
        "\n"
      ],
      "metadata": {
        "id": "zcgJDdLZMEby"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "unzip images in .zip\n",
        "\n"
      ],
      "metadata": {
        "id": "iAK0mh-yNDFd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## 2. Unzip dataset.zip and rename the folder on google drive\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "import os\n",
        "\n",
        "# Define Paths with Parameters\n",
        "dataset_path = \"/content/gdrive/MyDrive/vespA/100_1_dataset.zip\"  # @param {type:\"string\"}\n",
        "dataset_filename = \"100_1_sampleSize\"  # @param {type:\"string\"}\n",
        "\n",
        "# Unzip the Dataset (using the defined path)\n",
        "!unzip {dataset_path} -d '/content/'\n",
        "\n",
        "# Rename the Extracted Folder\n",
        "old_path = f'/content/{dataset_filename}'\n",
        "new_path = '/content/dataset'\n",
        "os.rename(old_path, new_path)"
      ],
      "metadata": {
        "id": "F29cF26uNl2L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "\n",
        "# Paths to directories and files\n",
        "image_dir = '/content/dataset/valid/images'\n",
        "output_dir = '/content/output_dir'\n",
        "os.makedirs(output_dir, exist_ok=True)  # Create the directory if it doesn't exist\n",
        "\n",
        "# File paths for saving calibration data\n",
        "calibration_data_path = os.path.join(output_dir, \"calibration_data.npy\")\n",
        "processed_data_path = os.path.join(output_dir, \"processed_calibration_data.npy\")\n",
        "\n",
        "# Initialize an empty list for calibration data\n",
        "calib_data = []\n",
        "\n",
        "# Process all image files in the directory\n",
        "for img_name in os.listdir(image_dir):\n",
        "    img_path = os.path.join(image_dir, img_name)\n",
        "    if img_name.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
        "        img = Image.open(img_path).resize((640, 640))  # Resize to desired dimensions\n",
        "        img_array = np.array(img) / 255.0  # Normalize to [0, 1]\n",
        "        calib_data.append(img_array)\n",
        "\n",
        "# Convert the calibration data to a NumPy array\n",
        "calib_data = np.array(calib_data)\n",
        "\n",
        "# Save the normalized calibration data\n",
        "np.save(calibration_data_path, calib_data)\n",
        "print(f\"Normalized calibration dataset saved with shape: {calib_data.shape} to {calibration_data_path}\")\n",
        "\n",
        "# Scale the normalized data back to [0, 255]\n",
        "processed_calibration_data = calib_data * 255.0\n",
        "\n",
        "# Save the processed calibration data\n",
        "np.save(processed_data_path, processed_calibration_data)\n",
        "print(f\"Processed calibration dataset saved with shape: {processed_calibration_data.shape} to {processed_data_path}\")"
      ],
      "metadata": {
        "id": "ifbGGXLdMFsB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aangepaste versie ivm teveel verbruik van RAM (nog verder verbeteren)"
      ],
      "metadata": {
        "id": "69VHpoKyR3j3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "# Paths to directories and files\n",
        "image_dir = '/content/dataset/valid/images'\n",
        "output_dir = '/content/output_dir'\n",
        "os.makedirs(output_dir, exist_ok=True)  # Create the directory if it doesn't exist\n",
        "\n",
        "# File paths for saving processed data\n",
        "calibration_data_path = os.path.join(output_dir, \"calibration_data.npy\")\n",
        "processed_data_path = os.path.join(output_dir, \"processed_calibration_data.npy\")\n",
        "\n",
        "# Process and save each image incrementally to avoid high memory usage\n",
        "with open(calibration_data_path, 'wb') as calib_file, open(processed_data_path, 'wb') as processed_file:\n",
        "    for img_name in os.listdir(image_dir):\n",
        "        img_path = os.path.join(image_dir, img_name)\n",
        "\n",
        "        if img_name.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
        "            # Resize and normalize the image\n",
        "            img = Image.open(img_path).resize((640, 640))\n",
        "            img_array = np.array(img) / 255.0  # Normalize to [0, 1]\n",
        "\n",
        "            # Append the normalized data directly to the file\n",
        "            np.save(calib_file, img_array, allow_pickle=False)\n",
        "            print(f\"Saved {img_name} normalized data to calibration file.\")\n",
        "\n",
        "            # Scale the normalized data back to [0, 255] and save incrementally\n",
        "            processed_calibration_data = img_array * 255.0\n",
        "            np.save(processed_file, processed_calibration_data, allow_pickle=False)\n",
        "            print(f\"Saved {img_name} processed calibration data to file.\")\n",
        "\n",
        "print(\"All images processed and saved.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BM2XJaAdL6W",
        "outputId": "130d0899-cd5d-4f28-d23e-e5f8b0ba23d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved train_vvel0359.jpg normalized data to calibration file.\n",
            "Saved train_vvel0359.jpg processed calibration data to file.\n",
            "Saved test_vcra0104.jpg normalized data to calibration file.\n",
            "Saved test_vcra0104.jpg processed calibration data to file.\n",
            "Saved train_vcra1859.jpg normalized data to calibration file.\n",
            "Saved train_vcra1859.jpg processed calibration data to file.\n",
            "Saved train_vvel0355.jpg normalized data to calibration file.\n",
            "Saved train_vvel0355.jpg processed calibration data to file.\n",
            "Saved valid_vzon0185.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0185.jpg processed calibration data to file.\n",
