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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Obrepal/TRA/blob/main/dzialaBURN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "e-S9kuBjM_2V",
        "outputId": "adef170e-ed5e-4f00-9704-02e7fecd7beb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
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      "execution_count": 2,
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          "text": [
            "Collecting huggingface_hub\n",
            "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n",
            "Installing collected packages: huggingface_hub\n",
            "Successfully installed huggingface_hub-0.17.3\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from huggingface_hub import hf_hub_download\n",
        "# yaml_file_path=hf_hub_download(repo_id=\"ibm-nasa-geospatial/Prithvi-100M\", filename=\"Prithvi_100M_config.yaml\", token=os.environ.get(\"token\"))\n",
        "checkpoint=hf_hub_download(repo_id=\"ibm-nasa-geospatial/Prithvi-100M\", filename='Prithvi_100M.pt', token=os.environ.get(\"token\"))\n"
      ],
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              "Downloading Prithvi_100M.pt:   0%|          | 0.00/454M [00:00<?, ?B/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "Training"
      ],
      "metadata": {
        "id": "gv4v2XdYMffb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/NASA-IMPACT/hls-foundation-os.git\n",
        "!pip install torch==1.11.0+cu115 torchvision==0.12.0+cu115 --extra-index-url https://download.pytorch.org/whl/cu115\n",
        "os.chdir(\"/content/hls-foundation-os\")\n",
        "!pip install -e .\n",
        "!pip install -U openmim\n",
        "!mim install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/index.html\n",
        "# !mim install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html\n",
        "# !mim install mmcv==2.0.0\n",
        "# !mim install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/index.html\n",
        "\n"
      ],
      "metadata": {
        "id": "QChZHiXwc0oc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "adcc8eb3-ff77-4be4-f3d8-38c06f2eee83"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'hls-foundation-os'...\n",
            "remote: Enumerating objects: 554, done.\u001b[K\n",
            "remote: Counting objects: 100% (162/162), done.\u001b[K\n",
            "remote: Compressing objects: 100% (61/61), done.\u001b[K\n",
            "remote: Total 554 (delta 129), reused 105 (delta 101), pack-reused 392\u001b[K\n",
            "Receiving objects: 100% (554/554), 1.20 MiB | 19.20 MiB/s, done.\n",
            "Resolving deltas: 100% (325/325), done.\n",
            "Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu115\n",
            "Collecting torch==1.11.0+cu115\n",
            "  Downloading https://download.pytorch.org/whl/cu115/torch-1.11.0%2Bcu115-cp310-cp310-linux_x86_64.whl (2138.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 GB\u001b[0m \u001b[31m506.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.12.0+cu115\n",
            "  Downloading https://download.pytorch.org/whl/cu115/torchvision-0.12.0%2Bcu115-cp310-cp310-linux_x86_64.whl (22.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.3/22.3 MB\u001b[0m \u001b[31m46.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.11.0+cu115) (4.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision==0.12.0+cu115) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision==0.12.0+cu115) (2.31.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision==0.12.0+cu115) (9.4.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.12.0+cu115) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.12.0+cu115) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.12.0+cu115) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.12.0+cu115) (2023.7.22)\n",
            "Installing collected packages: torch, torchvision\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.1+cu118\n",
            "    Uninstalling torch-2.0.1+cu118:\n",
            "      Successfully uninstalled torch-2.0.1+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.15.2+cu118\n",
            "    Uninstalling torchvision-0.15.2+cu118:\n",
            "      Successfully uninstalled torchvision-0.15.2+cu118\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",
            "torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.11.0+cu115 which is incompatible.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.11.0+cu115 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.11.0+cu115 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.11.0+cu115 torchvision-0.12.0+cu115\n",
            "Obtaining file:///content/hls-foundation-os\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280 (from geospatial-fm==0.1.0)\n",
            "  Cloning https://github.com/open-mmlab/mmsegmentation.git (to revision 186572a3ce64ac9b6b37e66d58c76515000c3280) to /tmp/pip-install-hlym5fid/mmsegmentation_9b1281e3691741499a99d47d9ce92283\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/open-mmlab/mmsegmentation.git /tmp/pip-install-hlym5fid/mmsegmentation_9b1281e3691741499a99d47d9ce92283\n",
            "  Running command git rev-parse -q --verify 'sha^186572a3ce64ac9b6b37e66d58c76515000c3280'\n",
            "  Running command git fetch -q https://github.com/open-mmlab/mmsegmentation.git 186572a3ce64ac9b6b37e66d58c76515000c3280\n",
            "  Running command git checkout -q 186572a3ce64ac9b6b37e66d58c76515000c3280\n",
            "  Resolved https://github.com/open-mmlab/mmsegmentation.git to commit 186572a3ce64ac9b6b37e66d58c76515000c3280\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting rasterio (from geospatial-fm==0.1.0)\n",
            "  Downloading rasterio-1.3.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tifffile in /usr/local/lib/python3.10/dist-packages (from geospatial-fm==0.1.0) (2023.9.26)\n",
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            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
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            "\u001b[?25hCollecting timm==0.4.12 (from geospatial-fm==0.1.0)\n",
            "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
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            "\u001b[?25hRequirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (from geospatial-fm==0.1.0) (2.13.0)\n",
            "Collecting imagecodecs (from geospatial-fm==0.1.0)\n",
            "  Downloading imagecodecs-2023.9.18-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.3/37.3 MB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.4 in /usr/local/lib/python3.10/dist-packages (from timm==0.4.12->geospatial-fm==0.1.0) (1.11.0+cu115)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from timm==0.4.12->geospatial-fm==0.1.0) (0.12.0+cu115)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imagecodecs->geospatial-fm==0.1.0) (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (3.7.1)\n",
            "Collecting mmcls>=0.20.1 (from mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0)\n",
            "  Downloading mmcls-0.25.0-py2.py3-none-any.whl (648 kB)\n",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (23.2)\n",
            "Requirement already satisfied: prettytable in /usr/local/lib/python3.10/dist-packages (from mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (3.9.0)\n",
            "Collecting affine (from rasterio->geospatial-fm==0.1.0)\n",
            "  Downloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio->geospatial-fm==0.1.0) (23.1.0)\n",
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            "Collecting snuggs>=1.4.1 (from rasterio->geospatial-fm==0.1.0)\n",
            "  Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
            "Requirement already satisfied: click-plugins in /usr/local/lib/python3.10/dist-packages (from rasterio->geospatial-fm==0.1.0) (1.1.1)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (0.12.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (4.43.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmsegmentation@ git+https://github.com/open-mmlab/mmsegmentation.git@186572a3ce64ac9b6b37e66d58c76515000c3280->geospatial-fm==0.1.0) (2.8.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard->geospatial-fm==0.1.0) (3.3.0)\n",
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            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->geospatial-fm==0.1.0) (0.5.0)\n",
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            "Building wheels for collected packages: mmsegmentation\n",
            "  Building wheel for mmsegmentation (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mmsegmentation: filename=mmsegmentation-0.30.0-py3-none-any.whl size=867096 sha256=41afb4dc7e2eda5be1df9fec8467de748f85cd23c411aa858327aff7b6a056e2\n",
            "  Stored in directory: /root/.cache/pip/wheels/a6/f9/e5/3f452217df3e52ccbf7263c0c1cd8eb81a5a08487a3b4ab4a4\n",
            "Successfully built mmsegmentation\n",
            "Installing collected packages: snuggs, imagecodecs, einops, affine, rasterio, timm, mmcls, mmsegmentation, geospatial-fm\n",
            "  Running setup.py develop for geospatial-fm\n",
            "Successfully installed affine-2.4.0 einops-0.7.0 geospatial-fm-0.1.0 imagecodecs-2023.9.18 mmcls-0.25.0 mmsegmentation-0.30.0 rasterio-1.3.8 snuggs-1.4.7 timm-0.4.12\n",
            "Collecting openmim\n",
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            "  Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->openmim) (1.16.0)\n",
            "Collecting oss2~=2.17.0 (from openxlab->opendatalab->openmim)\n",
            "  Downloading oss2-2.17.0.tar.gz (259 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m259.5/259.5 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting requests (from openmim)\n",
            "  Downloading requests-2.28.2-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rich (from openmim)\n",
            "  Downloading rich-13.4.2-py3-none-any.whl (239 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.4/239.4 kB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting setuptools~=60.2.0 (from openxlab->opendatalab->openmim)\n",
            "  Downloading setuptools-60.2.0-py3-none-any.whl (953 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m953.1/953.1 kB\u001b[0m \u001b[31m26.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tqdm (from opendatalab->openmim)\n",
            "  Downloading tqdm-4.65.2-py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.1/77.1 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting urllib3<1.27,>=1.21.1 (from requests->openmim)\n",
            "  Downloading urllib3-1.26.17-py2.py3-none-any.whl (143 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/143.4 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting crcmod>=1.7 (from oss2~=2.17.0->openxlab->opendatalab->openmim)\n",
            "  Downloading crcmod-1.7.tar.gz (89 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.7/89.7 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting aliyun-python-sdk-kms>=2.4.1 (from oss2~=2.17.0->openxlab->opendatalab->openmim)\n",
            "  Downloading aliyun_python_sdk_kms-2.16.2-py2.py3-none-any.whl (94 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.0/94.0 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aliyun-python-sdk-core>=2.13.12 (from oss2~=2.17.0->openxlab->opendatalab->openmim)\n",
            "  Downloading aliyun-python-sdk-core-2.14.0.tar.gz (443 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m443.0/443.0 kB\u001b[0m \u001b[31m27.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting jmespath<1.0.0,>=0.9.3 (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim)\n",
            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: cryptography>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (41.0.4)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (2.21)\n",
