notebooks/segmentation_connect_custom_dataset.ipynb

{
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5aISf1B-AGDQ"
   },
   "source": [
    "# SuperGradients Semantic Segmentation How to Connect Custom Dataset\n",
    "\n",
    "In this tutorial we will explore how you can connect your custom Semantic Segmentation dataset to SG.\n",
    "\n",
    "Since SG trainer is fully compatible with PyTorch data loaders, we will demonstrate how to build one and use it.\n",
    "\n",
    "The notebook is divided into 5 sections:\n",
    "1. Experiment setup\n",
    "2. Dataset definition: create a proxy dataset and create a dataloader\n",
    "3. Architecture definition: pre-trained PPLiteSeg on Cityscapes  \n",
    "4. Training setup\n",
    "5. Training and Evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1nPOPmc1lGp"
   },
   "source": [
    "#Install SG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VAssbjJw7Yt1"
   },
   "source": [
    "The cell below will install **super_gradients** which will automatically get all its dependencies. Let's import all the installed libraries to make sure they installed succesfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JKce1SM6voVH",
    "outputId": "e27e79a3-5b89-4869-bf1b-ea54ef60331f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m12.0/12.0 MB\u001B[0m \u001B[31m36.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m135.8/135.8 kB\u001B[0m \u001B[31m21.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m684.5/684.5 kB\u001B[0m \u001B[31m39.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m2.9/2.9 MB\u001B[0m \u001B[31m61.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m2.8/2.8 MB\u001B[0m \u001B[31m73.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m408.6/408.6 kB\u001B[0m \u001B[31m24.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m154.5/154.5 kB\u001B[0m \u001B[31m22.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m79.5/79.5 kB\u001B[0m \u001B[31m11.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m4.5/4.5 MB\u001B[0m \u001B[31m89.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m13.5/13.5 MB\u001B[0m \u001B[31m96.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m68.0/68.0 kB\u001B[0m \u001B[31m10.8 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",
      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m17.0/17.0 MB\u001B[0m \u001B[31m88.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m3.3/3.3 MB\u001B[0m \u001B[31m82.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m2.2/2.2 MB\u001B[0m \u001B[31m77.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m458.9/458.9 kB\u001B[0m \u001B[31m46.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m46.0/46.0 kB\u001B[0m \u001B[31m6.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m11.3/11.3 MB\u001B[0m \u001B[31m74.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m79.8/79.8 kB\u001B[0m \u001B[31m11.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m108.3/108.3 kB\u001B[0m \u001B[31m13.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m176.0/176.0 kB\u001B[0m \u001B[31m26.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m407.7/407.7 kB\u001B[0m \u001B[31m45.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m107.7/107.7 kB\u001B[0m \u001B[31m13.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m277.4/277.4 kB\u001B[0m \u001B[31m36.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m2.8/2.8 MB\u001B[0m \u001B[31m111.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m913.9/913.9 kB\u001B[0m \u001B[31m78.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m117.0/117.0 kB\u001B[0m \u001B[31m18.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m575.5/575.5 kB\u001B[0m \u001B[31m50.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m121.1/121.1 kB\u001B[0m \u001B[31m16.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m86.8/86.8 kB\u001B[0m \u001B[31m13.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m120.0/120.0 kB\u001B[0m \u001B[31m16.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m120.0/120.0 kB\u001B[0m \u001B[31m17.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m120.6/120.6 kB\u001B[0m \u001B[31m16.