            "Saved train_vcra0299.jpg normalized data to calibration file.\n",
            "Saved train_vcra0299.jpg processed calibration data to file.\n",
            "Saved train_vzon0930.jpg normalized data to calibration file.\n",
            "Saved train_vzon0930.jpg processed calibration data to file.\n",
            "Saved train_vvel0020.jpg normalized data to calibration file.\n",
            "Saved train_vvel0020.jpg processed calibration data to file.\n",
            "Saved train_vzon0796.jpg normalized data to calibration file.\n",
            "Saved train_vzon0796.jpg processed calibration data to file.\n",
            "Saved train_vzon0332.jpg normalized data to calibration file.\n",
            "Saved train_vzon0332.jpg processed calibration data to file.\n",
            "Saved train_amel1003.jpg normalized data to calibration file.\n",
            "Saved train_amel1003.jpg processed calibration data to file.\n",
            "Saved train_vvel0031.jpg normalized data to calibration file.\n",
            "Saved train_vvel0031.jpg processed calibration data to file.\n",
            "Saved train_amel1393.jpg normalized data to calibration file.\n",
            "Saved train_amel1393.jpg processed calibration data to file.\n",
            "Saved test_vvel0133.jpg normalized data to calibration file.\n",
            "Saved test_vvel0133.jpg processed calibration data to file.\n",
            "Saved valid_vvel0396.jpg normalized data to calibration file.\n",
            "Saved valid_vvel0396.jpg processed calibration data to file.\n",
            "Saved train_vcra1687.jpg normalized data to calibration file.\n",
            "Saved train_vcra1687.jpg processed calibration data to file.\n",
            "Saved test_vcra0231.jpg normalized data to calibration file.\n",
            "Saved test_vcra0231.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0545.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0545.jpg processed calibration data to file.\n",
            "Saved train_vzon1565.jpg normalized data to calibration file.\n",
            "Saved train_vzon1565.jpg processed calibration data to file.\n",
            "Saved train_vvel1500.jpg normalized data to calibration file.\n",
            "Saved train_vvel1500.jpg processed calibration data to file.\n",
            "Saved train_vvel1883.jpg normalized data to calibration file.\n",
            "Saved train_vvel1883.jpg processed calibration data to file.\n",
            "Saved train_vespsp1012.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1012.jpg processed calibration data to file.\n",
            "Saved train_amel0245.jpg normalized data to calibration file.\n",
            "Saved train_amel0245.jpg processed calibration data to file.\n",
            "Saved train_vzon1751.jpg normalized data to calibration file.\n",
            "Saved train_vzon1751.jpg processed calibration data to file.\n",
            "Saved train_vvel1502.jpg normalized data to calibration file.\n",
            "Saved train_vvel1502.jpg processed calibration data to file.\n",
            "Saved train_vespsp1648.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1648.jpg processed calibration data to file.\n",
            "Saved train_amel2147.jpg normalized data to calibration file.\n",
            "Saved train_amel2147.jpg processed calibration data to file.\n",
            "Saved valid_amel0421.jpg normalized data to calibration file.\n",
            "Saved valid_amel0421.jpg processed calibration data to file.\n",
            "Saved valid_vzon0417.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0417.jpg processed calibration data to file.\n",
            "Saved test_vespsp0067.jpg normalized data to calibration file.\n",
            "Saved test_vespsp0067.jpg processed calibration data to file.\n",
            "Saved train_vzon0752.jpg normalized data to calibration file.\n",
            "Saved train_vzon0752.jpg processed calibration data to file.\n",
            "Saved train_vzon1313.jpg normalized data to calibration file.\n",
            "Saved train_vzon1313.jpg processed calibration data to file.\n",
            "Saved train_amel1552.jpg normalized data to calibration file.\n",
            "Saved train_amel1552.jpg processed calibration data to file.\n",
            "Saved train_vvel1653.jpg normalized data to calibration file.\n",
            "Saved train_vvel1653.jpg processed calibration data to file.\n",
            "Saved train_vcra1123.jpg normalized data to calibration file.\n",
            "Saved train_vcra1123.jpg processed calibration data to file.\n",
            "Saved train_amel2075.jpg normalized data to calibration file.\n",
            "Saved train_amel2075.jpg processed calibration data to file.\n",
            "Saved train_vzon1321.jpg normalized data to calibration file.\n",
            "Saved train_vzon1321.jpg processed calibration data to file.\n",
            "Saved train_vvel0413.jpg normalized data to calibration file.\n",
            "Saved train_vvel0413.jpg processed calibration data to file.\n",
            "Saved train_vvel1126.jpg normalized data to calibration file.\n",
            "Saved train_vvel1126.jpg processed calibration data to file.\n",
            "Saved train_vespsp1622.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1622.jpg processed calibration data to file.\n",
            "Saved train_vcra0187.jpg normalized data to calibration file.\n",
            "Saved train_vcra0187.jpg processed calibration data to file.\n",
            "Saved test_vcra0222.jpg normalized data to calibration file.\n",
            "Saved test_vcra0222.jpg processed calibration data to file.\n",
            "Saved train_vzon0429.jpg normalized data to calibration file.\n",
            "Saved train_vzon0429.jpg processed calibration data to file.\n",
            "Saved train_vespsp1665.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1665.jpg processed calibration data to file.\n",
            "Saved train_vvel1321.jpg normalized data to calibration file.\n",
