codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxWWPVb-On3_"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://github.com/open-mmlab/mmyolo/raw/main/resources/mmyolo-logo.png\" width=\"600\"/>\n",
        "  <div>&nbsp;</div>\n",
        "  <div align=\"center\">\n",
        "    <b><font size=\"5\">MMYOLO 官方仓库</font></b>\n",
        "    <sup>\n",
        "      <a href=\"https://github.com/open-mmlab/mmyolo\">\n",
        "        <i><font size=\"4\">⭐Star</font></i>\n",
        "      </a>\n",
        "    </sup>\n",
        "    &nbsp;&nbsp;&nbsp;&nbsp;\n",
        "    <b><font size=\"5\">MMYOLO 开发计划</font></b>\n",
        "    <sup>\n",
        "      <a href=\"https://github.com/open-mmlab/mmyolo/issues/136\">\n",
        "        <i><font size=\"4\">💬欢迎留言</font></i>\n",
        "      </a>\n",
        "    </sup>\n",
        "    &nbsp;&nbsp;&nbsp;&nbsp;\n",
        "    <b><font size=\"5\">玩转 MMYOLO 系列视频教程</font></b>\n",
        "    <sup>\n",
        "      <a href=\"https://space.bilibili.com/1293512903/channel/collectiondetail?sid=788924\">\n",
        "        <i><font size=\"4\">👍一键三连</font></i>\n",
        "      </a>\n",
        "    </sup>\n",
        "    </br>\n",
        "    <font size=\"3\">MMYOLO 定位为 YOLO 系列工业核心算法库，提供统一全面的评测流程，轻松可定制的模块组件和支持多任务且高效的训练部署流程。</font>\n",
        "  </div>\n",
        "  <div>&nbsp;</div>\n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "\n",
        "[![PyPI](https://img.shields.io/pypi/v/mmyolo)](https://pypi.org/project/mmyolo)\n",
        "[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmyolo.readthedocs.io/en/latest/)\n",
        "[![deploy](https://github.com/open-mmlab/mmyolo/workflows/deploy/badge.svg)](https://github.com/open-mmlab/mmyolo/actions)\n",
        "[![codecov](https://codecov.io/gh/open-mmlab/mmyolo/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmyolo)\n",
        "[![license](https://img.shields.io/github/license/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/blob/master/LICENSE)\n",
        "[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)\n",
        "[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)\n",
        "\n",
        "[📘文档](https://mmyolo.readthedocs.io/zh_CN/latest/) |\n",
        "[🛠️安装教程](https://mmyolo.readthedocs.io/zh_CN/latest/install.html) |\n",
        "[👀模型库](https://mmyolo.readthedocs.io/zh_CN/latest/model_zoo.html) |\n",
        "[🆕更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html) |\n",
        "[🤔反馈问题](https://github.com/open-mmlab/mmyolo/issues/new/choose)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIK1y9AeWC0w"
      },
      "source": [
        "![封面](https://user-images.githubusercontent.com/27466624/203250285-38ae8deb-c54a-4d55-b2e7-5918de40ee98.png)\n",
        "# 10 分钟带你换遍主干网络\n",
        "\n",
        "- MMYOLO 中只有 YOLO 系列的主干网，想尝试用 MMDet 中的？\n",
        "- 用 YOLO 打比赛的时候想用 SwinTransformer？\n",
        "- 自监督预训练很火，也想在 YOLO 中试一试？\n",
        "\n",
        "\n",
        "<font size=5>🔥本教程带你一网打尽🔥</font>\n",
        "\n",
        "**注意**：\n",
        "1. 本文定稿时间为 2022.11.20。MMYOLO [![GitHub release](https://img.shields.io/github/release/open-mmlab/mmyolo.svg)](https://GitHub.com/open-mmlab/mmyolo/) 和其他 OpenMMLab 系列仓库在不断更新迭代，如果未来直接运行本脚本出错，请及时更新到最新代码或者锁定版本。\n",
        "\n",
        "2. 使用其他主干网络时，需要保证主干网络的输出通道与 Neck 的输入通道相匹配。\n",
        "3. 下面给出的配置文件，仅能确保训练可以正确运行，直接训练性能可能不是最优的。因为某些主干网络需要配套特定的学习率、优化器等超参数。后续会推出“训练技巧”补充训练调优相关内容。\n",
        "4. 词语 `主干网络`、`骨干网络`、`backbone` 都指代同一模块。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkT3NdKDOt1X"
      },
      "source": [
        "## 0 环境准备"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_mohlpPjfKR",
        "outputId": "dc91b21f-9a38-4203-fca2-f2943f0d09d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Python 3.7.15\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2021 NVIDIA Corporation\n",
            "Built on Sun_Feb_14_21:12:58_PST_2021\n",
            "Cuda compilation tools, release 11.2, V11.2.152\n",
            "Build cuda_11.2.r11.2/compiler.29618528_0\n",
            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "Copyright (C) 2017 Free Software Foundation, Inc.\n",
            "This is free software; see the source for copying conditions.  There is NO\n",
            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
            "\n",
            "1.12.1+cu113\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "#@title ### 0.1 检查环境版本\n",
        "# 检查 python 版本\n",
        "!python -V\n",
        "\n",
        "# 检查 nvcc 版本\n",
        "!nvcc -V\n",
        "\n",
        "# 检查 GCC 版本\n",
        "!gcc --version\n",
        "\n",
        "# 检查 PyTorch 版本\n",
        "import torch, torchvision\n",
        "print(torch.__version__)\n",
        "print(torch.cuda.is_available())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pf6qCA-OteY",
        "outputId": "ee8a7672-502a-484d-b40f-faa12445141b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Version: 0.3.3\n",
            "Summary: MIM Installs OpenMMLab packages\n",
            "Home-page: https://github.com/open-mmlab/mim\n",
            "Author: MIM Authors\n",
            "Author-email: openmmlab@gmail.com\n",
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            "Requires: requests, pandas, pip, model-index, tabulate, colorama, Click, rich\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmdet>=3.0.0rc2) (4.1.1)\n",
            "Installing collected packages: terminaltables, mmdet\n",
            "Successfully installed mmdet-3.0.0rc3 terminaltables-3.1.10\n",
            "Cloning into 'mmyolo'...\n",
            "remote: Enumerating objects: 2074, done.\u001b[K\n",
            "remote: Counting objects: 100% (102/102), done.\u001b[K\n",
            "remote: Compressing objects: 100% (88/88), done.\u001b[K\n",
            "remote: Total 2074 (delta 39), reused 35 (delta 13), pack-reused 1972\u001b[K\n",
            "Receiving objects: 100% (2074/2074), 1.99 MiB | 7.98 MiB/s, done.\n",
            "Resolving deltas: 100% (1167/1167), done.\n",
            "/content/mmyolo\n",
            "Using pip 21.1.3 from /usr/local/lib/python3.7/dist-packages/pip (python 3.7)\n",
            "Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/include/python3.7/UNKNOWN\n",
            "sysconfig: /usr/include/python3.7m/UNKNOWN\n",
            "Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/bin\n",
            "sysconfig: /usr/bin\n",
            "Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local\n",
            "sysconfig: /usr\n",
            "Additional context:\n",
            "user = False\n",
            "home = None\n",
            "root = None\n",
            "prefix = None\n",
            "Non-user install because site-packages writeable\n",
            "Created temporary directory: /tmp/pip-ephem-wheel-cache-2uhdg314\n",
            "Created temporary directory: /tmp/pip-req-tracker-jzlnf0le\n",
            "Initialized build tracking at /tmp/pip-req-tracker-jzlnf0le\n",
            "Created build tracker: /tmp/pip-req-tracker-jzlnf0le\n",
            "Entered build tracker: /tmp/pip-req-tracker-jzlnf0le\n",
            "Created temporary directory: /tmp/pip-install-16xdzblz\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html\n",
            "Obtaining file:///content/mmyolo\n",
            "  Added file:///content/mmyolo to build tracker '/tmp/pip-req-tracker-jzlnf0le'\n",
            "    Running setup.py (path:/content/mmyolo/setup.py) egg_info for package from file:///content/mmyolo\n",
            "    Created temporary directory: /tmp/pip-pip-egg-info-er3qcg59\n",
            "    Running command python setup.py egg_info\n",
            "    running egg_info\n",
            "    creating /tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info\n",
            "    writing /tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/PKG-INFO\n",
            "    writing dependency_links to /tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/dependency_links.txt\n",
            "    writing requirements to /tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/requires.txt\n",
            "    writing top-level names to /tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/top_level.txt\n",
            "    writing manifest file '/tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/SOURCES.txt'\n",
            "    reading manifest template 'MANIFEST.in'\n",
            "    warning: no files found matching 'mmyolo/VERSION'\n",
            "    warning: no files found matching 'mmyolo/.mim/model-index.yml'\n",
            "    warning: no files found matching 'mmyolo/.mim/demo/*/*'\n",
            "    warning: no files found matching '*.py' under directory 'mmyolo/.mim/configs'\n",
            "    warning: no files found matching '*.yml' under directory 'mmyolo/.mim/configs'\n",
            "    warning: no files found matching '*.sh' under directory 'mmyolo/.mim/tools'\n",
            "    warning: no files found matching '*.py' under directory 'mmyolo/.mim/tools'\n",
            "    adding license file 'LICENSE'\n",
            "    writing manifest file '/tmp/pip-pip-egg-info-er3qcg59/mmyolo.egg-info/SOURCES.txt'\n",
            "  Source in /content/mmyolo has version 0.1.3, which satisfies requirement mmyolo==0.1.3 from file:///content/mmyolo\n",
            "  Removed mmyolo==0.1.3 from file:///content/mmyolo from build tracker '/tmp/pip-req-tracker-jzlnf0le'\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmyolo==0.1.3) (1.21.6)\n",
            "Requirement already satisfied: mmcv<2.1.0,>=2.0.0rc1 in /usr/local/lib/python3.7/dist-packages (from mmyolo==0.1.3) (2.0.0rc2)\n",
            "Requirement already satisfied: mmdet>=3.0.0rc3 in /usr/local/lib/python3.7/dist-packages (from mmyolo==0.1.3) (3.0.0rc3)\n",
            "Requirement already satisfied: mmengine>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from mmyolo==0.1.3) (0.3.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (21.3)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (4.6.0.66)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (6.0)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (0.32.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (7.1.2)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmyolo==0.1.3) (2.4.0)\n",
            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from mmdet>=3.0.0rc3->mmyolo==0.1.3) (3.1.10)\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from mmdet>=3.0.0rc3->mmyolo==0.1.3) (2.0.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mmdet>=3.0.0rc3->mmyolo==0.1.3) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet>=3.0.0rc3->mmyolo==0.1.3) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from mmdet>=3.0.0rc3->mmyolo==0.1.3) (1.7.3)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from mmengine>=0.3.1->mmyolo==0.1.3) (2.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet>=3.0.0rc3->mmyolo==0.1.3) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet>=3.0.0rc3->mmyolo==0.1.3) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet>=3.0.0rc3->mmyolo==0.1.3) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet>=3.0.0rc3->mmyolo==0.1.3) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmdet>=3.0.0rc3->mmyolo==0.1.3) (4.1.1)\n",
            "Created temporary directory: /tmp/pip-unpack-vmw9uhnr\n",
            "Installing collected packages: mmyolo\n",
            "  Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "  distutils: /usr/local/lib/python3.7/dist-packages\n",
            "  sysconfig: /usr/lib/python3.7/site-packages\n",
            "  Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "  distutils: /usr/local/lib/python3.7/dist-packages\n",
            "  sysconfig: /usr/lib/python3.7/site-packages\n",
            "  Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "  distutils: /usr/local/include/python3.7/mmyolo\n",
            "  sysconfig: /usr/include/python3.7m/mmyolo\n",
            "  Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "  distutils: /usr/local/bin\n",
            "  sysconfig: /usr/bin\n",
            "  Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "  distutils: /usr/local\n",
            "  sysconfig: /usr\n",
            "  Additional context:\n",
            "  user = False\n",
            "  home = None\n",
            "  root = None\n",
            "  prefix = None\n",
            "  Running setup.py develop for mmyolo\n",
            "    Running command /usr/bin/python3 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmyolo/setup.py'\"'\"'; __file__='\"'\"'/content/mmyolo/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n",
            "    running develop\n",
            "    running egg_info\n",
            "    creating mmyolo.egg-info\n",
            "    writing mmyolo.egg-info/PKG-INFO\n",
            "    writing dependency_links to mmyolo.egg-info/dependency_links.txt\n",
            "    writing requirements to mmyolo.egg-info/requires.txt\n",
            "    writing top-level names to mmyolo.egg-info/top_level.txt\n",
            "    writing manifest file 'mmyolo.egg-info/SOURCES.txt'\n",
            "    reading manifest template 'MANIFEST.in'\n",
            "    warning: no files found matching 'mmyolo/VERSION'\n",
            "    warning: no files found matching 'mmyolo/.mim/demo/*/*'\n",
            "    adding license file 'LICENSE'\n",
            "    writing manifest file 'mmyolo.egg-info/SOURCES.txt'\n",
            "    running build_ext\n",
            "    Creating /usr/local/lib/python3.7/dist-packages/mmyolo.egg-link (link to .)\n",
            "    Adding mmyolo 0.1.3 to easy-install.pth file\n",
            "\n",
            "    Installed /content/mmyolo\n",
            "    /usr/local/lib/python3.7/dist-packages/torch/utils/cpp_extension.py:411: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n",
            "      warnings.warn(msg.format('we could not find ninja.'))\n",
            "Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/include/python3.7/UNKNOWN\n",
            "sysconfig: /usr/include/python3.7m/UNKNOWN\n",
            "Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/bin\n",
            "sysconfig: /usr/bin\n",
            "Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local\n",
            "sysconfig: /usr\n",
            "Additional context:\n",
            "user = False\n",
            "home = None\n",
            "root = None\n",
            "prefix = None\n",
            "Successfully installed mmyolo-0.1.3\n",
            "Removed build tracker: '/tmp/pip-req-tracker-jzlnf0le'\n"
          ]
        }
      ],
      "source": [
        "#@title ### 0.2 安装 MMYOLO\n",
        "# 使用 mim 安装 OpenMMlab 系列依赖包\n",
        "%pip install -U openmim\n",
        "%pip show openmim\n",
        "!mim install \"mmengine==0.3.1\"\n",
        "!mim install \"mmcv>=2.0.0rc2\"\n",
        "!mim install \"mmdet>=3.0.0rc2\"\n",
        "# 下载 MMYOLO 仓库\n",
        "!git clone https://github.com/open-mmlab/mmyolo.git -b dev\n",
        "%cd mmyolo \n",
        "# 使用 mim 以编辑模式安装 MMYOLO\n",
        "!mim install -v -e ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vtym1aNokLaz",
        "outputId": "e290fc79-7f50-4084-8747-9bf536f2c1b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Package    Version    Source\n",
            "---------  ---------  -----------------------------------------\n",
            "mmcv       2.0.0rc2   https://github.com/open-mmlab/mmcv\n",
            "mmdet      3.0.0rc3   https://github.com/open-mmlab/mmdetection\n",
            "mmengine   0.3.1      https://github.com/open-mmlab/mmengine\n",
            "mmyolo     0.1.3      /content/mmyolo\n"
          ]
        }
      ],
      "source": [
        "# 查看安装结果\n",
        "!mim list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rd2rIjHikQXC",
        "outputId": "3dfba3eb-b38e-4fa2-b02e-be13e3ccc10f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-11-20 07:55:27--  https://extremevision-js-userfile.oss-cn-hangzhou.aliyuncs.com/user-25117-files/ff6429ed-0f22-4759-aa1e-f3d3f8dc4b40/coco.zip\n",
            "Resolving extremevision-js-userfile.oss-cn-hangzhou.aliyuncs.com (extremevision-js-userfile.oss-cn-hangzhou.aliyuncs.com)... 47.110.23.49\n",
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          ]
        }
      ],
      "source": [
        "#@title ### 0.3 下载 COCO128 数据集作为示例\n",
        "# COCO128 取自 COCO2017 目标检测数据训练集前 128 张\n",
        "# 原始数据（YOLO标注格式）来自 https://www.kaggle.com/datasets/ultralytics/coco128\n",
        "# 转换 COCO 标注格式的脚本 https://github.com/open-mmlab/mmyolo/blob/main/tools/dataset_converters/yolo2coco.py\n",
        "!mkdir data\n",
        "!wget -P data https://extremevision-js-userfile.oss-cn-hangzhou.aliyuncs.com/user-25117-files/ff6429ed-0f22-4759-aa1e-f3d3f8dc4b40/coco.zip\n",
        "!unzip data/coco.zip -d data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CaQ_SvXEmiYN"
      },
      "outputs": [],
      "source": [
        "#@title ### 0.4 准备演示用的基础配置文件\n",
        "train_batch_size_per_gpu = 2 #@param {type:\"integer\"}\n",
        "train_num_workers = 2 #@param {type:\"integer\"}\n",
        "max_epochs = 1 #@param {type:\"integer\"}\n",
        "#@markdown >persistent_workers must be False if num_workers is 0.\n",
        "persistent_workers = True #@param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "config_base=f\"\"\"\n",
