Samurai.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/koji/GoogleColab/blob/main/Samurai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "install packages  \n",
        "https://github.com/yangchris11/samurai\n",
        "\n",
        "\n",
        "### Requirements\n",
        "- Linux with Python ≥ 3.10, PyTorch ≥ 2.3.1 and torchvision that matches the PyTorch installation. Install them together at https://pytorch.org to ensure this.\n",
        "  - Note older versions of Python or PyTorch may also work. However, the versions above are strongly recommended to provide all features such as torch.compile.\n",
        "- CUDA toolkits that match the CUDA version for your PyTorch installation. This should typically be CUDA 12.1 if you follow the default installation command.\n",
        "\n",
        "https://github.com/facebookresearch/sam2/blob/main/INSTALL.md"
      ],
      "metadata": {
        "id": "Pgoi70C9U8UP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "RqUX6ok0VRoL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22kU96fKU3Ib",
        "outputId": "dbd03715-61c3-4a07-9799-57725a40a04d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch                              2.5.1+cu121\n",
            "torchaudio                         2.5.1+cu121\n",
            "torchsummary                       1.5.1\n",
            "torchvision                        0.20.1+cu121\n"
          ]
        }
      ],
      "source": [
        "# check torch and torchvision\n",
        "!pip list | grep torch*"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib==3.7 tikzplotlib jpeg4py opencv-python lmdb pandas scipy loguru"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ioEx5iJAVHK_",
        "outputId": "3833d105-1ecc-4e49-cee6-c8a2a97a1cd1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting matplotlib==3.7\n",
            "  Downloading matplotlib-3.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)\n",
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            "Collecting jpeg4py\n",
            "  Downloading jpeg4py-0.1.4.tar.gz (12 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.10.0.84)\n",
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            "\u001b[?25hDownloading loguru-0.7.2-py3-none-any.whl (62 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: jpeg4py\n",
            "  Building wheel for jpeg4py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jpeg4py: filename=jpeg4py-0.1.4-py3-none-any.whl size=8422 sha256=eb625acedd0ba714b73e82d72670a45eff0a978de87831b7866e565e9d54066f\n",
            "  Stored in directory: /root/.cache/pip/wheels/86/c3/0f/348e6cadb3a27435e833d21d91707d653fb159d69f2a867a36\n",
            "Successfully built jpeg4py\n",
            "Installing collected packages: lmdb, loguru, matplotlib, jpeg4py, tikzplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.8.0\n",
            "    Uninstalling matplotlib-3.8.0:\n",
            "      Successfully uninstalled matplotlib-3.8.0\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",
            "bigframes 1.27.0 requires matplotlib>=3.7.1, but you have matplotlib 3.7.0 which is incompatible.\n",
            "plotnine 0.14.1 requires matplotlib>=3.8.0, but you have matplotlib 3.7.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed jpeg4py-0.1.4 lmdb-1.5.1 loguru-0.7.2 matplotlib-3.7.0 tikzplotlib-0.10.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/yangchris11/samurai.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BuBiwZaVp8T",
        "outputId": "0fa872e2-213c-4102-8a62-25625ad232c3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'samurai'...\n",
            "remote: Enumerating objects: 942, done.\u001b[K\n",
            "remote: Counting objects: 100% (172/172), done.\u001b[K\n",
            "remote: Compressing objects: 100% (134/134), done.\u001b[K\n",
            "remote: Total 942 (delta 57), reused 132 (delta 34), pack-reused 770 (from 1)\u001b[K\n",
            "Receiving objects: 100% (942/942), 242.93 MiB | 16.68 MiB/s, done.\n",
            "Resolving deltas: 100% (185/185), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd samurai/sam2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibN1FLSZV2RT",