            "Building wheels for collected packages: oss2, aliyun-python-sdk-core, crcmod\n",
            "  Building wheel for oss2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for oss2: filename=oss2-2.17.0-py3-none-any.whl size=112371 sha256=0b835a34abdb6f7ef2eb5a20b07a8642632ea0c82a183927202a340f9f065d6c\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/04/7b/7e61b8157fdf211c5131375240d0d86ca82e2a88ead9672c88\n",
            "  Building wheel for aliyun-python-sdk-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aliyun-python-sdk-core: filename=aliyun_python_sdk_core-2.14.0-py3-none-any.whl size=535290 sha256=c7beaa655163b97aaf57cf9828a2ee53a5e7de25407360f89e556e8ada121c65\n",
            "  Stored in directory: /root/.cache/pip/wheels/16/3c/68/b7eab618d9f1d5e7d386296f1e07e2cf36aaa1eb5161885038\n",
            "  Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31403 sha256=491d4cfcaffa29e893ac8584d61bdf5363b27b05e5b9d94e4fef2f684c063408\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n",
            "Successfully built oss2 aliyun-python-sdk-core crcmod\n",
            "Installing collected packages: crcmod, urllib3, tqdm, setuptools, pycryptodome, ordered-set, jmespath, colorama, rich, requests, model-index, aliyun-python-sdk-core, aliyun-python-sdk-kms, oss2, openxlab, opendatalab, openmim\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.6\n",
            "    Uninstalling urllib3-2.0.6:\n",
            "      Successfully uninstalled urllib3-2.0.6\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.66.1\n",
            "    Uninstalling tqdm-4.66.1:\n",
            "      Successfully uninstalled tqdm-4.66.1\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 67.7.2\n",
            "    Uninstalling setuptools-67.7.2:\n",
            "      Successfully uninstalled setuptools-67.7.2\n",
            "  Attempting uninstall: rich\n",
            "    Found existing installation: rich 13.6.0\n",
            "    Uninstalling rich-13.6.0:\n",
            "      Successfully uninstalled rich-13.6.0\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\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",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "cvxpy 1.3.2 requires setuptools>65.5.1, but you have setuptools 60.2.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.28.2 which is incompatible.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.11.0+cu115 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.11.0+cu115 which is incompatible.\n",
            "yfinance 0.2.30 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aliyun-python-sdk-core-2.14.0 aliyun-python-sdk-kms-2.16.2 colorama-0.4.6 crcmod-1.7 jmespath-0.10.0 model-index-0.1.11 opendatalab-0.0.10 openmim-0.3.9 openxlab-0.0.26 ordered-set-4.1.0 oss2-2.17.0 pycryptodome-3.19.0 requests-2.28.2 rich-13.4.2 setuptools-60.2.0 tqdm-4.65.2 urllib3-1.26.17\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "_distutils_hack",
                  "pkg_resources",
                  "requests",
                  "setuptools",
                  "tqdm",
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/index.html, https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/index.html\n",
            "Collecting mmcv-full==1.6.2\n",
            "  Downloading https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/mmcv_full-1.6.2-cp310-cp310-manylinux1_x86_64.whl (41.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.4/41.4 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting addict (from mmcv-full==1.6.2)\n",
            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.6.2) (1.23.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.6.2) (23.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.6.2) (9.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.6.2) (6.0.1)\n",
            "Collecting yapf (from mmcv-full==1.6.2)\n",
            "  Downloading yapf-0.40.2-py3-none-any.whl (254 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.7/254.7 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.10/dist-packages (from mmcv-full==1.6.2) (4.8.0.76)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.6.2) (6.8.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.6.2) (3.11.0)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full==1.6.2) (2.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmcv-full==1.6.2) (3.17.0)\n",
            "Installing collected packages: addict, yapf, mmcv-full\n",
            "Successfully installed addict-2.4.0 mmcv-full-1.6.2 yapf-0.40.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install einops"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wcclSWtVXGJ",
        "outputId": "a0fb3cd3-b98f-42f4-d3a1-aa968f4c8999"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (0.7.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !mim install mmcv-full==2.0.0\n",
        "# # !mim install mmcv>2.0.0# !pip install mmcv --upgrade\n",
        "# # print(mmcv.__version__)\n",
        "# import mmcv\n",
        "# print(mmcv.__version__)\n",
        "# !pwd"
      ],
      "metadata": {
        "id": "y9GdEnHs_1z0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from mmcv.runner import lERROR: Could not find a version that satisfies the requirement mmcv.runner (from versions: none)\n",
        "# ERROR: No matching distribution found for mmcv.runneroad_checkpoint\n",
        "# !mim install mmcv.runner\n",
        "# !pip install -U openmim\n",
        "# !mim install mmcv-full"
      ],
      "metadata": {
        "id": "Qx_RAax4OxuG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yapf==0.40.1"
      ],
      "metadata": {
        "id": "TXZSyfHEjzjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b8348f8-eaa4-445e-94a6-41efd2938132"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yapf==0.40.1\n",
            "  Downloading yapf-0.40.1-py3-none-any.whl (250 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/250.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m245.8/250.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.3/250.3 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf==0.40.1) (6.8.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf==0.40.1) (3.11.0)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf==0.40.1) (2.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf==0.40.1) (3.17.0)\n",
            "Installing collected packages: yapf\n",
            "  Attempting uninstall: yapf\n",
            "    Found existing installation: yapf 0.40.2\n",
            "    Uninstalling yapf-0.40.2:\n",
            "      Successfully uninstalled yapf-0.40.2\n",
            "Successfully installed yapf-0.40.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \"# !cd /content/hls-foundation-os\n",
        "!mim train mmsegmentation --launcher pytorch /content/hls-foundation-os/configs/burn_scars.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpF7tPzXdgya",
        "outputId": "b7579ab9-8bc0-4288-b756-d33d073de1ca"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using port 29764 for synchronization. \n",
            "Training command is /usr/bin/python3 -m torch.distributed.launch --nproc_per_node=1 --master_port=29764 /usr/local/lib/python3.10/dist-packages/mmseg/.mim/tools/train.py /content/hls-foundation-os/configs/burn_scars.py --launcher pytorch. \n",
            "/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated\n",
            "and will be removed in future. Use torchrun.\n",
            "Note that --use_env is set by default in torchrun.\n",
            "If your script expects `--local_rank` argument to be set, please\n",
            "change it to read from `os.environ['LOCAL_RANK']` instead. See \n",
            "https://pytorch.org/docs/stable/distributed.html#launch-utility for \n",
            "further instructions\n",
            "\n",
            "  warnings.warn(\n",
            "/content/hls-foundation-os\n",
            "HAAAAAAALAOASLSAADSODLASDO /content/hls-foundation-os/\n",
            "HAAAAAAALAOASLSAADSODLASDO /tmp/tmpiac0972i\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/local/lib/python3.10/dist-packages/mmseg/.mim/tools\n",
            "HAAAAAAALAOASLSAADSODLASDO /env/python\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/lib/python310.zip\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/lib/python3.10\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/lib/python3.10/lib-dynload\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/local/lib/python3.10/dist-packages\n",
            "HAAAAAAALAOASLSAADSODLASDO /content/hls-foundation-os\n",
            "HAAAAAAALAOASLSAADSODLASDO /usr/lib/python3/dist-packages\n",
            "2023-10-08 07:36:43,459 - mmseg - INFO - Multi-processing start method is `None`\n",
            "2023-10-08 07:36:43,464 - mmseg - INFO - OpenCV num_threads is `2\n",
            "2023-10-08 07:36:43,536 - mmseg - INFO - Environment info:\n",
            "------------------------------------------------------------\n",
            "sys.platform: linux\n",
            "Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]\n",
            "CUDA available: True\n",
            "GPU 0: Tesla T4\n",
            "CUDA_HOME: /usr/local/cuda\n",
            "NVCC: Cuda compilation tools, release 11.8, V11.8.89\n",
            "GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\n",
            "PyTorch: 1.11.0+cu115\n",
            "PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 7.3\n",
            "  - C++ Version: 201402\n",
            "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
            "  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n",
            "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
            "  - LAPACK is enabled (usually provided by MKL)\n",
            "  - NNPACK is enabled\n",
            "  - CPU capability usage: AVX2\n",
            "  - CUDA Runtime 11.5\n",
            "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
            "  - CuDNN 8.3.2\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.5, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
            "\n",
            "TorchVision: 0.12.0+cu115\n",
            "OpenCV: 4.8.0\n",
            "MMCV: 1.6.2\n",
            "MMCV Compiler: GCC 9.3\n",
            "MMCV CUDA Compiler: 11.5\n",
            "MMSegmentation: 0.30.0+4757c74\n",
            "------------------------------------------------------------\n",
            "\n",
            "2023-10-08 07:36:43,537 - mmseg - INFO - Distributed training: True\n",
            "2023-10-08 07:36:44,977 - mmseg - INFO - Config:\n",
            "path = '/usr/lib/python3/dist-packages'\n",
            "custom_imports = dict(imports=['geospatial_fm'])\n",
            "dist_params = dict(backend='nccl')\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume_from = None\n",
            "cudnn_benchmark = True\n",
            "dataset_type = 'GeospatialDataset'\n",
            "data_root = '/content/gdrive/MyDrive/hls'\n",
            "num_frames = 1\n",
            "img_size = 224\n",
            "num_workers = 4\n",
            "samples_per_gpu = 4\n",
            "img_norm_cfg = dict(\n",
            "    means=[\n",
            "        0.033349706741586264, 0.05701185520536176, 0.05889748132001316,\n",
            "        0.2323245113436119, 0.1972854853760658, 0.11944914225186566\n",
            "    ],\n",
            "    stds=[\n",
            "        0.02269135568823774, 0.026807560223070237, 0.04004109844362779,\n",
            "        0.07791732423672691, 0.08708738838140137, 0.07241979477437814\n",
            "    ])\n",
            "bands = [0, 1, 2, 3, 4, 5]\n",
            "tile_size = 224\n",
            "orig_nsize = 512\n",
            "crop_size = (224, 224)\n",
            "img_suffix = '_merged.tif'\n",
            "seg_map_suffix = '.mask.tif'\n",
            "ignore_index = -1\n",
            "image_nodata = -9999\n",
            "image_nodata_replace = 0\n",
            "image_to_float32 = True\n",
            "pretrained_weights_path = '/content/gdrive/MyDrive/Prithvi_100M.pt'\n",
