hubmap-effnetunet-tiled-data-no-schd-train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "127597ab",
   "metadata": {
    "papermill": {
     "duration": 0.010225,
     "end_time": "2022-08-05T09:51:50.313271",
     "exception": false,
     "start_time": "2022-08-05T09:51:50.303046",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "\n",
    "<h3 style=\"text-align:center; background-color:#C8FF33;padding:40px;border-radius: 30px;\">\n",
    "<p style=\"text-align: left\">See also this notebook:</p>\n",
    "    <p style=\"text-align: left\"><b><a href=\"https://www.kaggle.com/code/soumya9977/hubmap-multiorgan-segmentation-1-3-data-prep\"> &nbsp; HuBMAP multiOrgan Segmentation 1/3 [data prep]</a></b></p>\n",
    "    <p style=\"text-align: left\"><b>* HuBMAP: Vanilla Unet + W&B + Pytorch 2/3 [train]</b></p>\n",
    "    <p style=\"text-align: left\"><b><a href=\"https://www.kaggle.com/code/soumya9977/hubmap-vanilla-unet-pytorch-3-3-inference\"> &nbsp; HuBMAP: Vanilla Unet Pytorch 3/3 [inference]</a></b></p>\n",
    "</h3>\n",
    "\n",
    "\n",
    "## Please _DO_ upvote!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a04a6c",
   "metadata": {
    "papermill": {
     "duration": 0.008171,
     "end_time": "2022-08-05T09:51:50.330230",
     "exception": false,
     "start_time": "2022-08-05T09:51:50.322059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Changelog\n",
    "\n",
    "|| Version | Comments | LB |\n",
    "|---|  --- | --- | --- |\n",
    "|**Best**| -- | effNetUnet w/ 256x256, 50 epoch, no aug, Warmup CosineAnnealingLR lr sch, no pre/post processing, L2 norm, bs=8 | `--` |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "439555e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:51:50.348653Z",
     "iopub.status.busy": "2022-08-05T09:51:50.348204Z",
     "iopub.status.idle": "2022-08-05T09:52:20.246150Z",
     "shell.execute_reply": "2022-08-05T09:52:20.245019Z"
    },
    "papermill": {
     "duration": 29.910269,
     "end_time": "2022-08-05T09:52:20.248782",
     "exception": false,
     "start_time": "2022-08-05T09:51:50.338513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install git+https://github.com/qubvel/segmentation_models.pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85cf0872",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:20.273529Z",
     "iopub.status.busy": "2022-08-05T09:52:20.273216Z",
     "iopub.status.idle": "2022-08-05T09:52:20.277545Z",
     "shell.execute_reply": "2022-08-05T09:52:20.276580Z"
    },
    "papermill": {
     "duration": 0.018252,
     "end_time": "2022-08-05T09:52:20.279431",
     "exception": false,
     "start_time": "2022-08-05T09:52:20.261179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install -q tifffile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47351231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Aug 14 00:10:21 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |\n",
      "| 28%   39C    P8    33W / 250W |    542MiB / 11016MiB |     32%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A      2829      G   /usr/lib/xorg/Xorg                 18MiB |\n",
      "|    0   N/A  N/A      3116      G   /usr/bin/gnome-shell               71MiB |\n",
      "|    0   N/A  N/A      3390      G   /usr/lib/xorg/Xorg                166MiB |\n",
      "|    0   N/A  N/A      3593      G   /usr/bin/gnome-shell               39MiB |\n",
      "|    0   N/A  N/A      4360      G   ...isable-features=EventPath      112MiB |\n",
      "|    0   N/A  N/A      8616      G   ...RendererForSitePerProcess      125MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c131cd07",
   "metadata": {
    "papermill": {
     "duration": 0.010437,
     "end_time": "2022-08-05T09:52:20.300490",
     "exception": false,
     "start_time": "2022-08-05T09:52:20.290053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Import, SEED, Config:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9890593",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:20.323481Z",
     "iopub.status.busy": "2022-08-05T09:52:20.322598Z",
     "iopub.status.idle": "2022-08-05T09:52:27.377248Z",
     "shell.execute_reply": "2022-08-05T09:52:27.376205Z"
    },
    "papermill": {
     "duration": 7.06882,
     "end_time": "2022-08-05T09:52:27.379904",
     "exception": false,
     "start_time": "2022-08-05T09:52:20.311084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import warnings\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import gc\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "from collections import defaultdict\n",
    "from IPython.display import display\n",
    "\n",
    "#Pytorch Imports\n",
    "import torch\n",
    "import torchvision # torch package for vision related things\n",
    "import torch.nn.functional as F  # Parameterless functions, like (some) activation functions\n",
