{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F77yOqgkX8p4"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/open-mmlab/mmpose/blob/master/demo/MMPose_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_h0e90xzw0w"
   },
   "source": [
    "# MMPose Tutorial\n",
    "\n",
    "Welcome to MMPose colab tutorial! In this tutorial, we will show you how to\n",
    "- perform inference with an MMPose model\n",
    "- train a new mmpose model with your own datasets\n",
    "\n",
    "Let's start!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bMVTUneIzw0x"
   },
   "source": [
    "## Install MMPose\n",
    "\n",
    "We recommend to use a conda environment to install mmpose and its dependencies. And compilers `nvcc` and `gcc` are required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9dvKWH89zw0x",
    "outputId": "c3e29ad4-6a1b-4ef8-ec45-93196de7ffae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvcc: NVIDIA (R) Cuda compiler driver\n",
      "Copyright (c) 2005-2020 NVIDIA Corporation\n",
      "Built on Tue_Sep_15_19:10:02_PDT_2020\n",
      "Cuda compilation tools, release 11.1, V11.1.74\n",
      "Build cuda_11.1.TC455_06.29069683_0\n",
      "gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\n",
      "Copyright (C) 2019 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",
      "/home/PJLAB/liyining/anaconda3/envs/pt1.9/bin/python\n"
     ]
    }
   ],
   "source": [
    "# check NVCC version\n",
    "!nvcc -V\n",
    "\n",
    "# check GCC version\n",
    "!gcc --version\n",
    "\n",
    "# check python in conda environment\n",
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "26-3yY31zw0y",
    "outputId": "7e6f3bae-7cf0-47b1-e6fd-9a8fd1cec453"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
      "Requirement already satisfied: torch==1.10.0+cu111 in /usr/local/lib/python3.7/dist-packages (1.10.0+cu111)\n",
      "Requirement already satisfied: torchvision==0.11.0+cu111 in /usr/local/lib/python3.7/dist-packages (0.11.0+cu111)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.10.0+cu111) (3.10.0.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==0.11.0+cu111) (1.21.5)\n",
      "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.11.0+cu111) (7.1.2)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html\n",
      "Requirement already satisfied: mmcv-full in /usr/local/lib/python3.7/dist-packages (1.4.6)\n",
      "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (2.4.0)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (7.1.2)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (21.3)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (1.21.5)\n",
      "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (0.32.0)\n",
      "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (4.1.2.30)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (3.13)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv-full) (3.0.7)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Requirement already satisfied: mmdet in /usr/local/lib/python3.7/dist-packages (2.22.0)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet) (3.2.2)\n",
      "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from mmdet) (3.1.10)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mmdet) (1.15.0)\n",
      "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from mmdet) (2.0.4)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmdet) (1.21.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet) (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->mmdet) (3.0.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet) (1.3.2)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Cloning into 'mmpose'...\n",
      "remote: Enumerating objects: 17932, done.\u001b[K\n",
      "remote: Counting objects: 100% (2856/2856), done.\u001b[K\n",
      "remote: Compressing objects: 100% (1144/1144), done.\u001b[K\n",
      "remote: Total 17932 (delta 1864), reused 2414 (delta 1680), pack-reused 15076\u001b[K\n",
      "Receiving objects: 100% (17932/17932), 26.12 MiB | 30.22 MiB/s, done.\n",
      "Resolving deltas: 100% (12459/12459), done.\n",
      "/content/mmpose/mmpose\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Ignoring dataclasses: markers 'python_version == \"3.6\"' don't match your environment\n",
      "Collecting poseval@ git+https://github.com/svenkreiss/poseval.git\n",
      "  Cloning https://github.com/svenkreiss/poseval.git to /tmp/pip-install-0cofrb1n/poseval_1a913a96da95443db876d27e713e233a\n",
      "  Running command git clone -q https://github.com/svenkreiss/poseval.git /tmp/pip-install-0cofrb1n/poseval_1a913a96da95443db876d27e713e233a\n",
      "  Running command git submodule update --init --recursive -q\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from -r requirements/build.txt (line 2)) (1.21.5)\n",
      "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.7/dist-packages (from -r requirements/build.txt (line 3)) (1.10.0+cu111)\n",
      "Requirement already satisfied: chumpy in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 1)) (0.70)\n",
      "Requirement already satisfied: json_tricks in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 3)) (3.15.5)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 4)) (3.2.2)\n",
      "Requirement already satisfied: munkres in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (1.1.4)\n",
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      "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 8)) (7.1.2)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 9)) (1.4.1)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 10)) (0.11.0+cu111)\n",
      "Requirement already satisfied: xtcocotools>=1.8 in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 11)) (1.11.5)\n",
      "Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 1)) (3.7.1)\n",
      "Requirement already satisfied: flake8 in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 2)) (4.0.1)\n",
      "Requirement already satisfied: interrogate in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 3)) (1.5.0)\n",
      "Requirement already satisfied: isort==4.3.21 in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 4)) (4.3.21)\n",
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      "Requirement already satisfied: pytest-runner in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 6)) (6.0.0)\n",
      "Requirement already satisfied: smplx>=0.1.28 in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 7)) (0.1.28)\n",
      "Requirement already satisfied: xdoctest>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 8)) (0.15.10)\n",
      "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 9)) (0.32.0)\n",
      "Requirement already satisfied: albumentations>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 1)) (1.1.0)\n",
      "Requirement already satisfied: onnx in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 2)) (1.11.0)\n",
      "Requirement already satisfied: onnxruntime in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 3)) (1.10.0)\n",
      "Requirement already satisfied: pyrender in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 5)) (0.1.45)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 6)) (2.23.0)\n",
      "Requirement already satisfied: trimesh in /usr/local/lib/python3.7/dist-packages (from -r requirements/optional.txt (line 8)) (3.10.3)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from poseval@ git+https://github.com/svenkreiss/poseval.git->-r requirements/optional.txt (line 4)) (7.1.2)\n",
      "Requirement already satisfied: motmetrics>=1.2 in /usr/local/lib/python3.7/dist-packages (from poseval@ git+https://github.com/svenkreiss/poseval.git->-r requirements/optional.txt (line 4)) (1.2.0)\n",
      "Requirement already satisfied: shapely in /usr/local/lib/python3.7/dist-packages (from poseval@ git+https://github.com/svenkreiss/poseval.git->-r requirements/optional.txt (line 4)) (1.8.1.post1)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from poseval@ git+https://github.com/svenkreiss/poseval.git->-r requirements/optional.txt (line 4)) (4.63.0)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3->-r requirements/build.txt (line 3)) (3.10.0.2)\n",
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      "Requirement already satisfied: cython>=0.27.3 in /usr/local/lib/python3.7/dist-packages (from xtcocotools>=1.8->-r requirements/runtime.txt (line 11)) (0.29.28)\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->-r requirements/runtime.txt (line 4)) (3.0.7)\n",
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      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->-r requirements/runtime.txt (line 4)) (0.11.0)\n",
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      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from xdoctest>=0.10.0->-r requirements/tests.txt (line 8)) (1.15.0)\n",
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      "Requirement already satisfied: py in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (1.11.0)\n",
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      "Requirement already satisfied: PyOpenGL==3.1.0 in /usr/local/lib/python3.7/dist-packages (from pyrender->-r requirements/optional.txt (line 5)) (3.1.0)\n",
      "Requirement already satisfied: pyglet>=1.4.10 in /usr/local/lib/python3.7/dist-packages (from pyrender->-r requirements/optional.txt (line 5)) (1.5.0)\n",
      "Requirement already satisfied: freetype-py in /usr/local/lib/python3.7/dist-packages (from pyrender->-r requirements/optional.txt (line 5)) (2.2.0)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet>=1.4.10->pyrender->-r requirements/optional.txt (line 5)) (0.16.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->-r requirements/optional.txt (line 6)) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->-r requirements/optional.txt (line 6)) (3.0.4)\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->-r requirements/optional.txt (line 6)) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->-r requirements/optional.txt (line 6)) (2021.10.8)\n",
      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.7/dist-packages (from pytest-benchmark->motmetrics>=1.2->poseval@ git+https://github.com/svenkreiss/poseval.git->-r requirements/optional.txt (line 4)) (8.0.0)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Obtaining file:///content/mmpose/mmpose\n",
      "\u001b[33m  WARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Requirement already satisfied: chumpy in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (0.70)\n",
      "Requirement already satisfied: json_tricks in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (3.15.5)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (3.2.2)\n",
      "Requirement already satisfied: munkres in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (1.1.4)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (1.21.5)\n",
      "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (4.1.2.30)\n",
      "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (7.1.2)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (1.4.1)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (0.11.0+cu111)\n",
      "Requirement already satisfied: xtcocotools>=1.8 in /usr/local/lib/python3.7/dist-packages (from mmpose==0.24.0) (1.11.5)\n",
      "Requirement already satisfied: cython>=0.27.3 in /usr/local/lib/python3.7/dist-packages (from xtcocotools>=1.8->mmpose==0.24.0) (0.29.28)\n",
      "Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from xtcocotools>=1.8->mmpose==0.24.0) (57.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmpose==0.24.0) (1.3.2)\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->mmpose==0.24.0) (3.0.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmpose==0.24.0) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmpose==0.24.0) (0.11.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mmpose==0.24.0) (1.15.0)\n",
      "Requirement already satisfied: torch==1.10.0+cu111 in /usr/local/lib/python3.7/dist-packages (from torchvision->mmpose==0.24.0) (1.10.0+cu111)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.10.0+cu111->torchvision->mmpose==0.24.0) (3.10.0.2)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Installing collected packages: mmpose\n",
      "  Attempting uninstall: mmpose\n",
      "\u001b[33m    WARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "    Found existing installation: mmpose 0.24.0\n",
      "    Can't uninstall 'mmpose'. No files were found to uninstall.\n",
      "  Running setup.py develop for mmpose\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.7/dist-packages)\u001b[0m\n",
      "Successfully installed mmpose-0.24.0\n"
     ]
    }
   ],
   "source": [
    "# install dependencies: (use cu111 because colab has CUDA 11.1)\n",
    "%pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html\n",
    "\n",
    "# install mmcv-full thus we could use CUDA operators\n",
    "%pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html\n",
    "\n",
    "# install mmdet for inference demo\n",
    "%pip install mmdet\n",
    "\n",
    "# clone mmpose repo\n",
    "%rm -rf mmpose\n",
    "!git clone https://github.com/open-mmlab/mmpose.git\n",
    "%cd mmpose\n",
    "\n",
    "# install mmpose dependencies\n",
    "%pip install -r requirements.txt\n",
    "\n",
    "# install mmpose in develop mode\n",
    "%pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aIEhiA44zw0y",
    "outputId": "31e36b6e-29a7-4f21-dc47-22905c6a48ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 1.9.0+cu111 True\n",
      "torchvision version: 0.10.0+cu111\n",
      "mmpose version: 0.18.0\n",
      "cuda version: 11.1\n",
      "compiler information: GCC 9.3\n"
     ]
    }
   ],
   "source": [
    "# Check Pytorch installation\n",
    "import torch, torchvision\n",
    "\n",
    "print('torch version:', torch.__version__, torch.cuda.is_available())\n",
    "print('torchvision version:', torchvision.__version__)\n",
    "\n",
    "# Check MMPose installation\n",
    "import mmpose\n",
    "\n",
    "print('mmpose version:', mmpose.__version__)\n",
    "\n",
    "# Check mmcv installation\n",
    "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
    "\n",
    "print('cuda version:', get_compiling_cuda_version())\n",
    "print('compiler information:', get_compiler_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KyrovOnDzw0z"
   },
   "source": [
    "## Inference with an MMPose model\n",
    "\n",
    "MMPose provides high level APIs for model inference and training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "AaUNCi28zw0z",
    "outputId": "441a8335-7795-42f8-c48c-d37149ca85a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_http loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/PJLAB/liyining/anaconda3/envs/pt1.9/lib/python3.9/site-packages/mmdet/core/anchor/builder.py:16: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator`` \n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_http loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/PJLAB/liyining/anaconda3/envs/pt1.9/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
      "/home/PJLAB/liyining/anaconda3/envs/pt1.9/lib/python3.9/site-packages/mmdet/core/anchor/anchor_generator.py:324: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors`` \n",
      "  warnings.warn('``grid_anchors`` would be deprecated soon. '\n",
      "/home/PJLAB/liyining/anaconda3/envs/pt1.9/lib/python3.9/site-packages/mmdet/core/anchor/anchor_generator.py:360: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors`` \n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "from mmpose.apis import (inference_top_down_pose_model, init_pose_model,\n",
    "                         vis_pose_result, process_mmdet_results)\n",
    "from mmdet.apis import inference_detector, init_detector\n",
    "\n",
    "local_runtime = False\n",
    "\n",
    "try:\n",
    "    from google.colab.patches import cv2_imshow  # for image visualization in colab\n",
    "except:\n",
    "    local_runtime = True\n",
    "\n",
    "pose_config = 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py'\n",
