main_segmentation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAWaB8TITctz"
      },
      "source": [
        "# Сегментация тетрадей\n",
        "\n",
        "## Detectron2 baseline\n",
        "\n",
        "В данном ноутбуке представлен baseline модели сегментации текста в школьных тетрадях с помощью фреймворка detectron2. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAVYkC3zTct4"
      },
      "source": [
        "# 0. Установка библиотек"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFrm_e6hTct5",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "!python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html\n",
        "!pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "!pip install tensorflow==2.1.0\n",
        "!pip install -q albumentations==0.4.6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ugw2PDtuoVWz"
      },
      "outputs": [],
      "source": [
        "!pip install -q albumentations==0.4.6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "aMPWWEv6oVW0"
      },
      "outputs": [],
      "source": [
        "!python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "he1golRITct9"
      },
      "source": [
        "## 1. Загрузить необходимые библиотеки для создания и обучения модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvrZd8Zb-o72",
        "outputId": "87f3fd9a-c039-4e8e-8d84-47cb9393de31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "khz6vnpbTct9"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import random\n",
        "import json\n",
        "import os\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact, interact_manual\n",
        "import shutil\n",
        "import numpy as np\n",
        "import tqdm\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUrvsdxUTcuA"
      },
      "outputs": [],
      "source": [
        "import torch, torchvision\n",
        "import detectron2\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.engine import DefaultPredictor\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.utils.visualizer import Visualizer\n",
        "from detectron2.data import MetadataCatalog,DatasetCatalog\n",
        "from detectron2.data.datasets import register_coco_instances,load_coco_json\n",
        "from detectron2.data import detection_utils as utils\n",
        "from detectron2.engine import DefaultTrainer\n",
        "from detectron2.engine import HookBase\n",
        "\n",
        "# from detectron2.utils.logger import setup_logger\n",
        "# setup_logger()\n",
        "\n",
        "import logging\n",
        "logger = logging.getLogger('detectron2')\n",
        "logger.setLevel(logging.CRITICAL)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcOSoXNhTcuA"
      },
      "source": [
        "Прежде чем переходить к загрузке данных посмотрим, доступны ли нам GPU-мощности. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIvWAqZUTcuB",
        "outputId": "66213508-83f1-4ad8-a475-2f2d27b02c90",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU: True\n"
          ]
        }
      ],
      "source": [
        "print('GPU: ' + str(torch.cuda.is_available()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJ59StAsTcuB"
      },
      "source": [
        "# 2. Валидационный датасет"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4TZU__UTcuC"
      },
      "source": [
        "Для валидации наших моделей нам неплохо было создать из обучающих данных валидационный датасет. Для этого разделим наш датасет на две части - для обучения и для валидации. Для этого просто создадим два новых файлика с аннотациями, куда раздельно запишем исиходную информацию об аннотациях."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U4d_rkWPoVXK"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "!mkdir data\n",
        "with zipfile.ZipFile('train.zip') as zf: \n",
        "    zf.extractall('data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bzx606myTcuC"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "#Подгрузим аннотации train\n",
        "with open('data/train/annotations.json') as f:\n",
        "    annotations = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E5H2sjEWoVXM"
      },
      "outputs": [],
      "source": [
        "print('Харьков')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hCPrXcBFTcuD"
      },
      "outputs": [],
      "source": [
        "#Пустой словарь для аннотаций валидации\n",
        "annotations_val = {}\n",
        "#Список категорий такой же как в train\n",
        "annotations_val['categories'] = annotations['categories']\n",
        "\n",
        "#Пустой словарь для аннотаций нового train\n",
        "annotations_train = {}\n",
        "#Список категорий такой же как в train\n",
        "annotations_train['categories'] = annotations['categories']\n",
        "\n",
        "\n",
        "#Положим в валидацию каждое 10 изображение из исходного train, а остальные - в новый train\n",
        "annotations_val['images'] = []\n",
        "annotations_train['images'] = []\n",
        "for num,img in enumerate(annotations['images']):\n",
        "    if num%9==0:\n",
        "        annotations_val['images'].append(img)\n",
        "    else:\n",
        "        annotations_train['images'].append(img)\n",
        "\n",
        "#Положим в список аннотаций валидации только те аннотации, которые относятся к изображениям из валидации. \n",
        "#А в список аннотаций нового train - только те, которые относятся к нему\n",
        "val_img_id = [i['id'] for i in annotations_val['images']]\n",
        "train_img_id = [i['id'] for i in annotations_train['images']]\n",
        "\n",
        "annotations_val['annotations'] = []\n",
        "annotations_train['annotations'] = []\n",
        "\n",
        "for annot in annotations['annotations']:\n",
        "    if annot['image_id'] in val_img_id:\n",
        "        annotations_val['annotations'].append(annot)\n",
        "    elif annot['image_id'] in train_img_id:\n",
        "        annotations_train['annotations'].append(annot)\n",
        "    else:\n",
        "        print('Аннотации нет ни в одном наборе')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVjCoiePTcuF"
      },
      "source": [
        "Аннотации для валидации и новой обучающей выборки готовы, теперь просто сохраним их в формате json, и положим в папке. Назовем аннотации annotations_new.json, чтобы новая набор аннотаций для train (без множества val) не перезаписал исходные аннотации."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztXIsE_ATcuF"
      },
      "outputs": [],
      "source": [
        "if not os.path.exists('data/val'):\n",
        "    os.makedirs('data/val')\n",
        "if not os.path.exists('data/val/images'):\n",
        "    os.makedirs('data/val/images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGkLMvbzTcuG"
      },
      "source": [
        "Скопируем изображения, которые относятся к валидации, в папку val/images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2CuPMuBuTcuG"
      },
      "outputs": [],
      "source": [
        "for i in annotations_val['images']:\n",
        "    shutil.copy('data/train/images/'+i['file_name'],'data/val/images/')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgNTyoniTcuG"
      },
      "source": [
        "Запишем новые файлы с аннотациями для train и val."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jG8-d25CTcuG"
      },
      "outputs": [],
      "source": [
        "with open('data/val/annotations_new.json', 'w') as outfile:\n",
        "    json.dump(annotations_val, outfile)\n",
        "    \n",
        "with open('data/train/annotations_new.json', 'w') as outfile:\n",
        "    json.dump(annotations_train, outfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzJ950LxTcuH"
      },
      "source": [
        "# 3. Регистрация датасета"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pRN8FqiTcuH"
      },
      "source": [
        "Зарегистрируем выборки в detectron2 для дальнейшей подачи на обучение модели."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TVkVyIDsTcuH"
      },
      "outputs": [],
      "source": [
        "for d in ['train','val']:\n",
        "    DatasetCatalog.register(\"my_dataset_\"+d, lambda d=d: load_coco_json(\"data/{}/annotations_new.json\".format(d),\n",
        "    image_root= \"data/train/images\",\\\n",
        "    dataset_name=\"my_dataset_\"+d,extra_annotation_keys=['bbox_mode']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCjl7WTWTcuH"
      },
      "source": [
        "После регистрации можно загружать выборки, чтобы иметь возможность посмотреть на них глазами. Первой загрузим обучающую выборку в **dataset_dicts_train**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fhT0HVnyTcuH"
      },
      "outputs": [],
      "source": [
        "dataset_dicts_train = DatasetCatalog.get(\"my_dataset_train\")\n",
        "train_metadata = MetadataCatalog.get(\"my_dataset_train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lieGR8zsTcuI"
      },
      "source": [
        "И тестовую выборку в **dataset_dicts_val**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZLihM5xTcuI"
      },
      "outputs": [],
      "source": [
        "dataset_dicts_val = DatasetCatalog.get(\"my_dataset_val\")\n",
        "val_metadata = MetadataCatalog.get(\"my_dataset_val\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne7rrRsxTcuI",
        "outputId": "a8cc2dae-7985-4bc8-c570-ad22a79c9179"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Размер обучающей выборки (Картинки): 828\n",
            "Размер тестовой выборки (Картинки): 104\n"
          ]
        }
      ],
      "source": [
        "print('Размер обучающей выборки (Картинки): {}'.format(len(dataset_dicts_train)))\n",
        "print('Размер тестовой выборки (Картинки): {}'.format(len(dataset_dicts_val)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNUtoeCfTcuI"
      },
      "source": [
        "**Посмотрим на размеченные фотографии из валидации**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CTDJo2amTcuJ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from IPython.display import Image\n",
        "@interact\n",
        "def show_images(file=range(len(dataset_dicts_val))):\n",
        "    example = dataset_dicts_val[file]\n",
        "    image = utils.read_image(example[\"file_name\"], format=\"RGB\")\n",
        "    plt.figure(figsize=(3,3),dpi=200)\n",
        "    visualizer = Visualizer(image[:, :, ::-1], metadata=val_metadata, scale=0.5)\n",
        "    vis = visualizer.draw_dataset_dict(example)\n",
        "    plt.imshow(vis.get_image()[:, :,::-1])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eJT9nsjTcuJ"
      },
      "source": [
        "##   4 Обучение модели\n",
        "\n",
        "**4.1. Определяем конфигурацию**\n",
        "\n",
        "Прежде чем начать работать с самой моделью, нам нужно определить ее параметры и спецификацию обучения\n",
        "\n",
        "Создаем конфигурацию и загружаем архитектуру модели с предобученными весами (на COCO - датасете, содержащем $80$ популярных категорий объектов и более $300000$ изображений) для распознавания объектов."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sE4JT1APTcuJ"
      },
      "source": [
        "В целом, вы можете посмотреть и другие архитектуры в зоопарке [моделей](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY_jyBcVTcuK"
      },
      "source": [
        "**4.2. Обучаем модель**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3k0t6FWcla1o"
      },
      "outputs": [],
      "source": [
        "import detectron2.data.transforms as T\n",
        "from detectron2.data import DatasetMapper, build_detection_train_loader, build_detection_test_loader\n",
        "import albumentations as albu\n",
        "\n",
        "class My_Augs(T.Transform):\n",
        "    def __init__(self):\n",
        "        self.augs = albu.Compose([\n",
        "                                      albu.RandomGamma(gamma_limit=(90, 110), p=0.2),\n",
        "                                      albu.OneOf([albu.Blur(blur_limit=7, p=0.2),\n",
        "                                                  albu.MedianBlur(blur_limit=7, p=0.2)]),\n",
        "                                      albu.RGBShift(r_shift_limit=5, g_shift_limit=5, b_shift_limit=5, p=0.2),\n",
        "                                      albu.JpegCompression(quality_lower=80, p=0.2)])\n",
        "\n",
        "    def apply_image(self, img):\n",
        "        img = self.augs(image=img)['image']\n",
        "        return img\n",
        "\n",
        "    def apply_coords(self, coords):\n",
        "        return coords.astype(np.float32)\n",
        "\n",
        "class CustomResize(T.Augmentation):\n",
        "    def get_transform(self, image):\n",
        "        rand_scale = random.uniform(0.8, 1.1)\n",
        "        my_scale = 1.65\n",
        "        new_width = int((image.shape[1] * rand_scale) // my_scale)\n",
        "        new_height = int((image.shape[0] * rand_scale) // my_scale)\n",
        "        return T.ResizeTransform(image.shape[0], image.shape[1], new_height, new_width)\n",
        "\n",
        "class Trainer(DefaultTrainer):\n",
        "\n",
        "    @classmethod\n",
        "    def build_train_loader(cls, cfg):\n",
        "        mapper = DatasetMapper(cfg, is_train=True, augmentations=[\n",
        "                   CustomResize(),\n",
        "                   T.RandomBrightness(0.7, 1.3),\n",
        "                   T.RandomContrast(0.7, 1.3),\n",
        "                   T.RandomSaturation(0.7, 1.3),\n",
        "                   T.RandomLighting(0.9),\n",
        "                   My_Augs(),\n",
        "                   T.RandomFlip(prob=0.5, horizontal=True, vertical=False),\n",
        "                   T.RandomFlip(prob=0.3, horizontal=False, vertical=True)])\n",
        "        return build_detection_train_loader(cfg, mapper=mapper)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lNEjFAymO8PM"
      },
      "outputs": [],
      "source": [
        "cfg = get_cfg()\n",
        "cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\")) \n",
        "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\")\n",
        "cfg.SOLVER.BASE_LR = 0.001\n",
        "cfg.SOLVER.STEPS = (4000, 7000, 9000)\n",
        "cfg.SOLVER.GAMMA = 0.1\n",
        "cfg.SOLVER.MAX_ITER = 10000\n",
        "cfg.SOLVER.CHECKPOINT_PERIOD = 2000\n",
        "cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 256 #370\n",
        "cfg.SOLVER.IMS_PER_BATCH = 1\n",
        "\n",
        "\n",
        "cfg.DATASETS.TRAIN = (\"my_dataset_train\")\n",
        "cfg.DATASETS.TEST = ('my_dataset_train')\n",
        "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.1\n",
        "cfg.INPUT.FORMAT = 'BGR'\n",
        "cfg.DATALOADER.NUM_WORKERS = 2\n",
        "cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1\n",
        "\n",
        "cfg.OUTPUT_DIR = 'output'\n",
        "os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFw0HFD0P_ES",
        "outputId": "8de96e01-b775-4fdc-9860-e8b453b4d588"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 19:40:23 d2.engine.defaults]: \u001b[0mModel:\n",
            "GeneralizedRCNN(\n",
            "  (backbone): FPN(\n",
            "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (top_block): LastLevelMaxPool()\n",
            "    (bottom_up): ResNet(\n",
            "      (stem): BasicStem(\n",
            "        (conv1): Conv2d(\n",
            "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
            "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "        )\n",
            "      )\n",
            "      (res2): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res3): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res4): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (4): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (5): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (6): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (7): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (8): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (9): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (10): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (11): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (12): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (13): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (14): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (15): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (16): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (17): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (18): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (19): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (20): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (21): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (22): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res5): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (proposal_generator): RPN(\n",
            "    (rpn_head): StandardRPNHead(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (anchor_generator): DefaultAnchorGenerator(\n",
            "      (cell_anchors): BufferList()\n",
            "    )\n",
            "  )\n",
            "  (roi_heads): StandardROIHeads(\n",
            "    (box_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (box_head): FastRCNNConvFCHead(\n",
            "      (flatten): Flatten()\n",
            "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
            "      (fc_relu1): ReLU()\n",
            "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "      (fc_relu2): ReLU()\n",
            "    )\n",
            "    (box_predictor): FastRCNNOutputLayers(\n",
            "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
            "      (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n",
            "    )\n",
            "    (mask_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (mask_head): MaskRCNNConvUpsampleHead(\n",
            "      (mask_fcn1): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn2): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn3): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn4): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
            "      (deconv_relu): ReLU()\n",
            "      (predictor): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 19:40:23 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [<__main__.CustomResize object at 0x7f3350fe5090>, RandomBrightness(intensity_min=0.7, intensity_max=1.3), RandomContrast(intensity_min=0.7, intensity_max=1.3), RandomSaturation(intensity_min=0.7, intensity_max=1.3), RandomLighting(scale=0.9), My_Augs(), RandomFlip(prob=0.5), RandomFlip(prob=0.3, horizontal=False, vertical=True)]\n",
            "\u001b[32m[03/01 19:40:26 d2.data.datasets.coco]: \u001b[0mLoading data/train/annotations_new.json takes 3.12 seconds.\n",
            "\u001b[32m[03/01 19:40:26 d2.data.datasets.coco]: \u001b[0mLoaded 828 images in COCO format from data/train/annotations_new.json\n",
            "\u001b[32m[03/01 19:40:26 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 828 images left.\n",
            "\u001b[32m[03/01 19:40:26 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
            "\u001b[36m|  category  | #instances   |\n",
            "|:----------:|:-------------|\n",
            "|    text    | 144154       |\n",
            "|            |              |\u001b[0m\n",
            "\u001b[32m[03/01 19:40:26 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n",
            "\u001b[32m[03/01 19:40:26 d2.data.common]: \u001b[0mSerializing 828 elements to byte tensors and concatenating them all ...\n",
            "\u001b[32m[03/01 19:40:27 d2.data.common]: \u001b[0mSerialized dataset takes 65.07 MiB\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2022-03-01 19:40:27.401232: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 19:40:29 d2.engine.train_loop]: \u001b[0mStarting training from iteration 0\n",