            "num_layers = 12\n",
            "patch_size = 16\n",
            "embed_dim = 768\n",
            "num_heads = 12\n",
            "tubelet_size = 1\n",
            "output_embed_dim = 768\n",
            "max_intervals = 2000\n",
            "evaluation_interval = 400\n",
            "experiment = 'BurnExperiment'\n",
            "project_dir = '/content/gdrive/MyDrive/results_u_net'\n",
            "work_dir = '/content/gdrive/MyDrive/results_u_net/BurnExperiment'\n",
            "save_path = '/content/gdrive/MyDrive/results_u_net/BurnExperiment'\n",
            "train_pipeline = [\n",
            "    dict(\n",
            "        type='LoadGeospatialImageFromFile',\n",
            "        to_float32=True,\n",
            "        channels_last=True),\n",
            "    dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),\n",
            "    dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),\n",
            "    dict(type='RandomFlip', prob=0.5),\n",
            "    dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),\n",
            "    dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)),\n",
            "    dict(\n",
            "        type='TorchNormalize',\n",
            "        means=[\n",
            "            0.033349706741586264, 0.05701185520536176, 0.05889748132001316,\n",
            "            0.2323245113436119, 0.1972854853760658, 0.11944914225186566\n",
            "        ],\n",
            "        stds=[\n",
            "            0.02269135568823774, 0.026807560223070237, 0.04004109844362779,\n",
            "            0.07791732423672691, 0.08708738838140137, 0.07241979477437814\n",
            "        ]),\n",
            "    dict(type='TorchRandomCrop', crop_size=(224, 224)),\n",
            "    dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)),\n",
            "    dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, 224, 224)),\n",
            "    dict(\n",
            "        type='CastTensor',\n",
            "        keys=['gt_semantic_seg'],\n",
            "        new_type='torch.LongTensor'),\n",
            "    dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n",
            "]\n",
            "test_pipeline = [\n",
            "    dict(\n",
            "        type='LoadGeospatialImageFromFile',\n",
            "        to_float32=True,\n",
            "        channels_last=True),\n",
            "    dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),\n",
            "    dict(type='ToTensor', keys=['img']),\n",
            "    dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)),\n",
            "    dict(\n",
            "        type='TorchNormalize',\n",
            "        means=[\n",
            "            0.033349706741586264, 0.05701185520536176, 0.05889748132001316,\n",
            "            0.2323245113436119, 0.1972854853760658, 0.11944914225186566\n",
            "        ],\n",
            "        stds=[\n",
            "            0.02269135568823774, 0.026807560223070237, 0.04004109844362779,\n",
            "            0.07791732423672691, 0.08708738838140137, 0.07241979477437814\n",
            "        ]),\n",
            "    dict(\n",
            "        type='Reshape',\n",
            "        keys=['img'],\n",
            "        new_shape=(6, 1, -1, -1),\n",
            "        look_up=dict({\n",
            "            '2': 1,\n",
            "            '3': 2\n",
            "        })),\n",
            "    dict(type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),\n",
            "    dict(\n",
            "        type='CollectTestList',\n",
            "        keys=['img'],\n",
            "        meta_keys=[\n",
            "            'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename',\n",
            "            'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape',\n",
            "            'scale_factor', 'img_norm_cfg'\n",
            "        ])\n",
            "]\n",
            "CLASSES = ('Unburnt land', 'Burn scar')\n",
            "data = dict(\n",
            "    samples_per_gpu=4,\n",
            "    workers_per_gpu=4,\n",
            "    train=dict(\n",
            "        type='GeospatialDataset',\n",
            "        CLASSES=('Unburnt land', 'Burn scar'),\n",
            "        data_root='/content/gdrive/MyDrive/hls',\n",
            "        img_dir='training',\n",
            "        ann_dir='training',\n",
            "        img_suffix='_merged.tif',\n",
            "        seg_map_suffix='.mask.tif',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadGeospatialImageFromFile',\n",
            "                to_float32=True,\n",
            "                channels_last=True),\n",
            "            dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),\n",
            "            dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),\n",
            "            dict(type='RandomFlip', prob=0.5),\n",
            "            dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),\n",
            "            dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)),\n",
            "            dict(\n",
            "                type='TorchNormalize',\n",
            "                means=[\n",
            "                    0.033349706741586264, 0.05701185520536176,\n",
            "                    0.05889748132001316, 0.2323245113436119,\n",
            "                    0.1972854853760658, 0.11944914225186566\n",
            "                ],\n",
            "                stds=[\n",
            "                    0.02269135568823774, 0.026807560223070237,\n",
            "                    0.04004109844362779, 0.07791732423672691,\n",
            "                    0.08708738838140137, 0.07241979477437814\n",
            "                ]),\n",
            "            dict(type='TorchRandomCrop', crop_size=(224, 224)),\n",
            "            dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)),\n",
            "            dict(\n",
            "                type='Reshape',\n",
            "                keys=['gt_semantic_seg'],\n",
            "                new_shape=(1, 224, 224)),\n",
            "            dict(\n",
            "                type='CastTensor',\n",
            "                keys=['gt_semantic_seg'],\n",
            "                new_type='torch.LongTensor'),\n",
            "            dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n",
            "        ],\n",
            "        ignore_index=-1),\n",
            "    val=dict(\n",
            "        type='GeospatialDataset',\n",
            "        CLASSES=('Unburnt land', 'Burn scar'),\n",
            "        data_root='/content/gdrive/MyDrive/hls',\n",
            "        img_dir='validation',\n",
            "        ann_dir='validation',\n",
            "        img_suffix='_merged.tif',\n",
            "        seg_map_suffix='.mask.tif',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadGeospatialImageFromFile',\n",
            "                to_float32=True,\n",
            "                channels_last=True),\n",
            "            dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),\n",
            "            dict(type='ToTensor', keys=['img']),\n",
            "            dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)),\n",
            "            dict(\n",
            "                type='TorchNormalize',\n",
            "                means=[\n",
            "                    0.033349706741586264, 0.05701185520536176,\n",
            "                    0.05889748132001316, 0.2323245113436119,\n",
            "                    0.1972854853760658, 0.11944914225186566\n",
            "                ],\n",
            "                stds=[\n",
            "                    0.02269135568823774, 0.026807560223070237,\n",
            "                    0.04004109844362779, 0.07791732423672691,\n",
            "                    0.08708738838140137, 0.07241979477437814\n",
            "                ]),\n",
            "            dict(\n",
            "                type='Reshape',\n",
            "                keys=['img'],\n",
            "                new_shape=(6, 1, -1, -1),\n",
            "                look_up=dict({\n",
            "                    '2': 1,\n",
            "                    '3': 2\n",
            "                })),\n",
            "            dict(\n",
            "                type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),\n",
            "            dict(\n",
            "                type='CollectTestList',\n",
            "                keys=['img'],\n",
            "                meta_keys=[\n",
            "                    'img_info', 'seg_fields', 'img_prefix', 'seg_prefix',\n",
            "                    'filename', 'ori_filename', 'img', 'img_shape',\n",
            "                    'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg'\n",
            "                ])\n",
            "        ],\n",
            "        ignore_index=-1),\n",
            "    test=dict(\n",
            "        type='GeospatialDataset',\n",
            "        CLASSES=('Unburnt land', 'Burn scar'),\n",
            "        data_root='/content/gdrive/MyDrive/hls',\n",
            "        img_dir='validation',\n",
            "        ann_dir='validation',\n",
            "        img_suffix='_merged.tif',\n",
            "        seg_map_suffix='.mask.tif',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadGeospatialImageFromFile',\n",
            "                to_float32=True,\n",
            "                channels_last=True),\n",
            "            dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),\n",
            "            dict(type='ToTensor', keys=['img']),\n",
            "            dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)),\n",
            "            dict(\n",
            "                type='TorchNormalize',\n",
            "                means=[\n",
            "                    0.033349706741586264, 0.05701185520536176,\n",
            "                    0.05889748132001316, 0.2323245113436119,\n",
            "                    0.1972854853760658, 0.11944914225186566\n",
            "                ],\n",
            "                stds=[\n",
            "                    0.02269135568823774, 0.026807560223070237,\n",
            "                    0.04004109844362779, 0.07791732423672691,\n",
            "                    0.08708738838140137, 0.07241979477437814\n",
            "                ]),\n",
            "            dict(\n",
            "                type='Reshape',\n",
            "                keys=['img'],\n",
            "                new_shape=(6, 1, -1, -1),\n",
            "                look_up=dict({\n",
            "                    '2': 1,\n",
            "                    '3': 2\n",
            "                })),\n",
            "            dict(\n",
            "                type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),\n",
            "            dict(\n",
            "                type='CollectTestList',\n",
            "                keys=['img'],\n",
            "                meta_keys=[\n",
            "                    'img_info', 'seg_fields', 'img_prefix', 'seg_prefix',\n",
            "                    'filename', 'ori_filename', 'img', 'img_shape',\n",
            "                    'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg'\n",
            "                ])\n",
            "        ],\n",
            "        ignore_index=-1))\n",
            "optimizer = dict(type='Adam', lr=1.3e-05, betas=(0.9, 0.999))\n",
            "optimizer_config = dict(grad_clip=None)\n",
            "lr_config = dict(\n",
            "    policy='poly',\n",
            "    warmup='linear',\n",