    "pose_checkpoint = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'\n",
    "det_config = 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py'\n",
    "det_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'\n",
    "\n",
    "# initialize pose model\n",
    "pose_model = init_pose_model(pose_config, pose_checkpoint)\n",
    "# initialize detector\n",
    "det_model = init_detector(det_config, det_checkpoint)\n",
    "\n",
    "img = 'tests/data/coco/000000196141.jpg'\n",
    "\n",
    "# inference detection\n",
    "mmdet_results = inference_detector(det_model, img)\n",
    "\n",
    "# extract person (COCO_ID=1) bounding boxes from the detection results\n",
    "person_results = process_mmdet_results(mmdet_results, cat_id=1)\n",
    "\n",
    "# inference pose\n",
    "pose_results, returned_outputs = inference_top_down_pose_model(\n",
    "    pose_model,\n",
    "    img,\n",
    "    person_results,\n",
    "    bbox_thr=0.3,\n",
    "    format='xyxy',\n",
    "    dataset=pose_model.cfg.data.test.type)\n",
    "\n",
    "# show pose estimation results\n",
    "vis_result = vis_pose_result(\n",
    "    pose_model,\n",
    "    img,\n",
    "    pose_results,\n",
    "    dataset=pose_model.cfg.data.test.type,\n",
    "    show=False)\n",
    "# reduce image size\n",
    "vis_result = cv2.resize(vis_result, dsize=None, fx=0.5, fy=0.5)\n",
    "\n",
    "if local_runtime:\n",
    "    from IPython.display import Image, display\n",
    "    import tempfile\n",
    "    import os.path as osp\n",
    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
    "        file_name = osp.join(tmpdir, 'pose_results.png')\n",
    "        cv2.imwrite(file_name, vis_result)\n",
    "        display(Image(file_name))\n",
    "else:\n",
    "    cv2_imshow(vis_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mOulhU_Wsr_S"
   },
   "source": [
    "## Train a pose estimation model on a customized dataset\n",
    "\n",
    "To train a model on a customized dataset with MMPose, there are usually three steps:\n",
    "1. Support the dataset in MMPose\n",
    "1. Create a config\n",
    "1. Perform training and evaluation\n",
    "\n",
    "### Add a new dataset\n",
    "\n",
    "There are two methods to support a customized dataset in MMPose. The first one is to convert the data to a supported format (e.g. COCO) and use the corresponding dataset class (e.g. TopdownCOCODataset), as described in the [document](https://mmpose.readthedocs.io/en/latest/tutorials/2_new_dataset.html#reorganize-dataset-to-existing-format). The second one is to add a new dataset class. In this tutorial, we give an example of the second method.\n",
    "\n",
    "We first download the demo dataset, which contains 100 samples (75 for training and 25 for validation) selected from COCO train2017 dataset. The annotations are stored in a different format from the original COCO format.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tlSP8JNr9pEr",
    "outputId": "aee224ab-4469-40c6-8b41-8591d92aafb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘data’: File exists\n",
      "/home/PJLAB/liyining/openmmlab/mmpose/data\n",
      "--2021-09-22 22:27:21--  https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/datasets/coco_tiny.tar\n",
      "Resolving openmmlab.oss-cn-hangzhou.aliyuncs.com (openmmlab.oss-cn-hangzhou.aliyuncs.com)... 124.160.145.51\n",
      "Connecting to openmmlab.oss-cn-hangzhou.aliyuncs.com (openmmlab.oss-cn-hangzhou.aliyuncs.com)|124.160.145.51|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 16558080 (16M) [application/x-tar]\n",
      "Saving to: ‘coco_tiny.tar.1’\n",
      "\n",
      "coco_tiny.tar.1     100%[===================>]  15.79M  14.7MB/s    in 1.1s    \n",
      "\n",
      "2021-09-22 22:27:24 (14.7 MB/s) - ‘coco_tiny.tar.1’ saved [16558080/16558080]\n",
      "\n",
      "/home/PJLAB/liyining/openmmlab/mmpose\n"
     ]
    }
   ],
   "source": [
    "# download dataset\n",
    "%mkdir data\n",
    "%cd data\n",
    "!wget https://download.openmmlab.com/mmpose/datasets/coco_tiny.tar\n",
    "!tar -xf coco_tiny.tar\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UDzqo6pwB-Zz",
    "outputId": "96bb444c-94c5-4b8a-cc63-0a94f16ebf95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\r\n",
      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n",
      "\u001b[01;34mdata/coco_tiny\u001b[00m\n",
      "├── \u001b[01;34mimages\u001b[00m\n",
      "│   ├── \u001b[01;35m000000012754.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000017741.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000019157.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000019523.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000019608.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000022816.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000031092.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000032124.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000037209.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000050713.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000057703.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000064909.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000076942.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000079754.jpg\u001b[00m\n",
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      "│   ├── \u001b[01;35m000000105647.jpg\u001b[00m\n",
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      "│   ├── \u001b[01;35m000000117891.jpg\u001b[00m\n",
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      "│   ├── \u001b[01;35m000000161112.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000175737.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000177069.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000184659.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000209468.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000210060.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000215867.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000216861.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000227224.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000246265.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000254919.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000263687.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000264628.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000268927.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000271177.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000275219.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000277542.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000279140.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000286813.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000297980.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000301641.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000312341.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000325768.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000332221.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000345071.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000346965.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000347836.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000349437.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000360735.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000362343.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000364079.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000364113.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000386279.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000386968.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000388619.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000390137.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000390241.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000390298.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000390348.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000398606.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000400456.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000402514.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000403255.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000403432.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000410350.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000453065.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000457254.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000464153.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000464515.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000465418.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000480591.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000484279.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000494014.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000515289.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000516805.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000521994.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000528962.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000534736.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000535588.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000537548.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000553698.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000555622.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000566456.jpg\u001b[00m\n",
      "│   ├── \u001b[01;35m000000567171.jpg\u001b[00m\n",
      "│   └── \u001b[01;35m000000568961.jpg\u001b[00m\n",
      "├── train.json\n",
      "└── val.json\n",
      "\n",
      "1 directory, 99 files\n"
     ]
    }
   ],
   "source": [
    "# check the directory structure\n",
    "!apt-get -q install tree\n",
    "!tree data/coco_tiny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ef-045CUCdb3",
    "outputId": "5a39b30a-8e6c-4754-8908-9ea13b91c22b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> 75\n",
      "{'bbox': [267.03, 104.32, 229.19, 320],\n",
      " 'image_file': '000000537548.jpg',\n",
      " 'image_size': [640, 480],\n",
      " 'keypoints': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 325, 160, 2, 398,\n",
      "               177, 2, 0, 0, 0, 437, 238, 2, 0, 0, 0, 477, 270, 2, 287, 255, 1,\n",
      "               339, 267, 2, 0, 0, 0, 423, 314, 2, 0, 0, 0, 355, 367, 2]}\n"
     ]
    }
   ],
   "source": [
    "# check the annotation format\n",
    "import json\n",
    "import pprint\n",
    "\n",
    "anns = json.load(open('data/coco_tiny/train.json'))\n",
    "\n",
    "print(type(anns), len(anns))\n",
    "pprint.pprint(anns[0], compact=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r4Dt1io8D7m8"
   },
   "source": [
    "After downloading the data, we implement a new dataset class to load data samples for model training and validation. Assume that we are going to train a top-down pose estimation model (refer to [Top-down Pose Estimation](https://github.com/open-mmlab/mmpose/tree/master/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap#readme) for a brief introduction), the new dataset class inherits `TopDownBaseDataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "WR9ZVXuPFy4v"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os.path as osp\n",
    "from collections import OrderedDict\n",
    "import tempfile\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from mmpose.core.evaluation.top_down_eval import (keypoint_nme,\n",
    "                                                  keypoint_pck_accuracy)\n",
    "from mmpose.datasets.builder import DATASETS\n",
    "from mmpose.datasets.datasets.base import Kpt2dSviewRgbImgTopDownDataset\n",
    "\n",
    "\n",
    "@DATASETS.register_module()\n",
    "class TopDownCOCOTinyDataset(Kpt2dSviewRgbImgTopDownDataset):\n",
    "\n",
    "    def __init__(self,\n",
    "                 ann_file,\n",
    "                 img_prefix,\n",
    "                 data_cfg,\n",
    "                 pipeline,\n",
    "                 dataset_info=None,\n",
    "                 test_mode=False):\n",
    "        super().__init__(\n",
    "            ann_file,\n",
    "            img_prefix,\n",
    "            data_cfg,\n",
    "            pipeline,\n",
    "            dataset_info,\n",
    "            coco_style=False,\n",
    "            test_mode=test_mode)\n",
    "\n",
    "        # flip_pairs, upper_body_ids and lower_body_ids will be used\n",
    "        # in some data augmentations like random flip\n",
    "        self.ann_info['flip_pairs'] = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],\n",
    "                                       [11, 12], [13, 14], [15, 16]]\n",
    "        self.ann_info['upper_body_ids'] = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\n",
    "        self.ann_info['lower_body_ids'] = (11, 12, 13, 14, 15, 16)\n",
    "\n",
    "        self.ann_info['joint_weights'] = None\n",
    "        self.ann_info['use_different_joint_weights'] = False\n",
    "\n",
    "        self.dataset_name = 'coco_tiny'\n",
    "        self.db = self._get_db()\n",
    "\n",
    "    def _get_db(self):\n",
    "        with open(self.ann_file) as f:\n",
    "            anns = json.load(f)\n",
    "\n",
    "        db = []\n",
    "        for idx, ann in enumerate(anns):\n",
    "            # get image path\n",
    "            image_file = osp.join(self.img_prefix, ann['image_file'])\n",
    "            # get bbox\n",
    "            bbox = ann['bbox']\n",
    "            # get keypoints\n",
    "            keypoints = np.array(\n",
    "                ann['keypoints'], dtype=np.float32).reshape(-1, 3)\n",
    "            num_joints = keypoints.shape[0]\n",
    "            joints_3d = np.zeros((num_joints, 3), dtype=np.float32)\n",
    "            joints_3d[:, :2] = keypoints[:, :2]\n",
    "            joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)\n",
    "            joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])\n",
    "\n",
    "            sample = {\n",
    "                'image_file': image_file,\n",
    "                'bbox': bbox,\n",
    "                'rotation': 0,\n",
    "                'joints_3d': joints_3d,\n",
    "                'joints_3d_visible': joints_3d_visible,\n",
    "                'bbox_score': 1,\n",
    "                'bbox_id': idx,\n",
    "            }\n",
    "            db.append(sample)\n",
    "\n",
    "        return db\n",
    "\n",
    "    def evaluate(self, results, res_folder=None, metric='PCK', **kwargs):\n",
    "        \"\"\"Evaluate keypoint detection results. The pose prediction results will\n",
    "        be saved in `${res_folder}/result_keypoints.json`.\n",
    "\n",
    "        Note:\n",
    "        batch_size: N\n",
    "        num_keypoints: K\n",
    "        heatmap height: H\n",
    "        heatmap width: W\n",
    "\n",
    "        Args:\n",
    "        results (list(preds, boxes, image_path, output_heatmap))\n",
    "            :preds (np.ndarray[N,K,3]): The first two dimensions are\n",
    "                coordinates, score is the third dimension of the array.\n",
    "            :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]\n",
    "                , scale[1],area, score]\n",
    "            :image_paths (list[str]): For example, ['Test/source/0.jpg']\n",
    "            :output_heatmap (np.ndarray[N, K, H, W]): model outputs.\n",
    "\n",
    "        res_folder (str, optional): The folder to save the testing\n",
    "                results. If not specified, a temp folder will be created.\n",
    "                Default: None.\n",
    "        metric (str | list[str]): Metric to be performed.\n",
    "            Options: 'PCK', 'NME'.\n",
    "\n",
    "        Returns:\n",
    "            dict: Evaluation results for evaluation metric.\n",
    "        \"\"\"\n",
    "        metrics = metric if isinstance(metric, list) else [metric]\n",
    "        allowed_metrics = ['PCK', 'NME']\n",
    "        for metric in metrics:\n",
    "            if metric not in allowed_metrics:\n",