            "\u001b[32m[03/01 19:40:42 d2.utils.events]: \u001b[0m eta: 1:27:04  iter: 19  total_loss: 1.009  loss_cls: 0.2753  loss_box_reg: 0.3268  loss_mask: 0.2761  loss_rpn_cls: 0.02348  loss_rpn_loc: 0.1245  time: 0.5797  data_time: 0.1417  lr: 1.9981e-05  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:40:52 d2.utils.events]: \u001b[0m eta: 1:19:54  iter: 39  total_loss: 0.8942  loss_cls: 0.2074  loss_box_reg: 0.3025  loss_mask: 0.258  loss_rpn_cls: 0.02644  loss_rpn_loc: 0.1181  time: 0.5310  data_time: 0.0107  lr: 3.9961e-05  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:03 d2.utils.events]: \u001b[0m eta: 1:21:37  iter: 59  total_loss: 1.023  loss_cls: 0.2672  loss_box_reg: 0.3255  loss_mask: 0.2674  loss_rpn_cls: 0.02368  loss_rpn_loc: 0.1306  time: 0.5346  data_time: 0.0824  lr: 5.9941e-05  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:15 d2.utils.events]: \u001b[0m eta: 1:24:32  iter: 79  total_loss: 1.039  loss_cls: 0.2481  loss_box_reg: 0.3461  loss_mask: 0.2711  loss_rpn_cls: 0.02389  loss_rpn_loc: 0.1155  time: 0.5467  data_time: 0.0723  lr: 7.9921e-05  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:25 d2.utils.events]: \u001b[0m eta: 1:24:22  iter: 99  total_loss: 0.9765  loss_cls: 0.2255  loss_box_reg: 0.3286  loss_mask: 0.2646  loss_rpn_cls: 0.02988  loss_rpn_loc: 0.1189  time: 0.5411  data_time: 0.0212  lr: 9.9901e-05  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:35 d2.utils.events]: \u001b[0m eta: 1:22:38  iter: 119  total_loss: 1.076  loss_cls: 0.2534  loss_box_reg: 0.3686  loss_mask: 0.2651  loss_rpn_cls: 0.02563  loss_rpn_loc: 0.1128  time: 0.5340  data_time: 0.0417  lr: 0.00011988  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:47 d2.utils.events]: \u001b[0m eta: 1:22:58  iter: 139  total_loss: 1.019  loss_cls: 0.2238  loss_box_reg: 0.3457  loss_mask: 0.2728  loss_rpn_cls: 0.02805  loss_rpn_loc: 0.1307  time: 0.5414  data_time: 0.1011  lr: 0.00013986  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:41:57 d2.utils.events]: \u001b[0m eta: 1:23:00  iter: 159  total_loss: 0.9253  loss_cls: 0.2234  loss_box_reg: 0.3032  loss_mask: 0.2476  loss_rpn_cls: 0.02144  loss_rpn_loc: 0.1233  time: 0.5411  data_time: 0.0604  lr: 0.00015984  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:42:07 d2.utils.events]: \u001b[0m eta: 1:23:02  iter: 179  total_loss: 0.9888  loss_cls: 0.2434  loss_box_reg: 0.3031  loss_mask: 0.2501  loss_rpn_cls: 0.02227  loss_rpn_loc: 0.1223  time: 0.5365  data_time: 0.0241  lr: 0.00017982  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:42:18 d2.utils.events]: \u001b[0m eta: 1:22:53  iter: 199  total_loss: 1.005  loss_cls: 0.2548  loss_box_reg: 0.3381  loss_mask: 0.2638  loss_rpn_cls: 0.02684  loss_rpn_loc: 0.1307  time: 0.5347  data_time: 0.0383  lr: 0.0001998  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:42:28 d2.utils.events]: \u001b[0m eta: 1:22:43  iter: 219  total_loss: 0.9634  loss_cls: 0.2506  loss_box_reg: 0.3196  loss_mask: 0.2473  loss_rpn_cls: 0.0221  loss_rpn_loc: 0.1193  time: 0.5321  data_time: 0.0041  lr: 0.00021978  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:42:38 d2.utils.events]: \u001b[0m eta: 1:22:21  iter: 239  total_loss: 0.9949  loss_cls: 0.2381  loss_box_reg: 0.3164  loss_mask: 0.2676  loss_rpn_cls: 0.01696  loss_rpn_loc: 0.12  time: 0.5278  data_time: 0.0162  lr: 0.00023976  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:42:50 d2.utils.events]: \u001b[0m eta: 1:22:21  iter: 259  total_loss: 0.9326  loss_cls: 0.2131  loss_box_reg: 0.296  loss_mask: 0.2435  loss_rpn_cls: 0.01917  loss_rpn_loc: 0.1117  time: 0.5346  data_time: 0.1165  lr: 0.00025974  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:00 d2.utils.events]: \u001b[0m eta: 1:22:01  iter: 279  total_loss: 1.052  loss_cls: 0.268  loss_box_reg: 0.3645  loss_mask: 0.2763  loss_rpn_cls: 0.02264  loss_rpn_loc: 0.1365  time: 0.5320  data_time: 0.0117  lr: 0.00027972  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:12 d2.utils.events]: \u001b[0m eta: 1:22:01  iter: 299  total_loss: 1.005  loss_cls: 0.2268  loss_box_reg: 0.3501  loss_mask: 0.2788  loss_rpn_cls: 0.01817  loss_rpn_loc: 0.1198  time: 0.5364  data_time: 0.0990  lr: 0.0002997  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:23 d2.utils.events]: \u001b[0m eta: 1:22:05  iter: 319  total_loss: 1.02  loss_cls: 0.2716  loss_box_reg: 0.3312  loss_mask: 0.2555  loss_rpn_cls: 0.01794  loss_rpn_loc: 0.1186  time: 0.5367  data_time: 0.0337  lr: 0.00031968  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:33 d2.utils.events]: \u001b[0m eta: 1:21:54  iter: 339  total_loss: 1.031  loss_cls: 0.2445  loss_box_reg: 0.3345  loss_mask: 0.2899  loss_rpn_cls: 0.02197  loss_rpn_loc: 0.1218  time: 0.5362  data_time: 0.0214  lr: 0.00033966  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:44 d2.utils.events]: \u001b[0m eta: 1:21:59  iter: 359  total_loss: 1.041  loss_cls: 0.2606  loss_box_reg: 0.3402  loss_mask: 0.2672  loss_rpn_cls: 0.02085  loss_rpn_loc: 0.1337  time: 0.5370  data_time: 0.0582  lr: 0.00035964  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:43:55 d2.utils.events]: \u001b[0m eta: 1:21:57  iter: 379  total_loss: 0.931  loss_cls: 0.2247  loss_box_reg: 0.3074  loss_mask: 0.2519  loss_rpn_cls: 0.01117  loss_rpn_loc: 0.1199  time: 0.5373  data_time: 0.0488  lr: 0.00037962  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:05 d2.utils.events]: \u001b[0m eta: 1:21:47  iter: 399  total_loss: 1.062  loss_cls: 0.2575  loss_box_reg: 0.3382  loss_mask: 0.2665  loss_rpn_cls: 0.02268  loss_rpn_loc: 0.1363  time: 0.5353  data_time: 0.0059  lr: 0.0003996  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:15 d2.utils.events]: \u001b[0m eta: 1:21:29  iter: 419  total_loss: 0.9919  loss_cls: 0.2647  loss_box_reg: 0.3363  loss_mask: 0.2687  loss_rpn_cls: 0.02389  loss_rpn_loc: 0.1209  time: 0.5338  data_time: 0.0071  lr: 0.00041958  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:26 d2.utils.events]: \u001b[0m eta: 1:21:18  iter: 439  total_loss: 1.014  loss_cls: 0.2488  loss_box_reg: 0.3465  loss_mask: 0.2685  loss_rpn_cls: 0.02358  loss_rpn_loc: 0.1256  time: 0.5336  data_time: 0.0248  lr: 0.00043956  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:37 d2.utils.events]: \u001b[0m eta: 1:21:27  iter: 459  total_loss: 0.94  loss_cls: 0.2256  loss_box_reg: 0.3284  loss_mask: 0.2636  loss_rpn_cls: 0.01865  loss_rpn_loc: 0.1181  time: 0.5335  data_time: 0.0316  lr: 0.00045954  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:46 d2.utils.events]: \u001b[0m eta: 1:20:58  iter: 479  total_loss: 1.025  loss_cls: 0.2839  loss_box_reg: 0.3193  loss_mask: 0.2729  loss_rpn_cls: 0.01389  loss_rpn_loc: 0.1214  time: 0.5315  data_time: 0.0148  lr: 0.00047952  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:44:57 d2.utils.events]: \u001b[0m eta: 1:21:06  iter: 499  total_loss: 1.013  loss_cls: 0.2291  loss_box_reg: 0.3462  loss_mask: 0.2752  loss_rpn_cls: 0.02127  loss_rpn_loc: 0.1251  time: 0.5311  data_time: 0.0129  lr: 0.0004995  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:07 d2.utils.events]: \u001b[0m eta: 1:21:02  iter: 519  total_loss: 0.9606  loss_cls: 0.2311  loss_box_reg: 0.3294  loss_mask: 0.2456  loss_rpn_cls: 0.02458  loss_rpn_loc: 0.125  time: 0.5306  data_time: 0.0141  lr: 0.00051948  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:17 d2.utils.events]: \u001b[0m eta: 1:20:52  iter: 539  total_loss: 1.074  loss_cls: 0.2666  loss_box_reg: 0.323  loss_mask: 0.2744  loss_rpn_cls: 0.02501  loss_rpn_loc: 0.1282  time: 0.5299  data_time: 0.0242  lr: 0.00053946  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:28 d2.utils.events]: \u001b[0m eta: 1:20:42  iter: 559  total_loss: 1.042  loss_cls: 0.2659  loss_box_reg: 0.3585  loss_mask: 0.265  loss_rpn_cls: 0.01633  loss_rpn_loc: 0.133  time: 0.5294  data_time: 0.0279  lr: 0.00055944  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:38 d2.utils.events]: \u001b[0m eta: 1:20:03  iter: 579  total_loss: 0.9986  loss_cls: 0.2443  loss_box_reg: 0.3185  loss_mask: 0.2683  loss_rpn_cls: 0.02434  loss_rpn_loc: 0.1277  time: 0.5282  data_time: 0.0300  lr: 0.00057942  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:48 d2.utils.events]: \u001b[0m eta: 1:19:41  iter: 599  total_loss: 1.027  loss_cls: 0.2656  loss_box_reg: 0.3227  loss_mask: 0.2604  loss_rpn_cls: 0.02103  loss_rpn_loc: 0.1209  time: 0.5278  data_time: 0.0353  lr: 0.0005994  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:45:59 d2.utils.events]: \u001b[0m eta: 1:19:47  iter: 619  total_loss: 1.072  loss_cls: 0.2636  loss_box_reg: 0.3313  loss_mask: 0.2866  loss_rpn_cls: 0.03142  loss_rpn_loc: 0.1363  time: 0.5290  data_time: 0.0676  lr: 0.00061938  max_mem: 15437M\n",
            "\u001b[32m[03/01 19:46:11 d2.utils.events]: \u001b[0m eta: 1:20:01  iter: 639  total_loss: 1.054  loss_cls: 0.2613  loss_box_reg: 0.3484  loss_mask: 0.2739  loss_rpn_cls: 0.02136  loss_rpn_loc: 0.1244  time: 0.5308  data_time: 0.0345  lr: 0.00063936  max_mem: 15848M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[32m[03/01 19:46:32 d2.utils.events]: \u001b[0m eta: 1:20:01  iter: 679  total_loss: 1.1  loss_cls: 0.2781  loss_box_reg: 0.3371  loss_mask: 0.2679  loss_rpn_cls: 0.02486  loss_rpn_loc: 0.1388  time: 0.5310  data_time: 0.0119  lr: 0.00067932  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:46:44 d2.utils.events]: \u001b[0m eta: 1:19:49  iter: 699  total_loss: 0.9966  loss_cls: 0.2425  loss_box_reg: 0.3215  loss_mask: 0.2486  loss_rpn_cls: 0.02297  loss_rpn_loc: 0.1268  time: 0.5317  data_time: 0.0758  lr: 0.0006993  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:46:54 d2.utils.events]: \u001b[0m eta: 1:19:35  iter: 719  total_loss: 1.015  loss_cls: 0.2364  loss_box_reg: 0.3362  loss_mask: 0.266  loss_rpn_cls: 0.01625  loss_rpn_loc: 0.118  time: 0.5320  data_time: 0.0568  lr: 0.00071928  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:05 d2.utils.events]: \u001b[0m eta: 1:19:21  iter: 739  total_loss: 0.8723  loss_cls: 0.2196  loss_box_reg: 0.3031  loss_mask: 0.2435  loss_rpn_cls: 0.01457  loss_rpn_loc: 0.1145  time: 0.5315  data_time: 0.0217  lr: 0.00073926  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:15 d2.utils.events]: \u001b[0m eta: 1:19:07  iter: 759  total_loss: 0.9701  loss_cls: 0.2419  loss_box_reg: 0.3462  loss_mask: 0.2701  loss_rpn_cls: 0.02103  loss_rpn_loc: 0.1132  time: 0.5316  data_time: 0.0425  lr: 0.00075924  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:26 d2.utils.events]: \u001b[0m eta: 1:18:49  iter: 779  total_loss: 0.9708  loss_cls: 0.266  loss_box_reg: 0.3133  loss_mask: 0.2542  loss_rpn_cls: 0.02213  loss_rpn_loc: 0.1227  time: 0.5310  data_time: 0.0309  lr: 0.00077922  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:36 d2.utils.events]: \u001b[0m eta: 1:18:39  iter: 799  total_loss: 1.048  loss_cls: 0.2943  loss_box_reg: 0.3622  loss_mask: 0.2645  loss_rpn_cls: 0.02394  loss_rpn_loc: 0.1296  time: 0.5308  data_time: 0.0436  lr: 0.0007992  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:46 d2.utils.events]: \u001b[0m eta: 1:18:21  iter: 819  total_loss: 1.025  loss_cls: 0.2419  loss_box_reg: 0.3377  loss_mask: 0.2507  loss_rpn_cls: 0.01782  loss_rpn_loc: 0.1333  time: 0.5304  data_time: 0.0216  lr: 0.00081918  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:47:58 d2.utils.events]: \u001b[0m eta: 1:18:11  iter: 839  total_loss: 1.076  loss_cls: 0.2985  loss_box_reg: 0.3597  loss_mask: 0.2781  loss_rpn_cls: 0.02443  loss_rpn_loc: 0.1254  time: 0.5317  data_time: 0.0640  lr: 0.00083916  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:48:09 d2.utils.events]: \u001b[0m eta: 1:18:01  iter: 859  total_loss: 1.01  loss_cls: 0.2554  loss_box_reg: 0.3428  loss_mask: 0.2536  loss_rpn_cls: 0.02072  loss_rpn_loc: 0.1218  time: 0.5322  data_time: 0.0466  lr: 0.00085914  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:48:20 d2.utils.events]: \u001b[0m eta: 1:17:47  iter: 879  total_loss: 1.081  loss_cls: 0.2658  loss_box_reg: 0.3592  loss_mask: 0.2673  loss_rpn_cls: 0.01607  loss_rpn_loc: 0.1289  time: 0.5328  data_time: 0.0635  lr: 0.00087912  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:48:31 d2.utils.events]: \u001b[0m eta: 1:17:32  iter: 899  total_loss: 0.9802  loss_cls: 0.2301  loss_box_reg: 0.3104  loss_mask: 0.2585  loss_rpn_cls: 0.02096  loss_rpn_loc: 0.1308  time: 0.5323  data_time: 0.0299  lr: 0.0008991  max_mem: 15848M\n",
            "\u001b[32m[03/01 19:48:42 d2.utils.events]: \u001b[0m eta: 1:17:21  iter: 919  total_loss: 0.9936  loss_cls: 0.2402  loss_box_reg: 0.3262  loss_mask: 0.2585  loss_rpn_cls: 0.02407  loss_rpn_loc: 0.1213  time: 0.5326  data_time: 0.0573  lr: 0.00091908  max_mem: 15947M\n",
            "\u001b[32m[03/01 19:48:51 d2.utils.events]: \u001b[0m eta: 1:16:59  iter: 939  total_loss: 1.107  loss_cls: 0.2876  loss_box_reg: 0.3435  loss_mask: 0.2647  loss_rpn_cls: 0.02431  loss_rpn_loc: 0.128  time: 0.5318  data_time: 0.0110  lr: 0.00093906  max_mem: 15947M\n",
            "\u001b[32m[03/01 19:49:01 d2.utils.events]: \u001b[0m eta: 1:16:30  iter: 959  total_loss: 0.9862  loss_cls: 0.2509  loss_box_reg: 0.3332  loss_mask: 0.2425  loss_rpn_cls: 0.0241  loss_rpn_loc: 0.1294  time: 0.5311  data_time: 0.0140  lr: 0.00095904  max_mem: 15947M\n",
            "\u001b[32m[03/01 19:49:12 d2.utils.events]: \u001b[0m eta: 1:16:35  iter: 979  total_loss: 0.9493  loss_cls: 0.2183  loss_box_reg: 0.3179  loss_mask: 0.25  loss_rpn_cls: 0.02346  loss_rpn_loc: 0.122  time: 0.5308  data_time: 0.0090  lr: 0.00097902  max_mem: 15947M\n",
            "\u001b[32m[03/01 19:49:22 d2.utils.events]: \u001b[0m eta: 1:16:29  iter: 999  total_loss: 1.073  loss_cls: 0.2913  loss_box_reg: 0.3544  loss_mask: 0.2666  loss_rpn_cls: 0.01359  loss_rpn_loc: 0.1264  time: 0.5302  data_time: 0.0097  lr: 0.000999  max_mem: 15947M\n",
            "\u001b[32m[03/01 19:49:33 d2.utils.events]: \u001b[0m eta: 1:16:25  iter: 1019  total_loss: 1.058  loss_cls: 0.2801  loss_box_reg: 0.3456  loss_mask: 0.2626  loss_rpn_cls: 0.02495  loss_rpn_loc: 0.1362  time: 0.5310  data_time: 0.0359  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:49:44 d2.utils.events]: \u001b[0m eta: 1:16:31  iter: 1039  total_loss: 0.9577  loss_cls: 0.233  loss_box_reg: 0.3269  loss_mask: 0.2619  loss_rpn_cls: 0.02187  loss_rpn_loc: 0.1226  time: 0.5313  data_time: 0.0290  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:49:54 d2.utils.events]: \u001b[0m eta: 1:16:05  iter: 1059  total_loss: 0.8982  loss_cls: 0.2267  loss_box_reg: 0.2944  loss_mask: 0.23  loss_rpn_cls: 0.0143  loss_rpn_loc: 0.1244  time: 0.5303  data_time: 0.0095  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:03 d2.utils.events]: \u001b[0m eta: 1:15:29  iter: 1079  total_loss: 1.168  loss_cls: 0.3082  loss_box_reg: 0.3976  loss_mask: 0.2805  loss_rpn_cls: 0.0373  loss_rpn_loc: 0.142  time: 0.5293  data_time: 0.0498  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:13 d2.utils.events]: \u001b[0m eta: 1:15:18  iter: 1099  total_loss: 1.014  loss_cls: 0.2312  loss_box_reg: 0.3469  loss_mask: 0.259  loss_rpn_cls: 0.02573  loss_rpn_loc: 0.1332  time: 0.5288  data_time: 0.0101  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:24 d2.utils.events]: \u001b[0m eta: 1:15:27  iter: 1119  total_loss: 1.052  loss_cls: 0.2476  loss_box_reg: 0.3386  loss_mask: 0.2557  loss_rpn_cls: 0.0289  loss_rpn_loc: 0.12  time: 0.5289  data_time: 0.0361  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:34 d2.utils.events]: \u001b[0m eta: 1:15:13  iter: 1139  total_loss: 0.9674  loss_cls: 0.2417  loss_box_reg: 0.3122  loss_mask: 0.2656  loss_rpn_cls: 0.02169  loss_rpn_loc: 0.1369  time: 0.5285  data_time: 0.0233  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:45 d2.utils.events]: \u001b[0m eta: 1:15:10  iter: 1159  total_loss: 0.9549  loss_cls: 0.2203  loss_box_reg: 0.3243  loss_mask: 0.2611  loss_rpn_cls: 0.02074  loss_rpn_loc: 0.1227  time: 0.5285  data_time: 0.0311  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:50:55 d2.utils.events]: \u001b[0m eta: 1:14:53  iter: 1179  total_loss: 0.98  loss_cls: 0.2683  loss_box_reg: 0.3307  loss_mask: 0.2603  loss_rpn_cls: 0.01524  loss_rpn_loc: 0.1244  time: 0.5281  data_time: 0.0044  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:04 d2.utils.events]: \u001b[0m eta: 1:14:33  iter: 1199  total_loss: 1  loss_cls: 0.2368  loss_box_reg: 0.3556  loss_mask: 0.2749  loss_rpn_cls: 0.01877  loss_rpn_loc: 0.1197  time: 0.5270  data_time: 0.0081  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:15 d2.utils.events]: \u001b[0m eta: 1:14:36  iter: 1219  total_loss: 1.097  loss_cls: 0.3144  loss_box_reg: 0.3642  loss_mask: 0.2599  loss_rpn_cls: 0.02031  loss_rpn_loc: 0.1278  time: 0.5272  data_time: 0.0429  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:25 d2.utils.events]: \u001b[0m eta: 1:14:33  iter: 1239  total_loss: 0.9921  loss_cls: 0.2673  loss_box_reg: 0.3317  loss_mask: 0.2583  loss_rpn_cls: 0.01433  loss_rpn_loc: 0.1176  time: 0.5268  data_time: 0.0124  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:35 d2.utils.events]: \u001b[0m eta: 1:14:16  iter: 1259  total_loss: 1.025  loss_cls: 0.2684  loss_box_reg: 0.3282  loss_mask: 0.2443  loss_rpn_cls: 0.02198  loss_rpn_loc: 0.1312  time: 0.5261  data_time: 0.0097  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:45 d2.utils.events]: \u001b[0m eta: 1:14:12  iter: 1279  total_loss: 0.9264  loss_cls: 0.2187  loss_box_reg: 0.308  loss_mask: 0.2543  loss_rpn_cls: 0.01941  loss_rpn_loc: 0.1146  time: 0.5257  data_time: 0.0075  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:51:55 d2.utils.events]: \u001b[0m eta: 1:14:02  iter: 1299  total_loss: 1.11  loss_cls: 0.3126  loss_box_reg: 0.3685  loss_mask: 0.2867  loss_rpn_cls: 0.02652  loss_rpn_loc: 0.1362  time: 0.5255  data_time: 0.0171  lr: 0.001  max_mem: 16744M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 19:52:05 d2.utils.events]: \u001b[0m eta: 1:13:41  iter: 1319  total_loss: 1.073  loss_cls: 0.2794  loss_box_reg: 0.3416  loss_mask: 0.2732  loss_rpn_cls: 0.02548  loss_rpn_loc: 0.1339  time: 0.5252  data_time: 0.0151  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:52:16 d2.utils.events]: \u001b[0m eta: 1:13:31  iter: 1339  total_loss: 1.008  loss_cls: 0.2444  loss_box_reg: 0.3515  loss_mask: 0.2838  loss_rpn_cls: 0.02005  loss_rpn_loc: 0.1342  time: 0.5252  data_time: 0.0421  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:52:25 d2.utils.events]: \u001b[0m eta: 1:13:06  iter: 1359  total_loss: 0.9257  loss_cls: 0.2307  loss_box_reg: 0.2942  loss_mask: 0.2402  loss_rpn_cls: 0.0184  loss_rpn_loc: 0.1196  time: 0.5242  data_time: 0.0181  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:52:35 d2.utils.events]: \u001b[0m eta: 1:12:29  iter: 1379  total_loss: 1.066  loss_cls: 0.2893  loss_box_reg: 0.3436  loss_mask: 0.2695  loss_rpn_cls: 0.03677  loss_rpn_loc: 0.1302  time: 0.5237  data_time: 0.0462  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:52:45 d2.utils.events]: \u001b[0m eta: 1:12:12  iter: 1399  total_loss: 0.977  loss_cls: 0.2573  loss_box_reg: 0.3144  loss_mask: 0.2496  loss_rpn_cls: 0.02444  loss_rpn_loc: 0.1326  time: 0.5236  data_time: 0.0129  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:52:55 d2.utils.events]: \u001b[0m eta: 1:11:56  iter: 1419  total_loss: 0.993  loss_cls: 0.2325  loss_box_reg: 0.3476  loss_mask: 0.2738  loss_rpn_cls: 0.02334  loss_rpn_loc: 0.125  time: 0.5230  data_time: 0.0253  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:05 d2.utils.events]: \u001b[0m eta: 1:11:44  iter: 1439  total_loss: 0.9983  loss_cls: 0.2766  loss_box_reg: 0.3217  loss_mask: 0.2514  loss_rpn_cls: 0.02367  loss_rpn_loc: 0.1298  time: 0.5230  data_time: 0.0253  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:15 d2.utils.events]: \u001b[0m eta: 1:11:20  iter: 1459  total_loss: 0.9889  loss_cls: 0.2491  loss_box_reg: 0.3257  loss_mask: 0.2571  loss_rpn_cls: 0.01543  loss_rpn_loc: 0.13  time: 0.5225  data_time: 0.0183  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:27 d2.utils.events]: \u001b[0m eta: 1:11:20  iter: 1479  total_loss: 1.044  loss_cls: 0.2682  loss_box_reg: 0.3409  loss_mask: 0.2681  loss_rpn_cls: 0.02076  loss_rpn_loc: 0.1181  time: 0.5237  data_time: 0.0880  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:37 d2.utils.events]: \u001b[0m eta: 1:10:58  iter: 1499  total_loss: 0.9963  loss_cls: 0.2394  loss_box_reg: 0.3187  loss_mask: 0.2399  loss_rpn_cls: 0.02294  loss_rpn_loc: 0.1285  time: 0.5230  data_time: 0.0074  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:46 d2.utils.events]: \u001b[0m eta: 1:10:38  iter: 1519  total_loss: 1.146  loss_cls: 0.2868  loss_box_reg: 0.3345  loss_mask: 0.3122  loss_rpn_cls: 0.03331  loss_rpn_loc: 0.1524  time: 0.5226  data_time: 0.0365  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:53:57 d2.utils.events]: \u001b[0m eta: 1:10:33  iter: 1539  total_loss: 0.9954  loss_cls: 0.2438  loss_box_reg: 0.3026  loss_mask: 0.2464  loss_rpn_cls: 0.01445  loss_rpn_loc: 0.1332  time: 0.5223  data_time: 0.0071  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:07 d2.utils.events]: \u001b[0m eta: 1:10:18  iter: 1559  total_loss: 1.051  loss_cls: 0.2709  loss_box_reg: 0.3578  loss_mask: 0.2735  loss_rpn_cls: 0.01861  loss_rpn_loc: 0.1185  time: 0.5221  data_time: 0.0367  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:17 d2.utils.events]: \u001b[0m eta: 1:10:20  iter: 1579  total_loss: 0.9964  loss_cls: 0.2541  loss_box_reg: 0.3394  loss_mask: 0.2558  loss_rpn_cls: 0.03178  loss_rpn_loc: 0.1273  time: 0.5218  data_time: 0.0278  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:29 d2.utils.events]: \u001b[0m eta: 1:10:17  iter: 1599  total_loss: 1.004  loss_cls: 0.2502  loss_box_reg: 0.3386  loss_mask: 0.2524  loss_rpn_cls: 0.01548  loss_rpn_loc: 0.1259  time: 0.5229  data_time: 0.1214  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:39 d2.utils.events]: \u001b[0m eta: 1:10:05  iter: 1619  total_loss: 0.9439  loss_cls: 0.2257  loss_box_reg: 0.3444  loss_mask: 0.2665  loss_rpn_cls: 0.01897  loss_rpn_loc: 0.1254  time: 0.5228  data_time: 0.0056  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:49 d2.utils.events]: \u001b[0m eta: 1:09:41  iter: 1639  total_loss: 0.9441  loss_cls: 0.2312  loss_box_reg: 0.3056  loss_mask: 0.2587  loss_rpn_cls: 0.02944  loss_rpn_loc: 0.1275  time: 0.5226  data_time: 0.0128  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:54:59 d2.utils.events]: \u001b[0m eta: 1:09:23  iter: 1659  total_loss: 0.9423  loss_cls: 0.2458  loss_box_reg: 0.3127  loss_mask: 0.2559  loss_rpn_cls: 0.02228  loss_rpn_loc: 0.127  time: 0.5223  data_time: 0.0210  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:55:10 d2.utils.events]: \u001b[0m eta: 1:09:13  iter: 1679  total_loss: 1.033  loss_cls: 0.2561  loss_box_reg: 0.3568  loss_mask: 0.2729  loss_rpn_cls: 0.024  loss_rpn_loc: 0.1238  time: 0.5225  data_time: 0.0382  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:55:21 d2.utils.events]: \u001b[0m eta: 1:09:04  iter: 1699  total_loss: 0.9634  loss_cls: 0.2363  loss_box_reg: 0.3247  loss_mask: 0.2673  loss_rpn_cls: 0.01963  loss_rpn_loc: 0.1231  time: 0.5225  data_time: 0.0370  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:55:31 d2.utils.events]: \u001b[0m eta: 1:08:53  iter: 1719  total_loss: 0.9484  loss_cls: 0.2497  loss_box_reg: 0.3083  loss_mask: 0.2425  loss_rpn_cls: 0.01545  loss_rpn_loc: 0.1217  time: 0.5223  data_time: 0.0239  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:55:41 d2.utils.events]: \u001b[0m eta: 1:08:21  iter: 1739  total_loss: 1.145  loss_cls: 0.2676  loss_box_reg: 0.3568  loss_mask: 0.256  loss_rpn_cls: 0.01984  loss_rpn_loc: 0.1375  time: 0.5222  data_time: 0.0594  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:55:52 d2.utils.events]: \u001b[0m eta: 1:08:20  iter: 1759  total_loss: 1.043  loss_cls: 0.2731  loss_box_reg: 0.3248  loss_mask: 0.2921  loss_rpn_cls: 0.02024  loss_rpn_loc: 0.136  time: 0.5228  data_time: 0.0649  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:03 d2.utils.events]: \u001b[0m eta: 1:08:25  iter: 1779  total_loss: 1.03  loss_cls: 0.2511  loss_box_reg: 0.3148  loss_mask: 0.2528  loss_rpn_cls: 0.01725  loss_rpn_loc: 0.128  time: 0.5228  data_time: 0.0358  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:13 d2.utils.events]: \u001b[0m eta: 1:08:15  iter: 1799  total_loss: 0.9648  loss_cls: 0.2339  loss_box_reg: 0.3123  loss_mask: 0.2462  loss_rpn_cls: 0.01697  loss_rpn_loc: 0.1196  time: 0.5225  data_time: 0.0261  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:23 d2.utils.events]: \u001b[0m eta: 1:08:05  iter: 1819  total_loss: 1.032  loss_cls: 