            "Saved train_vvel1321.jpg processed calibration data to file.\n",
            "Saved valid_vzon0216.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0216.jpg processed calibration data to file.\n",
            "Saved valid_vcra0522.jpg normalized data to calibration file.\n",
            "Saved valid_vcra0522.jpg processed calibration data to file.\n",
            "Saved train_vespsp1355.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1355.jpg processed calibration data to file.\n",
            "Saved train_vespsp0254.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0254.jpg processed calibration data to file.\n",
            "Saved train_vcra0178.jpg normalized data to calibration file.\n",
            "Saved train_vcra0178.jpg processed calibration data to file.\n",
            "Saved train_vespsp0238.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0238.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0127.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0127.jpg processed calibration data to file.\n",
            "Saved train_vcra1504.jpg normalized data to calibration file.\n",
            "Saved train_vcra1504.jpg processed calibration data to file.\n",
            "Saved train_vespsp0293.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0293.jpg processed calibration data to file.\n",
            "Saved valid_vzon0245.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0245.jpg processed calibration data to file.\n",
            "Saved train_amel0190.jpg normalized data to calibration file.\n",
            "Saved train_amel0190.jpg processed calibration data to file.\n",
            "Saved train_vespsp0556.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0556.jpg processed calibration data to file.\n",
            "Saved train_vzon0167.jpg normalized data to calibration file.\n",
            "Saved train_vzon0167.jpg processed calibration data to file.\n",
            "Saved train_amel0412.jpg normalized data to calibration file.\n",
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            "Saved valid_vvel0261.jpg processed calibration data to file.\n",
            "Saved train_vespsp0597.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0597.jpg processed calibration data to file.\n",
            "Saved train_vvel1289.jpg normalized data to calibration file.\n",
            "Saved train_vvel1289.jpg processed calibration data to file.\n",
            "Saved train_vvel1402.jpg normalized data to calibration file.\n",
            "Saved train_vvel1402.jpg processed calibration data to file.\n",
            "Saved valid_vvel0370.jpg normalized data to calibration file.\n",
            "Saved valid_vvel0370.jpg processed calibration data to file.\n",
            "Saved train_vvel0840.jpg normalized data to calibration file.\n",
            "Saved train_vvel0840.jpg processed calibration data to file.\n",
            "Saved train_amel1205.jpg normalized data to calibration file.\n",
            "Saved train_amel1205.jpg processed calibration data to file.\n",
            "Saved train_amel0369.jpg normalized data to calibration file.\n",
            "Saved train_amel0369.jpg processed calibration data to file.\n",
            "Saved train_vespsp2028.jpg normalized data to calibration file.\n",
            "Saved train_vespsp2028.jpg processed calibration data to file.\n",
            "Saved train_vzon1480.jpg normalized data to calibration file.\n",
            "Saved train_vzon1480.jpg processed calibration data to file.\n",
            "Saved valid_vzon0122.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0122.jpg processed calibration data to file.\n",
            "Saved valid_vzon0347.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0347.jpg processed calibration data to file.\n",
            "Saved train_vvel1589.jpg normalized data to calibration file.\n",
            "Saved train_vvel1589.jpg processed calibration data to file.\n",
            "Saved train_vvel1306.jpg normalized data to calibration file.\n",
            "Saved train_vvel1306.jpg processed calibration data to file.\n",
            "Saved train_amel1276.jpg normalized data to calibration file.\n",
            "Saved train_amel1276.jpg processed calibration data to file.\n",
            "Saved train_vespsp0735.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0735.jpg processed calibration data to file.\n",
            "Saved train_amel1973.jpg normalized data to calibration file.\n",
            "Saved train_amel1973.jpg processed calibration data to file.\n",
            "Saved train_vzon2023.jpg normalized data to calibration file.\n",
            "Saved train_vzon2023.jpg processed calibration data to file.\n",
            "Saved train_vcra0878.jpg normalized data to calibration file.\n",
            "Saved train_vcra0878.jpg processed calibration data to file.\n",
            "Saved train_amel2037.jpg normalized data to calibration file.\n",
            "Saved train_amel2037.jpg processed calibration data to file.\n",
            "Saved valid_amel0204.jpg normalized data to calibration file.\n",
            "Saved valid_amel0204.jpg processed calibration data to file.\n",
            "Saved train_vcra1437.jpg normalized data to calibration file.\n",
            "Saved train_vcra1437.jpg processed calibration data to file.\n",
            "Saved train_vespsp1483.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1483.jpg processed calibration data to file.\n",
            "Saved train_amel2200.jpg normalized data to calibration file.\n",
            "Saved train_amel2200.jpg processed calibration data to file.\n",
            "Saved valid_amel0149.jpg normalized data to calibration file.\n",
            "Saved valid_amel0149.jpg processed calibration data to file.\n",
            "Saved train_vespsp0009.jpg normalized data to calibration file.\n",
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            "Saved train_vcra0779.jpg normalized data to calibration file.\n",
