ePillID_tutorial_colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ePillID_tutorial_colab.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNa8Tdoe5TFpMkIe/Jg5MVW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/usuyama/ePillID-benchmark/blob/master/ePillID_tutorial_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9qKNoiuE8SO",
        "colab_type": "text"
      },
      "source": [
        "# ePillID-benchmark\n",
        "\n",
        "* https://github.com/usuyama/ePillID-benchmark\n",
        "* https://arxiv.org/abs/2005.14288\n",
        "\n",
        "\n",
        "```\n",
        "@inproceedings{usuyama2020epillid,\n",
        "  title={ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification},\n",
        "  author={Usuyama, Naoto and Delgado, Natalia Larios and Hall, Amanda K and Lundin, Jessica},\n",
        "  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},\n",
        "  year={2020}\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8V5k1jH-fx5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "9c64b8d9-837b-4155-b0d2-bc2266d679c6"
      },
      "source": [
        "import os\n",
        "\n",
        "if not os.path.exists(\"ePillID-benchmark\"):\n",
        "  !git clone https://github.com/usuyama/ePillID-benchmark.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ePillID-benchmark'...\n",
            "remote: Enumerating objects: 102, done.\u001b[K\n",
            "remote: Counting objects: 100% (102/102), done.\u001b[K\n",
            "remote: Compressing objects: 100% (78/78), done.\u001b[K\n",
            "remote: Total 102 (delta 24), reused 91 (delta 19), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (102/102), 341.24 KiB | 250.00 KiB/s, done.\n",
            "Resolving deltas: 100% (24/24), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2II6ZJ6yG4-i",
        "colab_type": "text"
      },
      "source": [
        "## Download the data and unzip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBajwYHW-Vsq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "5bac3d98-3cab-44b3-95e8-9baea836be90"
      },
      "source": [
        "if not os.path.exists(\"ePillID_data.zip\"):\n",
        "  !curl -o ePillID_data.zip \"https://pillidwus2.blob.core.windows.net/data/ePillID_data.zip?st=2020-04-30T09%3A34%3A02Z&se=2031-05-01T02%3A38%3A00Z&sp=rl&sv=2018-03-28&sr=b&sig=Xvmr0RFWOeOSw74iyQso%2F7WQHs6lTxbtyQ9jrBTuLKI%3D\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  152M  100  152M    0     0  8322k      0  0:00:18  0:00:18 --:--:-- 9702k\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgXZEot0-esr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eceff502-2b7d-46ad-f53d-dea84104ba8b"
      },
      "source": [
        "if not os.path.exists(\"ePillID_data\"):\n",
        "  !unzip ePillID_data.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "   creating: ePillID_data/folds/\n",
            "   creating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/\n",
            "   creating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/\n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_0.csv  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_2.csv  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_1.csv  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_3.csv  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/label_encoder.pickle  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_4.csv  \n",
            "  inflating: ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_all.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5reR8Cr3-jrz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "e14c6d37-bb83-4b3d-aa0f-58de6a49f698"
      },
      "source": [
        "if not os.path.exists(\"train_cv.py\"):\n",
        "  %cd ePillID-benchmark/src\n",
        "\n",
        "!echo `pwd`\n",
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ePillID-benchmark/src\n",
            "/content/ePillID-benchmark/src\n",
            "arguments.py\t       metrics.py\t    multihead_trainer.py  train_nocv.py\n",
            "classif_utils.py       metric_test_eval.py  pillid_datasets.py\n",
            "configs\t\t       metric_utils.py\t    sanitytest_eval.py\n",
            "image_augmentators.py  models\t\t    train_cv.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2pyaZFUC4XK",
        "colab_type": "text"
      },
      "source": [
        "## Enabling GPU on Colab\n",
        "\n",
        "Need to enable GPU from Notebook settings\n",
        "\n",
        "- Navigate to Edit-Notebook settings menu\n",
        "- Select GPU from the Hardware Accelerator dropdown list\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPsvy3pyANVF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "55b58a10-915e-4b8a-e507-ceebaa35043c"
      },
      "source": [
        "import torch\n",
        "\n",
        "if not torch.cuda.is_available():\n",
        "  raise Exception(\"GPU not availalbe. CPU training will be too slow.\")\n",
        "\n",
        "print(\"device name\", torch.cuda.get_device_name(0))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "device name Tesla P100-PCIE-16GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEtbs_yYLXeW",
        "colab_type": "text"
      },
      "source": [
        "## Install dependencies\n",
        "\n",
        "The library versions on Colab might be different from what we used in our experiments. Check our repo for full dependencies\n",
        " https://github.com/usuyama/ePillID-benchmark/tree/master/docker"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUMu8-bDAIK7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1c458ecb-3f18-45ad-c35b-a575ec0d88ae"
      },
      "source": [
        "!pip install azureml-sdk"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  Downloading https://files.pythonhosted.org/packages/14/e1/f91d7b2c5ec5c8c0374db4b95006a3e2911b5ef6991138149bb7d7a4a3ec/azureml_sdk-1.9.0-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/c4/16/ede281a41fedffee6dd055621ca8a894055a5934284fcefb93e9cb324cbd/azureml_train-1.9.0-py3-none-any.whl\n",
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            "\u001b[?25hCollecting msal<2.0.0,>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c4/a8/16816334bd2552ebdbb0255518f15698feb94278d92f4f890c38387f1892/msal-1.4.1-py2.py3-none-any.whl (48kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 7.3MB/s \n",
            "\u001b[?25hCollecting msal-extensions~=0.1.3\n",
            "  Downloading https://files.pythonhosted.org/packages/21/9b/8bc67822e98573fe0460e30ad0202ab9e0638a42878041c65a6fe857babe/msal_extensions-0.1.3-py2.py3-none-any.whl\n",
            "Collecting mccabe<0.7.0,>=0.6.0\n",
            "  Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n",
            "Collecting pycodestyle<2.6.0,>=2.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0e/0c/04a353e104d2f324f8ee5f4b32012618c1c86dd79e52a433b64fceed511b/pycodestyle-2.5.0-py2.py3-none-any.whl (51kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 7.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: entrypoints<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\"->azureml-train-core~=1.9.0->azureml-train~=1.9.0->azureml-sdk) (0.3)\n",
            "Collecting pyflakes<2.2.0,>=2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/84/f2/ed0ffb887f8138a8fe5a621b8c0bb9598bfb3989e029f6c6a85ee66628ee/pyflakes-2.1.1-py2.py3-none-any.whl (59kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 8.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata->jsonpickle->azureml-core~=1.9.0->azureml-sdk) (3.1.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.5.0->msrest>=0.5.1->azureml-core~=1.9.0->azureml-sdk) (3.1.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi!=1.11.3,>=1.8->cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*->azureml-core~=1.9.0->azureml-sdk) (2.20)\n",
            "Collecting portalocker~=1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/53/84/7b3146ec6378d28abc73ab484f09f47dfa008ad6f03f33d90a369f880e25/portalocker-1.7.0-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: fusepy\n",
            "  Building wheel for fusepy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fusepy: filename=fusepy-3.0.1-cp36-none-any.whl size=10504 sha256=bda2fecac83200fed97c175451df3642325d0a3149c05529aaf96c3b6a2b4c00\n",
            "  Stored in directory: /root/.cache/pip/wheels/4c/a5/91/7772af9e21c461f07bb40f26d928d7d231d224977dd8353bab\n",
            "Successfully built fusepy\n",
            "Installing collected packages: applicationinsights, azure-common, jsonpickle, isodate, msrest, cryptography, PyJWT, adal, msrestazure, azure-mgmt-authorization, azure-graphrbac, jeepney, SecretStorage, backports.weakref, backports.tempfile, azure-mgmt-resource, pyopenssl, websocket-client, docker, azure-mgmt-containerregistry, ndg-httpsclient, azure-mgmt-storage, pathspec, azure-mgmt-network, ruamel.yaml.clib, ruamel.yaml, azure-mgmt-keyvault, azureml-core, azureml-telemetry, mccabe, pycodestyle, pyflakes, flake8, azureml-train-restclients-hyperdrive, azureml-train-core, azureml-train, distro, dotnetcore2, azureml-dataprep-native, azure-core, msal, portalocker, msal-extensions, azure-identity, fusepy, azureml-dataprep, azureml-automl-core, azureml-train-automl-client, azureml-pipeline-core, azureml-pipeline-steps, azureml-pipeline, azureml-sdk\n",
            "Successfully installed PyJWT-1.7.1 SecretStorage-3.1.2 adal-1.2.4 applicationinsights-0.11.9 azure-common-1.1.25 azure-core-1.7.0 azure-graphrbac-0.61.1 azure-identity-1.2.0 azure-mgmt-authorization-0.60.0 azure-mgmt-containerregistry-2.8.0 azure-mgmt-keyvault-2.2.0 azure-mgmt-network-10.2.0 azure-mgmt-resource-10.1.0 azure-mgmt-storage-11.1.0 azureml-automl-core-1.9.0 azureml-core-1.9.0 azureml-dataprep-1.9.1 azureml-dataprep-native-14.2.1 azureml-pipeline-1.9.0 azureml-pipeline-core-1.9.0 azureml-pipeline-steps-1.9.0 azureml-sdk-1.9.0 azureml-telemetry-1.9.0 azureml-train-1.9.0 azureml-train-automl-client-1.9.0 azureml-train-core-1.9.0 azureml-train-restclients-hyperdrive-1.9.0 backports.tempfile-1.0 backports.weakref-1.0.post1 cryptography-2.9.2 distro-1.5.0 docker-4.2.2 dotnetcore2-2.1.14 flake8-3.7.9 fusepy-3.0.1 isodate-0.6.0 jeepney-0.4.3 jsonpickle-1.4.1 mccabe-0.6.1 msal-1.4.1 msal-extensions-0.1.3 msrest-0.6.17 msrestazure-0.6.4 ndg-httpsclient-0.5.1 pathspec-0.8.0 portalocker-1.7.0 pycodestyle-2.5.0 pyflakes-2.1.1 pyopenssl-19.1.0 ruamel.yaml-0.16.10 ruamel.yaml.clib-0.2.0 websocket-client-0.57.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51wHJxfnkh59",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1b02194-9212-48a2-9af2-70d14c0dd48a"