0.2431  loss_box_reg: 0.3148  loss_mask: 0.2666  loss_rpn_cls: 0.0201  loss_rpn_loc: 0.1376  time: 0.5224  data_time: 0.0102  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:33 d2.utils.events]: \u001b[0m eta: 1:07:54  iter: 1839  total_loss: 1.056  loss_cls: 0.2726  loss_box_reg: 0.3343  loss_mask: 0.2607  loss_rpn_cls: 0.01989  loss_rpn_loc: 0.1428  time: 0.5222  data_time: 0.0237  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:43 d2.utils.events]: \u001b[0m eta: 1:07:44  iter: 1859  total_loss: 1.03  loss_cls: 0.2672  loss_box_reg: 0.3456  loss_mask: 0.2603  loss_rpn_cls: 0.02169  loss_rpn_loc: 0.1203  time: 0.5218  data_time: 0.0046  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:56:54 d2.utils.events]: \u001b[0m eta: 1:07:44  iter: 1879  total_loss: 0.9348  loss_cls: 0.2322  loss_box_reg: 0.3103  loss_mask: 0.2518  loss_rpn_cls: 0.01219  loss_rpn_loc: 0.1195  time: 0.5219  data_time: 0.0117  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:57:04 d2.utils.events]: \u001b[0m eta: 1:07:45  iter: 1899  total_loss: 0.957  loss_cls: 0.2327  loss_box_reg: 0.3137  loss_mask: 0.2496  loss_rpn_cls: 0.02316  loss_rpn_loc: 0.1176  time: 0.5219  data_time: 0.0137  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:57:14 d2.utils.events]: \u001b[0m eta: 1:07:15  iter: 1919  total_loss: 1.03  loss_cls: 0.2783  loss_box_reg: 0.3162  loss_mask: 0.2433  loss_rpn_cls: 0.01823  loss_rpn_loc: 0.1231  time: 0.5216  data_time: 0.0265  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:57:24 d2.utils.events]: \u001b[0m eta: 1:07:22  iter: 1939  total_loss: 0.9292  loss_cls: 0.2383  loss_box_reg: 0.3101  loss_mask: 0.2492  loss_rpn_cls: 0.01973  loss_rpn_loc: 0.1144  time: 0.5215  data_time: 0.0229  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:57:34 d2.utils.events]: \u001b[0m eta: 1:07:12  iter: 1959  total_loss: 1.052  loss_cls: 0.2833  loss_box_reg: 0.3513  loss_mask: 0.2781  loss_rpn_cls: 0.02727  loss_rpn_loc: 0.1453  time: 0.5211  data_time: 0.0133  lr: 0.001  max_mem: 16744M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 19:57:45 d2.utils.events]: \u001b[0m eta: 1:07:02  iter: 1979  total_loss: 1.058  loss_cls: 0.2773  loss_box_reg: 0.3236  loss_mask: 0.2622  loss_rpn_cls: 0.01952  loss_rpn_loc: 0.1329  time: 0.5214  data_time: 0.0301  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:57:57 d2.utils.events]: \u001b[0m eta: 1:06:56  iter: 1999  total_loss: 0.9404  loss_cls: 0.224  loss_box_reg: 0.3326  loss_mask: 0.2781  loss_rpn_cls: 0.01907  loss_rpn_loc: 0.1159  time: 0.5214  data_time: 0.0105  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:06 d2.utils.events]: \u001b[0m eta: 1:06:24  iter: 2019  total_loss: 1.032  loss_cls: 0.2676  loss_box_reg: 0.3422  loss_mask: 0.2472  loss_rpn_cls: 0.03415  loss_rpn_loc: 0.1287  time: 0.5210  data_time: 0.0060  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:16 d2.utils.events]: \u001b[0m eta: 1:05:53  iter: 2039  total_loss: 1.061  loss_cls: 0.2564  loss_box_reg: 0.3392  loss_mask: 0.2925  loss_rpn_cls: 0.01699  loss_rpn_loc: 0.1366  time: 0.5207  data_time: 0.0142  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:27 d2.utils.events]: \u001b[0m eta: 1:05:46  iter: 2059  total_loss: 0.9324  loss_cls: 0.2515  loss_box_reg: 0.303  loss_mask: 0.2561  loss_rpn_cls: 0.0228  loss_rpn_loc: 0.1177  time: 0.5206  data_time: 0.0049  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:37 d2.utils.events]: \u001b[0m eta: 1:05:54  iter: 2079  total_loss: 0.9404  loss_cls: 0.2203  loss_box_reg: 0.3204  loss_mask: 0.2512  loss_rpn_cls: 0.02426  loss_rpn_loc: 0.1221  time: 0.5205  data_time: 0.0065  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:47 d2.utils.events]: \u001b[0m eta: 1:05:44  iter: 2099  total_loss: 1.054  loss_cls: 0.2745  loss_box_reg: 0.3417  loss_mask: 0.2668  loss_rpn_cls: 0.02124  loss_rpn_loc: 0.1388  time: 0.5203  data_time: 0.0380  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:58:57 d2.utils.events]: \u001b[0m eta: 1:05:33  iter: 2119  total_loss: 0.8964  loss_cls: 0.227  loss_box_reg: 0.3052  loss_mask: 0.252  loss_rpn_cls: 0.0238  loss_rpn_loc: 0.1207  time: 0.5203  data_time: 0.0514  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:07 d2.utils.events]: \u001b[0m eta: 1:05:12  iter: 2139  total_loss: 1.067  loss_cls: 0.2447  loss_box_reg: 0.3509  loss_mask: 0.2707  loss_rpn_cls: 0.02086  loss_rpn_loc: 0.1277  time: 0.5203  data_time: 0.0316  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:17 d2.utils.events]: \u001b[0m eta: 1:04:53  iter: 2159  total_loss: 0.9334  loss_cls: 0.2205  loss_box_reg: 0.3204  loss_mask: 0.2498  loss_rpn_cls: 0.01382  loss_rpn_loc: 0.1153  time: 0.5201  data_time: 0.0278  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:27 d2.utils.events]: \u001b[0m eta: 1:04:39  iter: 2179  total_loss: 0.9743  loss_cls: 0.2458  loss_box_reg: 0.2943  loss_mask: 0.2595  loss_rpn_cls: 0.01926  loss_rpn_loc: 0.1251  time: 0.5197  data_time: 0.0056  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:37 d2.utils.events]: \u001b[0m eta: 1:04:30  iter: 2199  total_loss: 0.9966  loss_cls: 0.2571  loss_box_reg: 0.3131  loss_mask: 0.2652  loss_rpn_cls: 0.02593  loss_rpn_loc: 0.123  time: 0.5195  data_time: 0.0062  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:46 d2.utils.events]: \u001b[0m eta: 1:03:54  iter: 2219  total_loss: 0.9189  loss_cls: 0.2344  loss_box_reg: 0.2969  loss_mask: 0.2492  loss_rpn_cls: 0.0169  loss_rpn_loc: 0.1268  time: 0.5190  data_time: 0.0112  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 19:59:57 d2.utils.events]: \u001b[0m eta: 1:03:39  iter: 2239  total_loss: 0.9712  loss_cls: 0.255  loss_box_reg: 0.3018  loss_mask: 0.2417  loss_rpn_cls: 0.01913  loss_rpn_loc: 0.1231  time: 0.5191  data_time: 0.0766  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:08 d2.utils.events]: \u001b[0m eta: 1:03:39  iter: 2259  total_loss: 0.9189  loss_cls: 0.2308  loss_box_reg: 0.2905  loss_mask: 0.2438  loss_rpn_cls: 0.01605  loss_rpn_loc: 0.1351  time: 0.5192  data_time: 0.0384  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:18 d2.utils.events]: \u001b[0m eta: 1:03:41  iter: 2279  total_loss: 0.9577  loss_cls: 0.2263  loss_box_reg: 0.3153  loss_mask: 0.2501  loss_rpn_cls: 0.02104  loss_rpn_loc: 0.124  time: 0.5193  data_time: 0.0304  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:28 d2.utils.events]: \u001b[0m eta: 1:03:18  iter: 2299  total_loss: 0.9791  loss_cls: 0.2298  loss_box_reg: 0.314  loss_mask: 0.2526  loss_rpn_cls: 0.01609  loss_rpn_loc: 0.1241  time: 0.5190  data_time: 0.0147  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:38 d2.utils.events]: \u001b[0m eta: 1:03:06  iter: 2319  total_loss: 1.086  loss_cls: 0.2633  loss_box_reg: 0.3388  loss_mask: 0.2742  loss_rpn_cls: 0.03001  loss_rpn_loc: 0.1403  time: 0.5189  data_time: 0.0438  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:48 d2.utils.events]: \u001b[0m eta: 1:02:54  iter: 2339  total_loss: 1.022  loss_cls: 0.2482  loss_box_reg: 0.3476  loss_mask: 0.2587  loss_rpn_cls: 0.02784  loss_rpn_loc: 0.1331  time: 0.5188  data_time: 0.0630  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:00:59 d2.utils.events]: \u001b[0m eta: 1:02:47  iter: 2359  total_loss: 1.011  loss_cls: 0.2369  loss_box_reg: 0.3174  loss_mask: 0.2706  loss_rpn_cls: 0.03114  loss_rpn_loc: 0.1178  time: 0.5188  data_time: 0.0587  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:01:08 d2.utils.events]: \u001b[0m eta: 1:02:40  iter: 2379  total_loss: 0.9901  loss_cls: 0.2392  loss_box_reg: 0.3238  loss_mask: 0.2688  loss_rpn_cls: 0.02092  loss_rpn_loc: 0.1213  time: 0.5186  data_time: 0.0145  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:01:19 d2.utils.events]: \u001b[0m eta: 1:02:39  iter: 2399  total_loss: 1.061  loss_cls: 0.2686  loss_box_reg: 0.3474  loss_mask: 0.2659  loss_rpn_cls: 0.01964  loss_rpn_loc: 0.1418  time: 0.5185  data_time: 0.0335  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:01:30 d2.utils.events]: \u001b[0m eta: 1:02:48  iter: 2419  total_loss: 1.081  loss_cls: 0.2903  loss_box_reg: 0.3385  loss_mask: 0.2545  loss_rpn_cls: 0.02352  loss_rpn_loc: 0.1308  time: 0.5187  data_time: 0.0086  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:01:40 d2.utils.events]: \u001b[0m eta: 1:02:42  iter: 2439  total_loss: 0.8951  loss_cls: 0.2334  loss_box_reg: 0.2936  loss_mask: 0.2254  loss_rpn_cls: 0.01075  loss_rpn_loc: 0.1099  time: 0.5188  data_time: 0.0228  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:01:51 d2.utils.events]: \u001b[0m eta: 1:03:03  iter: 2459  total_loss: 0.9689  loss_cls: 0.2414  loss_box_reg: 0.3032  loss_mask: 0.2662  loss_rpn_cls: 0.01361  loss_rpn_loc: 0.1225  time: 0.5188  data_time: 0.0087  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:02 d2.utils.events]: \u001b[0m eta: 1:02:48  iter: 2479  total_loss: 0.9068  loss_cls: 0.229  loss_box_reg: 0.3045  loss_mask: 0.2354  loss_rpn_cls: 0.01612  loss_rpn_loc: 0.1152  time: 0.5191  data_time: 0.0233  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:13 d2.utils.events]: \u001b[0m eta: 1:02:44  iter: 2499  total_loss: 1.073  loss_cls: 0.2471  loss_box_reg: 0.3381  loss_mask: 0.2728  loss_rpn_cls: 0.02935  loss_rpn_loc: 0.133  time: 0.5192  data_time: 0.0424  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:23 d2.utils.events]: \u001b[0m eta: 1:02:32  iter: 2519  total_loss: 1.067  loss_cls: 0.2654  loss_box_reg: 0.3507  loss_mask: 0.2714  loss_rpn_cls: 0.0245  loss_rpn_loc: 0.1361  time: 0.5193  data_time: 0.0186  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:33 d2.utils.events]: \u001b[0m eta: 1:02:18  iter: 2539  total_loss: 1.082  loss_cls: 0.2623  loss_box_reg: 0.3482  loss_mask: 0.272  loss_rpn_cls: 0.02707  loss_rpn_loc: 0.1364  time: 0.5192  data_time: 0.0240  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:43 d2.utils.events]: \u001b[0m eta: 1:02:13  iter: 2559  total_loss: 0.9539  loss_cls: 0.2274  loss_box_reg: 0.3027  loss_mask: 0.2615  loss_rpn_cls: 0.01583  loss_rpn_loc: 0.1308  time: 0.5192  data_time: 0.0184  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:02:54 d2.utils.events]: \u001b[0m eta: 1:02:06  iter: 2579  total_loss: 0.9455  loss_cls: 0.2114  loss_box_reg: 0.3166  loss_mask: 0.2573  loss_rpn_cls: 0.02082  loss_rpn_loc: 0.1164  time: 0.5191  data_time: 0.0090  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:03:04 d2.utils.events]: \u001b[0m eta: 1:01:44  iter: 2599  total_loss: 0.9415  loss_cls: 0.2305  loss_box_reg: 0.3346  loss_mask: 0.248  loss_rpn_cls: 0.01858  loss_rpn_loc: 0.1265  time: 0.5190  data_time: 0.0178  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:03:15 d2.utils.events]: \u001b[0m eta: 1:01:49  iter: 2619  total_loss: 0.905  loss_cls: 0.2211  loss_box_reg: 0.2781  loss_mask: 0.2418  loss_rpn_cls: 0.01546  loss_rpn_loc: 0.1117  time: 0.5193  data_time: 0.0216  lr: 0.001  max_mem: 16744M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:03:26 d2.utils.events]: \u001b[0m eta: 1:01:41  iter: 2639  total_loss: 0.9185  loss_cls: 0.2124  loss_box_reg: 0.3158  loss_mask: 0.2522  loss_rpn_cls: 0.02076  loss_rpn_loc: 0.1266  time: 0.5195  data_time: 0.0313  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:03:36 d2.utils.events]: \u001b[0m eta: 1:01:31  iter: 2659  total_loss: 0.9632  loss_cls: 0.2466  loss_box_reg: 0.2996  loss_mask: 0.2659  loss_rpn_cls: 0.01989  loss_rpn_loc: 0.1241  time: 0.5194  data_time: 0.0114  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:03:47 d2.utils.events]: \u001b[0m eta: 1:01:21  iter: 2679  total_loss: 1.106  loss_cls: 0.2538  loss_box_reg: 0.3413  loss_mask: 0.2733  loss_rpn_cls: 0.02496  loss_rpn_loc: 0.1314  time: 0.5194  data_time: 0.0305  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:03:57 d2.utils.events]: \u001b[0m eta: 1:01:09  iter: 2699  total_loss: 0.9693  loss_cls: 0.2451  loss_box_reg: 0.3245  loss_mask: 0.2582  loss_rpn_cls: 0.01903  loss_rpn_loc: 0.129  time: 0.5195  data_time: 0.0411  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:07 d2.utils.events]: \u001b[0m eta: 1:00:54  iter: 2719  total_loss: 0.9898  loss_cls: 0.2527  loss_box_reg: 0.3207  loss_mask: 0.2493  loss_rpn_cls: 0.01982  loss_rpn_loc: 0.128  time: 0.5193  data_time: 0.0210  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:17 d2.utils.events]: \u001b[0m eta: 1:00:44  iter: 2739  total_loss: 0.9042  loss_cls: 0.2225  loss_box_reg: 0.3079  loss_mask: 0.2534  loss_rpn_cls: 0.01235  loss_rpn_loc: 0.1135  time: 0.5191  data_time: 0.0148  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:27 d2.utils.events]: \u001b[0m eta: 1:00:27  iter: 2759  total_loss: 0.9606  loss_cls: 0.2545  loss_box_reg: 0.3417  loss_mask: 0.2528  loss_rpn_cls: 0.02065  loss_rpn_loc: 0.125  time: 0.5190  data_time: 0.0224  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:36 d2.utils.events]: \u001b[0m eta: 1:00:01  iter: 2779  total_loss: 0.9753  loss_cls: 0.2491  loss_box_reg: 0.3246  loss_mask: 0.2493  loss_rpn_cls: 0.01769  loss_rpn_loc: 0.124  time: 0.5186  data_time: 0.0083  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:46 d2.utils.events]: \u001b[0m eta: 0:59:49  iter: 2799  total_loss: 1.017  loss_cls: 0.2674  loss_box_reg: 0.337  loss_mask: 0.2595  loss_rpn_cls: 0.02532  loss_rpn_loc: 0.125  time: 0.5184  data_time: 0.0047  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:04:56 d2.utils.events]: \u001b[0m eta: 0:59:31  iter: 2819  total_loss: 0.9842  loss_cls: 0.2197  loss_box_reg: 0.3446  loss_mask: 0.2641  loss_rpn_cls: 0.02422  loss_rpn_loc: 0.1259  time: 0.5180  data_time: 0.0140  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:06 d2.utils.events]: \u001b[0m eta: 0:59:20  iter: 2839  total_loss: 1.004  loss_cls: 0.26  loss_box_reg: 0.3508  loss_mask: 0.2486  loss_rpn_cls: 0.0166  loss_rpn_loc: 0.1267  time: 0.5179  data_time: 0.0043  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:16 d2.utils.events]: \u001b[0m eta: 0:59:08  iter: 2859  total_loss: 1.045  loss_cls: 0.2643  loss_box_reg: 0.3462  loss_mask: 0.2874  loss_rpn_cls: 0.02939  loss_rpn_loc: 0.1293  time: 0.5178  data_time: 0.0252  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:26 d2.utils.events]: \u001b[0m eta: 0:58:52  iter: 2879  total_loss: 0.953  loss_cls: 0.2269  loss_box_reg: 0.3268  loss_mask: 0.2654  loss_rpn_cls: 0.01294  loss_rpn_loc: 0.1088  time: 0.5177  data_time: 0.0098  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:36 d2.utils.events]: \u001b[0m eta: 0:58:24  iter: 2899  total_loss: 0.9643  loss_cls: 0.2424  loss_box_reg: 0.2997  loss_mask: 0.2404  loss_rpn_cls: 0.02482  loss_rpn_loc: 0.1411  time: 0.5176  data_time: 0.0465  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:46 d2.utils.events]: \u001b[0m eta: 0:58:28  iter: 2919  total_loss: 0.8956  loss_cls: 0.2259  loss_box_reg: 0.3061  loss_mask: 0.2447  loss_rpn_cls: 0.01838  loss_rpn_loc: 0.1173  time: 0.5175  data_time: 0.0372  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:05:57 d2.utils.events]: \u001b[0m eta: 0:58:20  iter: 2939  total_loss: 0.8458  loss_cls: 0.2176  loss_box_reg: 0.3038  loss_mask: 0.2445  loss_rpn_cls: 0.01733  loss_rpn_loc: 0.1162  time: 0.5177  data_time: 0.0415  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:07 d2.utils.events]: \u001b[0m eta: 0:58:16  iter: 2959  total_loss: 0.8863  loss_cls: 0.2233  loss_box_reg: 0.2979  loss_mask: 0.2426  loss_rpn_cls: 0.01113  loss_rpn_loc: 0.1132  time: 0.5176  data_time: 0.0471  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:17 d2.utils.events]: \u001b[0m eta: 0:57:48  iter: 2979  total_loss: 0.8414  loss_cls: 0.1772  loss_box_reg: 0.2952  loss_mask: 0.2529  loss_rpn_cls: 0.01326  loss_rpn_loc: 0.1081  time: 0.5174  data_time: 0.0124  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:27 d2.utils.events]: \u001b[0m eta: 0:57:34  iter: 2999  total_loss: 0.9798  loss_cls: 0.2445  loss_box_reg: 0.3321  loss_mask: 0.2639  loss_rpn_cls: 0.01763  loss_rpn_loc: 0.1274  time: 0.5174  data_time: 0.0302  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:38 d2.utils.events]: \u001b[0m eta: 0:57:48  iter: 3019  total_loss: 0.9743  loss_cls: 0.2486  loss_box_reg: 0.3231  loss_mask: 0.2587  loss_rpn_cls: 0.01892  loss_rpn_loc: 0.1152  time: 0.5177  data_time: 0.0292  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:48 d2.utils.events]: \u001b[0m eta: 0:57:41  iter: 3039  total_loss: 0.9076  loss_cls: 0.2152  loss_box_reg: 0.2851  loss_mask: 0.2478  loss_rpn_cls: 0.01809  loss_rpn_loc: 0.1177  time: 0.5175  data_time: 0.0064  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:06:59 d2.utils.events]: \u001b[0m eta: 0:57:34  iter: 3059  total_loss: 0.9784  loss_cls: 0.2415  loss_box_reg: 0.3028  loss_mask: 0.2539  loss_rpn_cls: 0.01756  loss_rpn_loc: 0.1282  time: 0.5176  data_time: 0.0427  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:07:09 d2.utils.events]: \u001b[0m eta: 0:57:27  iter: 3079  total_loss: 0.9775  loss_cls: 0.2273  loss_box_reg: 0.3053  loss_mask: 0.2778  loss_rpn_cls: 0.02062  loss_rpn_loc: 0.1161  time: 0.5176  data_time: 0.0137  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:07:20 d2.utils.events]: \u001b[0m eta: 0:57:22  iter: 3099  total_loss: 0.8877  loss_cls: 0.2107  loss_box_reg: 0.301  loss_mask: 0.2402  loss_rpn_cls: 0.01618  loss_rpn_loc: 0.1046  time: 0.5177  data_time: 0.0364  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:07:30 d2.utils.events]: \u001b[0m eta: 0:57:14  iter: 3119  total_loss: 0.9623  loss_cls: 0.2164  loss_box_reg: 0.3114  loss_mask: 0.2481  loss_rpn_cls: 0.02333  loss_rpn_loc: 0.1266  time: 0.5176  data_time: 0.0440  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:07:40 d2.utils.events]: \u001b[0m eta: 0:56:57  iter: 3139  total_loss: 0.9468  loss_cls: 0.2274  loss_box_reg: 0.306  loss_mask: 0.2571  loss_rpn_cls: 0.02162  loss_rpn_loc: 0.114  time: 0.5174  data_time: 0.0040  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:07:51 d2.utils.events]: \u001b[0m eta: 0:56:54  iter: 3159  total_loss: 0.9639  loss_cls: 0.2471  loss_box_reg: 0.3423  loss_mask: 0.2603  loss_rpn_cls: 0.01545  loss_rpn_loc: 0.1141  time: 0.5179  data_time: 0.0962  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:02 d2.utils.events]: \u001b[0m eta: 0:56:48  iter: 3179  total_loss: 0.9286  loss_cls: 0.2268  loss_box_reg: 0.3225  loss_mask: 0.2507  loss_rpn_cls: 0.02262  loss_rpn_loc: 0.1127  time: 0.5178  data_time: 0.0040  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:11 d2.utils.events]: \u001b[0m eta: 0:56:36  iter: 3199  total_loss: 1.014  loss_cls: 0.2613  loss_box_reg: 0.3394  loss_mask: 0.251  loss_rpn_cls: 0.02113  loss_rpn_loc: 0.1234  time: 0.5176  data_time: 0.0145  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:21 d2.utils.events]: \u001b[0m eta: 0:56:38  iter: 3219  total_loss: 1.053  loss_cls: 0.2508  loss_box_reg: 0.322  loss_mask: 0.2789  loss_rpn_cls: 0.02165  loss_rpn_loc: 0.115  time: 0.5174  data_time: 0.0269  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:31 d2.utils.events]: \u001b[0m eta: 0:56:28  iter: 3239  total_loss: 0.9446  loss_cls: 0.2321  loss_box_reg: 0.292  loss_mask: 0.2563  loss_rpn_cls: 0.01487  loss_rpn_loc: 0.1197  time: 0.5174  data_time: 0.0415  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:44 d2.utils.events]: \u001b[0m eta: 0:56:18  iter: 3259  total_loss: 0.9065  loss_cls: 0.2441  loss_box_reg: 0.3057  loss_mask: 0.2522  loss_rpn_cls: 0.01829  loss_rpn_loc: 0.1149  time: 0.5179  data_time: 0.1104  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:08:55 d2.utils.events]: \u001b[0m eta: 0:55:58  iter: 3279  total_loss: 0.9848  loss_cls: 0.2291  loss_box_reg: 0.3365  loss_mask: 0.2655  loss_rpn_cls: 0.01216  loss_rpn_loc: 0.116  time: 0.5181  data_time: 0.0266  lr: 0.001  max_mem: 16744M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:09:06 d2.utils.events]: \u001b[0m eta: 0:55:53  iter: 3299  total_loss: 0.9713  loss_cls: 0.2417  loss_box_reg: 0.3318  loss_mask: 0.2749  loss_rpn_cls: 0.02074  loss_rpn_loc: 0.1219  time: 0.5184  data_time: 0.0830  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:09:16 d2.utils.events]: \u001b[0m eta: 0:55:54  iter: 3319  total_loss: 0.9408  loss_cls: 0.2396  loss_box_reg: 0.3003  loss_mask: 0.259  loss_rpn_cls: 0.01455  loss_rpn_loc: 0.1255  time: 0.5184  data_time: 0.0050  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:09:26 d2.utils.events]: \u001b[0m eta: 0:55:46  iter: 3339  total_loss: 0.858  loss_cls: 0.206  loss_box_reg: 0.3047  loss_mask: 0.2514  loss_rpn_cls: 0.01132  loss_rpn_loc: 0.1098  time: 0.5183  data_time: 0.0049  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:09:36 d2.utils.events]: \u001b[0m eta: 0:55:34  iter: 3359  total_loss: 0.9205  loss_cls: 0.2414  loss_box_reg: 0.3044  loss_mask: 0.2358  loss_rpn_cls: 0.0197  loss_rpn_loc: 0.1129  time: 0.5180  data_time: 0.0318  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:09:47 d2.utils.events]: \u001b[0m eta: 0:55:26  iter: 3379  total_loss: 0.9885  loss_cls: 0.2356  loss_box_reg: 0.3412  loss_mask: 0.2842  loss_rpn_cls: 0.02142  loss_rpn_loc: 0.1174  time: 0.5181  data_time: 0.0835  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:09:57 d2.utils.events]: \u001b[0m eta: 0:55:11  iter: 3399  total_loss: 0.9208  loss_cls: 0.2223  loss_box_reg: 0.3204  loss_mask: 0.2498  loss_rpn_cls: 0.01137  loss_rpn_loc: 0.1212  time: 0.5181  data_time: 0.0333  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:10:07 d2.utils.events]: \u001b[0m eta: 0:54:58  iter: 3419  total_loss: 1.056  loss_cls: 0.2408  loss_box_reg: 0.3419  loss_mask: 0.2692  loss_rpn_cls: 0.0189  loss_rpn_loc: 0.1282  time: 0.5182  data_time: 0.0343  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:10:17 d2.utils.events]: \u001b[0m eta: 0:54:37  iter: 3439  total_loss: 0.9587  loss_cls: 0.2297  loss_box_reg: 0.3087  loss_mask: 0.2486  loss_rpn_cls: 0.02003  loss_rpn_loc: 0.1201  time: 0.5180  data_time: 0.0303  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:10:28 d2.utils.events]: \u001b[0m eta: 0:54:33  iter: 3459  total_loss: 0.9081  loss_cls: 0.2131  loss_box_reg: 0.3111  loss_mask: 0.251  loss_rpn_cls: 0.01383  loss_rpn_loc: 0.1176  time: 0.5181  data_time: 0.0110  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:10:38 d2.utils.events]: \u001b[0m eta: 0:54:28  iter: 3479  total_loss: 0.9437  loss_cls: 0.2464  loss_box_reg: 0.3115  loss_mask: 0.26  loss_rpn_cls: 0.01955  loss_rpn_loc: 0.125  time: 0.5181  data_time: 0.0051  