            "    warmup_iters=300,\n",
            "    warmup_ratio=1e-06,\n",
            "    power=1.0,\n",
            "    min_lr=0.0,\n",
            "    by_epoch=False)\n",
            "log_config = dict(\n",
            "    interval=20,\n",
            "    hooks=[\n",
            "        dict(type='TextLoggerHook', by_epoch=False),\n",
            "        dict(type='TensorboardLoggerHook', by_epoch=False)\n",
            "    ])\n",
            "checkpoint_config = dict(\n",
            "    by_epoch=True,\n",
            "    interval=10,\n",
            "    out_dir='/content/gdrive/MyDrive/results_u_net/BurnExperiment')\n",
            "evaluation = dict(\n",
            "    interval=400,\n",
            "    metric='mIoU',\n",
            "    pre_eval=True,\n",
            "    save_best='mIoU',\n",
            "    by_epoch=False)\n",
            "loss_func = dict(\n",
            "    type='DiceLoss', use_sigmoid=False, loss_weight=1, ignore_index=-1)\n",
            "runner = dict(type='IterBasedRunner', max_iters=2000)\n",
            "workflow = [('train', 1)]\n",
            "norm_cfg = dict(type='BN', requires_grad=True)\n",
            "model = dict(\n",
            "    type='TemporalEncoderDecoder',\n",
            "    frozen_backbone=False,\n",
            "    backbone=dict(\n",
            "        type='TemporalViTEncoder',\n",
            "        pretrained='/content/gdrive/MyDrive/Prithvi_100M.pt',\n",
            "        img_size=224,\n",
            "        patch_size=16,\n",
            "        num_frames=1,\n",
            "        tubelet_size=1,\n",
            "        in_chans=6,\n",
            "        embed_dim=768,\n",
            "        depth=12,\n",
            "        num_heads=12,\n",
            "        mlp_ratio=4.0,\n",
            "        norm_pix_loss=False),\n",
            "    neck=dict(\n",
            "        type='ConvTransformerTokensToEmbeddingNeck',\n",
            "        embed_dim=768,\n",
            "        output_embed_dim=768,\n",
            "        drop_cls_token=True,\n",
            "        Hp=14,\n",
            "        Wp=14),\n",
            "    decode_head=dict(\n",
            "        num_classes=2,\n",
            "        in_channels=768,\n",
            "        type='FCNHead',\n",
            "        in_index=-1,\n",
            "        channels=256,\n",
            "        num_convs=1,\n",
            "        concat_input=False,\n",
            "        dropout_ratio=0.1,\n",
            "        norm_cfg=dict(type='BN', requires_grad=True),\n",
            "        align_corners=False,\n",
            "        loss_decode=dict(\n",
            "            type='DiceLoss', use_sigmoid=False, loss_weight=1,\n",
            "            ignore_index=-1)),\n",
            "    auxiliary_head=dict(\n",
            "        num_classes=2,\n",
            "        in_channels=768,\n",
            "        type='FCNHead',\n",
            "        in_index=-1,\n",
            "        channels=256,\n",
            "        num_convs=1,\n",
            "        concat_input=False,\n",
            "        dropout_ratio=0.1,\n",
            "        norm_cfg=dict(type='BN', requires_grad=True),\n",
            "        align_corners=False,\n",
            "        loss_decode=dict(\n",
            "            type='DiceLoss', use_sigmoid=False, loss_weight=1,\n",
            "            ignore_index=-1)),\n",
            "    train_cfg=dict(),\n",
            "    test_cfg=dict(mode='slide', stride=(112, 112), crop_size=(224, 224)))\n",
            "gpu_ids = range(0, 1)\n",
            "auto_resume = False\n",
            "\n",
            "2023-10-08 07:36:44,978 - mmseg - INFO - Set random seed to 1631652106, deterministic: False\n",
            "load from /content/gdrive/MyDrive/Prithvi_100M.pt\n",
            "load checkpoint from local path: /content/gdrive/MyDrive/Prithvi_100M.pt\n",
            "The model and loaded state dict do not match exactly\n",
            "\n",
            "size mismatch for pos_embed: copying a param with shape torch.Size([1, 589, 768]) from checkpoint, the shape in current model is torch.Size([1, 197, 768]).\n",
            "unexpected key in source state_dict: mask_token, decoder_pos_embed, decoder_embed.weight, decoder_embed.bias, decoder_blocks.0.norm1.weight, decoder_blocks.0.norm1.bias, decoder_blocks.0.attn.qkv.weight, decoder_blocks.0.attn.qkv.bias, decoder_blocks.0.attn.proj.weight, decoder_blocks.0.attn.proj.bias, decoder_blocks.0.norm2.weight, decoder_blocks.0.norm2.bias, decoder_blocks.0.mlp.fc1.weight, decoder_blocks.0.mlp.fc1.bias, decoder_blocks.0.mlp.fc2.weight, decoder_blocks.0.mlp.fc2.bias, decoder_blocks.1.norm1.weight, decoder_blocks.1.norm1.bias, decoder_blocks.1.attn.qkv.weight, decoder_blocks.1.attn.qkv.bias, decoder_blocks.1.attn.proj.weight, decoder_blocks.1.attn.proj.bias, decoder_blocks.1.norm2.weight, decoder_blocks.1.norm2.bias, decoder_blocks.1.mlp.fc1.weight, decoder_blocks.1.mlp.fc1.bias, decoder_blocks.1.mlp.fc2.weight, decoder_blocks.1.mlp.fc2.bias, decoder_blocks.2.norm1.weight, decoder_blocks.2.norm1.bias, decoder_blocks.2.attn.qkv.weight, decoder_blocks.2.attn.qkv.bias, decoder_blocks.2.attn.proj.weight, decoder_blocks.2.attn.proj.bias, decoder_blocks.2.norm2.weight, decoder_blocks.2.norm2.bias, decoder_blocks.2.mlp.fc1.weight, decoder_blocks.2.mlp.fc1.bias, decoder_blocks.2.mlp.fc2.weight, decoder_blocks.2.mlp.fc2.bias, decoder_blocks.3.norm1.weight, decoder_blocks.3.norm1.bias, decoder_blocks.3.attn.qkv.weight, decoder_blocks.3.attn.qkv.bias, decoder_blocks.3.attn.proj.weight, decoder_blocks.3.attn.proj.bias, decoder_blocks.3.norm2.weight, decoder_blocks.3.norm2.bias, decoder_blocks.3.mlp.fc1.weight, decoder_blocks.3.mlp.fc1.bias, decoder_blocks.3.mlp.fc2.weight, decoder_blocks.3.mlp.fc2.bias, decoder_blocks.4.norm1.weight, decoder_blocks.4.norm1.bias, decoder_blocks.4.attn.qkv.weight, decoder_blocks.4.attn.qkv.bias, decoder_blocks.4.attn.proj.weight, decoder_blocks.4.attn.proj.bias, decoder_blocks.4.norm2.weight, decoder_blocks.4.norm2.bias, decoder_blocks.4.mlp.fc1.weight, decoder_blocks.4.mlp.fc1.bias, decoder_blocks.4.mlp.fc2.weight, decoder_blocks.4.mlp.fc2.bias, decoder_blocks.5.norm1.weight, decoder_blocks.5.norm1.bias, decoder_blocks.5.attn.qkv.weight, decoder_blocks.5.attn.qkv.bias, decoder_blocks.5.attn.proj.weight, decoder_blocks.5.attn.proj.bias, decoder_blocks.5.norm2.weight, decoder_blocks.5.norm2.bias, decoder_blocks.5.mlp.fc1.weight, decoder_blocks.5.mlp.fc1.bias, decoder_blocks.5.mlp.fc2.weight, decoder_blocks.5.mlp.fc2.bias, decoder_blocks.6.norm1.weight, decoder_blocks.6.norm1.bias, decoder_blocks.6.attn.qkv.weight, decoder_blocks.6.attn.qkv.bias, decoder_blocks.6.attn.proj.weight, decoder_blocks.6.attn.proj.bias, decoder_blocks.6.norm2.weight, decoder_blocks.6.norm2.bias, decoder_blocks.6.mlp.fc1.weight, decoder_blocks.6.mlp.fc1.bias, decoder_blocks.6.mlp.fc2.weight, decoder_blocks.6.mlp.fc2.bias, decoder_blocks.7.norm1.weight, decoder_blocks.7.norm1.bias, decoder_blocks.7.attn.qkv.weight, decoder_blocks.7.attn.qkv.bias, decoder_blocks.7.attn.proj.weight, decoder_blocks.7.attn.proj.bias, decoder_blocks.7.norm2.weight, decoder_blocks.7.norm2.bias, decoder_blocks.7.mlp.fc1.weight, decoder_blocks.7.mlp.fc1.bias, decoder_blocks.7.mlp.fc2.weight, decoder_blocks.7.mlp.fc2.bias, decoder_norm.weight, decoder_norm.bias, decoder_pred.weight, decoder_pred.bias\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold\n",
            "  warnings.warn('For binary segmentation, we suggest using'\n",
            "2023-10-08 07:36:53,601 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
            "2023-10-08 07:36:53,603 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
            "2023-10-08 07:36:53,608 - mmseg - INFO - TemporalEncoderDecoder(\n",
            "  (backbone): TemporalViTEncoder(\n",
            "    (patch_embed): PatchEmbed(\n",
            "      (proj): Conv3d(6, 768, kernel_size=(1, 16, 16), stride=(1, 16, 16))\n",
            "      (norm): Identity()\n",
            "    )\n",
            "    (blocks): ModuleList(\n",
            "      (0): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (1): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (2): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (3): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (4): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (5): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (6): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (7): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (8): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (9): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (10): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "      (11): Block(\n",
            "        (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): Attention(\n",
            "          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "          (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (drop_path): Identity()\n",
            "        (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): Mlp(\n",
            "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          (act): GELU()\n",
            "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "          (drop): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (neck): ConvTransformerTokensToEmbeddingNeck(\n",
            "    (conv1): Sequential(\n",
            "      (0): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (1): Norm2d(\n",
            "        (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
            "      )\n",
            "      (2): GELU()\n",
            "    )\n",
            "    (conv2): Sequential(\n",
            "      (0): Conv2d(1536, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (1): Norm2d(\n",
            "        (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
            "      )\n",
            "      (2): GELU()\n",
            "      (3): Dropout(p=0, inplace=25)\n",
            "    )\n",
            "    (conv3): Sequential(\n",
            "      (0): Conv2d(1536, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (1): Norm2d(\n",
            "        (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
            "      )\n",
            "      (2): GELU()\n",
            "      (3): Dropout(p=0, inplace=25)\n",
            "    )\n",
            "    (fpn1): Sequential(\n",
            "      (0): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2))\n",
            "      (1): Norm2d(\n",
            "        (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
            "      )\n",
            "      (2): GELU()\n",
            "      (3): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (fpn2): Sequential(\n",
            "      (0): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2))\n",
            "      (1): Norm2d(\n",
            "        (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
            "      )\n",
            "      (2): GELU()\n",
            "      (3): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "  )\n",
            "  (decode_head): FCNHead(\n",
            "    input_transform=None, ignore_index=255, align_corners=False\n",
            "    (loss_decode): DiceLoss()\n",
            "    (conv_seg): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (dropout): Dropout2d(p=0.1, inplace=False)\n",
            "    (convs): Sequential(\n",
            "      (0): ConvModule(\n",
            "        (conv): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (activate): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
            "  (auxiliary_head): FCNHead(\n",
            "    input_transform=None, ignore_index=255, align_corners=False\n",
            "    (loss_decode): DiceLoss()\n",
            "    (conv_seg): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (dropout): Dropout2d(p=0.1, inplace=False)\n",
            "    (convs): Sequential(\n",
            "      (0): ConvModule(\n",