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m83.5/83.5 kB\u001B[0m \u001B[31m13.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m83.5/83.5 kB\u001B[0m \u001B[31m13.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m84.7/84.7 kB\u001B[0m \u001B[31m13.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m99.2/99.2 kB\u001B[0m \u001B[31m14.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m99.2/99.2 kB\u001B[0m \u001B[31m15.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m99.8/99.8 kB\u001B[0m \u001B[31m15.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m89.4/89.4 kB\u001B[0m \u001B[31m14.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m89.4/89.4 kB\u001B[0m \u001B[31m14.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m90.6/90.6 kB\u001B[0m \u001B[31m14.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m92.6/92.6 kB\u001B[0m \u001B[31m14.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m92.6/92.6 kB\u001B[0m \u001B[31m14.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m92.6/92.6 kB\u001B[0m \u001B[31m15.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m94.0/94.0 kB\u001B[0m \u001B[31m15.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m105.0/105.0 kB\u001B[0m \u001B[31m15.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m46.2/46.2 kB\u001B[0m \u001B[31m6.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m106.8/106.8 kB\u001B[0m \u001B[31m17.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m194.6/194.6 kB\u001B[0m \u001B[31m29.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m58.1/58.1 kB\u001B[0m \u001B[31m9.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Building wheel for pycocotools (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for termcolor (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for treelib (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for coverage (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for xhtml2pdf (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for stringcase (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for svglib (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "lida 0.0.10 requires fastapi, which is not installed.\n",
      "lida 0.0.10 requires kaleido, which is not installed.\n",
      "lida 0.0.10 requires python-multipart, which is not installed.\n",
      "lida 0.0.10 requires uvicorn, which is not installed.\n",
      "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.23.0 which is incompatible.\u001B[0m\u001B[31m\n",
      "\u001B[0m"
     ]
    }
   ],
   "source": [
    "! pip install -qq super-gradients==3.7.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "njthhNJR1pJm"
   },
   "source": [
    "# 1. Experiment setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YPym4wvpOcOJ"
   },
   "source": [
    "We will first initialize our **trainer** which will be in charge of everything, like training, evaluation, saving checkpoints, plotting etc.\n",
    "\n",
    "The **experiment name** argument is important as every checkpoints, logs and tensorboards to be saved in a directory with the same name. This directory will be created as a sub-directory of **ckpt_root_dir** as follow:\n",
    "\n",
    "```\n",
    "ckpt_root_dir\n",
    "|─── experiment_name_1\n",
    "│       ckpt_best.pth                     # Model checkpoint on best epoch\n",
    "│       ckpt_latest.pth                   # Model checkpoint on last epoch\n",
    "│       average_model.pth                 # Model checkpoint averaged over epochs\n",
    "│       events.out.tfevents.1659878383... # Tensorflow artifacts of a specific run\n",
    "│       log_Aug07_11_52_48.txt            # Trainer logs of a specific run\n",
    "└─── experiment_name_2\n",
    "        ...\n",
    "```\n",
    "In this notebook multi-gpu training is set as `OFF`, for Distributed training multi_gpu can be set as\n",
    " `MultiGPUMode.DISTRIBUTED_DATA_PARALLEL` or `MultiGPUMode.DATA_PARALLEL`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A2PlnTWpimnH"
   },
   "source": [
