SWIN_SSL_MOBY_Colab.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSJKWEZ1MUOY",
        "outputId": "7d33549f-99bc-4dfe-cc63-dd0bc606033a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Jun  1 19:37:54 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P0    25W / 300W |      0MiB / 16160MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0qt8BLHPVKS",
        "outputId": "1060c7e7-63b9-42cd-95fc-e51e668aa245"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "name, driver_version, memory.total [MiB]\n",
            "Tesla V100-SXM2-16GB, 460.32.03, 16160 MiB\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi --query-gpu=gpu_name,driver_version,memory.total --format=csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUsYlqUsK3t-"
      },
      "source": [
        "#Download dummy dataset, load csv and split to train val\n",
        "Change data format to class folder stucture train & val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bJKX7XwKpAP",
        "outputId": "ac838561-ce7d-4ee9-8852-c7af264e7448"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.5.18.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kaggle\n",
            "  Downloading kaggle-1.5.12.tar.gz (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 2.8 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.12-py3-none-any.whl size=73051 sha256=f479341f9f147fe5ba7964d55d8f1b01b3d8c3a38c6c031c922fa0c7dc73fe87\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/d6/58/5853130f941e75b2177d281eb7e44b4a98ed46dd155f556dc5\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Attempting uninstall: kaggle\n",
            "    Found existing installation: kaggle 1.5.12\n",
            "    Uninstalling kaggle-1.5.12:\n",
            "      Successfully uninstalled kaggle-1.5.12\n",
            "Successfully installed kaggle-1.5.12\n",
            "Downloading resized-2015-2019-blindness-detection-images.zip to /content/competitionKaggle\n",
            " 19% 3.36G/17.3G [01:37<06:45, 37.0MB/s]\n",
            "User cancelled operation\n",
            "Error in atexit._run_exitfuncs:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/util.py\", line 300, in _run_finalizers\n",
            "    finalizer()\n",
            "  File \"/usr/lib/python3.7/multiprocessing/util.py\", line 224, in __call__\n",
            "    res = self._callback(*self._args, **self._kwargs)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/pool.py\", line 594, in _terminate_pool\n",
            "    worker_handler.join()\n",
            "  File \"/usr/lib/python3.7/threading.py\", line 1044, in join\n",
            "    self._wait_for_tstate_lock()\n",
            "  File \"/usr/lib/python3.7/threading.py\", line 1060, in _wait_for_tstate_lock\n",
            "    elif lock.acquire(block, timeout):\n",
            "KeyboardInterrupt\n",
            "Archive:  /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip\n",
            "  End-of-central-directory signature not found.  Either this file is not\n",
            "  a zipfile, or it constitutes one disk of a multi-part archive.  In the\n",
            "  latter case the central directory and zipfile comment will be found on\n",
            "  the last disk(s) of this archive.\n",
            "unzip:  cannot find zipfile directory in one of /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip or\n",
            "        /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip.zip, and cannot find /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip.ZIP, period.\n"
          ]
        }
      ],
      "source": [
        "#Upload kaggle api key in /content/\n",
        "\n",
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! mkdir competitionKaggle\n",
        "! pip install --upgrade --force-reinstall --no-deps kaggle\n",
        "\n",
        "#https://www.kaggle.com/benjaminwarner/resized-2015-2019-blindness-detection-images\n",
        "!kaggle datasets download -p /content/competitionKaggle/ -d benjaminwarner/resized-2015-2019-blindness-detection-images\n",
        "\n",
        "!unzip /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip -d /content/competitionKaggle/\n",
        "!rm /content/competitionKaggle/resized-2015-2019-blindness-detection-images.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xCrlcBfkM3ku",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "6f45e3c2-9cff-4cad-e4d3-e8016ef06a23"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-9f4cecf4c9ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/competitionKaggle/labels/trainLabels15.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#\"/content/competitionKaggle/trainLabels.csv\")#\"../input/com3025-2022-image-classification-challenge/train.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'string'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m         )\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    705\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m                 \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m                 \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    708\u001b[0m             )\n\u001b[1;32m    709\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/competitionKaggle/labels/trainLabels15.csv'"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv(\"/content/competitionKaggle/labels/trainLabels15.csv\")#\"/content/competitionKaggle/trainLabels.csv\")#\"../input/com3025-2022-image-classification-challenge/train.csv\"\n",
        "train = train.astype('string')\n",
        "\n",
        "test = pd.read_csv(\"/content/competitionKaggle/labels/testLabels15.csv\")#\"/content/competitionKaggle/trainLabels.csv\")#\"../input/com3025-2022-image-classification-challenge/train.csv\"\n",
        "test = test.astype('string')\n",
        "\n",
        "train2 = pd.read_csv(\"/content/competitionKaggle/labels/trainLabels19.csv\")#\"/content/competitionKaggle/trainLabels.csv\")#\"../input/com3025-2022-image-classification-challenge/train.csv\"\n",
        "train2 = train2.astype('string')\n",
        "\n",
        "\n",
        "def append_ext(fn):#add file extention to filenames\n",
        "    return fn+\".jpg\"#\".jpeg\"#check converted file extention\n",
        "\n",
        "train[\"image\"]=train[\"image\"].apply(append_ext)\n",
        "\n",
        "test[\"image\"]=test[\"image\"].apply(append_ext)\n",
        "\n",
        "train2[\"id_code\"]=train2[\"id_code\"].apply(append_ext)\n",
        "\n",
        "#SAMPLE SMALLER FOR QUICK RUNS --------------------------------------------------------------------------------------------------------------------------------------------\n",
        "#and making demo dataset\n",
        "# train = train.sample(1000)\n",
        "# test = test.sample(1000)\n",
        "# train2 = train2.sample(1000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MYepyBvxdXU"
      },
      "source": [
        "Convert data from directories with all images into train/{classnumber}/ folder structure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X36d4CxTN4Js"
      },
      "outputs": [],
      "source": [
        "!mkdir /content/competitionKaggle/train\n",
        "!mkdir /content/competitionKaggle/val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFqxtwAOKLWn"
      },
      "outputs": [],
      "source": [
        "#very quick\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "def createFolderTreeFromDataframe(df, folder, source, ycolumn, imageCol):\n",
        "  for index, row in df.iterrows():#for every row\n",
        "    image_class = row[ycolumn]#load the class\n",
        "    if not os.path.exists(f'/content/competitionKaggle/{folder}/{image_class}'):#create class folder if it doesnt exist\n",
        "      os.makedirs(f'/content/competitionKaggle/{folder}/{image_class}')\n",
        "    image_name = row[imageCol]#load the filename\n",
        "    shutil.move(f\"/content/competitionKaggle/{source}/{image_name}\", f\"/content/competitionKaggle/{folder}/{image_class}/\")#move that file to the new dest\n",
        "\n",
        "createFolderTreeFromDataframe(train, \"train\", \"resized train 15\",'level', 'image')\n",
        "createFolderTreeFromDataframe(test, \"train\", \"resized test 15\",'level', 'image')#put test in train as well\n",
        "createFolderTreeFromDataframe(train2, \"train\", \"resized train 19\", 'diagnosis', 'id_code')#put test in train as well\n",
        "#92364 total images\n",
        "\n",
        "# createFolderTreeFromDataframe(test, \"val\", \"resized test 15\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAWZ-W51xdXW"
      },
      "source": [
        "If needed split into val with 20 of the images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DTRpM53diRgE"
      },
      "outputs": [],
      "source": [