      },
      "source": [
        "# check library versions\n",
        "!pip freeze"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "absl-py==0.9.0\n",
            "adal==1.2.4\n",
            "alabaster==0.7.12\n",
            "albumentations==0.1.12\n",
            "altair==4.1.0\n",
            "applicationinsights==0.11.9\n",
            "asgiref==3.2.10\n",
            "astor==0.8.1\n",
            "astropy==4.0.1.post1\n",
            "astunparse==1.6.3\n",
            "atari-py==0.2.6\n",
            "atomicwrites==1.4.0\n",
            "attrs==19.3.0\n",
            "audioread==2.1.8\n",
            "autograd==1.3\n",
            "azure-common==1.1.25\n",
            "azure-core==1.7.0\n",
            "azure-graphrbac==0.61.1\n",
            "azure-identity==1.2.0\n",
            "azure-mgmt-authorization==0.60.0\n",
            "azure-mgmt-containerregistry==2.8.0\n",
            "azure-mgmt-keyvault==2.2.0\n",
            "azure-mgmt-network==10.2.0\n",
            "azure-mgmt-resource==10.1.0\n",
            "azure-mgmt-storage==11.1.0\n",
            "azureml-automl-core==1.9.0\n",
            "azureml-core==1.9.0\n",
            "azureml-dataprep==1.9.1\n",
            "azureml-dataprep-native==14.2.1\n",
            "azureml-pipeline==1.9.0\n",
            "azureml-pipeline-core==1.9.0\n",
            "azureml-pipeline-steps==1.9.0\n",
            "azureml-sdk==1.9.0\n",
            "azureml-telemetry==1.9.0\n",
            "azureml-train==1.9.0\n",
            "azureml-train-automl-client==1.9.0\n",
            "azureml-train-core==1.9.0\n",
            "azureml-train-restclients-hyperdrive==1.9.0\n",
            "Babel==2.8.0\n",
            "backcall==0.2.0\n",
            "backports.tempfile==1.0\n",
            "backports.weakref==1.0.post1\n",
            "beautifulsoup4==4.6.3\n",
            "bleach==3.1.5\n",
            "blis==0.4.1\n",
            "bokeh==1.4.0\n",
            "boto==2.49.0\n",
            "boto3==1.14.9\n",
            "botocore==1.17.9\n",
            "Bottleneck==1.3.2\n",
            "branca==0.4.1\n",
            "bs4==0.0.1\n",
            "CacheControl==0.12.6\n",
            "cachetools==4.1.0\n",
            "catalogue==1.0.0\n",
            "certifi==2020.6.20\n",
            "cffi==1.14.0\n",
            "chainer==6.5.0\n",
            "chardet==3.0.4\n",
            "click==7.1.2\n",
            "cloudpickle==1.3.0\n",
            "cmake==3.12.0\n",
            "cmdstanpy==0.4.0\n",
            "colorlover==0.3.0\n",
            "community==1.0.0b1\n",
            "contextlib2==0.5.5\n",
            "convertdate==2.2.1\n",
            "coverage==3.7.1\n",
            "coveralls==0.5\n",
            "crcmod==1.7\n",
            "cryptography==2.9.2\n",
            "cufflinks==0.17.3\n",
            "cupy-cuda101==6.5.0\n",
            "cvxopt==1.2.5\n",
            "cvxpy==1.0.31\n",
            "cycler==0.10.0\n",
            "cymem==2.0.3\n",
            "Cython==0.29.20\n",
            "daft==0.0.4\n",
            "dask==2.12.0\n",
            "dataclasses==0.7\n",
            "datascience==0.10.6\n",
            "decorator==4.4.2\n",
            "defusedxml==0.6.0\n",
            "descartes==1.1.0\n",
            "dill==0.3.2\n",
            "distributed==1.25.3\n",
            "distro==1.5.0\n",
            "Django==3.0.7\n",
            "dlib==19.18.0\n",
            "docker==4.2.2\n",
            "docopt==0.6.2\n",
            "docutils==0.15.2\n",
            "dopamine-rl==1.0.5\n",
            "dotnetcore2==2.1.14\n",
            "earthengine-api==0.1.226\n",
            "easydict==1.9\n",
            "ecos==2.0.7.post1\n",
            "editdistance==0.5.3\n",
            "en-core-web-sm==2.2.5\n",
            "entrypoints==0.3\n",
            "ephem==3.7.7.1\n",
            "et-xmlfile==1.0.1\n",
            "fa2==0.3.5\n",
            "fancyimpute==0.4.3\n",
            "fastai==1.0.61\n",
            "fastdtw==0.3.4\n",
            "fastprogress==0.2.3\n",
            "fastrlock==0.5\n",
            "fbprophet==0.6\n",
            "feather-format==0.4.1\n",
            "featuretools==0.4.1\n",
            "filelock==3.0.12\n",
            "firebase-admin==4.1.0\n",
            "fix-yahoo-finance==0.0.22\n",
            "flake8==3.7.9\n",
            "Flask==1.1.2\n",
            "folium==0.8.3\n",
            "fsspec==0.7.4\n",
            "fusepy==3.0.1\n",
            "future==0.16.0\n",
            "gast==0.3.3\n",
            "GDAL==2.2.2\n",
            "gdown==3.6.4\n",
            "gensim==3.6.0\n",
            "geographiclib==1.50\n",
            "geopy==1.17.0\n",
            "gin-config==0.3.0\n",
            "glob2==0.7\n",
            "google==2.0.3\n",
            "google-api-core==1.16.0\n",
            "google-api-python-client==1.7.12\n",
            "google-auth==1.17.2\n",
            "google-auth-httplib2==0.0.3\n",
            "google-auth-oauthlib==0.4.1\n",
            "google-cloud-bigquery==1.21.0\n",
            "google-cloud-core==1.0.3\n",
            "google-cloud-datastore==1.8.0\n",
            "google-cloud-firestore==1.7.0\n",
            "google-cloud-language==1.2.0\n",
            "google-cloud-storage==1.18.1\n",
            "google-cloud-translate==1.5.0\n",
            "google-colab==1.0.0\n",
            "google-pasta==0.2.0\n",
            "google-resumable-media==0.4.1\n",
            "googleapis-common-protos==1.52.0\n",
            "googledrivedownloader==0.4\n",
            "graphviz==0.10.1\n",
            "grpcio==1.30.0\n",
            "gspread==3.0.1\n",
            "gspread-dataframe==3.0.7\n",
            "gym==0.17.2\n",
            "h5py==2.10.0\n",
            "HeapDict==1.0.1\n",
            "holidays==0.9.12\n",
            "html5lib==1.0.1\n",
            "httpimport==0.5.18\n",
            "httplib2==0.17.4\n",
            "httplib2shim==0.0.3\n",
            "humanize==0.5.1\n",
            "hyperopt==0.1.2\n",
            "ideep4py==2.0.0.post3\n",
            "idna==2.9\n",
            "image==1.5.32\n",
            "imageio==2.4.1\n",
            "imagesize==1.2.0\n",
            "imbalanced-learn==0.4.3\n",
            "imblearn==0.0\n",
            "imgaug==0.2.9\n",
            "importlib-metadata==1.6.1\n",
            "imutils==0.5.3\n",
            "inflect==2.1.0\n",
            "intel-openmp==2020.0.133\n",
            "intervaltree==2.1.0\n",
            "ipykernel==4.10.1\n",
            "ipython==5.5.0\n",
            "ipython-genutils==0.2.0\n",
            "ipython-sql==0.3.9\n",
            "ipywidgets==7.5.1\n",
            "isodate==0.6.0\n",
            "itsdangerous==1.1.0\n",
            "jax==0.1.69\n",
            "jaxlib==0.1.47\n",
            "jdcal==1.4.1\n",
            "jedi==0.17.1\n",
            "jeepney==0.4.3\n",
            "jieba==0.42.1\n",
            "Jinja2==2.11.2\n",
            "jmespath==0.10.0\n",
            "joblib==0.15.1\n",
            "jpeg4py==0.1.4\n",
            "jsonpickle==1.4.1\n",
            "jsonschema==2.6.0\n",
            "jupyter==1.0.0\n",
            "jupyter-client==5.3.4\n",
            "jupyter-console==5.2.0\n",
            "jupyter-core==4.6.3\n",
            "kaggle==1.5.6\n",
            "kapre==0.1.3.1\n",
            "Keras==2.3.1\n",
            "Keras-Applications==1.0.8\n",
            "Keras-Preprocessing==1.1.2\n",
            "keras-vis==0.4.1\n",
            "kiwisolver==1.2.0\n",
            "knnimpute==0.1.0\n",
            "librosa==0.6.3\n",
            "lightgbm==2.2.3\n",
            "llvmlite==0.31.0\n",
            "lmdb==0.98\n",
            "lucid==0.3.8\n",
            "LunarCalendar==0.0.9\n",
            "lxml==4.2.6\n",
            "Markdown==3.2.2\n",
            "MarkupSafe==1.1.1\n",
            "matplotlib==3.2.2\n",
            "matplotlib-venn==0.11.5\n",
            "mccabe==0.6.1\n",
            "missingno==0.4.2\n",
            "mistune==0.8.4\n",
            "mizani==0.6.0\n",
            "mkl==2019.0\n",
            "mlxtend==0.14.0\n",
            "more-itertools==8.4.0\n",
            "moviepy==0.2.3.5\n",
            "mpmath==1.1.0\n",
            "msal==1.4.1\n",
            "msal-extensions==0.1.3\n",
            "msgpack==1.0.0\n",
            "msrest==0.6.17\n",
            "msrestazure==0.6.4\n",
            "multiprocess==0.70.10\n",
            "multitasking==0.0.9\n",
            "murmurhash==1.0.2\n",
            "music21==5.5.0\n",
            "natsort==5.5.0\n",
            "nbconvert==5.6.1\n",
            "nbformat==5.0.7\n",
            "ndg-httpsclient==0.5.1\n",
            "networkx==2.4\n",
            "nibabel==3.0.2\n",
            "nltk==3.2.5\n",
            "notebook==5.2.2\n",
            "np-utils==0.5.12.1\n",
            "numba==0.48.0\n",
            "numexpr==2.7.1\n",
            "numpy==1.18.5\n",
            "nvidia-ml-py3==7.352.0\n",
            "oauth2client==4.1.3\n",
            "oauthlib==3.1.0\n",
            "okgrade==0.4.3\n",
            "opencv-contrib-python==4.1.2.30\n",
            "opencv-python==4.1.2.30\n",
            "openpyxl==2.5.9\n",
            "opt-einsum==3.2.1\n",
            "osqp==0.6.1\n",
            "packaging==20.4\n",
            "palettable==3.3.0\n",
            "pandas==1.0.5\n",
            "pandas-datareader==0.8.1\n",
            "pandas-gbq==0.11.0\n",
            "pandas-profiling==1.4.1\n",
            "pandocfilters==1.4.2\n",
            "parso==0.7.0\n",
            "pathlib==1.0.1\n",
            "pathspec==0.8.0\n",
            "patsy==0.5.1\n",