        "outputId": "421256b7-af76-47fa-c5a4-3b6ff91be98f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/samurai/sam2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzN2Uu3mWEOP",
        "outputId": "deaa72ca-ed2b-45f6-ef1c-0913fde9e032"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/samurai/sam2\n",
            "assets\t\t    CODE_OF_CONDUCT.md\tdocker-compose.yaml  LICENSE_cctorch  README.md    setup.py\n",
            "backend.Dockerfile  CONTRIBUTING.md\tINSTALL.md\t     MANIFEST.in      sam2\t   tools\n",
            "checkpoints\t    demo\t\tLICENSE\t\t     pyproject.toml   sav_dataset  training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## install sam2"
      ],
      "metadata": {
        "id": "3zuLnGmBWWEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -e .\n",
        "!pip install -e \".[notebooks]\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9CjFcmbFWVR0",
        "outputId": "cd844274-e3af-4531-d2aa-95ed2d9d4e88"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Obtaining file:///content/samurai/sam2\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from SAM-2==1.0) (2.5.1+cu121)\n",
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            "Collecting hydra-core>=1.3.2 (from SAM-2==1.0)\n",
            "  Downloading hydra_core-1.3.2-py3-none-any.whl.metadata (5.5 kB)\n",
            "Collecting iopath>=0.1.10 (from SAM-2==1.0)\n",
            "  Downloading iopath-0.1.10.tar.gz (42 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)\n",
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            "Building wheels for collected packages: SAM-2, antlr4-python3-runtime, iopath\n",
            "  Building editable for SAM-2 (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for SAM-2: filename=SAM_2-1.0-0.editable-cp310-cp310-linux_x86_64.whl size=13390 sha256=6bb142780a2919df8d7c68939be29eb7b89839f4893ef2537b5d9c9132717f03\n",
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            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
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            "  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31528 sha256=a435ef9bcf14f142bfab718848ac14d132ed741e92b9764d7469783061adacbd\n",
            "  Stored in directory: /root/.cache/pip/wheels/9a/a3/b6/ac0fcd1b4ed5cfeb3db92e6a0e476cfd48ed0df92b91080c1d\n",
            "Successfully built SAM-2 antlr4-python3-runtime iopath\n",
            "Installing collected packages: antlr4-python3-runtime, portalocker, omegaconf, iopath, hydra-core, SAM-2\n",
            "Successfully installed SAM-2-1.0 antlr4-python3-runtime-4.9.3 hydra-core-1.3.2 iopath-0.1.10 omegaconf-2.3.0 portalocker-3.0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
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              "id": "243ef5f3ecb34752900c5d1b5ec17351"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Obtaining file:///content/samurai/sam2\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting rfc3986-validator>=0.1.1 (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading rfc3986_validator-0.1.1-py2.py3-none-any.whl.metadata (1.7 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter>=1.0.0->SAM-2==1.0) (3.4.0)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter>=1.0.0->SAM-2==1.0) (2.2.3)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook->jupyter>=1.0.0->SAM-2==1.0) (1.17.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter>=1.0.0->SAM-2==1.0) (2.22)\n",
            "Collecting fqdn (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading fqdn-1.5.1-py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting isoduration (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading isoduration-20.11.0-py3-none-any.whl.metadata (5.7 kB)\n",
            "Requirement already satisfied: jsonpointer>1.13 in /usr/local/lib/python3.10/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0) (3.0.0)\n",
            "Collecting uri-template (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading uri_template-1.3.0-py3-none-any.whl.metadata (8.8 kB)\n",