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            "Saved train_amel1534.jpg normalized data to calibration file.\n",
            "Saved train_amel1534.jpg processed calibration data to file.\n",
            "Saved train_vespsp0552.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0552.jpg processed calibration data to file.\n",
            "Saved train_vvel1025.jpg normalized data to calibration file.\n",
            "Saved train_vvel1025.jpg processed calibration data to file.\n",
            "Saved valid_vcra0106.jpg normalized data to calibration file.\n",
            "Saved valid_vcra0106.jpg processed calibration data to file.\n",
            "Saved valid_vvel0243.jpg normalized data to calibration file.\n",
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            "Saved train_vespsp1996.jpg normalized data to calibration file.\n",
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            "Saved train_vcra0938.jpg normalized data to calibration file.\n",
            "Saved train_vcra0938.jpg processed calibration data to file.\n",
            "Saved valid_vvel0276.jpg normalized data to calibration file.\n",
            "Saved valid_vvel0276.jpg processed calibration data to file.\n",
            "Saved train_vcra0795.jpg normalized data to calibration file.\n",
            "Saved train_vcra0795.jpg processed calibration data to file.\n",
            "Saved train_vespsp0944.jpg normalized data to calibration file.\n",
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            "Saved valid_vvel0394.jpg normalized data to calibration file.\n",
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            "Saved train_amel2154.jpg normalized data to calibration file.\n",
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            "Saved valid_vcra0210.jpg normalized data to calibration file.\n",
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            "Saved valid_vespsp0121.jpg normalized data to calibration file.\n",
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            "Saved train_vvel0695.jpg normalized data to calibration file.\n",
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            "Saved valid_vzon0486.jpg normalized data to calibration file.\n",
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            "Saved valid_amel0495.jpg normalized data to calibration file.\n",
            "Saved valid_amel0495.jpg processed calibration data to file.\n",
            "Saved train_vespsp0326.jpg normalized data to calibration file.\n",
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            "Saved valid_vvel0018.jpg normalized data to calibration file.\n",
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            "Saved train_vzon0784.jpg normalized data to calibration file.\n",
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            "Saved valid_vespsp0349.jpg normalized data to calibration file.\n",
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            "Saved valid_vespsp0125.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0125.jpg processed calibration data to file.\n",
            "Saved train_vzon0157.jpg normalized data to calibration file.\n",
            "Saved train_vzon0157.jpg processed calibration data to file.\n",
            "Saved train_amel1428.jpg normalized data to calibration file.\n",
            "Saved train_amel1428.jpg processed calibration data to file.\n",
            "Saved train_vespsp0587.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0587.jpg processed calibration data to file.\n",
            "Saved train_vcra0020.jpg normalized data to calibration file.\n",
            "Saved train_vcra0020.jpg processed calibration data to file.\n",
            "Saved valid_vzon0015.jpg normalized data to calibration file.\n",
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            "Saved train_vcra1171.jpg normalized data to calibration file.\n",
            "Saved train_vcra1171.jpg processed calibration data to file.\n",
            "Saved train_vespsp1939.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1939.jpg processed calibration data to file.\n",
            "Saved train_amel1295.jpg normalized data to calibration file.\n",
            "Saved train_amel1295.jpg processed calibration data to file.\n",
            "Saved train_vvel0784.jpg normalized data to calibration file.\n",
            "Saved train_vvel0784.jpg processed calibration data to file.\n",
            "Saved train_amel1058.jpg normalized data to calibration file.\n",
            "Saved train_amel1058.jpg processed calibration data to file.\n",
            "Saved train_vzon0545.jpg normalized data to calibration file.\n",
            "Saved train_vzon0545.jpg processed calibration data to file.\n",
            "Saved train_vcra0863.jpg normalized data to calibration file.\n",
            "Saved train_vcra0863.jpg processed calibration data to file.\n",
            "Saved valid_vvel0116.jpg normalized data to calibration file.\n",
            "Saved valid_vvel0116.jpg processed calibration data to file.\n",
            "Saved train_vcra0282.jpg normalized data to calibration file.\n",
            "Saved train_vcra0282.jpg processed calibration data to file.\n",
            "Saved train_vcra1466.jpg normalized data to calibration file.\n",
            "Saved train_vcra1466.jpg processed calibration data to file.\n",
            "Saved train_vespsp0636.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0636.jpg processed calibration data to file.\n",
            "Saved valid_vzon0030.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0030.jpg processed calibration data to file.\n",
            "Saved train_amel0556.jpg normalized data to calibration file.\n",
            "Saved train_amel0556.jpg processed calibration data to file.\n",
            "Saved train_vvel0895.jpg normalized data to calibration file.\n",