            "        (conv): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (activate): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
            ")\n",
            "2023-10-08 07:36:53,767 - mmseg - INFO - Loaded 540 images\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:487: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "2023-10-08 07:36:55,066 - mmseg - INFO - Loaded 264 images\n",
            "2023-10-08 07:36:55,067 - mmseg - INFO - Start running, host: root@7203f3f0c50a, work_dir: /content/gdrive/MyDrive/results_u_net/BurnExperiment\n",
            "2023-10-08 07:36:55,067 - mmseg - INFO - Hooks will be executed in the following order:\n",
            "before_run:\n",
            "(VERY_HIGH   ) PolyLrUpdaterHook                  \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(LOW         ) DistEvalHook                       \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "before_train_epoch:\n",
            "(VERY_HIGH   ) PolyLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            "(LOW         ) DistEvalHook                       \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "before_train_iter:\n",
            "(VERY_HIGH   ) PolyLrUpdaterHook                  \n",
            "(LOW         ) IterTimerHook                      \n",
            "(LOW         ) DistEvalHook                       \n",
            " -------------------- \n",
            "after_train_iter:\n",
            "(ABOVE_NORMAL) OptimizerHook                      \n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(LOW         ) IterTimerHook                      \n",
            "(LOW         ) DistEvalHook                       \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "after_train_epoch:\n",
            "(NORMAL      ) CheckpointHook                     \n",
            "(LOW         ) DistEvalHook                       \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "before_val_epoch:\n",
            "(LOW         ) IterTimerHook                      \n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "before_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_iter:\n",
            "(LOW         ) IterTimerHook                      \n",
            " -------------------- \n",
            "after_val_epoch:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "after_run:\n",
            "(VERY_LOW    ) TextLoggerHook                     \n",
            "(VERY_LOW    ) TensorboardLoggerHook              \n",
            " -------------------- \n",
            "2023-10-08 07:36:55,068 - mmseg - INFO - workflow: [('train', 1)], max: 2000 iters\n",
            "2023-10-08 07:36:55,068 - mmseg - INFO - Checkpoints will be saved to /content/gdrive/MyDrive/results_u_net/BurnExperiment by HardDiskBackend.\n",
            "2023-10-08 07:36:55.571893: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-10-08 07:36:57.686640: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:487: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "2023-10-08 07:37:36,479 - mmcv - INFO - Reducer buckets have been rebuilt in this iteration.\n",
            "2023-10-08 07:37:57,721 - mmseg - INFO - Iter [20/2000]\tlr: 8.155e-07, eta: 1:13:54, time: 2.240, data_time: 0.327, memory: 8118, decode.loss_dice: 0.4671, decode.acc_seg: 33.7417, aux.loss_dice: 0.4453, aux.acc_seg: 67.8573, loss: 0.9124\n",
            "2023-10-08 07:38:20,798 - mmseg - INFO - Iter [40/2000]\tlr: 1.657e-06, eta: 0:55:25, time: 1.154, data_time: 0.004, memory: 8118, decode.loss_dice: 0.4664, decode.acc_seg: 34.7158, aux.loss_dice: 0.4439, aux.acc_seg: 70.2247, loss: 0.9103\n",
            "2023-10-08 07:38:44,682 - mmseg - INFO - Iter [60/2000]\tlr: 2.481e-06, eta: 0:49:26, time: 1.194, data_time: 0.004, memory: 8118, decode.loss_dice: 0.4669, decode.acc_seg: 36.7869, aux.loss_dice: 0.4415, aux.acc_seg: 76.7776, loss: 0.9084\n",
            "2023-10-08 07:39:09,297 - mmseg - INFO - Iter [80/2000]\tlr: 3.288e-06, eta: 0:46:32, time: 1.231, data_time: 0.004, memory: 8118, decode.loss_dice: 0.4371, decode.acc_seg: 46.3481, aux.loss_dice: 0.4130, aux.acc_seg: 78.2383, loss: 0.8501\n",
            "2023-10-08 07:39:35,053 - mmseg - INFO - Iter [100/2000]\tlr: 4.078e-06, eta: 0:45:00, time: 1.288, data_time: 0.004, memory: 8118, decode.loss_dice: 0.4351, decode.acc_seg: 57.8357, aux.loss_dice: 0.4097, aux.acc_seg: 82.7733, loss: 0.8448\n",
            "2023-10-08 07:40:00,473 - mmseg - INFO - Iter [120/2000]\tlr: 4.850e-06, eta: 0:43:44, time: 1.271, data_time: 0.004, memory: 8118, decode.loss_dice: 0.4089, decode.acc_seg: 73.7192, aux.loss_dice: 0.3805, aux.acc_seg: 86.6186, loss: 0.7894\n",
            "2023-10-08 07:40:28,188 - mmseg - INFO - Iter [140/2000]\tlr: 5.605e-06, eta: 0:43:14, time: 1.386, data_time: 0.143, memory: 8118, decode.loss_dice: 0.3658, decode.acc_seg: 80.8723, aux.loss_dice: 0.3411, aux.acc_seg: 84.8051, loss: 0.7069\n",
            "2023-10-08 07:40:53,379 - mmseg - INFO - Iter [160/2000]\tlr: 6.342e-06, eta: 0:42:15, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.3321, decode.acc_seg: 84.9750, aux.loss_dice: 0.3096, aux.acc_seg: 87.3436, loss: 0.6417\n",
            "2023-10-08 07:41:18,784 - mmseg - INFO - Iter [180/2000]\tlr: 7.062e-06, eta: 0:41:25, time: 1.270, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2837, decode.acc_seg: 90.3221, aux.loss_dice: 0.2652, aux.acc_seg: 91.1812, loss: 0.5489\n",
            "2023-10-08 07:41:44,036 - mmseg - INFO - Iter [200/2000]\tlr: 7.765e-06, eta: 0:40:39, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2675, decode.acc_seg: 90.3457, aux.loss_dice: 0.2509, aux.acc_seg: 90.6584, loss: 0.5185\n",
            "2023-10-08 07:42:09,261 - mmseg - INFO - Iter [220/2000]\tlr: 8.451e-06, eta: 0:39:57, time: 1.261, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2875, decode.acc_seg: 89.0355, aux.loss_dice: 0.2686, aux.acc_seg: 89.5287, loss: 0.5561\n",
            "2023-10-08 07:42:34,376 - mmseg - INFO - Iter [240/2000]\tlr: 9.119e-06, eta: 0:39:17, time: 1.256, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2509, decode.acc_seg: 88.6249, aux.loss_dice: 0.2386, aux.acc_seg: 88.8536, loss: 0.4895\n",
            "2023-10-08 07:42:59,527 - mmseg - INFO - Iter [260/2000]\tlr: 9.770e-06, eta: 0:38:39, time: 1.258, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2538, decode.acc_seg: 90.0143, aux.loss_dice: 0.2378, aux.acc_seg: 90.2988, loss: 0.4916\n",
            "2023-10-08 07:43:27,360 - mmseg - INFO - Iter [280/2000]\tlr: 1.040e-05, eta: 0:38:20, time: 1.392, data_time: 0.138, memory: 8118, decode.loss_dice: 0.2644, decode.acc_seg: 89.8651, aux.loss_dice: 0.2467, aux.acc_seg: 90.2641, loss: 0.5110\n",
            "2023-10-08 07:43:52,647 - mmseg - INFO - Iter [300/2000]\tlr: 1.102e-05, eta: 0:37:45, time: 1.264, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2375, decode.acc_seg: 91.8119, aux.loss_dice: 0.2164, aux.acc_seg: 92.0989, loss: 0.4539\n",
            "2023-10-08 07:44:17,840 - mmseg - INFO - Iter [320/2000]\tlr: 1.093e-05, eta: 0:37:10, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2252, decode.acc_seg: 90.1781, aux.loss_dice: 0.2105, aux.acc_seg: 90.3148, loss: 0.4356\n",
            "2023-10-08 07:44:42,960 - mmseg - INFO - Iter [340/2000]\tlr: 1.080e-05, eta: 0:36:37, time: 1.256, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2236, decode.acc_seg: 90.4931, aux.loss_dice: 0.2067, aux.acc_seg: 90.6419, loss: 0.4303\n",
            "2023-10-08 07:45:08,090 - mmseg - INFO - Iter [360/2000]\tlr: 1.067e-05, eta: 0:36:04, time: 1.256, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2420, decode.acc_seg: 91.0225, aux.loss_dice: 0.2252, aux.acc_seg: 91.3273, loss: 0.4672\n",
            "2023-10-08 07:45:33,179 - mmseg - INFO - Iter [380/2000]\tlr: 1.054e-05, eta: 0:35:32, time: 1.254, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2487, decode.acc_seg: 90.5800, aux.loss_dice: 0.2322, aux.acc_seg: 90.8237, loss: 0.4809\n",
            "2023-10-08 07:45:58,265 - mmseg - INFO - Iter [400/2000]\tlr: 1.041e-05, eta: 0:35:01, time: 1.254, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2120, decode.acc_seg: 92.3760, aux.loss_dice: 0.1950, aux.acc_seg: 92.4833, loss: 0.4070\n",
            "[>>] 264/264, 0.6 task/s, elapsed: 453s, ETA:     0s\n",
            "\n",
            "2023-10-08 07:53:31,845 - mmseg - INFO - per class results:\n",
            "2023-10-08 07:53:31,846 - mmseg - INFO - \n",
            "+--------------+-------+-------+\n",
            "|    Class     |  IoU  |  Acc  |\n",
            "+--------------+-------+-------+\n",
            "| Unburnt land | 91.87 | 92.85 |\n",
            "|  Burn scar   | 52.65 | 89.54 |\n",
            "+--------------+-------+-------+\n",
            "2023-10-08 07:53:31,847 - mmseg - INFO - Summary:\n",
            "2023-10-08 07:53:31,848 - mmseg - INFO - \n",
            "+-------+-------+------+\n",
            "|  aAcc |  mIoU | mAcc |\n",
            "+-------+-------+------+\n",
            "| 92.55 | 72.26 | 91.2 |\n",
            "+-------+-------+------+\n",
            "2023-10-08 07:53:52,429 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_400.pth.\n",
            "2023-10-08 07:53:52,429 - mmseg - INFO - Best mIoU is 0.7226 at 400 iter.\n",
            "2023-10-08 07:53:52,430 - mmseg - INFO - Iter(val) [264]\taAcc: 0.9255, mIoU: 0.7226, mAcc: 0.9120, IoU.Unburnt land: 0.9187, IoU.Burn scar: 0.5265, Acc.Unburnt land: 0.9285, Acc.Burn scar: 0.8954\n",
            "2023-10-08 07:54:20,759 - mmseg - INFO - Iter [420/2000]\tlr: 1.028e-05, eta: 1:04:26, time: 25.125, data_time: 23.856, memory: 8118, decode.loss_dice: 0.2465, decode.acc_seg: 91.8314, aux.loss_dice: 0.2300, aux.acc_seg: 92.0595, loss: 0.4765\n",
            "2023-10-08 07:54:46,781 - mmseg - INFO - Iter [440/2000]\tlr: 1.015e-05, eta: 1:02:16, time: 1.301, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1930, decode.acc_seg: 91.8616, aux.loss_dice: 0.1773, aux.acc_seg: 91.9897, loss: 0.3703\n",
            "2023-10-08 07:55:11,879 - mmseg - INFO - Iter [460/2000]\tlr: 1.002e-05, eta: 1:00:12, time: 1.255, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2006, decode.acc_seg: 92.2218, aux.loss_dice: 0.1847, aux.acc_seg: 92.3647, loss: 0.3853\n",
            "2023-10-08 07:55:36,881 - mmseg - INFO - Iter [480/2000]\tlr: 9.886e-06, eta: 0:58:15, time: 1.250, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2003, decode.acc_seg: 93.9840, aux.loss_dice: 0.1831, aux.acc_seg: 94.0960, loss: 0.3834\n",
            "2023-10-08 07:56:02,226 - mmseg - INFO - Iter [500/2000]\tlr: 9.756e-06, eta: 0:56:27, time: 1.267, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2311, decode.acc_seg: 92.9318, aux.loss_dice: 0.2143, aux.acc_seg: 93.0286, loss: 0.4455\n",
            "2023-10-08 07:56:27,623 - mmseg - INFO - Iter [520/2000]\tlr: 9.626e-06, eta: 0:54:46, time: 1.270, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2155, decode.acc_seg: 91.3953, aux.loss_dice: 0.2016, aux.acc_seg: 91.5121, loss: 0.4172\n",
            "2023-10-08 07:56:52,873 - mmseg - INFO - Iter [540/2000]\tlr: 9.496e-06, eta: 0:53:10, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2159, decode.acc_seg: 91.9735, aux.loss_dice: 0.2003, aux.acc_seg: 92.0675, loss: 0.4162\n",
            "2023-10-08 07:57:20,692 - mmseg - INFO - Iter [560/2000]\tlr: 9.366e-06, eta: 0:51:45, time: 1.391, data_time: 0.134, memory: 8118, decode.loss_dice: 0.2140, decode.acc_seg: 92.9347, aux.loss_dice: 0.1939, aux.acc_seg: 93.0678, loss: 0.4079\n",
            "2023-10-08 07:57:46,067 - mmseg - INFO - Iter [580/2000]\tlr: 9.237e-06, eta: 0:50:19, time: 1.269, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2067, decode.acc_seg: 92.2193, aux.loss_dice: 0.1949, aux.acc_seg: 92.3893, loss: 0.4016\n",
            "2023-10-08 07:58:11,378 - mmseg - INFO - Iter [600/2000]\tlr: 9.106e-06, eta: 0:48:56, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2048, decode.acc_seg: 94.4168, aux.loss_dice: 0.1870, aux.acc_seg: 94.4610, loss: 0.3918\n",