            "Requirement already satisfied: webcolors>=24.6.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0) (24.11.1)\n",
            "Collecting arrow>=0.15.0 (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading arrow-1.3.0-py3-none-any.whl.metadata (7.5 kB)\n",
            "Collecting types-python-dateutil>=2.8.10 (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter>=1.0.0->SAM-2==1.0)\n",
            "  Downloading types_python_dateutil-2.9.0.20241003-py3-none-any.whl.metadata (1.9 kB)\n",
            "Downloading eva_decord-0.6.1-py3-none-manylinux2010_x86_64.whl (13.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.6/13.6 MB\u001b[0m \u001b[31m70.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jupyter-1.1.1-py2.py3-none-any.whl (2.7 kB)\n",
            "Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
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            "\u001b[?25hDownloading jupyterlab-4.3.1-py3-none-any.whl (11.7 MB)\n",
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            "\u001b[?25hDownloading ipykernel-6.29.5-py3-none-any.whl (117 kB)\n",
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            "\u001b[?25hDownloading async_lru-2.0.4-py3-none-any.whl (6.1 kB)\n",
            "Downloading comm-0.2.2-py3-none-any.whl (7.2 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jupyter_server-2.14.2-py3-none-any.whl (383 kB)\n",
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            "\u001b[?25hDownloading jupyter_client-7.4.9-py3-none-any.whl (133 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.5/133.5 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jupyterlab_server-2.27.3-py3-none-any.whl (59 kB)\n",
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            "\u001b[?25hDownloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading json5-0.9.28-py3-none-any.whl (30 kB)\n",
            "Downloading jupyter_events-0.10.0-py3-none-any.whl (18 kB)\n",
            "Downloading jupyter_server_terminals-0.5.3-py3-none-any.whl (13 kB)\n",
            "Downloading overrides-7.7.0-py3-none-any.whl (17 kB)\n",
            "Downloading python_json_logger-2.0.7-py3-none-any.whl (8.1 kB)\n",
            "Downloading rfc3986_validator-0.1.1-py2.py3-none-any.whl (4.2 kB)\n",
            "Downloading rfc3339_validator-0.1.4-py2.py3-none-any.whl (3.5 kB)\n",
            "Downloading fqdn-1.5.1-py3-none-any.whl (9.1 kB)\n",
            "Downloading isoduration-20.11.0-py3-none-any.whl (11 kB)\n",
            "Downloading uri_template-1.3.0-py3-none-any.whl (11 kB)\n",
            "Downloading arrow-1.3.0-py3-none-any.whl (66 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading types_python_dateutil-2.9.0.20241003-py3-none-any.whl (9.7 kB)\n",
            "Building wheels for collected packages: SAM-2\n",
            "  Building editable for SAM-2 (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for SAM-2: filename=SAM_2-1.0-0.editable-cp310-cp310-linux_x86_64.whl size=13390 sha256=fa55da07b233e01ecf94c3ae7c8689ed9e8cbabe42fb2aa654e6c0a3099ac71d\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-o80nkt_z/wheels/8f/f4/b4/4cfd8cc457fb923556d69f1c5fd38928c37cfca759666f3851\n",
            "Successfully built SAM-2\n",
            "Installing collected packages: uri-template, types-python-dateutil, rfc3986-validator, rfc3339-validator, python-json-logger, overrides, json5, jedi, fqdn, eva-decord, comm, async-lru, matplotlib, jupyter-server-terminals, jupyter-client, arrow, isoduration, ipykernel, SAM-2, jupyter-events, jupyter-server, jupyterlab-server, jupyter-lsp, jupyterlab, jupyter\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.0\n",
            "    Uninstalling matplotlib-3.7.0:\n",
            "      Successfully uninstalled matplotlib-3.7.0\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 6.1.12\n",
            "    Uninstalling jupyter-client-6.1.12:\n",
            "      Successfully uninstalled jupyter-client-6.1.12\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 5.5.6\n",
            "    Uninstalling ipykernel-5.5.6:\n",
            "      Successfully uninstalled ipykernel-5.5.6\n",
            "  Attempting uninstall: SAM-2\n",