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:10:49 d2.utils.events]: \u001b[0m eta: 0:54:17  iter: 3499  total_loss: 0.8902  loss_cls: 0.2356  loss_box_reg: 0.2853  loss_mask: 0.2425  loss_rpn_cls: 0.0124  loss_rpn_loc: 0.1138  time: 0.5182  data_time: 0.0420  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:00 d2.utils.events]: \u001b[0m eta: 0:54:10  iter: 3519  total_loss: 0.9781  loss_cls: 0.2516  loss_box_reg: 0.3261  loss_mask: 0.2531  loss_rpn_cls: 0.01515  loss_rpn_loc: 0.112  time: 0.5184  data_time: 0.0639  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:10 d2.utils.events]: \u001b[0m eta: 0:53:58  iter: 3539  total_loss: 0.9921  loss_cls: 0.247  loss_box_reg: 0.3351  loss_mask: 0.2631  loss_rpn_cls: 0.02239  loss_rpn_loc: 0.1258  time: 0.5183  data_time: 0.0343  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:21 d2.utils.events]: \u001b[0m eta: 0:53:50  iter: 3559  total_loss: 1.058  loss_cls: 0.2919  loss_box_reg: 0.36  loss_mask: 0.2521  loss_rpn_cls: 0.01426  loss_rpn_loc: 0.1233  time: 0.5184  data_time: 0.0461  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:32 d2.utils.events]: \u001b[0m eta: 0:53:43  iter: 3579  total_loss: 0.95  loss_cls: 0.2129  loss_box_reg: 0.3061  loss_mask: 0.2446  loss_rpn_cls: 0.01722  loss_rpn_loc: 0.114  time: 0.5187  data_time: 0.0673  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:43 d2.utils.events]: \u001b[0m eta: 0:53:30  iter: 3599  total_loss: 0.9177  loss_cls: 0.2309  loss_box_reg: 0.3159  loss_mask: 0.2423  loss_rpn_cls: 0.0141  loss_rpn_loc: 0.1098  time: 0.5188  data_time: 0.0691  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:11:53 d2.utils.events]: \u001b[0m eta: 0:53:09  iter: 3619  total_loss: 0.9314  loss_cls: 0.2293  loss_box_reg: 0.3128  loss_mask: 0.246  loss_rpn_cls: 0.02046  loss_rpn_loc: 0.1311  time: 0.5187  data_time: 0.0330  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:03 d2.utils.events]: \u001b[0m eta: 0:52:55  iter: 3639  total_loss: 0.9847  loss_cls: 0.2249  loss_box_reg: 0.3232  loss_mask: 0.2459  loss_rpn_cls: 0.02276  loss_rpn_loc: 0.1321  time: 0.5186  data_time: 0.0135  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:13 d2.utils.events]: \u001b[0m eta: 0:52:37  iter: 3659  total_loss: 0.9328  loss_cls: 0.2501  loss_box_reg: 0.3004  loss_mask: 0.2431  loss_rpn_cls: 0.01323  loss_rpn_loc: 0.1288  time: 0.5185  data_time: 0.0048  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:23 d2.utils.events]: \u001b[0m eta: 0:52:29  iter: 3679  total_loss: 0.9711  loss_cls: 0.273  loss_box_reg: 0.3361  loss_mask: 0.2705  loss_rpn_cls: 0.01552  loss_rpn_loc: 0.1153  time: 0.5184  data_time: 0.0108  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:34 d2.utils.events]: \u001b[0m eta: 0:52:19  iter: 3699  total_loss: 0.9076  loss_cls: 0.2427  loss_box_reg: 0.2746  loss_mask: 0.2426  loss_rpn_cls: 0.01682  loss_rpn_loc: 0.1201  time: 0.5184  data_time: 0.0377  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:45 d2.utils.events]: \u001b[0m eta: 0:52:24  iter: 3719  total_loss: 0.9247  loss_cls: 0.2359  loss_box_reg: 0.2937  loss_mask: 0.255  loss_rpn_cls: 0.01547  loss_rpn_loc: 0.1118  time: 0.5187  data_time: 0.0257  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:12:56 d2.utils.events]: \u001b[0m eta: 0:52:20  iter: 3739  total_loss: 0.9156  loss_cls: 0.2289  loss_box_reg: 0.3387  loss_mask: 0.257  loss_rpn_cls: 0.01799  loss_rpn_loc: 0.1215  time: 0.5188  data_time: 0.0529  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:06 d2.utils.events]: \u001b[0m eta: 0:52:13  iter: 3759  total_loss: 0.9326  loss_cls: 0.2225  loss_box_reg: 0.3124  loss_mask: 0.2344  loss_rpn_cls: 0.01946  loss_rpn_loc: 0.1149  time: 0.5188  data_time: 0.0356  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:17 d2.utils.events]: \u001b[0m eta: 0:52:21  iter: 3779  total_loss: 0.9306  loss_cls: 0.2287  loss_box_reg: 0.299  loss_mask: 0.2394  loss_rpn_cls: 0.0125  loss_rpn_loc: 0.1173  time: 0.5190  data_time: 0.0480  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:27 d2.utils.events]: \u001b[0m eta: 0:52:11  iter: 3799  total_loss: 0.9628  loss_cls: 0.2503  loss_box_reg: 0.2969  loss_mask: 0.2437  loss_rpn_cls: 0.02569  loss_rpn_loc: 0.1231  time: 0.5189  data_time: 0.0243  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:37 d2.utils.events]: \u001b[0m eta: 0:52:05  iter: 3819  total_loss: 1.018  loss_cls: 0.2478  loss_box_reg: 0.3324  loss_mask: 0.2739  loss_rpn_cls: 0.01408  loss_rpn_loc: 0.1225  time: 0.5187  data_time: 0.0119  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:48 d2.utils.events]: \u001b[0m eta: 0:52:08  iter: 3839  total_loss: 0.9866  loss_cls: 0.2591  loss_box_reg: 0.2946  loss_mask: 0.2775  loss_rpn_cls: 0.01787  loss_rpn_loc: 0.1218  time: 0.5189  data_time: 0.0562  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:13:59 d2.utils.events]: \u001b[0m eta: 0:51:59  iter: 3859  total_loss: 0.9551  loss_cls: 0.2488  loss_box_reg: 0.2996  loss_mask: 0.2604  loss_rpn_cls: 0.02544  loss_rpn_loc: 0.1254  time: 0.5190  data_time: 0.0289  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:14:09 d2.utils.events]: \u001b[0m eta: 0:51:47  iter: 3879  total_loss: 1.104  loss_cls: 0.2598  loss_box_reg: 0.3739  loss_mask: 0.2867  loss_rpn_cls: 0.0199  loss_rpn_loc: 0.126  time: 0.5188  data_time: 0.0107  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:14:20 d2.utils.events]: \u001b[0m eta: 0:51:43  iter: 3899  total_loss: 0.8651  loss_cls: 0.2068  loss_box_reg: 0.2927  loss_mask: 0.2343  loss_rpn_cls: 0.01931  loss_rpn_loc: 0.1149  time: 0.5190  data_time: 0.0184  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:14:30 d2.utils.events]: \u001b[0m eta: 0:51:29  iter: 3919  total_loss: 0.9295  loss_cls: 0.2256  loss_box_reg: 0.3126  loss_mask: 0.2555  loss_rpn_cls: 0.01955  loss_rpn_loc: 0.1182  time: 0.5189  data_time: 0.0147  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:14:40 d2.utils.events]: \u001b[0m eta: 0:51:21  iter: 3939  total_loss: 0.9314  loss_cls: 0.2081  loss_box_reg: 0.3301  loss_mask: 0.2601  loss_rpn_cls: 0.01679  loss_rpn_loc: 0.115  time: 0.5189  data_time: 0.0312  lr: 0.001  max_mem: 16744M\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:14:50 d2.utils.events]: \u001b[0m eta: 0:51:10  iter: 3959  total_loss: 1.089  loss_cls: 0.267  loss_box_reg: 0.3642  loss_mask: 0.2742  loss_rpn_cls: 0.01404  loss_rpn_loc: 0.1319  time: 0.5187  data_time: 0.0157  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:01 d2.utils.events]: \u001b[0m eta: 0:51:09  iter: 3979  total_loss: 0.9354  loss_cls: 0.229  loss_box_reg: 0.3259  loss_mask: 0.2593  loss_rpn_cls: 0.02014  loss_rpn_loc: 0.1132  time: 0.5189  data_time: 0.0751  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:12 d2.utils.events]: \u001b[0m eta: 0:50:57  iter: 3999  total_loss: 1.021  loss_cls: 0.2421  loss_box_reg: 0.329  loss_mask: 0.2537  loss_rpn_cls: 0.01839  loss_rpn_loc: 0.1136  time: 0.5187  data_time: 0.0129  lr: 0.001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:22 d2.utils.events]: \u001b[0m eta: 0:50:36  iter: 4019  total_loss: 0.8913  loss_cls: 0.2285  loss_box_reg: 0.3032  loss_mask: 0.2413  loss_rpn_cls: 0.01508  loss_rpn_loc: 0.1122  time: 0.5186  data_time: 0.0056  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:32 d2.utils.events]: \u001b[0m eta: 0:50:24  iter: 4039  total_loss: 0.974  loss_cls: 0.2377  loss_box_reg: 0.3154  loss_mask: 0.2663  loss_rpn_cls: 0.01954  loss_rpn_loc: 0.11  time: 0.5185  data_time: 0.0488  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:43 d2.utils.events]: \u001b[0m eta: 0:50:16  iter: 4059  total_loss: 0.9068  loss_cls: 0.2162  loss_box_reg: 0.3112  loss_mask: 0.2461  loss_rpn_cls: 0.02098  loss_rpn_loc: 0.1074  time: 0.5187  data_time: 0.0572  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:15:53 d2.utils.events]: \u001b[0m eta: 0:50:05  iter: 4079  total_loss: 0.9031  loss_cls: 0.2403  loss_box_reg: 0.3102  loss_mask: 0.2432  loss_rpn_cls: 0.01493  loss_rpn_loc: 0.1127  time: 0.5186  data_time: 0.0463  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:03 d2.utils.events]: \u001b[0m eta: 0:49:50  iter: 4099  total_loss: 0.9833  loss_cls: 0.2399  loss_box_reg: 0.324  loss_mask: 0.262  loss_rpn_cls: 0.01462  loss_rpn_loc: 0.1156  time: 0.5186  data_time: 0.0328  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:14 d2.utils.events]: \u001b[0m eta: 0:49:32  iter: 4119  total_loss: 0.9078  loss_cls: 0.2126  loss_box_reg: 0.3035  loss_mask: 0.2608  loss_rpn_cls: 0.01829  loss_rpn_loc: 0.1095  time: 0.5185  data_time: 0.0192  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:23 d2.utils.events]: \u001b[0m eta: 0:49:21  iter: 4139  total_loss: 0.8962  loss_cls: 0.2184  loss_box_reg: 0.3019  loss_mask: 0.253  loss_rpn_cls: 0.0263  loss_rpn_loc: 0.1114  time: 0.5184  data_time: 0.0205  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:34 d2.utils.events]: \u001b[0m eta: 0:49:11  iter: 4159  total_loss: 0.9263  loss_cls: 0.2452  loss_box_reg: 0.3159  loss_mask: 0.2512  loss_rpn_cls: 0.01163  loss_rpn_loc: 0.1173  time: 0.5185  data_time: 0.0516  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:45 d2.utils.events]: \u001b[0m eta: 0:49:05  iter: 4179  total_loss: 0.8863  loss_cls: 0.2178  loss_box_reg: 0.3123  loss_mask: 0.2466  loss_rpn_cls: 0.01168  loss_rpn_loc: 0.1043  time: 0.5186  data_time: 0.0296  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:16:56 d2.utils.events]: \u001b[0m eta: 0:49:16  iter: 4199  total_loss: 0.8537  loss_cls: 0.2054  loss_box_reg: 0.2789  loss_mask: 0.2705  loss_rpn_cls: 0.01084  loss_rpn_loc: 0.1084  time: 0.5186  data_time: 0.0081  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:17:06 d2.utils.events]: \u001b[0m eta: 0:49:09  iter: 4219  total_loss: 0.9436  loss_cls: 0.2223  loss_box_reg: 0.3116  loss_mask: 0.2549  loss_rpn_cls: 0.02138  loss_rpn_loc: 0.1201  time: 0.5187  data_time: 0.0171  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:17:17 d2.utils.events]: \u001b[0m eta: 0:49:06  iter: 4239  total_loss: 0.8672  loss_cls: 0.1915  loss_box_reg: 0.2948  loss_mask: 0.239  loss_rpn_cls: 0.01386  loss_rpn_loc: 0.1087  time: 0.5189  data_time: 0.0351  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:17:30 d2.utils.events]: \u001b[0m eta: 0:48:55  iter: 4259  total_loss: 0.9034  loss_cls: 0.2092  loss_box_reg: 0.2961  loss_mask: 0.2541  loss_rpn_cls: 0.01386  loss_rpn_loc: 0.1051  time: 0.5193  data_time: 0.1126  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:17:40 d2.utils.events]: \u001b[0m eta: 0:48:41  iter: 4279  total_loss: 0.8124  loss_cls: 0.1825  loss_box_reg: 0.2843  loss_mask: 0.243  loss_rpn_cls: 0.01834  loss_rpn_loc: 0.09302  time: 0.5194  data_time: 0.0513  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:17:51 d2.utils.events]: \u001b[0m eta: 0:48:35  iter: 4299  total_loss: 0.882  loss_cls: 0.2042  loss_box_reg: 0.2939  loss_mask: 0.2629  loss_rpn_cls: 0.01719  loss_rpn_loc: 0.109  time: 0.5194  data_time: 0.0137  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:02 d2.utils.events]: \u001b[0m eta: 0:48:15  iter: 4319  total_loss: 0.9137  loss_cls: 0.2002  loss_box_reg: 0.2874  loss_mask: 0.2465  loss_rpn_cls: 0.01732  loss_rpn_loc: 0.116  time: 0.5195  data_time: 0.0587  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:12 d2.utils.events]: \u001b[0m eta: 0:48:04  iter: 4339  total_loss: 0.8469  loss_cls: 0.2066  loss_box_reg: 0.2851  loss_mask: 0.2472  loss_rpn_cls: 0.01316  loss_rpn_loc: 0.1049  time: 0.5195  data_time: 0.0446  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:22 d2.utils.events]: \u001b[0m eta: 0:47:56  iter: 4359  total_loss: 0.8232  loss_cls: 0.2057  loss_box_reg: 0.283  loss_mask: 0.2506  loss_rpn_cls: 0.01004  loss_rpn_loc: 0.09613  time: 0.5194  data_time: 0.0418  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:32 d2.utils.events]: \u001b[0m eta: 0:47:41  iter: 4379  total_loss: 0.9155  loss_cls: 0.2238  loss_box_reg: 0.2897  loss_mask: 0.2616  loss_rpn_cls: 0.01784  loss_rpn_loc: 0.1145  time: 0.5193  data_time: 0.0366  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:43 d2.utils.events]: \u001b[0m eta: 0:47:33  iter: 4399  total_loss: 0.931  loss_cls: 0.2521  loss_box_reg: 0.3106  loss_mask: 0.2569  loss_rpn_cls: 0.01996  loss_rpn_loc: 0.1052  time: 0.5194  data_time: 0.0535  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:18:53 d2.utils.events]: \u001b[0m eta: 0:47:13  iter: 4419  total_loss: 0.8711  loss_cls: 0.2116  loss_box_reg: 0.3167  loss_mask: 0.2334  loss_rpn_cls: 0.01247  loss_rpn_loc: 0.1148  time: 0.5192  data_time: 0.0417  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:03 d2.utils.events]: \u001b[0m eta: 0:47:05  iter: 4439  total_loss: 0.8687  loss_cls: 0.2214  loss_box_reg: 0.291  loss_mask: 0.2465  loss_rpn_cls: 0.01577  loss_rpn_loc: 0.1072  time: 0.5193  data_time: 0.0488  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:15 d2.utils.events]: \u001b[0m eta: 0:46:56  iter: 4459  total_loss: 0.9028  loss_cls: 0.2009  loss_box_reg: 0.2978  loss_mask: 0.2527  loss_rpn_cls: 0.01504  loss_rpn_loc: 0.1066  time: 0.5196  data_time: 0.0554  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:24 d2.utils.events]: \u001b[0m eta: 0:46:28  iter: 4479  total_loss: 0.8387  loss_cls: 0.2122  loss_box_reg: 0.3089  loss_mask: 0.2528  loss_rpn_cls: 0.01262  loss_rpn_loc: 0.1004  time: 0.5193  data_time: 0.0054  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:34 d2.utils.events]: \u001b[0m eta: 0:46:15  iter: 4499  total_loss: 0.8963  loss_cls: 0.2074  loss_box_reg: 0.2909  loss_mask: 0.2404  loss_rpn_cls: 0.01844  loss_rpn_loc: 0.1094  time: 0.5193  data_time: 0.0302  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:45 d2.utils.events]: \u001b[0m eta: 0:46:04  iter: 4519  total_loss: 0.9775  loss_cls: 0.2232  loss_box_reg: 0.3375  loss_mask: 0.2665  loss_rpn_cls: 0.01402  loss_rpn_loc: 0.117  time: 0.5194  data_time: 0.0802  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:19:56 d2.utils.events]: \u001b[0m eta: 0:45:58  iter: 4539  total_loss: 0.8968  loss_cls: 0.249  loss_box_reg: 0.2966  loss_mask: 0.2414  loss_rpn_cls: 0.009415  loss_rpn_loc: 0.1053  time: 0.5195  data_time: 0.0366  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:20:06 d2.utils.events]: \u001b[0m eta: 0:45:42  iter: 4559  total_loss: 0.9245  loss_cls: 0.251  loss_box_reg: 0.293  loss_mask: 0.245  loss_rpn_cls: 0.01737  loss_rpn_loc: 0.1087  time: 0.5194  data_time: 0.0116  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:20:16 d2.utils.events]: \u001b[0m eta: 0:45:23  iter: 4579  total_loss: 0.8467  loss_cls: 0.214  loss_box_reg: 0.2874  loss_mask: 0.2508  loss_rpn_cls: 0.01072  loss_rpn_loc: 0.1027  time: 0.5193  data_time: 0.0083  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:20:26 d2.utils.events]: \u001b[0m eta: 0:45:19  iter: 4599  total_loss: 0.9205  loss_cls: 0.2122  loss_box_reg: 0.3218  loss_mask: 0.2475  loss_rpn_cls: 0.01502  loss_rpn_loc: 0.1084  time: 0.5192  data_time: 0.0135  lr: 0.0001  max_mem: 16744M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:20:36 d2.utils.events]: \u001b[0m eta: 0:45:09  iter: 4619  total_loss: 0.95  loss_cls: 0.2305  loss_box_reg: 0.3173  loss_mask: 0.2481  loss_rpn_cls: 0.01584  loss_rpn_loc: 0.108  time: 0.5192  data_time: 0.0415  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:20:46 d2.utils.events]: \u001b[0m eta: 0:44:53  iter: 4639  total_loss: 0.8485  loss_cls: 0.1825  loss_box_reg: 0.2847  loss_mask: 0.2379  loss_rpn_cls: 0.01226  loss_rpn_loc: 0.1057  time: 0.5191  data_time: 0.0587  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:20:57 d2.utils.events]: \u001b[0m eta: 0:44:46  iter: 4659  total_loss: 0.8479  loss_cls: 0.2197  loss_box_reg: 0.2819  loss_mask: 0.2508  loss_rpn_cls: 0.01364  loss_rpn_loc: 0.11  time: 0.5191  data_time: 0.0349  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:07 d2.utils.events]: \u001b[0m eta: 0:44:36  iter: 4679  total_loss: 0.8946  loss_cls: 0.195  loss_box_reg: 0.27  loss_mask: 0.2411  loss_rpn_cls: 0.01732  loss_rpn_loc: 0.105  time: 0.5191  data_time: 0.0047  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:17 d2.utils.events]: \u001b[0m eta: 0:44:22  iter: 4699  total_loss: 0.9696  loss_cls: 0.2422  loss_box_reg: 0.3342  loss_mask: 0.2522  loss_rpn_cls: 0.01205  loss_rpn_loc: 0.112  time: 0.5191  data_time: 0.0313  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:28 d2.utils.events]: \u001b[0m eta: 0:44:11  iter: 4719  total_loss: 0.8567  loss_cls: 0.1993  loss_box_reg: 0.3071  loss_mask: 0.2389  loss_rpn_cls: 0.01382  loss_rpn_loc: 0.1057  time: 0.5192  data_time: 0.0408  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:38 d2.utils.events]: \u001b[0m eta: 0:44:00  iter: 4739  total_loss: 0.8631  loss_cls: 0.2008  loss_box_reg: 0.2899  loss_mask: 0.244  loss_rpn_cls: 0.01327  loss_rpn_loc: 0.1025  time: 0.5191  data_time: 0.0134  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:48 d2.utils.events]: \u001b[0m eta: 0:43:49  iter: 4759  total_loss: 0.9629  loss_cls: 0.2473  loss_box_reg: 0.3237  loss_mask: 0.2574  loss_rpn_cls: 0.01446  loss_rpn_loc: 0.1156  time: 0.5190  data_time: 0.0092  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:21:59 d2.utils.events]: \u001b[0m eta: 0:43:40  iter: 4779  total_loss: 0.8498  loss_cls: 0.2119  loss_box_reg: 0.2853  loss_mask: 0.2438  loss_rpn_cls: 0.01231  loss_rpn_loc: 0.09944  time: 0.5190  data_time: 0.0136  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:22:10 d2.utils.events]: \u001b[0m eta: 0:43:33  iter: 4799  total_loss: 0.8739  loss_cls: 0.2091  loss_box_reg: 0.3158  loss_mask: 0.2507  loss_rpn_cls: 0.01879  loss_rpn_loc: 0.1229  time: 0.5192  data_time: 0.0636  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:22:19 d2.utils.events]: \u001b[0m eta: 0:43:21  iter: 4819  total_loss: 0.9399  loss_cls: 0.2683  loss_box_reg: 0.2939  loss_mask: 0.2472  loss_rpn_cls: 0.02209  loss_rpn_loc: 0.1121  time: 0.5190  data_time: 0.0106  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:22:30 d2.utils.events]: \u001b[0m eta: 0:43:10  iter: 4839  total_loss: 0.8725  loss_cls: 0.1983  loss_box_reg: 0.3158  loss_mask: 0.248  loss_rpn_cls: 0.009761  loss_rpn_loc: 0.09732  time: 0.5191  data_time: 0.0233  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:22:41 d2.utils.events]: \u001b[0m eta: 0:42:59  iter: 4859  total_loss: 0.8458  loss_cls: 0.2037  loss_box_reg: 0.2772  loss_mask: 0.2368  loss_rpn_cls: 0.01474  loss_rpn_loc: 0.09774  time: 0.5191  data_time: 0.0505  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:22:52 d2.utils.events]: \u001b[0m eta: 0:42:48  iter: 4879  total_loss: 0.9027  loss_cls: 0.2258  loss_box_reg: 0.297  loss_mask: 0.2363  loss_rpn_cls: 0.01421  loss_rpn_loc: 0.1033  time: 0.5192  data_time: 0.0508  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:02 d2.utils.events]: \u001b[0m eta: 0:42:33  iter: 4899  total_loss: 0.8497  loss_cls: 0.2087  loss_box_reg: 0.2811  loss_mask: 0.2518  loss_rpn_cls: 0.01434  loss_rpn_loc: 0.1083  time: 0.5191  data_time: 0.0236  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:12 d2.utils.events]: \u001b[0m eta: 0:42:22  iter: 4919  total_loss: 0.9396  loss_cls: 0.2277  loss_box_reg: 0.3204  loss_mask: 0.2596  loss_rpn_cls: 0.01121  loss_rpn_loc: 0.1043  time: 0.5190  data_time: 0.0282  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:23 d2.utils.events]: \u001b[0m eta: 0:42:12  iter: 4939  total_loss: 0.8926  loss_cls: 0.21  loss_box_reg: 0.2987  loss_mask: 0.257  loss_rpn_cls: 0.01372  loss_rpn_loc: 0.106  time: 0.5192  data_time: 0.0652  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:35 d2.utils.events]: \u001b[0m eta: 0:42:07  iter: 4959  total_loss: 0.9385  loss_cls: 0.2187  loss_box_reg: 0.3199  loss_mask: 0.2617  loss_rpn_cls: 0.01487  loss_rpn_loc: 0.1158  time: 0.5196  data_time: 0.1211  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:45 d2.utils.events]: \u001b[0m eta: 0:41:57  iter: 4979  total_loss: 0.8391  loss_cls: 0.2001  loss_box_reg: 0.2684  loss_mask: 0.24  loss_rpn_cls: 0.009081  loss_rpn_loc: 0.1007  time: 0.5196  data_time: 0.0447  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:23:56 d2.utils.events]: \u001b[0m eta: 0:41:44  iter: 4999  total_loss: 0.9913  loss_cls: 0.2275  loss_box_reg: 0.3199  loss_mask: 0.2572  loss_rpn_cls: 0.01643  loss_rpn_loc: 0.1094  time: 0.5196  data_time: 0.0753  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:08 d2.utils.events]: \u001b[0m eta: 0:41:34  iter: 5019  total_loss: 0.9313  loss_cls: 0.2462  loss_box_reg: 0.3291  loss_mask: 0.28  loss_rpn_cls: 0.01559  loss_rpn_loc: 0.1145  time: 0.5199  data_time: 0.1060  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:17 d2.utils.events]: \u001b[0m eta: 0:41:28  iter: 5039  total_loss: 0.9649  loss_cls: 0.2627  loss_box_reg: 0.3156  loss_mask: 0.2478  loss_rpn_cls: 0.0209  loss_rpn_loc: 0.1128  time: 0.5197  data_time: 0.0232  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:27 d2.utils.events]: \u001b[0m eta: 0:41:11  iter: 5059  total_loss: 0.8593  loss_cls: 0.2212  loss_box_reg: 0.2833  loss_mask: 0.2368  loss_rpn_cls: 0.01223  loss_rpn_loc: 0.1011  time: 0.5196  data_time: 0.0240  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:37 d2.utils.events]: \u001b[0m eta: 0:40:59  iter: 5079  total_loss: 0.9684  loss_cls: 0.241  loss_box_reg: 0.318  loss_mask: 0.2865  loss_rpn_cls: 0.01099  loss_rpn_loc: 0.1145  time: 0.5194  data_time: 0.0123  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:47 d2.utils.events]: \u001b[0m eta: 0:40:51  iter: 5099  total_loss: 0.8706  loss_cls: 0.211  loss_box_reg: 0.3121  loss_mask: 0.2435  loss_rpn_cls: 0.01365  loss_rpn_loc: 0.1024  time: 0.5194  data_time: 0.0179  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:24:58 d2.utils.events]: \u001b[0m eta: 0:40:48  iter: 5119  total_loss: 0.9287  loss_cls: 0.2122  loss_box_reg: 0.3337  loss_mask: 0.2446  loss_rpn_cls: 0.01341  loss_rpn_loc: 0.1155  time: 0.5196  data_time: 0.0377  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:25:08 d2.utils.events]: \u001b[0m eta: 0:40:40  iter: 5139  total_loss: 0.8957  loss_cls: 0.2296  loss_box_reg: 0.3198  loss_mask: 0.2681  loss_rpn_cls: 0.007299  loss_rpn_loc: 0.09886  time: 0.5195  data_time: 0.0267  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:25:19 d2.utils.events]: \u001b[0m eta: 0:40:28  iter: 5159  total_loss: 0.9014  loss_cls: 0.2168  