    "import torchvision.datasets as datasets  # Standard datasets\n",
    "import torchvision.transforms as transforms  # Transformations we can perform on our dataset for augmentation\n",
    "from torch import optim  # For optimizers like SGD, Adam, etc.\n",
    "from torch import nn  # All neural network modules\n",
    "from torch.utils.data import Dataset, DataLoader  # Gives easier dataset managment by creating mini batches etc.\n",
    "from tqdm import tqdm  # For nice progress bar!\n",
    "from torchvision.transforms import Resize\n",
    "from torchaudio.transforms import MelSpectrogram, AmplitudeToDB\n",
    "from torch.optim import lr_scheduler\n",
    "# from tifffile import imread\n",
    "import segmentation_models_pytorch as smp\n",
    "# import transformers\n",
    "from sklearn.model_selection import StratifiedKFold, KFold, StratifiedGroupKFold\n",
    "\n",
    "from albumentations.pytorch.transforms import ToTensorV2\n",
    "import albumentations as A\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# For colored terminal text\n",
    "from colorama import Fore, Back, Style\n",
    "c_ = Fore.CYAN\n",
    "sr_ = Style.RESET_ALL\n",
    "b_ = Fore.BLUE\n",
    "\n",
    "# For descriptive error messages\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "511c4d9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:27.403599Z",
     "iopub.status.busy": "2022-08-05T09:52:27.403119Z",
     "iopub.status.idle": "2022-08-05T09:52:27.465160Z",
     "shell.execute_reply": "2022-08-05T09:52:27.464042Z"
    },
    "papermill": {
     "duration": 0.07637,
     "end_time": "2022-08-05T09:52:27.467286",
     "exception": false,
     "start_time": "2022-08-05T09:52:27.390916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Hyperparameters etc.\n",
    "# Hyperparameters\n",
    "CONFIG = {\n",
    "    \"in_channels\" :3,\n",
    "    \"num_classes\": 1,\n",
    "    \"BATCH_SIZE\" : 8,\n",
    "    \"NUM_EPOCHS\" : 100,\n",
    "    \"n_accumulate\": 1,\n",
    "    \"competition\": \"HuBMAP-Kaggle\", # HuBMAP-Kaggle\n",
    "    \"model_name\": \"Vanilla_Unet\",\n",
    "    \"LEARNING_RATE\": 1e-4,\n",
    "    \"DEVICE\": \"cuda\" if torch.cuda.is_available() else \"cpu\", \n",
    "    \"AUG\": \"No\",\n",
    "    \"SEED\": 2022,\n",
    "    \"opt\": 'Adam',\n",
    "    \"Normalization\": \"L2\",\n",
    "    \"img_size\": 256,\n",
    "    \"scheduler\": \"\",#'CosineAnnealingLR',\n",
    "    \"warmup_epochs\": 2,\n",
    "    \"n_folds\": 5,\n",
    "    \"folds_to_run\": [0],\n",
    "    \"min_lr\": 1e-6,\n",
    "    \"T_max\": 500, \n",
    "    \"T_0\": 20\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9598d5cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:27.490389Z",
     "iopub.status.busy": "2022-08-05T09:52:27.489594Z",
     "iopub.status.idle": "2022-08-05T09:52:27.503345Z",
     "shell.execute_reply": "2022-08-05T09:52:27.501874Z"
    },
    "papermill": {
     "duration": 0.02779,
     "end_time": "2022-08-05T09:52:27.505862",
     "exception": false,
     "start_time": "2022-08-05T09:52:27.478072",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> SEEDING DONE\n"
     ]
    }
   ],
   "source": [
    "def set_seed(seed = 42):\n",
    "    '''Sets the seed of the entire notebook so results are the same every time we run.\n",
    "    This is for REPRODUCIBILITY.'''\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    # When running on the CuDNN backend, two further options must be set\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # Set a fixed value for the hash seed\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    print('> SEEDING DONE')\n",
    "    \n",
    "set_seed(CONFIG[\"SEED\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b8c932",
   "metadata": {
    "papermill": {
     "duration": 0.01125,
     "end_time": "2022-08-05T09:52:27.528420",
     "exception": false,
     "start_time": "2022-08-05T09:52:27.517170",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# WandB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1eb64906",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:27.551734Z",
     "iopub.status.busy": "2022-08-05T09:52:27.550948Z",
     "iopub.status.idle": "2022-08-05T09:52:32.272374Z",
     "shell.execute_reply": "2022-08-05T09:52:32.271274Z"
    },
    "papermill": {
     "duration": 4.735091,
     "end_time": "2022-08-05T09:52:32.274508",
     "exception": false,