        "#----------------------put 20% of images in val\n",
        "import os\n",
        "import random\n",
        "random.seed(0)\n",
        "\n",
        "for i in range(5):\n",
        "  files = os.listdir(f\"/content/competitionKaggle/train/{i}\")\n",
        "  random.shuffle(files)\n",
        "  files = files[:int(len(files)*0.2)]\n",
        "  for fileName in files:\n",
        "    if not os.path.exists(f'/content/competitionKaggle/val/{i}'):#create class folder if it doesnt exist\n",
        "      os.makedirs(f'/content/competitionKaggle/val/{i}')\n",
        "    shutil.move(f\"/content/competitionKaggle/train/{i}/{fileName}\", f\"/content/competitionKaggle/val/{i}/\")#move that file to the new dest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WB8ApmUK6hzc"
      },
      "source": [
        "##make demo datset with val split or full train without train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Rajm3GE4xKB"
      },
      "outputs": [],
      "source": [
        "#move val and train to new dataset folder\n",
        "# !mkdir /content/competitionKaggle/demodataset\n",
        "# !mv /content/competitionKaggle/train /content/competitionKaggle/demodataset\n",
        "# !mv /content/competitionKaggle/val /content/competitionKaggle/demodataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQaXwsCA4NDt"
      },
      "outputs": [],
      "source": [
        "# #making demo dataset - delete after and not run again\n",
        "# !kaggle datasets init -p /content/competitionKaggle/demodataset\n",
        "# #change json metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ImEVLwDW4i99"
      },
      "outputs": [],
      "source": [
        "# !kaggle datasets create -p /content/competitionKaggle/demodataset/ --dir-mode zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obd9LzcZYwPk"
      },
      "source": [
        "#Downlaod my kaggle full train DR - for unsupervised learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHnDlwpyY5BV",
        "outputId": "4e6aa96e-223d-46f2-8e47-7a77ed7a7c87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.5.18.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "mkdir: cannot create directory ‘competitionKaggle’: File exists\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kaggle\n",
            "  Using cached kaggle-1.5.12-py3-none-any.whl\n",
            "Installing collected packages: kaggle\n",
            "  Attempting uninstall: kaggle\n",
            "    Found existing installation: kaggle 1.5.12\n",
            "    Uninstalling kaggle-1.5.12:\n",
            "      Successfully uninstalled kaggle-1.5.12\n",
            "Successfully installed kaggle-1.5.12\n"
          ]
        }
      ],
      "source": [
        "#Upload kaggle api key in /content/\n",
        "\n",
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! mkdir competitionKaggle\n",
        "! pip install --upgrade --force-reinstall --no-deps kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwte-qLGY9JT",
        "outputId": "7d52814b-d9cb-4be7-9e65-8d983424fd77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!kaggle datasets download -p /content/competitionKaggle/ -d gilescodes/dr-full-train-set\n",
        "!unzip /content/competitionKaggle/dr-full-train-set.zip -d /content/competitionKaggle/\n",
        "!rm /content/competitionKaggle/dr-full-train-set.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQnIMYIcKfHH"
      },
      "source": [
        "#MoBY SLL SWIN T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sn6qdNBzKMJn"
      },
      "source": [
        "## setup MOBY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "026TU_xwLGvI",
        "outputId": "17d99074-1fcf-4e50-df6c-08b4b9ca3d4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Transformer-SSL'...\n",
            "remote: Enumerating objects: 201, done.\u001b[K\n",
            "remote: Total 201 (delta 0), reused 0 (delta 0), pack-reused 201\u001b[K\n",
            "Receiving objects: 100% (201/201), 1.17 MiB | 5.12 MiB/s, done.\n",
            "Resolving deltas: 100% (95/95), done.\n",
            "/content/Transformer-SSL\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/SwinTransformer/Transformer-SSL.git\n",
        "%cd /content/Transformer-SSL\n",
        "\n",
        "# !pip3 install install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit==10.1\n",
        "\n",
        "#Experimenting with prepre-training---------------------------------------\n",
        "# !git clone --single-branch --branch Modified-MoBY-Pretrained https://github.com/Giles-Billenness/Swin-Transformer.git\n",
        "# %cd /content/Swin-Transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYdgJRuHLOTP",
        "outputId": "e5869f2d-3eeb-4466-aa55-8799c278611b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.8.0+cu101\n",
            "  Downloading https://download.pytorch.org/whl/cu101/torch-1.8.0%2Bcu101-cp37-cp37m-linux_x86_64.whl (763.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 763.5 MB 15 kB/s \n",
            "\u001b[?25hCollecting torchvision==0.9.0+cu101\n",
            "  Downloading https://download.pytorch.org/whl/cu101/torchvision-0.9.0%2Bcu101-cp37-cp37m-linux_x86_64.whl (17.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.3 MB 748 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu101) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu101) (4.2.0)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.0+cu101) (7.1.2)\n",
            "Installing collected packages: torch, torchvision\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.11.0+cu113\n",
            "    Uninstalling torch-1.11.0+cu113:\n",
            "      Successfully uninstalled torch-1.11.0+cu113\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.12.0+cu113\n",
            "    Uninstalling torchvision-0.12.0+cu113:\n",
            "      Successfully uninstalled torchvision-0.12.0+cu113\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.12.0 requires torch==1.11.0, but you have torch 1.8.0+cu101 which is incompatible.\n",
            "torchaudio 0.11.0+cu113 requires torch==1.11.0, but you have torch 1.8.0+cu101 which is incompatible.\u001b[0m\n",
            "Successfully installed torch-1.8.0+cu101 torchvision-0.9.0+cu101\n",
            "py37_cu101_pyt180\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/py37_cu101_pyt180/download.html\n",
            "Collecting apex\n",
            "  Downloading https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/py37_cu101_pyt180/apex-0.1-cp37-cp37m-linux_x86_64.whl (13.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.7 MB 772 kB/s \n",
            "\u001b[?25hInstalling collected packages: apex\n",
            "Successfully installed apex-0.1\n",
            "10.1\n",
            "1\n",
            "Tesla V100-SXM2-16GB\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n"
          ]
        }
      ],
      "source": [
        "#Install from VISSL notebook\n",
        "\n",
        "#pytorch ssl certificate invalid :(\n",
        "# !pip install --trusted-host pypi.org --trusted-host pytorch.org --trusted-host download.pytorch.org --trusted-host files.pypi.org --trusted-host files.pytorch.org --upgrade --force-reinstall torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# Install pytorch version 1.8\n",
        "!pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# install Apex by checking system settings: cuda version, pytorch version, and python version\n",
        "import sys\n",
        "import torch\n",
        "version_str=\"\".join([\n",
        "    f\"py3{sys.version_info.minor}_cu\",\n",
        "    torch.version.cuda.replace(\".\",\"\"),\n",
        "    f\"_pyt{torch.__version__[0:5:2]}\"\n",
        "])\n",
        "print(version_str)\n",
        "\n",
        "# install apex (pre-compiled with optimizer C++ extensions and CUDA kernels)\n",
        "!pip install apex -f https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/{version_str}/download.html\n",
        "\n",
        "import torch\n",
        "print(torch.version.cuda)\n",
        "print(torch.cuda.device_count())\n",
        "print(torch.cuda.get_device_name(0))\n",
        "\n",
        "!nvcc --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9kLSF5JLSXd",
        "outputId": "15340625-ba79-4cb6-cdf1-73bc9279eca3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Transformer-SSL\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting opencv-python==4.4.0.46\n",
            "  Downloading opencv_python-4.4.0.46-cp37-cp37m-manylinux2014_x86_64.whl (49.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 49.5 MB 2.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor==1.1.0 in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Collecting yacs==0.1.8\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Collecting diffdist\n",