            "Saved train_vvel0895.jpg processed calibration data to file.\n",
            "Saved train_vvel1333.jpg normalized data to calibration file.\n",
            "Saved train_vvel1333.jpg processed calibration data to file.\n",
            "Saved train_vzon0674.jpg normalized data to calibration file.\n",
            "Saved train_vzon0674.jpg processed calibration data to file.\n",
            "Saved train_vcra1915.jpg normalized data to calibration file.\n",
            "Saved train_vcra1915.jpg processed calibration data to file.\n",
            "Saved train_vcra0667.jpg normalized data to calibration file.\n",
            "Saved train_vcra0667.jpg processed calibration data to file.\n",
            "Saved test_amel0005.jpg normalized data to calibration file.\n",
            "Saved test_amel0005.jpg processed calibration data to file.\n",
            "Saved train_amel2196.jpg normalized data to calibration file.\n",
            "Saved train_amel2196.jpg processed calibration data to file.\n",
            "Saved train_vespsp0497.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0497.jpg processed calibration data to file.\n",
            "Saved train_vzon0763.jpg normalized data to calibration file.\n",
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            "Saved train_vcra1193.jpg normalized data to calibration file.\n",
            "Saved train_vcra1193.jpg processed calibration data to file.\n",
            "Saved train_vvel1159.jpg normalized data to calibration file.\n",
            "Saved train_vvel1159.jpg processed calibration data to file.\n",
            "Saved train_vvel1718.jpg normalized data to calibration file.\n",
            "Saved train_vvel1718.jpg processed calibration data to file.\n",
            "Saved valid_vzon0146.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0146.jpg processed calibration data to file.\n",
            "Saved train_amel0195.jpg normalized data to calibration file.\n",
            "Saved train_amel0195.jpg processed calibration data to file.\n",
            "Saved train_vespsp0008.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0008.jpg processed calibration data to file.\n",
            "Saved valid_amel0297.jpg normalized data to calibration file.\n",
            "Saved valid_amel0297.jpg processed calibration data to file.\n",
            "Saved train_vzon0453.jpg normalized data to calibration file.\n",
            "Saved train_vzon0453.jpg processed calibration data to file.\n",
            "Saved valid_amel0474.jpg normalized data to calibration file.\n",
            "Saved valid_amel0474.jpg processed calibration data to file.\n",
            "Saved train_amel1199.jpg normalized data to calibration file.\n",
            "Saved train_amel1199.jpg processed calibration data to file.\n",
            "Saved train_vespsp0269.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0269.jpg processed calibration data to file.\n",
            "Saved train_vvel1130.jpg normalized data to calibration file.\n",
            "Saved train_vvel1130.jpg processed calibration data to file.\n",
            "Saved train_vvel0033.jpg normalized data to calibration file.\n",
            "Saved train_vvel0033.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0292.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0292.jpg processed calibration data to file.\n",
            "Saved train_vzon2181.jpg normalized data to calibration file.\n",
            "Saved train_vzon2181.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0509.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0509.jpg processed calibration data to file.\n",
            "Saved train_vespsp1852.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1852.jpg processed calibration data to file.\n",
            "Saved train_vespsp0436.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0436.jpg processed calibration data to file.\n",
            "Saved valid_vzon0543.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0543.jpg processed calibration data to file.\n",
            "Saved train_vespsp2066.jpg normalized data to calibration file.\n",
            "Saved train_vespsp2066.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0060.jpg normalized data to calibration file.\n",
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            "Saved train_vvel0283.jpg normalized data to calibration file.\n",
            "Saved train_vvel0283.jpg processed calibration data to file.\n",
            "Saved train_vcra1934.jpg normalized data to calibration file.\n",
            "Saved train_vcra1934.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0186.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0186.jpg processed calibration data to file.\n",
            "Saved train_vespsp0147.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0147.jpg processed calibration data to file.\n",
            "Saved train_vcra1201.jpg normalized data to calibration file.\n",
            "Saved train_vcra1201.jpg processed calibration data to file.\n",
            "Saved train_vespsp1392.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1392.jpg processed calibration data to file.\n",
            "Saved train_vzon1527.jpg normalized data to calibration file.\n",
            "Saved train_vzon1527.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0553.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0553.jpg processed calibration data to file.\n",
            "Saved train_vzon1625.jpg normalized data to calibration file.\n",
            "Saved train_vzon1625.jpg processed calibration data to file.\n",
            "Saved valid_vzon0255.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0255.jpg processed calibration data to file.\n",
            "Saved train_vvel0333.jpg normalized data to calibration file.\n",
            "Saved train_vvel0333.jpg processed calibration data to file.\n",