    "                raise KeyError(f'metric {metric} is not supported')\n",
    "\n",
    "        if res_folder is not None:\n",
    "            tmp_folder = None\n",
    "            res_file = osp.join(res_folder, 'result_keypoints.json')\n",
    "        else:\n",
    "            tmp_folder = tempfile.TemporaryDirectory()\n",
    "            res_file = osp.join(tmp_folder.name, 'result_keypoints.json')\n",
    "\n",
    "        kpts = []\n",
    "        for result in results:\n",
    "            preds = result['preds']\n",
    "            boxes = result['boxes']\n",
    "            image_paths = result['image_paths']\n",
    "            bbox_ids = result['bbox_ids']\n",
    "\n",
    "            batch_size = len(image_paths)\n",
    "            for i in range(batch_size):\n",
    "                kpts.append({\n",
    "                    'keypoints': preds[i].tolist(),\n",
    "                    'center': boxes[i][0:2].tolist(),\n",
    "                    'scale': boxes[i][2:4].tolist(),\n",
    "                    'area': float(boxes[i][4]),\n",
    "                    'score': float(boxes[i][5]),\n",
    "                    'bbox_id': bbox_ids[i]\n",
    "                })\n",
    "        kpts = self._sort_and_unique_bboxes(kpts)\n",
    "\n",
    "        self._write_keypoint_results(kpts, res_file)\n",
    "        info_str = self._report_metric(res_file, metrics)\n",
    "        name_value = OrderedDict(info_str)\n",
    "\n",
    "        if tmp_folder is not None:\n",
    "            tmp_folder.cleanup()\n",
    "\n",
    "        return name_value\n",
    "\n",
    "    def _report_metric(self, res_file, metrics, pck_thr=0.3):\n",
    "        \"\"\"Keypoint evaluation.\n",
    "\n",
    "        Args:\n",
    "        res_file (str): Json file stored prediction results.\n",
    "        metrics (str | list[str]): Metric to be performed.\n",
    "            Options: 'PCK', 'NME'.\n",
    "        pck_thr (float): PCK threshold, default: 0.3.\n",
    "\n",
    "        Returns:\n",
    "        dict: Evaluation results for evaluation metric.\n",
    "        \"\"\"\n",
    "        info_str = []\n",
    "\n",
    "        with open(res_file, 'r') as fin:\n",
    "            preds = json.load(fin)\n",
    "        assert len(preds) == len(self.db)\n",
    "\n",
    "        outputs = []\n",
    "        gts = []\n",
    "        masks = []\n",
    "\n",
    "        for pred, item in zip(preds, self.db):\n",
    "            outputs.append(np.array(pred['keypoints'])[:, :-1])\n",
    "            gts.append(np.array(item['joints_3d'])[:, :-1])\n",
    "            masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)\n",
    "\n",
    "        outputs = np.array(outputs)\n",
    "        gts = np.array(gts)\n",
    "        masks = np.array(masks)\n",
    "\n",
    "        normalize_factor = self._get_normalize_factor(gts)\n",
    "\n",
    "        if 'PCK' in metrics:\n",
    "            _, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,\n",
    "                                              normalize_factor)\n",
    "            info_str.append(('PCK', pck))\n",
    "\n",
    "        if 'NME' in metrics:\n",
    "            info_str.append(\n",
    "                ('NME', keypoint_nme(outputs, gts, masks, normalize_factor)))\n",
    "\n",
    "        return info_str\n",
    "\n",
    "    @staticmethod\n",
    "    def _write_keypoint_results(keypoints, res_file):\n",
    "        \"\"\"Write results into a json file.\"\"\"\n",
    "\n",
    "        with open(res_file, 'w') as f:\n",
    "            json.dump(keypoints, f, sort_keys=True, indent=4)\n",
    "\n",
    "    @staticmethod\n",
    "    def _sort_and_unique_bboxes(kpts, key='bbox_id'):\n",
    "        \"\"\"sort kpts and remove the repeated ones.\"\"\"\n",
    "        kpts = sorted(kpts, key=lambda x: x[key])\n",
    "        num = len(kpts)\n",
    "        for i in range(num - 1, 0, -1):\n",
    "            if kpts[i][key] == kpts[i - 1][key]:\n",
    "                del kpts[i]\n",
    "\n",
    "        return kpts\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_normalize_factor(gts):\n",
    "        \"\"\"Get inter-ocular distance as the normalize factor, measured as the\n",
    "        Euclidean distance between the outer corners of the eyes.\n",
    "\n",
    "        Args:\n",
    "            gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.\n",
    "\n",
    "        Return:\n",
    "            np.ndarray[N, 2]: normalized factor\n",
    "        \"\"\"\n",
    "\n",
    "        interocular = np.linalg.norm(\n",
    "            gts[:, 0, :] - gts[:, 1, :], axis=1, keepdims=True)\n",
    "        return np.tile(interocular, [1, 2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gh05C4mBl_u-"
   },
   "source": [
    "### Create a config file\n",
    "\n",
    "In the next step, we create a config file which configures the model, dataset and runtime settings. More information can be found at [Learn about Configs](https://mmpose.readthedocs.io/en/latest/tutorials/0_config.html). A common practice to create a config file is deriving from a existing one. In this tutorial, we load a config file that trains a HRNet on COCO dataset, and modify it to adapt to the COCOTiny dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n-z89qCJoWwL",
    "outputId": "a3f6817e-b448-463d-d3df-2c5519efa99c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_info = dict(\n",
      "    dataset_name='coco',\n",
      "    paper_info=dict(\n",
      "        author=\n",
      "        'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n",
      "        title='Microsoft coco: Common objects in context',\n",
      "        container='European conference on computer vision',\n",
      "        year='2014',\n",
      "        homepage='http://cocodataset.org/'),\n",
      "    keypoint_info=dict({\n",
      "        0:\n",
      "        dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),\n",
      "        1:\n",
      "        dict(\n",
      "            name='left_eye',\n",
      "            id=1,\n",
      "            color=[51, 153, 255],\n",
      "            type='upper',\n",
      "            swap='right_eye'),\n",
      "        2:\n",
      "        dict(\n",
      "            name='right_eye',\n",
      "            id=2,\n",
      "            color=[51, 153, 255],\n",
      "            type='upper',\n",
      "            swap='left_eye'),\n",
      "        3:\n",
      "        dict(\n",
      "            name='left_ear',\n",
      "            id=3,\n",
      "            color=[51, 153, 255],\n",
      "            type='upper',\n",
      "            swap='right_ear'),\n",
      "        4:\n",
      "        dict(\n",
      "            name='right_ear',\n",
      "            id=4,\n",
      "            color=[51, 153, 255],\n",
      "            type='upper',\n",
      "            swap='left_ear'),\n",
      "        5:\n",
      "        dict(\n",
      "            name='left_shoulder',\n",
      "            id=5,\n",
      "            color=[0, 255, 0],\n",
      "            type='upper',\n",
      "            swap='right_shoulder'),\n",
      "        6:\n",
      "        dict(\n",
      "            name='right_shoulder',\n",
      "            id=6,\n",
      "            color=[255, 128, 0],\n",
      "            type='upper',\n",
      "            swap='left_shoulder'),\n",
      "        7:\n",
      "        dict(\n",
      "            name='left_elbow',\n",
      "            id=7,\n",
      "            color=[0, 255, 0],\n",
      "            type='upper',\n",
      "            swap='right_elbow'),\n",
      "        8:\n",
      "        dict(\n",
      "            name='right_elbow',\n",
      "            id=8,\n",
      "            color=[255, 128, 0],\n",
      "            type='upper',\n",
      "            swap='left_elbow'),\n",
      "        9:\n",
      "        dict(\n",
      "            name='left_wrist',\n",
      "            id=9,\n",
      "            color=[0, 255, 0],\n",
      "            type='upper',\n",
      "            swap='right_wrist'),\n",
      "        10:\n",
      "        dict(\n",
      "            name='right_wrist',\n",
      "            id=10,\n",
      "            color=[255, 128, 0],\n",
      "            type='upper',\n",
      "            swap='left_wrist'),\n",
      "        11:\n",
      "        dict(\n",
      "            name='left_hip',\n",
      "            id=11,\n",
      "            color=[0, 255, 0],\n",
      "            type='lower',\n",
      "            swap='right_hip'),\n",
      "        12:\n",
      "        dict(\n",
      "            name='right_hip',\n",
      "            id=12,\n",
      "            color=[255, 128, 0],\n",
      "            type='lower',\n",
      "            swap='left_hip'),\n",
      "        13:\n",
      "        dict(\n",
      "            name='left_knee',\n",
      "            id=13,\n",
      "            color=[0, 255, 0],\n",
      "            type='lower',\n",
      "            swap='right_knee'),\n",
      "        14:\n",
      "        dict(\n",
      "            name='right_knee',\n",
      "            id=14,\n",
      "            color=[255, 128, 0],\n",
      "            type='lower',\n",
      "            swap='left_knee'),\n",
      "        15:\n",
      "        dict(\n",
      "            name='left_ankle',\n",
      "            id=15,\n",
      "            color=[0, 255, 0],\n",
      "            type='lower',\n",
      "            swap='right_ankle'),\n",
      "        16:\n",
      "        dict(\n",
      "            name='right_ankle',\n",
      "            id=16,\n",
      "            color=[255, 128, 0],\n",
      "            type='lower',\n",
      "            swap='left_ankle')\n",
      "    }),\n",
      "    skeleton_info=dict({\n",
      "        0:\n",
      "        dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n",
      "        1:\n",
      "        dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n",
      "        2:\n",
      "        dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),\n",
      "        3:\n",
      "        dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),\n",
      "        4:\n",
      "        dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),\n",
      "        5:\n",
      "        dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),\n",
      "        6:\n",
      "        dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),\n",
      "        7:\n",
      "        dict(\n",
      "            link=('left_shoulder', 'right_shoulder'),\n",
      "            id=7,\n",
      "            color=[51, 153, 255]),\n",
      "        8:\n",
      "        dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),\n",
      "        9:\n",
      "        dict(\n",
      "            link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),\n",
      "        10:\n",
      "        dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),\n",
      "        11:\n",
      "        dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),\n",
      "        12:\n",
      "        dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),\n",
      "        13:\n",
      "        dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n",
      "        14:\n",
      "        dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n",
      "        15:\n",
      "        dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),\n",
      "        16:\n",
      "        dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),\n",
      "        17:\n",
      "        dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),\n",
      "        18:\n",
      "        dict(\n",
      "            link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])\n",
      "    }),\n",
      "    joint_weights=[\n",
      "        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0, 1.0, 1.2,\n",
      "        1.2, 1.5, 1.5\n",
      "    ],\n",
      "    sigmas=[\n",
      "        0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,\n",
      "        0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n",
      "    ])\n",
      "log_level = 'INFO'\n",
      "load_from = None\n",
      "resume_from = None\n",
      "dist_params = dict(backend='nccl')\n",
      "workflow = [('train', 1)]\n",
      "checkpoint_config = dict(interval=10)\n",
      "evaluation = dict(interval=10, metric='PCK', save_best='PCK')\n",
      "optimizer = dict(type='Adam', lr=0.0005)\n",
      "optimizer_config = dict(grad_clip=None)\n",
      "lr_config = dict(\n",
      "    policy='step',\n",
      "    warmup='linear',\n",
      "    warmup_iters=500,\n",
      "    warmup_ratio=0.001,\n",
      "    step=[170, 200])\n",
      "total_epochs = 40\n",
      "log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook')])\n",
      "channel_cfg = dict(\n",
      "    num_output_channels=17,\n",
      "    dataset_joints=17,\n",
      "    dataset_channel=[[\n",
      "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "    ]],\n",
      "    inference_channel=[\n",
      "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "    ])\n",
      "model = dict(\n",
      "    type='TopDown',\n",
      "    pretrained=\n",
      "    'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth',\n",
      "    backbone=dict(\n",
      "        type='HRNet',\n",
      "        in_channels=3,\n",
      "        extra=dict(\n",
      "            stage1=dict(\n",
      "                num_modules=1,\n",
      "                num_branches=1,\n",
      "                block='BOTTLENECK',\n",
      "                num_blocks=(4, ),\n",
      "                num_channels=(64, )),\n",
      "            stage2=dict(\n",
      "                num_modules=1,\n",
      "                num_branches=2,\n",
      "                block='BASIC',\n",
      "                num_blocks=(4, 4),\n",
      "                num_channels=(32, 64)),\n",
      "            stage3=dict(\n",
      "                num_modules=4,\n",
      "                num_branches=3,\n",
      "                block='BASIC',\n",
      "                num_blocks=(4, 4, 4),\n",
      "                num_channels=(32, 64, 128)),\n",
      "            stage4=dict(\n",
      "                num_modules=3,\n",
      "                num_branches=4,\n",
      "                block='BASIC',\n",
      "                num_blocks=(4, 4, 4, 4),\n",
      "                num_channels=(32, 64, 128, 256)))),\n",
      "    keypoint_head=dict(\n",
      "        type='TopdownHeatmapSimpleHead',\n",
      "        in_channels=32,\n",
      "        out_channels=17,\n",
      "        num_deconv_layers=0,\n",
      "        extra=dict(final_conv_kernel=1),\n",
      "        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),\n",
      "    train_cfg=dict(),\n",
      "    test_cfg=dict(\n",
      "        flip_test=True,\n",
      "        post_process='default',\n",
      "        shift_heatmap=True,\n",
      "        modulate_kernel=11))\n",
      "data_cfg = dict(\n",
      "    image_size=[192, 256],\n",
      "    heatmap_size=[48, 64],\n",
      "    num_output_channels=17,\n",
      "    num_joints=17,\n",
      "    dataset_channel=[[\n",
      "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "    ]],\n",
      "    inference_channel=[\n",
      "        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "    ],\n",
      "    soft_nms=False,\n",
      "    nms_thr=1.0,\n",
      "    oks_thr=0.9,\n",
      "    vis_thr=0.2,\n",
      "    use_gt_bbox=False,\n",
      "    det_bbox_thr=0.0,\n",
      "    bbox_file=\n",
      "    'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n",
      ")\n",
      "train_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(type='TopDownRandomFlip', flip_prob=0.5),\n",
      "    dict(\n",
      "        type='TopDownHalfBodyTransform',\n",
      "        num_joints_half_body=8,\n",
      "        prob_half_body=0.3),\n",
      "    dict(\n",
      "        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),\n",