    "Let's define **ckpt_root_dir** inside the Colab, later we can use it to start TensorBoard and monitor the run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_v1N3kXs3wo1",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "bf8d1fba-3fa0-41af-97a6-9bc23626e78e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The console stream is logged into /root/sg_logs/console.log\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[2023-11-13 12:01:12] INFO - crash_tips_setup.py - Crash tips is enabled. You can set your environment variable to CRASH_HANDLER=FALSE to disable it\n",
      "[2023-11-13 12:01:13] WARNING - __init__.py - Failed to import pytorch_quantization\n",
      "[2023-11-13 12:01:13] INFO - utils.py - NumExpr defaulting to 2 threads.\n",
      "[2023-11-13 12:01:24] WARNING - calibrator.py - Failed to import pytorch_quantization\n",
      "[2023-11-13 12:01:24] WARNING - export.py - Failed to import pytorch_quantization\n",
      "[2023-11-13 12:01:24] WARNING - selective_quantization_utils.py - Failed to import pytorch_quantization\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: boto3 required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: deprecated required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: coverage required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: sphinx-rtd-theme required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: torchmetrics required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: hydra-core required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: omegaconf required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: onnxruntime required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: onnx required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: einops required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: treelib required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: stringcase required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: rapidfuzz required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: json-tricks required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: onnx-simplifier required but not found\u001B[0m\n",
      "[2023-11-13 12:01:24] WARNING - env_sanity_check.py - \u001B[31mFailed to verify installed packages: data-gradients required but not found\u001B[0m\n"
     ]
    }
   ],
   "source": [
    "from super_gradients.training import Trainer, MultiGPUMode\n",
    "\n",
    "\n",
    "CHECKPOINT_DIR = './notebook_ckpts/'\n",
    "trainer = Trainer(experiment_name='transfer_learning_semantic_segementation_ppLite', ckpt_root_dir=CHECKPOINT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J9ZaMulSvwhr"
   },
   "source": [
    "# 2. Dataset definition\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1TXuJKkKzFJ"
   },
   "source": [
    "## 2.1 Generate Proxy Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y7us7VHRig7M"
   },
   "source": [
    "\n",
    "A proxy dataset generation is available merely to demonstrate an end-to-end training pipeline in this notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "wbdVYnIyjgv-"
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# creation of proxy dataset to demonstrate usage\n",
    "def generate_proxy_dataset(write_path: str, num_samples: int, num_classes: int, img_size: int = 256):\n",
    "    # Create training files and text\n",
    "    os.makedirs(os.path.join(write_path, 'images', 'train'), exist_ok=True)\n",
    "    os.makedirs(os.path.join(write_path, 'images', 'val'), exist_ok=True)\n",
    "    os.makedirs(os.path.join(write_path, 'labels', 'train'), exist_ok=True)\n",
    "    os.makedirs(os.path.join(write_path, 'labels', 'val'), exist_ok=True)\n",
    "\n",
    "    train_fp = open(os.path.join(write_path, 'train.txt'), 'w')\n",
    "    val_fp = open(os.path.join(write_path, 'val.txt'), 'w')\n",
    "\n",
    "    # Create random samples\n",
    "    for n in range(num_samples):\n",
    "        img = np.random.rand(img_size, img_size, 3) * 255\n",
    "        img = Image.fromarray(img.astype('uint8')).convert('RGB')\n",
    "\n",
    "        lbl = np.random.randint(0, num_classes, size=(img_size, img_size))\n",
    "        lbl = Image.fromarray(lbl.astype('uint8')).convert('L')\n",
    "\n",