            "pexpect==4.8.0\n",
            "pickleshare==0.7.5\n",
            "Pillow==7.0.0\n",
            "pip-tools==4.5.1\n",
            "plac==1.1.3\n",
            "plotly==4.4.1\n",
            "plotnine==0.6.0\n",
            "pluggy==0.7.1\n",
            "portalocker==1.7.0\n",
            "portpicker==1.3.1\n",
            "prefetch-generator==1.0.1\n",
            "preshed==3.0.2\n",
            "prettytable==0.7.2\n",
            "progressbar2==3.38.0\n",
            "prometheus-client==0.8.0\n",
            "promise==2.3\n",
            "prompt-toolkit==1.0.18\n",
            "protobuf==3.10.0\n",
            "psutil==5.4.8\n",
            "psycopg2==2.7.6.1\n",
            "ptyprocess==0.6.0\n",
            "py==1.8.2\n",
            "pyarrow==0.14.1\n",
            "pyasn1==0.4.8\n",
            "pyasn1-modules==0.2.8\n",
            "pycocotools==2.0.1\n",
            "pycodestyle==2.5.0\n",
            "pycparser==2.20\n",
            "pydata-google-auth==1.1.0\n",
            "pydot==1.3.0\n",
            "pydot-ng==2.0.0\n",
            "pydotplus==2.0.2\n",
            "PyDrive==1.3.1\n",
            "pyemd==0.5.1\n",
            "pyflakes==2.1.1\n",
            "pyglet==1.5.0\n",
            "Pygments==2.1.3\n",
            "pygobject==3.26.1\n",
            "PyJWT==1.7.1\n",
            "pymc3==3.7\n",
            "PyMeeus==0.3.7\n",
            "pymongo==3.10.1\n",
            "pymystem3==0.2.0\n",
            "PyOpenGL==3.1.5\n",
            "pyOpenSSL==19.1.0\n",
            "pyparsing==2.4.7\n",
            "pyrsistent==0.16.0\n",
            "pysndfile==1.3.8\n",
            "PySocks==1.7.1\n",
            "pystan==2.19.1.1\n",
            "pytest==3.6.4\n",
            "python-apt==1.6.5+ubuntu0.3\n",
            "python-chess==0.23.11\n",
            "python-dateutil==2.8.1\n",
            "python-louvain==0.14\n",
            "python-slugify==4.0.0\n",
            "python-utils==2.4.0\n",
            "pytz==2018.9\n",
            "PyWavelets==1.1.1\n",
            "PyYAML==3.13\n",
            "pyzmq==19.0.1\n",
            "qtconsole==4.7.5\n",
            "QtPy==1.9.0\n",
            "regex==2019.12.20\n",
            "requests==2.23.0\n",
            "requests-oauthlib==1.3.0\n",
            "resampy==0.2.2\n",
            "retrying==1.3.3\n",
            "rpy2==3.2.7\n",
            "rsa==4.6\n",
            "ruamel.yaml==0.16.10\n",
            "ruamel.yaml.clib==0.2.0\n",
            "s3fs==0.4.2\n",
            "s3transfer==0.3.3\n",
            "scikit-image==0.16.2\n",
            "scikit-learn==0.22.2.post1\n",
            "scipy==1.4.1\n",
            "screen-resolution-extra==0.0.0\n",
            "scs==2.1.2\n",
            "seaborn==0.10.1\n",
            "SecretStorage==3.1.2\n",
            "Send2Trash==1.5.0\n",
            "setuptools-git==1.2\n",
            "Shapely==1.7.0\n",
            "simplegeneric==0.8.1\n",
            "six==1.12.0\n",
            "sklearn==0.0\n",
            "sklearn-pandas==1.8.0\n",
            "smart-open==2.0.0\n",
            "snowballstemmer==2.0.0\n",
            "sortedcontainers==2.2.2\n",
            "spacy==2.2.4\n",
            "Sphinx==1.8.5\n",
            "sphinxcontrib-websupport==1.2.2\n",
            "SQLAlchemy==1.3.17\n",
            "sqlparse==0.3.1\n",
            "srsly==1.0.2\n",
            "statsmodels==0.10.2\n",
            "sympy==1.1.1\n",
            "tables==3.4.4\n",
            "tabulate==0.8.7\n",
            "tbb==2020.0.133\n",
            "tblib==1.6.0\n",
            "tensorboard==2.2.2\n",
            "tensorboard-plugin-wit==1.6.0.post3\n",
            "tensorboardcolab==0.0.22\n",
            "tensorflow==2.2.0\n",
            "tensorflow-addons==0.8.3\n",
            "tensorflow-datasets==2.1.0\n",
            "tensorflow-estimator==2.2.0\n",
            "tensorflow-gcs-config==2.2.0\n",
            "tensorflow-hub==0.8.0\n",
            "tensorflow-metadata==0.22.2\n",
            "tensorflow-privacy==0.2.2\n",
            "tensorflow-probability==0.10.0\n",
            "termcolor==1.1.0\n",
            "terminado==0.8.3\n",
            "testpath==0.4.4\n",
            "text-unidecode==1.3\n",
            "textblob==0.15.3\n",
            "textgenrnn==1.4.1\n",
            "Theano==1.0.4\n",
            "thinc==7.4.0\n",
            "tifffile==2020.6.3\n",
            "toolz==0.10.0\n",
            "torch==1.5.1+cu101\n",
            "torchsummary==1.5.1\n",
            "torchtext==0.3.1\n",
            "torchvision==0.6.1+cu101\n",
            "tornado==4.5.3\n",
            "tqdm==4.41.1\n",
            "traitlets==4.3.3\n",
            "tweepy==3.6.0\n",
            "typeguard==2.7.1\n",
            "typing==3.6.6\n",
            "typing-extensions==3.6.6\n",
            "tzlocal==1.5.1\n",
            "umap-learn==0.4.4\n",
            "uritemplate==3.0.1\n",
            "urllib3==1.24.3\n",
            "vega-datasets==0.8.0\n",
            "wasabi==0.7.0\n",
            "wcwidth==0.2.5\n",
            "webencodings==0.5.1\n",
            "websocket-client==0.57.0\n",
            "Werkzeug==1.0.1\n",
            "widgetsnbextension==3.5.1\n",
            "wordcloud==1.5.0\n",
            "wrapt==1.12.1\n",
            "xarray==0.15.1\n",
            "xgboost==0.90\n",
            "xkit==0.0.0\n",
            "xlrd==1.1.0\n",
            "xlwt==1.3.0\n",
            "yellowbrick==0.9.1\n",
            "zict==2.0.0\n",
            "zipp==3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myR3X4h8GxRt",
        "colab_type": "text"
      },
      "source": [
        "## Run training and evaluation using resnet18"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRB3kiiO_YYr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "14731012-0450-4652-b096-b6f9a2986731"
      },
      "source": [
        "!python train_nocv.py \\\n",
        " --appearance_network resnet18 \\\n",
        " --pooling CBP \\\n",
        " --max_epochs 10 \\\n",
        " --data_root_dir \\\n",
        " ../../ePillID_data"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(add_persp_aug=1, all_imgs_csv='../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_all.csv', appearance_network='resnet18', arcface_w=0.1, batch_size=48, ce_w=1.0, contrastive_w=1.0, data_root_dir='../../ePillID_data', dropout=0.0, focal_gamma=0.0, focal_w=0.0, img_dir='classification_data', init_lr=0.0001, label_encoder='../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/label_encoder_pytorch131.pickle', load_config=None, load_mod=None, lr_factor=0.5, lr_patience=2, max_epochs=10, metric_embedding_dim=2048, metric_evaluator_type='cosine', metric_margin=1.0, metric_simul_sidepairs_eval=1, optimizer='adam', pooling='CBP', results_dir='../../ePillID_data/classification_results', sidepairs_agg='post_mean', supress_warnings=False, test_imgs_csv='../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_4.csv', train_with_side_labels=1, triplet_w=1.0, val_imgs_csv='../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_3.csv')\n",
            "Attempted to add tag data_root_dir with value ../../ePillID_data\n",
            "Attempted to add tag img_dir with value classification_data\n",
            "Attempted to add tag supress_warnings with value False\n",
            "Attempted to add tag optimizer with value adam\n",
            "Attempted to add tag init_lr with value 0.0001\n",
            "Attempted to add tag dropout with value 0.0\n",
            "Attempted to add tag lr_patience with value 2\n",
            "Attempted to add tag lr_factor with value 0.5\n",
            "Attempted to add tag batch_size with value 48\n",
            "Attempted to add tag max_epochs with value 10\n",
            "Attempted to add tag add_persp_aug with value 1\n",
            "Attempted to add tag appearance_network with value resnet18\n",
            "Attempted to add tag pooling with value CBP\n",
            "Attempted to add tag metric_margin with value 1.0\n",
            "Attempted to add tag metric_embedding_dim with value 2048\n",
            "Attempted to add tag train_with_side_labels with value 1\n",
            "Attempted to add tag metric_simul_sidepairs_eval with value 1\n",
            "Attempted to add tag sidepairs_agg with value post_mean\n",
            "Attempted to add tag metric_evaluator_type with value cosine\n",
            "Attempted to add tag ce_w with value 1.0\n",
            "Attempted to add tag arcface_w with value 0.1\n",
            "Attempted to add tag contrastive_w with value 1.0\n",
            "Attempted to add tag triplet_w with value 1.0\n",
            "Attempted to add tag focal_w with value 0.0\n",
            "Attempted to add tag focal_gamma with value 0.0\n",
            "Attempted to add tag load_mod with value None\n",
            "Attempted to add tag results_dir with value ../../ePillID_data/classification_results\n",
            "Attempted to add tag load_config with value None\n",
            "Attempted to add tag all_imgs_csv with value ../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_all.csv\n",
            "Attempted to add tag val_imgs_csv with value ../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_3.csv\n",
            "Attempted to add tag test_imgs_csv with value ../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/pilltypeid_nih_sidelbls0.01_metric_5folds_4.csv\n",
            "Attempted to add tag label_encoder with value ../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/label_encoder_pytorch131.pickle\n",
            "save_path ../../ePillID_data/classification_results/lr0.0001_dr0.0_lrpatience2_lrfactor0.5_resnet18_202007121035\n",
            "WARNING - Fitting a new label encoder at ../../ePillID_data/folds/pilltypeid_nih_sidelbls0.01_metric_5folds/base/label_encoder_pytorch131.pickle\n",
            "all_images 13532 train 12042 val 745 test 745\n",
            "Attempted to log scalar metric all_images_size:\n",
            "13532\n",
            "Attempted to log scalar metric train_size:\n",
            "12042\n",
            "Attempted to log scalar metric val_size:\n",