      "    dict(type='TopDownAffine'),\n",
      "    dict(type='ToTensor'),\n",
      "    dict(\n",
      "        type='NormalizeTensor',\n",
      "        mean=[0.485, 0.456, 0.406],\n",
      "        std=[0.229, 0.224, 0.225]),\n",
      "    dict(type='TopDownGenerateTarget', sigma=2),\n",
      "    dict(\n",
      "        type='Collect',\n",
      "        keys=['img', 'target', 'target_weight'],\n",
      "        meta_keys=[\n",
      "            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',\n",
      "            'rotation', 'bbox_score', 'flip_pairs'\n",
      "        ])\n",
      "]\n",
      "val_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(type='TopDownAffine'),\n",
      "    dict(type='ToTensor'),\n",
      "    dict(\n",
      "        type='NormalizeTensor',\n",
      "        mean=[0.485, 0.456, 0.406],\n",
      "        std=[0.229, 0.224, 0.225]),\n",
      "    dict(\n",
      "        type='Collect',\n",
      "        keys=['img'],\n",
      "        meta_keys=[\n",
      "            'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n",
      "            'flip_pairs'\n",
      "        ])\n",
      "]\n",
      "test_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(type='TopDownAffine'),\n",
      "    dict(type='ToTensor'),\n",
      "    dict(\n",
      "        type='NormalizeTensor',\n",
      "        mean=[0.485, 0.456, 0.406],\n",
      "        std=[0.229, 0.224, 0.225]),\n",
      "    dict(\n",
      "        type='Collect',\n",
      "        keys=['img'],\n",
      "        meta_keys=[\n",
      "            'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n",
      "            'flip_pairs'\n",
      "        ])\n",
      "]\n",
      "data_root = 'data/coco_tiny'\n",
      "data = dict(\n",
      "    samples_per_gpu=16,\n",
      "    workers_per_gpu=2,\n",
      "    val_dataloader=dict(samples_per_gpu=16),\n",
      "    test_dataloader=dict(samples_per_gpu=16),\n",
      "    train=dict(\n",
      "        type='TopDownCOCOTinyDataset',\n",
      "        ann_file='data/coco_tiny/train.json',\n",
      "        img_prefix='data/coco_tiny/images/',\n",
      "        data_cfg=dict(\n",
      "            image_size=[192, 256],\n",
      "            heatmap_size=[48, 64],\n",
      "            num_output_channels=17,\n",
      "            num_joints=17,\n",
      "            dataset_channel=[[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ]],\n",
      "            inference_channel=[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ],\n",
      "            soft_nms=False,\n",
      "            nms_thr=1.0,\n",
      "            oks_thr=0.9,\n",
      "            vis_thr=0.2,\n",
      "            use_gt_bbox=False,\n",
      "            det_bbox_thr=0.0,\n",
      "            bbox_file=\n",
      "            'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n",
      "        ),\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(type='TopDownRandomFlip', flip_prob=0.5),\n",
      "            dict(\n",
      "                type='TopDownHalfBodyTransform',\n",
      "                num_joints_half_body=8,\n",
      "                prob_half_body=0.3),\n",
      "            dict(\n",
      "                type='TopDownGetRandomScaleRotation',\n",
      "                rot_factor=40,\n",
      "                scale_factor=0.5),\n",
      "            dict(type='TopDownAffine'),\n",
      "            dict(type='ToTensor'),\n",
      "            dict(\n",
      "                type='NormalizeTensor',\n",
      "                mean=[0.485, 0.456, 0.406],\n",
      "                std=[0.229, 0.224, 0.225]),\n",
      "            dict(type='TopDownGenerateTarget', sigma=2),\n",
      "            dict(\n",
      "                type='Collect',\n",
      "                keys=['img', 'target', 'target_weight'],\n",
      "                meta_keys=[\n",
      "                    'image_file', 'joints_3d', 'joints_3d_visible', 'center',\n",
      "                    'scale', 'rotation', 'bbox_score', 'flip_pairs'\n",
      "                ])\n",
      "        ],\n",
      "        dataset_info=dict(\n",
      "            dataset_name='coco',\n",
      "            paper_info=dict(\n",
      "                author=\n",
      "                'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n",
      "                title='Microsoft coco: Common objects in context',\n",
      "                container='European conference on computer vision',\n",
      "                year='2014',\n",
      "                homepage='http://cocodataset.org/'),\n",
      "            keypoint_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    name='nose',\n",
      "                    id=0,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap=''),\n",
      "                1:\n",
      "                dict(\n",
      "                    name='left_eye',\n",
      "                    id=1,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_eye'),\n",
      "                2:\n",
      "                dict(\n",
      "                    name='right_eye',\n",
      "                    id=2,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_eye'),\n",
      "                3:\n",
      "                dict(\n",
      "                    name='left_ear',\n",
      "                    id=3,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_ear'),\n",
      "                4:\n",
      "                dict(\n",
      "                    name='right_ear',\n",
      "                    id=4,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_ear'),\n",
      "                5:\n",
      "                dict(\n",
      "                    name='left_shoulder',\n",
      "                    id=5,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_shoulder'),\n",
      "                6:\n",
      "                dict(\n",
      "                    name='right_shoulder',\n",
      "                    id=6,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_shoulder'),\n",
      "                7:\n",
      "                dict(\n",
      "                    name='left_elbow',\n",
      "                    id=7,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_elbow'),\n",
      "                8:\n",
      "                dict(\n",
      "                    name='right_elbow',\n",
      "                    id=8,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_elbow'),\n",
      "                9:\n",
      "                dict(\n",
      "                    name='left_wrist',\n",
      "                    id=9,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_wrist'),\n",
      "                10:\n",
      "                dict(\n",
      "                    name='right_wrist',\n",
      "                    id=10,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_wrist'),\n",
      "                11:\n",
      "                dict(\n",
      "                    name='left_hip',\n",
      "                    id=11,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_hip'),\n",
      "                12:\n",
      "                dict(\n",
      "                    name='right_hip',\n",
      "                    id=12,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_hip'),\n",
      "                13:\n",
      "                dict(\n",
      "                    name='left_knee',\n",
      "                    id=13,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_knee'),\n",
      "                14:\n",
      "                dict(\n",
      "                    name='right_knee',\n",
      "                    id=14,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_knee'),\n",
      "                15:\n",
      "                dict(\n",
      "                    name='left_ankle',\n",
      "                    id=15,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_ankle'),\n",
      "                16:\n",
      "                dict(\n",
      "                    name='right_ankle',\n",
      "                    id=16,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_ankle')\n",
      "            }),\n",
      "            skeleton_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n",
      "                1:\n",
      "                dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n",
      "                2:\n",
      "                dict(\n",
      "                    link=('right_ankle', 'right_knee'),\n",
      "                    id=2,\n",
      "                    color=[255, 128, 0]),\n",
      "                3:\n",
      "                dict(\n",
      "                    link=('right_knee', 'right_hip'),\n",
      "                    id=3,\n",
      "                    color=[255, 128, 0]),\n",
      "                4:\n",
      "                dict(\n",
      "                    link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                5:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_hip'),\n",
      "                    id=5,\n",
      "                    color=[51, 153, 255]),\n",
      "                6:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_hip'),\n",
      "                    id=6,\n",
      "                    color=[51, 153, 255]),\n",
      "                7:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'right_shoulder'),\n",
      "                    id=7,\n",
      "                    color=[51, 153, 255]),\n",
      "                8:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_elbow'),\n",
      "                    id=8,\n",
      "                    color=[0, 255, 0]),\n",
      "                9:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_elbow'),\n",
      "                    id=9,\n",
      "                    color=[255, 128, 0]),\n",
      "                10:\n",
      "                dict(\n",
      "                    link=('left_elbow', 'left_wrist'),\n",
      "                    id=10,\n",
      "                    color=[0, 255, 0]),\n",
      "                11:\n",
      "                dict(\n",
      "                    link=('right_elbow', 'right_wrist'),\n",
      "                    id=11,\n",
      "                    color=[255, 128, 0]),\n",
      "                12:\n",
      "                dict(\n",
      "                    link=('left_eye', 'right_eye'),\n",
      "                    id=12,\n",
      "                    color=[51, 153, 255]),\n",
      "                13:\n",
      "                dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n",
      "                14:\n",
      "                dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n",
      "                15:\n",
      "                dict(\n",
      "                    link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                16:\n",
      "                dict(\n",
      "                    link=('right_eye', 'right_ear'),\n",
      "                    id=16,\n",
      "                    color=[51, 153, 255]),\n",
      "                17:\n",
      "                dict(\n",
      "                    link=('left_ear', 'left_shoulder'),\n",
      "                    id=17,\n",
      "                    color=[51, 153, 255]),\n",
      "                18:\n",
      "                dict(\n",
      "                    link=('right_ear', 'right_shoulder'),\n",
      "                    id=18,\n",
      "                    color=[51, 153, 255])\n",
      "            }),\n",
      "            joint_weights=[\n",
      "                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n",
      "                1.0, 1.2, 1.2, 1.5, 1.5\n",
      "            ],\n",
      "            sigmas=[\n",
      "                0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n",
      "                0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n",
      "            ])),\n",
      "    val=dict(\n",
      "        type='TopDownCOCOTinyDataset',\n",
      "        ann_file='data/coco_tiny/val.json',\n",
      "        img_prefix='data/coco_tiny/images/',\n",
      "        data_cfg=dict(\n",
      "            image_size=[192, 256],\n",
      "            heatmap_size=[48, 64],\n",
      "            num_output_channels=17,\n",
      "            num_joints=17,\n",
      "            dataset_channel=[[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ]],\n",
      "            inference_channel=[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ],\n",
      "            soft_nms=False,\n",
      "            nms_thr=1.0,\n",
      "            oks_thr=0.9,\n",
      "            vis_thr=0.2,\n",
      "            use_gt_bbox=False,\n",
      "            det_bbox_thr=0.0,\n",
      "            bbox_file=\n",
      "            'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n",
      "        ),\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(type='TopDownAffine'),\n",
      "            dict(type='ToTensor'),\n",
      "            dict(\n",
      "                type='NormalizeTensor',\n",
      "                mean=[0.485, 0.456, 0.406],\n",
      "                std=[0.229, 0.224, 0.225]),\n",
      "            dict(\n",
      "                type='Collect',\n",
      "                keys=['img'],\n",
      "                meta_keys=[\n",
      "                    'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n",
      "                    'flip_pairs'\n",
      "                ])\n",
      "        ],\n",
      "        dataset_info=dict(\n",
      "            dataset_name='coco',\n",
      "            paper_info=dict(\n",
      "                author=\n",
      "                'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n",
      "                title='Microsoft coco: Common objects in context',\n",
      "                container='European conference on computer vision',\n",
      "                year='2014',\n",
      "                homepage='http://cocodataset.org/'),\n",
      "            keypoint_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    name='nose',\n",
      "                    id=0,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap=''),\n",
      "                1:\n",
      "                dict(\n",
      "                    name='left_eye',\n",
      "                    id=1,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_eye'),\n",
      "                2:\n",
      "                dict(\n",
      "                    name='right_eye',\n",
      "                    id=2,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_eye'),\n",
      "                3:\n",
      "                dict(\n",
      "                    name='left_ear',\n",
      "                    id=3,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_ear'),\n",
      "                4:\n",
      "                dict(\n",
      "                    name='right_ear',\n",
      "                    id=4,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_ear'),\n",
      "                5:\n",
      "                dict(\n",
      "                    name='left_shoulder',\n",
      "                    id=5,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_shoulder'),\n",
      "                6:\n",
      "                dict(\n",
      "                    name='right_shoulder',\n",
      "                    id=6,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_shoulder'),\n",
      "                7:\n",
      "                dict(\n",
      "                    name='left_elbow',\n",
      "                    id=7,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_elbow'),\n",
      "                8:\n",
      "                dict(\n",
      "                    name='right_elbow',\n",
      "                    id=8,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_elbow'),\n",
      "                9:\n",
      "                dict(\n",
      "                    name='left_wrist',\n",
      "                    id=9,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_wrist'),\n",