            "    Found existing installation: SAM-2 1.0\n",
            "    Uninstalling SAM-2-1.0:\n",
            "      Successfully uninstalled SAM-2-1.0\n",
            "  Attempting uninstall: jupyter-server\n",
            "    Found existing installation: jupyter-server 1.24.0\n",
            "    Uninstalling jupyter-server-1.24.0:\n",
            "      Successfully uninstalled jupyter-server-1.24.0\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",
            "google-colab 1.0.0 requires ipykernel==5.5.6, but you have ipykernel 6.29.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed SAM-2-1.0 arrow-1.3.0 async-lru-2.0.4 comm-0.2.2 eva-decord-0.6.1 fqdn-1.5.1 ipykernel-6.29.5 isoduration-20.11.0 jedi-0.19.2 json5-0.9.28 jupyter-1.1.1 jupyter-client-7.4.9 jupyter-events-0.10.0 jupyter-lsp-2.2.5 jupyter-server-2.14.2 jupyter-server-terminals-0.5.3 jupyterlab-4.3.1 jupyterlab-server-2.27.3 matplotlib-3.9.2 overrides-7.7.0 python-json-logger-2.0.7 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 types-python-dateutil-2.9.0.20241003 uri-template-1.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## download checkpoints"
      ],
      "metadata": {
        "id": "4_6t7JmIWfdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/samurai/sam2/checkpoints\n",
        "!./download_ckpts.sh && \\\n",
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFQlYC1-WFzl",
        "outputId": "324e4382-9b21-4fca-9d8b-e7fcd14fc0c8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/samurai/sam2/checkpoints\n",
            "Downloading sam2.1_hiera_tiny.pt checkpoint...\n",
            "--2024-11-24 19:45:48--  https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 108.157.254.124, 108.157.254.121, 108.157.254.102, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|108.157.254.124|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 156008466 (149M) [application/vnd.snesdev-page-table]\n",
            "Saving to: ‘sam2.1_hiera_tiny.pt’\n",
            "\n",
            "sam2.1_hiera_tiny.p 100%[===================>] 148.78M   264MB/s    in 0.6s    \n",
            "\n",
            "2024-11-24 19:45:49 (264 MB/s) - ‘sam2.1_hiera_tiny.pt’ saved [156008466/156008466]\n",
            "\n",
            "Downloading sam2.1_hiera_small.pt checkpoint...\n",
            "--2024-11-24 19:45:49--  https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 108.157.254.124, 108.157.254.121, 108.157.254.102, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|108.157.254.124|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 184416285 (176M) [application/vnd.snesdev-page-table]\n",
            "Saving to: ‘sam2.1_hiera_small.pt’\n",
            "\n",
            "sam2.1_hiera_small. 100%[===================>] 175.87M   261MB/s    in 0.7s    \n",
            "\n",
            "2024-11-24 19:45:50 (261 MB/s) - ‘sam2.1_hiera_small.pt’ saved [184416285/184416285]\n",
            "\n",
            "Downloading sam2.1_hiera_base_plus.pt checkpoint...\n",
            "--2024-11-24 19:45:50--  https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 108.157.254.124, 108.157.254.121, 108.157.254.102, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|108.157.254.124|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 323606802 (309M) [application/vnd.snesdev-page-table]\n",
            "Saving to: ‘sam2.1_hiera_base_plus.pt’\n",
            "\n",
            "sam2.1_hiera_base_p 100%[===================>] 308.62M  23.9MB/s    in 14s     \n",
            "\n",
            "2024-11-24 19:46:05 (21.9 MB/s) - ‘sam2.1_hiera_base_plus.pt’ saved [323606802/323606802]\n",
            "\n",
            "Downloading sam2.1_hiera_large.pt checkpoint...\n",
            "--2024-11-24 19:46:05--  https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 108.157.254.124, 108.157.254.121, 108.157.254.102, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|108.157.254.124|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 898083611 (856M) [application/vnd.snesdev-page-table]\n",
            "Saving to: ‘sam2.1_hiera_large.pt’\n",
            "\n",
            "sam2.1_hiera_large. 100%[===================>] 856.48M  93.7MB/s    in 5.1s    \n",