        "_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py'\n",
        "\n",
        "train_batch_size_per_gpu = {train_batch_size_per_gpu}\n",
        "train_num_workers = {train_num_workers}\n",
        "max_epochs = {max_epochs} # （max_epochs 设置为 1 仅供演示）\n",
        "\n",
        "train_dataloader = dict(\n",
        "    batch_size=train_batch_size_per_gpu,\n",
        "    num_workers=train_num_workers)\n",
        "\n",
        "optim_wrapper = dict(\n",
        "    optimizer=dict(\n",
        "        batch_size_per_gpu=train_batch_size_per_gpu))\n",
        "\n",
        "default_hooks = dict(\n",
        "    param_scheduler=dict(\n",
        "        type='YOLOv5ParamSchedulerHook',\n",
        "        max_epochs=max_epochs))\n",
        "\n",
        "train_cfg = dict(\n",
        "    type='EpochBasedTrainLoop',\n",
        "    max_epochs=max_epochs)\n",
        "\"\"\"\n",
        "\n",
        "# 写入文件\n",
        "with open('./configs/yolov5/yolov5_s-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_base)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BthrwomDvUbD"
      },
      "source": [
        "## 1 使用 MMYOLO 中注册的主干网络\n",
        "\n",
        "<details close>\n",
        "<summary>Supported algorithms</summary>\n",
        "\n",
        "- [x] [YOLOv5](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5)\n",
        "- [x] [YOLOv6](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov6)\n",
        "- [x] [YOLOv7](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7)\n",
        "- [x] [YOLOX](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolox)\n",
        "- [x] [PPYOLO-E](https://github.com/open-mmlab/mmyolo/tree/main/configs/ppyoloe)\n",
        "- [x] [RTMDet](https://github.com/open-mmlab/mmyolo/tree/main/configs/rtmdet)\n",
        "\n",
        "</details>\n",
        "\n",
        "<div align=\"center\"> \n",
        "<img src=\"https://p0.meituan.net/travelcube/8ec8337d37c2545b8fcf355625854802145939.png\" width = \"410\" height = \"400\" alt=\"EfficientRep Backbone 结构图]\">\n",
        "</div>\n",
        "\n",
        "YOLOv6 中，美团基于 Rep 算子设计了一个高效的 Backbone。相比于 YOLOv5 采用的 CSP-Backbone，该 Backbone 能够高效利用硬件（如 GPU）算力的同时，还具有较强的表征能力[<sup>1</sup>](https://tech.meituan.com/2022/06/23/yolov6-a-fast-and-accurate-target-detection-framework-is-opening-source.html)。\n",
        "\n",
        "如果想将 `YOLOv6EfficientRep` 作为 `YOLOv5` 的主干网络，则配置文件如下："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hHqwED0aHfzW"
      },
      "outputs": [],
      "source": [
        "_base_ = './yolov5_s-v61_1xb2-1e_coco128.py'\n",
        "\n",
        "model = dict(\n",
        "    backbone=dict(\n",
        "        type='YOLOv6EfficientRep',\n",
        "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
        "        act_cfg=dict(type='ReLU', inplace=True))\n",
        ")\n",
        "\n",
        "config_YOLOv6EfficientRep = f\"\"\"\n",
        "_base_=\\'{_base_}\\'\n",
        "model={model}\n",
        "\"\"\"\n",
        "\n",
        "with open('./configs/yolov5/yolov5_s_efficientrep-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_YOLOv6EfficientRep)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEZj85YNoGg4",
        "outputId": "65d42d74-a09e-40f6-f4a6-ceb89149424c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11/20 08:02:42 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmyolo\" in the \"log_processor\" registry tree. As a workaround, the current \"log_processor\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmyolo\" is a correct scope, or whether the registry is initialized.\n",
            "11/20 08:02:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 648320955\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.2, V11.2.152\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.12.1+cu113\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.3\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  (built against CUDA 11.5)\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, 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: 0.13.1+cu113\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.3.1\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: True\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: None\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "11/20 08:02:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "default_scope = 'mmyolo'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(\n",
            "        type='YOLOv5ParamSchedulerHook',\n",
            "        scheduler_type='linear',\n",
            "        lr_factor=0.01,\n",
            "        max_epochs=1),\n",
            "    checkpoint=dict(\n",
            "        type='CheckpointHook', interval=10, save_best='auto',\n",
            "        max_keep_ckpts=3),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook'))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=True,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='visualizer')\n",
            "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "file_client_args = dict(backend='disk')\n",
            "data_root = 'data/coco/'\n",
            "dataset_type = 'YOLOv5CocoDataset'\n",
            "num_classes = 80\n",
            "img_scale = (640, 640)\n",
            "deepen_factor = 0.33\n",
            "widen_factor = 0.5\n",
            "max_epochs = 1\n",
            "save_epoch_intervals = 10\n",
            "train_batch_size_per_gpu = 2\n",
            "train_num_workers = 2\n",
            "val_batch_size_per_gpu = 1\n",
            "val_num_workers = 2\n",
            "persistent_workers = True\n",
            "batch_shapes_cfg = dict(\n",
            "    type='BatchShapePolicy',\n",
            "    batch_size=1,\n",
            "    img_size=640,\n",
            "    size_divisor=32,\n",
            "    extra_pad_ratio=0.5)\n",
            "anchors = [[(10, 13), (16, 30), (33, 23)], [(30, 61), (62, 45), (59, 119)],\n",
            "           [(116, 90), (156, 198), (373, 326)]]\n",
            "strides = [8, 16, 32]\n",
            "num_det_layers = 3\n",
            "model = dict(\n",
            "    type='YOLODetector',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmdet.DetDataPreprocessor',\n",
            "        mean=[0.0, 0.0, 0.0],\n",
            "        std=[255.0, 255.0, 255.0],\n",
            "        bgr_to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='YOLOv6EfficientRep',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=0.5,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='ReLU', inplace=True)),\n",
            "    neck=dict(\n",
            "        type='YOLOv5PAFPN',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=0.5,\n",
            "        in_channels=[256, 512, 1024],\n",
            "        out_channels=[256, 512, 1024],\n",
            "        num_csp_blocks=3,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='SiLU', inplace=True)),\n",
            "    bbox_head=dict(\n",
            "        type='YOLOv5Head',\n",
            "        head_module=dict(\n",
            "            type='YOLOv5HeadModule',\n",
            "            num_classes=80,\n",
            "            in_channels=[256, 512, 1024],\n",
            "            widen_factor=0.5,\n",
            "            featmap_strides=[8, 16, 32],\n",
            "            num_base_priors=3),\n",
            "        prior_generator=dict(\n",
            "            type='mmdet.YOLOAnchorGenerator',\n",
            "            base_sizes=[[(10, 13), (16, 30), (33, 23)],\n",
            "                        [(30, 61), (62, 45), (59, 119)],\n",
            "                        [(116, 90), (156, 198), (373, 326)]],\n",
            "            strides=[8, 16, 32]),\n",
            "        loss_cls=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.5),\n",
            "        loss_bbox=dict(\n",
            "            type='IoULoss',\n",
            "            iou_mode='ciou',\n",
            "            bbox_format='xywh',\n",
            "            eps=1e-07,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.05,\n",
            "            return_iou=True),\n",
            "        loss_obj=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=1.0),\n",
            "        prior_match_thr=4.0,\n",
            "        obj_level_weights=[4.0, 1.0, 0.4]),\n",
            "    test_cfg=dict(\n",
            "        multi_label=True,\n",
            "        nms_pre=30000,\n",
            "        score_thr=0.001,\n",
            "        nms=dict(type='nms', iou_threshold=0.65),\n",
            "        max_per_img=300))\n",
            "albu_train_transforms = [\n",
            "    dict(type='Blur', p=0.01),\n",
            "    dict(type='MedianBlur', p=0.01),\n",
            "    dict(type='ToGray', p=0.01),\n",
            "    dict(type='CLAHE', p=0.01)\n",
            "]\n",
            "pre_transform = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True)\n",
            "]\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='Mosaic',\n",
            "        img_scale=(640, 640),\n",
            "        pad_val=114.0,\n",
            "        pre_transform=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='YOLOv5RandomAffine',\n",
            "        max_rotate_degree=0.0,\n",
            "        max_shear_degree=0.0,\n",
            "        scaling_ratio_range=(0.5, 1.5),\n",
            "        border=(-320, -320),\n",
            "        border_val=(114, 114, 114)),\n",
            "    dict(\n",
            "        type='mmdet.Albu',\n",
            "        transforms=[\n",
            "            dict(type='Blur', p=0.01),\n",
            "            dict(type='MedianBlur', p=0.01),\n",
            "            dict(type='ToGray', p=0.01),\n",
            "            dict(type='CLAHE', p=0.01)\n",
            "        ],\n",
            "        bbox_params=dict(\n",
            "            type='BboxParams',\n",
            "            format='pascal_voc',\n",
            "            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "        keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "    dict(type='YOLOv5HSVRandomAug'),\n",
            "    dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',\n",
            "                   'flip_direction'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        ann_file='annotations/instances_train2017.json',\n",
            "        data_prefix=dict(img='train2017/'),\n",
            "        filter_cfg=dict(filter_empty_gt=False, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='Mosaic',\n",
            "                img_scale=(640, 640),\n",
            "                pad_val=114.0,\n",
            "                pre_transform=[\n",
            "                    dict(\n",
            "                        type='LoadImageFromFile',\n",
            "                        file_client_args=dict(backend='disk')),\n",
            "                    dict(type='LoadAnnotations', with_bbox=True)\n",
            "                ]),\n",
            "            dict(\n",
            "                type='YOLOv5RandomAffine',\n",
            "                max_rotate_degree=0.0,\n",
            "                max_shear_degree=0.0,\n",
            "                scaling_ratio_range=(0.5, 1.5),\n",
            "                border=(-320, -320),\n",
            "                border_val=(114, 114, 114)),\n",
            "            dict(\n",
            "                type='mmdet.Albu',\n",
            "                transforms=[\n",
            "                    dict(type='Blur', p=0.01),\n",
            "                    dict(type='MedianBlur', p=0.01),\n",
            "                    dict(type='ToGray', p=0.01),\n",
            "                    dict(type='CLAHE', p=0.01)\n",
            "                ],\n",
            "                bbox_params=dict(\n",
            "                    type='BboxParams',\n",
            "                    format='pascal_voc',\n",
            "                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "                keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "            dict(type='YOLOv5HSVRandomAug'),\n",
            "            dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'flip', 'flip_direction'))\n",
            "        ]))\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "    dict(\n",
            "        type='LetterResize',\n",
            "        scale=(640, 640),\n",
            "        allow_scale_up=False,\n",
            "        pad_val=dict(img=114)),\n",
            "    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor', 'pad_param'))\n",
            "]\n",
            "val_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "param_scheduler = None\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(\n",
            "        type='SGD',\n",
            "        lr=0.01,\n",
            "        momentum=0.937,\n",
            "        weight_decay=0.0005,\n",
            "        nesterov=True,\n",
            "        batch_size_per_gpu=2),\n",
            "    constructor='YOLOv5OptimizerConstructor')\n",
            "custom_hooks = [\n",
            "    dict(\n",
            "        type='EMAHook',\n",
            "        ema_type='ExpMomentumEMA',\n",
            "        momentum=0.0001,\n",
            "        update_buffers=True,\n",
            "        strict_load=False,\n",
            "        priority=49)\n",
            "]\n",
            "val_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "test_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1, val_interval=10)\n",
            "val_cfg = dict(type='ValLoop')\n",
            "test_cfg = dict(type='TestLoop')\n",
            "launcher = 'none'\n",
            "work_dir = './work_dirs/yolov5_s_efficientrep-v61_1xb2-1e_coco128'\n",
            "\n",
            "11/20 08:02:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Result has been saved to /content/mmyolo/work_dirs/yolov5_s_efficientrep-v61_1xb2-1e_coco128/modules_statistic_results.json\n",
            "11/20 08:02:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "11/20 08:02:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmyolo/work_dirs/yolov5_s_efficientrep-v61_1xb2-1e_coco128.\n",
            "11/20 08:03:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/63]  lr: 4.9000e-04  eta: 0:00:04  time: 0.3141  data_time: 0.0075  memory: 4902  loss: 0.5655  loss_cls: 0.2104  loss_obj: 0.1342  loss_bbox: 0.2209\n",
            "11/20 08:03:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: yolov5_s_efficientrep-v61_1xb2-1e_coco128_20221120_080242\n",
            "11/20 08:03:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "11/20 08:03:09 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers\n"
          ]
        }
      ],
      "source": [
        "# 启动训练\n",
        "!python tools/train.py configs/yolov5/yolov5_s_efficientrep-v61_1xb2-1e_coco128.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgEWPiW2qXsC"
      },
      "source": [
        "## 2 跨库使用主干网络\n",
        "OpenMMLab 2.0 体系中 MMYOLO、MMDetection、MMClassification、MMSelfsup 中的模型注册表都继承自 MMEngine 中的根注册表，允许这些 OpenMMLab 开源库直接使用彼此已经实现的模块。 因此用户可以在 MMYOLO 中使用来自 MMDetection、MMClassification、MMSelfsup 的主干网络，而无需重新实现。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqhhOC74t3IA"
      },
      "source": [
        "### 2.1 使用在 MMDetection 中实现的主干网络（SwinTransformer）\n",
        "\n",
        "目前 MMDetection [![GitHub release](https://img.shields.io/github/release/open-mmlab/mmdetection.svg)](https://GitHub.com/open-mmlab/mmdetection/) 已支持 16 种主干网络：\n",
        "<details close>\n",
        "<summary>Supported backbones</summary>\n",
        "\n",
        "<li>VGG (ICLR'2015)</li>\n",
        "        <li>ResNet (CVPR'2016)</li>\n",
        "        <li>ResNeXt (CVPR'2017)</li>\n",
        "        <li>MobileNetV2 (CVPR'2018)</li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/hrnet\">HRNet (CVPR'2019)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/empirical_attention\">Generalized Attention (ICCV'2019)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/gcnet\">GCNet (ICCVW'2019)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/res2net\">Res2Net (TPAMI'2020)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/regnet\">RegNet (CVPR'2020)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/resnest\">ResNeSt (ArXiv'2020)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/pvt\">PVT (ICCV'2021)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/swin\">Swin (CVPR'2021)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/pvt\">PVTv2 (ArXiv'2021)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/resnet_strikes_back\">ResNet strikes back (ArXiv'2021)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/efficientnet\">EfficientNet (ArXiv'2021)</a></li>\n",
        "        <li><a href=\"https://github.com/open-mmlab/mmdetection/tree/3.x/configs/convnext\">ConvNeXt (CVPR'2022)</a></li>\n",
        "\n",
        "</details>\n",
        "\n",
        "<div align=\"center\"> \n",
        "<img src=\"https://raw.githubusercontent.com/microsoft/Swin-Transformer/main/figures/teaser.png\" width = \"750\" height = \"400\" alt=\"EfficientRep Backbone 结构图]\">\n",