      "                10:\n",
      "                dict(\n",
      "                    name='right_wrist',\n",
      "                    id=10,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_wrist'),\n",
      "                11:\n",
      "                dict(\n",
      "                    name='left_hip',\n",
      "                    id=11,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_hip'),\n",
      "                12:\n",
      "                dict(\n",
      "                    name='right_hip',\n",
      "                    id=12,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_hip'),\n",
      "                13:\n",
      "                dict(\n",
      "                    name='left_knee',\n",
      "                    id=13,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_knee'),\n",
      "                14:\n",
      "                dict(\n",
      "                    name='right_knee',\n",
      "                    id=14,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_knee'),\n",
      "                15:\n",
      "                dict(\n",
      "                    name='left_ankle',\n",
      "                    id=15,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_ankle'),\n",
      "                16:\n",
      "                dict(\n",
      "                    name='right_ankle',\n",
      "                    id=16,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_ankle')\n",
      "            }),\n",
      "            skeleton_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n",
      "                1:\n",
      "                dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n",
      "                2:\n",
      "                dict(\n",
      "                    link=('right_ankle', 'right_knee'),\n",
      "                    id=2,\n",
      "                    color=[255, 128, 0]),\n",
      "                3:\n",
      "                dict(\n",
      "                    link=('right_knee', 'right_hip'),\n",
      "                    id=3,\n",
      "                    color=[255, 128, 0]),\n",
      "                4:\n",
      "                dict(\n",
      "                    link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                5:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_hip'),\n",
      "                    id=5,\n",
      "                    color=[51, 153, 255]),\n",
      "                6:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_hip'),\n",
      "                    id=6,\n",
      "                    color=[51, 153, 255]),\n",
      "                7:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'right_shoulder'),\n",
      "                    id=7,\n",
      "                    color=[51, 153, 255]),\n",
      "                8:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_elbow'),\n",
      "                    id=8,\n",
      "                    color=[0, 255, 0]),\n",
      "                9:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_elbow'),\n",
      "                    id=9,\n",
      "                    color=[255, 128, 0]),\n",
      "                10:\n",
      "                dict(\n",
      "                    link=('left_elbow', 'left_wrist'),\n",
      "                    id=10,\n",
      "                    color=[0, 255, 0]),\n",
      "                11:\n",
      "                dict(\n",
      "                    link=('right_elbow', 'right_wrist'),\n",
      "                    id=11,\n",
      "                    color=[255, 128, 0]),\n",
      "                12:\n",
      "                dict(\n",
      "                    link=('left_eye', 'right_eye'),\n",
      "                    id=12,\n",
      "                    color=[51, 153, 255]),\n",
      "                13:\n",
      "                dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n",
      "                14:\n",
      "                dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n",
      "                15:\n",
      "                dict(\n",
      "                    link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                16:\n",
      "                dict(\n",
      "                    link=('right_eye', 'right_ear'),\n",
      "                    id=16,\n",
      "                    color=[51, 153, 255]),\n",
      "                17:\n",
      "                dict(\n",
      "                    link=('left_ear', 'left_shoulder'),\n",
      "                    id=17,\n",
      "                    color=[51, 153, 255]),\n",
      "                18:\n",
      "                dict(\n",
      "                    link=('right_ear', 'right_shoulder'),\n",
      "                    id=18,\n",
      "                    color=[51, 153, 255])\n",
      "            }),\n",
      "            joint_weights=[\n",
      "                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n",
      "                1.0, 1.2, 1.2, 1.5, 1.5\n",
      "            ],\n",
      "            sigmas=[\n",
      "                0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n",
      "                0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n",
      "            ])),\n",
      "    test=dict(\n",
      "        type='TopDownCOCOTinyDataset',\n",
      "        ann_file='data/coco_tiny/val.json',\n",
      "        img_prefix='data/coco_tiny/images/',\n",
      "        data_cfg=dict(\n",
      "            image_size=[192, 256],\n",
      "            heatmap_size=[48, 64],\n",
      "            num_output_channels=17,\n",
      "            num_joints=17,\n",
      "            dataset_channel=[[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ]],\n",
      "            inference_channel=[\n",
      "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n",
      "            ],\n",
      "            soft_nms=False,\n",
      "            nms_thr=1.0,\n",
      "            oks_thr=0.9,\n",
      "            vis_thr=0.2,\n",
      "            use_gt_bbox=False,\n",
      "            det_bbox_thr=0.0,\n",
      "            bbox_file=\n",
      "            'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n",
      "        ),\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(type='TopDownAffine'),\n",
      "            dict(type='ToTensor'),\n",
      "            dict(\n",
      "                type='NormalizeTensor',\n",
      "                mean=[0.485, 0.456, 0.406],\n",
      "                std=[0.229, 0.224, 0.225]),\n",
      "            dict(\n",
      "                type='Collect',\n",
      "                keys=['img'],\n",
      "                meta_keys=[\n",
      "                    'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n",
      "                    'flip_pairs'\n",
      "                ])\n",
      "        ],\n",
      "        dataset_info=dict(\n",
      "            dataset_name='coco',\n",
      "            paper_info=dict(\n",
      "                author=\n",
      "                'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n",
      "                title='Microsoft coco: Common objects in context',\n",
      "                container='European conference on computer vision',\n",
      "                year='2014',\n",
      "                homepage='http://cocodataset.org/'),\n",
      "            keypoint_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    name='nose',\n",
      "                    id=0,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap=''),\n",
      "                1:\n",
      "                dict(\n",
      "                    name='left_eye',\n",
      "                    id=1,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_eye'),\n",
      "                2:\n",
      "                dict(\n",
      "                    name='right_eye',\n",
      "                    id=2,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_eye'),\n",
      "                3:\n",
      "                dict(\n",
      "                    name='left_ear',\n",
      "                    id=3,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='right_ear'),\n",
      "                4:\n",
      "                dict(\n",
      "                    name='right_ear',\n",
      "                    id=4,\n",
      "                    color=[51, 153, 255],\n",
      "                    type='upper',\n",
      "                    swap='left_ear'),\n",
      "                5:\n",
      "                dict(\n",
      "                    name='left_shoulder',\n",
      "                    id=5,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_shoulder'),\n",
      "                6:\n",
      "                dict(\n",
      "                    name='right_shoulder',\n",
      "                    id=6,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_shoulder'),\n",
      "                7:\n",
      "                dict(\n",
      "                    name='left_elbow',\n",
      "                    id=7,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_elbow'),\n",
      "                8:\n",
      "                dict(\n",
      "                    name='right_elbow',\n",
      "                    id=8,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_elbow'),\n",
      "                9:\n",
      "                dict(\n",
      "                    name='left_wrist',\n",
      "                    id=9,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='upper',\n",
      "                    swap='right_wrist'),\n",
      "                10:\n",
      "                dict(\n",
      "                    name='right_wrist',\n",
      "                    id=10,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='upper',\n",
      "                    swap='left_wrist'),\n",
      "                11:\n",
      "                dict(\n",
      "                    name='left_hip',\n",
      "                    id=11,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_hip'),\n",
      "                12:\n",
      "                dict(\n",
      "                    name='right_hip',\n",
      "                    id=12,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_hip'),\n",
      "                13:\n",
      "                dict(\n",
      "                    name='left_knee',\n",
      "                    id=13,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_knee'),\n",
      "                14:\n",
      "                dict(\n",
      "                    name='right_knee',\n",
      "                    id=14,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_knee'),\n",
      "                15:\n",
      "                dict(\n",
      "                    name='left_ankle',\n",
      "                    id=15,\n",
      "                    color=[0, 255, 0],\n",
      "                    type='lower',\n",
      "                    swap='right_ankle'),\n",
      "                16:\n",
      "                dict(\n",
      "                    name='right_ankle',\n",
      "                    id=16,\n",
      "                    color=[255, 128, 0],\n",
      "                    type='lower',\n",
      "                    swap='left_ankle')\n",
      "            }),\n",
      "            skeleton_info=dict({\n",
      "                0:\n",
      "                dict(\n",
      "                    link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n",
      "                1:\n",
      "                dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n",
      "                2:\n",
      "                dict(\n",
      "                    link=('right_ankle', 'right_knee'),\n",
      "                    id=2,\n",
      "                    color=[255, 128, 0]),\n",
      "                3:\n",
      "                dict(\n",
      "                    link=('right_knee', 'right_hip'),\n",
      "                    id=3,\n",
      "                    color=[255, 128, 0]),\n",
      "                4:\n",
      "                dict(\n",
      "                    link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                5:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_hip'),\n",
      "                    id=5,\n",
      "                    color=[51, 153, 255]),\n",
      "                6:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_hip'),\n",
      "                    id=6,\n",
      "                    color=[51, 153, 255]),\n",
      "                7:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'right_shoulder'),\n",
      "                    id=7,\n",
      "                    color=[51, 153, 255]),\n",
      "                8:\n",
      "                dict(\n",
      "                    link=('left_shoulder', 'left_elbow'),\n",
      "                    id=8,\n",
      "                    color=[0, 255, 0]),\n",
      "                9:\n",
      "                dict(\n",
      "                    link=('right_shoulder', 'right_elbow'),\n",
      "                    id=9,\n",
      "                    color=[255, 128, 0]),\n",
      "                10:\n",
      "                dict(\n",
      "                    link=('left_elbow', 'left_wrist'),\n",
      "                    id=10,\n",
      "                    color=[0, 255, 0]),\n",
      "                11:\n",
      "                dict(\n",
      "                    link=('right_elbow', 'right_wrist'),\n",
      "                    id=11,\n",
      "                    color=[255, 128, 0]),\n",
      "                12:\n",
      "                dict(\n",
      "                    link=('left_eye', 'right_eye'),\n",
      "                    id=12,\n",
      "                    color=[51, 153, 255]),\n",
      "                13:\n",
      "                dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n",
      "                14:\n",
      "                dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n",
      "                15:\n",
      "                dict(\n",
      "                    link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n",
      "                                                                 255]),\n",
      "                16:\n",
      "                dict(\n",
      "                    link=('right_eye', 'right_ear'),\n",
      "                    id=16,\n",
      "                    color=[51, 153, 255]),\n",
      "                17:\n",
      "                dict(\n",
      "                    link=('left_ear', 'left_shoulder'),\n",
      "                    id=17,\n",
      "                    color=[51, 153, 255]),\n",
      "                18:\n",
      "                dict(\n",
      "                    link=('right_ear', 'right_shoulder'),\n",
      "                    id=18,\n",
      "                    color=[51, 153, 255])\n",
      "            }),\n",
      "            joint_weights=[\n",
      "                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n",
      "                1.0, 1.2, 1.2, 1.5, 1.5\n",
      "            ],\n",
      "            sigmas=[\n",
      "                0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n",
      "                0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n",
      "            ])))\n",
      "work_dir = 'work_dirs/hrnet_w32_coco_tiny_256x192'\n",
      "gpu_ids = range(0, 1)\n",
      "seed = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from mmcv import Config\n",
    "\n",
    "cfg = Config.fromfile(\n",
    "    './configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_256x192.py'\n",
    ")\n",
    "\n",
    "# set basic configs\n",
    "cfg.data_root = 'data/coco_tiny'\n",
    "cfg.work_dir = 'work_dirs/hrnet_w32_coco_tiny_256x192'\n",
    "cfg.gpu_ids = range(1)\n",
    "cfg.seed = 0\n",
    "\n",
    "# set log interval\n",
    "cfg.log_config.interval = 1\n",
    "\n",
    "# set evaluation configs\n",
    "cfg.evaluation.interval = 10\n",