    "        im_string = '%000d.jpg' % n\n",
    "        lbl_string = '%000d.png' % n\n",
    "\n",
    "        img_train_fn = os.path.join(write_path, 'images', 'train', im_string)\n",
    "        img_val_fn = img_train_fn.replace(\"train\", \"val\")\n",
    "        img.save(img_train_fn)\n",
    "        img.save(img_val_fn)\n",
    "\n",
    "        lbl_train_fn = os.path.join(write_path, 'labels', 'train', lbl_string)\n",
    "        lbl_val_fn = lbl_train_fn.replace(\"train\", \"val\")\n",
    "        lbl.save(lbl_train_fn)\n",
    "        lbl.save(lbl_val_fn)\n",
    "\n",
    "        train_fp.write(f\"{img_train_fn} {lbl_train_fn}\\n\")\n",
    "        val_fp.write(f\"{img_val_fn} {lbl_val_fn}\\n\")\n",
    "\n",
    "    train_fp.close()\n",
    "    val_fp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "DXu4yfuZoiv0"
   },
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "data_dir = os.path.join(os.getcwd(), 'example_data')\n",
    "generate_proxy_dataset(data_dir, num_samples=10, num_classes=num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MDksFYrIqClt"
   },
   "source": [
    "## 2.2 Create Torch Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "AGziBKSIqaUu"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    \"\"\"\n",
    "    A PyTorch Dataset class to be used in a PyTorch DataLoader to create batches.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, data_folder, split):\n",
    "        \"\"\"\n",
    "        :param data_folder: folder where data files are stored\n",
    "        :param split: split, one of 'TRAIN' or 'TEST'\n",
    "        \"\"\"\n",
    "        self.data_folder = data_folder\n",
    "        self.split = split.lower()\n",
    "        assert self.split in {'train', 'val'}\n",
    "\n",
    "        # Read data files\n",
    "        with open(os.path.join(data_folder, self.split + '.txt'), 'r') as f:\n",
    "            data_lines = f.readlines()\n",
    "        self.samples_fn = [line.strip().split(\" \") for line in data_lines]\n",
    "\n",
    "        self.transforms = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        # Read image and label\n",
    "        image = Image.open(self.samples_fn[i][0]).convert('RGB')\n",
    "        label = Image.open(self.samples_fn[i][1])\n",
    "\n",
    "        image_tensor = self.transforms(image)\n",
    "        label_tensor = torch.from_numpy(np.array(label)).long()\n",
    "\n",
    "        return image_tensor, label_tensor\n",
    "\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples_fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "2B0hlas_1Rh-"
   },
   "outputs": [],
   "source": [
    "train_dataset = CustomDataset(data_dir, split=\"train\")\n",
    "val_dataset = CustomDataset(data_dir, split=\"val\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eIG5tsiuor9E"
   },
   "source": [
    "Let's have a look at the first sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ZsHqcq1jpN0F",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "5976e582-e08a-4642-bd04-eaf613d04a97"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([3, 256, 256]) torch.Size([256, 256])\n",
      "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n"
     ]
    }
   ],
   "source": [
    "img, lbl = train_dataset[0]\n",
    "print(img.shape, lbl.shape)\n",
    "print(torch.unique(lbl))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aWfFrYLzo9j8"
   },
   "source": [
    "## 2.C Create Torch Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "XrWjWfjXnw_r"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)\n",
    "val_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vB1sGPO8qwZJ"
   },
   "source": [
    "Lets' have a look at the first batch:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "O-KuZQ3XBduM",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "7addf002-9c95-4252-fad7-510b2a0ab333"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 256, 256])"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "next(iter(train_dataloader))[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fFfvyMHU32QF"
   },
   "source": [
    "\n",