        "</div>\n",
        "\n",
        "SwinTransformer 不仅获得了 ICCV 2021 最佳论文，在各种计算机视觉比赛中你也可以看到它的身影。\n",
        "\n",
        "如果想将 `SwinTransformer-Tiny` 作为 `YOLOv5` 的主干网络，则配置文件如下："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yLAi4JYArBI-"
      },
      "outputs": [],
      "source": [
        "_base_ = './yolov5_s-v61_1xb2-1e_coco128.py'\n",
        "\n",
        "widen_factor = 1.0\n",
        "channels = [192, 384, 768]\n",
        "checkpoint_file = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa\n",
        "\n",
        "model = dict(\n",
        "    backbone=dict(\n",
        "        _delete_=True, # 将 _base_ 中关于 backbone 的字段删除\n",
        "        type='mmdet.SwinTransformer', # 使用 mmdet 中的 SwinTransformer\n",
        "        embed_dims=96,\n",
        "        depths=[2, 2, 6, 2],\n",
        "        num_heads=[3, 6, 12, 24],\n",
        "        window_size=7,\n",
        "        mlp_ratio=4,\n",
        "        qkv_bias=True,\n",
        "        qk_scale=None,\n",
        "        drop_rate=0.,\n",
        "        attn_drop_rate=0.,\n",
        "        drop_path_rate=0.2,\n",
        "        patch_norm=True,\n",
        "        out_indices=(1, 2, 3),\n",
        "        with_cp=False,\n",
        "        convert_weights=True,\n",
        "        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),\n",
        "    neck=dict(\n",
        "        type='YOLOv5PAFPN',\n",
        "        widen_factor=widen_factor,\n",
        "        in_channels=channels, # 注意：SwinTransformer-Tiny 输出的3个通道是 [192, 384, 768]，和原先的 yolov5-s neck 不匹配，需要更改\n",
        "        out_channels=channels),\n",
        "    bbox_head=dict(\n",
        "        type='YOLOv5Head',\n",
        "        head_module=dict(\n",
        "            type='YOLOv5HeadModule',\n",
        "            in_channels=channels, # head 部分输入通道也要做相应更改\n",
        "            widen_factor=widen_factor))\n",
        ")\n",
        "\n",
        "config_swin_t = f\"\"\"\n",
        "_base_=\\'{_base_}\\'\n",
        "widen_factor={widen_factor}\n",
        "checkpoint_file=\\'{checkpoint_file}\\'\n",
        "model={model}\n",
        "\"\"\"\n",
        "\n",
        "with open('./configs/yolov5/yolov5_s_swin_t-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_swin_t)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "og5JqsH2rdw8",
        "outputId": "658b61cb-6eff-40e5-fb35-a9ac820cb181"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11/19 11:26:33 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmyolo\" in the \"log_processor\" registry tree. As a workaround, the current \"log_processor\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmyolo\" is a correct scope, or whether the registry is initialized.\n",
            "11/19 11:26:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 666285573\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.2, V11.2.152\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.12.1+cu113\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.3\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  (built against CUDA 11.5)\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, 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: 0.13.1+cu113\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.3.1\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: True\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: None\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "11/19 11:26:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "default_scope = 'mmyolo'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(\n",
            "        type='YOLOv5ParamSchedulerHook',\n",
            "        scheduler_type='linear',\n",
            "        lr_factor=0.01,\n",
            "        max_epochs=1),\n",
            "    checkpoint=dict(\n",
            "        type='CheckpointHook', interval=10, save_best='auto',\n",
            "        max_keep_ckpts=3),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook'))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=True,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='visualizer')\n",
            "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "file_client_args = dict(backend='disk')\n",
            "data_root = 'data/coco/'\n",
            "dataset_type = 'YOLOv5CocoDataset'\n",
            "num_classes = 80\n",
            "img_scale = (640, 640)\n",
            "deepen_factor = 0.33\n",
            "widen_factor = 1.0\n",
            "max_epochs = 1\n",
            "save_epoch_intervals = 10\n",
            "train_batch_size_per_gpu = 2\n",
            "train_num_workers = 2\n",
            "val_batch_size_per_gpu = 1\n",
            "val_num_workers = 2\n",
            "persistent_workers = True\n",
            "batch_shapes_cfg = dict(\n",
            "    type='BatchShapePolicy',\n",
            "    batch_size=1,\n",
            "    img_size=640,\n",
            "    size_divisor=32,\n",
            "    extra_pad_ratio=0.5)\n",
            "anchors = [[(10, 13), (16, 30), (33, 23)], [(30, 61), (62, 45), (59, 119)],\n",
            "           [(116, 90), (156, 198), (373, 326)]]\n",
            "strides = [8, 16, 32]\n",
            "num_det_layers = 3\n",
            "model = dict(\n",
            "    type='YOLODetector',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmdet.DetDataPreprocessor',\n",
            "        mean=[0.0, 0.0, 0.0],\n",
            "        std=[255.0, 255.0, 255.0],\n",
            "        bgr_to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmdet.SwinTransformer',\n",
            "        embed_dims=96,\n",
            "        depths=[2, 2, 6, 2],\n",
            "        num_heads=[3, 6, 12, 24],\n",
            "        window_size=7,\n",
            "        mlp_ratio=4,\n",
            "        qkv_bias=True,\n",
            "        qk_scale=None,\n",
            "        drop_rate=0.0,\n",
            "        attn_drop_rate=0.0,\n",
            "        drop_path_rate=0.2,\n",
            "        patch_norm=True,\n",
            "        out_indices=(1, 2, 3),\n",
            "        with_cp=False,\n",
            "        convert_weights=True,\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained',\n",
            "            checkpoint=\n",
            "            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'\n",
            "        )),\n",
            "    neck=dict(\n",
            "        type='YOLOv5PAFPN',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=1.0,\n",
            "        in_channels=[192, 384, 768],\n",
            "        out_channels=[192, 384, 768],\n",
            "        num_csp_blocks=3,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='SiLU', inplace=True)),\n",
            "    bbox_head=dict(\n",
            "        type='YOLOv5Head',\n",
            "        head_module=dict(\n",
            "            type='YOLOv5HeadModule',\n",
            "            num_classes=80,\n",
            "            in_channels=[192, 384, 768],\n",
            "            widen_factor=1.0,\n",
            "            featmap_strides=[8, 16, 32],\n",
            "            num_base_priors=3),\n",
            "        prior_generator=dict(\n",
            "            type='mmdet.YOLOAnchorGenerator',\n",
            "            base_sizes=[[(10, 13), (16, 30), (33, 23)],\n",
            "                        [(30, 61), (62, 45), (59, 119)],\n",
            "                        [(116, 90), (156, 198), (373, 326)]],\n",
            "            strides=[8, 16, 32]),\n",
            "        loss_cls=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.5),\n",
            "        loss_bbox=dict(\n",
            "            type='IoULoss',\n",
            "            iou_mode='ciou',\n",
            "            bbox_format='xywh',\n",
            "            eps=1e-07,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.05,\n",
            "            return_iou=True),\n",
            "        loss_obj=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=1.0),\n",
            "        prior_match_thr=4.0,\n",
            "        obj_level_weights=[4.0, 1.0, 0.4]),\n",
            "    test_cfg=dict(\n",
            "        multi_label=True,\n",
            "        nms_pre=30000,\n",
            "        score_thr=0.001,\n",
            "        nms=dict(type='nms', iou_threshold=0.65),\n",
            "        max_per_img=300))\n",
            "albu_train_transforms = [\n",
            "    dict(type='Blur', p=0.01),\n",
            "    dict(type='MedianBlur', p=0.01),\n",
            "    dict(type='ToGray', p=0.01),\n",
            "    dict(type='CLAHE', p=0.01)\n",
            "]\n",
            "pre_transform = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True)\n",
            "]\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='Mosaic',\n",
            "        img_scale=(640, 640),\n",
            "        pad_val=114.0,\n",
            "        pre_transform=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='YOLOv5RandomAffine',\n",
            "        max_rotate_degree=0.0,\n",
            "        max_shear_degree=0.0,\n",
            "        scaling_ratio_range=(0.5, 1.5),\n",
            "        border=(-320, -320),\n",
            "        border_val=(114, 114, 114)),\n",
            "    dict(\n",
            "        type='mmdet.Albu',\n",
            "        transforms=[\n",
            "            dict(type='Blur', p=0.01),\n",
            "            dict(type='MedianBlur', p=0.01),\n",
            "            dict(type='ToGray', p=0.01),\n",
            "            dict(type='CLAHE', p=0.01)\n",
            "        ],\n",
            "        bbox_params=dict(\n",
            "            type='BboxParams',\n",
            "            format='pascal_voc',\n",
            "            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "        keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "    dict(type='YOLOv5HSVRandomAug'),\n",
            "    dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',\n",
            "                   'flip_direction'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        ann_file='annotations/instances_train2017.json',\n",
            "        data_prefix=dict(img='train2017/'),\n",
            "        filter_cfg=dict(filter_empty_gt=False, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='Mosaic',\n",
            "                img_scale=(640, 640),\n",
            "                pad_val=114.0,\n",
            "                pre_transform=[\n",
            "                    dict(\n",
            "                        type='LoadImageFromFile',\n",
            "                        file_client_args=dict(backend='disk')),\n",
            "                    dict(type='LoadAnnotations', with_bbox=True)\n",
            "                ]),\n",
            "            dict(\n",
            "                type='YOLOv5RandomAffine',\n",
            "                max_rotate_degree=0.0,\n",
            "                max_shear_degree=0.0,\n",
            "                scaling_ratio_range=(0.5, 1.5),\n",
            "                border=(-320, -320),\n",
            "                border_val=(114, 114, 114)),\n",
            "            dict(\n",
            "                type='mmdet.Albu',\n",
            "                transforms=[\n",
            "                    dict(type='Blur', p=0.01),\n",
            "                    dict(type='MedianBlur', p=0.01),\n",
            "                    dict(type='ToGray', p=0.01),\n",
            "                    dict(type='CLAHE', p=0.01)\n",
            "                ],\n",
            "                bbox_params=dict(\n",
            "                    type='BboxParams',\n",
            "                    format='pascal_voc',\n",
            "                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "                keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "            dict(type='YOLOv5HSVRandomAug'),\n",
            "            dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'flip', 'flip_direction'))\n",
            "        ]))\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "    dict(\n",
            "        type='LetterResize',\n",
            "        scale=(640, 640),\n",
            "        allow_scale_up=False,\n",
            "        pad_val=dict(img=114)),\n",
            "    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor', 'pad_param'))\n",
            "]\n",
            "val_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "param_scheduler = None\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(\n",
            "        type='SGD',\n",
            "        lr=0.01,\n",
            "        momentum=0.937,\n",
            "        weight_decay=0.0005,\n",
            "        nesterov=True,\n",
            "        batch_size_per_gpu=2),\n",
            "    constructor='YOLOv5OptimizerConstructor')\n",
            "custom_hooks = [\n",
            "    dict(\n",
            "        type='EMAHook',\n",
            "        ema_type='ExpMomentumEMA',\n",
            "        momentum=0.0001,\n",
            "        update_buffers=True,\n",
            "        strict_load=False,\n",
            "        priority=49)\n",
            "]\n",
            "val_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "test_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1, val_interval=10)\n",
            "val_cfg = dict(type='ValLoop')\n",
            "test_cfg = dict(type='TestLoop')\n",
            "checkpoint_file = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'\n",
            "launcher = 'none'\n",
            "work_dir = './work_dirs/yolov5_s_swin_t-v61_1xb2-1e_coco128'\n",
            "\n",
            "11/19 11:26:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Result has been saved to /content/mmyolo/work_dirs/yolov5_s_swin_t-v61_1xb2-1e_coco128/modules_statistic_results.json\n",
            "11/19 11:26:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "11/19 11:26:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\n",
            "Downloading: \"https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\" to /root/.cache/torch/hub/checkpoints/swin_tiny_patch4_window7_224.pth\n",
            "100% 109M/109M [00:13<00:00, 8.74MB/s]\n",
            "11/19 11:27:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmyolo/work_dirs/yolov5_s_swin_t-v61_1xb2-1e_coco128.\n",
            "11/19 11:27:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/63]  lr: 4.9000e-04  eta: 0:00:04  time: 0.3812  data_time: 0.0055  memory: 8502  loss: 0.5635  loss_cls: 0.2091  loss_obj: 0.1338  loss_bbox: 0.2205\n",
            "11/19 11:27:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: yolov5_s_swin_t-v61_1xb2-1e_coco128_20221119_112633\n",
            "11/19 11:27:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "11/19 11:27:25 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers\n"
          ]
        }
      ],
      "source": [
        "# 启动训练\n",
        "!python tools/train.py configs/yolov5/yolov5_s_swin_t-v61_1xb2-1e_coco128.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3MIUWpCKWM4"
      },
      "source": [
        "### 2.2 使用在 MMClassification 中实现的主干网络（MobileNetV3）\n",
        "觉得 SwinTransformer 模型太大？\n",
        "\n",
        "目前 MMClassification [![GitHub release](https://img.shields.io/github/release/open-mmlab/mmclassification.svg)](https://GitHub.com/open-mmlab/mmclassification/) 已支持 30 种主干网络，其中也有很多轻量级网络：\n",
        "<details close>\n",
        "<summary>Supported backbones</summary>\n",
        "\n",
        "- [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/vgg)\n",
        "- [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)\n",
        "- [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)\n",
        "- [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)\n",
        "- [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)\n",
        "- [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/regnet)\n",
        "- [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)\n",
        "- [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)\n",
        "- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)\n",
        "- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v3)\n",
        "- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)\n",
        "- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)\n",
        "- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer)\n",
        "- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/tnt)\n",
        "- [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/master/configs/res2net)\n",
        "- [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/mlp_mixer)\n",
        "- [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit)\n",
        "- [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/conformer)\n",
        "- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/t2t_vit)\n",
        "- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/master/configs/twins)\n",
        "- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)\n",
        "- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)\n",