    "cfg.evaluation.metric = 'PCK'\n",
    "cfg.evaluation.save_best = 'PCK'\n",
    "\n",
    "# set learning rate policy\n",
    "lr_config = dict(\n",
    "    policy='step',\n",
    "    warmup='linear',\n",
    "    warmup_iters=10,\n",
    "    warmup_ratio=0.001,\n",
    "    step=[17, 35])\n",
    "cfg.total_epochs = 40\n",
    "\n",
    "# set batch size\n",
    "cfg.data.samples_per_gpu = 16\n",
    "cfg.data.val_dataloader = dict(samples_per_gpu=16)\n",
    "cfg.data.test_dataloader = dict(samples_per_gpu=16)\n",
    "\n",
    "# set dataset configs\n",
    "cfg.data.train.type = 'TopDownCOCOTinyDataset'\n",
    "cfg.data.train.ann_file = f'{cfg.data_root}/train.json'\n",
    "cfg.data.train.img_prefix = f'{cfg.data_root}/images/'\n",
    "\n",
    "cfg.data.val.type = 'TopDownCOCOTinyDataset'\n",
    "cfg.data.val.ann_file = f'{cfg.data_root}/val.json'\n",
    "cfg.data.val.img_prefix = f'{cfg.data_root}/images/'\n",
    "\n",
    "cfg.data.test.type = 'TopDownCOCOTinyDataset'\n",
    "cfg.data.test.ann_file = f'{cfg.data_root}/val.json'\n",
    "cfg.data.test.img_prefix = f'{cfg.data_root}/images/'\n",
    "\n",
    "print(cfg.pretty_text)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WQVa6wBDxVSW"
   },
   "source": [
    "### Train and Evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "c50b2c7b3d58486d9941509548a877e4",
      "ae33a61272f84a7981bc1f3008458688",
      "a0bf65a0401e465393ef8720ef3328ac",
      "a724d84941224553b1fab6c0b489213d",
      "210e7151c2ad44a3ba79d477f91d8b26",
      "a3dc245089464b159bbdd5fc71afa1bc",
      "864769e1e83c4b5d89baaa373c181f07",
      "9035c6e9fddd41d8b7dae395c93410a2",
      "1d31e1f7256d42669d76f54a8a844b79",
      "43ef0a1859c342dab6f6cd620ae78ba7",
      "90e3675160374766b5387ddb078fa3c5"
     ]
    },
    "id": "XJ5uVkwcxiyx",
    "outputId": "0693f2e3-f41d-46a8-d3ed-1add83735f91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_http loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth\" to /home/PJLAB/liyining/.cache/torch/hub/checkpoints/hrnet_w32-36af842e.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c50b2c7b3d58486d9941509548a877e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/126M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-22 22:37:43,193 - mmpose - WARNING - The model and loaded state dict do not match exactly\n",
      "\n",
      "unexpected key in source state_dict: head.0.0.0.conv1.weight, head.0.0.0.bn1.weight, head.0.0.0.bn1.bias, head.0.0.0.bn1.running_mean, head.0.0.0.bn1.running_var, head.0.0.0.bn1.num_batches_tracked, head.0.0.0.conv2.weight, head.0.0.0.bn2.weight, head.0.0.0.bn2.bias, head.0.0.0.bn2.running_mean, head.0.0.0.bn2.running_var, head.0.0.0.bn2.num_batches_tracked, head.0.0.0.conv3.weight, head.0.0.0.bn3.weight, head.0.0.0.bn3.bias, head.0.0.0.bn3.running_mean, head.0.0.0.bn3.running_var, head.0.0.0.bn3.num_batches_tracked, head.0.0.0.downsample.0.weight, head.0.0.0.downsample.1.weight, head.0.0.0.downsample.1.bias, head.0.0.0.downsample.1.running_mean, head.0.0.0.downsample.1.running_var, head.0.0.0.downsample.1.num_batches_tracked, head.0.1.0.conv1.weight, head.0.1.0.bn1.weight, head.0.1.0.bn1.bias, head.0.1.0.bn1.running_mean, head.0.1.0.bn1.running_var, head.0.1.0.bn1.num_batches_tracked, head.0.1.0.conv2.weight, head.0.1.0.bn2.weight, head.0.1.0.bn2.bias, head.0.1.0.bn2.running_mean, head.0.1.0.bn2.running_var, head.0.1.0.bn2.num_batches_tracked, head.0.1.0.conv3.weight, head.0.1.0.bn3.weight, head.0.1.0.bn3.bias, head.0.1.0.bn3.running_mean, head.0.1.0.bn3.running_var, head.0.1.0.bn3.num_batches_tracked, head.0.1.0.downsample.0.weight, head.0.1.0.downsample.1.weight, head.0.1.0.downsample.1.bias, head.0.1.0.downsample.1.running_mean, head.0.1.0.downsample.1.running_var, head.0.1.0.downsample.1.num_batches_tracked, head.0.2.0.conv1.weight, head.0.2.0.bn1.weight, head.0.2.0.bn1.bias, head.0.2.0.bn1.running_mean, head.0.2.0.bn1.running_var, head.0.2.0.bn1.num_batches_tracked, head.0.2.0.conv2.weight, head.0.2.0.bn2.weight, head.0.2.0.bn2.bias, head.0.2.0.bn2.running_mean, head.0.2.0.bn2.running_var, head.0.2.0.bn2.num_batches_tracked, head.0.2.0.conv3.weight, head.0.2.0.bn3.weight, head.0.2.0.bn3.bias, head.0.2.0.bn3.running_mean, head.0.2.0.bn3.running_var, head.0.2.0.bn3.num_batches_tracked, head.0.2.0.downsample.0.weight, head.0.2.0.downsample.1.weight, head.0.2.0.downsample.1.bias, head.0.2.0.downsample.1.running_mean, head.0.2.0.downsample.1.running_var, head.0.2.0.downsample.1.num_batches_tracked, head.1.0.0.conv1.weight, head.1.0.0.bn1.weight, head.1.0.0.bn1.bias, head.1.0.0.bn1.running_mean, head.1.0.0.bn1.running_var, head.1.0.0.bn1.num_batches_tracked, head.1.0.0.conv2.weight, head.1.0.0.bn2.weight, head.1.0.0.bn2.bias, head.1.0.0.bn2.running_mean, head.1.0.0.bn2.running_var, head.1.0.0.bn2.num_batches_tracked, head.1.0.0.conv3.weight, head.1.0.0.bn3.weight, head.1.0.0.bn3.bias, head.1.0.0.bn3.running_mean, head.1.0.0.bn3.running_var, head.1.0.0.bn3.num_batches_tracked, head.1.0.0.downsample.0.weight, head.1.0.0.downsample.1.weight, head.1.0.0.downsample.1.bias, head.1.0.0.downsample.1.running_mean, head.1.0.0.downsample.1.running_var, head.1.0.0.downsample.1.num_batches_tracked, head.1.1.0.conv1.weight, head.1.1.0.bn1.weight, head.1.1.0.bn1.bias, head.1.1.0.bn1.running_mean, head.1.1.0.bn1.running_var, head.1.1.0.bn1.num_batches_tracked, head.1.1.0.conv2.weight, head.1.1.0.bn2.weight, head.1.1.0.bn2.bias, head.1.1.0.bn2.running_mean, head.1.1.0.bn2.running_var, head.1.1.0.bn2.num_batches_tracked, head.1.1.0.conv3.weight, head.1.1.0.bn3.weight, head.1.1.0.bn3.bias, head.1.1.0.bn3.running_mean, head.1.1.0.bn3.running_var, head.1.1.0.bn3.num_batches_tracked, head.1.1.0.downsample.0.weight, head.1.1.0.downsample.1.weight, head.1.1.0.downsample.1.bias, head.1.1.0.downsample.1.running_mean, head.1.1.0.downsample.1.running_var, head.1.1.0.downsample.1.num_batches_tracked, head.2.0.0.conv1.weight, head.2.0.0.bn1.weight, head.2.0.0.bn1.bias, head.2.0.0.bn1.running_mean, head.2.0.0.bn1.running_var, head.2.0.0.bn1.num_batches_tracked, head.2.0.0.conv2.weight, head.2.0.0.bn2.weight, head.2.0.0.bn2.bias, head.2.0.0.bn2.running_mean, head.2.0.0.bn2.running_var, head.2.0.0.bn2.num_batches_tracked, head.2.0.0.conv3.weight, head.2.0.0.bn3.weight, head.2.0.0.bn3.bias, head.2.0.0.bn3.running_mean, head.2.0.0.bn3.running_var, head.2.0.0.bn3.num_batches_tracked, head.2.0.0.downsample.0.weight, head.2.0.0.downsample.1.weight, head.2.0.0.downsample.1.bias, head.2.0.0.downsample.1.running_mean, head.2.0.0.downsample.1.running_var, head.2.0.0.downsample.1.num_batches_tracked, head.3.0.0.conv1.weight, head.3.0.0.bn1.weight, head.3.0.0.bn1.bias, head.3.0.0.bn1.running_mean, head.3.0.0.bn1.running_var, head.3.0.0.bn1.num_batches_tracked, head.3.0.0.conv2.weight, head.3.0.0.bn2.weight, head.3.0.0.bn2.bias, head.3.0.0.bn2.running_mean, head.3.0.0.bn2.running_var, head.3.0.0.bn2.num_batches_tracked, head.3.0.0.conv3.weight, head.3.0.0.bn3.weight, head.3.0.0.bn3.bias, head.3.0.0.bn3.running_mean, head.3.0.0.bn3.running_var, head.3.0.0.bn3.num_batches_tracked, head.3.0.0.downsample.0.weight, head.3.0.0.downsample.1.weight, head.3.0.0.downsample.1.bias, head.3.0.0.downsample.1.running_mean, head.3.0.0.downsample.1.running_var, head.3.0.0.downsample.1.num_batches_tracked, fc.weight, fc.bias, stage4.2.fuse_layers.1.0.0.0.weight, stage4.2.fuse_layers.1.0.0.1.weight, stage4.2.fuse_layers.1.0.0.1.bias, stage4.2.fuse_layers.1.0.0.1.running_mean, stage4.2.fuse_layers.1.0.0.1.running_var, stage4.2.fuse_layers.1.0.0.1.num_batches_tracked, stage4.2.fuse_layers.1.2.0.weight, stage4.2.fuse_layers.1.2.1.weight, stage4.2.fuse_layers.1.2.1.bias, stage4.2.fuse_layers.1.2.1.running_mean, stage4.2.fuse_layers.1.2.1.running_var, stage4.2.fuse_layers.1.2.1.num_batches_tracked, stage4.2.fuse_layers.1.3.0.weight, stage4.2.fuse_layers.1.3.1.weight, stage4.2.fuse_layers.1.3.1.bias, stage4.2.fuse_layers.1.3.1.running_mean, stage4.2.fuse_layers.1.3.1.running_var, stage4.2.fuse_layers.1.3.1.num_batches_tracked, stage4.2.fuse_layers.2.0.0.0.weight, stage4.2.fuse_layers.2.0.0.1.weight, stage4.2.fuse_layers.2.0.0.1.bias, stage4.2.fuse_layers.2.0.0.1.running_mean, stage4.2.fuse_layers.2.0.0.1.running_var, stage4.2.fuse_layers.2.0.0.1.num_batches_tracked, stage4.2.fuse_layers.2.0.1.0.weight, stage4.2.fuse_layers.2.0.1.1.weight, stage4.2.fuse_layers.2.0.1.1.bias, stage4.2.fuse_layers.2.0.1.1.running_mean, stage4.2.fuse_layers.2.0.1.1.running_var, stage4.2.fuse_layers.2.0.1.1.num_batches_tracked, stage4.2.fuse_layers.2.1.0.0.weight, stage4.2.fuse_layers.2.1.0.1.weight, stage4.2.fuse_layers.2.1.0.1.bias, stage4.2.fuse_layers.2.1.0.1.running_mean, stage4.2.fuse_layers.2.1.0.1.running_var, stage4.2.fuse_layers.2.1.0.1.num_batches_tracked, stage4.2.fuse_layers.2.3.0.weight, stage4.2.fuse_layers.2.3.1.weight, stage4.2.fuse_layers.2.3.1.bias, stage4.2.fuse_layers.2.3.1.running_mean, stage4.2.fuse_layers.2.3.1.running_var, stage4.2.fuse_layers.2.3.1.num_batches_tracked, stage4.2.fuse_layers.3.0.0.0.weight, stage4.2.fuse_layers.3.0.0.1.weight, stage4.2.fuse_layers.3.0.0.1.bias, stage4.2.fuse_layers.3.0.0.1.running_mean, stage4.2.fuse_layers.3.0.0.1.running_var, stage4.2.fuse_layers.3.0.0.1.num_batches_tracked, stage4.2.fuse_layers.3.0.1.0.weight, stage4.2.fuse_layers.3.0.1.1.weight, stage4.2.fuse_layers.3.0.1.1.bias, stage4.2.fuse_layers.3.0.1.1.running_mean, stage4.2.fuse_layers.3.0.1.1.running_var, stage4.2.fuse_layers.3.0.1.1.num_batches_tracked, stage4.2.fuse_layers.3.0.2.0.weight, stage4.2.fuse_layers.3.0.2.1.weight, stage4.2.fuse_layers.3.0.2.1.bias, stage4.2.fuse_layers.3.0.2.1.running_mean, stage4.2.fuse_layers.3.0.2.1.running_var, stage4.2.fuse_layers.3.0.2.1.num_batches_tracked, stage4.2.fuse_layers.3.1.0.0.weight, stage4.2.fuse_layers.3.1.0.1.weight, stage4.2.fuse_layers.3.1.0.1.bias, stage4.2.fuse_layers.3.1.0.1.running_mean, stage4.2.fuse_layers.3.1.0.1.running_var, stage4.2.fuse_layers.3.1.0.1.num_batches_tracked, stage4.2.fuse_layers.3.1.1.0.weight, stage4.2.fuse_layers.3.1.1.1.weight, stage4.2.fuse_layers.3.1.1.1.bias, stage4.2.fuse_layers.3.1.1.1.running_mean, stage4.2.fuse_layers.3.1.1.1.running_var, stage4.2.fuse_layers.3.1.1.1.num_batches_tracked, stage4.2.fuse_layers.3.2.0.0.weight, stage4.2.fuse_layers.3.2.0.1.weight, stage4.2.fuse_layers.3.2.0.1.bias, stage4.2.fuse_layers.3.2.0.1.running_mean, stage4.2.fuse_layers.3.2.0.1.running_var, stage4.2.fuse_layers.3.2.0.1.num_batches_tracked\n",
      "\n",
      "2021-09-22 22:37:46,021 - mmpose - INFO - Start running, host: PJLAB\\liyining@shai14000065l, work_dir: /home/PJLAB/liyining/openmmlab/mmpose/work_dirs/hrnet_w32_coco_tiny_256x192\n",
      "2021-09-22 22:37:46,021 - mmpose - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(NORMAL      ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(NORMAL      ) EvalHook                           \n",
      "(LOW         ) IterTimerHook                      \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(NORMAL      ) EvalHook                           \n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) OptimizerHook                      \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(NORMAL      ) EvalHook                           \n",
      "(LOW         ) IterTimerHook                      \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(NORMAL      ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "2021-09-22 22:37:46,022 - mmpose - INFO - workflow: [('train', 1)], max: 40 epochs\n",
      "2021-09-22 22:37:48,774 - mmpose - INFO - Epoch [1][1/4]\tlr: 5.000e-07, eta: 0:07:17, time: 2.749, data_time: 2.287, memory: 2594, mse_loss: 0.0020, acc_pose: 0.0000, loss: 0.0020\n",
      "2021-09-22 22:37:49,164 - mmpose - INFO - Epoch [1][2/4]\tlr: 1.499e-06, eta: 0:04:08, time: 0.391, data_time: 0.002, memory: 2903, mse_loss: 0.0025, acc_pose: 0.0152, loss: 0.0025\n",
      "2021-09-22 22:37:49,549 - mmpose - INFO - Epoch [1][3/4]\tlr: 2.498e-06, eta: 0:03:04, time: 0.385, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.0270, loss: 0.0019\n",
      "2021-09-22 22:37:49,937 - mmpose - INFO - Epoch [1][4/4]\tlr: 3.497e-06, eta: 0:02:32, time: 0.388, data_time: 0.002, memory: 2903, mse_loss: 0.0020, acc_pose: 0.0104, loss: 0.0020\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:37:52,650 - mmpose - INFO - Epoch [2][1/4]\tlr: 4.496e-06, eta: 0:03:23, time: 2.656, data_time: 2.263, memory: 2903, mse_loss: 0.0024, acc_pose: 0.0206, loss: 0.0024\n",
      "2021-09-22 22:37:53,025 - mmpose - INFO - Epoch [2][2/4]\tlr: 5.495e-06, eta: 0:02:58, time: 0.375, data_time: 0.001, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0331, loss: 0.0022\n",
      "2021-09-22 22:37:53,395 - mmpose - INFO - Epoch [2][3/4]\tlr: 6.494e-06, eta: 0:02:39, time: 0.371, data_time: 0.001, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0065, loss: 0.0023\n",
      "2021-09-22 22:37:53,770 - mmpose - INFO - Epoch [2][4/4]\tlr: 7.493e-06, eta: 0:02:26, time: 0.375, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.0143, loss: 0.0020\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:37:56,509 - mmpose - INFO - Epoch [3][1/4]\tlr: 8.492e-06, eta: 0:02:54, time: 2.685, data_time: 2.248, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0183, loss: 0.0023\n",
      "2021-09-22 22:37:56,902 - mmpose - INFO - Epoch [3][2/4]\tlr: 9.491e-06, eta: 0:02:41, time: 0.393, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.0334, loss: 0.0020\n",
      "2021-09-22 22:37:57,269 - mmpose - INFO - Epoch [3][3/4]\tlr: 1.049e-05, eta: 0:02:30, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.0139, loss: 0.0017\n",
      "2021-09-22 22:37:57,632 - mmpose - INFO - Epoch [3][4/4]\tlr: 1.149e-05, eta: 0:02:21, time: 0.363, data_time: 0.001, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0119, loss: 0.0022\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:00,246 - mmpose - INFO - Epoch [4][1/4]\tlr: 1.249e-05, eta: 0:02:38, time: 2.552, data_time: 2.207, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0152, loss: 0.0023\n",
      "2021-09-22 22:38:00,579 - mmpose - INFO - Epoch [4][2/4]\tlr: 1.349e-05, eta: 0:02:29, time: 0.333, data_time: 0.002, memory: 2903, mse_loss: 0.0019, acc_pose: 0.0250, loss: 0.0019\n",
      "2021-09-22 22:38:00,946 - mmpose - INFO - Epoch [4][3/4]\tlr: 1.449e-05, eta: 0:02:22, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0025, acc_pose: 0.0127, loss: 0.0025\n",
      "2021-09-22 22:38:01,309 - mmpose - INFO - Epoch [4][4/4]\tlr: 1.549e-05, eta: 0:02:16, time: 0.363, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.0310, loss: 0.0020\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:03,971 - mmpose - INFO - Epoch [5][1/4]\tlr: 1.648e-05, eta: 0:02:29, time: 2.618, data_time: 2.265, memory: 2903, mse_loss: 0.0024, acc_pose: 0.0264, loss: 0.0024\n",
      "2021-09-22 22:38:04,319 - mmpose - INFO - Epoch [5][2/4]\tlr: 1.748e-05, eta: 0:02:22, time: 0.348, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.0147, loss: 0.0017\n",
      "2021-09-22 22:38:04,672 - mmpose - INFO - Epoch [5][3/4]\tlr: 1.848e-05, eta: 0:02:16, time: 0.354, data_time: 0.002, memory: 2903, mse_loss: 0.0020, acc_pose: 0.0065, loss: 0.0020\n",
      "2021-09-22 22:38:05,062 - mmpose - INFO - Epoch [5][4/4]\tlr: 1.948e-05, eta: 0:02:11, time: 0.390, data_time: 0.001, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0168, loss: 0.0023\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:07,651 - mmpose - INFO - Epoch [6][1/4]\tlr: 2.048e-05, eta: 0:02:21, time: 2.546, data_time: 2.202, memory: 2903, mse_loss: 0.0009, acc_pose: 0.0095, loss: 0.0009\n",
      "2021-09-22 22:38:07,981 - mmpose - INFO - Epoch [6][2/4]\tlr: 2.148e-05, eta: 0:02:16, time: 0.330, data_time: 0.002, memory: 2903, mse_loss: 0.0025, acc_pose: 0.0225, loss: 0.0025\n",
      "2021-09-22 22:38:08,311 - mmpose - INFO - Epoch [6][3/4]\tlr: 2.248e-05, eta: 0:02:11, time: 0.330, data_time: 0.001, memory: 2903, mse_loss: 0.0025, acc_pose: 0.0266, loss: 0.0025\n",
      "2021-09-22 22:38:08,638 - mmpose - INFO - Epoch [6][4/4]\tlr: 2.348e-05, eta: 0:02:06, time: 0.327, data_time: 0.001, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0281, loss: 0.0023\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:11,310 - mmpose - INFO - Epoch [7][1/4]\tlr: 2.448e-05, eta: 0:02:14, time: 2.614, data_time: 2.220, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0202, loss: 0.0022\n",