            "  Downloading diffdist-0.1.tar.gz (4.6 kB)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python==4.4.0.46) (1.21.6)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from yacs==0.1.8) (3.13)\n",
            "Building wheels for collected packages: diffdist\n",
            "  Building wheel for diffdist (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for diffdist: filename=diffdist-0.1-py3-none-any.whl size=6553 sha256=7959abf8a08d04ca7154d4e721e83ce4555fc1afc3a3ef99e66a6e85580a0619\n",
            "  Stored in directory: /root/.cache/pip/wheels/58/eb/5d/4878c7186753c09afacb2a43a74b45289488ebc62e9576565e\n",
            "Successfully built diffdist\n",
            "Installing collected packages: yacs, opencv-python, diffdist\n",
            "  Attempting uninstall: opencv-python\n",
            "    Found existing installation: opencv-python 4.1.2.30\n",
            "    Uninstalling opencv-python-4.1.2.30:\n",
            "      Successfully uninstalled opencv-python-4.1.2.30\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed diffdist-0.1 opencv-python-4.4.0.46 yacs-0.1.8\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting timm==0.4.9\n",
            "  Downloading timm-0.4.9-py3-none-any.whl (346 kB)\n",
            "\u001b[K     |████████████████████████████████| 346 kB 4.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm==0.4.9) (0.9.0+cu101)\n",
            "Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from timm==0.4.9) (1.8.0+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->timm==0.4.9) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->timm==0.4.9) (4.2.0)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision->timm==0.4.9) (7.1.2)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.4.9\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting PyYaml==5.1\n",
            "  Downloading PyYAML-5.1.tar.gz (274 kB)\n",
            "\u001b[K     |████████████████████████████████| 274 kB 4.3 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: PyYaml\n",
            "  Building wheel for PyYaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyYaml: filename=PyYAML-5.1-cp37-cp37m-linux_x86_64.whl size=44092 sha256=63ff56efb00b9f000467da674e43962d3768db7cd97769ce7c9e541f317ecb34\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/f5/10/d00a2bd30928b972790053b5de0c703ca87324f3fead0f2fd9\n",
            "Successfully built PyYaml\n",
            "Installing collected packages: PyYaml\n",
            "  Attempting uninstall: PyYaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYaml-5.1\n"
          ]
        }
      ],
      "source": [
        "%cd /content/Transformer-SSL\n",
        "!pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 diffdist\n",
        "\n",
        "# !pip install timm==0.3.2\n",
        "!pip install timm==0.4.9\n",
        "!pip install PyYaml==5.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INoBoFGCN2b-"
      },
      "source": [
        "## Run MoBY - change config to fit data first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmnoKcRwLVix",
        "outputId": "3e6de6d3-30ff-4c19-eb1c-10f256c9faf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Transformer-SSL\n",
            "=> merge config from configs/moby_swin_tiny.yaml\n",
            "RANK and WORLD_SIZE in environ: 0/1\n",
            "\u001b[32m[2022-06-01 20:02:16 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 231)\u001b[0m: INFO Full config saved to /content/drive/MyDrive/Colab Notebooks/FYP/Final Code/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default/config.json\n",
            "\u001b[32m[2022-06-01 20:02:16 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 234)\u001b[0m: INFO AMP_OPT_LEVEL: O1\n",
            "AUG:\n",
            "  AUTO_AUGMENT: rand-m9-mstd0.5-inc1\n",
            "  COLOR_JITTER: 0.4\n",
            "  CUTMIX: 1.0\n",
            "  CUTMIX_MINMAX: null\n",
            "  MIXUP: 0.8\n",
            "  MIXUP_MODE: batch\n",
            "  MIXUP_PROB: 1.0\n",
            "  MIXUP_SWITCH_PROB: 0.5\n",
            "  RECOUNT: 1\n",
            "  REMODE: pixel\n",
            "  REPROB: 0.25\n",
            "  SSL_AUG: true\n",
            "  SSL_AUG_CROP: 0.08\n",
            "  SSL_AUG_TYPE: byol\n",
            "  SSL_LINEAR_AUG: false\n",
            "BASE:\n",
            "- ''\n",
            "DATA:\n",
            "  BATCH_SIZE: 64\n",
            "  CACHE_MODE: part\n",
            "  DATASET: imagenet\n",
            "  DATA_PATH: /content/competitionKaggle\n",
            "  IMG_SIZE: 224\n",
            "  INTERPOLATION: bicubic\n",
            "  NUM_WORKERS: 8\n",
            "  PIN_MEMORY: true\n",
            "  ZIP_MODE: false\n",
            "EVAL_MODE: false\n",
            "LINEAR_EVAL:\n",
            "  PRETRAINED: ''\n",
            "LOCAL_RANK: 0\n",
            "MODEL:\n",
            "  DROP_PATH_RATE: 0.1\n",
            "  DROP_RATE: 0.0\n",
            "  LABEL_SMOOTHING: 0.1\n",
            "  MOBY:\n",
            "    CONTRAST_MOMENTUM: 0.99\n",
            "    CONTRAST_NUM_NEGATIVE: 4096\n",
            "    CONTRAST_TEMPERATURE: 0.2\n",
            "    ENCODER: swin\n",
            "    ONLINE_DROP_PATH_RATE: 0.2\n",
            "    PRED_NUM_LAYERS: 2\n",
            "    PROJ_NUM_LAYERS: 2\n",
            "    TARGET_DROP_PATH_RATE: 0.0\n",
            "  NAME: moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2\n",
            "  NUM_CLASSES: 5\n",
            "  RESUME: ''\n",
            "  SWIN:\n",
            "    APE: false\n",
            "    DEPTHS:\n",
            "    - 2\n",
            "    - 2\n",
            "    - 6\n",
            "    - 2\n",
            "    EMBED_DIM: 96\n",
            "    IN_CHANS: 3\n",
            "    MLP_RATIO: 4.0\n",
            "    NORM_BEFORE_MLP: ln\n",
            "    NUM_HEADS:\n",
            "    - 3\n",
            "    - 6\n",
            "    - 12\n",
            "    - 24\n",
            "    PATCH_NORM: true\n",
            "    PATCH_SIZE: 4\n",
            "    QKV_BIAS: true\n",
            "    QK_SCALE: null\n",
            "    WINDOW_SIZE: 7\n",
            "  TYPE: moby\n",
            "OUTPUT: /content/drive/MyDrive/Colab Notebooks/FYP/Final Code/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default\n",
            "PRINT_FREQ: 10\n",
            "SAVE_FREQ: 5\n",
            "SEED: 0\n",
            "TAG: default\n",
            "TEST:\n",
            "  CROP: true\n",
            "THROUGHPUT_MODE: false\n",
            "TRAIN:\n",
            "  ACCUMULATION_STEPS: 0\n",
            "  AUTO_RESUME: true\n",
            "  BASE_LR: 0.000125\n",
            "  CLIP_GRAD: 5.0\n",
            "  EPOCHS: 300\n",
            "  LR_SCHEDULER:\n",
            "    DECAY_EPOCHS: 30\n",
            "    DECAY_RATE: 0.1\n",
            "    NAME: cosine\n",
            "  MIN_LR: 6.25e-07\n",
            "  OPTIMIZER:\n",
            "    BETAS:\n",
            "    - 0.9\n",
            "    - 0.999\n",
            "    EPS: 1.0e-08\n",
            "    MOMENTUM: 0.9\n",
            "    NAME: adamw\n",
            "  START_EPOCH: 0\n",
            "  USE_CHECKPOINT: false\n",
            "  WARMUP_EPOCHS: 5\n",
            "  WARMUP_LR: 6.25e-08\n",
            "  WEIGHT_DECAY: 0.05\n",
            "\n",
            "local rank 0 / global rank 0 successfully build train dataset\n",
            "local rank 0 / global rank 0 successfully build val dataset\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "\u001b[32m[2022-06-01 20:02:16 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 82)\u001b[0m: INFO Creating model:moby/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2\n",
            "\u001b[32m[2022-06-01 20:02:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 85)\u001b[0m: INFO MoBY(\n",
            "  (encoder): SwinTransformer(\n",
            "    (patch_embed): PatchEmbed(\n",
            "      (proj): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
            "    (layers): ModuleList(\n",
            "      (0): BasicLayer(\n",
            "        dim=96, input_resolution=(56, 56), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=96, window_size=(7, 7), num_heads=3\n",
            "              (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=96, window_size=(7, 7), num_heads=3\n",
            "              (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(56, 56), dim=96\n",
            "          (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "          (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (1): BasicLayer(\n",
            "        dim=192, input_resolution=(28, 28), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=192, window_size=(7, 7), num_heads=6\n",