     "start_time": "2022-08-05T09:52:27.539417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Finishing last run (ID:2ft0alve) before initializing another..."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Synced <strong style=\"color:#cdcd00\">Vanilla_Unet_1</strong>: <a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle/runs/2ft0alve\" target=\"_blank\">https://wandb.ai/somusan/HuBMAP-Kaggle/runs/2ft0alve</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20220813_200502-2ft0alve/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Successfully finished last run (ID:2ft0alve). Initializing new run:<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.13.1 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.0"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/home/lakshita/somusan/hubmap_kaggle/nbs/wandb/run-20220814_001023-1jzupt3a</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle/runs/1jzupt3a\" target=\"_blank\">Vanilla_Unet_1</a></strong> to <a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import wandb\n",
    "\n",
    "# # try:\n",
    "# #     from kaggle_secrets import UserSecretsClient\n",
    "# #     user_secrets = UserSecretsClient()\n",
    "# #     api_key = user_secrets.get_secret(\"WANDB\")\n",
    "# #     wandb.login(key=api_key)\n",
    "# #     anonymous = None\n",
    "# # except:\n",
    "# #     anonymous = \"must\"\n",
    "# #     print('To use your W&B account,\\nGo to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. \\nGet your W&B access token from here: https://wandb.ai/authorize')\n",
    "# # wandb.init(project=\"PogChamp2 Baseline\")\n",
    "# run = wandb.init(project=CONFIG['competition'], \n",
    "#                  config=CONFIG,\n",
    "#                  job_type='Train',\n",
    "#                  tags=['semantic segmentation', CONFIG['model_name']],\n",
    "#                  anonymous='must',\n",
    "#                  name = \"Vanilla_Unet_1\",\n",
    "#                  notes = \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43856fc",
   "metadata": {
    "papermill": {
     "duration": 0.011192,
     "end_time": "2022-08-05T09:52:32.297838",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.286646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Tabular:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94df1d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kidney            99\n",
       "prostate          93\n",
       "largeintestine    58\n",
       "spleen            53\n",
       "lung              48\n",
       "Name: organ, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train.csv\")[\"organ\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0b011422",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:32.323255Z",
     "iopub.status.busy": "2022-08-05T09:52:32.322498Z",
     "iopub.status.idle": "2022-08-05T09:52:32.856029Z",
     "shell.execute_reply": "2022-08-05T09:52:32.855127Z"
    },
    "papermill": {
     "duration": 0.54857,
     "end_time": "2022-08-05T09:52:32.858294",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.309724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>img_path</th>\n",
       "      <th>mask_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/8388_04.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/8388_04.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/9450_04.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/9450_04.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/1500_03.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/1500_03.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/23961_06.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/23961_06.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/21086_00.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/21086_00.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3154</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/6611_05.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/6611_05.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3155</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/24961_00.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/24961_00.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/351_03.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/351_03.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/1229_04.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/1229_04.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/660_05.png</td>\n",
       "      <td>/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/660_05.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3159 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                             img_path  \\\n",
       "0      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/8388_04.png   \n",
       "1      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/9450_04.png   \n",
       "2      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/1500_03.png   \n",
       "3     /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/23961_06.png   \n",
       "4     /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/21086_00.png   \n",