        "- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)\n",
        "- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)\n",
        "- [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)\n",
        "- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)\n",
        "- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)\n",
        "- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)\n",
        "- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)\n",
        "- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)\n",
        "\n",
        "</details>\n",
        "\n",
        "如果想要使用在 MMClassification 中实现的主干网络，需要先安装 mmcls。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhdBMPTUrmYv",
        "outputId": "b26f4276-5501-4fce-86ea-ba638acbfeb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html\n",
            "Collecting mmcls>=1.0.0rc2\n",
            "  Downloading mmcls-1.0.0rc2-py2.py3-none-any.whl (642 kB)\n",
            "\u001b[K     |████████████████████████████████| 642 kB 26.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (1.21.6)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (21.3)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (12.6.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (3.2.2)\n",
            "Requirement already satisfied: mmengine<1.0.0,>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (0.3.1)\n",
            "Requirement already satisfied: mmcv<=2.0.0,>=2.0.0rc1 in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc2) (2.0.0rc2)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv<=2.0.0,>=2.0.0rc1->mmcls>=1.0.0rc2) (4.6.0.66)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv<=2.0.0,>=2.0.0rc1->mmcls>=1.0.0rc2) (0.32.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv<=2.0.0,>=2.0.0rc1->mmcls>=1.0.0rc2) (7.1.2)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv<=2.0.0,>=2.0.0rc1->mmcls>=1.0.0rc2) (2.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv<=2.0.0,>=2.0.0rc1->mmcls>=1.0.0rc2) (6.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from mmengine<1.0.0,>=0.2.0->mmcls>=1.0.0rc2) (2.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls>=1.0.0rc2) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls>=1.0.0rc2) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls>=1.0.0rc2) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls>=1.0.0rc2) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmcls>=1.0.0rc2) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mmcls>=1.0.0rc2) (1.15.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->mmcls>=1.0.0rc2) (2.6.1)\n",
            "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from rich->mmcls>=1.0.0rc2) (0.9.1)\n",
            "Installing collected packages: mmcls\n",
            "Successfully installed mmcls-1.0.0rc2\n"
          ]
        }
      ],
      "source": [
        "# 安装 mmcls\n",
        "!mim install \"mmcls>=1.0.0rc2\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQFq4lu-rnCz"
      },
      "source": [
        "如果想将 `MobileNetV3-small` 作为 `YOLOv5` 的主干网络，则配置文件如下："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQmnfdUprnuM"
      },
      "outputs": [],
      "source": [
        "_base_ = './yolov5_s-v61_1xb2-1e_coco128.py'\n",
        "\n",
        "# 导入 mmcls.models 使得可以调用 mmcls 中注册的模块\n",
        "custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)\n",
        "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth'  # noqa\n",
        "widen_factor = 1.0\n",
        "channels = [24, 48, 96]\n",
        "\n",
        "model = dict(\n",
        "    backbone=dict(\n",
        "        _delete_=True, # 将 _base_ 中关于 backbone 的字段删除\n",
        "        type='mmcls.MobileNetV3', # 使用 mmcls 中的 MobileNetV3\n",
        "        arch='small',\n",
        "        out_indices=(3, 8, 11), # 修改 out_indices\n",
        "        init_cfg=dict(\n",
        "            type='Pretrained',\n",
        "            checkpoint=checkpoint_file,\n",
        "            prefix='backbone.')), # MMCls 中主干网络的预训练权重含义 prefix='backbone.'，为了正常加载权重，需要把这个 prefix 去掉。\n",
        "    neck=dict(\n",
        "        type='YOLOv5PAFPN',\n",
        "        widen_factor=widen_factor,\n",
        "        in_channels=channels, # 注意：MobileNetV3-small 输出的3个通道是 [24, 48, 96]，和原先的 yolov5-s neck 不匹配，需要更改\n",
        "        out_channels=channels),\n",
        "    bbox_head=dict(\n",
        "        type='YOLOv5Head',\n",
        "        head_module=dict(\n",
        "            type='YOLOv5HeadModule',\n",
        "            in_channels=channels, # head 部分输入通道也要做相应更改\n",
        "            widen_factor=widen_factor))\n",
        ")\n",
        "\n",
        "config_mobilenetv3_s = f\"\"\"\n",
        "_base_=\\'{_base_}\\'\n",
        "widen_factor={widen_factor}\n",
        "checkpoint_file=\\'{checkpoint_file}\\'\n",
        "model={model}\n",
        "\"\"\"\n",
        "\n",
        "with open('./configs/yolov5/yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_mobilenetv3_s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZC2BXgyr8br",
        "outputId": "51bd676d-4e63-4e38-befd-d7c49e2438c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11/19 11:27:50 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmyolo\" in the \"log_processor\" registry tree. As a workaround, the current \"log_processor\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmyolo\" is a correct scope, or whether the registry is initialized.\n",
            "11/19 11:27:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 896685858\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.2, V11.2.152\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.12.1+cu113\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.3\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  (built against CUDA 11.5)\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, 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: 0.13.1+cu113\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.3.1\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: True\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: None\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "11/19 11:27:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "default_scope = 'mmyolo'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(\n",
            "        type='YOLOv5ParamSchedulerHook',\n",
            "        scheduler_type='linear',\n",
            "        lr_factor=0.01,\n",
            "        max_epochs=1),\n",
            "    checkpoint=dict(\n",
            "        type='CheckpointHook', interval=10, save_best='auto',\n",
            "        max_keep_ckpts=3),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook'))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=True,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='visualizer')\n",
            "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "file_client_args = dict(backend='disk')\n",
            "data_root = 'data/coco/'\n",
            "dataset_type = 'YOLOv5CocoDataset'\n",
            "num_classes = 80\n",
            "img_scale = (640, 640)\n",
            "deepen_factor = 0.33\n",
            "widen_factor = 1.0\n",
            "max_epochs = 1\n",
            "save_epoch_intervals = 10\n",
            "train_batch_size_per_gpu = 2\n",
            "train_num_workers = 2\n",
            "val_batch_size_per_gpu = 1\n",
            "val_num_workers = 2\n",
            "persistent_workers = True\n",
            "batch_shapes_cfg = dict(\n",
            "    type='BatchShapePolicy',\n",
            "    batch_size=1,\n",
            "    img_size=640,\n",
            "    size_divisor=32,\n",
            "    extra_pad_ratio=0.5)\n",
            "anchors = [[(10, 13), (16, 30), (33, 23)], [(30, 61), (62, 45), (59, 119)],\n",
            "           [(116, 90), (156, 198), (373, 326)]]\n",
            "strides = [8, 16, 32]\n",
            "num_det_layers = 3\n",
            "model = dict(\n",
            "    type='YOLODetector',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmdet.DetDataPreprocessor',\n",
            "        mean=[0.0, 0.0, 0.0],\n",
            "        std=[255.0, 255.0, 255.0],\n",
            "        bgr_to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmcls.MobileNetV3',\n",
            "        arch='small',\n",
            "        out_indices=(3, 8, 11),\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained',\n",
            "            checkpoint=\n",
            "            'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth',\n",
            "            prefix='backbone.')),\n",
            "    neck=dict(\n",
            "        type='YOLOv5PAFPN',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=1.0,\n",
            "        in_channels=[24, 48, 96],\n",
            "        out_channels=[24, 48, 96],\n",
            "        num_csp_blocks=3,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='SiLU', inplace=True)),\n",
            "    bbox_head=dict(\n",
            "        type='YOLOv5Head',\n",
            "        head_module=dict(\n",
            "            type='YOLOv5HeadModule',\n",
            "            num_classes=80,\n",
            "            in_channels=[24, 48, 96],\n",
            "            widen_factor=1.0,\n",
            "            featmap_strides=[8, 16, 32],\n",
            "            num_base_priors=3),\n",
            "        prior_generator=dict(\n",
            "            type='mmdet.YOLOAnchorGenerator',\n",
            "            base_sizes=[[(10, 13), (16, 30), (33, 23)],\n",
            "                        [(30, 61), (62, 45), (59, 119)],\n",
            "                        [(116, 90), (156, 198), (373, 326)]],\n",
            "            strides=[8, 16, 32]),\n",
            "        loss_cls=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.5),\n",
            "        loss_bbox=dict(\n",
            "            type='IoULoss',\n",
            "            iou_mode='ciou',\n",
            "            bbox_format='xywh',\n",
            "            eps=1e-07,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.05,\n",
            "            return_iou=True),\n",
            "        loss_obj=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=1.0),\n",
            "        prior_match_thr=4.0,\n",
            "        obj_level_weights=[4.0, 1.0, 0.4]),\n",
            "    test_cfg=dict(\n",
            "        multi_label=True,\n",
            "        nms_pre=30000,\n",
            "        score_thr=0.001,\n",
            "        nms=dict(type='nms', iou_threshold=0.65),\n",
            "        max_per_img=300))\n",
            "albu_train_transforms = [\n",
            "    dict(type='Blur', p=0.01),\n",
            "    dict(type='MedianBlur', p=0.01),\n",
            "    dict(type='ToGray', p=0.01),\n",
            "    dict(type='CLAHE', p=0.01)\n",
            "]\n",
            "pre_transform = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True)\n",
            "]\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='Mosaic',\n",
            "        img_scale=(640, 640),\n",
            "        pad_val=114.0,\n",
            "        pre_transform=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='YOLOv5RandomAffine',\n",
            "        max_rotate_degree=0.0,\n",
            "        max_shear_degree=0.0,\n",
            "        scaling_ratio_range=(0.5, 1.5),\n",
            "        border=(-320, -320),\n",
            "        border_val=(114, 114, 114)),\n",
            "    dict(\n",
            "        type='mmdet.Albu',\n",
            "        transforms=[\n",
            "            dict(type='Blur', p=0.01),\n",
            "            dict(type='MedianBlur', p=0.01),\n",
            "            dict(type='ToGray', p=0.01),\n",
            "            dict(type='CLAHE', p=0.01)\n",
            "        ],\n",
            "        bbox_params=dict(\n",
            "            type='BboxParams',\n",
            "            format='pascal_voc',\n",
            "            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "        keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "    dict(type='YOLOv5HSVRandomAug'),\n",
            "    dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',\n",
            "                   'flip_direction'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        ann_file='annotations/instances_train2017.json',\n",
            "        data_prefix=dict(img='train2017/'),\n",
            "        filter_cfg=dict(filter_empty_gt=False, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='Mosaic',\n",
            "                img_scale=(640, 640),\n",
            "                pad_val=114.0,\n",
            "                pre_transform=[\n",
            "                    dict(\n",
            "                        type='LoadImageFromFile',\n",
            "                        file_client_args=dict(backend='disk')),\n",
            "                    dict(type='LoadAnnotations', with_bbox=True)\n",
            "                ]),\n",
            "            dict(\n",
            "                type='YOLOv5RandomAffine',\n",
            "                max_rotate_degree=0.0,\n",
            "                max_shear_degree=0.0,\n",
            "                scaling_ratio_range=(0.5, 1.5),\n",
            "                border=(-320, -320),\n",
            "                border_val=(114, 114, 114)),\n",
            "            dict(\n",
            "                type='mmdet.Albu',\n",
            "                transforms=[\n",
            "                    dict(type='Blur', p=0.01),\n",
            "                    dict(type='MedianBlur', p=0.01),\n",
            "                    dict(type='ToGray', p=0.01),\n",
            "                    dict(type='CLAHE', p=0.01)\n",
            "                ],\n",
            "                bbox_params=dict(\n",
            "                    type='BboxParams',\n",
            "                    format='pascal_voc',\n",
            "                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "                keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "            dict(type='YOLOv5HSVRandomAug'),\n",
            "            dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'flip', 'flip_direction'))\n",
            "        ]))\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "    dict(\n",
            "        type='LetterResize',\n",
            "        scale=(640, 640),\n",
            "        allow_scale_up=False,\n",
            "        pad_val=dict(img=114)),\n",
            "    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor', 'pad_param'))\n",
            "]\n",
            "val_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "param_scheduler = None\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(\n",
            "        type='SGD',\n",
            "        lr=0.01,\n",
            "        momentum=0.937,\n",
            "        weight_decay=0.0005,\n",
            "        nesterov=True,\n",
            "        batch_size_per_gpu=2),\n",
            "    constructor='YOLOv5OptimizerConstructor')\n",
            "custom_hooks = [\n",
            "    dict(\n",
            "        type='EMAHook',\n",
            "        ema_type='ExpMomentumEMA',\n",
            "        momentum=0.0001,\n",
            "        update_buffers=True,\n",
            "        strict_load=False,\n",
            "        priority=49)\n",
            "]\n",
            "val_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "test_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1, val_interval=10)\n",
            "val_cfg = dict(type='ValLoop')\n",
            "test_cfg = dict(type='TestLoop')\n",
            "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth'\n",
            "launcher = 'none'\n",
            "work_dir = './work_dirs/yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128'\n",
            "\n",
            "11/19 11:27:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Result has been saved to /content/mmyolo/work_dirs/yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128/modules_statistic_results.json\n",
            "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/hsigmoid.py:36: UserWarning: In MMCV v1.4.4, we modified the default value of args to align with PyTorch official. Previous Implementation: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1). Current Implementation: Hsigmoid(x) = min(max((x + 3) / 6, 0), 1).\n",
            "  'In MMCV v1.4.4, we modified the default value of args to align '\n",
            "11/19 11:27:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "11/19 11:28:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load backbone. in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth\n",
            "http loads checkpoint from path: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth\n",
            "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_small-8427ecf0.pth\n",
            "100% 9.82M/9.82M [00:00<00:00, 24.2MB/s]\n",
            "11/19 11:28:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmyolo/work_dirs/yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128.\n",