            "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=192, window_size=(7, 7), num_heads=6\n",
            "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(28, 28), dim=192\n",
            "          (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (2): BasicLayer(\n",
            "        dim=384, input_resolution=(14, 14), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(14, 14), dim=384\n",
            "          (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "          (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (3): BasicLayer(\n",
            "        dim=768, input_resolution=(7, 7), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=768, window_size=(7, 7), num_heads=24\n",
            "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=768, window_size=(7, 7), num_heads=24\n",
            "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    (avgpool): AdaptiveAvgPool1d(output_size=1)\n",
            "    (head): Identity()\n",
            "  )\n",
            "  (encoder_k): SwinTransformer(\n",
            "    (patch_embed): PatchEmbed(\n",
            "      (proj): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
            "    (layers): ModuleList(\n",
            "      (0): BasicLayer(\n",
            "        dim=96, input_resolution=(56, 56), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=96, window_size=(7, 7), num_heads=3\n",
            "              (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=96, window_size=(7, 7), num_heads=3\n",
            "              (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(56, 56), dim=96\n",
            "          (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "          (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (1): BasicLayer(\n",
            "        dim=192, input_resolution=(28, 28), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=192, window_size=(7, 7), num_heads=6\n",
            "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=192, window_size=(7, 7), num_heads=6\n",
            "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(28, 28), dim=192\n",
            "          (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (2): BasicLayer(\n",
            "        dim=384, input_resolution=(14, 14), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=384, window_size=(7, 7), num_heads=12\n",
            "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (downsample): PatchMerging(\n",
            "          input_resolution=(14, 14), dim=384\n",
            "          (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "          (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (3): BasicLayer(\n",
            "        dim=768, input_resolution=(7, 7), depth=2\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=768, window_size=(7, 7), num_heads=24\n",
            "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=768, window_size=(7, 7), num_heads=24\n",
            "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    (avgpool): AdaptiveAvgPool1d(output_size=1)\n",
            "    (head): Identity()\n",
            "  )\n",
            "  (projector): MoBYMLP(\n",
            "    (linear_hidden): Sequential(\n",
            "      (0): Identity()\n",
            "      (1): Linear(in_features=768, out_features=4096, bias=True)\n",
            "      (2): SyncBatchNorm(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "    (linear_out): Linear(in_features=4096, out_features=256, bias=True)\n",
            "  )\n",
            "  (projector_k): MoBYMLP(\n",
            "    (linear_hidden): Sequential(\n",
            "      (0): Identity()\n",
            "      (1): Linear(in_features=768, out_features=4096, bias=True)\n",
            "      (2): SyncBatchNorm(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "    (linear_out): Linear(in_features=4096, out_features=256, bias=True)\n",
            "  )\n",
            "  (predictor): MoBYMLP(\n",
            "    (linear_hidden): Sequential(\n",
            "      (0): Identity()\n",
            "      (1): Linear(in_features=256, out_features=4096, bias=True)\n",
            "      (2): SyncBatchNorm(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "    (linear_out): Linear(in_features=4096, out_features=256, bias=True)\n",
            "  )\n",
            ")\n",
            "Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.\n",
            "\n",
            "Defaults for this optimization level are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n",
            "Processing user overrides (additional kwargs that are not None)...\n",
            "After processing overrides, optimization options are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n",
            "\u001b[32m[2022-06-01 20:02:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 94)\u001b[0m: INFO number of params: 33835898\n",
            "All checkpoints founded in /content/drive/MyDrive/Colab Notebooks/FYP/Final Code/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default: []\n",
            "\u001b[32m[2022-06-01 20:02:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 111)\u001b[0m: INFO no checkpoint found in /content/drive/MyDrive/Colab Notebooks/FYP/Final Code/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default, ignoring auto resume\n",
            "\u001b[32m[2022-06-01 20:02:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 116)\u001b[0m: INFO Start self-supervised pre-training\n",
            "\u001b[32m[2022-06-01 20:02:37 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][0/1443]\teta 7:58:38 lr 0.000000\ttime 19.9023 (19.9023)\tloss 16.5959 (16.5959)\tgrad_norm 31.1601 (31.1601)\tmem 12773MB\n",
            "\u001b[32m[2022-06-01 20:02:53 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][10/1443]\teta 1:17:25 lr 0.000000\ttime 1.3260 (3.2416)\tloss 16.6212 (16.6042)\tgrad_norm 26.5684 (29.1137)\tmem 13187MB\n",
            "\u001b[32m[2022-06-01 20:03:10 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][20/1443]\teta 0:59:56 lr 0.000000\ttime 1.4077 (2.5273)\tloss 16.5354 (16.5795)\tgrad_norm 20.7110 (26.8007)\tmem 13187MB\n",
            "\u001b[32m[2022-06-01 20:03:28 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][30/1443]\teta 0:54:04 lr 0.000001\ttime 1.3251 (2.2959)\tloss 16.4547 (16.5298)\tgrad_norm 17.1579 (24.6021)\tmem 13187MB\n",
            "\u001b[32m[2022-06-01 20:03:52 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][40/1443]\teta 0:54:10 lr 0.000001\ttime 5.8317 (2.3172)\tloss 16.2894 (16.4870)\tgrad_norm 15.1429 (22.6259)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:04:10 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][50/1443]\teta 0:51:20 lr 0.000001\ttime 1.3687 (2.2113)\tloss 16.3256 (16.4539)\tgrad_norm 11.2208 (20.6943)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:04:28 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][60/1443]\teta 0:49:30 lr 0.000001\ttime 1.3277 (2.1478)\tloss 16.2620 (16.4259)\tgrad_norm 10.3961 (19.0606)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:04:46 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][70/1443]\teta 0:48:02 lr 0.000001\ttime 1.3286 (2.0995)\tloss 16.3744 (16.4185)\tgrad_norm 9.7624 (17.7304)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:05:09 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][80/1443]\teta 0:48:10 lr 0.000001\ttime 5.6174 (2.1209)\tloss 16.2146 (16.3996)\tgrad_norm 9.0249 (16.7415)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:05:28 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][90/1443]\teta 0:47:22 lr 0.000002\ttime 1.3431 (2.1012)\tloss 16.1880 (16.3858)\tgrad_norm 9.2773 (15.9777)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:05:47 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][100/1443]\teta 0:46:25 lr 0.000002\ttime 1.3786 (2.0739)\tloss 16.2245 (16.3672)\tgrad_norm 9.9733 (15.3438)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:06:05 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][110/1443]\teta 0:45:35 lr 0.000002\ttime 1.3845 (2.0521)\tloss 16.0451 (16.3525)\tgrad_norm 9.3794 (14.8073)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:06:27 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][120/1443]\teta 0:45:31 lr 0.000002\ttime 5.2632 (2.0645)\tloss 16.2545 (16.3343)\tgrad_norm 9.5673 (14.3524)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:06:46 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][130/1443]\teta 0:44:50 lr 0.000002\ttime 1.5041 (2.0494)\tloss 15.9523 (16.3193)\tgrad_norm 10.0689 (13.9676)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:07:04 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][140/1443]\teta 0:44:10 lr 0.000002\ttime 1.4070 (2.0338)\tloss 16.0509 (16.3023)\tgrad_norm 9.4246 (13.6312)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:07:23 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][150/1443]\teta 0:43:36 lr 0.000003\ttime 1.3434 (2.0236)\tloss 15.9335 (16.2888)\tgrad_norm 9.5542 (13.3420)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:07:45 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][160/1443]\teta 0:43:30 lr 0.000003\ttime 5.0476 (2.0346)\tloss 16.0768 (16.2765)\tgrad_norm 10.4722 (13.1019)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:08:04 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][170/1443]\teta 0:43:02 lr 0.000003\ttime 1.4063 (2.0285)\tloss 15.9906 (16.2624)\tgrad_norm 9.4066 (12.8757)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:08:23 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][180/1443]\teta 0:42:31 lr 0.000003\ttime 1.4604 (2.0199)\tloss 15.9514 (16.2495)\tgrad_norm 8.6740 (12.6681)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:08:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][190/1443]\teta 0:41:56 lr 0.000003\ttime 1.4407 (2.0081)\tloss 15.7199 (16.2351)\tgrad_norm 9.4374 (12.4844)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:09:03 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][200/1443]\teta 0:41:48 lr 0.000004\ttime 4.6361 (2.0182)\tloss 15.8403 (16.2229)\tgrad_norm 8.2280 (12.2975)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:09:22 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][210/1443]\teta 0:41:24 lr 0.000004\ttime 1.4986 (2.0154)\tloss 15.8285 (16.2057)\tgrad_norm 8.7873 (12.1352)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:09:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][220/1443]\teta 0:40:54 lr 0.000004\ttime 1.3817 (2.0067)\tloss 15.8551 (16.1934)\tgrad_norm 8.8924 (12.0183)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:09:59 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][230/1443]\teta 0:40:26 lr 0.000004\ttime 1.7457 (2.0003)\tloss 15.6033 (16.1804)\tgrad_norm 10.5398 (11.9070)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:10:24 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][240/1443]\teta 0:40:28 lr 0.000004\ttime 5.8869 (2.0189)\tloss 15.5901 (16.1639)\tgrad_norm 8.2101 (11.7859)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:10:46 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][250/1443]\teta 0:40:16 lr 0.000004\ttime 1.8338 (2.0252)\tloss 16.0217 (16.1475)\tgrad_norm 8.9762 (11.6845)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:11:05 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][260/1443]\teta 0:39:51 lr 0.000005\ttime 1.3552 (2.0214)\tloss 16.1579 (16.1350)\tgrad_norm 12.6372 (11.6011)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:11:23 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][270/1443]\teta 0:39:22 lr 0.000005\ttime 1.3993 (2.0141)\tloss 15.6241 (16.1172)\tgrad_norm 9.6368 (11.5107)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:11:46 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][280/1443]\teta 0:39:14 lr 0.000005\ttime 6.0566 (2.0246)\tloss 15.6689 (16.1034)\tgrad_norm 8.5353 (11.4400)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:12:04 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][290/1443]\teta 0:38:46 lr 0.000005\ttime 1.5408 (2.0178)\tloss 15.7193 (16.0876)\tgrad_norm 9.5880 (11.3571)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:12:23 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][300/1443]\teta 0:38:20 lr 0.000005\ttime 1.3249 (2.0124)\tloss 15.6410 (16.0749)\tgrad_norm 9.5366 (11.2967)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:12:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][310/1443]\teta 0:37:54 lr 0.000005\ttime 1.3476 (2.0073)\tloss 15.4666 (16.0637)\tgrad_norm 11.3490 (11.2537)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:13:04 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][320/1443]\teta 0:37:44 lr 0.000006\ttime 6.0841 (2.0161)\tloss 15.4942 (16.0506)\tgrad_norm 8.1359 (11.1929)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:13:23 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][330/1443]\teta 0:37:18 lr 0.000006\ttime 1.4196 (2.0116)\tloss 15.5843 (16.0333)\tgrad_norm 12.0286 (11.1299)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:13:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][340/1443]\teta 0:36:52 lr 0.000006\ttime 1.3449 (2.0055)\tloss 15.8303 (16.0261)\tgrad_norm 9.9361 (11.0896)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:13:59 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][350/1443]\teta 0:36:25 lr 0.000006\ttime 1.4006 (2.0000)\tloss 15.3649 (16.0098)\tgrad_norm 7.9222 (11.0274)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:14:21 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][360/1443]\teta 0:36:11 lr 0.000006\ttime 5.5170 (2.0051)\tloss 15.9028 (15.9966)\tgrad_norm 8.5944 (10.9861)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:14:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][370/1443]\teta 0:35:49 lr 0.000006\ttime 1.4133 (2.0037)\tloss 15.5021 (15.9841)\tgrad_norm 8.4733 (10.9411)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:14:59 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][380/1443]\teta 0:35:24 lr 0.000007\ttime 1.3424 (1.9985)\tloss 15.6454 (15.9773)\tgrad_norm 8.9922 (10.9031)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:15:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][390/1443]\teta 0:35:00 lr 0.000007\ttime 1.3874 (1.9946)\tloss 15.7529 (15.9653)\tgrad_norm 9.0895 (10.8575)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:15:39 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][400/1443]\teta 0:34:46 lr 0.000007\ttime 5.9104 (2.0003)\tloss 15.3287 (15.9564)\tgrad_norm 7.7627 (10.8084)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:15:58 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][410/1443]\teta 0:34:23 lr 0.000007\ttime 1.4185 (1.9975)\tloss 15.6663 (15.9470)\tgrad_norm 8.8785 (10.7606)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:16:16 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][420/1443]\teta 0:33:59 lr 0.000007\ttime 1.3983 (1.9932)\tloss 15.7740 (15.9407)\tgrad_norm 6.9902 (10.6973)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:16:35 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][430/1443]\teta 0:33:36 lr 0.000008\ttime 1.3894 (1.9902)\tloss 15.3493 (15.9296)\tgrad_norm 7.3543 (10.6580)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:16:57 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][440/1443]\teta 0:33:21 lr 0.000008\ttime 5.0402 (1.9953)\tloss 15.9329 (15.9233)\tgrad_norm 9.0712 (10.6250)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:17:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][450/1443]\teta 0:33:00 lr 0.000008\ttime 1.3872 (1.9946)\tloss 15.2134 (15.9144)\tgrad_norm 8.4607 (10.5813)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:17:35 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][460/1443]\teta 0:32:37 lr 0.000008\ttime 1.3680 (1.9914)\tloss 15.6068 (15.9043)\tgrad_norm 11.5685 (10.5404)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:17:54 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][470/1443]\teta 0:32:14 lr 0.000008\ttime 1.3560 (1.9882)\tloss 15.4123 (15.8979)\tgrad_norm 8.7187 (10.5052)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:18:17 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][480/1443]\teta 0:32:00 lr 0.000008\ttime 5.6270 (1.9945)\tloss 15.7397 (15.8929)\tgrad_norm 12.1765 (10.5052)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:18:35 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][490/1443]\teta 0:31:38 lr 0.000009\ttime 1.4377 (1.9922)\tloss 15.6894 (15.8865)\tgrad_norm 8.6811 (10.4931)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:18:54 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][500/1443]\teta 0:31:15 lr 0.000009\ttime 1.4572 (1.9889)\tloss 15.4241 (15.8788)\tgrad_norm 7.1904 (10.4576)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:19:14 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][510/1443]\teta 0:30:55 lr 0.000009\ttime 1.3735 (1.9890)\tloss 15.6445 (15.8716)\tgrad_norm 8.5955 (10.4331)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:19:35 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][520/1443]\teta 0:30:38 lr 0.000009\ttime 4.4341 (1.9922)\tloss 15.5180 (15.8654)\tgrad_norm 8.4121 (10.4046)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:19:54 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][530/1443]\teta 0:30:17 lr 0.000009\ttime 1.3496 (1.9909)\tloss 15.1858 (15.8579)\tgrad_norm 8.0763 (10.3881)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:20:13 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][540/1443]\teta 