       "...                                                                               ...   \n",
       "3154   /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/6611_05.png   \n",
       "3155  /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/24961_00.png   \n",
       "3156    /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/351_03.png   \n",
       "3157   /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/1229_04.png   \n",
       "3158    /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/660_05.png   \n",
       "\n",
       "                                                                                  mask_path  \n",
       "0      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/8388_04.png  \n",
       "1      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/9450_04.png  \n",
       "2      /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/1500_03.png  \n",
       "3     /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/23961_06.png  \n",
       "4     /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/21086_00.png  \n",
       "...                                                                                     ...  \n",
       "3154   /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/6611_05.png  \n",
       "3155  /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/24961_00.png  \n",
       "3156    /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/351_03.png  \n",
       "3157   /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/1229_04.png  \n",
       "3158    /home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/660_05.png  \n",
       "\n",
       "[3159 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !ls ../input/hubmap-tiled-data-generation/tiled_data\n",
    "DATA_DIR = \"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/\"\n",
    "\n",
    "img_list = [os.path.join(DATA_DIR+'images',i) for i in os.listdir(DATA_DIR+\"images\")]\n",
    "mask_list = [os.path.join(DATA_DIR+'binary_masks',i) for i in os.listdir(DATA_DIR+\"binary_masks\")]\n",
    "\n",
    "data_dict = {\n",
    "    \"img_path\": img_list,\n",
    "    \"mask_path\": mask_list\n",
    "}\n",
    "\n",
    "train_df = pd.DataFrame.from_dict(data_dict)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "display(train_df)\n",
    "# plt.imshow(plt.imread(mask_list[3]))\n",
    "# img_list[0:5],mask_list[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbfd1170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/8388_04.png',\n",
       " '/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/binary_masks/8388_04.png')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"img_path\"].iloc[0], train_df[\"mask_path\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e9c9b174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc4fd53eb00>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_cee = plt.imread(train_df[\"mask_path\"].iloc[9]).astype(np.float16)\n",
    "a = mask_cee\n",
    "new_a = np.where(a > 0, 1, a)\n",
    "plt.imshow(new_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "475a1b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13333334 0.13725491 0.14509805 0.14901961 0.15294118 0.16078432\n",
      " 0.16470589 0.16862746 0.17254902 0.1764706  0.18039216 0.18431373\n",
      " 0.1882353  0.19215687 0.19607843 0.2        0.20392157 0.20784314\n",
      " 0.21176471 0.21568628 0.21960784 0.22352941 0.22745098 0.23137255\n",
      " 0.23529412 0.23921569 0.24313726 0.24705882 0.2509804  0.25490198\n",
      " 0.25882354 0.2627451  0.26666668 0.27058825 0.27450982 0.2784314\n",
      " 0.28235295 0.28627452 0.2901961  0.29411766 0.29803923 0.3019608\n",
      " 0.30588236 0.30980393 0.3137255  0.31764707 0.32156864 0.3254902\n",
      " 0.32941177 0.33333334 0.3372549  0.34117648 0.34509805 0.34901962\n",
      " 0.3529412  0.35686275 0.36078432 0.3647059  0.36862746 0.37254903\n",
      " 0.3764706  0.38039216 0.38431373 0.3882353  0.39215687 0.39607844\n",
      " 0.4        0.40392157 0.40784314 0.4117647  0.41568628 0.41960785\n",
      " 0.42352942 0.42745098 0.43137255 0.43529412 0.4392157  0.44313726\n",
      " 0.44705883 0.4509804  0.45490196 0.45882353 0.4627451  0.46666667\n",
      " 0.47058824 0.4745098  0.47843137 0.48235294 0.4862745  0.49019608\n",
      " 0.49411765 0.49803922 0.5019608  0.5058824  0.50980395 0.5137255\n",
      " 0.5176471  0.52156866 0.5254902  0.5294118  0.53333336 0.5372549\n",
      " 0.5411765  0.54509807 0.54901963 0.5529412  0.5568628  0.56078434\n",
      " 0.5647059  0.5686275  0.57254905 0.5764706  0.5803922  0.58431375\n",
      " 0.5882353  0.5921569  0.59607846 0.6        0.6039216  0.60784316\n",
      " 0.6117647  0.6156863  0.61960787 0.62352943 0.627451   0.6313726\n",