    "# 3. Architecture definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EpqgjQjl4awr"
   },
   "source": [
    "SG includes implementations of many different architectures for object detection tasks that can be found [here](https://github.com/Deci-AI/super-gradients#implemented-model-architectures)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GNM64JAa4sbF"
   },
   "source": [
    "Create a PPLiteSeg nn.Module, with 1 class segmentation head classifier. For simplicity `use_aux_head` is set as `False`\n",
    "and extra Auxiliary heads aren't used for training.\n",
    "\n",
    "Other segmentation modules can be used for this task such as, DDRNet, STDC and RegSeg.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "YDK4btf04Gbu",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "11d5aad2-dc94-4231-8e61-c4b968857370"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Downloading: \"https://sghub.deci.ai/models/pp_lite_t_seg75_cityscapes.pth\" to /root/.cache/torch/hub/checkpoints/pp_lite_t_seg75_cityscapes.pth\n",
      "100%|██████████| 31.4M/31.4M [00:00<00:00, 223MB/s]\n",
      "[2023-11-13 12:03:09] INFO - checkpoint_utils.py - Successfully loaded pretrained weights for architecture pp_lite_t_seg75\n"
     ]
    }
   ],
   "source": [
    "from super_gradients.training import models\n",
    "from super_gradients.common.object_names import Models\n",
    "\n",
    "model = models.get(model_name=Models.PP_LITE_T_SEG75,\n",
    "                   arch_params={\"use_aux_heads\": False},\n",
    "                   num_classes=num_classes,\n",
    "                   pretrained_weights=\"cityscapes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40UcYJ3u5JyF"
   },
   "source": [
    "That being said, SG allows you to use one of SG implemented architectures or your custom architecture, as long as it inherits torch.nn.Module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LYPVR-XM4GsZ"
   },
   "source": [
    "# 4. Training setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6K_56lDV8azX"
   },
   "source": [
    "\n",
    "Here we define the training recipe. The full parameters can be found here  [training parameters supported](https://deci-ai.github.io/super-gradients/user_guide.html#training-parameters).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "3eRe0hBz4G1n"
   },
   "outputs": [],
   "source": [
    "from super_gradients.training.metrics.segmentation_metrics import IoU\n",
    "from super_gradients.training.utils.callbacks import BinarySegmentationVisualizationCallback, Phase\n",
    "\n",
    "\n",
    "train_params = {\"max_epochs\": 10,\n",
    "                \"lr_mode\": \"cosine\",\n",
    "                \"initial_lr\": 0.005,\n",
    "                \"optimizer\": \"SGD\",\n",
    "                \"loss\": \"CrossEntropyLoss\",\n",
    "                \"average_best_models\": False,\n",
    "                \"metric_to_watch\": \"IoU\",\n",
    "                \"greater_metric_to_watch_is_better\": True,\n",
    "                \"train_metrics_list\": [IoU(num_classes=10)],\n",
    "                \"valid_metrics_list\": [IoU(num_classes=10)],\n",
    "                \"loss_logging_items_names\": [\"loss\"],\n",
    "                \"phase_callbacks\": [BinarySegmentationVisualizationCallback(phase=Phase.VALIDATION_BATCH_END,\n",
    "                                                                            freq=1,\n",
    "                                                                            last_img_idx_in_batch=4)],\n",
    "\n",
    "                }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D3tVVUhy4OqP"
   },
   "source": [
    "# 5. Training and evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8tKUuxbe9NlQ"
   },
   "source": [
    "The logs and the checkpoint for the latest epoch will be kept in your experiment folder.\n",
    "\n",
    "To start training we'll call train(...) and provide it with the objects we construted above: the model, the training parameters and the data loaders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "-Ojnc1bk9L3s",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "2c2ac68b-75f7-48c0-db0f-7b579c743013"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[2023-11-13 12:03:20] WARNING - sg_trainer.py - Train dataset size % batch_size != 0 and drop_last=False, this might result in smaller last batch.\n",