            "Saved train_vespsp1708.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1708.jpg processed calibration data to file.\n",
            "Saved train_vespsp1112.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1112.jpg processed calibration data to file.\n",
            "Saved train_amel0433.jpg normalized data to calibration file.\n",
            "Saved train_amel0433.jpg processed calibration data to file.\n",
            "Saved train_vzon1279.jpg normalized data to calibration file.\n",
            "Saved train_vzon1279.jpg processed calibration data to file.\n",
            "Saved train_vespsp0469.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0469.jpg processed calibration data to file.\n",
            "Saved valid_amel0029.jpg normalized data to calibration file.\n",
            "Saved valid_amel0029.jpg processed calibration data to file.\n",
            "Saved valid_vzon0235.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0235.jpg processed calibration data to file.\n",
            "Saved train_amel0230.jpg normalized data to calibration file.\n",
            "Saved train_amel0230.jpg processed calibration data to file.\n",
            "Saved train_amel0118.jpg normalized data to calibration file.\n",
            "Saved train_amel0118.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0192.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0192.jpg processed calibration data to file.\n",
            "Saved test_vcra0138.jpg normalized data to calibration file.\n",
            "Saved test_vcra0138.jpg processed calibration data to file.\n",
            "Saved train_amel1236.jpg normalized data to calibration file.\n",
            "Saved train_amel1236.jpg processed calibration data to file.\n",
            "Saved train_vespsp1639.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1639.jpg processed calibration data to file.\n",
            "Saved train_vespsp0660.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0660.jpg processed calibration data to file.\n",
            "Saved train_vvel2010.jpg normalized data to calibration file.\n",
            "Saved train_vvel2010.jpg processed calibration data to file.\n",
            "Saved train_vcra2015.jpg normalized data to calibration file.\n",
            "Saved train_vcra2015.jpg processed calibration data to file.\n",
            "Saved train_vcra1350.jpg normalized data to calibration file.\n",
            "Saved train_vcra1350.jpg processed calibration data to file.\n",
            "Saved train_vzon0715.jpg normalized data to calibration file.\n",
            "Saved train_vzon0715.jpg processed calibration data to file.\n",
            "Saved train_vcra0005.jpg normalized data to calibration file.\n",
            "Saved train_vcra0005.jpg processed calibration data to file.\n",
            "Saved train_amel1089.jpg normalized data to calibration file.\n",
            "Saved train_amel1089.jpg processed calibration data to file.\n",
            "Saved train_vvel0014.jpg normalized data to calibration file.\n",
            "Saved train_vvel0014.jpg processed calibration data to file.\n",
            "Saved valid_vzon0233.jpg normalized data to calibration file.\n",
            "Saved valid_vzon0233.jpg processed calibration data to file.\n",
            "Saved test_amel0113.jpg normalized data to calibration file.\n",
            "Saved test_amel0113.jpg processed calibration data to file.\n",
            "Saved test_vvel0235.jpg normalized data to calibration file.\n",
            "Saved test_vvel0235.jpg processed calibration data to file.\n",
            "Saved train_vcra1326.jpg normalized data to calibration file.\n",
            "Saved train_vcra1326.jpg processed calibration data to file.\n",
            "Saved train_vzon0292.jpg normalized data to calibration file.\n",
            "Saved train_vzon0292.jpg processed calibration data to file.\n",
            "Saved train_vcra0452.jpg normalized data to calibration file.\n",
            "Saved train_vcra0452.jpg processed calibration data to file.\n",
            "Saved train_vzon0535.jpg normalized data to calibration file.\n",
            "Saved train_vzon0535.jpg processed calibration data to file.\n",
            "Saved train_vespsp0312.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0312.jpg processed calibration data to file.\n",
            "Saved train_amel0381.jpg normalized data to calibration file.\n",
            "Saved train_amel0381.jpg processed calibration data to file.\n",
            "Saved test_vespsp0178.jpg normalized data to calibration file.\n",
            "Saved test_vespsp0178.jpg processed calibration data to file.\n",
            "Saved train_vcra0947.jpg normalized data to calibration file.\n",
            "Saved train_vcra0947.jpg processed calibration data to file.\n",
            "Saved train_amel1716.jpg normalized data to calibration file.\n",
            "Saved train_amel1716.jpg processed calibration data to file.\n",
            "Saved train_amel0681.jpg normalized data to calibration file.\n",
            "Saved train_amel0681.jpg processed calibration data to file.\n",
            "Saved train_vvel0079.jpg normalized data to calibration file.\n",
            "Saved train_vvel0079.jpg processed calibration data to file.\n",
            "Saved train_vcra0780.jpg normalized data to calibration file.\n",
            "Saved train_vcra0780.jpg processed calibration data to file.\n",
            "Saved test_vvel0125.jpg normalized data to calibration file.\n",
            "Saved test_vvel0125.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0230.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0230.jpg processed calibration data to file.\n",
            "Saved train_vcra0093.jpg normalized data to calibration file.\n",
            "Saved train_vcra0093.jpg processed calibration data to file.\n",
            "Saved train_vvel0127.jpg normalized data to calibration file.\n",
            "Saved train_vvel0127.jpg processed calibration data to file.\n",