      " 0.63529414 0.6392157  0.6431373  0.64705884 0.6509804  0.654902\n",
      " 0.65882355 0.6627451  0.6666667  0.67058825 0.6745098  0.6784314\n",
      " 0.68235296 0.6862745  0.6901961  0.69411767 0.69803923 0.7019608\n",
      " 0.7058824  0.70980394 0.7137255  0.7176471  0.72156864 0.7254902\n",
      " 0.7294118  0.73333335 0.7372549  0.7411765  0.74509805 0.7490196\n",
      " 0.7529412  0.75686276 0.7607843  0.7647059  0.76862746 0.77254903\n",
      " 0.7764706  0.78039217 0.78431374 0.7882353  0.7921569  0.79607844\n",
      " 0.8        0.8039216  0.80784315 0.8117647  0.8156863  0.81960785\n",
      " 0.8235294  0.827451   0.83137256 0.8352941  0.8392157  0.84313726\n",
      " 0.84705883 0.8509804  0.85490197 0.85882354 0.8627451  0.8666667\n",
      " 0.87058824 0.8745098  0.8784314  0.88235295 0.8862745  0.8901961\n",
      " 0.89411765 0.8980392  0.9019608  0.90588236 0.9098039  0.9137255\n",
      " 0.91764706 0.92156863 0.9254902  0.92941177 0.93333334 0.9372549\n",
      " 0.9411765  0.94509804 0.9490196  0.9529412  0.95686275 0.9607843 ]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "img_cee = plt.imread(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/tiled_data/images/62_00.png\")\n",
    "plt.imshow(img_cee)\n",
    "print(np.unique(img_cee))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "94bd3be2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:32.883849Z",
     "iopub.status.busy": "2022-08-05T09:52:32.883053Z",
     "iopub.status.idle": "2022-08-05T09:52:32.893548Z",
     "shell.execute_reply": "2022-08-05T09:52:32.892586Z"
    },
    "papermill": {
     "duration": 0.024948,
     "end_time": "2022-08-05T09:52:32.895509",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.870561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2527, 632)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df, test_df = train_test_split(train_df, test_size=0.2, random_state=CONFIG[\"SEED\"])\n",
    "train_df.__len__(), len(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "18bc5c33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:32.920541Z",
     "iopub.status.busy": "2022-08-05T09:52:32.919705Z",
     "iopub.status.idle": "2022-08-05T09:52:32.925373Z",
     "shell.execute_reply": "2022-08-05T09:52:32.924187Z"
    },
    "papermill": {
     "duration": 0.020319,
     "end_time": "2022-08-05T09:52:32.927390",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.907071",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %timeit img_temp = plt.imread(train_df[\"mask_path\"].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05c1e2e",
   "metadata": {
    "papermill": {
     "duration": 0.016348,
     "end_time": "2022-08-05T09:52:32.955024",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.938676",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Pytorch Data Class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c25b780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 1.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = np.array([0, 0.74582646, 0.74582646])\n",
    "std = np.array([0.24745085, 0.26182273, 0.25782376])\n",
    "a = mean\n",
    "new_a = np.where(a == 0.74582646, 1, a)\n",
    "new_a\n",
    "# torch.from_numpy(mean).to(torch.uint8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f1b09bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:32.983027Z",
     "iopub.status.busy": "2022-08-05T09:52:32.982735Z",
     "iopub.status.idle": "2022-08-05T09:52:32.995058Z",
     "shell.execute_reply": "2022-08-05T09:52:32.994200Z"
    },
    "papermill": {
     "duration": 0.027924,
     "end_time": "2022-08-05T09:52:32.997893",
     "exception": false,
     "start_time": "2022-08-05T09:52:32.969969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mean = np.array([0.7720342, 0.74582646, 0.76392896])\n",
    "std = np.array([0.24745085, 0.26182273, 0.25782376])\n",
    "\n",
    "\n",
    "def img2tensor(img,dtype:np.dtype=np.float32):\n",
    "    if img.ndim==2 : img = np.expand_dims(img,2)\n",
    "    img = np.transpose(img,(2,0,1))\n",
    "    return torch.from_numpy(img.astype(dtype, copy=False))\n",
    "\n",
    "\n",
    "\n",
    "class hubmap_data(Dataset):\n",
    "    def __init__(self, df, transform=None):\n",
    "        self.df = df\n",
    "        self.img_paths = df[\"img_path\"].to_numpy()\n",
    "        self.mask_paths = df[\"mask_path\"].to_numpy()\n",
    "        self.transform = transform\n",
    "        \n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        # load data from the pre-processed npy files\n",
    "        img_file = self.img_paths[index]\n",
    "\n",
    "        mask_file = self.mask_paths[index]\n",
    "        \n",
    "        img = cv2.cvtColor(cv2.imread(img_file), cv2.COLOR_BGR2RGB)\n",