0:29:56 lr 0.000009\ttime 1.8074 (1.9892)\tloss 15.3835 (15.8531)\tgrad_norm 11.2140 (10.3728)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:20:32 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][550/1443]\teta 0:29:34 lr 0.000010\ttime 1.3663 (1.9869)\tloss 15.7681 (15.8455)\tgrad_norm 8.5837 (10.3471)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:20:53 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][560/1443]\teta 0:29:16 lr 0.000010\ttime 3.9637 (1.9896)\tloss 15.4436 (15.8386)\tgrad_norm 8.0611 (10.3211)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:21:12 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][570/1443]\teta 0:28:55 lr 0.000010\ttime 1.3278 (1.9877)\tloss 14.8400 (15.8312)\tgrad_norm 7.6805 (10.2995)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:21:32 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][580/1443]\teta 0:28:35 lr 0.000010\ttime 2.6486 (1.9873)\tloss 15.3168 (15.8252)\tgrad_norm 7.2022 (10.2759)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:21:50 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][590/1443]\teta 0:28:12 lr 0.000010\ttime 1.4356 (1.9847)\tloss 15.7200 (15.8217)\tgrad_norm 8.4531 (10.2501)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:22:11 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][600/1443]\teta 0:27:54 lr 0.000010\ttime 3.4450 (1.9861)\tloss 15.6990 (15.8147)\tgrad_norm 9.8910 (10.2390)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:22:31 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][610/1443]\teta 0:27:34 lr 0.000011\ttime 1.4068 (1.9860)\tloss 15.2843 (15.8077)\tgrad_norm 7.5262 (10.2156)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:22:51 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][620/1443]\teta 0:27:14 lr 0.000011\ttime 3.2281 (1.9864)\tloss 15.2616 (15.8030)\tgrad_norm 9.7182 (10.2100)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:23:09 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][630/1443]\teta 0:26:52 lr 0.000011\ttime 1.4330 (1.9840)\tloss 15.5696 (15.7990)\tgrad_norm 9.0915 (10.2007)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:23:28 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][640/1443]\teta 0:26:32 lr 0.000011\ttime 2.3085 (1.9832)\tloss 15.0473 (15.7919)\tgrad_norm 6.6573 (10.1805)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:23:50 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][650/1443]\teta 0:26:15 lr 0.000011\ttime 1.4346 (1.9863)\tloss 15.2457 (15.7861)\tgrad_norm 10.0148 (10.1613)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:24:11 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][660/1443]\teta 0:25:55 lr 0.000011\ttime 3.2907 (1.9869)\tloss 15.7868 (15.7803)\tgrad_norm 8.4347 (10.1347)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:24:29 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][670/1443]\teta 0:25:34 lr 0.000012\ttime 1.3320 (1.9846)\tloss 15.5900 (15.7760)\tgrad_norm 8.6586 (10.1342)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:24:48 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][680/1443]\teta 0:25:13 lr 0.000012\ttime 2.5359 (1.9837)\tloss 15.4822 (15.7733)\tgrad_norm 6.7840 (10.1176)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:25:08 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][690/1443]\teta 0:24:54 lr 0.000012\ttime 1.4243 (1.9845)\tloss 15.3345 (15.7700)\tgrad_norm 9.8853 (10.1040)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:25:29 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][700/1443]\teta 0:24:34 lr 0.000012\ttime 3.1674 (1.9851)\tloss 15.4296 (15.7656)\tgrad_norm 7.6222 (10.0955)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:25:47 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][710/1443]\teta 0:24:13 lr 0.000012\ttime 1.3571 (1.9832)\tloss 15.5302 (15.7601)\tgrad_norm 7.6331 (10.0732)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:26:07 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][720/1443]\teta 0:23:53 lr 0.000013\ttime 2.3246 (1.9825)\tloss 15.5627 (15.7560)\tgrad_norm 7.7304 (10.0718)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:26:27 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][730/1443]\teta 0:23:33 lr 0.000013\ttime 1.4220 (1.9828)\tloss 15.6849 (15.7516)\tgrad_norm 13.4549 (10.0906)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:26:47 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][740/1443]\teta 0:23:14 lr 0.000013\ttime 3.3380 (1.9830)\tloss 15.4761 (15.7490)\tgrad_norm 9.6394 (10.0921)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:27:05 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][750/1443]\teta 0:22:52 lr 0.000013\ttime 1.3137 (1.9808)\tloss 15.7042 (15.7438)\tgrad_norm 7.1986 (10.0796)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:27:25 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][760/1443]\teta 0:22:33 lr 0.000013\ttime 2.8395 (1.9816)\tloss 15.2199 (15.7398)\tgrad_norm 9.4206 (10.0927)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:27:44 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][770/1443]\teta 0:22:12 lr 0.000013\ttime 1.3600 (1.9806)\tloss 15.4298 (15.7354)\tgrad_norm 6.6848 (10.0679)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:28:05 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][780/1443]\teta 0:21:53 lr 0.000014\ttime 3.2651 (1.9814)\tloss 15.3328 (15.7308)\tgrad_norm 8.9934 (10.0814)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:28:24 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][790/1443]\teta 0:21:33 lr 0.000014\ttime 1.3543 (1.9803)\tloss 15.2633 (15.7253)\tgrad_norm 7.8850 (10.0671)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:28:43 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][800/1443]\teta 0:21:13 lr 0.000014\ttime 2.2536 (1.9803)\tloss 15.3186 (15.7224)\tgrad_norm 11.0436 (10.0612)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:29:04 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][810/1443]\teta 0:20:54 lr 0.000014\ttime 1.3075 (1.9812)\tloss 15.5112 (15.7197)\tgrad_norm 10.1023 (10.0445)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:29:24 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][820/1443]\teta 0:20:34 lr 0.000014\ttime 2.5936 (1.9810)\tloss 15.6279 (15.7166)\tgrad_norm 6.5504 (10.0150)\tmem 13188MB\n",
            "\u001b[32m[2022-06-01 20:29:41 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_main.py 177)\u001b[0m: INFO Train: [0/300][830/1443]\teta 0:20:12 lr 0.000014\ttime 1.3901 (1.9786)\tloss 15.0931 (15.7127)\tgrad_norm 7.2664 (9.9992)\tmem 13188MB\n",
            "Killing subprocess 551\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n",
            "    \"__main__\", mod_spec)\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n",
            "    exec(code, run_globals)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 340, in <module>\n",
            "    main()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 332, in main\n",
            "    time.sleep(1)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 295, in sigkill_handler\n",
            "    print(f\"Killing subprocess {process.pid}\")\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 295, in sigkill_handler\n",
            "    print(f\"Killing subprocess {process.pid}\")\n",
            "RuntimeError: reentrant call inside <_io.BufferedWriter name='<stdout>'>\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Transformer-SSL/moby_main.py\", line 236, in <module>\n",
            "    main(config)\n",
            "  File \"/content/Transformer-SSL/moby_main.py\", line 121, in main\n",
            "    train_one_epoch(config, model, data_loader_train, optimizer, epoch, lr_scheduler)\n",
            "  File \"/content/Transformer-SSL/moby_main.py\", line 146, in train_one_epoch\n",
            "    loss = model(samples_1, samples_2)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/parallel/distributed.py\", line 705, in forward\n",
            "    output = self.module(*inputs[0], **kwargs[0])\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/Transformer-SSL/models/moby.py\", line 139, in forward\n",
            "    feat_1 = self.encoder(im_1)  # queries: NxC\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/Transformer-SSL/models/swin_transformer.py\", line 583, in forward\n",
            "    x = self.forward_features(x)\n",
            "  File \"/content/Transformer-SSL/models/swin_transformer.py\", line 575, in forward_features\n",
            "    x = layer(x)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/Transformer-SSL/models/swin_transformer.py\", line 401, in forward\n",
            "    x = blk(x)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/Transformer-SSL/models/swin_transformer.py\", line 260, in forward\n",