      "2021-09-22 22:38:11,678 - mmpose - INFO - Epoch [7][2/4]\tlr: 2.548e-05, eta: 0:02:10, time: 0.368, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.0059, loss: 0.0018\n",
      "2021-09-22 22:38:12,049 - mmpose - INFO - Epoch [7][3/4]\tlr: 2.647e-05, eta: 0:02:06, time: 0.371, data_time: 0.002, memory: 2903, mse_loss: 0.0019, acc_pose: 0.0231, loss: 0.0019\n",
      "2021-09-22 22:38:12,418 - mmpose - INFO - Epoch [7][4/4]\tlr: 2.747e-05, eta: 0:02:02, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.0564, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:15,087 - mmpose - INFO - Epoch [8][1/4]\tlr: 2.847e-05, eta: 0:02:09, time: 2.617, data_time: 2.224, memory: 2903, mse_loss: 0.0019, acc_pose: 0.0576, loss: 0.0019\n",
      "2021-09-22 22:38:15,457 - mmpose - INFO - Epoch [8][2/4]\tlr: 2.947e-05, eta: 0:02:05, time: 0.370, data_time: 0.002, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0451, loss: 0.0022\n",
      "2021-09-22 22:38:15,824 - mmpose - INFO - Epoch [8][3/4]\tlr: 3.047e-05, eta: 0:02:02, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0021, acc_pose: 0.0161, loss: 0.0021\n",
      "2021-09-22 22:38:16,191 - mmpose - INFO - Epoch [8][4/4]\tlr: 3.147e-05, eta: 0:01:59, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0024, acc_pose: 0.0953, loss: 0.0024\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:18,866 - mmpose - INFO - Epoch [9][1/4]\tlr: 3.247e-05, eta: 0:02:04, time: 2.622, data_time: 2.226, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0718, loss: 0.0022\n",
      "2021-09-22 22:38:19,231 - mmpose - INFO - Epoch [9][2/4]\tlr: 3.347e-05, eta: 0:02:01, time: 0.364, data_time: 0.002, memory: 2903, mse_loss: 0.0022, acc_pose: 0.0466, loss: 0.0022\n",
      "2021-09-22 22:38:19,596 - mmpose - INFO - Epoch [9][3/4]\tlr: 3.447e-05, eta: 0:01:58, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.0562, loss: 0.0018\n",
      "2021-09-22 22:38:19,963 - mmpose - INFO - Epoch [9][4/4]\tlr: 3.547e-05, eta: 0:01:55, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0021, acc_pose: 0.0830, loss: 0.0021\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:22,631 - mmpose - INFO - Epoch [10][1/4]\tlr: 3.646e-05, eta: 0:02:00, time: 2.611, data_time: 2.220, memory: 2903, mse_loss: 0.0021, acc_pose: 0.0687, loss: 0.0021\n",
      "2021-09-22 22:38:22,995 - mmpose - INFO - Epoch [10][2/4]\tlr: 3.746e-05, eta: 0:01:57, time: 0.365, data_time: 0.002, memory: 2903, mse_loss: 0.0017, acc_pose: 0.1515, loss: 0.0017\n",
      "2021-09-22 22:38:23,361 - mmpose - INFO - Epoch [10][3/4]\tlr: 3.846e-05, eta: 0:01:54, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1117, loss: 0.0020\n",
      "2021-09-22 22:38:23,729 - mmpose - INFO - Epoch [10][4/4]\tlr: 3.946e-05, eta: 0:01:51, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0023, acc_pose: 0.0976, loss: 0.0023\n",
      "2021-09-22 22:38:23,778 - mmpose - INFO - Saving checkpoint at 10 epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                                  ] 0/25, elapsed: 0s, ETA:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 43.4 task/s, elapsed: 1s, ETA:     0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-22 22:38:25,434 - mmpose - INFO - Now best checkpoint is saved as best_PCK_epoch_10.pth.\n",
      "2021-09-22 22:38:25,434 - mmpose - INFO - Best PCK is 0.2753 at 10 epoch.\n",
      "2021-09-22 22:38:25,435 - mmpose - INFO - Epoch(val) [10][2]\tPCK: 0.2753\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:28,080 - mmpose - INFO - Epoch [11][1/4]\tlr: 4.046e-05, eta: 0:01:55, time: 2.639, data_time: 2.248, memory: 2903, mse_loss: 0.0018, acc_pose: 0.1022, loss: 0.0018\n",
      "2021-09-22 22:38:28,448 - mmpose - INFO - Epoch [11][2/4]\tlr: 4.146e-05, eta: 0:01:53, time: 0.368, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.0652, loss: 0.0018\n",
      "2021-09-22 22:38:28,813 - mmpose - INFO - Epoch [11][3/4]\tlr: 4.246e-05, eta: 0:01:50, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.1531, loss: 0.0019\n",
      "2021-09-22 22:38:29,178 - mmpose - INFO - Epoch [11][4/4]\tlr: 4.346e-05, eta: 0:01:47, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1465, loss: 0.0020\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:31,838 - mmpose - INFO - Epoch [12][1/4]\tlr: 4.446e-05, eta: 0:01:51, time: 2.608, data_time: 2.218, memory: 2903, mse_loss: 0.0018, acc_pose: 0.0605, loss: 0.0018\n",
      "2021-09-22 22:38:32,206 - mmpose - INFO - Epoch [12][2/4]\tlr: 4.545e-05, eta: 0:01:48, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0022, acc_pose: 0.1361, loss: 0.0022\n",
      "2021-09-22 22:38:32,574 - mmpose - INFO - Epoch [12][3/4]\tlr: 4.645e-05, eta: 0:01:46, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.1523, loss: 0.0019\n",
      "2021-09-22 22:38:32,942 - mmpose - INFO - Epoch [12][4/4]\tlr: 4.745e-05, eta: 0:01:44, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0022, acc_pose: 0.1340, loss: 0.0022\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:35,606 - mmpose - INFO - Epoch [13][1/4]\tlr: 4.845e-05, eta: 0:01:47, time: 2.613, data_time: 2.217, memory: 2903, mse_loss: 0.0021, acc_pose: 0.1284, loss: 0.0021\n",
      "2021-09-22 22:38:35,973 - mmpose - INFO - Epoch [13][2/4]\tlr: 4.945e-05, eta: 0:01:44, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0019, acc_pose: 0.1190, loss: 0.0019\n",
      "2021-09-22 22:38:36,348 - mmpose - INFO - Epoch [13][3/4]\tlr: 5.045e-05, eta: 0:01:42, time: 0.375, data_time: 0.001, memory: 2903, mse_loss: 0.0022, acc_pose: 0.1670, loss: 0.0022\n",
      "2021-09-22 22:38:36,724 - mmpose - INFO - Epoch [13][4/4]\tlr: 5.145e-05, eta: 0:01:40, time: 0.376, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1706, loss: 0.0020\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:39,416 - mmpose - INFO - Epoch [14][1/4]\tlr: 5.245e-05, eta: 0:01:43, time: 2.641, data_time: 2.245, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1876, loss: 0.0020\n",
      "2021-09-22 22:38:39,786 - mmpose - INFO - Epoch [14][2/4]\tlr: 5.345e-05, eta: 0:01:40, time: 0.371, data_time: 0.002, memory: 2903, mse_loss: 0.0022, acc_pose: 0.1800, loss: 0.0022\n",
      "2021-09-22 22:38:40,159 - mmpose - INFO - Epoch [14][3/4]\tlr: 5.445e-05, eta: 0:01:38, time: 0.373, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1617, loss: 0.0020\n",
      "2021-09-22 22:38:40,527 - mmpose - INFO - Epoch [14][4/4]\tlr: 5.544e-05, eta: 0:01:36, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.1060, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:43,178 - mmpose - INFO - Epoch [15][1/4]\tlr: 5.644e-05, eta: 0:01:38, time: 2.601, data_time: 2.203, memory: 2903, mse_loss: 0.0020, acc_pose: 0.2289, loss: 0.0020\n",
      "2021-09-22 22:38:43,544 - mmpose - INFO - Epoch [15][2/4]\tlr: 5.744e-05, eta: 0:01:36, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0016, acc_pose: 0.1636, loss: 0.0016\n",
      "2021-09-22 22:38:43,910 - mmpose - INFO - Epoch [15][3/4]\tlr: 5.844e-05, eta: 0:01:34, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0021, acc_pose: 0.1721, loss: 0.0021\n",
      "2021-09-22 22:38:44,276 - mmpose - INFO - Epoch [15][4/4]\tlr: 5.944e-05, eta: 0:01:33, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.1038, loss: 0.0017\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:46,914 - mmpose - INFO - Epoch [16][1/4]\tlr: 6.044e-05, eta: 0:01:34, time: 2.587, data_time: 2.198, memory: 2903, mse_loss: 0.0020, acc_pose: 0.1295, loss: 0.0020\n",
      "2021-09-22 22:38:47,283 - mmpose - INFO - Epoch [16][2/4]\tlr: 6.144e-05, eta: 0:01:32, time: 0.369, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.1358, loss: 0.0018\n",
      "2021-09-22 22:38:47,651 - mmpose - INFO - Epoch [16][3/4]\tlr: 6.244e-05, eta: 0:01:31, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.1543, loss: 0.0018\n",
      "2021-09-22 22:38:48,019 - mmpose - INFO - Epoch [16][4/4]\tlr: 6.344e-05, eta: 0:01:29, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.1155, loss: 0.0017\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:50,700 - mmpose - INFO - Epoch [17][1/4]\tlr: 6.444e-05, eta: 0:01:30, time: 2.611, data_time: 2.217, memory: 2903, mse_loss: 0.0019, acc_pose: 0.2150, loss: 0.0019\n",
      "2021-09-22 22:38:51,070 - mmpose - INFO - Epoch [17][2/4]\tlr: 6.544e-05, eta: 0:01:29, time: 0.370, data_time: 0.002, memory: 2903, mse_loss: 0.0022, acc_pose: 0.1850, loss: 0.0022\n",
      "2021-09-22 22:38:51,439 - mmpose - INFO - Epoch [17][3/4]\tlr: 6.643e-05, eta: 0:01:27, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.1244, loss: 0.0019\n",
      "2021-09-22 22:38:51,805 - mmpose - INFO - Epoch [17][4/4]\tlr: 6.743e-05, eta: 0:01:25, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2272, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:54,470 - mmpose - INFO - Epoch [18][1/4]\tlr: 6.843e-05, eta: 0:01:26, time: 2.614, data_time: 2.218, memory: 2903, mse_loss: 0.0020, acc_pose: 0.2409, loss: 0.0020\n",
      "2021-09-22 22:38:54,840 - mmpose - INFO - Epoch [18][2/4]\tlr: 6.943e-05, eta: 0:01:25, time: 0.370, data_time: 0.002, memory: 2903, mse_loss: 0.0017, acc_pose: 0.1534, loss: 0.0017\n",
      "2021-09-22 22:38:55,209 - mmpose - INFO - Epoch [18][3/4]\tlr: 7.043e-05, eta: 0:01:23, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.3068, loss: 0.0018\n",
      "2021-09-22 22:38:55,575 - mmpose - INFO - Epoch [18][4/4]\tlr: 7.143e-05, eta: 0:01:21, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2066, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:38:58,277 - mmpose - INFO - Epoch [19][1/4]\tlr: 7.243e-05, eta: 0:01:22, time: 2.636, data_time: 2.228, memory: 2903, mse_loss: 0.0019, acc_pose: 0.2946, loss: 0.0019\n",
      "2021-09-22 22:38:58,651 - mmpose - INFO - Epoch [19][2/4]\tlr: 7.343e-05, eta: 0:01:21, time: 0.374, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.2669, loss: 0.0014\n",
      "2021-09-22 22:38:59,019 - mmpose - INFO - Epoch [19][3/4]\tlr: 7.443e-05, eta: 0:01:19, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.2514, loss: 0.0020\n",
      "2021-09-22 22:38:59,388 - mmpose - INFO - Epoch [19][4/4]\tlr: 7.543e-05, eta: 0:01:18, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.2052, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:02,074 - mmpose - INFO - Epoch [20][1/4]\tlr: 7.642e-05, eta: 0:01:19, time: 2.634, data_time: 2.231, memory: 2903, mse_loss: 0.0021, acc_pose: 0.1846, loss: 0.0021\n",
      "2021-09-22 22:39:02,443 - mmpose - INFO - Epoch [20][2/4]\tlr: 7.742e-05, eta: 0:01:17, time: 0.369, data_time: 0.002, memory: 2903, mse_loss: 0.0013, acc_pose: 0.1537, loss: 0.0013\n",
      "2021-09-22 22:39:02,811 - mmpose - INFO - Epoch [20][3/4]\tlr: 7.842e-05, eta: 0:01:15, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.2114, loss: 0.0017\n",
      "2021-09-22 22:39:03,180 - mmpose - INFO - Epoch [20][4/4]\tlr: 7.942e-05, eta: 0:01:14, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.2147, loss: 0.0020\n",
      "2021-09-22 22:39:03,231 - mmpose - INFO - Saving checkpoint at 20 epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                                  ] 0/25, elapsed: 0s, ETA:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 45.0 task/s, elapsed: 1s, ETA:     0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-22 22:39:04,788 - mmpose - INFO - Now best checkpoint is saved as best_PCK_epoch_20.pth.\n",
      "2021-09-22 22:39:04,789 - mmpose - INFO - Best PCK is 0.3123 at 20 epoch.\n",
      "2021-09-22 22:39:04,789 - mmpose - INFO - Epoch(val) [20][2]\tPCK: 0.3123\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:07,402 - mmpose - INFO - Epoch [21][1/4]\tlr: 8.042e-05, eta: 0:01:15, time: 2.609, data_time: 2.218, memory: 2903, mse_loss: 0.0017, acc_pose: 0.2806, loss: 0.0017\n",
      "2021-09-22 22:39:07,769 - mmpose - INFO - Epoch [21][2/4]\tlr: 8.142e-05, eta: 0:01:13, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0017, acc_pose: 0.2352, loss: 0.0017\n",
      "2021-09-22 22:39:08,136 - mmpose - INFO - Epoch [21][3/4]\tlr: 8.242e-05, eta: 0:01:12, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0021, acc_pose: 0.2968, loss: 0.0021\n",
      "2021-09-22 22:39:08,502 - mmpose - INFO - Epoch [21][4/4]\tlr: 8.342e-05, eta: 0:01:10, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0015, acc_pose: 0.1867, loss: 0.0015\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:11,188 - mmpose - INFO - Epoch [22][1/4]\tlr: 8.442e-05, eta: 0:01:11, time: 2.635, data_time: 2.244, memory: 2903, mse_loss: 0.0019, acc_pose: 0.3474, loss: 0.0019\n",
      "2021-09-22 22:39:11,561 - mmpose - INFO - Epoch [22][2/4]\tlr: 8.542e-05, eta: 0:01:09, time: 0.373, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.2988, loss: 0.0016\n",
      "2021-09-22 22:39:11,929 - mmpose - INFO - Epoch [22][3/4]\tlr: 8.641e-05, eta: 0:01:08, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2864, loss: 0.0018\n",
      "2021-09-22 22:39:12,292 - mmpose - INFO - Epoch [22][4/4]\tlr: 8.741e-05, eta: 0:01:07, time: 0.363, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2130, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:14,985 - mmpose - INFO - Epoch [23][1/4]\tlr: 8.841e-05, eta: 0:01:07, time: 2.625, data_time: 2.227, memory: 2903, mse_loss: 0.0016, acc_pose: 0.2869, loss: 0.0016\n",
      "2021-09-22 22:39:15,352 - mmpose - INFO - Epoch [23][2/4]\tlr: 8.941e-05, eta: 0:01:06, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2948, loss: 0.0018\n",
      "2021-09-22 22:39:15,732 - mmpose - INFO - Epoch [23][3/4]\tlr: 9.041e-05, eta: 0:01:04, time: 0.381, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2796, loss: 0.0018\n",
      "2021-09-22 22:39:16,098 - mmpose - INFO - Epoch [23][4/4]\tlr: 9.141e-05, eta: 0:01:03, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.2982, loss: 0.0017\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:18,773 - mmpose - INFO - Epoch [24][1/4]\tlr: 9.241e-05, eta: 0:01:03, time: 2.624, data_time: 2.226, memory: 2903, mse_loss: 0.0016, acc_pose: 0.3208, loss: 0.0016\n",
      "2021-09-22 22:39:19,142 - mmpose - INFO - Epoch [24][2/4]\tlr: 9.341e-05, eta: 0:01:02, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.2067, loss: 0.0018\n",
      "2021-09-22 22:39:19,512 - mmpose - INFO - Epoch [24][3/4]\tlr: 9.441e-05, eta: 0:01:00, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0020, acc_pose: 0.2734, loss: 0.0020\n",
      "2021-09-22 22:39:19,879 - mmpose - INFO - Epoch [24][4/4]\tlr: 9.540e-05, eta: 0:00:59, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.3253, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:22,523 - mmpose - INFO - Epoch [25][1/4]\tlr: 9.640e-05, eta: 0:00:59, time: 2.593, data_time: 2.211, memory: 2903, mse_loss: 0.0020, acc_pose: 0.3644, loss: 0.0020\n",
      "2021-09-22 22:39:22,893 - mmpose - INFO - Epoch [25][2/4]\tlr: 9.740e-05, eta: 0:00:58, time: 0.371, data_time: 0.002, memory: 2903, mse_loss: 0.0014, acc_pose: 0.3229, loss: 0.0014\n",
      "2021-09-22 22:39:23,260 - mmpose - INFO - Epoch [25][3/4]\tlr: 9.840e-05, eta: 0:00:57, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0015, acc_pose: 0.3083, loss: 0.0015\n",
      "2021-09-22 22:39:23,625 - mmpose - INFO - Epoch [25][4/4]\tlr: 9.940e-05, eta: 0:00:55, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0015, acc_pose: 0.2692, loss: 0.0015\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:26,300 - mmpose - INFO - Epoch [26][1/4]\tlr: 1.004e-04, eta: 0:00:55, time: 2.623, data_time: 2.235, memory: 2903, mse_loss: 0.0017, acc_pose: 0.3494, loss: 0.0017\n",
      "2021-09-22 22:39:26,667 - mmpose - INFO - Epoch [26][2/4]\tlr: 1.014e-04, eta: 0:00:54, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.3283, loss: 0.0013\n",
      "2021-09-22 22:39:27,033 - mmpose - INFO - Epoch [26][3/4]\tlr: 1.024e-04, eta: 0:00:53, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.3560, loss: 0.0017\n",