    "        mask = cv2.imread((mask_file),cv2.IMREAD_GRAYSCALE)\n",
    "        if self.transform is not None:\n",
    "            transformed = self.transform(image=img, mask=mask)\n",
    "            img = transformed[\"image\"]\n",
    "            mask = transformed[\"mask\"]\n",
    "            mask=mask.reshape((1, mask.shape[0], mask.shape[1]))\n",
    "\n",
    "            if mask.shape[-1] == 1:\n",
    "                mask = mask.permute(2,0,1)\n",
    "                \n",
    "\n",
    "        return  img, mask \n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7436e795",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:33.023379Z",
     "iopub.status.busy": "2022-08-05T09:52:33.023081Z",
     "iopub.status.idle": "2022-08-05T09:52:33.029268Z",
     "shell.execute_reply": "2022-08-05T09:52:33.028285Z"
    },
    "papermill": {
     "duration": 0.020846,
     "end_time": "2022-08-05T09:52:33.031715",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.010869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_transform = A.Compose([\n",
    "        A.Resize(256, 256, p=1.0),\n",
    "        A.HorizontalFlip(),\n",
    "        A.VerticalFlip(),\n",
    "        A.RandomRotate90(),\n",
    "        A.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=15, p=0.9, \n",
    "                         border_mode=cv2.BORDER_REFLECT),\n",
    "        A.OneOf([\n",
    "            A.OpticalDistortion(p=0.3),\n",
    "            A.GridDistortion(p=.1),\n",
    "            A.PiecewiseAffine(p=0.3),\n",
    "        ], p=0.3),\n",
    "        A.OneOf([\n",
    "            A.CLAHE(clip_limit=2),\n",
    "        ], p=0.3),\n",
    "        ToTensorV2(),\n",
    "    ], p=1.0)\n",
    "\n",
    "\n",
    "test_transform = A.Compose(\n",
    "    [\n",
    "        A.Resize(256, 256, p=1.0),\n",
    "        ToTensorV2(),\n",
    "    ], p=1.0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1bb1a4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.uint8"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_ = hubmap_data(train_df, train_transform)\n",
    "train_data_[0][1].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ea6b65e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:33.058784Z",
     "iopub.status.busy": "2022-08-05T09:52:33.058514Z",
     "iopub.status.idle": "2022-08-05T09:52:33.228268Z",
     "shell.execute_reply": "2022-08-05T09:52:33.227188Z"
    },
    "papermill": {
     "duration": 0.184058,
     "end_time": "2022-08-05T09:52:33.230557",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.046499",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 256, 256]) torch.Size([1, 256, 256])\n",
      "torch.uint8\n"
     ]
    }
   ],
   "source": [
    "train_data = hubmap_data(train_df,train_transform)\n",
    "test_data = hubmap_data(test_df, test_transform) # CHANGED [change the augmentation method]\n",
    "\n",
    "print(train_data[10][0].shape, train_data[10][1].shape)\n",
    "# test_data[10][0].shape, test_data[10][1].shape\n",
    "print(train_data[10][1].dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff67748",
   "metadata": {},
   "source": [
    "### Ideas:\n",
    "- change loss\n",
    "- tta\n",
    "- scheduler\n",
    "- w/o imagenet training\n",
    "- optimizer for encoder and decoder seperately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8194007f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:33.257081Z",
     "iopub.status.busy": "2022-08-05T09:52:33.255503Z",
     "iopub.status.idle": "2022-08-05T09:52:33.779084Z",
     "shell.execute_reply": "2022-08-05T09:52:33.778081Z"
    },
    "papermill": {
     "duration": 0.539499,
     "end_time": "2022-08-05T09:52:33.781835",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.242336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_img = train_data[54][0]\n",
    "temp_mask = train_data[54][1]\n",
    "# plt.imshow(temp_mask.detach().numpy())\n",
    "# plt.imshow(temp_img.permute(1,2,0).detach().numpy())\n",
    "\n",
    "\n",
    "img_trfm = (temp_img.permute(1,2,0)).detach().numpy()\n",
    "mask_trfm = (temp_mask.permute(1,2,0)).detach().numpy()\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(img_trfm)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(mask_trfm)\n",
    "print(np.unique(mask_trfm))\n",
    "# temp_img.permute(1,2,0).detach().numpy() # 0.00784314 0.00392157"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3429341b",
   "metadata": {
    "papermill": {
     "duration": 0.014292,
     "end_time": "2022-08-05T09:52:33.814368",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.800076",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Unet Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2aceb20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:33.843991Z",
     "iopub.status.busy": "2022-08-05T09:52:33.843672Z",