            "    attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/Transformer-SSL/models/swin_transformer.py\", line 125, in forward\n",
            "    attn = (q @ k.transpose(-2, -1))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/apex/amp/wrap.py\", line 28, in wrapper\n",
            "    return orig_fn(*new_args, **kwargs)\n",
            "KeyboardInterrupt\n"
          ]
        }
      ],
      "source": [
        "%cd /content/Transformer-SSL\n",
        "\n",
        "#or use --opts flag\n",
        "batch_size = 64#64#32 #16 #8 #4 ish (grad overflows)\n",
        "image_size = 224*1 #*2 #*3 #*4\n",
        "TRAIN_EPOCHS = 300\n",
        "SWIN_WINDOW_SIZE=7\n",
        "# TRAIN_WARMUP_EPOCHS = 1\n",
        "NUM_CLASSES = 5\n",
        "\n",
        "SAVE_FREQ = 5#Save checkpoints every 5 epochs\n",
        "\n",
        "!python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345  \"/content/Transformer-SSL/moby_main.py\" \\\n",
        "--cfg configs/moby_swin_tiny.yaml --data-path '/content/competitionKaggle' --batch-size $batch_size \\\n",
        "--output '/content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/' \\\n",
        "--opts TRAIN.EPOCHS $TRAIN_EPOCHS MODEL.NUM_CLASSES $NUM_CLASSES DATA.IMG_SIZE $image_size \\\n",
        "SAVE_FREQ $SAVE_FREQ \\\n",
        "MODEL.SWIN.WINDOW_SIZE $SWIN_WINDOW_SIZE\n",
        "# TRAIN.WARMUP_EPOCHS $TRAIN_WARMUP_EPOCHS\n",
        "# --pretrained \"/content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/swin_tiny_patch4_window7_224.pth\" \\# doesnt work for moby\n",
        "\n",
        "#Image Net pretrained models\n",
        "#https://github.com/microsoft/Swin-Transformer\n",
        "#https://github.com/SwinTransformer/Transformer-SSL\n",
        "# #MoBy-PreTrained\n",
        "# # /content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\n",
        "# #supervised\n",
        "# #swin_tiny_patch4_window7_224.pth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYC0u9BrXF3G"
      },
      "source": [
        "## Linear Evaluation\n",
        "To evaluate a pre-trained MoBY with Swin Transformer on ImageNet-1K linear evaluation, run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yh7fOkOQU1Y4"
      },
      "outputs": [],
      "source": [
        "# !python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 moby_linear.py \\\n",
        "# --cfg <config-file> --data-path <imagenet-path>\n",
        "\n",
        "# # Notes:\n",
        "# # Make sure the <config-file>, <output-directory> and <tag> are the same as in the pre-training stage.\n",
        "# # Note that some configurations are fixed in moby_linear.py for simplicity.\n",
        "# # For example, to evaluate MoBY Swin-T with 8 GPU on a single node on ImageNet-1K linear evluation, run:\n",
        "\n",
        "# !python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345  moby_linear.py \\\n",
        "# --cfg configs/moby_swin_tiny.yaml --data-path <imagenet-path> --batch-size 64"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeizcf7lXJ5j"
      },
      "source": [
        "## Evaluate\n",
        "To evaluate a MoBY with Swin Transformer linear evaluation model on ImageNet-1K, run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDfi_Y2qU4Bh",
        "outputId": "b244f656-fd20-4d09-da8b-ab01fe6da1e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/Transformer-SSL\n",
            "=> merge config from configs/moby_swin_tiny.yaml\n",
            "RANK and WORLD_SIZE in environ: 0/1\n",
            "\u001b[32m[2022-04-22 00:39:38 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 380)\u001b[0m: INFO Full config saved to output/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default/linear/config.json\n",
            "\u001b[32m[2022-04-22 00:39:38 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 383)\u001b[0m: INFO AMP_OPT_LEVEL: O1\n",
            "AUG:\n",
            "  AUTO_AUGMENT: rand-m9-mstd0.5-inc1\n",
            "  COLOR_JITTER: 0.4\n",
            "  CUTMIX: 0.0\n",
            "  CUTMIX_MINMAX: null\n",
            "  MIXUP: 0.0\n",
            "  MIXUP_MODE: batch\n",
            "  MIXUP_PROB: 1.0\n",
            "  MIXUP_SWITCH_PROB: 0.5\n",
            "  RECOUNT: 1\n",
            "  REMODE: pixel\n",
            "  REPROB: 0.25\n",
            "  SSL_AUG: false\n",
            "  SSL_AUG_CROP: 0.08\n",
            "  SSL_AUG_TYPE: byol\n",
            "  SSL_LINEAR_AUG: true\n",
            "BASE:\n",
            "- ''\n",
            "DATA:\n",
            "  BATCH_SIZE: 32\n",
            "  CACHE_MODE: part\n",
            "  DATASET: imagenet\n",
            "  DATA_PATH: /content/competitionKaggle/\n",
            "  IMG_SIZE: 224\n",
            "  INTERPOLATION: bicubic\n",
            "  NUM_WORKERS: 8\n",
            "  PIN_MEMORY: true\n",
            "  ZIP_MODE: false\n",
            "EVAL_MODE: true\n",
            "LINEAR_EVAL:\n",
            "  PRETRAINED: /content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\n",
            "LOCAL_RANK: 0\n",
            "MODEL:\n",
            "  DROP_PATH_RATE: 0.2\n",
            "  DROP_RATE: 0.0\n",
            "  LABEL_SMOOTHING: 0.1\n",
            "  MOBY:\n",
            "    CONTRAST_MOMENTUM: 0.99\n",
            "    CONTRAST_NUM_NEGATIVE: 4096\n",
            "    CONTRAST_TEMPERATURE: 0.2\n",
            "    ENCODER: swin\n",
            "    ONLINE_DROP_PATH_RATE: 0.2\n",
            "    PRED_NUM_LAYERS: 2\n",
            "    PROJ_NUM_LAYERS: 2\n",
            "    TARGET_DROP_PATH_RATE: 0.0\n",
            "  NAME: moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2\n",
            "  NUM_CLASSES: 5\n",
            "  RESUME: ''\n",
            "  SWIN:\n",
            "    APE: false\n",
            "    DEPTHS:\n",
            "    - 2\n",
            "    - 2\n",
            "    - 6\n",
            "    - 2\n",
            "    EMBED_DIM: 96\n",
            "    IN_CHANS: 3\n",
            "    MLP_RATIO: 4.0\n",
            "    NORM_BEFORE_MLP: ln\n",
            "    NUM_HEADS:\n",
            "    - 3\n",
            "    - 6\n",
            "    - 12\n",
            "    - 24\n",
            "    PATCH_NORM: true\n",
            "    PATCH_SIZE: 4\n",
            "    QKV_BIAS: true\n",
            "    QK_SCALE: null\n",
            "    WINDOW_SIZE: 7\n",
            "  TYPE: linear\n",
            "OUTPUT: output/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default/linear\n",
            "PRINT_FREQ: 10\n",
            "SAVE_FREQ: 1\n",
            "SEED: 0\n",
            "TAG: default\n",
            "TEST:\n",
            "  CROP: true\n",
            "THROUGHPUT_MODE: false\n",
            "TRAIN:\n",
            "  ACCUMULATION_STEPS: 0\n",
            "  AUTO_RESUME: true\n",
            "  BASE_LR: 0.0625\n",
            "  CLIP_GRAD: 5.0\n",
            "  EPOCHS: 100\n",
            "  LR_SCHEDULER:\n",
            "    DECAY_EPOCHS: 30\n",
            "    DECAY_RATE: 0.1\n",
            "    NAME: cosine\n",
            "  MIN_LR: 3.125e-07\n",
            "  OPTIMIZER:\n",
            "    BETAS:\n",
            "    - 0.9\n",
            "    - 0.999\n",
            "    EPS: 1.0e-08\n",
            "    MOMENTUM: 0.9\n",
            "    NAME: sgd\n",
            "  START_EPOCH: 0\n",
            "  USE_CHECKPOINT: false\n",
            "  WARMUP_EPOCHS: 5\n",
            "  WARMUP_LR: 3.125e-08\n",
            "  WEIGHT_DECAY: 0.0\n",
            "\n",
            "local rank 0 / global rank 0 successfully build train dataset\n",
            "local rank 0 / global rank 0 successfully build val dataset\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "\u001b[32m[2022-04-22 00:39:39 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 109)\u001b[0m: INFO Creating model:linear/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2\n",
            "\u001b[32m[2022-04-22 00:39:39 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 112)\u001b[0m: INFO SwinTransformer(\n",
            "  (patch_embed): PatchEmbed(\n",
            "    (proj): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "    (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (pos_drop): Dropout(p=0.0, inplace=False)\n",
            "  (layers): ModuleList(\n",
            "    (0): BasicLayer(\n",
            "      dim=96, input_resolution=(56, 56), depth=2\n",
            "      (blocks): ModuleList(\n",
            "        (0): SwinTransformerBlock(\n",
            "          dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=96, window_size=(7, 7), num_heads=3\n",
            "            (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): Identity()\n",
            "          (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinTransformerBlock(\n",