            "745\n",
            "Attempted to log scalar metric test_size:\n",
            "745\n",
            "ref_only_df 9804 cons_train_df 2238 cons_val_df 745\n",
            "Configuring data_dir ../../ePillID_data/classification_data\n",
            "Adding perspective transform to augmentation\n",
            "train_dataset 12042 val_dataset 10549\n",
            "n_classes=4902\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
            "100% 44.7M/44.7M [00:00<00:00, 297MB/s]\n",
            "treat front/back as different classes (first half: front, second half: back), n_classes=9804\n",
            "MultiheadModel(\n",
            "  (embedding_model): EmbeddingModel(\n",
            "    (base_model): Newmodel(\n",
            "      (features): Sequential(\n",
            "        (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU(inplace=True)\n",
            "        (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "        (4): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (5): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (6): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (7): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (representation): CBP(\n",
            "        (conv_dr_block): Sequential(\n",
            "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (classifier): Linear(in_features=8192, out_features=2048, bias=True)\n",
            "    )\n",
            "    (dropout): Dropout(p=0.0, inplace=False)\n",
            "    (emb): Sequential(\n",
            "      (0): Linear(in_features=2048, out_features=1000, bias=True)\n",
            "      (1): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Linear(in_features=1000, out_features=2048, bias=True)\n",
            "      (4): Tanh()\n",
            "    )\n",
            "  )\n",
            "  (binary_head): BinaryHead(\n",
            "    (fc): Sequential(\n",
            "      (0): Linear(in_features=2048, out_features=9804, bias=True)\n",
            "    )\n",
            "  )\n",
            "  (margin_head): MarginHead(\n",
            "    (fc): MarginLinear()\n",
            "  )\n",
            ")\n",
            "Starting multihead training\n",
            "loss_weights {'ce': 1.0, 'arcface': 0.1, 'contrastive': 1.0, 'triplet': 1.0, 'focal': 0.0}\n",
            "Will use metric evaluator for validation\n",
            "Epoch 0/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 0 train:  36% 89/250 [02:11<03:58,  1.48s/it, acc1=0.00, acc5=0.00, arcface=25.98, ce=10.09, cont=0.51, cont_neg_dist=0.55, cont_pos_dist=0.48, loss=14.18, lr=1.0e-04, metric_loss=1.49, trip=0.98, trip_neg_dist=0.51, trip_pos_dist=0.48]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.0007206341647705206\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.0024021138915169735\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000006\n",
            "Attempted to log scalar metric train_loss:\n",
            "14.181264803325389\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.490588911502315\n",
            "Attempted to log scalar metric train_ce:\n",
            "10.09229173346434\n",
            "Attempted to log scalar metric train_arcface:\n",
            "25.98384007782195\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.5142821225514642\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.9763067880989467\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.4833195070444764\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.5076689785458273\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.4833195071160822\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.5450180659266631\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.02434947150135086\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.5770436456757853\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.06169855881058095\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.6699328013009036\n",
            "Phase: val\n",
            "Epoch 0 val:  14% 30/219 [00:25<02:41,  1.17it/s, acc1=0.00, acc5=0.00, arcface=25.73, ce=10.06, cont=0.48, cont_neg_dist=0.74, cont_pos_dist=0.23, loss=14.01, metric_loss=1.38, trip=0.90, trip_neg_dist=0.33, trip_pos_dist=0.23]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.0014295926087238347\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.0028591852094591984\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.01190061207922\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.3810994127463068\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.057549657269492\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.73251469839122\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.48335573058029513\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.8977436716851377\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.23139104684108658\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.33479648845560733\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.231391043707366\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.7355060882663863\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.10340544161452075\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.6865443778831622\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.5041150445590203\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9923495136876376\n",
            "#### Checkpoint ###\n",
            "Evaluating metric metrics\n",
            "100% 32/32 [00:11<00:00,  2.72it/s]\n",
            "query_img_paths 745 nunique 745 ../../ePillID_data/classification_data/fcn_mix_weight/dc_224/10.jpg\n",
            "query_labels 745 nunique 192\n",
            "100% 409/409 [02:31<00:00,  2.70it/s]\n",
            "Completed the eval model function. query:torch.Size([745, 2048]), ref:torch.Size([9804, 2048])\n",
            "Creating Torch cosine sim provider for metric evaluator\n",
            "Found 4902 classes in the references\n",
            "Evaluation will be performed in 611 simulated 2-side pairs from 613 consumer images of 141 labels.\n",
            "Calculate distance matrix between all the labels 4902 and consumer-queries 611\n",
            "Avg. time elapsed per metric query 0m 0s\n",
            "qry_lbl_sim_matrix torch.Size([611, 4902])\n",
            "torch.Size([611, 4902])\n",
            "torch.Size([611])\n",
            "torch.Size([611])\n",
            "torch.Size([611, 4902])\n",
            "torch.Size([611])\n",
            "torch.Size([611])\n",
            "Evaluation complete in 2m 45s\n",
            "{'top1-acc': 0.1243862509727478, 'top5-acc': 0.3600654602050781}\n",
            "all_avg_precision (611, 7) (611, 4902) (611, 4902) 4902 611\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "Attempted to log scalar metric val_top1-acc_metric:\n",
            "0.1243862509727478\n",
            "Attempted to log scalar metric val_top5-acc_metric:\n",
            "0.3600654602050781\n",
            "Attempted to log scalar metric val_map_metric:\n",
            "0.2412337364769986\n",
            "Attempted to log scalar metric val_map_at_1_metric:\n",
            "0.12438625204582651\n",
            "Attempted to log scalar metric val_gap_metric:\n",
            "0.014708584567388279\n",
            "Attempted to log scalar metric val_gap_at_1_metric:\n",
            "0.12842170508737197\n",
            "Attempted to log scalar metric val_micro-ap_metric:\n",
            "0.014708584567388279\n",
            "Saving the best model state dict, 0.014708584567388279, 0\n",
            "\n",
            "Epoch 1/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 1 train:  36% 89/250 [02:08<03:51,  1.44s/it, acc1=0.00, acc5=0.02, arcface=24.46, ce=8.17, cont=0.47, cont_neg_dist=0.59, cont_pos_dist=0.36, loss=11.98, lr=1.0e-04, metric_loss=1.36, trip=0.89, trip_neg_dist=0.47, trip_pos_dist=0.36]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.0045530793935296176\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.016295231590857617\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000009\n",
            "Attempted to log scalar metric train_loss:\n",
            "11.975992743699864\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.363016843395841\n",
            "Attempted to log scalar metric train_ce:\n",
            "8.167063101515506\n",
            "Attempted to log scalar metric train_arcface:\n",
            "24.459126226341528\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.4746836909081134\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.8883331486597753\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.3589628064632416\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.47154993052942207\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.3589628078133227\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.5899929730279977\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.11258712406618049\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.7114906665798497\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.231030165214675\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.8376676250711015\n",
            "Phase: val\n",
            "Epoch 1 val:  14% 30/219 [00:23<02:28,  1.27it/s, acc1=0.00, acc5=0.01, arcface=25.92, ce=10.64, cont=0.45, cont_neg_dist=0.66, cont_pos_dist=0.24, loss=14.49, metric_loss=1.26, trip=0.81, trip_neg_dist=0.43, trip_pos_dist=0.24]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.0029197080101627504\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.011678832084158042\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.494759712080016\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.2619166573033715\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.64105525747703\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.91787728776027\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.450235925016612\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.8116807230632671\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.2414658467266217\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.4316966466802306\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.24146585026619372\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.6577692766867774\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.19023079995360892\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.8001743036751892\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.4163034264205837\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9331881231072259\n",