      "[2023-11-13 12:03:28] INFO - sg_trainer.py - Starting a new run with `run_id=RUN_20231113_120328_854372`\n",
      "[2023-11-13 12:03:28] INFO - sg_trainer.py - Checkpoints directory: ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The console stream is now moved to ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/console_Nov13_12_03_28.txt\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[2023-11-13 12:03:29] INFO - sg_trainer_utils.py - TRAINING PARAMETERS:\n",
      "    - Mode:                         Single GPU\n",
      "    - Number of GPUs:               1          (1 available on the machine)\n",
      "    - Full dataset size:            10         (len(train_set))\n",
      "    - Batch size per GPU:           4          (batch_size)\n",
      "    - Batch Accumulate:             1          (batch_accumulate)\n",
      "    - Total batch size:             4          (num_gpus * batch_size)\n",
      "    - Effective Batch size:         4          (num_gpus * batch_size * batch_accumulate)\n",
      "    - Iterations per epoch:         3          (len(train_loader))\n",
      "    - Gradient updates per epoch:   3          (len(train_loader) / batch_accumulate)\n",
      "\n",
      "[2023-11-13 12:03:29] INFO - sg_trainer.py - Started training for 10 epochs (0/9)\n",
      "\n",
      "Train epoch 0: 100%|██████████| 3/3 [00:08<00:00,  2.99s/it, CrossEntropyLoss=3.04, IoU=0.0464, gpu_mem=0.686]\n",
      "Validating: 100%|██████████| 3/3 [00:00<00:00,  3.41it/s]\n",
      "[2023-11-13 12:03:39] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:39] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.015268713235855103\n",
      "Train epoch 1:   0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 0\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 3.0408\n",
      "│   └── Iou = 0.0464\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.9924\n",
      "    └── Iou = 0.0153\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 1: 100%|██████████| 3/3 [00:00<00:00,  4.61it/s, CrossEntropyLoss=2.73, IoU=0.0493, gpu_mem=0.686]\n",
      "Validating epoch 1: 100%|██████████| 3/3 [00:00<00:00,  5.66it/s]\n",
      "[2023-11-13 12:03:41] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:41] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.03659294173121452\n",
      "Train epoch 2:   0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 1\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.7343\n",
      "│   │   ├── Epoch N-1      = 3.0408 (\u001B[32m↘ -0.3065\u001B[0m)\n",
      "│   │   └── Best until now = 3.0408 (\u001B[32m↘ -0.3065\u001B[0m)\n",
      "│   └── Iou = 0.0493\n",
      "│       ├── Epoch N-1      = 0.0464 (\u001B[32m↗ 0.0029\u001B[0m)\n",
      "│       └── Best until now = 0.0464 (\u001B[32m↗ 0.0029\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.4724\n",
      "    │   ├── Epoch N-1      = 2.9924 (\u001B[32m↘ -0.52\u001B[0m)\n",
      "    │   └── Best until now = 2.9924 (\u001B[32m↘ -0.52\u001B[0m)\n",
      "    └── Iou = 0.0366\n",
      "        ├── Epoch N-1      = 0.0153 (\u001B[32m↗ 0.0213\u001B[0m)\n",
      "        └── Best until now = 0.0153 (\u001B[32m↗ 0.0213\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 2: 100%|██████████| 3/3 [00:00<00:00,  6.72it/s, CrossEntropyLoss=2.59, IoU=0.0517, gpu_mem=0.686]\n",
      "Validating epoch 2: 100%|██████████| 3/3 [00:00<00:00,  4.14it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 2\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.5864\n",
      "│   │   ├── Epoch N-1      = 2.7343 (\u001B[32m↘ -0.1479\u001B[0m)\n",
      "│   │   └── Best until now = 2.7343 (\u001B[32m↘ -0.1479\u001B[0m)\n",
      "│   └── Iou = 0.0517\n",
      "│       ├── Epoch N-1      = 0.0493 (\u001B[32m↗ 0.0024\u001B[0m)\n",
      "│       └── Best until now = 0.0493 (\u001B[32m↗ 0.0024\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.7345\n",