            "11/19 11:28:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/63]  lr: 4.9000e-04  eta: 0:00:02  time: 0.1847  data_time: 0.0106  memory: 1145  loss: 0.5764  loss_cls: 0.2093  loss_obj: 0.1373  loss_bbox: 0.2298\n",
            "11/19 11:28:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128_20221119_112750\n",
            "11/19 11:28:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "11/19 11:28:14 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers\n"
          ]
        }
      ],
      "source": [
        "# 启动训练\n",
        "!python tools/train.py configs/yolov5/yolov5_s_mobilenetv3_s-v61_1xb2-1e_coco128.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fz43offklfzF"
      },
      "source": [
        "### 2.3 通过 MMClassification 使用 timm 中实现的主干网络（MobileViT V2）\n",
        "MMClassification 中的主干网络还不够？\n",
        "\n",
        "试试通过 MMClassification 加载 timm [![GitHub release](https://img.shields.io/github/release/rwightman/pytorch-image-models.svg)](https://GitHub.com/rwightman/pytorch-image-models/) 中实现的主干网络：\n",
        "<details close>\n",
        "<summary>Supported backbones</summary>\n",
        "\n",
        "* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723\n",
        "* BEiT - https://arxiv.org/abs/2106.08254\n",
        "* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370\n",
        "* Bottleneck Transformers - https://arxiv.org/abs/2101.11605\n",
        "* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239\n",
        "* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399\n",
        "* CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803\n",
        "* ConvNeXt - https://arxiv.org/abs/2201.03545\n",
        "* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697\n",
        "* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929\n",
        "* DeiT - https://arxiv.org/abs/2012.12877\n",
        "* DeiT-III - https://arxiv.org/pdf/2204.07118.pdf\n",
        "* DenseNet - https://arxiv.org/abs/1608.06993\n",
        "* DLA - https://arxiv.org/abs/1707.06484\n",
        "* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629\n",
        "* EdgeNeXt - https://arxiv.org/abs/2206.10589\n",
        "* EfficientFormer - https://arxiv.org/abs/2206.01191\n",
        "* EfficientNet (MBConvNet Family)\n",
        "    * EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252\n",
        "    * EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665\n",
        "    * EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946\n",
        "    * EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html\n",
        "    * EfficientNet V2 - https://arxiv.org/abs/2104.00298\n",
        "    * FBNet-C - https://arxiv.org/abs/1812.03443\n",
        "    * MixNet - https://arxiv.org/abs/1907.09595\n",
        "    * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626\n",
        "    * MobileNet-V2 - https://arxiv.org/abs/1801.04381\n",
        "    * Single-Path NAS - https://arxiv.org/abs/1904.02877\n",
        "    * TinyNet - https://arxiv.org/abs/2010.14819\n",
        "* GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959\n",
        "* GhostNet - https://arxiv.org/abs/1911.11907\n",
        "* gMLP - https://arxiv.org/abs/2105.08050\n",
        "* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090\n",
        "* Halo Nets - https://arxiv.org/abs/2103.12731\n",
        "* HRNet - https://arxiv.org/abs/1908.07919\n",
        "* Inception-V3 - https://arxiv.org/abs/1512.00567\n",
        "* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261\n",
        "* Lambda Networks - https://arxiv.org/abs/2102.08602\n",
        "* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136\n",
        "* MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697\n",
        "* MLP-Mixer - https://arxiv.org/abs/2105.01601\n",
        "* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244\n",
        "  * FBNet-V3 - https://arxiv.org/abs/2006.02049\n",
        "  * HardCoRe-NAS - https://arxiv.org/abs/2102.11646\n",
        "  * LCNet - https://arxiv.org/abs/2109.15099\n",
        "* MobileViT - https://arxiv.org/abs/2110.02178\n",
        "* MobileViT-V2 - https://arxiv.org/abs/2206.02680\n",
        "* MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526\n",
        "* NASNet-A - https://arxiv.org/abs/1707.07012\n",
        "* NesT - https://arxiv.org/abs/2105.12723\n",
        "* NFNet-F - https://arxiv.org/abs/2102.06171\n",
        "* NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692\n",
        "* PNasNet - https://arxiv.org/abs/1712.00559\n",
        "* PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418\n",
        "* Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302\n",
        "* PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797\n",
        "* RegNet - https://arxiv.org/abs/2003.13678\n",
        "* RegNetZ - https://arxiv.org/abs/2103.06877\n",
        "* RepVGG - https://arxiv.org/abs/2101.03697\n",
        "* ResMLP - https://arxiv.org/abs/2105.03404\n",
        "* ResNet/ResNeXt\n",
        "    * ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385\n",
        "    * ResNeXt - https://arxiv.org/abs/1611.05431\n",
        "    * 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187\n",
        "    * Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932\n",
        "    * Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546\n",
        "    * ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4\n",
        "    * Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507\n",
        "    * ResNet-RS - https://arxiv.org/abs/2103.07579\n",
        "* Res2Net - https://arxiv.org/abs/1904.01169\n",
        "* ResNeSt - https://arxiv.org/abs/2004.08955\n",
        "* ReXNet - https://arxiv.org/abs/2007.00992\n",
        "* SelecSLS - https://arxiv.org/abs/1907.00837\n",
        "* Selective Kernel Networks - https://arxiv.org/abs/1903.06586\n",
        "* Sequencer2D - https://arxiv.org/abs/2205.01972\n",
        "* Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725\n",
        "* Swin Transformer - https://arxiv.org/abs/2103.14030\n",
        "* Swin Transformer V2 - https://arxiv.org/abs/2111.09883\n",
        "* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112\n",
        "* TResNet - https://arxiv.org/abs/2003.13630\n",
        "* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf\n",
        "* Visformer - https://arxiv.org/abs/2104.12533\n",
        "* Vision Transformer - https://arxiv.org/abs/2010.11929\n",
        "* VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112\n",
        "* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667\n",
        "* Xception - https://arxiv.org/abs/1610.02357\n",
        "* Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611\n",
        "* Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611\n",
        "* XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681\n",
        "\n",
        "</details>\n",
        "\n",
        "如果想要使用在 timm 中实现的主干网络，需要先安装 timm。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vyNPZsFmluO",
        "outputId": "491f4809-6468-4ad3-be77-06cad71df3e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting timm\n",
            "  Downloading timm-0.6.11-py3-none-any.whl (548 kB)\n",
            "\u001b[K     |████████████████████████████████| 548 kB 35.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from timm) (6.0)\n",
            "Collecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.11.0-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 37.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from timm) (1.12.1+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm) (0.13.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->timm) (4.1.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (21.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (3.8.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (4.64.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (4.13.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub->timm) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub->timm) (3.10.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (2022.9.24)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (1.21.6)\n",
            "Installing collected packages: huggingface-hub, timm\n",
            "Successfully installed huggingface-hub-0.11.0 timm-0.6.11\n"
          ]
        }
      ],
      "source": [
        "# 安装 timm\n",
        "%pip install timm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jlBsDU8m7nm",
        "outputId": "05b84405-4c1a-4b35-bbff-a11e72afd8e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "num of models: 765\n",
            "['adv_inception_v3', 'bat_resnext26ts', 'beit_base_patch16_224', 'beit_base_patch16_224_in22k', 'beit_base_patch16_384', 'beit_large_patch16_224', 'beit_large_patch16_224_in22k', 'beit_large_patch16_384', 'beit_large_patch16_512', 'beitv2_base_patch16_224', 'beitv2_base_patch16_224_in22k', 'beitv2_large_patch16_224', 'beitv2_large_patch16_224_in22k', 'botnet26t_256', 'cait_m36_384', 'cait_m48_448', 'cait_s24_224', 'cait_s24_384', 'cait_s36_384', 'cait_xs24_384', 'cait_xxs24_224', 'cait_xxs24_384', 'cait_xxs36_224', 'cait_xxs36_384', 'coat_lite_mini', 'coat_lite_small', 'coat_lite_tiny', 'coat_mini', 'coat_tiny', 'coatnet_0_rw_224', 'coatnet_1_rw_224', 'coatnet_bn_0_rw_224', 'coatnet_nano_rw_224', 'coatnet_rmlp_1_rw_224', 'coatnet_rmlp_nano_rw_224', 'convit_base', 'convit_small', 'convit_tiny', 'convmixer_768_32', 'convmixer_1024_20_ks9_p14', 'convmixer_1536_20', 'convnext_atto', 'convnext_atto_ols', 'convnext_base', 'convnext_base_384_in22ft1k', 'convnext_base_in22ft1k', 'convnext_base_in22k', 'convnext_femto', 'convnext_femto_ols', 'convnext_large', 'convnext_large_384_in22ft1k', 'convnext_large_in22ft1k', 'convnext_large_in22k', 'convnext_nano', 'convnext_nano_ols', 'convnext_pico', 'convnext_pico_ols', 'convnext_small', 'convnext_small_384_in22ft1k', 'convnext_small_in22ft1k', 'convnext_small_in22k', 'convnext_tiny', 'convnext_tiny_384_in22ft1k', 'convnext_tiny_hnf', 'convnext_tiny_in22ft1k', 'convnext_tiny_in22k', 'convnext_xlarge_384_in22ft1k', 'convnext_xlarge_in22ft1k', 'convnext_xlarge_in22k', 'crossvit_9_240', 'crossvit_9_dagger_240', 'crossvit_15_240', 'crossvit_15_dagger_240', 'crossvit_15_dagger_408', 'crossvit_18_240', 'crossvit_18_dagger_240', 'crossvit_18_dagger_408', 'crossvit_base_240', 'crossvit_small_240', 'crossvit_tiny_240', 'cs3darknet_focus_l', 'cs3darknet_focus_m', 'cs3darknet_l', 'cs3darknet_m', 'cs3darknet_x', 'cs3edgenet_x', 'cs3se_edgenet_x', 'cs3sedarknet_l', 'cs3sedarknet_x', 'cspdarknet53', 'cspresnet50', 'cspresnext50', 'darknet53', 'darknetaa53', 'deit3_base_patch16_224', 'deit3_base_patch16_224_in21ft1k', 'deit3_base_patch16_384', 'deit3_base_patch16_384_in21ft1k', 'deit3_huge_patch14_224', 'deit3_huge_patch14_224_in21ft1k', 'deit3_large_patch16_224', 'deit3_large_patch16_224_in21ft1k', 'deit3_large_patch16_384', 'deit3_large_patch16_384_in21ft1k', 'deit3_medium_patch16_224', 'deit3_medium_patch16_224_in21ft1k', 'deit3_small_patch16_224', 'deit3_small_patch16_224_in21ft1k', 'deit3_small_patch16_384', 'deit3_small_patch16_384_in21ft1k', 'deit_base_distilled_patch16_224', 'deit_base_distilled_patch16_384', 'deit_base_patch16_224', 'deit_base_patch16_384', 'deit_small_distilled_patch16_224', 'deit_small_patch16_224', 'deit_tiny_distilled_patch16_224', 'deit_tiny_patch16_224', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'densenetblur121d', 'dla34', 'dla46_c', 'dla46x_c', 'dla60', 'dla60_res2net', 'dla60_res2next', 'dla60x', 'dla60x_c', 'dla102', 'dla102x', 'dla102x2', 'dla169', 'dm_nfnet_f0', 'dm_nfnet_f1', 'dm_nfnet_f2', 'dm_nfnet_f3', 'dm_nfnet_f4', 'dm_nfnet_f5', 'dm_nfnet_f6', 'dpn68', 'dpn68b', 'dpn92', 'dpn98', 'dpn107', 'dpn131', 'eca_botnext26ts_256', 'eca_halonext26ts', 'eca_nfnet_l0', 'eca_nfnet_l1', 'eca_nfnet_l2', 'eca_resnet33ts', 'eca_resnext26ts', 'ecaresnet26t', 'ecaresnet50d', 'ecaresnet50d_pruned', 'ecaresnet50t', 'ecaresnet101d', 'ecaresnet101d_pruned', 'ecaresnet269d', 'ecaresnetlight', 'edgenext_base', 'edgenext_small', 'edgenext_small_rw', 'edgenext_x_small', 'edgenext_xx_small', 'efficientformer_l1', 'efficientformer_l3', 'efficientformer_l7', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b1_pruned', 'efficientnet_b2', 'efficientnet_b2_pruned', 'efficientnet_b3', 'efficientnet_b3_pruned', 'efficientnet_b4', 'efficientnet_el', 'efficientnet_el_pruned', 'efficientnet_em', 'efficientnet_es', 'efficientnet_es_pruned', 'efficientnet_lite0', 'efficientnetv2_rw_m', 'efficientnetv2_rw_s', 'efficientnetv2_rw_t', 'ens_adv_inception_resnet_v2', 'ese_vovnet19b_dw', 'ese_vovnet39b', 'fbnetc_100', 'fbnetv3_b', 'fbnetv3_d', 'fbnetv3_g', 'gc_efficientnetv2_rw_t', 'gcresnet33ts', 'gcresnet50t', 'gcresnext26ts', 'gcresnext50ts', 'gcvit_base', 'gcvit_small', 'gcvit_tiny', 'gcvit_xtiny', 'gcvit_xxtiny', 'gernet_l', 'gernet_m', 'gernet_s', 'ghostnet_100', 'gluon_inception_v3', 'gluon_resnet18_v1b', 'gluon_resnet34_v1b', 'gluon_resnet50_v1b', 'gluon_resnet50_v1c', 'gluon_resnet50_v1d', 'gluon_resnet50_v1s', 'gluon_resnet101_v1b', 'gluon_resnet101_v1c', 'gluon_resnet101_v1d', 'gluon_resnet101_v1s', 'gluon_resnet152_v1b', 'gluon_resnet152_v1c', 'gluon_resnet152_v1d', 'gluon_resnet152_v1s', 'gluon_resnext50_32x4d', 'gluon_resnext101_32x4d', 'gluon_resnext101_64x4d', 'gluon_senet154', 'gluon_seresnext50_32x4d', 'gluon_seresnext101_32x4d', 'gluon_seresnext101_64x4d', 'gluon_xception65', 'gmixer_24_224', 'gmlp_s16_224', 'halo2botnet50ts_256', 'halonet26t', 'halonet50ts', 'haloregnetz_b', 'hardcorenas_a', 'hardcorenas_b', 'hardcorenas_c', 'hardcorenas_d', 'hardcorenas_e', 'hardcorenas_f', 'hrnet_w18', 'hrnet_w18_small', 'hrnet_w18_small_v2', 'hrnet_w30', 'hrnet_w32', 'hrnet_w40', 'hrnet_w44', 'hrnet_w48', 'hrnet_w64', 'ig_resnext101_32x8d', 'ig_resnext101_32x16d', 'ig_resnext101_32x32d', 'ig_resnext101_32x48d', 'inception_resnet_v2', 'inception_v3', 'inception_v4', 'jx_nest_base', 'jx_nest_small', 'jx_nest_tiny', 'lambda_resnet26rpt_256', 'lambda_resnet26t', 'lambda_resnet50ts', 'lamhalobotnet50ts_256', 'lcnet_050', 'lcnet_075', 'lcnet_100', 'legacy_senet154', 'legacy_seresnet18', 'legacy_seresnet34', 'legacy_seresnet50', 'legacy_seresnet101', 'legacy_seresnet152', 'legacy_seresnext26_32x4d', 'legacy_seresnext50_32x4d', 'legacy_seresnext101_32x4d', 'levit_128', 'levit_128s', 'levit_192', 'levit_256', 'levit_384', 'maxvit_nano_rw_256', 'maxvit_rmlp_nano_rw_256', 'maxvit_rmlp_pico_rw_256', 'maxvit_rmlp_tiny_rw_256', 'maxvit_tiny_rw_224', 'mixer_b16_224', 'mixer_b16_224_in21k', 'mixer_b16_224_miil', 'mixer_b16_224_miil_in21k', 'mixer_l16_224', 'mixer_l16_224_in21k', 'mixnet_l', 'mixnet_m', 'mixnet_s', 'mixnet_xl', 'mnasnet_100', 'mnasnet_small', 'mobilenetv2_050', 'mobilenetv2_100', 'mobilenetv2_110d', 'mobilenetv2_120d', 'mobilenetv2_140', 'mobilenetv3_large_100', 'mobilenetv3_large_100_miil', 'mobilenetv3_large_100_miil_in21k', 'mobilenetv3_rw', 'mobilenetv3_small_050', 'mobilenetv3_small_075', 'mobilenetv3_small_100', 'mobilevit_s', 'mobilevit_xs', 'mobilevit_xxs', 'mobilevitv2_050', 'mobilevitv2_075', 'mobilevitv2_100', 'mobilevitv2_125', 'mobilevitv2_150', 'mobilevitv2_150_384_in22ft1k', 'mobilevitv2_150_in22ft1k', 'mobilevitv2_175', 'mobilevitv2_175_384_in22ft1k', 'mobilevitv2_175_in22ft1k', 'mobilevitv2_200', 'mobilevitv2_200_384_in22ft1k', 'mobilevitv2_200_in22ft1k', 'mvitv2_base', 'mvitv2_large', 'mvitv2_small', 'mvitv2_tiny', 'nasnetalarge', 'nf_regnet_b1', 'nf_resnet50', 'nfnet_l0', 'pit_b_224', 'pit_b_distilled_224', 'pit_s_224', 'pit_s_distilled_224', 'pit_ti_224', 'pit_ti_distilled_224', 'pit_xs_224', 'pit_xs_distilled_224', 'pnasnet5large', 'poolformer_m36', 'poolformer_m48', 'poolformer_s12', 'poolformer_s24', 'poolformer_s36', 'pvt_v2_b0', 'pvt_v2_b1', 'pvt_v2_b2', 'pvt_v2_b2_li', 'pvt_v2_b3', 'pvt_v2_b4', 'pvt_v2_b5', 'regnetv_040', 'regnetv_064', 'regnetx_002', 'regnetx_004', 'regnetx_006', 'regnetx_008', 'regnetx_016', 'regnetx_032', 'regnetx_040', 'regnetx_064', 'regnetx_080', 'regnetx_120', 'regnetx_160', 'regnetx_320', 'regnety_002', 'regnety_004', 'regnety_006', 'regnety_008', 'regnety_016', 'regnety_032', 'regnety_040', 'regnety_064', 'regnety_080', 'regnety_120', 'regnety_160', 'regnety_320', 'regnetz_040', 'regnetz_040h', 'regnetz_b16', 'regnetz_c16', 'regnetz_c16_evos', 'regnetz_d8', 'regnetz_d8_evos', 'regnetz_d32', 'regnetz_e8', 'repvgg_a2', 'repvgg_b0', 'repvgg_b1', 'repvgg_b1g4', 'repvgg_b2', 'repvgg_b2g4', 'repvgg_b3', 'repvgg_b3g4', 'res2net50_14w_8s', 'res2net50_26w_4s', 'res2net50_26w_6s', 'res2net50_26w_8s', 'res2net50_48w_2s', 'res2net101_26w_4s', 'res2next50', 'resmlp_12_224', 'resmlp_12_224_dino', 'resmlp_12_distilled_224', 'resmlp_24_224', 'resmlp_24_224_dino', 'resmlp_24_distilled_224', 'resmlp_36_224', 'resmlp_36_distilled_224', 'resmlp_big_24_224', 'resmlp_big_24_224_in22ft1k', 'resmlp_big_24_distilled_224', 'resnest14d', 'resnest26d', 'resnest50d', 'resnest50d_1s4x24d', 'resnest50d_4s2x40d', 'resnest101e', 'resnest200e', 'resnest269e', 'resnet10t', 'resnet14t', 'resnet18', 'resnet18d', 'resnet26', 'resnet26d', 'resnet26t', 'resnet32ts', 'resnet33ts', 'resnet34', 'resnet34d', 'resnet50', 'resnet50_gn', 'resnet50d', 'resnet51q', 'resnet61q', 'resnet101', 'resnet101d', 'resnet152', 'resnet152d', 'resnet200d', 'resnetaa50', 'resnetblur50', 'resnetrs50', 