            "Saved valid_vespsp0551.jpg normalized data to calibration file.\n",
            "Saved valid_vespsp0551.jpg processed calibration data to file.\n",
            "Saved train_vespsp1985.jpg normalized data to calibration file.\n",
            "Saved train_vespsp1985.jpg processed calibration data to file.\n",
            "Saved train_vespsp0251.jpg normalized data to calibration file.\n",
            "Saved train_vespsp0251.jpg processed calibration data to file.\n",
            "Saved valid_vcra0442.jpg normalized data to calibration file.\n",
            "Saved valid_vcra0442.jpg processed calibration data to file.\n",
            "Saved test_vcra0254.jpg normalized data to calibration file.\n",
            "Saved test_vcra0254.jpg processed calibration data to file.\n",
            "All images processed and saved.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we’re finally ready to optimize it with this script, you can find sample .alls files here, I referenced yolo10nms.json as a base to create my alls file.\n",
        "\n",
        "Note that the change_output_activation applied to my CLS_layer, you can go back and verify this with Netron like specified above.\n",
        "\n"
      ],
      "metadata": {
        "id": "HpQ99d8JMMG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from hailo_sdk_client import ClientRunner\n",
        "\n",
        "# Define your model's HAR file name\n",
        "model_name = \"best_opset9\"\n",
        "hailo_model_har_name = f\"{model_name}_hailo_model.har\"\n",
        "hailo_model_har_name = \"modified_run_3_renamed_hailo_model.har\"\n",
        "\n",
        "# Ensure the HAR file exists\n",
        "assert os.path.isfile(hailo_model_har_name), \"Please provide a valid path for the HAR file\"\n",
        "\n",
        "# Initialize the ClientRunner with the HAR file\n",
        "runner = ClientRunner(har=hailo_model_har_name)\n",
        "\n",
        "# Define the model script to add a normalization layer\n",
        "# Normalization for [0, 1] range\n",
        "alls = \\\"\\\"\\\"\n",
        "normalization1 = normalization([0.0, 0.0, 0.0], [255.0, 255.0, 255.0])\n",
        "change_output_activation(conv54, sigmoid)\n",
        "change_output_activation(conv62, sigmoid)\n",
        "change_output_activation(conv80, sigmoid)\n",
        "nms_postprocess(\"/content/nms_layer_config.json\", meta_arch=yolov8, engine=cpu)\n",
        "performance_param(compiler_optimization_level=max)\n",
        "\\\"\\\"\\\"\n",
        "\n",
        "# Load the model script into the ClientRunner\n",
        "runner.load_model_script(alls)\n",
        "\n",
        "# Define a calibration dataset\n",
        "# Replace 'calib_dataset' with the actual dataset you're using for calibration\n",
        "# For example, if it's a directory of images, prepare the dataset accordingly\n",
        "calib_dataset = \"/content/output_dir/processed_calibration_data.npy\"\n",
        "\n",
        "# Perform optimization with the calibration dataset\n",
        "runner.optimize(calib_dataset)\n",
        "\n",
        "# Save the optimized model to a new Quantized HAR file\n",
        "quantized_model_har_path = f\"{model_name}_quantized_model.har\"\n",
        "runner.save_har(quantized_model_har_path)\n",
        "\n",
        "print(f\"Quantized HAR file saved to: {quantized_model_har_path}\")"
      ],
      "metadata": {
        "id": "Duj6NKtbf3QN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"optimize_model.py\", \"w\") as f:\n",
        "\n",
        "    f.write(\"\"\"\n",
        "\n",
        "import os\n",
        "from hailo_sdk_client import ClientRunner\n",
        "\n",
        "# Define your model's HAR file name\n",
        "model_name = \"best_opset9\"\n",
        "hailo_model_har_name = f\"{model_name}_hailo_model.har\"\n",
        "hailo_model_har_name = \"best_opset9_hailo_model.har\"\n",
        "\n",
        "# Ensure the HAR file exists\n",
        "assert os.path.isfile(\"/content/best_opset9_hailo_model.har\")\n",
        "\n",
        "# Initialize the ClientRunner with the HAR file\n",
        "runner = ClientRunner(har=hailo_model_har_name)\n",
        "\n",
        "# Define the model script to add a normalization layer\n",
        "# Normalization for [0, 1] range\n",
        "alls = \\\"\\\"\\\"\n",
        "normalization1 = normalization([0.0, 0.0, 0.0], [255.0, 255.0, 255.0])\n",
        "change_output_activation(conv54, sigmoid)\n",
        "change_output_activation(conv62, sigmoid)\n",
        "change_output_activation(conv80, sigmoid)\n",
        "nms_postprocess(\"/content/nms_layer_config.json\", meta_arch=yolov8, engine=cpu)\n",
        "performance_param(compiler_optimization_level=max)\n",
        "\\\"\\\"\\\"\n",
        "\n",
        "# Load the model script into the ClientRunner\n",
        "runner.load_model_script(alls)\n",
        "\n",
        "# Define a calibration dataset\n",
        "# Replace 'calib_dataset' with the actual dataset you're using for calibration\n",
        "# For example, if it's a directory of images, prepare the dataset accordingly\n",
        "calib_dataset = \"/content/output_dir/processed_calibration_data.npy\"\n",
        "\n",
        "# Perform optimization with the calibration dataset\n",
        "runner.optimize(calib_dataset)\n",
        "\n",
        "# Save the optimized model to a new Quantized HAR file\n",
        "quantized_model_har_path = f\"{model_name}_quantized_model.har\"\n",
        "runner.save_har(quantized_model_har_path)\n",
        "\n",
        "print(f\"Quantized HAR file saved to: {quantized_model_har_path}\")\n",
        "\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "8zXSH2RlMHj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now run it"
      ],
      "metadata": {
        "id": "BH33weHVMX08"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!my_env/bin/python optimize_model.py"
      ],
      "metadata": {