      "    │   ├── Epoch N-1      = 2.4724 (\u001B[31m↗ 0.2621\u001B[0m)\n",
      "    │   └── Best until now = 2.4724 (\u001B[31m↗ 0.2621\u001B[0m)\n",
      "    └── Iou = 0.0301\n",
      "        ├── Epoch N-1      = 0.0366 (\u001B[31m↘ -0.0065\u001B[0m)\n",
      "        └── Best until now = 0.0366 (\u001B[31m↘ -0.0065\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 3: 100%|██████████| 3/3 [00:00<00:00,  6.46it/s, CrossEntropyLoss=2.52, IoU=0.0471, gpu_mem=0.686]\n",
      "Validating epoch 3: 100%|██████████| 3/3 [00:00<00:00,  3.83it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 3\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.517\n",
      "│   │   ├── Epoch N-1      = 2.5864 (\u001B[32m↘ -0.0693\u001B[0m)\n",
      "│   │   └── Best until now = 2.5864 (\u001B[32m↘ -0.0693\u001B[0m)\n",
      "│   └── Iou = 0.0471\n",
      "│       ├── Epoch N-1      = 0.0517 (\u001B[31m↘ -0.0046\u001B[0m)\n",
      "│       └── Best until now = 0.0517 (\u001B[31m↘ -0.0046\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.9355\n",
      "    │   ├── Epoch N-1      = 2.7345 (\u001B[31m↗ 0.201\u001B[0m)\n",
      "    │   └── Best until now = 2.4724 (\u001B[31m↗ 0.4631\u001B[0m)\n",
      "    └── Iou = 0.0264\n",
      "        ├── Epoch N-1      = 0.0301 (\u001B[31m↘ -0.0037\u001B[0m)\n",
      "        └── Best until now = 0.0366 (\u001B[31m↘ -0.0102\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 4: 100%|██████████| 3/3 [00:00<00:00,  6.13it/s, CrossEntropyLoss=2.46, IoU=0.0458, gpu_mem=0.686]\n",
      "Validating epoch 4: 100%|██████████| 3/3 [00:00<00:00,  4.07it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 4\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.4638\n",
      "│   │   ├── Epoch N-1      = 2.517  (\u001B[32m↘ -0.0533\u001B[0m)\n",
      "│   │   └── Best until now = 2.517  (\u001B[32m↘ -0.0533\u001B[0m)\n",
      "│   └── Iou = 0.0458\n",
      "│       ├── Epoch N-1      = 0.0471 (\u001B[31m↘ -0.0013\u001B[0m)\n",
      "│       └── Best until now = 0.0517 (\u001B[31m↘ -0.0059\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.8244\n",
      "    │   ├── Epoch N-1      = 2.9355 (\u001B[32m↘ -0.1111\u001B[0m)\n",
      "    │   └── Best until now = 2.4724 (\u001B[31m↗ 0.352\u001B[0m)\n",
      "    └── Iou = 0.0319\n",
      "        ├── Epoch N-1      = 0.0264 (\u001B[32m↗ 0.0055\u001B[0m)\n",
      "        └── Best until now = 0.0366 (\u001B[31m↘ -0.0047\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 5: 100%|██████████| 3/3 [00:00<00:00,  6.76it/s, CrossEntropyLoss=2.44, IoU=0.0473, gpu_mem=0.686]\n",
      "Validating epoch 5: 100%|██████████| 3/3 [00:00<00:00,  5.36it/s]\n",
      "[2023-11-13 12:03:48] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:48] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.04010358452796936\n",
      "Train epoch 6:   0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 5\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.4423\n",
      "│   │   ├── Epoch N-1      = 2.4638 (\u001B[32m↘ -0.0215\u001B[0m)\n",
      "│   │   └── Best until now = 2.4638 (\u001B[32m↘ -0.0215\u001B[0m)\n",
      "│   └── Iou = 0.0473\n",
      "│       ├── Epoch N-1      = 0.0458 (\u001B[32m↗ 0.0015\u001B[0m)\n",
      "│       └── Best until now = 0.0517 (\u001B[31m↘ -0.0044\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.5476\n",
      "    │   ├── Epoch N-1      = 2.8244 (\u001B[32m↘ -0.2768\u001B[0m)\n",
      "    │   └── Best until now = 2.4724 (\u001B[31m↗ 0.0752\u001B[0m)\n",
      "    └── Iou = 0.0401\n",
      "        ├── Epoch N-1      = 0.0319 (\u001B[32m↗ 0.0082\u001B[0m)\n",
      "        └── Best until now = 0.0366 (\u001B[32m↗ 0.0035\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 6: 100%|██████████| 3/3 [00:00<00:00,  8.34it/s, CrossEntropyLoss=2.4, IoU=0.0496, gpu_mem=0.686]\n",
      "Validating epoch 6: 100%|██████████| 3/3 [00:00<00:00,  5.47it/s]\n",
      "[2023-11-13 12:03:49] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:49] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.04576006531715393\n",
