Jupyter Notebooks/TFJS/YOLOv8_TFJS_Conversion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Custom Trained YOLOv8 Model to TFJS Model Conversion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Dependencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TlzxNSQjjefg",
    "outputId": "f0b20d93-72f6-4f70-f82f-bb1f8619e065"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting onnx-tf\n",
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      "\u001b[?25hCollecting onnx>=1.10.2 (from onnx-tf)\n",
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      "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from onnx-tf) (6.0.1)\n",
      "Collecting tensorflow-addons (from onnx-tf)\n",
      "  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from onnx>=1.10.2->onnx-tf) (1.25.2)\n",
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      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons->onnx-tf) (24.0)\n",
      "Collecting typeguard<3.0.0,>=2.7 (from tensorflow-addons->onnx-tf)\n",
      "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
      "Installing collected packages: typeguard, onnx, tensorflow-addons, onnx-tf\n",
      "Successfully installed onnx-1.16.0 onnx-tf-1.10.0 tensorflow-addons-0.23.0 typeguard-2.13.3\n"
     ]
    }
   ],
   "source": [
    "!pip install onnx-tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e45gkv-Fjj9s",
    "outputId": "e0fe9711-2477-46f6-c9d2-081ba444a73c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
      "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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      "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
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      "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
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      "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
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      "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.8.0->ultralytics)\n",
      "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
      "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
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      "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, thop, ultralytics\n",
      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238 ultralytics-8.2.2\n"
     ]
    }
   ],
   "source": [
    "!pip install ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zHRxYMwRjqzd",
    "outputId": "0e28eefb-af78-4ee8-f9b2-c9b558cab862"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import onnx\n",
    "from onnx_tf.backend import prepare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Convert ONNX Model to SavedModel Format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MuFfEe5qkUG3"
   },
   "outputs": [],
   "source": [
    "tf_model_dir=\"TFJS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q9gJojvBkYdS",
    "outputId": "508570b8-afb0-4c6f-95e9-66e4c518e44b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1260: resize_nearest_neighbor (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.image.resize(...method=ResizeMethod.NEAREST_NEIGHBOR...)` instead.\n",
      "INFO:absl:Function `__call__` contains input name(s) x, y with unsupported characters which will be renamed to transpose_196_x, add_44_y in the SavedModel.\n",
      "INFO:absl:Found untraced functions such as gen_tensor_dict while saving (showing 1 of 1). These functions will not be directly callable after loading.\n",
      "INFO:absl:Writing fingerprint to TFJS/fingerprint.pb\n"
     ]
    }
   ],
   "source": [
    "onnx_model = onnx.load(\"/content/best.onnx\")\n",
    "tf_rep = prepare(onnx_model)\n",
    "tf_rep.export_graph(tf_model_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Convert into TFJS Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "l6-fWKNCkiNK",
    "outputId": "77fb414d-d315-4128-dcac-0fb88f93cf80"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'TFJS-fp32'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfjs_model_dir = f\"{tf_model_dir}-fp32\"\n",
    "tfjs_model_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "MgD0CcUSkilW",
    "outputId": "c765b59a-5544-416b-cfdc-de756d40d8c4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model --signature_name=serving_default --saved_model_tags=serve \"TFJS\" \"TFJS-fp32\"'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfjs_convert_command = f\"\"\"tensorflowjs_converter\n",