            "2023-10-08 07:58:36,536 - mmseg - INFO - Iter [620/2000]\tlr: 8.976e-06, eta: 0:47:37, time: 1.258, data_time: 0.004, memory: 8118, decode.loss_dice: 0.2133, decode.acc_seg: 90.8893, aux.loss_dice: 0.2016, aux.acc_seg: 90.9647, loss: 0.4150\n",
            "2023-10-08 07:59:01,712 - mmseg - INFO - Iter [640/2000]\tlr: 8.846e-06, eta: 0:46:21, time: 1.259, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1878, decode.acc_seg: 92.6380, aux.loss_dice: 0.1716, aux.acc_seg: 92.7318, loss: 0.3594\n",
            "2023-10-08 07:59:26,920 - mmseg - INFO - Iter [660/2000]\tlr: 8.716e-06, eta: 0:45:08, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1990, decode.acc_seg: 94.0638, aux.loss_dice: 0.1835, aux.acc_seg: 94.1665, loss: 0.3825\n",
            "2023-10-08 07:59:55,235 - mmseg - INFO - Iter [680/2000]\tlr: 8.586e-06, eta: 0:44:04, time: 1.416, data_time: 0.156, memory: 8118, decode.loss_dice: 0.1926, decode.acc_seg: 92.0154, aux.loss_dice: 0.1784, aux.acc_seg: 92.0464, loss: 0.3710\n",
            "2023-10-08 08:00:20,560 - mmseg - INFO - Iter [700/2000]\tlr: 8.457e-06, eta: 0:42:57, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1839, decode.acc_seg: 92.9545, aux.loss_dice: 0.1724, aux.acc_seg: 93.0351, loss: 0.3563\n",
            "2023-10-08 08:00:45,892 - mmseg - INFO - Iter [720/2000]\tlr: 8.327e-06, eta: 0:41:51, time: 1.267, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1623, decode.acc_seg: 92.7978, aux.loss_dice: 0.1508, aux.acc_seg: 92.8162, loss: 0.3131\n",
            "2023-10-08 08:01:11,178 - mmseg - INFO - Iter [740/2000]\tlr: 8.197e-06, eta: 0:40:48, time: 1.264, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1987, decode.acc_seg: 91.6505, aux.loss_dice: 0.1875, aux.acc_seg: 91.6592, loss: 0.3862\n",
            "2023-10-08 08:01:36,536 - mmseg - INFO - Iter [760/2000]\tlr: 8.066e-06, eta: 0:39:47, time: 1.268, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1959, decode.acc_seg: 94.4533, aux.loss_dice: 0.1792, aux.acc_seg: 94.5048, loss: 0.3751\n",
            "2023-10-08 08:02:01,798 - mmseg - INFO - Iter [780/2000]\tlr: 7.936e-06, eta: 0:38:48, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1649, decode.acc_seg: 92.8121, aux.loss_dice: 0.1527, aux.acc_seg: 92.8329, loss: 0.3176\n",
            "2023-10-08 08:02:27,019 - mmseg - INFO - Iter [800/2000]\tlr: 7.807e-06, eta: 0:37:51, time: 1.261, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1867, decode.acc_seg: 94.0594, aux.loss_dice: 0.1721, aux.acc_seg: 94.1207, loss: 0.3588\n",
            "[>>] 264/264, 0.7 task/s, elapsed: 357s, ETA:     0s\n",
            "\n",
            "2023-10-08 08:08:23,934 - mmseg - INFO - per class results:\n",
            "2023-10-08 08:08:23,935 - mmseg - INFO - \n",
            "+--------------+-------+-------+\n",
            "|    Class     |  IoU  |  Acc  |\n",
            "+--------------+-------+-------+\n",
            "| Unburnt land | 95.89 | 97.56 |\n",
            "|  Burn scar   | 66.92 | 82.94 |\n",
            "+--------------+-------+-------+\n",
            "2023-10-08 08:08:23,935 - mmseg - INFO - Summary:\n",
            "2023-10-08 08:08:23,936 - mmseg - INFO - \n",
            "+------+------+-------+\n",
            "| aAcc | mIoU |  mAcc |\n",
            "+------+------+-------+\n",
            "| 96.2 | 81.4 | 90.25 |\n",
            "+------+------+-------+\n",
            "2023-10-08 08:08:23,947 - mmseg - INFO - The previous best checkpoint /content/gdrive/MyDrive/results_u_net/BurnExperiment/best_mIoU_iter_400.pth was removed\n",
            "2023-10-08 08:08:37,340 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_800.pth.\n",
            "2023-10-08 08:08:37,341 - mmseg - INFO - Best mIoU is 0.8140 at 800 iter.\n",
            "2023-10-08 08:08:37,341 - mmseg - INFO - Iter(val) [264]\taAcc: 0.9620, mIoU: 0.8140, mAcc: 0.9025, IoU.Unburnt land: 0.9589, IoU.Burn scar: 0.6692, Acc.Unburnt land: 0.9756, Acc.Burn scar: 0.8294\n",
            "2023-10-08 08:09:05,818 - mmseg - INFO - Iter [820/2000]\tlr: 7.677e-06, eta: 0:45:52, time: 19.940, data_time: 18.676, memory: 8118, decode.loss_dice: 0.1814, decode.acc_seg: 93.7193, aux.loss_dice: 0.1673, aux.acc_seg: 93.7484, loss: 0.3487\n",
            "2023-10-08 08:09:31,910 - mmseg - INFO - Iter [840/2000]\tlr: 7.546e-06, eta: 0:44:37, time: 1.305, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1472, decode.acc_seg: 94.5793, aux.loss_dice: 0.1309, aux.acc_seg: 94.6083, loss: 0.2781\n",
            "2023-10-08 08:09:57,004 - mmseg - INFO - Iter [860/2000]\tlr: 7.417e-06, eta: 0:43:23, time: 1.255, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1393, decode.acc_seg: 93.8102, aux.loss_dice: 0.1294, aux.acc_seg: 93.8795, loss: 0.2687\n",
            "2023-10-08 08:10:21,963 - mmseg - INFO - Iter [880/2000]\tlr: 7.286e-06, eta: 0:42:11, time: 1.248, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1729, decode.acc_seg: 94.1671, aux.loss_dice: 0.1598, aux.acc_seg: 94.1548, loss: 0.3327\n",
            "2023-10-08 08:10:47,360 - mmseg - INFO - Iter [900/2000]\tlr: 7.156e-06, eta: 0:41:02, time: 1.270, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1825, decode.acc_seg: 92.8232, aux.loss_dice: 0.1691, aux.acc_seg: 92.9265, loss: 0.3516\n",
            "2023-10-08 08:11:12,742 - mmseg - INFO - Iter [920/2000]\tlr: 7.026e-06, eta: 0:39:54, time: 1.269, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1519, decode.acc_seg: 94.0263, aux.loss_dice: 0.1424, aux.acc_seg: 94.0284, loss: 0.2943\n",
            "2023-10-08 08:11:38,132 - mmseg - INFO - Iter [940/2000]\tlr: 6.896e-06, eta: 0:38:48, time: 1.269, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1702, decode.acc_seg: 93.8765, aux.loss_dice: 0.1599, aux.acc_seg: 93.9227, loss: 0.3301\n",
            "2023-10-08 08:12:05,830 - mmseg - INFO - Iter [960/2000]\tlr: 6.766e-06, eta: 0:37:47, time: 1.385, data_time: 0.134, memory: 8118, decode.loss_dice: 0.1903, decode.acc_seg: 92.2926, aux.loss_dice: 0.1793, aux.acc_seg: 92.3341, loss: 0.3696\n",
            "2023-10-08 08:12:31,166 - mmseg - INFO - Iter [980/2000]\tlr: 6.636e-06, eta: 0:36:44, time: 1.267, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1490, decode.acc_seg: 93.5034, aux.loss_dice: 0.1391, aux.acc_seg: 93.5318, loss: 0.2880\n",
            "2023-10-08 08:12:56,497 - mmseg - INFO - Exp name: burn_scars.py\n",
            "2023-10-08 08:12:56,498 - mmseg - INFO - Iter [1000/2000]\tlr: 6.506e-06, eta: 0:35:43, time: 1.267, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1672, decode.acc_seg: 92.5000, aux.loss_dice: 0.1576, aux.acc_seg: 92.4734, loss: 0.3248\n",
            "2023-10-08 08:13:21,761 - mmseg - INFO - Iter [1020/2000]\tlr: 6.376e-06, eta: 0:34:43, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1779, decode.acc_seg: 93.8035, aux.loss_dice: 0.1676, aux.acc_seg: 93.8690, loss: 0.3455\n",
            "2023-10-08 08:13:47,077 - mmseg - INFO - Iter [1040/2000]\tlr: 6.247e-06, eta: 0:33:45, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1649, decode.acc_seg: 93.6270, aux.loss_dice: 0.1559, aux.acc_seg: 93.6527, loss: 0.3208\n",
            "2023-10-08 08:14:12,340 - mmseg - INFO - Iter [1060/2000]\tlr: 6.116e-06, eta: 0:32:48, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1537, decode.acc_seg: 94.5780, aux.loss_dice: 0.1439, aux.acc_seg: 94.5837, loss: 0.2976\n",
            "2023-10-08 08:14:37,563 - mmseg - INFO - Iter [1080/2000]\tlr: 5.987e-06, eta: 0:31:52, time: 1.261, data_time: 0.003, memory: 8118, decode.loss_dice: 0.1530, decode.acc_seg: 95.2693, aux.loss_dice: 0.1396, aux.acc_seg: 95.2745, loss: 0.2926\n",
            "2023-10-08 08:15:05,610 - mmseg - INFO - Iter [1100/2000]\tlr: 5.856e-06, eta: 0:30:59, time: 1.402, data_time: 0.147, memory: 8118, decode.loss_dice: 0.1317, decode.acc_seg: 95.6015, aux.loss_dice: 0.1174, aux.acc_seg: 95.6097, loss: 0.2491\n",
            "2023-10-08 08:15:30,992 - mmseg - INFO - Iter [1120/2000]\tlr: 5.726e-06, eta: 0:30:05, time: 1.269, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1562, decode.acc_seg: 92.4579, aux.loss_dice: 0.1482, aux.acc_seg: 92.4754, loss: 0.3044\n",
            "2023-10-08 08:15:56,303 - mmseg - INFO - Iter [1140/2000]\tlr: 5.596e-06, eta: 0:29:12, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1683, decode.acc_seg: 93.4471, aux.loss_dice: 0.1591, aux.acc_seg: 93.5145, loss: 0.3274\n",
            "2023-10-08 08:16:21,506 - mmseg - INFO - Iter [1160/2000]\tlr: 5.466e-06, eta: 0:28:20, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1749, decode.acc_seg: 93.7378, aux.loss_dice: 0.1662, aux.acc_seg: 93.7359, loss: 0.3411\n",
            "2023-10-08 08:16:46,696 - mmseg - INFO - Iter [1180/2000]\tlr: 5.336e-06, eta: 0:27:29, time: 1.260, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1600, decode.acc_seg: 93.0012, aux.loss_dice: 0.1509, aux.acc_seg: 93.0351, loss: 0.3108\n",
            "2023-10-08 08:17:11,924 - mmseg - INFO - Iter [1200/2000]\tlr: 5.206e-06, eta: 0:26:39, time: 1.261, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1395, decode.acc_seg: 95.5367, aux.loss_dice: 0.1269, aux.acc_seg: 95.5386, loss: 0.2664\n",
            "[>>] 264/264, 0.7 task/s, elapsed: 357s, ETA:     0s\n",
            "\n",
            "2023-10-08 08:23:09,534 - mmseg - INFO - per class results:\n",
            "2023-10-08 08:23:09,535 - mmseg - INFO - \n",
            "+--------------+-------+-------+\n",
            "|    Class     |  IoU  |  Acc  |\n",
            "+--------------+-------+-------+\n",
            "| Unburnt land | 95.43 | 96.67 |\n",
            "|  Burn scar   |  65.8 | 87.29 |\n",
            "+--------------+-------+-------+\n",
            "2023-10-08 08:23:09,535 - mmseg - INFO - Summary:\n",
            "2023-10-08 08:23:09,535 - mmseg - INFO - \n",
            "+------+-------+-------+\n",
            "| aAcc |  mIoU |  mAcc |\n",
            "+------+-------+-------+\n",
            "| 95.8 | 80.62 | 91.98 |\n",
            "+------+-------+-------+\n",
            "2023-10-08 08:23:09,536 - mmseg - INFO - Iter(val) [264]\taAcc: 0.9580, mIoU: 0.8062, mAcc: 0.9198, IoU.Unburnt land: 0.9543, IoU.Burn scar: 0.6580, Acc.Unburnt land: 0.9667, Acc.Burn scar: 0.8729\n",
            "2023-10-08 08:23:37,456 - mmseg - INFO - Iter [1220/2000]\tlr: 5.076e-06, eta: 0:29:40, time: 19.277, data_time: 18.025, memory: 8118, decode.loss_dice: 0.1441, decode.acc_seg: 94.8846, aux.loss_dice: 0.1334, aux.acc_seg: 94.9181, loss: 0.2774\n",
            "2023-10-08 08:24:02,710 - mmseg - INFO - Iter [1240/2000]\tlr: 4.946e-06, eta: 0:28:42, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1509, decode.acc_seg: 94.5939, aux.loss_dice: 0.1416, aux.acc_seg: 94.6211, loss: 0.2925\n",
            "2023-10-08 08:24:28,061 - mmseg - INFO - Iter [1260/2000]\tlr: 4.817e-06, eta: 0:27:45, time: 1.268, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1607, decode.acc_seg: 93.8028, aux.loss_dice: 0.1497, aux.acc_seg: 93.8398, loss: 0.3104\n",
            "2023-10-08 08:24:53,258 - mmseg - INFO - Iter [1280/2000]\tlr: 4.686e-06, eta: 0:26:48, time: 1.260, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1467, decode.acc_seg: 95.5345, aux.loss_dice: 0.1342, aux.acc_seg: 95.5679, loss: 0.2809\n",
            "2023-10-08 08:25:18,450 - mmseg - INFO - Iter [1300/2000]\tlr: 4.557e-06, eta: 0:25:53, time: 1.260, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1432, decode.acc_seg: 92.7456, aux.loss_dice: 0.1338, aux.acc_seg: 92.7723, loss: 0.2770\n",
            "2023-10-08 08:25:43,583 - mmseg - INFO - Iter [1320/2000]\tlr: 4.426e-06, eta: 0:24:59, time: 1.257, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1247, decode.acc_seg: 93.8977, aux.loss_dice: 0.1180, aux.acc_seg: 93.8712, loss: 0.2428\n",
            "2023-10-08 08:26:08,735 - mmseg - INFO - Iter [1340/2000]\tlr: 4.297e-06, eta: 0:24:05, time: 1.258, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1631, decode.acc_seg: 93.8588, aux.loss_dice: 0.1539, aux.acc_seg: 93.9353, loss: 0.3170\n",
            "2023-10-08 08:26:36,652 - mmseg - INFO - Iter [1360/2000]\tlr: 4.166e-06, eta: 0:23:14, time: 1.396, data_time: 0.143, memory: 8118, decode.loss_dice: 0.1404, decode.acc_seg: 94.8887, aux.loss_dice: 0.1280, aux.acc_seg: 94.8856, loss: 0.2684\n",