'resnetrs101', 'resnetrs152', 'resnetrs200', 'resnetrs270', 'resnetrs350', 'resnetrs420', 'resnetv2_50', 'resnetv2_50d_evos', 'resnetv2_50d_gn', 'resnetv2_50x1_bit_distilled', 'resnetv2_50x1_bitm', 'resnetv2_50x1_bitm_in21k', 'resnetv2_50x3_bitm', 'resnetv2_50x3_bitm_in21k', 'resnetv2_101', 'resnetv2_101x1_bitm', 'resnetv2_101x1_bitm_in21k', 'resnetv2_101x3_bitm', 'resnetv2_101x3_bitm_in21k', 'resnetv2_152x2_bit_teacher', 'resnetv2_152x2_bit_teacher_384', 'resnetv2_152x2_bitm', 'resnetv2_152x2_bitm_in21k', 'resnetv2_152x4_bitm', 'resnetv2_152x4_bitm_in21k', 'resnext26ts', 'resnext50_32x4d', 'resnext50d_32x4d', 'resnext101_32x8d', 'resnext101_64x4d', 'rexnet_100', 'rexnet_130', 'rexnet_150', 'rexnet_200', 'sebotnet33ts_256', 'sehalonet33ts', 'selecsls42b', 'selecsls60', 'selecsls60b', 'semnasnet_075', 'semnasnet_100', 'sequencer2d_l', 'sequencer2d_m', 'sequencer2d_s', 'seresnet33ts', 'seresnet50', 'seresnet152d', 'seresnext26d_32x4d', 'seresnext26t_32x4d', 'seresnext26ts', 'seresnext50_32x4d', 'seresnext101_32x8d', 'seresnext101d_32x8d', 'seresnextaa101d_32x8d', 'skresnet18', 'skresnet34', 'skresnext50_32x4d', 'spnasnet_100', 'ssl_resnet18', 'ssl_resnet50', 'ssl_resnext50_32x4d', 'ssl_resnext101_32x4d', 'ssl_resnext101_32x8d', 'ssl_resnext101_32x16d', 'swin_base_patch4_window7_224', 'swin_base_patch4_window7_224_in22k', 'swin_base_patch4_window12_384', 'swin_base_patch4_window12_384_in22k', 'swin_large_patch4_window7_224', 'swin_large_patch4_window7_224_in22k', 'swin_large_patch4_window12_384', 'swin_large_patch4_window12_384_in22k', 'swin_s3_base_224', 'swin_s3_small_224', 'swin_s3_tiny_224', 'swin_small_patch4_window7_224', 'swin_tiny_patch4_window7_224', 'swinv2_base_window8_256', 'swinv2_base_window12_192_22k', 'swinv2_base_window12to16_192to256_22kft1k', 'swinv2_base_window12to24_192to384_22kft1k', 'swinv2_base_window16_256', 'swinv2_cr_small_224', 'swinv2_cr_small_ns_224', 'swinv2_cr_tiny_ns_224', 'swinv2_large_window12_192_22k', 'swinv2_large_window12to16_192to256_22kft1k', 'swinv2_large_window12to24_192to384_22kft1k', 'swinv2_small_window8_256', 'swinv2_small_window16_256', 'swinv2_tiny_window8_256', 'swinv2_tiny_window16_256', 'swsl_resnet18', 'swsl_resnet50', 'swsl_resnext50_32x4d', 'swsl_resnext101_32x4d', 'swsl_resnext101_32x8d', 'swsl_resnext101_32x16d', 'tf_efficientnet_b0', 'tf_efficientnet_b0_ap', 'tf_efficientnet_b0_ns', 'tf_efficientnet_b1', 'tf_efficientnet_b1_ap', 'tf_efficientnet_b1_ns', 'tf_efficientnet_b2', 'tf_efficientnet_b2_ap', 'tf_efficientnet_b2_ns', 'tf_efficientnet_b3', 'tf_efficientnet_b3_ap', 'tf_efficientnet_b3_ns', 'tf_efficientnet_b4', 'tf_efficientnet_b4_ap', 'tf_efficientnet_b4_ns', 'tf_efficientnet_b5', 'tf_efficientnet_b5_ap', 'tf_efficientnet_b5_ns', 'tf_efficientnet_b6', 'tf_efficientnet_b6_ap', 'tf_efficientnet_b6_ns', 'tf_efficientnet_b7', 'tf_efficientnet_b7_ap', 'tf_efficientnet_b7_ns', 'tf_efficientnet_b8', 'tf_efficientnet_b8_ap', 'tf_efficientnet_cc_b0_4e', 'tf_efficientnet_cc_b0_8e', 'tf_efficientnet_cc_b1_8e', 'tf_efficientnet_el', 'tf_efficientnet_em', 'tf_efficientnet_es', 'tf_efficientnet_l2_ns', 'tf_efficientnet_l2_ns_475', 'tf_efficientnet_lite0', 'tf_efficientnet_lite1', 'tf_efficientnet_lite2', 'tf_efficientnet_lite3', 'tf_efficientnet_lite4', 'tf_efficientnetv2_b0', 'tf_efficientnetv2_b1', 'tf_efficientnetv2_b2', 'tf_efficientnetv2_b3', 'tf_efficientnetv2_l', 'tf_efficientnetv2_l_in21ft1k', 'tf_efficientnetv2_l_in21k', 'tf_efficientnetv2_m', 'tf_efficientnetv2_m_in21ft1k', 'tf_efficientnetv2_m_in21k', 'tf_efficientnetv2_s', 'tf_efficientnetv2_s_in21ft1k', 'tf_efficientnetv2_s_in21k', 'tf_efficientnetv2_xl_in21ft1k', 'tf_efficientnetv2_xl_in21k', 'tf_inception_v3', 'tf_mixnet_l', 'tf_mixnet_m', 'tf_mixnet_s', 'tf_mobilenetv3_large_075', 'tf_mobilenetv3_large_100', 'tf_mobilenetv3_large_minimal_100', 'tf_mobilenetv3_small_075', 'tf_mobilenetv3_small_100', 'tf_mobilenetv3_small_minimal_100', 'tinynet_a', 'tinynet_b', 'tinynet_c', 'tinynet_d', 'tinynet_e', 'tnt_s_patch16_224', 'tresnet_l', 'tresnet_l_448', 'tresnet_m', 'tresnet_m_448', 'tresnet_m_miil_in21k', 'tresnet_v2_l', 'tresnet_xl', 'tresnet_xl_448', 'tv_densenet121', 'tv_resnet34', 'tv_resnet50', 'tv_resnet101', 'tv_resnet152', 'tv_resnext50_32x4d', 'twins_pcpvt_base', 'twins_pcpvt_large', 'twins_pcpvt_small', 'twins_svt_base', 'twins_svt_large', 'twins_svt_small', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', 'visformer_small', 'vit_base_patch8_224', 'vit_base_patch8_224_dino', 'vit_base_patch8_224_in21k', 'vit_base_patch16_224', 'vit_base_patch16_224_dino', 'vit_base_patch16_224_in21k', 'vit_base_patch16_224_miil', 'vit_base_patch16_224_miil_in21k', 'vit_base_patch16_224_sam', 'vit_base_patch16_384', 'vit_base_patch16_rpn_224', 'vit_base_patch32_224', 'vit_base_patch32_224_clip_laion2b', 'vit_base_patch32_224_in21k', 'vit_base_patch32_224_sam', 'vit_base_patch32_384', 'vit_base_r50_s16_224_in21k', 'vit_base_r50_s16_384', 'vit_giant_patch14_224_clip_laion2b', 'vit_huge_patch14_224_clip_laion2b', 'vit_huge_patch14_224_in21k', 'vit_large_patch14_224_clip_laion2b', 'vit_large_patch16_224', 'vit_large_patch16_224_in21k', 'vit_large_patch16_384', 'vit_large_patch32_224_in21k', 'vit_large_patch32_384', 'vit_large_r50_s32_224', 'vit_large_r50_s32_224_in21k', 'vit_large_r50_s32_384', 'vit_relpos_base_patch16_224', 'vit_relpos_base_patch16_clsgap_224', 'vit_relpos_base_patch32_plus_rpn_256', 'vit_relpos_medium_patch16_224', 'vit_relpos_medium_patch16_cls_224', 'vit_relpos_medium_patch16_rpn_224', 'vit_relpos_small_patch16_224', 'vit_small_patch8_224_dino', 'vit_small_patch16_224', 'vit_small_patch16_224_dino', 'vit_small_patch16_224_in21k', 'vit_small_patch16_384', 'vit_small_patch32_224', 'vit_small_patch32_224_in21k', 'vit_small_patch32_384', 'vit_small_r26_s32_224', 'vit_small_r26_s32_224_in21k', 'vit_small_r26_s32_384', 'vit_srelpos_medium_patch16_224', 'vit_srelpos_small_patch16_224', 'vit_tiny_patch16_224', 'vit_tiny_patch16_224_in21k', 'vit_tiny_patch16_384', 'vit_tiny_r_s16_p8_224', 'vit_tiny_r_s16_p8_224_in21k', 'vit_tiny_r_s16_p8_384', 'volo_d1_224', 'volo_d1_384', 'volo_d2_224', 'volo_d2_384', 'volo_d3_224', 'volo_d3_448', 'volo_d4_224', 'volo_d4_448', 'volo_d5_224', 'volo_d5_448', 'volo_d5_512', 'wide_resnet50_2', 'wide_resnet101_2', 'xception', 'xception41', 'xception41p', 'xception65', 'xception65p', 'xception71', 'xcit_large_24_p8_224', 'xcit_large_24_p8_224_dist', 'xcit_large_24_p8_384_dist', 'xcit_large_24_p16_224', 'xcit_large_24_p16_224_dist', 'xcit_large_24_p16_384_dist', 'xcit_medium_24_p8_224', 'xcit_medium_24_p8_224_dist', 'xcit_medium_24_p8_384_dist', 'xcit_medium_24_p16_224', 'xcit_medium_24_p16_224_dist', 'xcit_medium_24_p16_384_dist', 'xcit_nano_12_p8_224', 'xcit_nano_12_p8_224_dist', 'xcit_nano_12_p8_384_dist', 'xcit_nano_12_p16_224', 'xcit_nano_12_p16_224_dist', 'xcit_nano_12_p16_384_dist', 'xcit_small_12_p8_224', 'xcit_small_12_p8_224_dist', 'xcit_small_12_p8_384_dist', 'xcit_small_12_p16_224', 'xcit_small_12_p16_224_dist', 'xcit_small_12_p16_384_dist', 'xcit_small_24_p8_224', 'xcit_small_24_p8_224_dist', 'xcit_small_24_p8_384_dist', 'xcit_small_24_p16_224', 'xcit_small_24_p16_224_dist', 'xcit_small_24_p16_384_dist', 'xcit_tiny_12_p8_224', 'xcit_tiny_12_p8_224_dist', 'xcit_tiny_12_p8_384_dist', 'xcit_tiny_12_p16_224', 'xcit_tiny_12_p16_224_dist', 'xcit_tiny_12_p16_384_dist', 'xcit_tiny_24_p8_224', 'xcit_tiny_24_p8_224_dist', 'xcit_tiny_24_p8_384_dist', 'xcit_tiny_24_p16_224', 'xcit_tiny_24_p16_224_dist', 'xcit_tiny_24_p16_384_dist']\n"
          ]
        }
      ],
      "source": [
        "# 查看 timm 中支持的模型\n",
        "import timm\n",
        "model_names = timm.list_models(pretrained=True)\n",
        "print(f'num of models: {len(model_names)}')\n",
        "print(model_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbT36avNnppt"
      },
      "source": [
        "如果想将 timm 中 `mobilevitv2_050` 作为 `YOLOv5` 的主干网络，则配置文件如下："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KjYbb-LTmqH1"
      },
      "outputs": [],
      "source": [
        "_base_ = './yolov5_s-v61_1xb2-1e_coco128.py'\n",
        "\n",
        "# 导入 mmcls.models 使得可以调用 mmcls 中注册的模块\n",
        "custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)\n",
        "\n",
        "widen_factor = 1.0\n",
        "channels = [128, 192, 256]\n",
        "\n",
        "model = dict(\n",
        "    backbone=dict(\n",
        "        _delete_=True,  # 将 _base_ 中关于 backbone 的字段删除\n",
        "        type='mmcls.TIMMBackbone',  # 使用 mmcls 中的 timm 主干网络\n",
        "        model_name='mobilevitv2_050',  # 使用 TIMM 中的 mobilevitv2_050\n",
        "        features_only=True,\n",
        "        pretrained=True,\n",
        "        out_indices=(2, 3, 4)),\n",
        "    neck=dict(\n",
        "        type='YOLOv5PAFPN',\n",
        "        widen_factor=widen_factor,\n",
        "        in_channels=channels,  # 注意：mobilevitv2_050 输出的3个通道是 [128, 192, 256]，和原先的 yolov5-s neck 不匹配，需要更改\n",
        "        out_channels=channels),\n",
        "    bbox_head=dict(\n",
        "        type='YOLOv5Head',\n",
        "        head_module=dict(\n",
        "            type='YOLOv5HeadModule',\n",
        "            in_channels=channels,  # head 部分输入通道也要做相应更改\n",
        "            widen_factor=widen_factor))\n",
        ")\n",
        "\n",
        "config_mobilevitv2_050 = f\"\"\"\n",
        "_base_=\\'{_base_}\\'\n",
        "widen_factor={widen_factor}\n",
        "model={model}\n",
        "\"\"\"\n",
        "\n",
        "with open('./configs/yolov5/yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_mobilevitv2_050)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLIcdPKNoaHM",
        "outputId": "33724fe3-7d80-4732-c1df-c89294e55f09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11/19 11:32:01 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmyolo\" in the \"log_processor\" registry tree. As a workaround, the current \"log_processor\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmyolo\" is a correct scope, or whether the registry is initialized.\n",
            "11/19 11:32:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 31316808\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.2, V11.2.152\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.12.1+cu113\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.3\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  (built against CUDA 11.5)\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, 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: 0.13.1+cu113\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.3.1\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: True\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: None\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "11/19 11:32:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "default_scope = 'mmyolo'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(\n",
            "        type='YOLOv5ParamSchedulerHook',\n",
            "        scheduler_type='linear',\n",
            "        lr_factor=0.01,\n",
            "        max_epochs=1),\n",
            "    checkpoint=dict(\n",
            "        type='CheckpointHook', interval=10, save_best='auto',\n",
            "        max_keep_ckpts=3),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook'))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=True,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='visualizer')\n",
            "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "file_client_args = dict(backend='disk')\n",
            "data_root = 'data/coco/'\n",
            "dataset_type = 'YOLOv5CocoDataset'\n",
            "num_classes = 80\n",
            "img_scale = (640, 640)\n",
            "deepen_factor = 0.33\n",
            "widen_factor = 1.0\n",
            "max_epochs = 1\n",
            "save_epoch_intervals = 10\n",
            "train_batch_size_per_gpu = 2\n",
            "train_num_workers = 2\n",
            "val_batch_size_per_gpu = 1\n",
            "val_num_workers = 2\n",
            "persistent_workers = True\n",
            "batch_shapes_cfg = dict(\n",
            "    type='BatchShapePolicy',\n",
            "    batch_size=1,\n",
            "    img_size=640,\n",
            "    size_divisor=32,\n",
            "    extra_pad_ratio=0.5)\n",
            "anchors = [[(10, 13), (16, 30), (33, 23)], [(30, 61), (62, 45), (59, 119)],\n",
            "           [(116, 90), (156, 198), (373, 326)]]\n",
            "strides = [8, 16, 32]\n",
            "num_det_layers = 3\n",
            "model = dict(\n",
            "    type='YOLODetector',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmdet.DetDataPreprocessor',\n",
            "        mean=[0.0, 0.0, 0.0],\n",
            "        std=[255.0, 255.0, 255.0],\n",
            "        bgr_to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmcls.TIMMBackbone',\n",
            "        model_name='mobilevitv2_050',\n",
            "        features_only=True,\n",
            "        pretrained=True,\n",
            "        out_indices=(2, 3, 4)),\n",
            "    neck=dict(\n",
            "        type='YOLOv5PAFPN',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=1.0,\n",
            "        in_channels=[128, 192, 256],\n",
            "        out_channels=[128, 192, 256],\n",
            "        num_csp_blocks=3,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='SiLU', inplace=True)),\n",
            "    bbox_head=dict(\n",
            "        type='YOLOv5Head',\n",
            "        head_module=dict(\n",
            "            type='YOLOv5HeadModule',\n",
            "            num_classes=80,\n",
            "            in_channels=[128, 192, 256],\n",
            "            widen_factor=1.0,\n",
            "            featmap_strides=[8, 16, 32],\n",
            "            num_base_priors=3),\n",
            "        prior_generator=dict(\n",
            "            type='mmdet.YOLOAnchorGenerator',\n",
            "            base_sizes=[[(10, 13), (16, 30), (33, 23)],\n",
            "                        [(30, 61), (62, 45), (59, 119)],\n",
            "                        [(116, 90), (156, 198), (373, 326)]],\n",
            "            strides=[8, 16, 32]),\n",
            "        loss_cls=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.5),\n",
            "        loss_bbox=dict(\n",
            "            type='IoULoss',\n",
            "            iou_mode='ciou',\n",
            "            bbox_format='xywh',\n",
            "            eps=1e-07,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.05,\n",
            "            return_iou=True),\n",
            "        loss_obj=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=1.0),\n",
            "        prior_match_thr=4.0,\n",
            "        obj_level_weights=[4.0, 1.0, 0.4]),\n",
            "    test_cfg=dict(\n",
            "        multi_label=True,\n",
            "        nms_pre=30000,\n",
            "        score_thr=0.001,\n",
            "        nms=dict(type='nms', iou_threshold=0.65),\n",
            "        max_per_img=300))\n",
            "albu_train_transforms = [\n",
            "    dict(type='Blur', p=0.01),\n",
            "    dict(type='MedianBlur', p=0.01),\n",
            "    dict(type='ToGray', p=0.01),\n",
            "    dict(type='CLAHE', p=0.01)\n",
            "]\n",
            "pre_transform = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True)\n",
            "]\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='Mosaic',\n",
            "        img_scale=(640, 640),\n",
            "        pad_val=114.0,\n",
            "        pre_transform=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='YOLOv5RandomAffine',\n",
            "        max_rotate_degree=0.0,\n",
            "        max_shear_degree=0.0,\n",
            "        scaling_ratio_range=(0.5, 1.5),\n",
            "        border=(-320, -320),\n",
            "        border_val=(114, 114, 114)),\n",
            "    dict(\n",
            "        type='mmdet.Albu',\n",
            "        transforms=[\n",
            "            dict(type='Blur', p=0.01),\n",
            "            dict(type='MedianBlur', p=0.01),\n",
            "            dict(type='ToGray', p=0.01),\n",
            "            dict(type='CLAHE', p=0.01)\n",
            "        ],\n",
            "        bbox_params=dict(\n",
            "            type='BboxParams',\n",
            "            format='pascal_voc',\n",
            "            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "        keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "    dict(type='YOLOv5HSVRandomAug'),\n",
            "    dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',\n",
            "                   'flip_direction'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        ann_file='annotations/instances_train2017.json',\n",
            "        data_prefix=dict(img='train2017/'),\n",
            "        filter_cfg=dict(filter_empty_gt=False, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='Mosaic',\n",
            "                img_scale=(640, 640),\n",
            "                pad_val=114.0,\n",
            "                pre_transform=[\n",
            "                    dict(\n",
            "                        type='LoadImageFromFile',\n",