            "          dim=96, input_resolution=(56, 56), num_heads=3, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=96, window_size=(7, 7), num_heads=3\n",
            "            (qkv): Linear(in_features=96, out_features=288, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=96, out_features=96, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (downsample): PatchMerging(\n",
            "        input_resolution=(56, 56), dim=96\n",
            "        (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "        (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicLayer(\n",
            "      dim=192, input_resolution=(28, 28), depth=2\n",
            "      (blocks): ModuleList(\n",
            "        (0): SwinTransformerBlock(\n",
            "          dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=192, window_size=(7, 7), num_heads=6\n",
            "            (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinTransformerBlock(\n",
            "          dim=192, input_resolution=(28, 28), num_heads=6, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=192, window_size=(7, 7), num_heads=6\n",
            "            (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=192, out_features=192, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (downsample): PatchMerging(\n",
            "        input_resolution=(28, 28), dim=192\n",
            "        (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "        (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "    )\n",
            "    (2): BasicLayer(\n",
            "      dim=384, input_resolution=(14, 14), depth=6\n",
            "      (blocks): ModuleList(\n",
            "        (0): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (2): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (3): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (4): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (5): SwinTransformerBlock(\n",
            "          dim=384, input_resolution=(14, 14), num_heads=12, window_size=7, shift_size=3, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=384, window_size=(7, 7), num_heads=12\n",
            "            (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=384, out_features=384, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (downsample): PatchMerging(\n",
            "        input_resolution=(14, 14), dim=384\n",
            "        (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "        (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "    )\n",
            "    (3): BasicLayer(\n",
            "      dim=768, input_resolution=(7, 7), depth=2\n",
            "      (blocks): ModuleList(\n",
            "        (0): SwinTransformerBlock(\n",
            "          dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=768, window_size=(7, 7), num_heads=24\n",
            "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinTransformerBlock(\n",
            "          dim=768, input_resolution=(7, 7), num_heads=24, window_size=7, shift_size=0, mlp_ratio=4.0\n",
            "          (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (attn): WindowAttention(\n",
            "            dim=768, window_size=(7, 7), num_heads=24\n",
            "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
            "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "            (softmax): Softmax(dim=-1)\n",
            "          )\n",
            "          (drop_path): DropPath()\n",
            "          (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): Mlp(\n",
            "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "            (act): GELU()\n",
            "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (drop): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "  (avgpool): AdaptiveAvgPool1d(output_size=1)\n",
            "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
            ")\n",
            "Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.\n",
            "\n",
            "Defaults for this optimization level are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n",
            "Processing user overrides (additional kwargs that are not None)...\n",
            "After processing overrides, optimization options are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(utils.py 32)\u001b[0m: INFO loaded pretrained checkpoint from: /content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 129)\u001b[0m: INFO number of params: 769000\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 132)\u001b[0m: INFO number of GFLOPs: 4.49440512\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 154)\u001b[0m: INFO auto resuming from output/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default/linear/checkpoint.pth\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(utils.py 38)\u001b[0m: INFO ==============> Resuming form output/moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2/default/linear/checkpoint.pth....................\n",
            "\u001b[32m[2022-04-22 00:39:40 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(utils.py 45)\u001b[0m: INFO _IncompatibleKeys(missing_keys=['patch_embed.proj.weight', 'patch_embed.proj.bias', 'patch_embed.norm.weight', 'patch_embed.norm.bias', 'layers.0.blocks.0.norm1.weight', 'layers.0.blocks.0.norm1.bias', 'layers.0.blocks.0.attn.relative_position_bias_table', 'layers.0.blocks.0.attn.relative_position_index', 'layers.0.blocks.0.attn.qkv.weight', 'layers.0.blocks.0.attn.qkv.bias', 'layers.0.blocks.0.attn.proj.weight', 'layers.0.blocks.0.attn.proj.bias', 'layers.0.blocks.0.norm2.weight', 'layers.0.blocks.0.norm2.bias', 'layers.0.blocks.0.mlp.fc1.weight', 'layers.0.blocks.0.mlp.fc1.bias', 'layers.0.blocks.0.mlp.fc2.weight', 'layers.0.blocks.0.mlp.fc2.bias', 'layers.0.blocks.1.attn_mask', 'layers.0.blocks.1.norm1.weight', 'layers.0.blocks.1.norm1.bias', 'layers.0.blocks.1.attn.relative_position_bias_table', 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            "\u001b[32m[2022-04-22 00:39:44 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 306)\u001b[0m: INFO Test: [0/578]\tTime 3.302 (3.302)\tLoss 7.0406 (7.0406)\tAcc@1 0.000 (0.000)\tAcc@5 0.000 (0.000)\tMem 722MB\n",
            "\u001b[32m[2022-04-22 00:39:46 moby__swin_tiny__patch4_window7_224__odpr02_tdpr0_cm099_ct02_queue4096_proj2_pred2]\u001b[0m\u001b[33m(moby_linear.py 306)\u001b[0m: INFO Test: [10/578]\tTime 0.114 (0.540)\tLoss 7.2537 (7.1406)\tAcc@1 0.000 (0.000)\tAcc@5 0.000 (0.000)\tMem 782MB\n",
            "Killing subprocess 2524\n",
            "Main process received SIGINT, exiting\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "# # !python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 moby_linear.py \\\n",
        "# # --cfg <config-file> --resume <checkpoint> --data-path <imagenet-path> --eval\n",
        "\n",
        "# # # For example, to evaluate the provided MoBY Swin-T linear evaluation model with a single GPU:\n",
        "# %cd /content/Transformer-SSL\n",
        "\n",
        "# batch_size = 32#64#32 #16 #8 #4 ish (grad overflows)\n",
        "# image_size = 224*1 #*2 #*3 #*4\n",
        "# START_EPOCH = 0\n",
        "# TRAIN_EPOCHS = 300\n",
        "# SWIN_WINDOW_SIZE=7\n",
        "# # TRAIN_WARMUP_EPOCHS = 1\n",
        "# NUM_CLASSES = 5\n",
        "# OUTPUTPath = \"/content/Transformer-SSL/output/\"\n",
        "\n",
        "# pretrainedModelPath = \"/content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\"\n",
        "\n",
        "# !python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 moby_linear.py \\\n",
        "# --cfg configs/moby_swin_tiny.yaml \\\n",
        "# --data-path \"/content/competitionKaggle/\" \\\n",
        "# --batch-size $batch_size \\\n",
        "# --opts MODEL.NUM_CLASSES $NUM_CLASSES DATA.IMG_SIZE $image_size TRAIN.EPOCHS $TRAIN_EPOCHS TRAIN.START_EPOCH $START_EPOCH \\\n",
        "# MODEL.SWIN.WINDOW_SIZE $SWIN_WINDOW_SIZE LINEAR_EVAL.PRETRAINED \"/content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\"   --eval\n",
        "\n",
        "# #comment out the overrides in linear parsing part for pretrained path\n",
        "# #change \"self.transform\" to \"[self.transform]\" in /content/Transformer-SSL/data/custom_image_folder.py\n",
        "\n",
        "# # --resume \"/content/drive/MyDrive/Colab Notebooks/FYP/MOBY SSL SWIN/moby_swin_t_300ep_pretrained.pth\" \\\n",
        "# # OUTPUT $OUTPUTPath"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "BUsYlqUsK3t-",
        "obd9LzcZYwPk",
        "hQnIMYIcKfHH",
        "sn6qdNBzKMJn",
        "INoBoFGCN2b-",
        "aYC0u9BrXF3G",
        "aeizcf7lXJ5j",
        "gx_EeRLVkaCi",
        "FR_QuS3YLEsX"
      ],
      "name": "SWIN SSL MOBY Colab.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}