     "iopub.status.idle": "2022-08-05T09:52:33.849640Z",
     "shell.execute_reply": "2022-08-05T09:52:33.848431Z"
    },
    "papermill": {
     "duration": 0.024248,
     "end_time": "2022-08-05T09:52:33.852750",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.828502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def init_model():\n",
    "    model =  smp.Unet(\n",
    "                 encoder_name='efficientnet-b0',\n",
    "                 encoder_weights='imagenet',\n",
    "                 in_channels=3,\n",
    "                 classes=1)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802c70bb",
   "metadata": {
    "papermill": {
     "duration": 0.014268,
     "end_time": "2022-08-05T09:52:33.883667",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.869399",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aba74a9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:33.913202Z",
     "iopub.status.busy": "2022-08-05T09:52:33.912907Z",
     "iopub.status.idle": "2022-08-05T09:52:49.264360Z",
     "shell.execute_reply": "2022-08-05T09:52:49.263331Z"
    },
    "papermill": {
     "duration": 15.369001,
     "end_time": "2022-08-05T09:52:49.266850",
     "exception": false,
     "start_time": "2022-08-05T09:52:33.897849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 256, 256])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_on_gpu = torch.cuda.is_available()\n",
    "model = init_model() #UNet(n_channels=CONFIG[\"in_channels\"], n_classes=CONFIG[\"num_classes\"])\n",
    "if train_on_gpu:\n",
    "    model.cuda()\n",
    "\n",
    "model(temp_img.unsqueeze(0).float().cuda()).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27e7792",
   "metadata": {
    "papermill": {
     "duration": 0.019147,
     "end_time": "2022-08-05T09:52:49.301050",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.281903",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c1a87870",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.331963Z",
     "iopub.status.busy": "2022-08-05T09:52:49.331657Z",
     "iopub.status.idle": "2022-08-05T09:52:49.338361Z",
     "shell.execute_reply": "2022-08-05T09:52:49.337420Z"
    },
    "papermill": {
     "duration": 0.024757,
     "end_time": "2022-08-05T09:52:49.340801",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.316044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# REMARK shuffle = False [result might change with shuffle =True]\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "               dataset=train_data,\n",
    "               batch_size=16,#CONFIG[\"BATCH_SIZE\"],\n",
    "               shuffle=False,\n",
    "               num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "              dataset=test_data,\n",
    "              batch_size=CONFIG[\"BATCH_SIZE\"],\n",
    "              shuffle=False,\n",
    "              num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6a9e2ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.uint8\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ds = HuBMAPDataset(tfms=albu_aug())\n",
    "# dl = torch.utils.data.DataLoader(ds,batch_size=64,shuffle=False,num_workers=NUM_WORKERS)\n",
    "it = iter(train_loader)\n",
    "imgs,masks = next(it)\n",
    "print(masks.dtype)\n",
    "plt.figure(figsize=(16,16))\n",
    "for i,(img,mask) in enumerate(zip(imgs,masks)):\n",
    "    img = ((img.permute(1,2,0))).numpy().astype(np.uint8)  # H , W , C\n",
    "    plt.subplot(8,8,i+1)\n",
    "    plt.imshow(img,vmin=0,vmax=255)\n",
    "    plt.imshow(mask.squeeze().numpy(), alpha=0.2)\n",
    "    plt.axis('off')\n",
    "    plt.subplots_adjust(wspace=None, hspace=None)\n",
    "    \n",
    "# del ds,dl,imgs,masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "79f948df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.374997Z",
     "iopub.status.busy": "2022-08-05T09:52:49.374730Z",
     "iopub.status.idle": "2022-08-05T09:52:49.384377Z",
     "shell.execute_reply": "2022-08-05T09:52:49.381573Z"
    },
    "papermill": {
     "duration": 0.028638,
     "end_time": "2022-08-05T09:52:49.387408",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.358770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_scheduler(optimizer):\n",
    "    num_steps = 12\n",
    "      \n",
    "    if CONFIG[\"scheduler\"] == 'CosineAnnealingLR':\n",
    "        scheduler = lr_scheduler.CosineAnnealingLR(optimizer,T_max=CONFIG['T_max'], \n",
    "                                                   eta_min=CONFIG['min_lr'])\n",
    "        \n",
    "    elif CONFIG[\"scheduler\"] == 'ReduceLROnPlateau':\n",
    "        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=7, threshold=0.0001, min_lr=1e-6)\n",
    "    elif CONFIG[\"scheduler\"] == 'ExponentialLR':\n",