            "                        file_client_args=dict(backend='disk')),\n",
            "                    dict(type='LoadAnnotations', with_bbox=True)\n",
            "                ]),\n",
            "            dict(\n",
            "                type='YOLOv5RandomAffine',\n",
            "                max_rotate_degree=0.0,\n",
            "                max_shear_degree=0.0,\n",
            "                scaling_ratio_range=(0.5, 1.5),\n",
            "                border=(-320, -320),\n",
            "                border_val=(114, 114, 114)),\n",
            "            dict(\n",
            "                type='mmdet.Albu',\n",
            "                transforms=[\n",
            "                    dict(type='Blur', p=0.01),\n",
            "                    dict(type='MedianBlur', p=0.01),\n",
            "                    dict(type='ToGray', p=0.01),\n",
            "                    dict(type='CLAHE', p=0.01)\n",
            "                ],\n",
            "                bbox_params=dict(\n",
            "                    type='BboxParams',\n",
            "                    format='pascal_voc',\n",
            "                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "                keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "            dict(type='YOLOv5HSVRandomAug'),\n",
            "            dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'flip', 'flip_direction'))\n",
            "        ]))\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "    dict(\n",
            "        type='LetterResize',\n",
            "        scale=(640, 640),\n",
            "        allow_scale_up=False,\n",
            "        pad_val=dict(img=114)),\n",
            "    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor', 'pad_param'))\n",
            "]\n",
            "val_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "param_scheduler = None\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(\n",
            "        type='SGD',\n",
            "        lr=0.01,\n",
            "        momentum=0.937,\n",
            "        weight_decay=0.0005,\n",
            "        nesterov=True,\n",
            "        batch_size_per_gpu=2),\n",
            "    constructor='YOLOv5OptimizerConstructor')\n",
            "custom_hooks = [\n",
            "    dict(\n",
            "        type='EMAHook',\n",
            "        ema_type='ExpMomentumEMA',\n",
            "        momentum=0.0001,\n",
            "        update_buffers=True,\n",
            "        strict_load=False,\n",
            "        priority=49)\n",
            "]\n",
            "val_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "test_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1, val_interval=10)\n",
            "val_cfg = dict(type='ValLoop')\n",
            "test_cfg = dict(type='TestLoop')\n",
            "launcher = 'none'\n",
            "work_dir = './work_dirs/yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128'\n",
            "\n",
            "11/19 11:32:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Result has been saved to /content/mmyolo/work_dirs/yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128/modules_statistic_results.json\n",
            "11/19 11:32:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - backbone out_indices: (2, 3, 4)\n",
            "11/19 11:32:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - backbone out_channels: [128, 192, 256]\n",
            "11/19 11:32:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - backbone out_strides: [8, 16, 32]\n",
            "11/19 11:32:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "/usr/local/lib/python3.7/dist-packages/mmengine/model/base_module.py:124: UserWarning: init_weights of TIMMBackbone has been called more than once.\n",
            "  warnings.warn(f'init_weights of {self.__class__.__name__} has '\n",
            "11/19 11:32:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmyolo/work_dirs/yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128.\n",
            "11/19 11:32:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/63]  lr: 4.9000e-04  eta: 0:00:02  time: 0.2264  data_time: 0.0062  memory: 5409  loss: 0.5606  loss_cls: 0.2027  loss_obj: 0.1421  loss_bbox: 0.2158\n",
            "11/19 11:32:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128_20221119_113201\n",
            "11/19 11:32:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "11/19 11:32:21 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers\n"
          ]
        }
      ],
      "source": [
        "# 启动训练\n",
        "!python tools/train.py configs/yolov5/yolov5_s_mobilevitv2_050-v61_1xb2-1e_coco128.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2_3IshhsDj_"
      },
      "source": [
        "### 2.4 使用在 MMSelfSup 中实现的主干网络（MoCo v3-ResNet）\n",
        "目前 MMSelfSup [![GitHub release](https://img.shields.io/github/release/open-mmlab/mmselfsup.svg)](https://GitHub.com/open-mmlab/mmselfsup/) 已支持 18 种自监督算法：\n",
        "\n",
        "<details open>\n",
        "<summary>Supported algorithms</summary>\n",
        "\n",
        "- [x] [Relative Location (ICCV'2015)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/relative_loc)\n",
        "- [x] [Rotation Prediction (ICLR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/rotation_pred)\n",
        "- [x] [DeepCluster (ECCV'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/deepcluster)\n",
        "- [x] [NPID (CVPR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/npid)\n",
        "- [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/odc)\n",
        "- [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov1)\n",
        "- [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simclr)\n",
        "- [x] [MoCo v2 (ArXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/byol)\n",
        "- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov2)\n",
        "- [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/swav)\n",
        "- [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/densecl)\n",
        "- [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simsiam)\n",
        "- [x] [Barlow Twins (ICML'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/barlowtwins)\n",
        "- [x] [MoCo v3 (ICCV'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov3)\n",
        "- [x] [MAE (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mae)\n",
        "- [x] [SimMIM (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simmim)\n",
        "- [x] [MaskFeat (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/maskfeat)\n",
        "- [x] [CAE (ArXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/cae)\n",
        "\n",
        "</details>\n",
        "\n",
        "如果想要使用在 MMSelfSup 中实现的主干网络，需要先安装 mmselfsup。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8HNMjaHsBK0",
        "outputId": "7b3b9942-ed60-4c58-fc7d-ae501a9b3766"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html\n",
            "Collecting mmselfsup>=1.0.0rc3\n",
            "  Downloading mmselfsup-1.0.0rc3-py3-none-any.whl (348 kB)\n",
            "\u001b[K     |████████████████████████████████| 348 kB 23.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (1.0.2)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (22.1.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (21.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (3.2.2)\n",
            "Requirement already satisfied: mmcls>=1.0.0rc0 in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (1.0.0rc2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (1.15.0)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (2.9.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (0.16.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (1.7.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (4.64.1)\n",
            "Requirement already satisfied: mmdet<3.1.0,>=3.0.0rc0 in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (3.0.0rc3)\n",
            "Collecting mmsegmentation<1.1.0,>=1.0.0rc0\n",
            "  Downloading mmsegmentation-1.0.0rc1-py3-none-any.whl (806 kB)\n",
            "\u001b[K     |████████████████████████████████| 806 kB 62.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: mmcv<2.1.0,>=2.0.0rc1 in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (2.0.0rc2)\n",
            "Requirement already satisfied: mmengine<1.0.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from mmselfsup>=1.0.0rc3) (0.3.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.7/dist-packages (from mmcls>=1.0.0rc0->mmselfsup>=1.0.0rc3) (12.6.0)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmselfsup>=1.0.0rc3) (4.6.0.66)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmselfsup>=1.0.0rc3) (0.32.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmselfsup>=1.0.0rc3) (7.1.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmselfsup>=1.0.0rc3) (6.0)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv<2.1.0,>=2.0.0rc1->mmselfsup>=1.0.0rc3) (2.4.0)\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from mmdet<3.1.0,>=3.0.0rc0->mmselfsup>=1.0.0rc3) (2.0.6)\n",
            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from mmdet<3.1.0,>=3.0.0rc0->mmselfsup>=1.0.0rc3) (3.1.10)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from mmengine<1.0.0,>=0.1.0->mmselfsup>=1.0.0rc3) (2.1.0)\n",
            "Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from mmsegmentation<1.1.0,>=1.0.0rc0->mmselfsup>=1.0.0rc3) (3.5.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmselfsup>=1.0.0rc3) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmselfsup>=1.0.0rc3) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmselfsup>=1.0.0rc3) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmselfsup>=1.0.0rc3) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmselfsup>=1.0.0rc3) (4.1.1)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->prettytable->mmsegmentation<1.1.0,>=1.0.0rc0->mmselfsup>=1.0.0rc3) (3.10.0)\n",
            "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from rich->mmcls>=1.0.0rc0->mmselfsup>=1.0.0rc3) (0.9.1)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->mmcls>=1.0.0rc0->mmselfsup>=1.0.0rc3) (2.6.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->mmselfsup>=1.0.0rc3) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->mmselfsup>=1.0.0rc3) (3.1.0)\n",
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            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->mmselfsup>=1.0.0rc3) (1.0.1)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->mmselfsup>=1.0.0rc3) (3.4.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->mmselfsup>=1.0.0rc3) (0.4.6)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->mmselfsup>=1.0.0rc3) (1.8.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->mmselfsup>=1.0.0rc3) (57.4.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->mmselfsup>=1.0.0rc3) (5.2.0)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmselfsup>=1.0.0rc3) (1.3.1)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmselfsup>=1.0.0rc3) (1.24.3)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmselfsup>=1.0.0rc3) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmselfsup>=1.0.0rc3) (2022.9.24)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmselfsup>=1.0.0rc3) (3.2.2)\n",
            "Installing collected packages: mmsegmentation, mmselfsup\n",
            "Successfully installed mmsegmentation-1.0.0rc1 mmselfsup-1.0.0rc3\n"
          ]
        }
      ],
      "source": [
        "# 安装 mmselfsup\n",
        "!mim install \"mmselfsup>=1.0.0rc3\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0qxhim1sN_N"
      },
      "source": [
        "如果想将 MMSelfSup 中 `MoCo v3`  自监督训练的 `ResNet-50` 作为 `YOLOv5` 的主干网络，则配置文件如下："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dTm6fVN0sLpH"
      },
      "outputs": [],
      "source": [
        "_base_ = './yolov5_s-v61_1xb2-1e_coco128.py'\n",
        "\n",
        "# 导入 mmselfsup.models 使得可以调用 mmselfsup 中注册的模块\n",
        "custom_imports = dict(imports=['mmselfsup.models'], allow_failed_imports=False)\n",
        "checkpoint_file = 'https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth'  # noqa\n",
        "widen_factor = 1.0\n",
        "channels = [512, 1024, 2048]\n",
        "\n",
        "model = dict(\n",
        "    backbone=dict(\n",
        "        _delete_=True, # 将 _base_ 中关于 backbone 的字段删除\n",
        "        type='mmselfsup.ResNet',\n",
        "        depth=50,\n",
        "        num_stages=4,\n",
        "        out_indices=(2, 3, 4), # 注意：MMSelfSup 中 ResNet 的 out_indices 比 MMdet 和 MMCls 的要大 1\n",
        "        frozen_stages=1,\n",
        "        norm_cfg=dict(type='BN', requires_grad=True),\n",
        "        norm_eval=True,\n",
        "        style='pytorch',\n",
        "        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),\n",
        "    neck=dict(\n",
        "        type='YOLOv5PAFPN',\n",
        "        widen_factor=widen_factor,\n",
        "        in_channels=channels, # 注意：ResNet-50 输出的3个通道是 [512, 1024, 2048]，和原先的 yolov5-s neck 不匹配，需要更改\n",
        "        out_channels=channels),\n",
        "    bbox_head=dict(\n",
        "        type='YOLOv5Head',\n",
        "        head_module=dict(\n",
        "            type='YOLOv5HeadModule',\n",
        "            in_channels=channels, # head 部分输入通道也要做相应更改\n",
        "            widen_factor=widen_factor))\n",
        ")\n",
        "\n",
        "config_res50_mocov3 = f\"\"\"\n",
        "_base_=\\'{_base_}\\'\n",
        "widen_factor={widen_factor}\n",
        "checkpoint_file=\\'{checkpoint_file}\\'\n",
        "model={model}\n",
        "\"\"\"\n",
        "with open('./configs/yolov5/yolov5_s_res50_mocov3-v61_1xb2-1e_coco128.py', 'w') as f:\n",
        "    f.write(config_res50_mocov3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dLIVIzOsZtc",
        "outputId": "b90d6c77-138f-4f78-95b1-d77051df040d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11/19 11:33:24 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmyolo\" in the \"log_processor\" registry tree. As a workaround, the current \"log_processor\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmyolo\" is a correct scope, or whether the registry is initialized.\n",
            "11/19 11:33:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
            "    CUDA available: True\n",
            "    numpy_random_seed: 918106889\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 11.2, V11.2.152\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "    PyTorch: 1.12.1+cu113\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.3\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  (built against CUDA 11.5)\n",
            "  - Magma 2.5.2\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, 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: 0.13.1+cu113\n",
            "    OpenCV: 4.6.0\n",
            "    MMEngine: 0.3.1\n",
            "\n",
            "Runtime environment:\n",
            "    cudnn_benchmark: True\n",
            "    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n",
            "    dist_cfg: {'backend': 'nccl'}\n",
            "    seed: None\n",
            "    Distributed launcher: none\n",
            "    Distributed training: False\n",
            "    GPU number: 1\n",
            "------------------------------------------------------------\n",
            "\n",
            "11/19 11:33:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n",
            "default_scope = 'mmyolo'\n",
            "default_hooks = dict(\n",
            "    timer=dict(type='IterTimerHook'),\n",
            "    logger=dict(type='LoggerHook', interval=50),\n",
            "    param_scheduler=dict(\n",
            "        type='YOLOv5ParamSchedulerHook',\n",
            "        scheduler_type='linear',\n",
            "        lr_factor=0.01,\n",
            "        max_epochs=1),\n",
            "    checkpoint=dict(\n",
            "        type='CheckpointHook', interval=10, save_best='auto',\n",
            "        max_keep_ckpts=3),\n",
            "    sampler_seed=dict(type='DistSamplerSeedHook'),\n",
            "    visualization=dict(type='mmdet.DetVisualizationHook'))\n",
            "env_cfg = dict(\n",
            "    cudnn_benchmark=True,\n",
            "    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n",
            "    dist_cfg=dict(backend='nccl'))\n",
            "vis_backends = [dict(type='LocalVisBackend')]\n",
            "visualizer = dict(\n",
            "    type='mmdet.DetLocalVisualizer',\n",
            "    vis_backends=[dict(type='LocalVisBackend')],\n",
            "    name='visualizer')\n",
            "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n",
            "log_level = 'INFO'\n",
            "load_from = None\n",
            "resume = False\n",
            "file_client_args = dict(backend='disk')\n",
            "data_root = 'data/coco/'\n",
            "dataset_type = 'YOLOv5CocoDataset'\n",
            "num_classes = 80\n",
            "img_scale = (640, 640)\n",
            "deepen_factor = 0.33\n",
            "widen_factor = 1.0\n",
            "max_epochs = 1\n",
            "save_epoch_intervals = 10\n",
            "train_batch_size_per_gpu = 2\n",