loss_box_reg: 0.2971  loss_mask: 0.2475  loss_rpn_cls: 0.01479  loss_rpn_loc: 0.105  time: 0.5195  data_time: 0.0084  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:25:29 d2.utils.events]: \u001b[0m eta: 0:40:16  iter: 5179  total_loss: 0.9109  loss_cls: 0.2073  loss_box_reg: 0.2856  loss_mask: 0.2498  loss_rpn_cls: 0.01692  loss_rpn_loc: 0.1073  time: 0.5194  data_time: 0.0066  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:25:39 d2.utils.events]: \u001b[0m eta: 0:39:57  iter: 5199  total_loss: 0.9136  loss_cls: 0.2112  loss_box_reg: 0.2832  loss_mask: 0.2584  loss_rpn_cls: 0.01741  loss_rpn_loc: 0.1124  time: 0.5194  data_time: 0.0317  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:25:49 d2.utils.events]: \u001b[0m eta: 0:39:47  iter: 5219  total_loss: 0.9005  loss_cls: 0.2128  loss_box_reg: 0.2969  loss_mask: 0.2766  loss_rpn_cls: 0.01051  loss_rpn_loc: 0.1024  time: 0.5194  data_time: 0.0119  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:26:00 d2.utils.events]: \u001b[0m eta: 0:39:33  iter: 5239  total_loss: 0.9789  loss_cls: 0.246  loss_box_reg: 0.3163  loss_mask: 0.2652  loss_rpn_cls: 0.01154  loss_rpn_loc: 0.1074  time: 0.5194  data_time: 0.0208  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:26:09 d2.utils.events]: \u001b[0m eta: 0:39:08  iter: 5259  total_loss: 0.9151  loss_cls: 0.2285  loss_box_reg: 0.2756  loss_mask: 0.2598  loss_rpn_cls: 0.01662  loss_rpn_loc: 0.1088  time: 0.5191  data_time: 0.0069  lr: 0.0001  max_mem: 16744M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:26:18 d2.utils.events]: \u001b[0m eta: 0:38:52  iter: 5279  total_loss: 0.8102  loss_cls: 0.2032  loss_box_reg: 0.2551  loss_mask: 0.238  loss_rpn_cls: 0.007746  loss_rpn_loc: 0.09838  time: 0.5190  data_time: 0.0090  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:26:28 d2.utils.events]: \u001b[0m eta: 0:38:40  iter: 5299  total_loss: 0.8555  loss_cls: 0.196  loss_box_reg: 0.3007  loss_mask: 0.2449  loss_rpn_cls: 0.009142  loss_rpn_loc: 0.1018  time: 0.5188  data_time: 0.0059  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:26:38 d2.utils.events]: \u001b[0m eta: 0:38:37  iter: 5319  total_loss: 0.8786  loss_cls: 0.2116  loss_box_reg: 0.2771  loss_mask: 0.2566  loss_rpn_cls: 0.01754  loss_rpn_loc: 0.1133  time: 0.5188  data_time: 0.0095  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:26:50 d2.utils.events]: \u001b[0m eta: 0:38:38  iter: 5339  total_loss: 0.8648  loss_cls: 0.1873  loss_box_reg: 0.2945  loss_mask: 0.243  loss_rpn_cls: 0.01501  loss_rpn_loc: 0.09978  time: 0.5190  data_time: 0.0727  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:01 d2.utils.events]: \u001b[0m eta: 0:38:35  iter: 5359  total_loss: 0.865  loss_cls: 0.2053  loss_box_reg: 0.2861  loss_mask: 0.2507  loss_rpn_cls: 0.007884  loss_rpn_loc: 0.09978  time: 0.5191  data_time: 0.0538  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:11 d2.utils.events]: \u001b[0m eta: 0:38:28  iter: 5379  total_loss: 0.9187  loss_cls: 0.2419  loss_box_reg: 0.324  loss_mask: 0.2626  loss_rpn_cls: 0.01676  loss_rpn_loc: 0.1114  time: 0.5191  data_time: 0.0122  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:21 d2.utils.events]: \u001b[0m eta: 0:38:17  iter: 5399  total_loss: 0.8899  loss_cls: 0.2211  loss_box_reg: 0.3039  loss_mask: 0.2447  loss_rpn_cls: 0.02174  loss_rpn_loc: 0.1033  time: 0.5190  data_time: 0.0229  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:32 d2.utils.events]: \u001b[0m eta: 0:38:12  iter: 5419  total_loss: 0.8821  loss_cls: 0.2144  loss_box_reg: 0.3081  loss_mask: 0.2553  loss_rpn_cls: 0.01449  loss_rpn_loc: 0.1011  time: 0.5191  data_time: 0.0581  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:42 d2.utils.events]: \u001b[0m eta: 0:38:00  iter: 5439  total_loss: 0.9965  loss_cls: 0.255  loss_box_reg: 0.3199  loss_mask: 0.2627  loss_rpn_cls: 0.01529  loss_rpn_loc: 0.1059  time: 0.5190  data_time: 0.0267  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:27:52 d2.utils.events]: \u001b[0m eta: 0:37:44  iter: 5459  total_loss: 0.8425  loss_cls: 0.2033  loss_box_reg: 0.3015  loss_mask: 0.2356  loss_rpn_cls: 0.01608  loss_rpn_loc: 0.1094  time: 0.5190  data_time: 0.0133  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:02 d2.utils.events]: \u001b[0m eta: 0:37:35  iter: 5479  total_loss: 0.9146  loss_cls: 0.1986  loss_box_reg: 0.3028  loss_mask: 0.2504  loss_rpn_cls: 0.009552  loss_rpn_loc: 0.102  time: 0.5189  data_time: 0.0194  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:14 d2.utils.events]: \u001b[0m eta: 0:37:31  iter: 5499  total_loss: 0.8214  loss_cls: 0.177  loss_box_reg: 0.2796  loss_mask: 0.2357  loss_rpn_cls: 0.01161  loss_rpn_loc: 0.09383  time: 0.5191  data_time: 0.0553  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:24 d2.utils.events]: \u001b[0m eta: 0:37:17  iter: 5519  total_loss: 0.8712  loss_cls: 0.2274  loss_box_reg: 0.2993  loss_mask: 0.2376  loss_rpn_cls: 0.01179  loss_rpn_loc: 0.1138  time: 0.5192  data_time: 0.0659  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:34 d2.utils.events]: \u001b[0m eta: 0:37:06  iter: 5539  total_loss: 0.8542  loss_cls: 0.1968  loss_box_reg: 0.2839  loss_mask: 0.2379  loss_rpn_cls: 0.01307  loss_rpn_loc: 0.09388  time: 0.5191  data_time: 0.0265  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:45 d2.utils.events]: \u001b[0m eta: 0:36:58  iter: 5559  total_loss: 0.8189  loss_cls: 0.185  loss_box_reg: 0.2693  loss_mask: 0.2374  loss_rpn_cls: 0.008947  loss_rpn_loc: 0.09621  time: 0.5192  data_time: 0.0478  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:28:56 d2.utils.events]: \u001b[0m eta: 0:36:52  iter: 5579  total_loss: 0.903  loss_cls: 0.2122  loss_box_reg: 0.272  loss_mask: 0.2392  loss_rpn_cls: 0.01418  loss_rpn_loc: 0.1098  time: 0.5192  data_time: 0.0482  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:07 d2.utils.events]: \u001b[0m eta: 0:36:42  iter: 5599  total_loss: 0.903  loss_cls: 0.227  loss_box_reg: 0.2966  loss_mask: 0.2438  loss_rpn_cls: 0.009859  loss_rpn_loc: 0.1065  time: 0.5193  data_time: 0.0422  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:17 d2.utils.events]: \u001b[0m eta: 0:36:31  iter: 5619  total_loss: 0.9774  loss_cls: 0.25  loss_box_reg: 0.2918  loss_mask: 0.2396  loss_rpn_cls: 0.0137  loss_rpn_loc: 0.1032  time: 0.5193  data_time: 0.0468  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:28 d2.utils.events]: \u001b[0m eta: 0:36:22  iter: 5639  total_loss: 0.9486  loss_cls: 0.2214  loss_box_reg: 0.304  loss_mask: 0.2344  loss_rpn_cls: 0.01481  loss_rpn_loc: 0.108  time: 0.5193  data_time: 0.0663  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:37 d2.utils.events]: \u001b[0m eta: 0:36:11  iter: 5659  total_loss: 0.8507  loss_cls: 0.1809  loss_box_reg: 0.2913  loss_mask: 0.259  loss_rpn_cls: 0.01491  loss_rpn_loc: 0.09997  time: 0.5192  data_time: 0.0059  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:49 d2.utils.events]: \u001b[0m eta: 0:36:04  iter: 5679  total_loss: 0.9239  loss_cls: 0.1989  loss_box_reg: 0.313  loss_mask: 0.2574  loss_rpn_cls: 0.01512  loss_rpn_loc: 0.112  time: 0.5194  data_time: 0.0692  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:29:59 d2.utils.events]: \u001b[0m eta: 0:35:53  iter: 5699  total_loss: 0.9224  loss_cls: 0.2204  loss_box_reg: 0.304  loss_mask: 0.2632  loss_rpn_cls: 0.01266  loss_rpn_loc: 0.1027  time: 0.5194  data_time: 0.0133  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:30:09 d2.utils.events]: \u001b[0m eta: 0:35:42  iter: 5719  total_loss: 0.8847  loss_cls: 0.2077  loss_box_reg: 0.2952  loss_mask: 0.2615  loss_rpn_cls: 0.01427  loss_rpn_loc: 0.1153  time: 0.5193  data_time: 0.0111  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:30:20 d2.utils.events]: \u001b[0m eta: 0:35:32  iter: 5739  total_loss: 0.8827  loss_cls: 0.1964  loss_box_reg: 0.2971  loss_mask: 0.257  loss_rpn_cls: 0.02068  loss_rpn_loc: 0.1049  time: 0.5193  data_time: 0.0271  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:30:29 d2.utils.events]: \u001b[0m eta: 0:35:21  iter: 5759  total_loss: 0.8385  loss_cls: 0.2098  loss_box_reg: 0.2829  loss_mask: 0.2406  loss_rpn_cls: 0.01039  loss_rpn_loc: 0.1024  time: 0.5192  data_time: 0.0212  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:30:41 d2.utils.events]: \u001b[0m eta: 0:35:11  iter: 5779  total_loss: 1.023  loss_cls: 0.2757  loss_box_reg: 0.3414  loss_mask: 0.2441  loss_rpn_cls: 0.01834  loss_rpn_loc: 0.1116  time: 0.5194  data_time: 0.0617  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:30:51 d2.utils.events]: \u001b[0m eta: 0:35:00  iter: 5799  total_loss: 0.9293  loss_cls: 0.2022  loss_box_reg: 0.2876  loss_mask: 0.2425  loss_rpn_cls: 0.0143  loss_rpn_loc: 0.1088  time: 0.5194  data_time: 0.0438  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:02 d2.utils.events]: \u001b[0m eta: 0:34:51  iter: 5819  total_loss: 0.8114  loss_cls: 0.1982  loss_box_reg: 0.2898  loss_mask: 0.2358  loss_rpn_cls: 0.01114  loss_rpn_loc: 0.09906  time: 0.5194  data_time: 0.0157  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:12 d2.utils.events]: \u001b[0m eta: 0:34:41  iter: 5839  total_loss: 0.9462  loss_cls: 0.1997  loss_box_reg: 0.3317  loss_mask: 0.263  loss_rpn_cls: 0.01728  loss_rpn_loc: 0.1064  time: 0.5195  data_time: 0.0211  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:23 d2.utils.events]: \u001b[0m eta: 0:34:32  iter: 5859  total_loss: 0.8686  loss_cls: 0.2046  loss_box_reg: 0.2946  loss_mask: 0.2485  loss_rpn_cls: 0.006087  loss_rpn_loc: 0.1044  time: 0.5195  data_time: 0.0145  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:33 d2.utils.events]: \u001b[0m eta: 0:34:21  iter: 5879  total_loss: 0.9601  loss_cls: 0.2573  loss_box_reg: 0.3199  loss_mask: 0.2584  loss_rpn_cls: 0.01093  loss_rpn_loc: 0.1113  time: 0.5195  data_time: 0.0309  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:44 d2.utils.events]: \u001b[0m eta: 0:34:13  iter: 5899  total_loss: 0.8977  loss_cls: 0.2165  loss_box_reg: 0.323  loss_mask: 0.258  loss_rpn_cls: 0.01654  loss_rpn_loc: 0.1092  time: 0.5196  data_time: 0.0360  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:31:54 d2.utils.events]: \u001b[0m eta: 0:34:03  iter: 5919  total_loss: 0.8454  loss_cls: 0.2149  loss_box_reg: 0.2826  loss_mask: 0.2406  loss_rpn_cls: 0.01182  loss_rpn_loc: 0.1002  time: 0.5195  data_time: 0.0150  lr: 0.0001  max_mem: 16744M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:32:04 d2.utils.events]: \u001b[0m eta: 0:33:51  iter: 5939  total_loss: 0.9157  loss_cls: 0.222  loss_box_reg: 0.3067  loss_mask: 0.2433  loss_rpn_cls: 0.01953  loss_rpn_loc: 0.1086  time: 0.5194  data_time: 0.0487  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:32:13 d2.utils.events]: \u001b[0m eta: 0:33:33  iter: 5959  total_loss: 0.9117  loss_cls: 0.222  loss_box_reg: 0.2773  loss_mask: 0.2525  loss_rpn_cls: 0.0215  loss_rpn_loc: 0.111  time: 0.5192  data_time: 0.0170  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:32:23 d2.utils.events]: \u001b[0m eta: 0:33:21  iter: 5979  total_loss: 0.8553  loss_cls: 0.2155  loss_box_reg: 0.2882  loss_mask: 0.2283  loss_rpn_cls: 0.01044  loss_rpn_loc: 0.1049  time: 0.5191  data_time: 0.0126  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:32:34 d2.utils.events]: \u001b[0m eta: 0:33:06  iter: 5999  total_loss: 0.8373  loss_cls: 0.1994  loss_box_reg: 0.2746  loss_mask: 0.2441  loss_rpn_cls: 0.01283  loss_rpn_loc: 0.1021  time: 0.5190  data_time: 0.0172  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:32:44 d2.utils.events]: \u001b[0m eta: 0:32:56  iter: 6019  total_loss: 0.9047  loss_cls: 0.2191  loss_box_reg: 0.3088  loss_mask: 0.2384  loss_rpn_cls: 0.01211  loss_rpn_loc: 0.1027  time: 0.5189  data_time: 0.0072  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:32:54 d2.utils.events]: \u001b[0m eta: 0:32:46  iter: 6039  total_loss: 0.8833  loss_cls: 0.1994  loss_box_reg: 0.2786  loss_mask: 0.2452  loss_rpn_cls: 0.01763  loss_rpn_loc: 0.1079  time: 0.5189  data_time: 0.0252  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:33:04 d2.utils.events]: \u001b[0m eta: 0:32:39  iter: 6059  total_loss: 0.8138  loss_cls: 0.1825  loss_box_reg: 0.2856  loss_mask: 0.23  loss_rpn_cls: 0.01904  loss_rpn_loc: 0.09843  time: 0.5188  data_time: 0.0223  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:33:16 d2.utils.events]: \u001b[0m eta: 0:32:30  iter: 6079  total_loss: 0.971  loss_cls: 0.2687  loss_box_reg: 0.3218  loss_mask: 0.2693  loss_rpn_cls: 0.01049  loss_rpn_loc: 0.1113  time: 0.5190  data_time: 0.0929  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:33:28 d2.utils.events]: \u001b[0m eta: 0:32:29  iter: 6099  total_loss: 0.8517  loss_cls: 0.2186  loss_box_reg: 0.2631  loss_mask: 0.2418  loss_rpn_cls: 0.01065  loss_rpn_loc: 0.09555  time: 0.5193  data_time: 0.0579  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:33:38 d2.utils.events]: \u001b[0m eta: 0:32:10  iter: 6119  total_loss: 0.8637  loss_cls: 0.1952  loss_box_reg: 0.2933  loss_mask: 0.2384  loss_rpn_cls: 0.01408  loss_rpn_loc: 0.1082  time: 0.5192  data_time: 0.0376  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:33:48 d2.utils.events]: \u001b[0m eta: 0:31:59  iter: 6139  total_loss: 0.8144  loss_cls: 0.1854  loss_box_reg: 0.2743  loss_mask: 0.2416  loss_rpn_cls: 0.01699  loss_rpn_loc: 0.102  time: 0.5192  data_time: 0.0418  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:00 d2.utils.events]: \u001b[0m eta: 0:31:50  iter: 6159  total_loss: 0.9012  loss_cls: 0.2034  loss_box_reg: 0.3109  loss_mask: 0.2469  loss_rpn_cls: 0.01246  loss_rpn_loc: 0.1024  time: 0.5194  data_time: 0.1056  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:11 d2.utils.events]: \u001b[0m eta: 0:31:41  iter: 6179  total_loss: 0.9586  loss_cls: 0.2602  loss_box_reg: 0.2962  loss_mask: 0.2544  loss_rpn_cls: 0.02063  loss_rpn_loc: 0.1078  time: 0.5195  data_time: 0.0420  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:21 d2.utils.events]: \u001b[0m eta: 0:31:31  iter: 6199  total_loss: 0.8685  loss_cls: 0.1876  loss_box_reg: 0.2882  loss_mask: 0.2526  loss_rpn_cls: 0.01664  loss_rpn_loc: 0.1061  time: 0.5194  data_time: 0.0315  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:31 d2.utils.events]: \u001b[0m eta: 0:31:20  iter: 6219  total_loss: 0.841  loss_cls: 0.1894  loss_box_reg: 0.2886  loss_mask: 0.2478  loss_rpn_cls: 0.01249  loss_rpn_loc: 0.09815  time: 0.5193  data_time: 0.0122  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:41 d2.utils.events]: \u001b[0m eta: 0:31:05  iter: 6239  total_loss: 0.9726  loss_cls: 0.2264  loss_box_reg: 0.3275  loss_mask: 0.2632  loss_rpn_cls: 0.01872  loss_rpn_loc: 0.1142  time: 0.5193  data_time: 0.0536  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:34:51 d2.utils.events]: \u001b[0m eta: 0:31:07  iter: 6259  total_loss: 0.8946  loss_cls: 0.216  loss_box_reg: 0.2886  loss_mask: 0.254  loss_rpn_cls: 0.006934  loss_rpn_loc: 0.1082  time: 0.5193  data_time: 0.0350  lr: 0.0001  max_mem: 16744M\n",
            "\u001b[32m[03/01 20:35:03 d2.utils.events]: \u001b[0m eta: 0:31:01  iter: 6279  total_loss: 0.9713  loss_cls: 0.2441  loss_box_reg: 0.3192  loss_mask: 0.256  loss_rpn_cls: 0.01401  loss_rpn_loc: 0.1111  time: 0.5195  data_time: 0.0407  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:35:14 d2.utils.events]: \u001b[0m eta: 0:30:52  iter: 6299  total_loss: 0.95  loss_cls: 0.2258  loss_box_reg: 0.3263  loss_mask: 0.2647  loss_rpn_cls: 0.01094  loss_rpn_loc: 0.113  time: 0.5197  data_time: 0.0436  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:35:24 d2.utils.events]: \u001b[0m eta: 0:30:42  iter: 6319  total_loss: 0.9437  loss_cls: 0.2278  loss_box_reg: 0.3023  loss_mask: 0.2601  loss_rpn_cls: 0.01439  loss_rpn_loc: 0.1132  time: 0.5196  data_time: 0.0330  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:35:35 d2.utils.events]: \u001b[0m eta: 0:30:31  iter: 6339  total_loss: 0.8285  loss_cls: 0.1817  loss_box_reg: 0.2978  loss_mask: 0.2455  loss_rpn_cls: 0.01234  loss_rpn_loc: 0.1005  time: 0.5197  data_time: 0.0148  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:35:45 d2.utils.events]: \u001b[0m eta: 0:30:20  iter: 6359  total_loss: 0.9165  loss_cls: 0.2037  loss_box_reg: 0.3048  loss_mask: 0.2529  loss_rpn_cls: 0.01802  loss_rpn_loc: 0.09888  time: 0.5196  data_time: 0.0218  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:35:55 d2.utils.events]: \u001b[0m eta: 0:30:11  iter: 6379  total_loss: 0.9301  loss_cls: 0.2141  loss_box_reg: 0.2973  loss_mask: 0.2582  loss_rpn_cls: 0.009718  loss_rpn_loc: 0.1038  time: 0.5196  data_time: 0.0227  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:06 d2.utils.events]: \u001b[0m eta: 0:30:01  iter: 6399  total_loss: 0.9055  loss_cls: 0.2122  loss_box_reg: 0.3  loss_mask: 0.2481  loss_rpn_cls: 0.01647  loss_rpn_loc: 0.1003  time: 0.5196  data_time: 0.0423  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:16 d2.utils.events]: \u001b[0m eta: 0:29:51  iter: 6419  total_loss: 0.8842  loss_cls: 0.1842  loss_box_reg: 0.2954  loss_mask: 0.2548  loss_rpn_cls: 0.01414  loss_rpn_loc: 0.09976  time: 0.5195  data_time: 0.0167  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:26 d2.utils.events]: \u001b[0m eta: 0:29:43  iter: 6439  total_loss: 0.8613  loss_cls: 0.2113  loss_box_reg: 0.2888  loss_mask: 0.248  loss_rpn_cls: 0.008478  loss_rpn_loc: 0.1066  time: 0.5195  data_time: 0.0331  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:36 d2.utils.events]: \u001b[0m eta: 0:29:38  iter: 6459  total_loss: 0.8847  loss_cls: 0.2119  loss_box_reg: 0.3179  loss_mask: 0.2446  loss_rpn_cls: 0.01178  loss_rpn_loc: 0.1023  time: 0.5195  data_time: 0.0247  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:48 d2.utils.events]: \u001b[0m eta: 0:29:31  iter: 6479  total_loss: 0.8987  loss_cls: 0.2135  loss_box_reg: 0.2976  loss_mask: 0.2507  loss_rpn_cls: 0.01379  loss_rpn_loc: 0.1034  time: 0.5197  data_time: 0.0711  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:36:58 d2.utils.events]: \u001b[0m eta: 0:29:18  iter: 6499  total_loss: 0.8938  loss_cls: 0.1997  loss_box_reg: 0.3047  loss_mask: 0.2502  loss_rpn_cls: 0.009445  loss_rpn_loc: 0.1111  time: 0.5197  data_time: 0.0118  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:37:08 d2.utils.events]: \u001b[0m eta: 0:29:15  iter: 6519  total_loss: 0.8118  loss_cls: 0.1769  loss_box_reg: 0.2842  loss_mask: 0.2452  loss_rpn_cls: 0.01079  loss_rpn_loc: 0.1062  time: 0.5195  data_time: 0.0056  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:37:18 d2.utils.events]: \u001b[0m eta: 0:29:03  iter: 6539  total_loss: 0.8232  loss_cls: 0.1865  loss_box_reg: 0.2746  loss_mask: 0.2276  loss_rpn_cls: 0.01179  loss_rpn_loc: 0.1014  time: 0.5194  data_time: 0.0296  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:37:28 d2.utils.events]: \u001b[0m eta: 0:28:53  iter: 6559  total_loss: 0.9306  loss_cls: 0.2459  loss_box_reg: 0.2952  loss_mask: 0.2478  loss_rpn_cls: 0.01919  loss_rpn_loc: 0.1097  time: 0.5194  data_time: 0.0318  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:37:38 d2.utils.events]: \u001b[0m eta: 0:28:44  iter: 6579  total_loss: 0.8812  loss_cls: 0.2182  loss_box_reg: 0.3049  loss_mask: 0.2383  loss_rpn_cls: 0.01852  loss_rpn_loc: 0.1115  time: 0.5194  data_time: 0.0100  lr: 0.0001  max_mem: 16994M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:37:48 d2.utils.events]: \u001b[0m eta: 0:28:28  iter: 6599  total_loss: 0.8984  loss_cls: 0.2343  loss_box_reg: 0.2892  loss_mask: 0.2503  loss_rpn_cls: 0.01202  loss_rpn_loc: 0.1107  time: 0.5193  data_time: 0.0309  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:37:59 d2.utils.events]: \u001b[0m eta: 0:28:24  iter: 6619  total_loss: 0.8713  loss_cls: 0.2041  loss_box_reg: 0.3003  loss_mask: 0.2315  loss_rpn_cls: 0.01127  loss_rpn_loc: 0.1024  time: 0.5193  data_time: 0.0307  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:38:09 d2.utils.events]: \u001b[0m eta: 0:28:15  iter: 6639  total_loss: 0.7974  loss_cls: 0.169  loss_box_reg: 0.2711  loss_mask: 0.2394  loss_rpn_cls: 0.009251  loss_rpn_loc: 0.09921  time: 0.5193  data_time: 0.0164  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:38:19 d2.utils.events]: \u001b[0m eta: 0:28:05  iter: 6659  total_loss: 0.8702  loss_cls: 0.216  loss_box_reg: 0.2813  loss_mask: 0.2459  loss_rpn_cls: 0.01544  loss_rpn_loc: 0.1067  time: 0.5192  data_time: 0.0219  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:38:29 d2.utils.events]: \u001b[0m eta: 0:27:49  iter: 6679  total_loss: 0.8559  loss_cls: 0.208  loss_box_reg: 0.2825  loss_mask: 0.2564  loss_rpn_cls: 0.01394  loss_rpn_loc: 0.1034  time: 0.5192  data_time: 0.0479  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:38:40 d2.utils.events]: \u001b[0m eta: 0:27:35  iter: 6699  total_loss: 0.8884  loss_cls: 0.2053  loss_box_reg: 0.2807  loss_mask: 0.2513  loss_rpn_cls: 0.01651  loss_rpn_loc: 0.1155  time: 0.5193  data_time: 0.0657  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:38:50 d2.utils.events]: \u001b[0m eta: 0:27:24  iter: 6719  total_loss: 0.8553  loss_cls: 0.2114  loss_box_reg: 0.3173  loss_mask: 0.2459  loss_rpn_cls: 0.01666  loss_rpn_loc: 0.1044  time: 0.5192  data_time: 0.0315  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:00 d2.utils.events]: \u001b[0m eta: 0:27:14  iter: 6739  total_loss: 0.8969  loss_cls: 0.2008  loss_box_reg: 0.3189  loss_mask: 0.2608  loss_rpn_cls: 0.01485  loss_rpn_loc: 0.1066  time: 0.5192  data_time: 0.0603  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:11 d2.utils.events]: \u001b[0m eta: 0:27:05  iter: 6759  total_loss: 0.9502  loss_cls: 0.2399  loss_box_reg: 0.3244  loss_mask: 0.2497  loss_rpn_cls: 0.01406  loss_rpn_loc: 0.119  time: 0.5192  data_time: 0.0463  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:22 d2.utils.events]: \u001b[0m eta: 0:26:54  iter: 6779  total_loss: 0.8838  loss_cls: 0.2346  loss_box_reg: 0.2812  loss_mask: 0.2521  loss_rpn_cls: 0.0142  loss_rpn_loc: 0.1075  time: 0.5193  data_time: 0.0534  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:33 d2.utils.events]: \u001b[0m eta: 0:26:45  iter: 6799  total_loss: 0.8745  loss_cls: 0.225  loss_box_reg: 0.3044  loss_mask: 0.232  loss_rpn_cls: 0.00733  loss_rpn_loc: 0.1091  time: 0.5195  data_time: 0.0803  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:43 d2.utils.events]: \u001b[0m eta: 0:26:34  iter: 6819  total_loss: 0.9203  loss_cls: 0.2078  loss_box_reg: 0.2956  loss_mask: 0.2451  loss_rpn_cls: 