        "id": "Kfq92L58MayG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f26d3e69-86b1-49c2-e47a-27cec65cf6d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\u001b[32minfo\u001b[0m] ParsedPerformanceParam command, setting optimization_level(max=2)\n",
            "[\u001b[32minfo\u001b[0m] Loading model script commands to best_opset9 from string\n",
            "[\u001b[32minfo\u001b[0m] ParsedPerformanceParam command, setting optimization_level(max=2)\n",
            "[\u001b[32minfo\u001b[0m] The activation function of layer best_opset9/conv42 was replaced by a Sigmoid\n",
            "[\u001b[32minfo\u001b[0m] The activation function of layer best_opset9/conv65 was replaced by a Sigmoid\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/optimize_model.py\", line 37, in <module>\n",
            "    runner.optimize(calib_dataset)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_common/states/states.py\", line 16, in wrapped_func\n",
            "    return func(self, *args, **kwargs)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py\", line 2093, in optimize\n",
            "    self._optimize(calib_data, data_type=data_type, work_dir=work_dir)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_common/states/states.py\", line 16, in wrapped_func\n",
            "    return func(self, *args, **kwargs)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py\", line 1934, in _optimize\n",
            "    self._optimize_full_precision(calib_data=calib_data, data_type=data_type)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py\", line 1999, in _optimize_full_precision\n",
            "    self._sdk_backend.optimize_full_precision(calib_data=calib_data, data_type=data_type)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py\", line 1497, in optimize_full_precision\n",
            "    model, params = self._apply_model_modification_commands(model, params, update_model_and_params)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py\", line 1388, in _apply_model_modification_commands\n",
            "    model, params = command.apply(model, params, hw_consts=self.hw_arch.consts)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/script_parser/nms_postprocess_command.py\", line 387, in apply\n",
            "    pp_creator = create_nms_postprocess(\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/tools/core_postprocess/nms_postprocess.py\", line 1767, in create_nms_postprocess\n",
            "    pp_creator.prepare_hn_and_weights(hw_consts, engine, dfl_on_nn_core=dfl_on_nn_core)\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/tools/core_postprocess/nms_postprocess.py\", line 1125, in prepare_hn_and_weights\n",
            "    super().prepare_hn_and_weights(\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/tools/core_postprocess/nms_postprocess.py\", line 1089, in prepare_hn_and_weights\n",
            "    self.add_postprocess_layer_to_hn()\n",
            "  File \"/content/my_env/lib/python3.10/site-packages/hailo_sdk_client/tools/core_postprocess/nms_postprocess.py\", line 1040, in add_postprocess_layer_to_hn\n",
            "    raise NMSConfigPostprocessException(f\"The layer {encoded_layer.name} doesn't have one output layer\")\n",
            "hailo_sdk_client.tools.core_postprocess.nms_postprocess.NMSConfigPostprocessException: The layer best_opset9/conv41 doesn't have one output layer\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compiling model"
      ],
      "metadata": {
        "id": "Xqvg9ACHMg6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from hailo_sdk_client import ClientRunner\n",
        "\n",
        "# Define the quantized model HAR file\n",
        "model_name = \"modified_run_3_renamed\"\n",
        "quantized_model_har_path = f\"{model_name}_quantized_model.har\"\n",
        "\n",
        "# Initialize the ClientRunner with the HAR file\n",
        "runner = ClientRunner(har=quantized_model_har_path)\n",
        "print(\"[info] ClientRunner initialized successfully.\")\n",
        "\n",
        "# Compile the model\n",
        "try:\n",
        "    hef = runner.compile()\n",
        "    print(\"[info] Compilation completed successfully.\")\n",
        "except Exception as e:\n",
        "    print(f\"[error] Failed to compile the model: {e}\")\n",
        "    raise\n",
        "file_name = f\"{model_name}.hef\"\n",
        "with open(file_name, \"wb\") as f:\n",
        "    f.write(hef)"
      ],
      "metadata": {
        "id": "Ay75npCQMjZg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "now run"
      ],
      "metadata": {
        "id": "gNppU7pCM94D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!my_env/bin/python compile_model.py\n"
      ],
      "metadata": {
        "id": "bKKPT3ayM-r-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zip and Download the results to your local computer\n",
        "Data is lost when closing the page!!!\n",
        "\n",
        "If an error occurs:\n",
        "Click Runtime in the toolbar on the top\n",
        "Select Restart session / Sessie opnieuw starten from the drop down menu\n",
        "Run this codeblock again"
      ],
      "metadata": {
        "id": "2GczIZpaLcgE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DON'T FORGET TO DOWNLOAD THE RESULTS!!!\n",
        "\n",
        "# Data is lost when closing the page!!!\n",
        "\n",
        "# IN MOST CASES AN ERROR OCCURS:\n",
        "# Click `Runtime`, select `Restart session` / `Sessie opnieuw opstarten`\n",
        "# then run this codeblock again\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "try:\n",
        "  !zip -r /content/runs.zip /content/runs\n",
        "  files.download('/content/runs.zip')\n",
        "except Exception as e:\n",
        "  print(f\"An error occurred: {e}\")\n",
        "  print(\"Click 'Runtime' -> 'Restart session' and try running the code again.\")"
      ],
      "metadata": {
        "id": "bmKFC6x9LgBb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}