      "Train epoch 7:  33%|███▎      | 1/3 [00:00<00:00,  5.54it/s, CrossEntropyLoss=2.37, IoU=0.0513, gpu_mem=0.686]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 6\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.4006\n",
      "│   │   ├── Epoch N-1      = 2.4423 (\u001B[32m↘ -0.0418\u001B[0m)\n",
      "│   │   └── Best until now = 2.4423 (\u001B[32m↘ -0.0418\u001B[0m)\n",
      "│   └── Iou = 0.0496\n",
      "│       ├── Epoch N-1      = 0.0473 (\u001B[32m↗ 0.0023\u001B[0m)\n",
      "│       └── Best until now = 0.0517 (\u001B[31m↘ -0.0021\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.4681\n",
      "    │   ├── Epoch N-1      = 2.5476 (\u001B[32m↘ -0.0795\u001B[0m)\n",
      "    │   └── Best until now = 2.4724 (\u001B[32m↘ -0.0043\u001B[0m)\n",
      "    └── Iou = 0.0458\n",
      "        ├── Epoch N-1      = 0.0401 (\u001B[32m↗ 0.0057\u001B[0m)\n",
      "        └── Best until now = 0.0401 (\u001B[32m↗ 0.0057\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 7: 100%|██████████| 3/3 [00:00<00:00,  8.82it/s, CrossEntropyLoss=2.38, IoU=0.0522, gpu_mem=0.686]\n",
      "Validating epoch 7: 100%|██████████| 3/3 [00:00<00:00,  5.36it/s]\n",
      "[2023-11-13 12:03:50] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:50] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.049313418567180634\n",
      "Train epoch 8:  33%|███▎      | 1/3 [00:00<00:00,  5.65it/s, CrossEntropyLoss=2.37, IoU=0.0524, gpu_mem=0.686]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 7\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.3774\n",
      "│   │   ├── Epoch N-1      = 2.4006 (\u001B[32m↘ -0.0232\u001B[0m)\n",
      "│   │   └── Best until now = 2.4006 (\u001B[32m↘ -0.0232\u001B[0m)\n",
      "│   └── Iou = 0.0522\n",
      "│       ├── Epoch N-1      = 0.0496 (\u001B[32m↗ 0.0026\u001B[0m)\n",
      "│       └── Best until now = 0.0517 (\u001B[32m↗ 0.0005\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.4264\n",
      "    │   ├── Epoch N-1      = 2.4681 (\u001B[32m↘ -0.0417\u001B[0m)\n",
      "    │   └── Best until now = 2.4681 (\u001B[32m↘ -0.0417\u001B[0m)\n",
      "    └── Iou = 0.0493\n",
      "        ├── Epoch N-1      = 0.0458 (\u001B[32m↗ 0.0036\u001B[0m)\n",
      "        └── Best until now = 0.0458 (\u001B[32m↗ 0.0036\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 8: 100%|██████████| 3/3 [00:00<00:00,  8.79it/s, CrossEntropyLoss=2.38, IoU=0.0524, gpu_mem=0.686]\n",
      "Validating epoch 8: 100%|██████████| 3/3 [00:00<00:00,  5.50it/s]\n",
      "[2023-11-13 12:03:54] INFO - base_sg_logger.py - Checkpoint saved in ./notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231113_120328_854372/ckpt_best.pth\n",
      "[2023-11-13 12:03:54] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.05060109123587608\n",
      "Train epoch 9:   0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "===========================================================\n",
      "SUMMARY OF EPOCH 8\n",
      "├── Train\n",
      "│   ├── Crossentropyloss = 2.378\n",
      "│   │   ├── Epoch N-1      = 2.3774 (\u001B[31m↗ 0.0007\u001B[0m)\n",
      "│   │   └── Best until now = 2.3774 (\u001B[31m↗ 0.0007\u001B[0m)\n",
      "│   └── Iou = 0.0524\n",
      "│       ├── Epoch N-1      = 0.0522 (\u001B[32m↗ 0.0003\u001B[0m)\n",
      "│       └── Best until now = 0.0522 (\u001B[32m↗ 0.0003\u001B[0m)\n",
      "└── Validation\n",
      "    ├── Crossentropyloss = 2.4043\n",
      "    │   ├── Epoch N-1      = 2.4264 (\u001B[32m↘ -0.0221\u001B[0m)\n",
      "    │   └── Best until now = 2.4264 (\u001B[32m↘ -0.0221\u001B[0m)\n",
      "    └── Iou = 0.0506\n",
      "        ├── Epoch N-1      = 0.0493 (\u001B[32m↗ 0.0013\u001B[0m)\n",
      "        └── Best until now = 0.0493 (\u001B[32m↗ 0.0013\u001B[0m)\n",
      "\n",
      "===========================================================\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Train epoch 9: 100%|██████████| 3/3 [00:00<00:00,  8.55it/s, CrossEntropyLoss=2.37, IoU=0.0524, gpu_mem=0.686]\n",
      "Validating epoch 9: 100%|██████████| 3/3 [00:00<00:00,  5.53it/s]\n"
     ]
    }
   ],
   "source": [
    "trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=val_dataloader)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}