0.01604  loss_rpn_loc: 0.103  time: 0.5193  data_time: 0.0240  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:39:53 d2.utils.events]: \u001b[0m eta: 0:26:27  iter: 6839  total_loss: 0.8174  loss_cls: 0.1967  loss_box_reg: 0.2841  loss_mask: 0.2574  loss_rpn_cls: 0.01286  loss_rpn_loc: 0.1041  time: 0.5194  data_time: 0.0145  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:04 d2.utils.events]: \u001b[0m eta: 0:26:18  iter: 6859  total_loss: 0.8816  loss_cls: 0.2058  loss_box_reg: 0.2915  loss_mask: 0.2346  loss_rpn_cls: 0.01536  loss_rpn_loc: 0.1083  time: 0.5195  data_time: 0.0138  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:15 d2.utils.events]: \u001b[0m eta: 0:26:08  iter: 6879  total_loss: 0.8565  loss_cls: 0.1946  loss_box_reg: 0.2821  loss_mask: 0.2424  loss_rpn_cls: 0.01713  loss_rpn_loc: 0.1159  time: 0.5194  data_time: 0.0251  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:25 d2.utils.events]: \u001b[0m eta: 0:25:54  iter: 6899  total_loss: 0.8748  loss_cls: 0.2026  loss_box_reg: 0.2842  loss_mask: 0.2397  loss_rpn_cls: 0.01037  loss_rpn_loc: 0.09878  time: 0.5195  data_time: 0.0290  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:36 d2.utils.events]: \u001b[0m eta: 0:25:47  iter: 6919  total_loss: 0.8063  loss_cls: 0.1968  loss_box_reg: 0.2681  loss_mask: 0.2243  loss_rpn_cls: 0.01624  loss_rpn_loc: 0.1015  time: 0.5196  data_time: 0.0736  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:47 d2.utils.events]: \u001b[0m eta: 0:25:40  iter: 6939  total_loss: 0.9121  loss_cls: 0.1999  loss_box_reg: 0.3039  loss_mask: 0.2733  loss_rpn_cls: 0.01345  loss_rpn_loc: 0.1053  time: 0.5196  data_time: 0.0422  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:40:58 d2.utils.events]: \u001b[0m eta: 0:25:38  iter: 6959  total_loss: 0.9852  loss_cls: 0.229  loss_box_reg: 0.3085  loss_mask: 0.2694  loss_rpn_cls: 0.01614  loss_rpn_loc: 0.1134  time: 0.5196  data_time: 0.0064  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:41:08 d2.utils.events]: \u001b[0m eta: 0:25:27  iter: 6979  total_loss: 0.8406  loss_cls: 0.2024  loss_box_reg: 0.2933  loss_mask: 0.2521  loss_rpn_cls: 0.01495  loss_rpn_loc: 0.112  time: 0.5196  data_time: 0.0214  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:41:20 d2.utils.events]: \u001b[0m eta: 0:25:31  iter: 6999  total_loss: 0.8585  loss_cls: 0.2364  loss_box_reg: 0.2848  loss_mask: 0.252  loss_rpn_cls: 0.0102  loss_rpn_loc: 0.09766  time: 0.5198  data_time: 0.0789  lr: 0.0001  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:41:31 d2.utils.events]: \u001b[0m eta: 0:25:21  iter: 7019  total_loss: 0.8861  loss_cls: 0.2051  loss_box_reg: 0.2804  loss_mask: 0.2291  loss_rpn_cls: 0.01453  loss_rpn_loc: 0.1012  time: 0.5199  data_time: 0.0710  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:41:41 d2.utils.events]: \u001b[0m eta: 0:25:12  iter: 7039  total_loss: 0.8685  loss_cls: 0.2292  loss_box_reg: 0.2965  loss_mask: 0.2402  loss_rpn_cls: 0.009617  loss_rpn_loc: 0.09865  time: 0.5199  data_time: 0.0304  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:41:52 d2.utils.events]: \u001b[0m eta: 0:25:01  iter: 7059  total_loss: 0.8458  loss_cls: 0.215  loss_box_reg: 0.3  loss_mask: 0.2403  loss_rpn_cls: 0.02028  loss_rpn_loc: 0.1112  time: 0.5200  data_time: 0.0583  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:03 d2.utils.events]: \u001b[0m eta: 0:24:52  iter: 7079  total_loss: 0.8777  loss_cls: 0.1972  loss_box_reg: 0.3038  loss_mask: 0.254  loss_rpn_cls: 0.01245  loss_rpn_loc: 0.1033  time: 0.5200  data_time: 0.0050  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:14 d2.utils.events]: \u001b[0m eta: 0:24:37  iter: 7099  total_loss: 0.8871  loss_cls: 0.2103  loss_box_reg: 0.3006  loss_mask: 0.2532  loss_rpn_cls: 0.01855  loss_rpn_loc: 0.1011  time: 0.5202  data_time: 0.1069  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:24 d2.utils.events]: \u001b[0m eta: 0:24:29  iter: 7119  total_loss: 0.8547  loss_cls: 0.2118  loss_box_reg: 0.2873  loss_mask: 0.2487  loss_rpn_cls: 0.01175  loss_rpn_loc: 0.1058  time: 0.5201  data_time: 0.0045  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:36 d2.utils.events]: \u001b[0m eta: 0:24:22  iter: 7139  total_loss: 0.8204  loss_cls: 0.1935  loss_box_reg: 0.2736  loss_mask: 0.2419  loss_rpn_cls: 0.009778  loss_rpn_loc: 0.1045  time: 0.5203  data_time: 0.0657  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:46 d2.utils.events]: \u001b[0m eta: 0:24:10  iter: 7159  total_loss: 0.8601  loss_cls: 0.2283  loss_box_reg: 0.2902  loss_mask: 0.2469  loss_rpn_cls: 0.0154  loss_rpn_loc: 0.1129  time: 0.5202  data_time: 0.0209  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:42:57 d2.utils.events]: \u001b[0m eta: 0:23:57  iter: 7179  total_loss: 0.8523  loss_cls: 0.2063  loss_box_reg: 0.2934  loss_mask: 0.2555  loss_rpn_cls: 0.01295  loss_rpn_loc: 0.1096  time: 0.5203  data_time: 0.0596  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:43:08 d2.utils.events]: \u001b[0m eta: 0:23:51  iter: 7199  total_loss: 0.9492  loss_cls: 0.2123  loss_box_reg: 0.3145  loss_mask: 0.2529  loss_rpn_cls: 0.01472  loss_rpn_loc: 0.111  time: 0.5204  data_time: 0.0410  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:43:18 d2.utils.events]: \u001b[0m eta: 0:23:39  iter: 7219  total_loss: 0.8651  loss_cls: 0.2242  loss_box_reg: 0.2665  loss_mask: 0.2413  loss_rpn_cls: 0.01527  loss_rpn_loc: 0.1088  time: 0.5203  data_time: 0.0405  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:43:29 d2.utils.events]: \u001b[0m eta: 0:23:37  iter: 7239  total_loss: 0.9896  loss_cls: 0.2431  loss_box_reg: 0.313  loss_mask: 0.2427  loss_rpn_cls: 0.01503  loss_rpn_loc: 0.1115  time: 0.5204  data_time: 0.0361  lr: 1e-05  max_mem: 16994M\n"
          ]
        },
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          "output_type": "stream",
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            "\u001b[32m[03/01 20:43:40 d2.utils.events]: \u001b[0m eta: 0:23:24  iter: 7259  total_loss: 0.8414  loss_cls: 0.199  loss_box_reg: 0.2774  loss_mask: 0.2361  loss_rpn_cls: 0.01858  loss_rpn_loc: 0.1  time: 0.5204  data_time: 0.0633  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:43:50 d2.utils.events]: \u001b[0m eta: 0:23:09  iter: 7279  total_loss: 0.8847  loss_cls: 0.2267  loss_box_reg: 0.2997  loss_mask: 0.2438  loss_rpn_cls: 0.02014  loss_rpn_loc: 0.1179  time: 0.5205  data_time: 0.0475  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:01 d2.utils.events]: \u001b[0m eta: 0:22:56  iter: 7299  total_loss: 0.8687  loss_cls: 0.2286  loss_box_reg: 0.2974  loss_mask: 0.2467  loss_rpn_cls: 0.009317  loss_rpn_loc: 0.101  time: 0.5205  data_time: 0.0357  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:11 d2.utils.events]: \u001b[0m eta: 0:22:46  iter: 7319  total_loss: 0.9549  loss_cls: 0.2256  loss_box_reg: 0.3016  loss_mask: 0.2534  loss_rpn_cls: 0.01327  loss_rpn_loc: 0.09869  time: 0.5205  data_time: 0.0271  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:21 d2.utils.events]: \u001b[0m eta: 0:22:35  iter: 7339  total_loss: 0.8553  loss_cls: 0.2425  loss_box_reg: 0.2921  loss_mask: 0.2439  loss_rpn_cls: 0.01807  loss_rpn_loc: 0.0993  time: 0.5204  data_time: 0.0094  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:32 d2.utils.events]: \u001b[0m eta: 0:22:26  iter: 7359  total_loss: 0.7966  loss_cls: 0.1711  loss_box_reg: 0.2587  loss_mask: 0.2398  loss_rpn_cls: 0.007363  loss_rpn_loc: 0.1024  time: 0.5204  data_time: 0.0196  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:42 d2.utils.events]: \u001b[0m eta: 0:22:13  iter: 7379  total_loss: 0.7662  loss_cls: 0.1809  loss_box_reg: 0.2614  loss_mask: 0.2306  loss_rpn_cls: 0.008136  loss_rpn_loc: 0.09668  time: 0.5204  data_time: 0.0114  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:44:52 d2.utils.events]: \u001b[0m eta: 0:22:05  iter: 7399  total_loss: 0.8454  loss_cls: 0.2104  loss_box_reg: 0.2959  loss_mask: 0.2499  loss_rpn_cls: 0.006531  loss_rpn_loc: 0.09484  time: 0.5204  data_time: 0.0523  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:02 d2.utils.events]: \u001b[0m eta: 0:21:50  iter: 7419  total_loss: 0.8471  loss_cls: 0.225  loss_box_reg: 0.2961  loss_mask: 0.2532  loss_rpn_cls: 0.01179  loss_rpn_loc: 0.1014  time: 0.5204  data_time: 0.0120  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:13 d2.utils.events]: \u001b[0m eta: 0:21:42  iter: 7439  total_loss: 0.9097  loss_cls: 0.2067  loss_box_reg: 0.3153  loss_mask: 0.2386  loss_rpn_cls: 0.008241  loss_rpn_loc: 0.1004  time: 0.5204  data_time: 0.0480  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:24 d2.utils.events]: \u001b[0m eta: 0:21:29  iter: 7459  total_loss: 0.9273  loss_cls: 0.2489  loss_box_reg: 0.3205  loss_mask: 0.2575  loss_rpn_cls: 0.01179  loss_rpn_loc: 0.1112  time: 0.5205  data_time: 0.0370  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:34 d2.utils.events]: \u001b[0m eta: 0:21:13  iter: 7479  total_loss: 0.8634  loss_cls: 0.2163  loss_box_reg: 0.2833  loss_mask: 0.2564  loss_rpn_cls: 0.01466  loss_rpn_loc: 0.1024  time: 0.5204  data_time: 0.0293  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:45 d2.utils.events]: \u001b[0m eta: 0:21:03  iter: 7499  total_loss: 0.8391  loss_cls: 0.2024  loss_box_reg: 0.2673  loss_mask: 0.2334  loss_rpn_cls: 0.007882  loss_rpn_loc: 0.09527  time: 0.5204  data_time: 0.0471  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:45:56 d2.utils.events]: \u001b[0m eta: 0:20:53  iter: 7519  total_loss: 0.8571  loss_cls: 0.1874  loss_box_reg: 0.2912  loss_mask: 0.2481  loss_rpn_cls: 0.01129  loss_rpn_loc: 0.09932  time: 0.5205  data_time: 0.0736  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:07 d2.utils.events]: \u001b[0m eta: 0:20:46  iter: 7539  total_loss: 0.9475  loss_cls: 0.2232  loss_box_reg: 0.3136  loss_mask: 0.276  loss_rpn_cls: 0.01789  loss_rpn_loc: 0.1053  time: 0.5206  data_time: 0.0384  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:17 d2.utils.events]: \u001b[0m eta: 0:20:36  iter: 7559  total_loss: 0.8727  loss_cls: 0.1989  loss_box_reg: 0.2907  loss_mask: 0.2394  loss_rpn_cls: 0.01657  loss_rpn_loc: 0.1091  time: 0.5206  data_time: 0.0222  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:28 d2.utils.events]: \u001b[0m eta: 0:20:29  iter: 7579  total_loss: 0.9417  loss_cls: 0.2055  loss_box_reg: 0.3083  loss_mask: 0.258  loss_rpn_cls: 0.0125  loss_rpn_loc: 0.1111  time: 0.5206  data_time: 0.0152  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:38 d2.utils.events]: \u001b[0m eta: 0:20:25  iter: 7599  total_loss: 0.9149  loss_cls: 0.2254  loss_box_reg: 0.3089  loss_mask: 0.2527  loss_rpn_cls: 0.02103  loss_rpn_loc: 0.1035  time: 0.5206  data_time: 0.0066  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:49 d2.utils.events]: \u001b[0m eta: 0:20:13  iter: 7619  total_loss: 0.8005  loss_cls: 0.1912  loss_box_reg: 0.2911  loss_mask: 0.2414  loss_rpn_cls: 0.01047  loss_rpn_loc: 0.1044  time: 0.5207  data_time: 0.0505  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:46:59 d2.utils.events]: \u001b[0m eta: 0:19:55  iter: 7639  total_loss: 0.8469  loss_cls: 0.2247  loss_box_reg: 0.3005  loss_mask: 0.2301  loss_rpn_cls: 0.01185  loss_rpn_loc: 0.09262  time: 0.5206  data_time: 0.0333  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:47:09 d2.utils.events]: \u001b[0m eta: 0:19:48  iter: 7659  total_loss: 0.8939  loss_cls: 0.2018  loss_box_reg: 0.2913  loss_mask: 0.2408  loss_rpn_cls: 0.01348  loss_rpn_loc: 0.105  time: 0.5206  data_time: 0.0143  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:47:20 d2.utils.events]: \u001b[0m eta: 0:19:37  iter: 7679  total_loss: 0.9185  loss_cls: 0.2213  loss_box_reg: 0.2997  loss_mask: 0.2573  loss_rpn_cls: 0.01297  loss_rpn_loc: 0.1129  time: 0.5206  data_time: 0.0645  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:47:30 d2.utils.events]: \u001b[0m eta: 0:19:33  iter: 7699  total_loss: 0.9198  loss_cls: 0.2078  loss_box_reg: 0.3233  loss_mask: 0.2495  loss_rpn_cls: 0.01834  loss_rpn_loc: 0.1116  time: 0.5206  data_time: 0.0299  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:47:41 d2.utils.events]: \u001b[0m eta: 0:19:20  iter: 7719  total_loss: 0.8972  loss_cls: 0.2182  loss_box_reg: 0.2951  loss_mask: 0.2471  loss_rpn_cls: 0.02054  loss_rpn_loc: 0.1121  time: 0.5206  data_time: 0.0461  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:47:51 d2.utils.events]: \u001b[0m eta: 0:19:07  iter: 7739  total_loss: 0.8174  loss_cls: 0.1903  loss_box_reg: 0.2925  loss_mask: 0.2446  loss_rpn_cls: 0.009521  loss_rpn_loc: 0.09425  time: 0.5206  data_time: 0.0357  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:01 d2.utils.events]: \u001b[0m eta: 0:19:00  iter: 7759  total_loss: 0.8855  loss_cls: 0.1986  loss_box_reg: 0.3035  loss_mask: 0.2448  loss_rpn_cls: 0.01184  loss_rpn_loc: 0.1041  time: 0.5206  data_time: 0.0215  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:13 d2.utils.events]: \u001b[0m eta: 0:18:51  iter: 7779  total_loss: 0.845  loss_cls: 0.2067  loss_box_reg: 0.2951  loss_mask: 0.2518  loss_rpn_cls: 0.01196  loss_rpn_loc: 0.1044  time: 0.5208  data_time: 0.0915  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:25 d2.utils.events]: \u001b[0m eta: 0:18:44  iter: 7799  total_loss: 0.8494  loss_cls: 0.2141  loss_box_reg: 0.269  loss_mask: 0.2345  loss_rpn_cls: 0.01056  loss_rpn_loc: 0.1006  time: 0.5209  data_time: 0.0464  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:35 d2.utils.events]: \u001b[0m eta: 0:18:35  iter: 7819  total_loss: 0.916  loss_cls: 0.2307  loss_box_reg: 0.3074  loss_mask: 0.246  loss_rpn_cls: 0.01228  loss_rpn_loc: 0.1019  time: 0.5209  data_time: 0.0281  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:46 d2.utils.events]: \u001b[0m eta: 0:18:24  iter: 7839  total_loss: 0.8494  loss_cls: 0.2042  loss_box_reg: 0.2821  loss_mask: 0.2454  loss_rpn_cls: 0.01089  loss_rpn_loc: 0.09856  time: 0.5209  data_time: 0.0257  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:48:56 d2.utils.events]: \u001b[0m eta: 0:18:07  iter: 7859  total_loss: 0.8741  loss_cls: 0.2094  loss_box_reg: 0.3085  loss_mask: 0.2401  loss_rpn_cls: 0.01911  loss_rpn_loc: 0.1115  time: 0.5210  data_time: 0.0603  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:49:07 d2.utils.events]: \u001b[0m eta: 0:18:00  iter: 7879  total_loss: 0.8278  loss_cls: 0.1807  loss_box_reg: 0.2814  loss_mask: 0.2363  loss_rpn_cls: 0.01213  loss_rpn_loc: 0.09571  time: 0.5210  data_time: 0.0295  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:49:18 d2.utils.events]: \u001b[0m eta: 0:17:50  iter: 7899  total_loss: 0.8586  loss_cls: 0.2362  loss_box_reg: 0.2772  loss_mask: 0.2423  loss_rpn_cls: 0.01583  loss_rpn_loc: 0.09766  time: 0.5210  data_time: 0.0211  lr: 1e-05  max_mem: 16994M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:49:28 d2.utils.events]: \u001b[0m eta: 0:17:38  iter: 7919  total_loss: 0.9357  loss_cls: 0.2305  loss_box_reg: 0.315  loss_mask: 0.2552  loss_rpn_cls: 0.01552  loss_rpn_loc: 0.1078  time: 0.5210  data_time: 0.0165  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:49:39 d2.utils.events]: \u001b[0m eta: 0:17:26  iter: 7939  total_loss: 0.8308  loss_cls: 0.2041  loss_box_reg: 0.2834  loss_mask: 0.2421  loss_rpn_cls: 0.005664  loss_rpn_loc: 0.09904  time: 0.5211  data_time: 0.0617  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:49:49 d2.utils.events]: \u001b[0m eta: 0:17:10  iter: 7959  total_loss: 0.833  loss_cls: 0.193  loss_box_reg: 0.2761  loss_mask: 0.2353  loss_rpn_cls: 0.01586  loss_rpn_loc: 0.1064  time: 0.5211  data_time: 0.0357  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:50:00 d2.utils.events]: \u001b[0m eta: 0:17:00  iter: 7979  total_loss: 0.8589  loss_cls: 0.1882  loss_box_reg: 0.2782  loss_mask: 0.2472  loss_rpn_cls: 0.009512  loss_rpn_loc: 0.1122  time: 0.5210  data_time: 0.0352  lr: 1e-05  max_mem: 16994M\n",
            "\u001b[32m[03/01 20:50:11 d2.utils.events]: \u001b[0m eta: 0:16:47  iter: 7999  total_loss: 0.9052  loss_cls: 0.1993  loss_box_reg: 0.2911  loss_mask: 0.2552  loss_rpn_cls: 0.02046  loss_rpn_loc: 0.1132  time: 0.5210  data_time: 0.0080  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:50:21 d2.utils.events]: \u001b[0m eta: 0:16:38  iter: 8019  total_loss: 0.881  loss_cls: 0.1998  loss_box_reg: 0.2944  loss_mask: 0.249  loss_rpn_cls: 0.01463  loss_rpn_loc: 0.1085  time: 0.5209  data_time: 0.0060  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:50:31 d2.utils.events]: \u001b[0m eta: 0:16:30  iter: 8039  total_loss: 0.8365  loss_cls: 0.1885  loss_box_reg: 0.2877  loss_mask: 0.2525  loss_rpn_cls: 0.007819  loss_rpn_loc: 0.09966  time: 0.5209  data_time: 0.0222  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:50:42 d2.utils.events]: \u001b[0m eta: 0:16:20  iter: 8059  total_loss: 1.001  loss_cls: 0.2282  loss_box_reg: 0.338  loss_mask: 0.2837  loss_rpn_cls: 0.009845  loss_rpn_loc: 0.1077  time: 0.5209  data_time: 0.0441  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:50:52 d2.utils.events]: \u001b[0m eta: 0:16:07  iter: 8079  total_loss: 0.8952  loss_cls: 0.2167  loss_box_reg: 0.2986  loss_mask: 0.26  loss_rpn_cls: 0.0159  loss_rpn_loc: 0.1043  time: 0.5209  data_time: 0.0332  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:03 d2.utils.events]: \u001b[0m eta: 0:15:58  iter: 8099  total_loss: 0.7947  loss_cls: 0.1826  loss_box_reg: 0.2678  loss_mask: 0.2403  loss_rpn_cls: 0.008093  loss_rpn_loc: 0.09449  time: 0.5209  data_time: 0.0269  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:12 d2.utils.events]: \u001b[0m eta: 0:15:44  iter: 8119  total_loss: 0.9389  loss_cls: 0.2351  loss_box_reg: 0.317  loss_mask: 0.2561  loss_rpn_cls: 0.02074  loss_rpn_loc: 0.1125  time: 0.5208  data_time: 0.0198  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:22 d2.utils.events]: \u001b[0m eta: 0:15:34  iter: 8139  total_loss: 0.8943  loss_cls: 0.2067  loss_box_reg: 0.3169  loss_mask: 0.2472  loss_rpn_cls: 0.01485  loss_rpn_loc: 0.1068  time: 0.5208  data_time: 0.0332  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:33 d2.utils.events]: \u001b[0m eta: 0:15:24  iter: 8159  total_loss: 0.8927  loss_cls: 0.2469  loss_box_reg: 0.2869  loss_mask: 0.2463  loss_rpn_cls: 0.01595  loss_rpn_loc: 0.1196  time: 0.5208  data_time: 0.0121  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:44 d2.utils.events]: \u001b[0m eta: 0:15:17  iter: 8179  total_loss: 0.8884  loss_cls: 0.2167  loss_box_reg: 0.2887  loss_mask: 0.2369  loss_rpn_cls: 0.01907  loss_rpn_loc: 0.09131  time: 0.5209  data_time: 0.0807  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:51:56 d2.utils.events]: \u001b[0m eta: 0:15:09  iter: 8199  total_loss: 0.8887  loss_cls: 0.2092  loss_box_reg: 0.3047  loss_mask: 0.2385  loss_rpn_cls: 0.01762  loss_rpn_loc: 0.1123  time: 0.5210  data_time: 0.0444  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:52:06 d2.utils.events]: \u001b[0m eta: 0:15:02  iter: 8219  total_loss: 0.8292  loss_cls: 0.1622  loss_box_reg: 0.2767  loss_mask: 0.2536  loss_rpn_cls: 0.01247  loss_rpn_loc: 0.09463  time: 0.5210  data_time: 0.0166  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:52:18 d2.utils.events]: \u001b[0m eta: 0:14:48  iter: 8239  total_loss: 0.8008  loss_cls: 0.185  loss_box_reg: 0.2676  loss_mask: 0.2351  loss_rpn_cls: 0.01056  loss_rpn_loc: 0.0973  time: 0.5212  data_time: 0.0996  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:52:30 d2.utils.events]: \u001b[0m eta: 0:14:38  iter: 8259  total_loss: 0.8635  loss_cls: 0.1931  loss_box_reg: 0.293  loss_mask: 0.2456  loss_rpn_cls: 0.01527  loss_rpn_loc: 0.1081  time: 0.5213  data_time: 0.0637  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:52:41 d2.utils.events]: \u001b[0m eta: 0:14:30  iter: 8279  total_loss: 0.997  loss_cls: 0.252  loss_box_reg: 0.3285  loss_mask: 0.2527  loss_rpn_cls: 0.02377  loss_rpn_loc: 0.1193  time: 0.5214  data_time: 0.0599  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:52:51 d2.utils.events]: \u001b[0m eta: 0:14:22  iter: 8299  total_loss: 0.9582  loss_cls: 0.2339  loss_box_reg: 0.3248  loss_mask: 0.2654  loss_rpn_cls: 0.01418  loss_rpn_loc: 0.1085  time: 0.5214  data_time: 0.0465  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:01 d2.utils.events]: \u001b[0m eta: 0:14:09  iter: 8319  total_loss: 0.7651  loss_cls: 0.1493  loss_box_reg: 0.2599  loss_mask: 0.2275  loss_rpn_cls: 0.005636  loss_rpn_loc: 0.0894  time: 0.5214  data_time: 0.0071  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:11 d2.utils.events]: \u001b[0m eta: 0:13:59  iter: 8339  total_loss: 0.8314  loss_cls: 0.1838  loss_box_reg: 0.2873  loss_mask: 0.241  loss_rpn_cls: 0.01367  loss_rpn_loc: 0.1064  time: 0.5213  data_time: 0.0122  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:21 d2.utils.events]: \u001b[0m eta: 0:13:49  iter: 8359  total_loss: 0.8164  loss_cls: 0.1956  loss_box_reg: 0.2905  loss_mask: 0.2411  loss_rpn_cls: 0.009378  loss_rpn_loc: 0.09646  time: 0.5213  data_time: 0.0052  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:31 d2.utils.events]: \u001b[0m eta: 0:13:38  iter: 8379  total_loss: 0.8791  loss_cls: 0.2061  loss_box_reg: 0.2899  loss_mask: 0.2566  loss_rpn_cls: 0.01242  loss_rpn_loc: 0.1037  time: 0.5212  data_time: 0.0270  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:41 d2.utils.events]: \u001b[0m eta: 0:13:27  iter: 8399  total_loss: 0.9127  loss_cls: 0.2142  loss_box_reg: 0.3036  loss_mask: 0.2218  loss_rpn_cls: 0.008917  loss_rpn_loc: 0.1074  time: 0.5211  data_time: 0.0419  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:53:51 d2.utils.events]: \u001b[0m eta: 0:13:18  iter: 8419  total_loss: 0.911  loss_cls: 0.2149  loss_box_reg: 0.2752  loss_mask: 0.2521  loss_rpn_cls: 0.02076  loss_rpn_loc: 0.1121  time: 0.5210  data_time: 0.0323  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:01 d2.utils.events]: \u001b[0m eta: 0:13:07  iter: 8439  total_loss: 0.8762  loss_cls: 0.221  loss_box_reg: 0.3094  loss_mask: 0.2376  loss_rpn_cls: 0.0152  loss_rpn_loc: 0.1035  time: 0.5210  data_time: 0.0322  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:11 d2.utils.events]: \u001b[0m eta: 0:12:57  iter: 8459  total_loss: 0.8548  loss_cls: 0.2127  loss_box_reg: 0.2833  loss_mask: 0.2441  loss_rpn_cls: 0.01054  loss_rpn_loc: 0.101  time: 0.5210  data_time: 0.0105  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:22 d2.utils.events]: \u001b[0m eta: 0:12:47  iter: 8479  total_loss: 