            "\n",
            "2024-11-24 19:46:10 (169 MB/s) - ‘sam2.1_hiera_large.pt’ saved [898083611/898083611]\n",
            "\n",
            "All checkpoints are downloaded successfully.\n",
            "/bin/bash: line 1: fg: no job control\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## download data from hugging face\n",
        "https://huggingface.co/datasets/l-lt/LaSOT"
      ],
      "metadata": {
        "id": "SoPBUfu2rQYn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Define the data directory\n",
        "data_directory = '/content/samurai/data/LaSOT'\n",
        "\n",
        "# Create the data directory if it does not exist\n",
        "try:\n",
        "    os.makedirs(data_directory, exist_ok=True)\n",
        "    print(f\"Directory '{data_directory}' created successfully or already exists.\")\n",
        "except OSError as error:\n",
        "    print(f\"Error creating directory '{data_directory}': {error}\")\n",
        "\n",
        "# Define the content to be written to the file\n",
        "content = '''cat-1\n",
        "cat-20'''\n",
        "\n",
        "# Define the file path\n",
        "file_path = os.path.join(data_directory, 'testing_set.txt')\n",
        "\n",
        "# Write the content to the file\n",
        "try:\n",
        "    with open(file_path, 'w') as f:\n",
        "        f.write(content)\n",
        "    print(f\"Content written to file '{file_path}' successfully.\")\n",
        "except IOError as error:\n",
        "    print(f\"Error writing to file '{file_path}': {error}\")\n",
        "\n",
        "# Print the file path\n",
        "print(f'File path: {file_path}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFgomtmhWHG9",
        "outputId": "a2b2e594-d629-471c-f52e-d200c4ea806b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "file path: /content/samurai/data/LaSOT/testing_set.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from huggingface_hub import hf_hub_download\n",
        "import zipfile\n",
        "import shutil\n",
        "\n",
        "def download_and_extract_lasot(base_dir=\"/content/samurai/data\"):\n",
        "    try:\n",
        "        # Create LaSOT and cat directories\n",
        "        lasot_dir = os.path.join(base_dir, \"LaSOT\")\n",
        "        cat_dir = os.path.join(lasot_dir, \"cat\")\n",
        "        os.makedirs(cat_dir, exist_ok=True)\n",
        "\n",
        "        # Create directory to save the ZIP file\n",
        "        zip_dir = os.path.join(base_dir, \"zips\")\n",
        "        os.makedirs(zip_dir, exist_ok=True)\n",
        "\n",
        "        print(\"Downloading dataset...\")\n",
        "        zip_path = hf_hub_download(\n",
        "            repo_id=\"l-lt/LaSOT\",\n",
        "            filename=\"cat.zip\",\n",
        "            repo_type=\"dataset\",\n",
        "            local_dir=zip_dir\n",
        "        )\n",
        "        print(f\"Downloaded to: {zip_path}\")\n",
        "\n",
        "        # Extract ZIP file to cat directory\n",
        "        print(\"Extracting ZIP file to cat directory...\")\n",
        "        with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "            zip_ref.extractall(cat_dir)\n",
        "\n",
        "        print(\"\\nCreated directory structure:\")\n",
        "        print(\"LaSOT/\")\n",
        "        print(\"└── cat/\")\n",
        "        # Display the first few cat folders\n",
        "        for item in sorted(os.listdir(cat_dir))[:6]:\n",
        "            print(f\"    ├── {item}/\")\n",
        "        print(\"    └── ...\")\n",
        "\n",
        "        return lasot_dir\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"An error occurred: {str(e)}\")\n",
        "        return None\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    extract_path = download_and_extract_lasot()\n",
        "    if extract_path:\n",
        "        print(\"\\nDownload and extraction completed successfully!\")\n",
        "    else:\n",
        "        print(\"\\nDownload and extraction failed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrfUsOgQa-ji",