            "\n",
            "Epoch 2/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 2 train:  35% 88/250 [02:05<03:51,  1.43s/it, acc1=0.01, acc5=0.04, arcface=23.61, ce=7.22, cont=0.43, cont_neg_dist=0.54, cont_pos_dist=0.32, loss=10.81, lr=1.0e-04, metric_loss=1.23, trip=0.80, trip_neg_dist=0.52, trip_pos_dist=0.32]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.008452064849110624\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.039603960929958895\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000011\n",
            "Attempted to log scalar metric train_loss:\n",
            "10.814330386125187\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.229790917213802\n",
            "Attempted to log scalar metric train_ce:\n",
            "7.224017652443888\n",
            "Attempted to log scalar metric train_arcface:\n",
            "23.605218034296097\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.4276369059756596\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.8021540081578733\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.3171013547773779\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.5168167527429669\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.31710135489339664\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.5362163782264775\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.19971539796558901\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.8121321531831673\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.21911502333308086\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.8213211895222263\n",
            "Phase: val\n",
            "Epoch 2 val:  14% 30/219 [00:24<02:32,  1.24it/s, acc1=0.01, acc5=0.01, arcface=25.84, ce=10.56, cont=0.42, cont_neg_dist=0.65, cont_pos_dist=0.19, loss=14.32, metric_loss=1.18, trip=0.76, trip_neg_dist=0.44, trip_pos_dist=0.19]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.0056298382470173205\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.009148487253645454\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.324274626187586\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.1807624393579912\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.559050717712875\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.84461429469775\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.42100002596195446\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.7597624023643537\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.1941543648485753\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.43623961716932486\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.19415436440596653\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.6453038067932345\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.24208525232074957\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.8519708892696063\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.45114944238726795\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9765678843155079\n",
            "\n",
            "Epoch 3/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 3 train:  36% 89/250 [02:08<03:51,  1.44s/it, acc1=0.02, acc5=0.08, arcface=23.02, ce=6.58, cont=0.39, cont_neg_dist=0.47, cont_pos_dist=0.29, loss=9.98, lr=1.0e-04, metric_loss=1.10, trip=0.72, trip_neg_dist=0.58, trip_pos_dist=0.29]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.0202236498918543\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.07875327249071994\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000005\n",
            "Attempted to log scalar metric train_loss:\n",
            "9.984760896608769\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.101591215626047\n",
            "Attempted to log scalar metric train_ce:\n",
            "6.581159111887905\n",
            "Attempted to log scalar metric train_arcface:\n",
            "23.02010470573431\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.3855483503872628\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.716042860140552\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.2941368080728694\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.5801380334727272\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.2941368085330003\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.4745877257718609\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.2860012253998578\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.8881371145761803\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.18045091723886064\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.7964245926300764\n",
            "Phase: val\n",
            "Epoch 3 val:  13% 29/219 [00:23<02:34,  1.23it/s, acc1=0.01, acc5=0.02, arcface=25.62, ce=10.49, cont=0.39, cont_neg_dist=0.59, cont_pos_dist=0.19, loss=14.13, metric_loss=1.08, trip=0.69, trip_neg_dist=0.50, trip_pos_dist=0.19]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.010263929707530744\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.024926686286653156\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.13131366936692\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.0815161290493878\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.488214604665806\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.61582752546607\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.3905233671544584\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6909927599066513\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.19171610269091122\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.5000846717385767\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.19171609954804095\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.5900567438134753\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.3083685690476655\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9138590228465381\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.39834064426543436\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9731053778969923\n",
            "\n",
            "Epoch 4/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 4 train:  36% 89/250 [02:06<03:48,  1.42s/it, acc1=0.03, acc5=0.14, arcface=22.47, ce=6.05, cont=0.37, cont_neg_dist=0.46, cont_pos_dist=0.27, loss=9.35, lr=1.0e-04, metric_loss=1.05, trip=0.68, trip_neg_dist=0.59, trip_pos_dist=0.27]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.031556299713266796\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.13674396501077923\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000003\n",
            "Attempted to log scalar metric train_loss:\n",
            "9.347977865776775\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.0462504244437778\n",
            "Attempted to log scalar metric train_ce:\n",
            "6.054412094109828\n",
            "Attempted to log scalar metric train_arcface:\n",
            "22.473153060741637\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.3659978718057136\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.6802525486553966\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.2728994077921596\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.5934331428169011\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.27289940649177213\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.45785713322286553\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.3205337350247415\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9095175791810879\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.1849577267310934\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.8221154179515795\n",
            "Phase: val\n",
            "Epoch 4 val:  13% 29/219 [00:23<02:33,  1.24it/s, acc1=0.00, acc5=0.02, arcface=25.61, ce=10.59, cont=0.39, cont_neg_dist=0.61, cont_pos_dist=0.16, loss=14.22, metric_loss=1.07, trip=0.69, trip_neg_dist=0.48, trip_pos_dist=0.16]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.0036656891236141803\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.016129032457438557\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.224456540300698\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.0748148414571264\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.588560152613173\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.610815860658796\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.388428144178782\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6863866919034387\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.16254297759285016\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.47654249349878247\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.16254297550492933\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.6143496504663366\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.31399951590593234\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9316470986766511\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.45180667496140725\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9880757645245901\n",
            "\n",
            "Epoch 5/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 5 train:  36% 89/250 [02:06<03:48,  1.42s/it, acc1=0.05, acc5=0.19, arcface=22.09, ce=5.66, cont=0.36, cont_neg_dist=0.43, cont_pos_dist=0.28, loss=8.89, lr=1.0e-04, metric_loss=1.02, trip=0.67, trip_neg_dist=0.61, trip_pos_dist=0.28]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.049231508532923705\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.19356388269183758\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000007\n",
            "Attempted to log scalar metric train_loss:\n",