0.802  loss_cls: 0.1797  loss_box_reg: 0.2699  loss_mask: 0.2437  loss_rpn_cls: 0.01471  loss_rpn_loc: 0.09633  time: 0.5211  data_time: 0.0779  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:32 d2.utils.events]: \u001b[0m eta: 0:12:37  iter: 8499  total_loss: 0.8616  loss_cls: 0.2073  loss_box_reg: 0.2746  loss_mask: 0.2528  loss_rpn_cls: 0.01306  loss_rpn_loc: 0.1011  time: 0.5210  data_time: 0.0071  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:42 d2.utils.events]: \u001b[0m eta: 0:12:27  iter: 8519  total_loss: 0.8724  loss_cls: 0.2175  loss_box_reg: 0.2841  loss_mask: 0.2491  loss_rpn_cls: 0.008545  loss_rpn_loc: 0.1008  time: 0.5209  data_time: 0.0109  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:54:52 d2.utils.events]: \u001b[0m eta: 0:12:16  iter: 8539  total_loss: 0.8732  loss_cls: 0.2064  loss_box_reg: 0.2724  loss_mask: 0.2356  loss_rpn_cls: 0.01532  loss_rpn_loc: 0.09612  time: 0.5208  data_time: 0.0075  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:55:01 d2.utils.events]: \u001b[0m eta: 0:12:04  iter: 8559  total_loss: 0.8753  loss_cls: 0.225  loss_box_reg: 0.3001  loss_mask: 0.2449  loss_rpn_cls: 0.01624  loss_rpn_loc: 0.1044  time: 0.5208  data_time: 0.0045  lr: 1e-05  max_mem: 17403M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 20:55:12 d2.utils.events]: \u001b[0m eta: 0:11:53  iter: 8579  total_loss: 0.786  loss_cls: 0.182  loss_box_reg: 0.2651  loss_mask: 0.2435  loss_rpn_cls: 0.009943  loss_rpn_loc: 0.09846  time: 0.5208  data_time: 0.0244  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:55:23 d2.utils.events]: \u001b[0m eta: 0:11:43  iter: 8599  total_loss: 0.9223  loss_cls: 0.2155  loss_box_reg: 0.301  loss_mask: 0.2481  loss_rpn_cls: 0.01621  loss_rpn_loc: 0.1064  time: 0.5209  data_time: 0.0880  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:55:34 d2.utils.events]: \u001b[0m eta: 0:11:33  iter: 8619  total_loss: 0.9147  loss_cls: 0.2109  loss_box_reg: 0.3127  loss_mask: 0.2494  loss_rpn_cls: 0.008284  loss_rpn_loc: 0.1072  time: 0.5209  data_time: 0.0433  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:55:44 d2.utils.events]: \u001b[0m eta: 0:11:26  iter: 8639  total_loss: 0.8661  loss_cls: 0.2134  loss_box_reg: 0.2995  loss_mask: 0.2558  loss_rpn_cls: 0.00829  loss_rpn_loc: 0.09709  time: 0.5209  data_time: 0.0074  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:55:54 d2.utils.events]: \u001b[0m eta: 0:11:16  iter: 8659  total_loss: 0.8493  loss_cls: 0.1997  loss_box_reg: 0.2761  loss_mask: 0.235  loss_rpn_cls: 0.0147  loss_rpn_loc: 0.09955  time: 0.5208  data_time: 0.0092  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:04 d2.utils.events]: \u001b[0m eta: 0:11:06  iter: 8679  total_loss: 0.8959  loss_cls: 0.2284  loss_box_reg: 0.2967  loss_mask: 0.2755  loss_rpn_cls: 0.01901  loss_rpn_loc: 0.1049  time: 0.5208  data_time: 0.0125  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:15 d2.utils.events]: \u001b[0m eta: 0:10:56  iter: 8699  total_loss: 0.9308  loss_cls: 0.1997  loss_box_reg: 0.3081  loss_mask: 0.2667  loss_rpn_cls: 0.01429  loss_rpn_loc: 0.1064  time: 0.5209  data_time: 0.0567  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:27 d2.utils.events]: \u001b[0m eta: 0:10:50  iter: 8719  total_loss: 0.9519  loss_cls: 0.2525  loss_box_reg: 0.3001  loss_mask: 0.2477  loss_rpn_cls: 0.02025  loss_rpn_loc: 0.09756  time: 0.5210  data_time: 0.0733  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:38 d2.utils.events]: \u001b[0m eta: 0:10:43  iter: 8739  total_loss: 0.8616  loss_cls: 0.2075  loss_box_reg: 0.2654  loss_mask: 0.2455  loss_rpn_cls: 0.01366  loss_rpn_loc: 0.09905  time: 0.5210  data_time: 0.0622  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:48 d2.utils.events]: \u001b[0m eta: 0:10:33  iter: 8759  total_loss: 0.8898  loss_cls: 0.1905  loss_box_reg: 0.285  loss_mask: 0.2497  loss_rpn_cls: 0.01726  loss_rpn_loc: 0.1111  time: 0.5211  data_time: 0.0135  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:56:59 d2.utils.events]: \u001b[0m eta: 0:10:22  iter: 8779  total_loss: 0.8825  loss_cls: 0.2291  loss_box_reg: 0.2912  loss_mask: 0.2392  loss_rpn_cls: 0.008988  loss_rpn_loc: 0.1081  time: 0.5210  data_time: 0.0092  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:57:09 d2.utils.events]: \u001b[0m eta: 0:10:08  iter: 8799  total_loss: 0.8866  loss_cls: 0.2124  loss_box_reg: 0.2906  loss_mask: 0.2599  loss_rpn_cls: 0.01387  loss_rpn_loc: 0.106  time: 0.5210  data_time: 0.0066  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:57:19 d2.utils.events]: \u001b[0m eta: 0:09:59  iter: 8819  total_loss: 0.8832  loss_cls: 0.2042  loss_box_reg: 0.2836  loss_mask: 0.2624  loss_rpn_cls: 0.0135  loss_rpn_loc: 0.09791  time: 0.5210  data_time: 0.0056  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:57:29 d2.utils.events]: \u001b[0m eta: 0:09:45  iter: 8839  total_loss: 1.003  loss_cls: 0.2531  loss_box_reg: 0.3506  loss_mask: 0.2669  loss_rpn_cls: 0.02636  loss_rpn_loc: 0.1144  time: 0.5209  data_time: 0.0227  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:57:40 d2.utils.events]: \u001b[0m eta: 0:09:35  iter: 8859  total_loss: 0.8365  loss_cls: 0.2226  loss_box_reg: 0.2791  loss_mask: 0.2382  loss_rpn_cls: 0.01786  loss_rpn_loc: 0.1048  time: 0.5210  data_time: 0.0717  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:57:51 d2.utils.events]: \u001b[0m eta: 0:09:25  iter: 8879  total_loss: 0.8993  loss_cls: 0.201  loss_box_reg: 0.2882  loss_mask: 0.25  loss_rpn_cls: 0.01305  loss_rpn_loc: 0.1076  time: 0.5211  data_time: 0.0575  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:00 d2.utils.events]: \u001b[0m eta: 0:09:14  iter: 8899  total_loss: 0.9288  loss_cls: 0.2375  loss_box_reg: 0.3188  loss_mask: 0.2607  loss_rpn_cls: 0.01614  loss_rpn_loc: 0.1114  time: 0.5210  data_time: 0.0093  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:11 d2.utils.events]: \u001b[0m eta: 0:09:04  iter: 8919  total_loss: 0.8965  loss_cls: 0.2  loss_box_reg: 0.3013  loss_mask: 0.2592  loss_rpn_cls: 0.02232  loss_rpn_loc: 0.1075  time: 0.5210  data_time: 0.0342  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:21 d2.utils.events]: \u001b[0m eta: 0:08:52  iter: 8939  total_loss: 0.8699  loss_cls: 0.2044  loss_box_reg: 0.2863  loss_mask: 0.2565  loss_rpn_cls: 0.008904  loss_rpn_loc: 0.1003  time: 0.5209  data_time: 0.0047  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:32 d2.utils.events]: \u001b[0m eta: 0:08:44  iter: 8959  total_loss: 0.8653  loss_cls: 0.1808  loss_box_reg: 0.3038  loss_mask: 0.2614  loss_rpn_cls: 0.01757  loss_rpn_loc: 0.1093  time: 0.5210  data_time: 0.0570  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:41 d2.utils.events]: \u001b[0m eta: 0:08:32  iter: 8979  total_loss: 0.7717  loss_cls: 0.1876  loss_box_reg: 0.2585  loss_mask: 0.2306  loss_rpn_cls: 0.009393  loss_rpn_loc: 0.0929  time: 0.5209  data_time: 0.0147  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:58:51 d2.utils.events]: \u001b[0m eta: 0:08:24  iter: 8999  total_loss: 0.9004  loss_cls: 0.1962  loss_box_reg: 0.3022  loss_mask: 0.2582  loss_rpn_cls: 0.01592  loss_rpn_loc: 0.1094  time: 0.5208  data_time: 0.0075  lr: 1e-05  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:02 d2.utils.events]: \u001b[0m eta: 0:08:14  iter: 9019  total_loss: 0.8828  loss_cls: 0.2275  loss_box_reg: 0.2962  loss_mask: 0.2477  loss_rpn_cls: 0.01646  loss_rpn_loc: 0.09939  time: 0.5208  data_time: 0.0109  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:13 d2.utils.events]: \u001b[0m eta: 0:08:04  iter: 9039  total_loss: 0.818  loss_cls: 0.1921  loss_box_reg: 0.2866  loss_mask: 0.2431  loss_rpn_cls: 0.01314  loss_rpn_loc: 0.09516  time: 0.5209  data_time: 0.0362  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:22 d2.utils.events]: \u001b[0m eta: 0:07:54  iter: 9059  total_loss: 0.9307  loss_cls: 0.2283  loss_box_reg: 0.3046  loss_mask: 0.2563  loss_rpn_cls: 0.01676  loss_rpn_loc: 0.1054  time: 0.5208  data_time: 0.0098  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:32 d2.utils.events]: \u001b[0m eta: 0:07:44  iter: 9079  total_loss: 0.8941  loss_cls: 0.226  loss_box_reg: 0.2815  loss_mask: 0.246  loss_rpn_cls: 0.01473  loss_rpn_loc: 0.1077  time: 0.5207  data_time: 0.0419  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:43 d2.utils.events]: \u001b[0m eta: 0:07:34  iter: 9099  total_loss: 0.8403  loss_cls: 0.2097  loss_box_reg: 0.2802  loss_mask: 0.2465  loss_rpn_cls: 0.01503  loss_rpn_loc: 0.1013  time: 0.5207  data_time: 0.0225  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 20:59:53 d2.utils.events]: \u001b[0m eta: 0:07:24  iter: 9119  total_loss: 0.8792  loss_cls: 0.2037  loss_box_reg: 0.2903  loss_mask: 0.2433  loss_rpn_cls: 0.00984  loss_rpn_loc: 0.1057  time: 0.5207  data_time: 0.0214  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:00:03 d2.utils.events]: \u001b[0m eta: 0:07:14  iter: 9139  total_loss: 0.884  loss_cls: 0.2107  loss_box_reg: 0.2875  loss_mask: 0.2379  loss_rpn_cls: 0.01342  loss_rpn_loc: 0.1122  time: 0.5207  data_time: 0.0329  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:00:14 d2.utils.events]: \u001b[0m eta: 0:07:04  iter: 9159  total_loss: 0.9115  loss_cls: 0.2088  loss_box_reg: 0.3094  loss_mask: 0.2719  loss_rpn_cls: 0.01679  loss_rpn_loc: 0.1065  time: 0.5207  data_time: 0.0151  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:00:24 d2.utils.events]: \u001b[0m eta: 0:06:52  iter: 9179  total_loss: 0.8913  loss_cls: 0.2324  loss_box_reg: 0.3008  loss_mask: 0.2434  loss_rpn_cls: 0.01171  loss_rpn_loc: 0.1058  time: 0.5207  data_time: 0.0350  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:00:34 d2.utils.events]: \u001b[0m eta: 0:06:39  iter: 9199  total_loss: 0.888  loss_cls: 0.2099  loss_box_reg: 0.3074  loss_mask: 0.2368  loss_rpn_cls: 0.01838  loss_rpn_loc: 0.1077  time: 0.5206  data_time: 0.0044  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:00:44 d2.utils.events]: \u001b[0m eta: 0:06:30  iter: 9219  total_loss: 0.9255  loss_cls: 0.2398  loss_box_reg: 0.305  loss_mask: 0.2504  loss_rpn_cls: 0.009875  loss_rpn_loc: 0.1071  time: 0.5206  data_time: 0.0214  lr: 1e-06  max_mem: 17403M\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 21:00:54 d2.utils.events]: \u001b[0m eta: 0:06:20  iter: 9239  total_loss: 0.8546  loss_cls: 0.2001  loss_box_reg: 0.2824  loss_mask: 0.2491  loss_rpn_cls: 0.01256  loss_rpn_loc: 0.1025  time: 0.5206  data_time: 0.0196  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:01:04 d2.utils.events]: \u001b[0m eta: 0:06:09  iter: 9259  total_loss: 0.8011  loss_cls: 0.2112  loss_box_reg: 0.2615  loss_mask: 0.2305  loss_rpn_cls: 0.01655  loss_rpn_loc: 0.09336  time: 0.5204  data_time: 0.0164  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:01:15 d2.utils.events]: \u001b[0m eta: 0:06:00  iter: 9279  total_loss: 0.8894  loss_cls: 0.2311  loss_box_reg: 0.2925  loss_mask: 0.2367  loss_rpn_cls: 0.01765  loss_rpn_loc: 0.1168  time: 0.5205  data_time: 0.0387  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:01:28 d2.utils.events]: \u001b[0m eta: 0:05:50  iter: 9299  total_loss: 0.8515  loss_cls: 0.1887  loss_box_reg: 0.3028  loss_mask: 0.2446  loss_rpn_cls: 0.01143  loss_rpn_loc: 0.1018  time: 0.5208  data_time: 0.1253  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:01:38 d2.utils.events]: \u001b[0m eta: 0:05:41  iter: 9319  total_loss: 0.9195  loss_cls: 0.2058  loss_box_reg: 0.3082  loss_mask: 0.2379  loss_rpn_cls: 0.01181  loss_rpn_loc: 0.1063  time: 0.5208  data_time: 0.0662  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:01:49 d2.utils.events]: \u001b[0m eta: 0:05:30  iter: 9339  total_loss: 0.8488  loss_cls: 0.1924  loss_box_reg: 0.2786  loss_mask: 0.2374  loss_rpn_cls: 0.0132  loss_rpn_loc: 0.1119  time: 0.5208  data_time: 0.0472  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:00 d2.utils.events]: \u001b[0m eta: 0:05:21  iter: 9359  total_loss: 0.8237  loss_cls: 0.1884  loss_box_reg: 0.2735  loss_mask: 0.2329  loss_rpn_cls: 0.007678  loss_rpn_loc: 0.09505  time: 0.5209  data_time: 0.0628  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:09 d2.utils.events]: \u001b[0m eta: 0:05:11  iter: 9379  total_loss: 0.8051  loss_cls: 0.1824  loss_box_reg: 0.2736  loss_mask: 0.24  loss_rpn_cls: 0.01527  loss_rpn_loc: 0.1007  time: 0.5208  data_time: 0.0053  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:20 d2.utils.events]: \u001b[0m eta: 0:05:02  iter: 9399  total_loss: 0.7917  loss_cls: 0.1635  loss_box_reg: 0.2626  loss_mask: 0.2453  loss_rpn_cls: 0.009212  loss_rpn_loc: 0.09526  time: 0.5208  data_time: 0.0134  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:32 d2.utils.events]: \u001b[0m eta: 0:04:53  iter: 9419  total_loss: 0.7943  loss_cls: 0.1859  loss_box_reg: 0.2758  loss_mask: 0.238  loss_rpn_cls: 0.01333  loss_rpn_loc: 0.0981  time: 0.5209  data_time: 0.0314  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:42 d2.utils.events]: \u001b[0m eta: 0:04:44  iter: 9439  total_loss: 0.902  loss_cls: 0.2103  loss_box_reg: 0.3023  loss_mask: 0.2594  loss_rpn_cls: 0.006826  loss_rpn_loc: 0.09972  time: 0.5210  data_time: 0.0359  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:02:53 d2.utils.events]: \u001b[0m eta: 0:04:34  iter: 9459  total_loss: 0.8896  loss_cls: 0.2157  loss_box_reg: 0.2924  loss_mask: 0.2674  loss_rpn_cls: 0.01774  loss_rpn_loc: 0.106  time: 0.5210  data_time: 0.0117  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:03 d2.utils.events]: \u001b[0m eta: 0:04:24  iter: 9479  total_loss: 0.8695  loss_cls: 0.1842  loss_box_reg: 0.2831  loss_mask: 0.2489  loss_rpn_cls: 0.009936  loss_rpn_loc: 0.1058  time: 0.5210  data_time: 0.0113  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:14 d2.utils.events]: \u001b[0m eta: 0:04:13  iter: 9499  total_loss: 0.8248  loss_cls: 0.1779  loss_box_reg: 0.2743  loss_mask: 0.2612  loss_rpn_cls: 0.01525  loss_rpn_loc: 0.09466  time: 0.5210  data_time: 0.0499  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:24 d2.utils.events]: \u001b[0m eta: 0:04:03  iter: 9519  total_loss: 0.8216  loss_cls: 0.2038  loss_box_reg: 0.2705  loss_mask: 0.251  loss_rpn_cls: 0.01129  loss_rpn_loc: 0.1044  time: 0.5210  data_time: 0.0388  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:34 d2.utils.events]: \u001b[0m eta: 0:03:52  iter: 9539  total_loss: 0.8441  loss_cls: 0.2154  loss_box_reg: 0.2793  loss_mask: 0.2422  loss_rpn_cls: 0.02071  loss_rpn_loc: 0.1071  time: 0.5209  data_time: 0.0129  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:45 d2.utils.events]: \u001b[0m eta: 0:03:43  iter: 9559  total_loss: 0.8406  loss_cls: 0.1954  loss_box_reg: 0.2861  loss_mask: 0.2407  loss_rpn_cls: 0.01228  loss_rpn_loc: 0.0993  time: 0.5210  data_time: 0.0593  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:03:55 d2.utils.events]: \u001b[0m eta: 0:03:32  iter: 9579  total_loss: 0.8771  loss_cls: 0.1997  loss_box_reg: 0.3125  loss_mask: 0.2507  loss_rpn_cls: 0.006749  loss_rpn_loc: 0.1028  time: 0.5210  data_time: 0.0041  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:05 d2.utils.events]: \u001b[0m eta: 0:03:21  iter: 9599  total_loss: 0.8563  loss_cls: 0.2146  loss_box_reg: 0.274  loss_mask: 0.2443  loss_rpn_cls: 0.01157  loss_rpn_loc: 0.1058  time: 0.5209  data_time: 0.0356  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:16 d2.utils.events]: \u001b[0m eta: 0:03:11  iter: 9619  total_loss: 0.8834  loss_cls: 0.2213  loss_box_reg: 0.2841  loss_mask: 0.252  loss_rpn_cls: 0.0105  loss_rpn_loc: 0.1049  time: 0.5209  data_time: 0.0322  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:26 d2.utils.events]: \u001b[0m eta: 0:03:01  iter: 9639  total_loss: 0.9002  loss_cls: 0.2168  loss_box_reg: 0.3193  loss_mask: 0.2393  loss_rpn_cls: 0.01543  loss_rpn_loc: 0.1072  time: 0.5209  data_time: 0.0253  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:37 d2.utils.events]: \u001b[0m eta: 0:02:51  iter: 9659  total_loss: 0.9155  loss_cls: 0.214  loss_box_reg: 0.2997  loss_mask: 0.245  loss_rpn_cls: 0.01609  loss_rpn_loc: 0.1061  time: 0.5209  data_time: 0.0707  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:47 d2.utils.events]: \u001b[0m eta: 0:02:41  iter: 9679  total_loss: 0.8996  loss_cls: 0.2233  loss_box_reg: 0.298  loss_mask: 0.2559  loss_rpn_cls: 0.01973  loss_rpn_loc: 0.1057  time: 0.5209  data_time: 0.0212  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:04:57 d2.utils.events]: \u001b[0m eta: 0:02:30  iter: 9699  total_loss: 0.8716  loss_cls: 0.2075  loss_box_reg: 0.287  loss_mask: 0.2531  loss_rpn_cls: 0.009179  loss_rpn_loc: 0.1035  time: 0.5208  data_time: 0.0215  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:07 d2.utils.events]: \u001b[0m eta: 0:02:20  iter: 9719  total_loss: 0.8702  loss_cls: 0.1903  loss_box_reg: 0.288  loss_mask: 0.2387  loss_rpn_cls: 0.01533  loss_rpn_loc: 0.1018  time: 0.5209  data_time: 0.0507  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:17 d2.utils.events]: \u001b[0m eta: 0:02:09  iter: 9739  total_loss: 0.9335  loss_cls: 0.2443  loss_box_reg: 0.2987  loss_mask: 0.2494  loss_rpn_cls: 0.01934  loss_rpn_loc: 0.09946  time: 0.5208  data_time: 0.0053  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:27 d2.utils.events]: \u001b[0m eta: 0:01:59  iter: 9759  total_loss: 0.8797  loss_cls: 0.2162  loss_box_reg: 0.2896  loss_mask: 0.2356  loss_rpn_cls: 0.01452  loss_rpn_loc: 0.09817  time: 0.5208  data_time: 0.0596  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:37 d2.utils.events]: \u001b[0m eta: 0:01:49  iter: 9779  total_loss: 0.8393  loss_cls: 0.1953  loss_box_reg: 0.2715  loss_mask: 0.2423  loss_rpn_cls: 0.01131  loss_rpn_loc: 0.09925  time: 0.5207  data_time: 0.0052  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:47 d2.utils.events]: \u001b[0m eta: 0:01:39  iter: 9799  total_loss: 0.9579  loss_cls: 0.2382  loss_box_reg: 0.3202  loss_mask: 0.2583  loss_rpn_cls: 0.01686  loss_rpn_loc: 0.113  time: 0.5207  data_time: 0.0316  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:05:58 d2.utils.events]: \u001b[0m eta: 0:01:29  iter: 9819  total_loss: 0.9388  loss_cls: 0.2267  loss_box_reg: 0.3037  loss_mask: 0.2669  loss_rpn_cls: 0.01553  loss_rpn_loc: 0.1041  time: 0.5207  data_time: 0.0464  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:06:09 d2.utils.events]: \u001b[0m eta: 0:01:19  iter: 9839  total_loss: 0.8207  loss_cls: 0.1829  loss_box_reg: 0.271  loss_mask: 0.2518  loss_rpn_cls: 0.01462  loss_rpn_loc: 0.09683  time: 0.5207  data_time: 0.0597  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:06:20 d2.utils.events]: \u001b[0m eta: 0:01:09  iter: 9859  total_loss: 0.8836  loss_cls: 0.1955  loss_box_reg: 0.2772  loss_mask: 0.2427  loss_rpn_cls: 0.01436  loss_rpn_loc: 0.1091  time: 0.5208  data_time: 0.0289  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:06:31 d2.utils.events]: \u001b[0m eta: 0:00:59  iter: 9879  total_loss: 0.9522  loss_cls: 0.2034  loss_box_reg: 0.3005  loss_mask: 0.2546  loss_rpn_cls: 0.02017  loss_rpn_loc: 0.1168  time: 0.5208  data_time: 0.0432  lr: 1e-06  max_mem: 17403M\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m[03/01 21:06:41 d2.utils.events]: \u001b[0m eta: 0:00:50  iter: 9899  total_loss: 0.8332  loss_cls: 0.1838  loss_box_reg: 0.2883  loss_mask: 0.2419  loss_rpn_cls: 0.01296  loss_rpn_loc: 0.1008  time: 0.5208  data_time: 0.0050  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:06:51 d2.utils.events]: \u001b[0m eta: 0:00:39  iter: 9919  total_loss: 0.8701  loss_cls: 0.2124  loss_box_reg: 0.28  loss_mask: 0.2365  loss_rpn_cls: 0.01215  loss_rpn_loc: 0.1023  time: 0.5208  data_time: 0.0125  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:07:02 d2.utils.events]: \u001b[0m eta: 0:00:30  iter: 9939  total_loss: 0.8623  loss_cls: 0.2153  loss_box_reg: 0.2866  loss_mask: 0.2401  loss_rpn_cls: 0.01224  loss_rpn_loc: 0.09965  time: 0.5208  data_time: 0.0419  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:07:12 d2.utils.events]: \u001b[0m eta: 0:00:19  iter: 9959  total_loss: 0.8623  loss_cls: 0.2166  loss_box_reg: 0.309  loss_mask: 0.2501  loss_rpn_cls: 0.008048  loss_rpn_loc: 0.09257  time: 0.5208  data_time: 0.0110  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:07:24 d2.utils.events]: \u001b[0m eta: 0:00:10  iter: 9979  total_loss: 0.838  loss_cls: 0.1976  loss_box_reg: 0.2913  loss_mask: 0.2409  loss_rpn_cls: 0.007319  loss_rpn_loc: 0.1041  time: 0.5209  data_time: 0.0785  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:07:37 d2.utils.events]: \u001b[0m eta: 0:00:00  iter: 9999  total_loss: 0.9033  loss_cls: 0.2186  loss_box_reg: 0.3103  loss_mask: 0.2556  loss_rpn_cls: 0.01007  loss_rpn_loc: 0.1058  time: 0.5209  data_time: 0.0056  lr: 1e-06  max_mem: 17403M\n",
            "\u001b[32m[03/01 21:07:37 d2.engine.hooks]: \u001b[0mOverall training speed: 9998 iterations in 1:26:47 (0.5209 s / it)\n",
            "\u001b[32m[03/01 21:07:37 d2.engine.hooks]: \u001b[0mTotal training time: 1:27:04 (0:00:17 on hooks)\n"
          ]
        },
        {
          "ename": "KeyError",