            "train_num_workers = 2\n",
            "val_batch_size_per_gpu = 1\n",
            "val_num_workers = 2\n",
            "persistent_workers = True\n",
            "batch_shapes_cfg = dict(\n",
            "    type='BatchShapePolicy',\n",
            "    batch_size=1,\n",
            "    img_size=640,\n",
            "    size_divisor=32,\n",
            "    extra_pad_ratio=0.5)\n",
            "anchors = [[(10, 13), (16, 30), (33, 23)], [(30, 61), (62, 45), (59, 119)],\n",
            "           [(116, 90), (156, 198), (373, 326)]]\n",
            "strides = [8, 16, 32]\n",
            "num_det_layers = 3\n",
            "model = dict(\n",
            "    type='YOLODetector',\n",
            "    data_preprocessor=dict(\n",
            "        type='mmdet.DetDataPreprocessor',\n",
            "        mean=[0.0, 0.0, 0.0],\n",
            "        std=[255.0, 255.0, 255.0],\n",
            "        bgr_to_rgb=True),\n",
            "    backbone=dict(\n",
            "        type='mmselfsup.ResNet',\n",
            "        depth=50,\n",
            "        num_stages=4,\n",
            "        out_indices=(2, 3, 4),\n",
            "        frozen_stages=1,\n",
            "        norm_cfg=dict(type='BN', requires_grad=True),\n",
            "        norm_eval=True,\n",
            "        style='pytorch',\n",
            "        init_cfg=dict(\n",
            "            type='Pretrained',\n",
            "            checkpoint=\n",
            "            'https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth'\n",
            "        )),\n",
            "    neck=dict(\n",
            "        type='YOLOv5PAFPN',\n",
            "        deepen_factor=0.33,\n",
            "        widen_factor=1.0,\n",
            "        in_channels=[512, 1024, 2048],\n",
            "        out_channels=[512, 1024, 2048],\n",
            "        num_csp_blocks=3,\n",
            "        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),\n",
            "        act_cfg=dict(type='SiLU', inplace=True)),\n",
            "    bbox_head=dict(\n",
            "        type='YOLOv5Head',\n",
            "        head_module=dict(\n",
            "            type='YOLOv5HeadModule',\n",
            "            num_classes=80,\n",
            "            in_channels=[512, 1024, 2048],\n",
            "            widen_factor=1.0,\n",
            "            featmap_strides=[8, 16, 32],\n",
            "            num_base_priors=3),\n",
            "        prior_generator=dict(\n",
            "            type='mmdet.YOLOAnchorGenerator',\n",
            "            base_sizes=[[(10, 13), (16, 30), (33, 23)],\n",
            "                        [(30, 61), (62, 45), (59, 119)],\n",
            "                        [(116, 90), (156, 198), (373, 326)]],\n",
            "            strides=[8, 16, 32]),\n",
            "        loss_cls=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.5),\n",
            "        loss_bbox=dict(\n",
            "            type='IoULoss',\n",
            "            iou_mode='ciou',\n",
            "            bbox_format='xywh',\n",
            "            eps=1e-07,\n",
            "            reduction='mean',\n",
            "            loss_weight=0.05,\n",
            "            return_iou=True),\n",
            "        loss_obj=dict(\n",
            "            type='mmdet.CrossEntropyLoss',\n",
            "            use_sigmoid=True,\n",
            "            reduction='mean',\n",
            "            loss_weight=1.0),\n",
            "        prior_match_thr=4.0,\n",
            "        obj_level_weights=[4.0, 1.0, 0.4]),\n",
            "    test_cfg=dict(\n",
            "        multi_label=True,\n",
            "        nms_pre=30000,\n",
            "        score_thr=0.001,\n",
            "        nms=dict(type='nms', iou_threshold=0.65),\n",
            "        max_per_img=300))\n",
            "albu_train_transforms = [\n",
            "    dict(type='Blur', p=0.01),\n",
            "    dict(type='MedianBlur', p=0.01),\n",
            "    dict(type='ToGray', p=0.01),\n",
            "    dict(type='CLAHE', p=0.01)\n",
            "]\n",
            "pre_transform = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True)\n",
            "]\n",
            "train_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='LoadAnnotations', with_bbox=True),\n",
            "    dict(\n",
            "        type='Mosaic',\n",
            "        img_scale=(640, 640),\n",
            "        pad_val=114.0,\n",
            "        pre_transform=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True)\n",
            "        ]),\n",
            "    dict(\n",
            "        type='YOLOv5RandomAffine',\n",
            "        max_rotate_degree=0.0,\n",
            "        max_shear_degree=0.0,\n",
            "        scaling_ratio_range=(0.5, 1.5),\n",
            "        border=(-320, -320),\n",
            "        border_val=(114, 114, 114)),\n",
            "    dict(\n",
            "        type='mmdet.Albu',\n",
            "        transforms=[\n",
            "            dict(type='Blur', p=0.01),\n",
            "            dict(type='MedianBlur', p=0.01),\n",
            "            dict(type='ToGray', p=0.01),\n",
            "            dict(type='CLAHE', p=0.01)\n",
            "        ],\n",
            "        bbox_params=dict(\n",
            "            type='BboxParams',\n",
            "            format='pascal_voc',\n",
            "            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "        keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "    dict(type='YOLOv5HSVRandomAug'),\n",
            "    dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',\n",
            "                   'flip_direction'))\n",
            "]\n",
            "train_dataloader = dict(\n",
            "    batch_size=2,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=True),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        ann_file='annotations/instances_train2017.json',\n",
            "        data_prefix=dict(img='train2017/'),\n",
            "        filter_cfg=dict(filter_empty_gt=False, min_size=32),\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='LoadAnnotations', with_bbox=True),\n",
            "            dict(\n",
            "                type='Mosaic',\n",
            "                img_scale=(640, 640),\n",
            "                pad_val=114.0,\n",
            "                pre_transform=[\n",
            "                    dict(\n",
            "                        type='LoadImageFromFile',\n",
            "                        file_client_args=dict(backend='disk')),\n",
            "                    dict(type='LoadAnnotations', with_bbox=True)\n",
            "                ]),\n",
            "            dict(\n",
            "                type='YOLOv5RandomAffine',\n",
            "                max_rotate_degree=0.0,\n",
            "                max_shear_degree=0.0,\n",
            "                scaling_ratio_range=(0.5, 1.5),\n",
            "                border=(-320, -320),\n",
            "                border_val=(114, 114, 114)),\n",
            "            dict(\n",
            "                type='mmdet.Albu',\n",
            "                transforms=[\n",
            "                    dict(type='Blur', p=0.01),\n",
            "                    dict(type='MedianBlur', p=0.01),\n",
            "                    dict(type='ToGray', p=0.01),\n",
            "                    dict(type='CLAHE', p=0.01)\n",
            "                ],\n",
            "                bbox_params=dict(\n",
            "                    type='BboxParams',\n",
            "                    format='pascal_voc',\n",
            "                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),\n",
            "                keymap=dict(img='image', gt_bboxes='bboxes')),\n",
            "            dict(type='YOLOv5HSVRandomAug'),\n",
            "            dict(type='mmdet.RandomFlip', prob=0.5),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'flip', 'flip_direction'))\n",
            "        ]))\n",
            "test_pipeline = [\n",
            "    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n",
            "    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "    dict(\n",
            "        type='LetterResize',\n",
            "        scale=(640, 640),\n",
            "        allow_scale_up=False,\n",
            "        pad_val=dict(img=114)),\n",
            "    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "    dict(\n",
            "        type='mmdet.PackDetInputs',\n",
            "        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                   'scale_factor', 'pad_param'))\n",
            "]\n",
            "val_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "test_dataloader = dict(\n",
            "    batch_size=1,\n",
            "    num_workers=2,\n",
            "    persistent_workers=True,\n",
            "    pin_memory=True,\n",
            "    drop_last=False,\n",
            "    sampler=dict(type='DefaultSampler', shuffle=False),\n",
            "    dataset=dict(\n",
            "        type='YOLOv5CocoDataset',\n",
            "        data_root='data/coco/',\n",
            "        test_mode=True,\n",
            "        data_prefix=dict(img='val2017/'),\n",
            "        ann_file='annotations/instances_val2017.json',\n",
            "        pipeline=[\n",
            "            dict(\n",
            "                type='LoadImageFromFile',\n",
            "                file_client_args=dict(backend='disk')),\n",
            "            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),\n",
            "            dict(\n",
            "                type='LetterResize',\n",
            "                scale=(640, 640),\n",
            "                allow_scale_up=False,\n",
            "                pad_val=dict(img=114)),\n",
            "            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),\n",
            "            dict(\n",
            "                type='mmdet.PackDetInputs',\n",
            "                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n",
            "                           'scale_factor', 'pad_param'))\n",
            "        ],\n",
            "        batch_shapes_cfg=dict(\n",
            "            type='BatchShapePolicy',\n",
            "            batch_size=1,\n",
            "            img_size=640,\n",
            "            size_divisor=32,\n",
            "            extra_pad_ratio=0.5)))\n",
            "param_scheduler = None\n",
            "optim_wrapper = dict(\n",
            "    type='OptimWrapper',\n",
            "    optimizer=dict(\n",
            "        type='SGD',\n",
            "        lr=0.01,\n",
            "        momentum=0.937,\n",
            "        weight_decay=0.0005,\n",
            "        nesterov=True,\n",
            "        batch_size_per_gpu=2),\n",
            "    constructor='YOLOv5OptimizerConstructor')\n",
            "custom_hooks = [\n",
            "    dict(\n",
            "        type='EMAHook',\n",
            "        ema_type='ExpMomentumEMA',\n",
            "        momentum=0.0001,\n",
            "        update_buffers=True,\n",
            "        strict_load=False,\n",
            "        priority=49)\n",
            "]\n",
            "val_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "test_evaluator = dict(\n",
            "    type='mmdet.CocoMetric',\n",
            "    proposal_nums=(100, 1, 10),\n",
            "    ann_file='data/coco/annotations/instances_val2017.json',\n",
            "    metric='bbox')\n",
            "train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1, val_interval=10)\n",
            "val_cfg = dict(type='ValLoop')\n",
            "test_cfg = dict(type='TestLoop')\n",
            "checkpoint_file = 'https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth'\n",
            "launcher = 'none'\n",
            "work_dir = './work_dirs/yolov5_s_res50_mocov3-v61_1xb2-1e_coco128'\n",
            "\n",
            "11/19 11:33:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Result has been saved to /content/mmyolo/work_dirs/yolov5_s_res50_mocov3-v61_1xb2-1e_coco128/modules_statistic_results.json\n",
            "11/19 11:33:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "11/19 11:33:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth\n",
            "11/19 11:33:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - http loads checkpoint from path: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth\n",
            "11/19 11:33:34 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The model and loaded state dict do not match exactly\n",
            "\n",
            "unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std, backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.conv3.weight, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.num_batches_tracked, 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layer4.2.conv1.weight, layer4.2.bn1.weight, layer4.2.bn1.bias, layer4.2.bn1.running_mean, layer4.2.bn1.running_var, layer4.2.conv2.weight, layer4.2.bn2.weight, layer4.2.bn2.bias, layer4.2.bn2.running_mean, layer4.2.bn2.running_var, layer4.2.conv3.weight, layer4.2.bn3.weight, layer4.2.bn3.bias, layer4.2.bn3.running_mean, layer4.2.bn3.running_var\n",
            "\n",
            "11/19 11:33:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmyolo/work_dirs/yolov5_s_res50_mocov3-v61_1xb2-1e_coco128.\n",
            "11/19 11:34:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/63]  lr: 4.9000e-04  eta: 0:00:07  time: 0.5597  data_time: 0.0066  memory: 10724  loss: 0.5685  loss_cls: 0.2051  loss_obj: 0.1493  loss_bbox: 0.2142\n",
            "11/19 11:34:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: yolov5_s_res50_mocov3-v61_1xb2-1e_coco128_20221119_113324\n",
            "11/19 11:34:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n",
            "11/19 11:34:08 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers\n",
            "tcmalloc: large alloc 1085169664 bytes == 0xb7884000 @  0x7fca5fe3f615 0x58ead6 0x4f355e 0x58f8af 0x58fb26 0x7fca39d8a07f 0x7fca39d5a974 0x7fca13a84ef5 0x7fca13a7f441 0x7fca13a86549 0x7fca39d5af3b 0x7fca399e4f61 0x58f6e4 0x590691 0x510946 0x5b575e 0x58ff2e 0x50c4fc 0x5b4ee6 0x58ff2e 0x510325 0x5b4ee6 0x58ff2e 0x50c4fc 0x5b4ee6 0x4bad0a 0x50e18c 0x5b575e 0x4bad0a 0x4d3249 0x591e56\n"
          ]
        }
      ],
      "source": [
        "# 启动训练\n",
        "!python tools/train.py configs/yolov5/yolov5_s_res50_mocov3-v61_1xb2-1e_coco128.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4Pz4W9juPNt"
      },
      "source": [
        "## 3 彩蛋：如何确定主干网络输出通道数\n",
        "PPYOLO-E 最大模型 x 中的 `widen_factor` 为 1.25。假设我们想要构建一个更大的网络，将  `widen_factor` 设为 1.5，此时其主干网络 `PPYOLOECSPResNet` 的输出通道数会是多少呢？\n",
        "\n",
        "下面我们就来看一下。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bMQ5T9gseWL",
        "outputId": "ca64d6bf-5169-4895-acd6-93e72bc8786c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/hsigmoid.py:36: UserWarning: In MMCV v1.4.4, we modified the default value of args to align with PyTorch official. Previous Implementation: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1). Current Implementation: Hsigmoid(x) = min(max((x + 3) / 6, 0), 1).\n",
            "  'In MMCV v1.4.4, we modified the default value of args to align '\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[torch.Size([1, 384, 80, 80]), torch.Size([1, 768, 40, 40]), torch.Size([1, 1536, 20, 20])]\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from mmyolo.models import PPYOLOECSPResNet\n",
        "from mmyolo.utils import register_all_modules\n",
        "\n",
        "# 注册所有模块\n",
        "register_all_modules()\n",
        "\n",
        "imgs = torch.randn(1, 3, 640, 640)\n",
        "out_indices=(2, 3, 4)\n",
        "model = PPYOLOECSPResNet(arch='P5', widen_factor=1.5, out_indices=out_indices)\n",
        "out = model(imgs)\n",
        "out_shapes = [out[i].shape for i in range(len(out_indices))]\n",
        "print(out_shapes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiAxPHXSr1O_"
      },
      "source": [
        "**小问题：**\n",
        "\n",
        "为什么第二节的配置文件中 `widen_factor` 都要设置为 1.0？是必须的吗？"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMu-ZotLNsPo"
      },
      "source": [
        " ## 4 总结\n",
        "- 本教程以 YOLOv5 算法为例简短的介绍了如何更换各种主干网络。更多教程和示例详见 MMYOLO 的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/)。\n",
        "\n",
        "- 如果你有任何需求或者建议，欢迎在 MMYOLO [开发计划](https://github.com/open-mmlab/mmyolo/issues/136) 中留言。也欢迎添加小助手微信 **OpenMMLabwx**，邀你加入 MMYOLO 微信群，每天都有热心大佬在线答疑哦~\n",
        "\n",
        "- 如果觉得 MMYOLO 好用的话帮点个 [![GitHub stars](https://img.shields.io/github/stars/open-mmlab/mmyolo.svg?style=social&label=Star&maxAge=2592000)](https://github.com/open-mmlab/mmyolo)，你的支持就是我们动力。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ByJUYUy4vVCU"
      },
      "outputs": [],
      "source": []
    }
  ],
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