    "                 --input_format=tf_saved_model\n",
    "                 --output_format=tfjs_graph_model\n",
    "                 --signature_name=serving_default\n",
    "                 --saved_model_tags=serve\n",
    "                 \"{tf_model_dir}\"\n",
    "                 \"{tfjs_model_dir}\"\n",
    "                 \"\"\"\n",
    "tfjs_convert_command = \" \".join(tfjs_convert_command.split())\n",
    "tfjs_convert_command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zdn89y7qkint",
    "outputId": "2ec31fe1-6375-4121-b887-fab70f16781e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflowjs\n",
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      "Installing collected packages: namex, wurlitzer, packaging, optree, ml-dtypes, h5py, tensorboard, keras, tensorflow, tf-keras, tensorflow-decision-forests, tensorflowjs\n",
      "  Attempting uninstall: packaging\n",
      "    Found existing installation: packaging 24.0\n",
      "    Uninstalling packaging-24.0:\n",
      "      Successfully uninstalled packaging-24.0\n",
      "  Attempting uninstall: ml-dtypes\n",
      "    Found existing installation: ml-dtypes 0.2.0\n",
      "    Uninstalling ml-dtypes-0.2.0:\n",
      "      Successfully uninstalled ml-dtypes-0.2.0\n",
      "  Attempting uninstall: h5py\n",
      "    Found existing installation: h5py 3.9.0\n",
      "    Uninstalling h5py-3.9.0:\n",
      "      Successfully uninstalled h5py-3.9.0\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.15.2\n",
      "    Uninstalling tensorboard-2.15.2:\n",
      "      Successfully uninstalled tensorboard-2.15.2\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.15.0\n",
      "    Uninstalling keras-2.15.0:\n",
      "      Successfully uninstalled keras-2.15.0\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.15.0\n",
      "    Uninstalling tensorflow-2.15.0:\n",
      "      Successfully uninstalled tensorflow-2.15.0\n",
      "  Attempting uninstall: tf-keras\n",
      "    Found existing installation: tf_keras 2.15.1\n",
      "    Uninstalling tf_keras-2.15.1:\n",
      "      Successfully uninstalled tf_keras-2.15.1\n",
      "Successfully installed h5py-3.11.0 keras-3.2.1 ml-dtypes-0.3.2 namex-0.0.8 optree-0.11.0 packaging-23.2 tensorboard-2.16.2 tensorflow-2.16.1 tensorflow-decision-forests-1.9.0 tensorflowjs-4.18.0 tf-keras-2.16.0 wurlitzer-3.0.3\n"
     ]
    }
   ],
   "source": [
    "!sudo pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RO8uP2rUkipz",
    "outputId": "9c7afa83-7aa6-427a-d9a9-efb4141e41b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exporting TensorFlow SavedModel to TensorFlow.js Graph model...\n",
      "2024-04-21 12:17:04.493757: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
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      "weight StatefulPartitionedCall/onnx_tf_prefix_/model.22/Constant_2 with shape (3,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/Where_2 with shape (0, 1) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/Cast_6 with shape (1,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/GatherV2_2 with shape (0,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/zeros_2 with shape () and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/onnx_tf_prefix_Constant_247 with shape (2,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/SelectV2_5 with shape (1,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/SelectV2_6 with shape (1,) and dtype int64 was auto converted to the type int32\n",
      "weight StatefulPartitionedCall/SelectV2_2 with shape (1,) and dtype int64 was auto converted to the type int32\n"
     ]
    }
   ],
   "source": [
    "print(\"Exporting TensorFlow SavedModel to TensorFlow.js Graph model...\")\n",
    "conversion_result = %sx $tfjs_convert_command\n",
    "print(\"\\n\".join(conversion_result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Save the TFJS Model folder as Zip File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5EyG0xlok-NF",
    "outputId": "a6b839a0-2833-441d-f639-426b2a2c31ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: TFJS-fp32/ (stored 0%)\n",
      "  adding: TFJS-fp32/group1-shard1of3.bin (deflated 11%)\n",
      "  adding: TFJS-fp32/group1-shard2of3.bin (deflated 7%)\n",
      "  adding: TFJS-fp32/group1-shard3of3.bin (deflated 7%)\n",
      "  adding: TFJS-fp32/model.json (deflated 95%)\n"
     ]
    }
   ],
   "source": [
    "!zip -r yolov8FT-tfjs.zip TFJS-fp32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}