            "8.888641305203397\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.0216636920668092\n",
            "Attempted to log scalar metric train_ce:\n",
            "5.658051463643824\n",
            "Attempted to log scalar metric train_arcface:\n",
            "22.089261207067544\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.35653600166126814\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.6651276887164564\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.27892674600184825\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.6143619008755936\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.2789267477338756\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.4337073244032782\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.33543515487374537\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9149350201783816\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.1547805766694026\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.7782847471264491\n",
            "Phase: val\n",
            "Epoch 5 val:  13% 29/219 [00:23<02:32,  1.25it/s, acc1=0.02, acc5=0.03, arcface=25.63, ce=10.63, cont=0.38, cont_neg_dist=0.59, cont_pos_dist=0.16, loss=14.25, metric_loss=1.05, trip=0.68, trip_neg_dist=0.49, trip_pos_dist=0.16]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.015362107019868749\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.029992685378709673\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.249756432137701\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.052924406685442\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.63367186380985\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.631599896479106\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.3750338893391917\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6778905122665567\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.16295451425467145\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.48660334438481745\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.16295451415562384\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.5852443239097229\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.323648830130146\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9320664254358444\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.4222898097540991\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9866151270783837\n",
            "#### Checkpoint ###\n",
            "Evaluating metric metrics\n",
            "100% 32/32 [00:11<00:00,  2.80it/s]\n",
            "query_img_paths 745 nunique 745 ../../ePillID_data/classification_data/fcn_mix_weight/dc_224/10.jpg\n",
            "query_labels 745 nunique 192\n",
            "100% 409/409 [02:25<00:00,  2.81it/s]\n",
            "Completed the eval model function. query:torch.Size([745, 2048]), ref:torch.Size([9804, 2048])\n",
            "Creating Torch cosine sim provider for metric evaluator\n",
            "Found 4902 classes in the references\n",
            "Evaluation will be performed in 611 simulated 2-side pairs from 613 consumer images of 141 labels.\n",
            "Calculate distance matrix between all the labels 4902 and consumer-queries 611\n",
            "Avg. time elapsed per metric query 0m 0s\n",
            "qry_lbl_sim_matrix torch.Size([611, 4902])\n",
            "torch.Size([611, 4902])\n",
            "torch.Size([611])\n",
            "torch.Size([611])\n",
            "torch.Size([611, 4902])\n",
            "torch.Size([611])\n",
            "torch.Size([611])\n",
            "Evaluation complete in 2m 38s\n",
            "{'top1-acc': 0.607201337814331, 'top5-acc': 0.8756137490272522}\n",
            "all_avg_precision (611, 7) (611, 4902) (611, 4902) 4902 611\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "Attempted to log scalar metric val_top1-acc_metric:\n",
            "0.607201337814331\n",
            "Attempted to log scalar metric val_top5-acc_metric:\n",
            "0.8756137490272522\n",
            "Attempted to log scalar metric val_map_metric:\n",
            "0.7259299484520537\n",
            "Attempted to log scalar metric val_map_at_1_metric:\n",
            "0.6072013093289689\n",
            "Attempted to log scalar metric val_gap_metric:\n",
            "0.29401833317665305\n",
            "Attempted to log scalar metric val_gap_at_1_metric:\n",
            "0.7074460911114795\n",
            "Attempted to log scalar metric val_micro-ap_metric:\n",
            "0.29401833317665305\n",
            "Saving the best model state dict, 0.29401833317665305, 1\n",
            "\n",
            "Epoch 6/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 6 train:  36% 89/250 [02:11<03:56,  1.47s/it, acc1=0.07, acc5=0.27, arcface=21.78, ce=5.40, cont=0.35, cont_neg_dist=0.41, cont_pos_dist=0.29, loss=8.59, lr=1.0e-04, metric_loss=1.01, trip=0.66, trip_neg_dist=0.63, trip_pos_dist=0.29]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.07480879688375482\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.27198853075636076\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000007\n",
            "Attempted to log scalar metric train_loss:\n",
            "8.587736394619622\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "1.0100188939965706\n",
            "Attempted to log scalar metric train_ce:\n",
            "5.399635184905269\n",
            "Attempted to log scalar metric train_arcface:\n",
            "21.780822646321575\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.35025838785315105\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.6597605048755387\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.28640146802107186\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.6270231463993727\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.2864014664209472\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.41368135437012177\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.3406216783783009\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9289804448996145\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.12727988794917455\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.7596161769734857\n",
            "Phase: val\n",
            "Epoch 6 val:  14% 30/219 [00:24<02:35,  1.21it/s, acc1=0.01, acc5=0.02, arcface=25.44, ce=10.45, cont=0.36, cont_neg_dist=0.54, cont_pos_dist=0.18, loss=14.01, metric_loss=1.02, trip=0.66, trip_neg_dist=0.53, trip_pos_dist=0.18]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.007953723915465397\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.023138105925859444\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.00975194670373\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.017142134779877\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.44832564400144\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.442840363784537\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.3615572559859385\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6555848805178617\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.18357417270735837\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.5307313174398613\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.1835741732480391\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.5366571597506242\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.347157144732503\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9444803780822139\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.3530829865025851\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9693337381682513\n",
            "\n",
            "Epoch 7/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 7 train:  36% 89/250 [02:10<03:56,  1.47s/it, acc1=0.10, acc5=0.35, arcface=21.46, ce=4.99, cont=0.33, cont_neg_dist=0.38, cont_pos_dist=0.27, loss=8.08, lr=1.0e-04, metric_loss=0.95, trip=0.62, trip_neg_dist=0.65, trip_pos_dist=0.27]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.10291308643600575\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.35482330832527215\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000006\n",
            "Attempted to log scalar metric train_loss:\n",
            "8.083669007678429\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "0.9502369382889245\n",
            "Attempted to log scalar metric train_ce:\n",
            "4.987831663834901\n",
            "Attempted to log scalar metric train_arcface:\n",
            "21.456003438663572\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.3294282859331842\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.6208086549424511\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.27388658924996\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.653532355569225\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.2738865895553699\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.3844568783806209\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.37964576631926494\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9387906976313669\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.11057028882525105\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.7047428830372588\n",
            "Phase: val\n",
            "Epoch 7 val:  14% 30/219 [00:23<02:29,  1.27it/s, acc1=0.01, acc5=0.02, arcface=25.26, ce=10.23, cont=0.35, cont_neg_dist=0.52, cont_pos_dist=0.19, loss=13.74, metric_loss=0.99, trip=0.63, trip_neg_dist=0.56, trip_pos_dist=0.19]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.008771929910341114\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.023391812996209016\n",
            "Attempted to log scalar metric val_loss:\n",