            "2023-10-08 08:27:01,996 - mmseg - INFO - Iter [1380/2000]\tlr: 4.036e-06, eta: 0:22:22, time: 1.267, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1549, decode.acc_seg: 95.0733, aux.loss_dice: 0.1438, aux.acc_seg: 95.0939, loss: 0.2987\n",
            "2023-10-08 08:27:27,224 - mmseg - INFO - Iter [1400/2000]\tlr: 3.906e-06, eta: 0:21:31, time: 1.261, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1667, decode.acc_seg: 93.8383, aux.loss_dice: 0.1590, aux.acc_seg: 93.8277, loss: 0.3257\n",
            "2023-10-08 08:27:52,411 - mmseg - INFO - Iter [1420/2000]\tlr: 3.776e-06, eta: 0:20:41, time: 1.259, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1525, decode.acc_seg: 93.6992, aux.loss_dice: 0.1433, aux.acc_seg: 93.7267, loss: 0.2959\n",
            "2023-10-08 08:28:17,523 - mmseg - INFO - Iter [1440/2000]\tlr: 3.646e-06, eta: 0:19:51, time: 1.256, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1376, decode.acc_seg: 93.7092, aux.loss_dice: 0.1281, aux.acc_seg: 93.7303, loss: 0.2657\n",
            "2023-10-08 08:28:42,726 - mmseg - INFO - Iter [1460/2000]\tlr: 3.516e-06, eta: 0:19:02, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1342, decode.acc_seg: 95.1572, aux.loss_dice: 0.1266, aux.acc_seg: 95.1421, loss: 0.2608\n",
            "2023-10-08 08:29:07,990 - mmseg - INFO - Iter [1480/2000]\tlr: 3.386e-06, eta: 0:18:14, time: 1.263, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1130, decode.acc_seg: 94.5800, aux.loss_dice: 0.1033, aux.acc_seg: 94.5960, loss: 0.2163\n",
            "2023-10-08 08:29:35,835 - mmseg - INFO - Iter [1500/2000]\tlr: 3.256e-06, eta: 0:17:27, time: 1.392, data_time: 0.136, memory: 8118, decode.loss_dice: 0.1362, decode.acc_seg: 95.1635, aux.loss_dice: 0.1269, aux.acc_seg: 95.1836, loss: 0.2631\n",
            "2023-10-08 08:30:01,169 - mmseg - INFO - Iter [1520/2000]\tlr: 3.127e-06, eta: 0:16:40, time: 1.267, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1562, decode.acc_seg: 93.9290, aux.loss_dice: 0.1493, aux.acc_seg: 93.9436, loss: 0.3054\n",
            "2023-10-08 08:30:26,482 - mmseg - INFO - Iter [1540/2000]\tlr: 2.997e-06, eta: 0:15:53, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1559, decode.acc_seg: 94.0378, aux.loss_dice: 0.1459, aux.acc_seg: 94.1036, loss: 0.3018\n",
            "2023-10-08 08:30:51,799 - mmseg - INFO - Iter [1560/2000]\tlr: 2.866e-06, eta: 0:15:07, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1291, decode.acc_seg: 93.4908, aux.loss_dice: 0.1199, aux.acc_seg: 93.4940, loss: 0.2490\n",
            "2023-10-08 08:31:17,110 - mmseg - INFO - Iter [1580/2000]\tlr: 2.736e-06, eta: 0:14:22, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1956, decode.acc_seg: 92.9669, aux.loss_dice: 0.1857, aux.acc_seg: 92.9902, loss: 0.3812\n",
            "2023-10-08 08:31:42,472 - mmseg - INFO - Iter [1600/2000]\tlr: 2.606e-06, eta: 0:13:37, time: 1.268, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1716, decode.acc_seg: 92.7146, aux.loss_dice: 0.1625, aux.acc_seg: 92.7501, loss: 0.3341\n",
            "[>>] 264/264, 0.7 task/s, elapsed: 357s, ETA:     0s\n",
            "\n",
            "2023-10-08 08:37:39,782 - mmseg - INFO - per class results:\n",
            "2023-10-08 08:37:39,783 - mmseg - INFO - \n",
            "+--------------+-------+-------+\n",
            "|    Class     |  IoU  |  Acc  |\n",
            "+--------------+-------+-------+\n",
            "| Unburnt land | 95.65 | 96.95 |\n",
            "|  Burn scar   | 66.74 | 86.72 |\n",
            "+--------------+-------+-------+\n",
            "2023-10-08 08:37:39,783 - mmseg - INFO - Summary:\n",
            "2023-10-08 08:37:39,784 - mmseg - INFO - \n",
            "+------+------+-------+\n",
            "| aAcc | mIoU |  mAcc |\n",
            "+------+------+-------+\n",
            "| 96.0 | 81.2 | 91.83 |\n",
            "+------+------+-------+\n",
            "2023-10-08 08:37:39,784 - mmseg - INFO - Iter(val) [264]\taAcc: 0.9600, mIoU: 0.8120, mAcc: 0.9183, IoU.Unburnt land: 0.9565, IoU.Burn scar: 0.6674, Acc.Unburnt land: 0.9695, Acc.Burn scar: 0.8672\n",
            "2023-10-08 08:38:04,931 - mmseg - INFO - Iter [1620/2000]\tlr: 2.476e-06, eta: 0:14:16, time: 19.123, data_time: 17.869, memory: 8118, decode.loss_dice: 0.1356, decode.acc_seg: 95.3804, aux.loss_dice: 0.1280, aux.acc_seg: 95.3827, loss: 0.2636\n",
            "2023-10-08 08:38:33,190 - mmseg - INFO - Iter [1640/2000]\tlr: 2.346e-06, eta: 0:13:27, time: 1.413, data_time: 0.151, memory: 8118, decode.loss_dice: 0.1394, decode.acc_seg: 94.2574, aux.loss_dice: 0.1294, aux.acc_seg: 94.3007, loss: 0.2688\n",
            "2023-10-08 08:38:58,542 - mmseg - INFO - Iter [1660/2000]\tlr: 2.216e-06, eta: 0:12:38, time: 1.268, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1547, decode.acc_seg: 93.3240, aux.loss_dice: 0.1470, aux.acc_seg: 93.3355, loss: 0.3017\n",
            "2023-10-08 08:39:23,683 - mmseg - INFO - Iter [1680/2000]\tlr: 2.086e-06, eta: 0:11:50, time: 1.257, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1272, decode.acc_seg: 95.3319, aux.loss_dice: 0.1191, aux.acc_seg: 95.3490, loss: 0.2463\n",
            "2023-10-08 08:39:48,820 - mmseg - INFO - Iter [1700/2000]\tlr: 1.956e-06, eta: 0:11:02, time: 1.257, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1597, decode.acc_seg: 93.9146, aux.loss_dice: 0.1525, aux.acc_seg: 93.9238, loss: 0.3122\n",
            "2023-10-08 08:40:13,988 - mmseg - INFO - Iter [1720/2000]\tlr: 1.826e-06, eta: 0:10:15, time: 1.258, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1454, decode.acc_seg: 94.9263, aux.loss_dice: 0.1382, aux.acc_seg: 94.9279, loss: 0.2835\n",
            "2023-10-08 08:40:39,267 - mmseg - INFO - Iter [1740/2000]\tlr: 1.696e-06, eta: 0:09:28, time: 1.264, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1370, decode.acc_seg: 94.5768, aux.loss_dice: 0.1279, aux.acc_seg: 94.5667, loss: 0.2649\n",
            "2023-10-08 08:41:07,272 - mmseg - INFO - Iter [1760/2000]\tlr: 1.567e-06, eta: 0:08:42, time: 1.400, data_time: 0.139, memory: 8118, decode.loss_dice: 0.1530, decode.acc_seg: 94.5802, aux.loss_dice: 0.1454, aux.acc_seg: 94.6001, loss: 0.2984\n",
            "2023-10-08 08:41:32,588 - mmseg - INFO - Iter [1780/2000]\tlr: 1.437e-06, eta: 0:07:57, time: 1.266, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1171, decode.acc_seg: 94.8569, aux.loss_dice: 0.1107, aux.acc_seg: 94.8630, loss: 0.2278\n",
            "2023-10-08 08:41:57,840 - mmseg - INFO - Iter [1800/2000]\tlr: 1.307e-06, eta: 0:07:11, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1364, decode.acc_seg: 94.4051, aux.loss_dice: 0.1277, aux.acc_seg: 94.4289, loss: 0.2641\n",
            "2023-10-08 08:42:23,094 - mmseg - INFO - Iter [1820/2000]\tlr: 1.177e-06, eta: 0:06:26, time: 1.263, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1585, decode.acc_seg: 95.0947, aux.loss_dice: 0.1472, aux.acc_seg: 95.1163, loss: 0.3056\n",
            "2023-10-08 08:42:48,269 - mmseg - INFO - Iter [1840/2000]\tlr: 1.047e-06, eta: 0:05:42, time: 1.259, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1354, decode.acc_seg: 94.9202, aux.loss_dice: 0.1240, aux.acc_seg: 94.9469, loss: 0.2593\n",
            "2023-10-08 08:43:13,421 - mmseg - INFO - Iter [1860/2000]\tlr: 9.165e-07, eta: 0:04:58, time: 1.258, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1509, decode.acc_seg: 93.8649, aux.loss_dice: 0.1430, aux.acc_seg: 93.8878, loss: 0.2939\n",
            "2023-10-08 08:43:38,596 - mmseg - INFO - Iter [1880/2000]\tlr: 7.865e-07, eta: 0:04:14, time: 1.259, data_time: 0.005, memory: 8118, decode.loss_dice: 0.1447, decode.acc_seg: 94.8914, aux.loss_dice: 0.1355, aux.acc_seg: 94.9034, loss: 0.2802\n",
            "2023-10-08 08:44:06,463 - mmseg - INFO - Iter [1900/2000]\tlr: 6.565e-07, eta: 0:03:31, time: 1.393, data_time: 0.138, memory: 8118, decode.loss_dice: 0.1418, decode.acc_seg: 94.4776, aux.loss_dice: 0.1351, aux.acc_seg: 94.4791, loss: 0.2769\n",
            "2023-10-08 08:44:31,823 - mmseg - INFO - Iter [1920/2000]\tlr: 5.265e-07, eta: 0:02:48, time: 1.268, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1422, decode.acc_seg: 95.9524, aux.loss_dice: 0.1319, aux.acc_seg: 95.9505, loss: 0.2741\n",
            "2023-10-08 08:44:57,026 - mmseg - INFO - Iter [1940/2000]\tlr: 3.965e-07, eta: 0:02:05, time: 1.260, data_time: 0.004, memory: 8118, decode.loss_dice: 0.0912, decode.acc_seg: 96.4541, aux.loss_dice: 0.0838, aux.acc_seg: 96.4487, loss: 0.1750\n",
            "2023-10-08 08:45:22,209 - mmseg - INFO - Iter [1960/2000]\tlr: 2.665e-07, eta: 0:01:23, time: 1.259, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1385, decode.acc_seg: 94.6143, aux.loss_dice: 0.1278, aux.acc_seg: 94.6320, loss: 0.2663\n",
            "2023-10-08 08:45:47,333 - mmseg - INFO - Iter [1980/2000]\tlr: 1.365e-07, eta: 0:00:41, time: 1.256, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1367, decode.acc_seg: 94.3882, aux.loss_dice: 0.1290, aux.acc_seg: 94.3841, loss: 0.2657\n",
            "2023-10-08 08:46:12,435 - mmseg - INFO - Exp name: burn_scars.py\n",
            "2023-10-08 08:46:12,436 - mmseg - INFO - Iter [2000/2000]\tlr: 6.500e-09, eta: 0:00:00, time: 1.255, data_time: 0.004, memory: 8118, decode.loss_dice: 0.1257, decode.acc_seg: 94.4630, aux.loss_dice: 0.1203, aux.acc_seg: 94.4583, loss: 0.2460\n",
            "[>>] 264/264, 0.7 task/s, elapsed: 358s, ETA:     0s\n",
            "\n",
            "2023-10-08 08:52:10,222 - mmseg - INFO - per class results:\n",
            "2023-10-08 08:52:10,223 - mmseg - INFO - \n",
            "+--------------+-------+-------+\n",
            "|    Class     |  IoU  |  Acc  |\n",
            "+--------------+-------+-------+\n",
            "| Unburnt land | 95.75 | 96.95 |\n",
            "|  Burn scar   | 67.53 |  87.7 |\n",
            "+--------------+-------+-------+\n",
            "2023-10-08 08:52:10,223 - mmseg - INFO - Summary:\n",
            "2023-10-08 08:52:10,223 - mmseg - INFO - \n",
            "+------+-------+-------+\n",
            "| aAcc |  mIoU |  mAcc |\n",
            "+------+-------+-------+\n",
            "| 96.1 | 81.64 | 92.33 |\n",
            "+------+-------+-------+\n",
            "2023-10-08 08:53:00,724 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_2000.pth.\n",
            "2023-10-08 08:53:00,724 - mmseg - INFO - Best mIoU is 0.8164 at 2000 iter.\n",
            "2023-10-08 08:53:00,725 - mmseg - INFO - Exp name: burn_scars.py\n",
            "2023-10-08 08:53:00,725 - mmseg - INFO - Iter(val) [264]\taAcc: 0.9610, mIoU: 0.8164, mAcc: 0.9233, IoU.Unburnt land: 0.9575, IoU.Burn scar: 0.6753, Acc.Unburnt land: 0.9695, Acc.Burn scar: 0.8770\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/mim\", line 8, in <module>\n",
            "    sys.exit(cli())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1157, in __call__\n",
            "    return self.main(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1078, in main\n",
            "    rv = self.invoke(ctx)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1688, in invoke\n",
            "    return _process_result(sub_ctx.command.invoke(sub_ctx))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1434, in invoke\n",
            "    return ctx.invoke(self.callback, **ctx.params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 783, in invoke\n",
            "    return __callback(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/mim/commands/train.py\", line 100, in cli\n",
            "    is_success, msg = train(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/mim/commands/train.py\", line 261, in train\n",
            "    ret = subprocess.check_call(\n",
            "  File \"/usr/lib/python3.10/subprocess.py\", line 369, in check_call\n",
            "    raise CalledProcessError(retcode, cmd)\n",