      "2021-09-22 22:39:27,402 - mmpose - INFO - Epoch [26][4/4]\tlr: 1.034e-04, eta: 0:00:52, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.2936, loss: 0.0019\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:30,106 - mmpose - INFO - Epoch [27][1/4]\tlr: 1.044e-04, eta: 0:00:52, time: 2.643, data_time: 2.248, memory: 2903, mse_loss: 0.0016, acc_pose: 0.3084, loss: 0.0016\n",
      "2021-09-22 22:39:30,476 - mmpose - INFO - Epoch [27][2/4]\tlr: 1.054e-04, eta: 0:00:50, time: 0.371, data_time: 0.002, memory: 2903, mse_loss: 0.0020, acc_pose: 0.3418, loss: 0.0020\n",
      "2021-09-22 22:39:30,845 - mmpose - INFO - Epoch [27][3/4]\tlr: 1.064e-04, eta: 0:00:49, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0015, acc_pose: 0.3162, loss: 0.0015\n",
      "2021-09-22 22:39:31,211 - mmpose - INFO - Epoch [27][4/4]\tlr: 1.074e-04, eta: 0:00:48, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.3371, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:33,896 - mmpose - INFO - Epoch [28][1/4]\tlr: 1.084e-04, eta: 0:00:48, time: 2.633, data_time: 2.233, memory: 2903, mse_loss: 0.0019, acc_pose: 0.3924, loss: 0.0019\n",
      "2021-09-22 22:39:34,263 - mmpose - INFO - Epoch [28][2/4]\tlr: 1.094e-04, eta: 0:00:47, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.3889, loss: 0.0019\n",
      "2021-09-22 22:39:34,629 - mmpose - INFO - Epoch [28][3/4]\tlr: 1.104e-04, eta: 0:00:45, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.2687, loss: 0.0013\n",
      "2021-09-22 22:39:34,994 - mmpose - INFO - Epoch [28][4/4]\tlr: 1.114e-04, eta: 0:00:44, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0019, acc_pose: 0.3294, loss: 0.0019\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:37,690 - mmpose - INFO - Epoch [29][1/4]\tlr: 1.124e-04, eta: 0:00:44, time: 2.642, data_time: 2.247, memory: 2903, mse_loss: 0.0019, acc_pose: 0.4194, loss: 0.0019\n",
      "2021-09-22 22:39:38,056 - mmpose - INFO - Epoch [29][2/4]\tlr: 1.134e-04, eta: 0:00:43, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.3326, loss: 0.0017\n",
      "2021-09-22 22:39:38,423 - mmpose - INFO - Epoch [29][3/4]\tlr: 1.144e-04, eta: 0:00:42, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.3295, loss: 0.0017\n",
      "2021-09-22 22:39:38,788 - mmpose - INFO - Epoch [29][4/4]\tlr: 1.154e-04, eta: 0:00:40, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.3882, loss: 0.0014\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:41,450 - mmpose - INFO - Epoch [30][1/4]\tlr: 1.164e-04, eta: 0:00:40, time: 2.609, data_time: 2.216, memory: 2903, mse_loss: 0.0017, acc_pose: 0.3309, loss: 0.0017\n",
      "2021-09-22 22:39:41,816 - mmpose - INFO - Epoch [30][2/4]\tlr: 1.174e-04, eta: 0:00:39, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0014, acc_pose: 0.3749, loss: 0.0014\n",
      "2021-09-22 22:39:42,184 - mmpose - INFO - Epoch [30][3/4]\tlr: 1.184e-04, eta: 0:00:38, time: 0.369, data_time: 0.002, memory: 2903, mse_loss: 0.0018, acc_pose: 0.4279, loss: 0.0018\n",
      "2021-09-22 22:39:42,550 - mmpose - INFO - Epoch [30][4/4]\tlr: 1.194e-04, eta: 0:00:37, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.3873, loss: 0.0016\n",
      "2021-09-22 22:39:42,599 - mmpose - INFO - Saving checkpoint at 30 epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                                  ] 0/25, elapsed: 0s, ETA:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 44.1 task/s, elapsed: 1s, ETA:     0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-22 22:39:44,183 - mmpose - INFO - Now best checkpoint is saved as best_PCK_epoch_30.pth.\n",
      "2021-09-22 22:39:44,183 - mmpose - INFO - Best PCK is 0.3288 at 30 epoch.\n",
      "2021-09-22 22:39:44,184 - mmpose - INFO - Epoch(val) [30][2]\tPCK: 0.3288\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:46,788 - mmpose - INFO - Epoch [31][1/4]\tlr: 1.204e-04, eta: 0:00:36, time: 2.599, data_time: 2.210, memory: 2903, mse_loss: 0.0015, acc_pose: 0.3854, loss: 0.0015\n",
      "2021-09-22 22:39:47,154 - mmpose - INFO - Epoch [31][2/4]\tlr: 1.214e-04, eta: 0:00:35, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0012, acc_pose: 0.3277, loss: 0.0012\n",
      "2021-09-22 22:39:47,521 - mmpose - INFO - Epoch [31][3/4]\tlr: 1.224e-04, eta: 0:00:34, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0019, acc_pose: 0.3654, loss: 0.0019\n",
      "2021-09-22 22:39:47,887 - mmpose - INFO - Epoch [31][4/4]\tlr: 1.234e-04, eta: 0:00:33, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0015, acc_pose: 0.4014, loss: 0.0015\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:50,571 - mmpose - INFO - Epoch [32][1/4]\tlr: 1.244e-04, eta: 0:00:33, time: 2.633, data_time: 2.242, memory: 2903, mse_loss: 0.0019, acc_pose: 0.4077, loss: 0.0019\n",
      "2021-09-22 22:39:50,936 - mmpose - INFO - Epoch [32][2/4]\tlr: 1.254e-04, eta: 0:00:31, time: 0.366, data_time: 0.002, memory: 2903, mse_loss: 0.0015, acc_pose: 0.3948, loss: 0.0015\n",
      "2021-09-22 22:39:51,302 - mmpose - INFO - Epoch [32][3/4]\tlr: 1.264e-04, eta: 0:00:30, time: 0.365, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.3251, loss: 0.0013\n",
      "2021-09-22 22:39:51,664 - mmpose - INFO - Epoch [32][4/4]\tlr: 1.274e-04, eta: 0:00:29, time: 0.362, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4011, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:54,329 - mmpose - INFO - Epoch [33][1/4]\tlr: 1.284e-04, eta: 0:00:29, time: 2.616, data_time: 2.218, memory: 2903, mse_loss: 0.0014, acc_pose: 0.4166, loss: 0.0014\n",
      "2021-09-22 22:39:54,695 - mmpose - INFO - Epoch [33][2/4]\tlr: 1.294e-04, eta: 0:00:28, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4266, loss: 0.0016\n",
      "2021-09-22 22:39:55,062 - mmpose - INFO - Epoch [33][3/4]\tlr: 1.304e-04, eta: 0:00:27, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.3923, loss: 0.0014\n",
      "2021-09-22 22:39:55,429 - mmpose - INFO - Epoch [33][4/4]\tlr: 1.314e-04, eta: 0:00:26, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.4607, loss: 0.0017\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:39:58,079 - mmpose - INFO - Epoch [34][1/4]\tlr: 1.324e-04, eta: 0:00:25, time: 2.598, data_time: 2.215, memory: 2903, mse_loss: 0.0015, acc_pose: 0.3104, loss: 0.0015\n",
      "2021-09-22 22:39:58,443 - mmpose - INFO - Epoch [34][2/4]\tlr: 1.334e-04, eta: 0:00:24, time: 0.365, data_time: 0.003, memory: 2903, mse_loss: 0.0018, acc_pose: 0.4616, loss: 0.0018\n",
      "2021-09-22 22:39:58,808 - mmpose - INFO - Epoch [34][3/4]\tlr: 1.344e-04, eta: 0:00:23, time: 0.366, data_time: 0.001, memory: 2903, mse_loss: 0.0010, acc_pose: 0.3579, loss: 0.0010\n",
      "2021-09-22 22:39:59,176 - mmpose - INFO - Epoch [34][4/4]\tlr: 1.354e-04, eta: 0:00:22, time: 0.367, data_time: 0.001, memory: 2903, mse_loss: 0.0018, acc_pose: 0.4007, loss: 0.0018\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:01,843 - mmpose - INFO - Epoch [35][1/4]\tlr: 1.364e-04, eta: 0:00:21, time: 2.616, data_time: 2.227, memory: 2903, mse_loss: 0.0018, acc_pose: 0.4073, loss: 0.0018\n",
      "2021-09-22 22:40:02,211 - mmpose - INFO - Epoch [35][2/4]\tlr: 1.374e-04, eta: 0:00:20, time: 0.368, data_time: 0.001, memory: 2903, mse_loss: 0.0017, acc_pose: 0.5594, loss: 0.0017\n",
      "2021-09-22 22:40:02,582 - mmpose - INFO - Epoch [35][3/4]\tlr: 1.384e-04, eta: 0:00:19, time: 0.371, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.4707, loss: 0.0013\n",
      "2021-09-22 22:40:02,951 - mmpose - INFO - Epoch [35][4/4]\tlr: 1.394e-04, eta: 0:00:18, time: 0.369, data_time: 0.002, memory: 2903, mse_loss: 0.0015, acc_pose: 0.4522, loss: 0.0015\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:05,626 - mmpose - INFO - Epoch [36][1/4]\tlr: 1.404e-04, eta: 0:00:17, time: 2.622, data_time: 2.224, memory: 2903, mse_loss: 0.0013, acc_pose: 0.3195, loss: 0.0013\n",
      "2021-09-22 22:40:05,995 - mmpose - INFO - Epoch [36][2/4]\tlr: 1.414e-04, eta: 0:00:16, time: 0.369, data_time: 0.002, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4603, loss: 0.0016\n",
      "2021-09-22 22:40:06,364 - mmpose - INFO - Epoch [36][3/4]\tlr: 1.424e-04, eta: 0:00:15, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.3914, loss: 0.0016\n",
      "2021-09-22 22:40:06,733 - mmpose - INFO - Epoch [36][4/4]\tlr: 1.434e-04, eta: 0:00:14, time: 0.369, data_time: 0.001, memory: 2903, mse_loss: 0.0015, acc_pose: 0.5051, loss: 0.0015\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:09,418 - mmpose - INFO - Epoch [37][1/4]\tlr: 1.444e-04, eta: 0:00:14, time: 2.632, data_time: 2.231, memory: 2903, mse_loss: 0.0014, acc_pose: 0.4651, loss: 0.0014\n",
      "2021-09-22 22:40:09,789 - mmpose - INFO - Epoch [37][2/4]\tlr: 1.454e-04, eta: 0:00:13, time: 0.371, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4974, loss: 0.0016\n",
      "2021-09-22 22:40:10,162 - mmpose - INFO - Epoch [37][3/4]\tlr: 1.464e-04, eta: 0:00:12, time: 0.374, data_time: 0.002, memory: 2903, mse_loss: 0.0016, acc_pose: 0.5292, loss: 0.0016\n",
      "2021-09-22 22:40:10,533 - mmpose - INFO - Epoch [37][4/4]\tlr: 1.474e-04, eta: 0:00:11, time: 0.371, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.4183, loss: 0.0014\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:13,213 - mmpose - INFO - Epoch [38][1/4]\tlr: 1.484e-04, eta: 0:00:10, time: 2.628, data_time: 2.229, memory: 2903, mse_loss: 0.0014, acc_pose: 0.4511, loss: 0.0014\n",
      "2021-09-22 22:40:13,587 - mmpose - INFO - Epoch [38][2/4]\tlr: 1.494e-04, eta: 0:00:09, time: 0.374, data_time: 0.002, memory: 2903, mse_loss: 0.0013, acc_pose: 0.5198, loss: 0.0013\n",
      "2021-09-22 22:40:13,959 - mmpose - INFO - Epoch [38][3/4]\tlr: 1.504e-04, eta: 0:00:08, time: 0.371, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.5084, loss: 0.0014\n",
      "2021-09-22 22:40:14,338 - mmpose - INFO - Epoch [38][4/4]\tlr: 1.513e-04, eta: 0:00:07, time: 0.379, data_time: 0.002, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4849, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:16,996 - mmpose - INFO - Epoch [39][1/4]\tlr: 1.523e-04, eta: 0:00:06, time: 2.606, data_time: 2.221, memory: 2903, mse_loss: 0.0015, acc_pose: 0.4523, loss: 0.0015\n",
      "2021-09-22 22:40:17,363 - mmpose - INFO - Epoch [39][2/4]\tlr: 1.533e-04, eta: 0:00:05, time: 0.367, data_time: 0.002, memory: 2903, mse_loss: 0.0013, acc_pose: 0.5011, loss: 0.0013\n",
      "2021-09-22 22:40:17,739 - mmpose - INFO - Epoch [39][3/4]\tlr: 1.543e-04, eta: 0:00:04, time: 0.376, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.5854, loss: 0.0013\n",
      "2021-09-22 22:40:18,109 - mmpose - INFO - Epoch [39][4/4]\tlr: 1.553e-04, eta: 0:00:03, time: 0.370, data_time: 0.001, memory: 2903, mse_loss: 0.0016, acc_pose: 0.4886, loss: 0.0016\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "2021-09-22 22:40:20,760 - mmpose - INFO - Epoch [40][1/4]\tlr: 1.563e-04, eta: 0:00:02, time: 2.599, data_time: 2.234, memory: 2903, mse_loss: 0.0014, acc_pose: 0.4787, loss: 0.0014\n",
      "2021-09-22 22:40:21,109 - mmpose - INFO - Epoch [40][2/4]\tlr: 1.573e-04, eta: 0:00:01, time: 0.350, data_time: 0.001, memory: 2903, mse_loss: 0.0013, acc_pose: 0.5198, loss: 0.0013\n",
      "2021-09-22 22:40:21,459 - mmpose - INFO - Epoch [40][3/4]\tlr: 1.583e-04, eta: 0:00:00, time: 0.350, data_time: 0.001, memory: 2903, mse_loss: 0.0012, acc_pose: 0.5001, loss: 0.0012\n",
      "2021-09-22 22:40:21,805 - mmpose - INFO - Epoch [40][4/4]\tlr: 1.593e-04, eta: 0:00:00, time: 0.345, data_time: 0.001, memory: 2903, mse_loss: 0.0014, acc_pose: 0.5597, loss: 0.0014\n",
      "2021-09-22 22:40:21,852 - mmpose - INFO - Saving checkpoint at 40 epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                                  ] 0/25, elapsed: 0s, ETA:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
      "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 47.2 task/s, elapsed: 1s, ETA:     0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-22 22:40:23,387 - mmpose - INFO - Now best checkpoint is saved as best_PCK_epoch_40.pth.\n",
      "2021-09-22 22:40:23,388 - mmpose - INFO - Best PCK is 0.3473 at 40 epoch.\n",
      "2021-09-22 22:40:23,388 - mmpose - INFO - Epoch(val) [40][2]\tPCK: 0.3473\n"
     ]
    }
   ],
   "source": [
    "from mmpose.datasets import build_dataset\n",
    "from mmpose.models import build_posenet\n",
    "from mmpose.apis import train_model\n",
    "import mmcv\n",
    "\n",
    "# build dataset\n",
    "datasets = [build_dataset(cfg.data.train)]\n",
    "\n",
    "# build model\n",
    "model = build_posenet(cfg.model)\n",
    "\n",
    "# create work_dir\n",
    "mmcv.mkdir_or_exist(cfg.work_dir)\n",
    "\n",
    "# train model\n",
    "train_model(\n",
    "    model, datasets, cfg, distributed=False, validate=True, meta=dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iY2EWSp1zKoz"
   },
   "source": [
    "Test the trained model. Since the model is trained on a toy dataset coco-tiny, its performance would be as good as the ones in our model zoo. Here we mainly show how to inference and visualize a local model checkpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "id": "i0rk9eCVzT_D",
    "outputId": "722542be-ab38-4ca4-86c4-dce2cfb95c4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_local loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/SENSETIME/liyining/anaconda3/envs/colab/lib/python3.9/site-packages/mmdet/core/anchor/builder.py:15: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator`` \n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use load_from_http loader\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/SENSETIME/liyining/anaconda3/envs/colab/lib/python3.9/site-packages/mmdet/core/anchor/anchor_generator.py:323: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors`` \n",
      "  warnings.warn('``grid_anchors`` would be deprecated soon. '\n",
      "/home/SENSETIME/liyining/anaconda3/envs/colab/lib/python3.9/site-packages/mmdet/core/anchor/anchor_generator.py:359: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors`` \n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mmpose.apis import (inference_top_down_pose_model, init_pose_model,\n",
    "                         vis_pose_result, process_mmdet_results)\n",
    "from mmdet.apis import inference_detector, init_detector\n",
    "\n",
    "local_runtime = False\n",
    "\n",
    "try:\n",
    "    from google.colab.patches import cv2_imshow  # for image visualization in colab\n",
    "except:\n",
    "    local_runtime = True\n",
    "\n",
    "pose_checkpoint = 'work_dirs/hrnet_w32_coco_tiny_256x192/latest.pth'\n",
    "det_config = 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py'\n",
    "det_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'\n",
    "\n",
    "# initialize pose model\n",
    "pose_model = init_pose_model(cfg, pose_checkpoint)\n",
    "# initialize detector\n",
    "det_model = init_detector(det_config, det_checkpoint)\n",
    "\n",
    "img = 'tests/data/coco/000000196141.jpg'\n",
    "\n",
    "# inference detection\n",
    "mmdet_results = inference_detector(det_model, img)\n",
    "\n",
    "# extract person (COCO_ID=1) bounding boxes from the detection results\n",
    "person_results = process_mmdet_results(mmdet_results, cat_id=1)\n",
    "\n",
    "# inference pose\n",
    "pose_results, returned_outputs = inference_top_down_pose_model(\n",
    "    pose_model,\n",
    "    img,\n",
    "    person_results,\n",
    "    bbox_thr=0.3,\n",
    "    format='xyxy',\n",
    "    dataset='TopDownCocoDataset')\n",
    "\n",
    "# show pose estimation results\n",
    "vis_result = vis_pose_result(\n",
    "    pose_model,\n",
    "    img,\n",
    "    pose_results,\n",
    "    kpt_score_thr=0.,\n",
    "    dataset='TopDownCocoDataset',\n",
    "    show=False)\n",
    "\n",
    "# reduce image size\n",
    "vis_result = cv2.resize(vis_result, dsize=None, fx=0.5, fy=0.5)\n",
    "\n",
    "if local_runtime:\n",
    "    from IPython.display import Image, display\n",
    "    import tempfile\n",
    "    import os.path as osp\n",
    "    import cv2\n",
    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
    "        file_name = osp.join(tmpdir, 'pose_results.png')\n",
    "        cv2.imwrite(file_name, vis_result)\n",
    "        display(Image(file_name))\n",
    "else:\n",
    "    cv2_imshow(vis_result)"
   ]
  }
 ],
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