            "13.74205880276641\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "0.9862531126416915\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.229480426911024\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.26325087519417\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.35463246616006594\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6316206527340134\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.19170538199079387\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.5612849408651099\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.19170537950361477\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.5163420450599755\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.36957955887431604\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9258223361670356\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.3246366655563607\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9509661681685764\n",
            "\n",
            "Epoch 8/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 8 train:  36% 89/250 [02:08<03:51,  1.44s/it, acc1=0.14, acc5=0.42, arcface=21.32, ce=4.78, cont=0.32, cont_neg_dist=0.37, cont_pos_dist=0.27, loss=7.85, lr=1.0e-04, metric_loss=0.93, trip=0.61, trip_neg_dist=0.66, trip_pos_dist=0.27]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.1422715236012666\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.41620449048440256\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000006\n",
            "Attempted to log scalar metric train_loss:\n",
            "7.849918243940808\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "0.933405112467072\n",
            "Attempted to log scalar metric train_ce:\n",
            "4.784757685540583\n",
            "Attempted to log scalar metric train_arcface:\n",
            "21.31755330932264\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.32209381759022987\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.611311295793118\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.2725889514779929\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.6612980463025645\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.2725889484039519\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.3714722844323292\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.3887090948245716\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9542345783849739\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.09888333602837729\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.6949210424537515\n",
            "Phase: val\n",
            "Epoch 8 val:  14% 30/219 [00:24<02:32,  1.24it/s, acc1=0.02, acc5=0.03, arcface=25.17, ce=10.05, cont=0.34, cont_neg_dist=0.50, cont_pos_dist=0.19, loss=13.55, metric_loss=0.98, trip=0.64, trip_neg_dist=0.55, trip_pos_dist=0.19]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.015272727248343554\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.03272727277739482\n",
            "Attempted to log scalar metric val_loss:\n",
            "13.549212667291815\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "0.9804939888173884\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.051239307750356\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.174792898004704\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.343833777514371\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6366602023731579\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.18665670913501378\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.5501428939130184\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.18665671065776018\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.500798415833146\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.36348618477800465\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9500471587177521\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.3141417051753858\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9523948259457456\n",
            "\n",
            "Epoch 9/9\n",
            "----------\n",
            "Phase: train\n",
            "Epoch 9 train:  36% 89/250 [02:10<03:56,  1.47s/it, acc1=0.19, acc5=0.50, arcface=20.89, ce=4.44, cont=0.31, cont_neg_dist=0.35, cont_pos_dist=0.27, loss=7.42, lr=1.0e-04, metric_loss=0.90, trip=0.59, trip_neg_dist=0.69, trip_pos_dist=0.27]\n",
            "Attempted to log scalar metric train_acc1:\n",
            "0.19063465828830892\n",
            "Attempted to log scalar metric train_acc5:\n",
            "0.495127173495287\n",
            "Attempted to log scalar metric train_lr:\n",
            "0.00010000000000000009\n",
            "Attempted to log scalar metric train_loss:\n",
            "7.424094261339903\n",
            "Attempted to log scalar metric train_metric_loss:\n",
            "0.8996468508983129\n",
            "Attempted to log scalar metric train_ce:\n",
            "4.435548806717539\n",
            "Attempted to log scalar metric train_arcface:\n",
            "20.888985718404534\n",
            "Attempted to log scalar metric train_contrastive:\n",
            "0.31051045200349825\n",
            "Attempted to log scalar metric train_triplet:\n",
            "0.589136401434423\n",
            "Attempted to log scalar metric train_triplet_pos_distances:\n",
            "0.27326536451646793\n",
            "Attempted to log scalar metric train_triplet_neg_distances:\n",
            "0.6855722311166058\n",
            "Attempted to log scalar metric train_contrastive_pos_distances:\n",
            "0.27326536503421417\n",
            "Attempted to log scalar metric train_contrastive_neg_distances:\n",
            "0.34617763439965554\n",
            "Attempted to log scalar metric train_triplet_dist_diff:\n",
            "0.4123068666001379\n",
            "Attempted to log scalar metric train_triplet_pw-avg-precision:\n",
            "0.9618216188860745\n",
            "Attempted to log scalar metric train_contrastive_dist_diff:\n",
            "0.07291226936544137\n",
            "Attempted to log scalar metric train_contrastive_pw-avg-precision:\n",
            "0.6390542694289862\n",
            "Phase: val\n",
            "Epoch 9 val:  14% 30/219 [00:24<02:33,  1.23it/s, acc1=0.02, acc5=0.04, arcface=25.16, ce=10.09, cont=0.34, cont_neg_dist=0.50, cont_pos_dist=0.18, loss=13.56, metric_loss=0.95, trip=0.61, trip_neg_dist=0.57, trip_pos_dist=0.18]\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.017946877343645647\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.035893755046983715\n",
            "Attempted to log scalar metric val_loss:\n",
            "13.558453240843019\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "0.9486763575739425\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.09347260537459\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.163042479252866\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.33973737498538065\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.6089389830806319\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.179223024915674\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.5705743311039858\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.1792230238894821\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.5005237346827728\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.3913513061883118\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.9474103955166084\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.3213007107932907\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9478750369178421\n",
            "\n",
            "Training complete in 31m 7s\n",
            "completed train()\n",
            "val_metrics {'best_epoch': 1, 'acc1': 0.0029197080101627504, 'acc5': 0.011678832084158042, 'loss': 14.494759712080016, 'metric_loss': 1.2619166573033715, 'ce': 10.64105525747703, 'arcface': 25.91787728776027, 'contrastive': 0.450235925016612, 'triplet': 0.8116807230632671, 'triplet_pos_distances': 0.2414658467266217, 'triplet_neg_distances': 0.4316966466802306, 'contrastive_pos_distances': 0.24146585026619372, 'contrastive_neg_distances': 0.6577692766867774, 'triplet_dist_diff': 0.19023079995360892, 'triplet_pw-avg-precision': 0.8001743036751892, 'contrastive_dist_diff': 0.4163034264205837, 'contrastive_pw-avg-precision': 0.9331881231072259, 'top1-acc': 0.607201337814331, 'top5-acc': 0.8756137490272522, 'map': 0.7259299484520537, 'map_at_1': 0.6072013093289689, 'gap': 0.29401833317665305, 'gap_at_1': 0.7074460911114795, 'micro-ap': 0.29401833317665305}\n",
            "Attempted to log scalar metric val_acc1:\n",
            "0.0029197080101627504\n",
            "Attempted to log scalar metric val_acc5:\n",
            "0.011678832084158042\n",
            "Attempted to log scalar metric val_loss:\n",
            "14.494759712080016\n",
            "Attempted to log scalar metric val_metric_loss:\n",
            "1.2619166573033715\n",
            "Attempted to log scalar metric val_ce:\n",
            "10.64105525747703\n",
            "Attempted to log scalar metric val_arcface:\n",
            "25.91787728776027\n",
            "Attempted to log scalar metric val_contrastive:\n",
            "0.450235925016612\n",
            "Attempted to log scalar metric val_triplet:\n",
            "0.8116807230632671\n",
            "Attempted to log scalar metric val_triplet_pos_distances:\n",
            "0.2414658467266217\n",
            "Attempted to log scalar metric val_triplet_neg_distances:\n",
            "0.4316966466802306\n",
            "Attempted to log scalar metric val_contrastive_pos_distances:\n",
            "0.24146585026619372\n",
            "Attempted to log scalar metric val_contrastive_neg_distances:\n",
            "0.6577692766867774\n",
            "Attempted to log scalar metric val_triplet_dist_diff:\n",
            "0.19023079995360892\n",
            "Attempted to log scalar metric val_triplet_pw-avg-precision:\n",
            "0.8001743036751892\n",
            "Attempted to log scalar metric val_contrastive_dist_diff:\n",
            "0.4163034264205837\n",
            "Attempted to log scalar metric val_contrastive_pw-avg-precision:\n",
            "0.9331881231072259\n",
            "Attempted to log scalar metric val_top1-acc:\n",
            "0.607201337814331\n",
            "Attempted to log scalar metric val_top5-acc:\n",
            "0.8756137490272522\n",
            "Attempted to log scalar metric val_map:\n",
            "0.7259299484520537\n",
            "Attempted to log scalar metric val_map_at_1:\n",