            "subprocess.CalledProcessError: Command '['/usr/bin/python3', '-m', 'torch.distributed.launch', '--nproc_per_node=1', '--master_port=29764', '/usr/local/lib/python3.10/dist-packages/mmseg/.mim/tools/train.py', '/content/hls-foundation-os/configs/burn_scars.py', '--launcher', 'pytorch']' died with <Signals.SIGSEGV: 11>.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir(\"/content/hls-foundation-os/\")\n",
        "!pwd\n",
        "import geospatial_fm"
      ],
      "metadata": {
        "id": "227HQZzKaLB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "App"
      ],
      "metadata": {
        "id": "4_W_R2k6Mb05"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "######### pull files\n",
        "import os\n",
        "from huggingface_hub import hf_hub_download\n",
        "config_path=hf_hub_download(repo_id=\"ibm-nasa-geospatial/Prithvi-100M-burn-scar\", filename=\"burn_scars_Prithvi_100M.py\", token=os.environ.get(\"token\"))\n",
        "ckpt=hf_hub_download(repo_id=\"ibm-nasa-geospatial/Prithvi-100M-burn-scar\", filename='burn_scars_Prithvi_100M.pth', token=os.environ.get(\"token\"))\n",
        "##########\n",
        "\n",
        "\n",
        "import argparse\n",
        "from mmcv import Config\n",
        "\n",
        "from mmseg.models import build_segmentor\n",
        "\n",
        "from mmseg.datasets.pipelines import Compose, LoadImageFromFile\n",
        "\n",
        "import rasterio\n",
        "import torch\n",
        "\n",
        "from mmseg.apis import init_segmentor\n",
        "\n",
        "from mmcv.parallel import collate, scatter\n",
        "\n",
        "import numpy as np\n",
        "import glob\n",
        "import os\n",
        "\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import gradio as gr\n",
        "from functools import partial\n",
        "\n",
        "import pdb\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def open_tiff(fname):\n",
        "\n",
        "    with rasterio.open(fname, \"r\") as src:\n",
        "\n",
        "        data = src.read()\n",
        "\n",
        "    return data\n",
        "\n",
        "def write_tiff(img_wrt, filename, metadata):\n",
        "\n",
        "    \"\"\"\n",
        "    It writes a raster image to file.\n",
        "    :param img_wrt: numpy array containing the data (can be 2D for single band or 3D for multiple bands)\n",
        "    :param filename: file path to the output file\n",
        "    :param metadata: metadata to use to write the raster to disk\n",
        "    :return:\n",
        "    \"\"\"\n",
        "\n",
        "    with rasterio.open(filename, \"w\", **metadata) as dest:\n",
        "\n",
        "        if len(img_wrt.shape) == 2:\n",
        "\n",
        "            img_wrt = img_wrt[None]\n",
        "\n",
        "        for i in range(img_wrt.shape[0]):\n",
        "            dest.write(img_wrt[i, :, :], i + 1)\n",
        "\n",
        "    return filename\n",
        "\n",
        "\n",
        "def get_meta(fname):\n",
        "\n",
        "    with rasterio.open(fname, \"r\") as src:\n",
        "\n",
        "        meta = src.meta\n",
        "\n",
        "    return meta\n",
        "\n",
        "def preprocess_example(example_list):\n",
        "\n",
        "    example_list = [os.path.join(os.path.abspath(''), x) for x in example_list]\n",
        "\n",
        "    return example_list\n",
        "\n",
        "\n",
        "def inference_segmentor(model, imgs, custom_test_pipeline=None):\n",
        "    \"\"\"Inference image(s) with the segmentor.\n",
        "    Args:\n",
        "        model (nn.Module): The loaded segmentor.\n",
        "        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\n",
        "            images.\n",
        "    Returns:\n",
        "        (list[Tensor]): The segmentation result.\n",
        "    \"\"\"\n",
        "    cfg = model.cfg\n",
        "    device = next(model.parameters()).device  # model device\n",
        "    # build the data pipeline\n",
        "    test_pipeline = [LoadImageFromFile()] + cfg.data.test.pipeline[1:] if custom_test_pipeline == None else custom_test_pipeline\n",
        "    test_pipeline = Compose(test_pipeline)\n",
        "    # prepare data\n",
        "    data = []\n",
        "    imgs = imgs if isinstance(imgs, list) else [imgs]\n",
        "    for img in imgs:\n",
        "        img_data = {'img_info': {'filename': img}}\n",
        "        img_data = test_pipeline(img_data)\n",
        "        data.append(img_data)\n",
        "    # print(data.shape)\n",
        "\n",
        "    data = collate(data, samples_per_gpu=len(imgs))\n",
        "    if next(model.parameters()).is_cuda:\n",
        "        # data = collate(data, samples_per_gpu=len(imgs))\n",
        "        # scatter to specified GPU\n",
        "        data = scatter(data, [device])[0]\n",
        "    else:\n",
        "        # img_metas = scatter(data['img_metas'],'cpu')\n",
        "        # data['img_metas'] = [i.data[0] for i in data['img_metas']]\n",
        "\n",
        "        img_metas = data['img_metas'].data[0]\n",
        "        img = data['img']\n",
        "        data = {'img': img, 'img_metas':img_metas}\n",
        "\n",
        "    with torch.no_grad():\n",
        "        result = model(return_loss=False, rescale=True, **data)\n",
        "    return result\n",
        "\n",
        "\n",
        "def inference_on_file(target_image, model, custom_test_pipeline):\n",
        "\n",
        "    target_image = target_image.name\n",
        "    # print(type(target_image))\n",
        "\n",
        "    # output_image = target_image.replace('.tif', '_pred.tif')\n",
        "    time_taken=-1\n",
        "\n",
        "    st = time.time()\n",
        "    print('Running inference...')\n",
        "    try:\n",
        "        result = inference_segmentor(model, target_image, custom_test_pipeline)\n",
        "    except:\n",
        "        print('Error: Try different channels order.')\n",
        "        model.cfg.data.test.pipeline[0]['channels_last'] = True\n",
        "        result = inference_segmentor(model, target_image, custom_test_pipeline)\n",
        "    print(\"Output has shape: \" + str(result[0].shape))\n",
        "\n",
        "    # prep outputs\n",
        "    mask = open_tiff(target_image)\n",
        "    rgb = mask[[5, 3, 2], :, :].transpose((1,2,0))\n",
        "    meta = get_meta(target_image)\n",
        "    mask = np.where(mask == meta['nodata'], 1, 0)\n",
        "    mask = np.max(mask, axis=0)[None]\n",
        "    rgb = np.where(mask.transpose((1,2,0)) == 1, 0, rgb)\n",
        "    rgb = np.where(rgb < 0, 0, rgb)\n",
        "    rgb = np.where(rgb > 1, 1, rgb)\n",
        "\n",
        "    prediction = np.where(mask == 1, 0, result[0]*255)\n",
        "    et = time.time()\n",
        "    time_taken = np.round(et - st, 1)\n",
        "    print(f'Inference completed in {str(time_taken)} seconds')\n",
        "\n",
        "    return rgb, prediction[0]\n",
        "\n",
        "\n",
        "def process_test_pipeline(custom_test_pipeline, bands=None):\n",
        "\n",
        "    # change extracted bands if necessary\n",
        "    if bands is not None:\n",
        "\n",
        "        extract_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'] == 'BandsExtract' ]\n",
        "\n",
        "        if len(extract_index) > 0:\n",
        "\n",
        "            custom_test_pipeline[extract_index[0]]['bands'] = eval(bands)\n",
        "\n",
        "    collect_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'].find('Collect') > -1]\n",
        "\n",
        "    # adapt collected keys if necessary\n",
        "    if len(collect_index) > 0:\n",
        "\n",
        "        keys = ['img_info', 'filename', 'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg']\n",
        "        custom_test_pipeline[collect_index[0]]['meta_keys'] = keys\n",
        "\n",
        "    return custom_test_pipeline\n",
        "\n",
        "config = Config.fromfile(config_path)\n",
        "config.model.backbone.pretrained=None\n",
        "model = init_segmentor(config, ckpt, device='cpu')\n",
        "custom_test_pipeline=process_test_pipeline(model.cfg.data.test.pipeline, None)\n",
        "\n",
        "func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline)\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "\n",
        "    gr.Markdown(value='# Prithvi burn scars detection')\n",
        "    gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. This demo showcases how the model was finetuned to detect burn scars. More detailes can be found [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar).\\n\n",
        "    The user needs to provide an HLS geotiff image, including the following channels in reflectance units (e.g. 0-1): Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.\n",
        "    ''')\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            inp = gr.File()\n",
        "            btn = gr.Button(\"Submit\")\n",
        "\n",
        "    with gr.Row():\n",
        "        gr.Markdown(value='### Input color composite (SWIR, Narrow NIR, Red)')\n",
        "        gr.Markdown(value='### Model prediction (Black: No burn scar; White: Burn scar)')\n",
        "\n",
        "    with gr.Row():\n",
        "        out1=gr.Image(image_mode='RGB')\n",
        "        out2 = gr.Image(image_mode='L')\n",
        "\n",
        "    btn.click(fn=func, inputs=inp, outputs=[out1, out2])\n",
        "\n",
        "    with gr.Row():\n",
        "        gr.Examples(examples=[\"subsetted_512x512_HLS.S30.T10TGS.2020245.v1.4_merged.tif\",\n",
        "                     \"subsetted_512x512_HLS.S30.T10TGS.2018285.v1.4_merged.tif\",\n",
        "                     \"subsetted_512x512_HLS.S30.T10UGV.2020218.v1.4_merged.tif\"],\n",
        "                    inputs=inp,\n",
        "                    outputs=[out1, out2],\n",
        "                    preprocess=preprocess_example,\n",
        "                    fn=func,\n",
        "                    cache_examples=True,\n",
        "    )\n",
        "\n",
        "demo.launch()"
      ],
      "metadata": {
        "id": "gRxjdD0aMVnt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447,
          "referenced_widgets": [
            "97401e5bf0824532a1c800418242593d",
            "3125a3d8935e4509893062fe55f8e202",
            "64051d83a6ec473b8a6b58b4f3b2213b",
            "5335eec3b5bb46a5a7dad263ba09df45",
            "20a0720d98834b17bf0c6c3fb39c3a00",
            "42096348c6744bd3b81ef7be3067b6b2",
            "3c13ff14d059405e805b2c16954bec21",
            "7a165e0258504bd88dcff181fe50df2e",
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            "e57496483c3245d3b7198eefbdd06ea3",
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            "e21ca08cae604cbdaaf74122242a2392",
            "b13e87442855439aa78e3c3cd2b77fea",
            "436872be79a445599f84a32a73843960",
            "b6cba5b7bc6d48a2bf4c1be142493160",
            "2b5a328d8b144b64b6b0da19e6214a62",
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            "ca4b11b91c8d4d8799c692eee7eeb27b",
            "4ab34cd573eb4fc3ae7fe568a86cd881"
          ]
        },
        "outputId": "6210a6ba-a132-4bc4-8ea6-f4aae001fd82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cars_Prithvi_100M.py:   0%|          | 0.00/6.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97401e5bf0824532a1c800418242593d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ars_Prithvi_100M.pth:   0%|          | 0.00/1.20G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e57496483c3245d3b7198eefbdd06ea3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-80d5046fcb21>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmmseg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipelines\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCompose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLoadImageFromFile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rasterio'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q7PSx-LRKMIa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}