          "evalue": "\"Dataset 'm' is not registered! Available datasets are: coco_2014_train, coco_2014_val, coco_2014_minival, coco_2014_minival_100, coco_2014_valminusminival, coco_2017_train, coco_2017_val, coco_2017_test, coco_2017_test-dev, coco_2017_val_100, keypoints_coco_2014_train, keypoints_coco_2014_val, keypoints_coco_2014_minival, keypoints_coco_2014_valminusminival, keypoints_coco_2014_minival_100, keypoints_coco_2017_train, keypoints_coco_2017_val, keypoints_coco_2017_val_100, coco_2017_train_panoptic_separated, coco_2017_train_panoptic_stuffonly, coco_2017_train_panoptic, coco_2017_val_panoptic_separated, coco_2017_val_panoptic_stuffonly, coco_2017_val_panoptic, coco_2017_val_100_panoptic_separated, coco_2017_val_100_panoptic_stuffonly, coco_2017_val_100_panoptic, lvis_v1_train, lvis_v1_val, lvis_v1_test_dev, lvis_v1_test_challenge, lvis_v0.5_train, lvis_v0.5_val, lvis_v0.5_val_rand_100, lvis_v0.5_test, lvis_v0.5_train_cocofied, lvis_v0.5_val_cocofied, cityscapes_fine_instance_seg_train, cityscapes_fine_sem_seg_train, cityscapes_fine_instance_seg_val, cityscapes_fine_sem_seg_val, cityscapes_fine_instance_seg_test, cityscapes_fine_sem_seg_test, cityscapes_fine_panoptic_train, cityscapes_fine_panoptic_val, voc_2007_trainval, voc_2007_train, voc_2007_val, voc_2007_test, voc_2012_trainval, voc_2012_train, voc_2012_val, ade20k_sem_seg_train, ade20k_sem_seg_val, my_dataset_train, my_dataset_val\"",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/data/catalog.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;32mtry\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[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\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;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/user/conda/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1026\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   1028\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'm'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipykernel_343/1514359701.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnew_trainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnew_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresume_or_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresume\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[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnew_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0mOrderedDict\u001b[0m \u001b[0mof\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mevaluation\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0menabled\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mOtherwise\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \"\"\"\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_iter\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    432\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTEST\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEXPECTED_RESULTS\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_main_process\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[1;32m    433\u001b[0m             assert hasattr(\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/train_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, start_iter, max_iter)\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_train\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    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbefore_train\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~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/train_loop.py\u001b[0m in \u001b[0;36mafter_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mh\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m             \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_train\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    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbefore_step\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~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/hooks.py\u001b[0m in \u001b[0;36mafter_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0;31m# This condition is to prevent the eval from running after a failed training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_eval\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    376\u001b[0m         \u001b[0;31m# func is likely a closure that holds reference to the trainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;31m# therefore we clean it to avoid circular reference in the end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/hooks.py\u001b[0m in \u001b[0;36m_do_eval\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_eval\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--> 345\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\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    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36mtest_and_save_results\u001b[0;34m()\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mtest_and_save_results\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[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_eval_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\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    401\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_eval_results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36mtest\u001b[0;34m(cls, cfg, model, evaluators)\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOrderedDict\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    536\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDATASETS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTEST\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--> 537\u001b[0;31m             \u001b[0mdata_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_test_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_name\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    538\u001b[0m             \u001b[0;31m# When evaluators are passed in as arguments,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0;31m# implicitly assume that evaluators can be created before data_loader.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36mbuild_test_loader\u001b[0;34m(cls, cfg, dataset_name)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0mOverwrite\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0myou\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0md\u001b[0m \u001b[0mlike\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdifferent\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m         \"\"\"\n\u001b[0;32m--> 496\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuild_detection_test_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_name\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    497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/config/config.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0m_called_with_cfg\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m                     \u001b[0mexplicit_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_args_from_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_config\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    202\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0morig_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mexplicit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\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;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/config/config.py\u001b[0m in \u001b[0;36m_get_args_from_config\u001b[0;34m(from_config_func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msupported_arg_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0mextra_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrom_config_func\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    239\u001b[0m         \u001b[0;31m# forward the other arguments to __init__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/data/build.py\u001b[0m in \u001b[0;36m_test_loader_from_config\u001b[0;34m(cfg, dataset_name, mapper)\u001b[0m\n\u001b[1;32m    401\u001b[0m         ]\n\u001b[1;32m    402\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMODEL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLOAD_PROPOSALS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m         \u001b[0;32melse\u001b[0m \u001b[0;32mNone\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    404\u001b[0m     )\n\u001b[1;32m    405\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmapper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/data/build.py\u001b[0m in \u001b[0;36mget_detection_dataset_dicts\u001b[0;34m(names, filter_empty, min_keypoints, proposal_files)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m     \u001b[0mdataset_dicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mDatasetCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\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    228\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdicts\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_dicts\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    229\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdicts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Dataset '{}' is empty!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/data/build.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m     \u001b[0mdataset_dicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mDatasetCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\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    228\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdicts\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_dicts\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    229\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdicts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Dataset '{}' is empty!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.imgenv-first-server-gpu-0/lib/python3.7/site-packages/detectron2/data/catalog.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m     55\u001b[0m                     \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m                 )\n\u001b[0;32m---> 57\u001b[0;31m             ) from e\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\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     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"Dataset 'm' is not registered! Available datasets are: coco_2014_train, coco_2014_val, coco_2014_minival, coco_2014_minival_100, coco_2014_valminusminival, coco_2017_train, coco_2017_val, coco_2017_test, coco_2017_test-dev, coco_2017_val_100, keypoints_coco_2014_train, keypoints_coco_2014_val, keypoints_coco_2014_minival, keypoints_coco_2014_valminusminival, keypoints_coco_2014_minival_100, keypoints_coco_2017_train, keypoints_coco_2017_val, keypoints_coco_2017_val_100, coco_2017_train_panoptic_separated, coco_2017_train_panoptic_stuffonly, coco_2017_train_panoptic, coco_2017_val_panoptic_separated, coco_2017_val_panoptic_stuffonly, coco_2017_val_panoptic, coco_2017_val_100_panoptic_separated, coco_2017_val_100_panoptic_stuffonly, coco_2017_val_100_panoptic, lvis_v1_train, lvis_v1_val, lvis_v1_test_dev, lvis_v1_test_challenge, lvis_v0.5_train, lvis_v0.5_val, lvis_v0.5_val_rand_100, lvis_v0.5_test, lvis_v0.5_train_cocofied, lvis_v0.5_val_cocofied, cityscapes_fine_instance_seg_train, cityscapes_fine_sem_seg_train, cityscapes_fine_instance_seg_val, cityscapes_fine_sem_seg_val, cityscapes_fine_instance_seg_test, cityscapes_fine_sem_seg_test, cityscapes_fine_panoptic_train, cityscapes_fine_panoptic_val, voc_2007_trainval, voc_2007_train, voc_2007_val, voc_2007_test, voc_2012_trainval, voc_2012_train, voc_2012_val, ade20k_sem_seg_train, ade20k_sem_seg_val, my_dataset_train, my_dataset_val\""
          ]
        }
      ],
      "source": [
        "new_trainer = Trainer(cfg)\n",
        "new_trainer.resume_or_load(resume=False)\n",
        "new_trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3wjVEzBTcuK"
      },
      "source": [
        "Используем обученную модель для проверки качества на валидации."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CGAl4sjxTcuK"
      },
      "outputs": [],
      "source": [
        "cfg.MODEL.WEIGHTS = \"output/model_final.pth\" \n",
        "\n",
        "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.1\n",
        "cfg.DATASETS.TEST = (\"my_dataset_val\", )\n",
        "#Изменение размера исходных изображений для тестового датасета\n",
        "cfg.INPUT.MIN_SIZE_TEST= 1700 \n",
        "cfg.INPUT.MAX_SIZE_TEST = 2450 \n",
        "cfg.INPUT.FORMAT = 'BGR'\n",
        "\n",
        "cfg.TEST.DETECTIONS_PER_IMAGE = 1000\n",
        "\n",
        "predictor = DefaultPredictor(cfg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czZVEbRfTcuL"
      },
      "source": [
        "Сделаем предсказания для тестового датасета и сразу же нарисуем его.\n",
        "\n",
        "Вы можете выбрать из выпадающего списка номер изображения, и посмотреть разметку на всем валидационном датасете."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343,
          "referenced_widgets": [
            "ed1a8b7f604b4bc3914c36b6adbf92e2",
            "44d2f91fc8b64fb2856f62a5ec734726",
            "b04c5509a93f4d6193d312973330424f",
            "821ca0c5822a400b96f8efd689e27f06",
            "3c740b3dc6d64f5b9a2307d21cd5b56f",
            "cb6ce49dfe434664a66afa3ffa2f6efb",
            "867520a058df4f40b93cea57736993f4",
            "1c13712847fb4d0989a5fe1e80d196db"
          ]
        },
        "id": "gRmGql2TTcuL",
        "outputId": "224de7c4-0b84-4c71-f357-2be332d97f7a",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c13712847fb4d0989a5fe1e80d196db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='file', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "@interact\n",
        "def show_images(file=range(len(dataset_dicts_val))):\n",
        "    \n",
        "    example = dataset_dicts_val[file]\n",
        "    im = cv2.imread(example[\"file_name\"])\n",
        "    outputs = predictor(im)\n",
        "    fig, axs = plt.subplots(nrows=1,ncols=2,figsize=(4,4),dpi=200)\n",
        "    v = Visualizer(im[:, :],\n",
        "                  metadata=val_metadata, \n",
        "                  scale=0.4 )\n",
        "    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "    axs[0].imshow(im[:, :, ::-1])\n",
        "    axs[1].imshow(v.get_image()[:, :, ::-1])\n",
        "    axs[0].axis('off')\n",
        "    axs[1].axis('off')\n",
        "    axs[0].set_title('Original')\n",
        "    axs[1].set_title('Predict')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0swF9k_-TcuL"
      },
      "source": [
        "Можно непосредственно в коде изменить номер изображения, которое Вы хотите обработать."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "9SNlt0hwTcuL",
        "outputId": "13038730-8495-48cc-bb7b-2f1195d964c6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "id_image_selected = 3\n",
        "example = dataset_dicts_val[id_image_selected]\n",
        "im = cv2.imread(example[\"file_name\"])\n",
        "outputs = predictor(im)\n",
        "plt.figure(figsize=(7,7))\n",
        "v = Visualizer(im[:, :],\n",
        "              metadata=val_metadata, \n",
        "              scale=0.4 )\n",
        "v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "plt.imshow(v.get_image()[:, :, ::-1])\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gJN3HzVTcuM"
      },
      "source": [
        "В качестве предсказаний для каждого изображения из тестового набора требуется получить бинарную маску, в которой `1` означает, что данный пиксель относится к классу текста."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C89HZtZNTcuM"
      },
      "source": [
        "Давайте на примере одного изображения переведем формат выхода Detectron2 в требуемый формат для соревнования.\n",
        "\n",
        "`outputs` - результат предсказания модели на данном изображении из предыдущего блока с кодом"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O6VMbNJJTcuM"
      },
      "outputs": [],
      "source": [
        "prediction = outputs['instances'].pred_masks.cpu().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4M2NyA0TcuM",
        "outputId": "6a9ba3d1-1296-4e44-e787-622cdbf3a00f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(150, 4032, 3024)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prediction.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYDkB2NGTcuM"
      },
      "source": [
        "В `prediction` находится массив бинарных матриц. Каждая матрица отвечает за отдельную задетектированную маску текста. В нашем случае модель задетектировала 80 текстовых масок. Давайте провизуализируем одну из них."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPRenhGsTcuN",
        "outputId": "56dae232-c02a-438c-cdf6-015a6ef808e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       ...,\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False]])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prediction[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "zhJHBLp_TcuN",
        "outputId": "17e47bb6-02d7-454a-e3f7-49b4b6a16afb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3296788810>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(prediction[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INUXXU4HTcuN"
      },
      "source": [
        "Отлично. Теперь, для того, чтобы получить бинарную маску со всем задетектированным текстом для изображения, нам нужно объединить все маски в одну. Для этого мы просто поэелементно сложим все наши матрицы. Там, где после сложения остались нули - модель не задетектировала никакого текста."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C5YwJKImTcuN"
      },
      "outputs": [],
      "source": [
        "mask = np.add.reduce(prediction) # Складываем матрицы токенов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "idyzuPEVTcuN"
      },
      "outputs": [],
      "source": [
        "mask = mask > 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "RHlLdHm-TcuN",
        "outputId": "20ce3270-c86b-48d0-9a1c-e7e4b4241ab6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3294e1d990>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(mask)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYDzY4KDTcuO"
      },
      "source": [
        "Итак, нам нужно полуить такую маску для каждого изображения из валидационной выборки, а затем посчитать метрику F1-score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yBvZI_RbTcuO"
      },
      "outputs": [],
      "source": [
        "#Подгрузим аннотации train\n",
        "with open('data/val/annotations_new.json') as f:\n",
        "    annotations_val = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lfZdl1WfTcuO"
      },
      "outputs": [],
      "source": [
        "val_images = annotations_val['images']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9CevIqNbTcuO"
      },
      "outputs": [],
      "source": [
        "val_predictions = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uCkPni3STcuP"
      },
      "outputs": [],
      "source": [
        "for val_img in tqdm.tqdm_notebook(val_images):\n",
        "    file_name = val_img['file_name']\n",
        "    img_path = os.path.join('data/val/images/',file_name)\n",
        "    im = cv2.imread(img_path)\n",
        "    outputs = predictor(im)\n",
        "    prediction = outputs['instances'].pred_masks.cpu().numpy()\n",
        "    mask = np.add.reduce(prediction)\n",
        "    mask = mask > 0\n",
        "    val_predictions[file_name] = mask"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9MDBvzkCTcuP"
      },
      "source": [
        "Для сохрания предсказаний и загрузки бинарных масок бы будет использовать формат `.npz`. Он позволяет хранить большие массивы в компактном виде. Вот [ссылка](https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html) на документацию."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yFqtNaQHTcuP"
      },
      "outputs": [],
      "source": [
        "np.savez_compressed('val_pred.npz',**val_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKbJ-nfwTcuP"
      },
      "outputs": [],
      "source": [
        "loaded_train = np.load('data/train/binary.npz')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rrpWjNM5TcuQ"
      },
      "outputs": [],
      "source": [
        "loaded_val_pred = np.load('val_pred.npz')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_yYmXyGmTcuQ"
      },
      "source": [
        "Мы используем среднюю метрика F1-score. То есть считаем F1-score для каждого изображения, а затем усредняем результаты. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9KAbddgTcuQ"
      },
      "source": [
        "Реализация из sklearn работает довольно долго, попэтому мы будем использовать свою."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FbsB5I7aTcuQ"
      },
      "outputs": [],
      "source": [
        "def f1_loss(y_true, y_pred):\n",
        "        \n",
        "    \n",
        "    tp = np.sum(y_true & y_pred)\n",
        "    tn = np.sum(~y_true & ~y_pred)\n",
        "    fp = np.sum(~y_true & y_pred)\n",
        "    fn = np.sum(y_true & ~y_pred)\n",
        "    \n",
        "    epsilon = 1e-7\n",
        "    \n",
        "    precision = tp / (tp + fp + epsilon)\n",
        "    recall = tp / (tp + fn + epsilon)\n",
        "    \n",
        "    f1 = 2* precision*recall / ( precision + recall + epsilon)\n",
        "\n",
        "    return f1 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9415559c372f483298228fc3d05ecc72",
            "56002040bbb04b2e9a7ca9b98afc3222",
            "eae17eb238d64998bdeeaaf360b7731b",
            "eb5f6960cbde4e35b0cb51703312a440",
            "67758088ff574b759ba7efe89672763e",
            "b35ad6abaf4e48f79b75a45508ebb1e6",
            "8d42e23763d8451bb4f418b24675f5d9",
            "0c6526b90e284b6bbc3cf2ac874c20c9",
            "47324f0b7b9843a7acb13848475ce206",
            "61796247a44b42d380d1a7fd693312a8",
            "a7cf196ddea74fd69bd3e6c7cd3634e6",
            "870d18706a624a68adfc06667541844c"
          ]
        },
        "id": "yp7BRLoJTcuQ",
        "outputId": "2f69b6da-d916-4fc1-efb6-885e0e9acabe",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "870d18706a624a68adfc06667541844c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/104 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "f1_scores = []\n",
        "for key in tqdm.tqdm_notebook(loaded_val_pred.files):\n",
        "    pred = loaded_val_pred[key].reshape(-1)\n",
        "    true = loaded_train[key].reshape(-1)\n",
        "    \n",
        "    f1_img = f1_loss(true,pred)\n",
        "    f1_scores.append(f1_img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHra8QC0TcuQ"
      },
      "source": [
        "Получившаяся метрика на валидации."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5MjhRceTcuQ",
        "outputId": "2f6c6f5f-3d15-4f7d-9d84-13d95d869cf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8865043378492855"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.mean(f1_scores)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "second_segmentation (1) (1).ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "0c6526b90e284b6bbc3cf2ac874c20c9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
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        "3c740b3dc6d64f5b9a2307d21cd5b56f": {
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          "model_module_version": "1.5.0",
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          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
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