            "0.6072013093289689\n",
            "Attempted to log scalar metric val_gap:\n",
            "0.29401833317665305\n",
            "Attempted to log scalar metric val_gap_at_1:\n",
            "0.7074460911114795\n",
            "Attempted to log scalar metric val_micro-ap:\n",
            "0.29401833317665305\n",
            "Evaluating holdout-logit\n",
            "Eval holdout-logit: 745 images from 745 total images\n",
            "100% 32/32 [00:11<00:00,  2.73it/s]\n",
            "query_img_paths 745 nunique 745 ../../ePillID_data/classification_data/fcn_mix_weight/dc_224/1010.jpg\n",
            "query_labels 745 nunique 192\n",
            "Evaluation will be performed in 626 simulated 2-side pairs from 630 consumer images of 145 labels.\n",
            "query_outputs.shape torch.Size([745, 4902])\n",
            "Calculate distance matrix between all the labels 4902 and consumer-queries 626\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "all_avg_precision (1252, 7) (1252, 4902) (1252, 4902) 4902 1252\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "qry_lbl_sim_matrix.shape torch.Size([626, 4902])\n",
            "Avg. time elapsed per metric query 0m 0s\n",
            "qry_lbl_sim_matrix torch.Size([626, 4902])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "Evaluation complete in 0m 34s\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "Attempted to log image metric holdout-logit_11:07:43_PR-curve:\n",
            "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>\n",
            "Attempted to log scalar metric holdout-logit_raw-top1-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_raw-top5-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_f_top1-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_f_top5-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_f_map:\n",
            "0.0021902022036048973\n",
            "Attempted to log scalar metric holdout-logit_f_map_at_1:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_f_gap:\n",
            "0.00029412145540964256\n",
            "Attempted to log scalar metric holdout-logit_f_gap_at_1:\n",
            "nan\n",
            "Attempted to log scalar metric holdout-logit_f_micro-ap:\n",
            "0.00029412145540964256\n",
            "Attempted to log scalar metric holdout-logit_b_top1-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_b_top5-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_b_map:\n",
            "0.0022567145330392945\n",
            "Attempted to log scalar metric holdout-logit_b_map_at_1:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_b_gap:\n",
            "0.0002936019589902961\n",
            "Attempted to log scalar metric holdout-logit_b_gap_at_1:\n",
            "nan\n",
            "Attempted to log scalar metric holdout-logit_b_micro-ap:\n",
            "0.0002936019589902961\n",
            "Attempted to log scalar metric holdout-logit_s_map:\n",
            "0.002223458368322096\n",
            "Attempted to log scalar metric holdout-logit_s_map_at_1:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_s_gap:\n",
            "0.0002925258613456077\n",
            "Attempted to log scalar metric holdout-logit_s_gap_at_1:\n",
            "nan\n",
            "Attempted to log scalar metric holdout-logit_s_micro-ap:\n",
            "0.0002925258613456077\n",
            "Attempted to log scalar metric holdout-logit_top1-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_top5-acc:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_map:\n",
            "0.0023165816681888418\n",
            "Attempted to log scalar metric holdout-logit_map_at_1:\n",
            "0.0\n",
            "Attempted to log scalar metric holdout-logit_gap:\n",
            "0.0002994241933345158\n",
            "Attempted to log scalar metric holdout-logit_gap_at_1:\n",
            "nan\n",
            "Attempted to log scalar metric holdout-logit_micro-ap:\n",
            "0.0002994241933345158\n",
            "Evaluating holdout\n",
            "Eval holdout: 745 images from 745 total images\n",
            "100% 32/32 [00:11<00:00,  2.77it/s]\n",
            "query_img_paths 745 nunique 745 ../../ePillID_data/classification_data/fcn_mix_weight/dc_224/1010.jpg\n",
            "query_labels 745 nunique 192\n",
            "100% 409/409 [02:31<00:00,  2.70it/s]\n",
            "Completed the eval model function. query:torch.Size([745, 2048]), ref:torch.Size([9804, 2048])\n",
            "Creating Torch cosine sim provider for metric evaluator\n",
            "Found 4902 classes in the references\n",
            "Evaluation will be performed in 626 simulated 2-side pairs from 630 consumer images of 145 labels.\n",
            "Calculate distance matrix between all the labels 4902 and consumer-queries 626\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "all_avg_precision (1252, 7) (1252, 4902) (1252, 4902) 4902 1252\n",
            "Avg. time elapsed per metric query 0m 0s\n",
            "qry_lbl_sim_matrix torch.Size([626, 4902])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "torch.Size([626, 4902])\n",
            "torch.Size([626])\n",
            "torch.Size([626])\n",
            "Evaluation complete in 3m 5s\n",
            "{'f_top1-acc': 0.32108625769615173, 'f_top5-acc': 0.7044728398323059, 'f_map': 0.4858046749224538, 'f_map_at_1': 0.3210862619808307, 'f_gap': 0.11160219176728114, 'f_gap_at_1': 0.3800656182108245, 'f_micro-ap': 0.11160219176728114, 'f_PR-curve': <module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>, 'b_top1-acc': 0.3482428193092346, 'b_top5-acc': 0.6757188439369202, 'b_map': 0.49554494084137163, 'b_map_at_1': 0.34824281150159747, 'b_gap': 0.10315381146142404, 'b_gap_at_1': 0.35874156153775516, 'b_micro-ap': 0.10315381146142404, 'b_PR-curve': <module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>, 's_map': 0.4906748078819127, 's_map_at_1': 0.33466453674121405, 's_gap': 0.10655505355145217, 's_gap_at_1': 0.36627639843727844, 's_micro-ap': 0.10655505355145217, 's_PR-curve': <module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>, 'top1-acc': 0.6006389856338501, 'top5-acc': 0.8993610143661499}\n",
            "all_avg_precision (626, 7) (626, 4902) (626, 4902) 4902 626\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
            "  recall = tps / tps[-1]\n",
            "Attempted to log image metric holdout_11:10:55_PR-curve:\n",
            "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>\n",
            "Attempted to log scalar metric holdout_f_top1-acc:\n",
            "0.32108625769615173\n",
            "Attempted to log scalar metric holdout_f_top5-acc:\n",
            "0.7044728398323059\n",
            "Attempted to log scalar metric holdout_f_map:\n",
            "0.4858046749224538\n",
            "Attempted to log scalar metric holdout_f_map_at_1:\n",
            "0.3210862619808307\n",
            "Attempted to log scalar metric holdout_f_gap:\n",
            "0.11160219176728114\n",
            "Attempted to log scalar metric holdout_f_gap_at_1:\n",
            "0.3800656182108245\n",
            "Attempted to log scalar metric holdout_f_micro-ap:\n",
            "0.11160219176728114\n",
            "Attempted to log scalar metric holdout_b_top1-acc:\n",
            "0.3482428193092346\n",
            "Attempted to log scalar metric holdout_b_top5-acc:\n",
            "0.6757188439369202\n",
            "Attempted to log scalar metric holdout_b_map:\n",
            "0.49554494084137163\n",
            "Attempted to log scalar metric holdout_b_map_at_1:\n",
            "0.34824281150159747\n",
            "Attempted to log scalar metric holdout_b_gap:\n",
            "0.10315381146142404\n",
            "Attempted to log scalar metric holdout_b_gap_at_1:\n",
            "0.35874156153775516\n",
            "Attempted to log scalar metric holdout_b_micro-ap:\n",
            "0.10315381146142404\n",
            "Attempted to log scalar metric holdout_s_map:\n",
            "0.4906748078819127\n",
            "Attempted to log scalar metric holdout_s_map_at_1:\n",
            "0.33466453674121405\n",
            "Attempted to log scalar metric holdout_s_gap:\n",
            "0.10655505355145217\n",
            "Attempted to log scalar metric holdout_s_gap_at_1:\n",
            "0.36627639843727844\n",
            "Attempted to log scalar metric holdout_s_micro-ap:\n",
            "0.10655505355145217\n",
            "Attempted to log scalar metric holdout_top1-acc:\n",
            "0.6006389856338501\n",
            "Attempted to log scalar metric holdout_top5-acc:\n",
            "0.8993610143661499\n",
            "Attempted to log scalar metric holdout_map:\n",
            "0.7323913819773646\n",
            "Attempted to log scalar metric holdout_map_at_1:\n",
            "0.6006389776357828\n",
            "Attempted to log scalar metric holdout_gap:\n",
            "0.28388663046072643\n",
            "Attempted to log scalar metric holdout_gap_at_1:\n",
            "0.6827520578674207\n",
            "Attempted to log scalar metric holdout_micro-ap:\n",
            "0.28388663046072643\n",
            "saving predictions ../../ePillID_data/classification_results/lr0.0001_dr0.0_lrpatience2_lrfactor0.5_resnet18_202007121035\n",
            "saving predictions outputs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrQT4_45J5Lp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7628402f-0406-4ad9-f212-25f535ae9a6b"
      },
      "source": [
        "# Check predictions\n",
        "!ls outputs"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "eval_predictions_pilltypeid_nih_sidelbls0.01_metric_5folds_3.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVIqQjkXkw1s",
        "colab_type": "text"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "* Try train_cv.py for cross-validation\n",
        "* Try different models e.g. resnet50 and BCNN\n",
        "* Increase max_epochs (need 200-300 epochs to converge)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4SYEoRyL8rg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}