        "outputId": "24a11ed5-0200-4997-acd0-e21e935d50d0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading dataset...\n",
            "Downloaded to: /content/samurai/data/zips/cat.zip\n",
            "Extracting ZIP file to cat directory...\n",
            "\n",
            "Created directory structure:\n",
            "LaSOT/\n",
            "└── cat/\n",
            "    ├── cat-1/\n",
            "    ├── cat-10/\n",
            "    ├── cat-11/\n",
            "    ├── cat-12/\n",
            "    ├── cat-13/\n",
            "    └── ...\n",
            "\n",
            "Download and extraction completed successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/samurai\n",
        "!pwd\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-3b3ir5bUTN",
        "outputId": "878a2c2c-146a-4216-fb0b-a4c63008edae"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/samurai\n",
            "/content/samurai\n",
            "assets\tdata  lib  LICENSE  README.md  sam2  scripts  visualization\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python scripts/main_inference.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsLICrc8bmlY",
        "outputId": "be656f68-8aa3-495a-c899-a47d6a3facc8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[91mRunning video [1/2]: cat-1\n",
            " with 2051 frames\u001b[0m\n",
            "/content/samurai/sam2/sam2/modeling/sam/transformer.py:23: UserWarning: Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.\n",
            "  OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()\n",
            "\u001b[93mSAMURAI mode: True\u001b[0m\n",
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            "frame loading (JPEG):  92% 1892/2051 [02:27<00:17,  9.31it/s]\n",
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            "frame loading (JPEG):  93% 1900/2051 [02:28<00:18,  8.19it/s]\n",
            "frame loading (JPEG):  93% 1904/2051 [02:28<00:17,  8.42it/s]\n",
            "frame loading (JPEG):  93% 1909/2051 [02:29<00:15,  9.15it/s]\n",
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            "frame loading (JPEG):  94% 1919/2051 [02:30<00:10, 12.58it/s]\n",
            "frame loading (JPEG):  94% 1927/2051 [02:30<00:08, 14.16it/s]\n",
            "frame loading (JPEG):  94% 1933/2051 [02:31<00:08, 14.75it/s]\n",
            "frame loading (JPEG):  95% 1939/2051 [02:31<00:07, 14.31it/s]\n",
            "frame loading (JPEG):  95% 1945/2051 [02:31<00:07, 14.68it/s]\n",
            "frame loading (JPEG):  95% 1951/2051 [02:32<00:07, 14.21it/s]\n",
            "frame loading (JPEG):  95% 1957/2051 [02:32<00:06, 14.13it/s]\n",
            "frame loading (JPEG):  96% 1963/2051 [02:33<00:06, 14.20it/s]\n",
            "frame loading (JPEG):  96% 1969/2051 [02:33<00:05, 14.39it/s]\n",
            "frame loading (JPEG):  96% 1977/2051 [02:34<00:05, 14.59it/s]\n",
            "frame loading (JPEG):  97% 1981/2051 [02:34<00:04, 14.10it/s]\n",
            "frame loading (JPEG):  97% 1987/2051 [02:34<00:04, 13.73it/s]\n",
            "frame loading (JPEG):  97% 1993/2051 [02:35<00:04, 13.88it/s]\n",
            "frame loading (JPEG):  98% 2001/2051 [02:35<00:03, 14.66it/s]\n",
            "frame loading (JPEG):  98% 2007/2051 [02:36<00:02, 14.67it/s]\n",
            "frame loading (JPEG):  98% 2013/2051 [02:36<00:02, 14.74it/s]\n",
            "frame loading (JPEG):  98% 2019/2051 [02:37<00:02, 14.98it/s]\n",
            "frame loading (JPEG):  99% 2025/2051 [02:37<00:01, 14.39it/s]\n",
            "frame loading (JPEG):  99% 2033/2051 [02:38<00:01, 15.08it/s]\n",
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            "frame loading (JPEG): 100% 2051/2051 [02:39<00:00, 12.88it/s]\n",
            "\n",
            "propagate in video:  17% 351/2051 [02:35<12:13,  2.32it/s]\u001b[A\n",
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            "\u001b[91mRunning video [2/2]: cat-20 with 1261 frames\u001b[0m\n",
            "\u001b[93mSAMURAI mode: True\u001b[0m\n",
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            "\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aLhHflOFcPpC"
      },
      "execution_count": 11,
      "outputs": []
    }
  ]
}