    "        scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.85)\n",
    "    elif CONFIG['scheduler'] == 'CosineAnnealingWarmRestarts':\n",
    "        scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0=CONFIG['T_0'], \n",
    "                                                             eta_min=CONFIG['min_lr'])\n",
    "        \n",
    "    return scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fb77db",
   "metadata": {
    "papermill": {
     "duration": 0.014869,
     "end_time": "2022-08-05T09:52:49.417956",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.403087",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Loss, Optimizer & Metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f28c9442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.449190Z",
     "iopub.status.busy": "2022-08-05T09:52:49.448291Z",
     "iopub.status.idle": "2022-08-05T09:52:49.462990Z",
     "shell.execute_reply": "2022-08-05T09:52:49.462154Z"
    },
    "papermill": {
     "duration": 0.03351,
     "end_time": "2022-08-05T09:52:49.465858",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.432348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#PyTorch\n",
    "class DiceLoss(nn.Module):\n",
    "    def __init__(self, weight=None, size_average=True):\n",
    "        super(DiceLoss, self).__init__()\n",
    "\n",
    "    def forward(self, inputs, targets, smooth=1):\n",
    "        \n",
    "        #comment out if your model contains a sigmoid or equivalent activation layer\n",
    "        inputs = F.sigmoid(inputs)       \n",
    "        \n",
    "        #flatten label and prediction tensors\n",
    "        inputs = inputs.view(-1)\n",
    "        targets = targets.view(-1)\n",
    "        \n",
    "        intersection = (inputs * targets).sum()                            \n",
    "        dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)  \n",
    "        \n",
    "        return 1 - dice\n",
    "\n",
    "loss_fn = DiceLoss() #Dice()\n",
    "JaccardLoss = smp.losses.JaccardLoss(mode='multilabel')\n",
    "DiceLoss    = smp.losses.DiceLoss(mode='multilabel')\n",
    "BCELoss     = smp.losses.SoftBCEWithLogitsLoss()\n",
    "LovaszLoss  = smp.losses.LovaszLoss(mode='multilabel', per_image=False)\n",
    "TverskyLoss = smp.losses.TverskyLoss(mode='multilabel', log_loss=False)\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=CONFIG[\"LEARNING_RATE\"],weight_decay=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "85e48d73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.498009Z",
     "iopub.status.busy": "2022-08-05T09:52:49.497742Z",
     "iopub.status.idle": "2022-08-05T09:52:49.510793Z",
     "shell.execute_reply": "2022-08-05T09:52:49.509956Z"
    },
    "papermill": {
     "duration": 0.031154,
     "end_time": "2022-08-05T09:52:49.513498",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.482344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class DiceCoef(nn.Module):\n",
    "    def __init__(self, weight=None, size_average=True):\n",
    "        super().__init__()\n",
    "\n",
    "    def forward(self, y_pred, y_true, smooth=1.):\n",
    "        y_true = y_true.view(-1)\n",
    "        y_pred = y_pred.view(-1)\n",
    "        \n",
    "        #Round off y_pred\n",
    "        y_pred = torch.round((y_pred - y_pred.min()) / (y_pred.max() - y_pred.min()))\n",
    "        \n",
    "        intersection = (y_true * y_pred).sum()\n",
    "        dice = (2.0*intersection + smooth)/(y_true.sum() + y_pred.sum() + smooth)\n",
    "        \n",
    "        return dice\n",
    "\n",
    "def dice_coef(y_true, y_pred, thr=0.5, dim=(2,3), epsilon=0.001):\n",
    "    y_true = y_true.to(torch.float32)\n",
    "    y_pred = (y_pred>thr).to(torch.float32)\n",
    "    inter = (y_true*y_pred).sum(dim=dim)\n",
    "    den = y_true.sum(dim=dim) + y_pred.sum(dim=dim)\n",
    "    dice = ((2*inter+epsilon)/(den+epsilon)).mean(dim=(1,0))\n",
    "    return dice\n",
    "\n",
    "def iou_coef(y_true, y_pred, thr=0.5, dim=(2,3), epsilon=0.001):\n",
    "    y_true = y_true.to(torch.float32)\n",
    "    y_pred = (y_pred>thr).to(torch.float32)\n",
    "    inter = (y_true*y_pred).sum(dim=dim)\n",
    "    union = (y_true + y_pred - y_true*y_pred).sum(dim=dim)\n",
    "    iou = ((inter+epsilon)/(union+epsilon)).mean(dim=(1,0))\n",
    "    return iou\n",
    "\n",
    "dice_coe = DiceCoef()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90557ca",
   "metadata": {
    "papermill": {
     "duration": 0.014435,
     "end_time": "2022-08-05T09:52:49.546341",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.531906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# model save class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5ab72315",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.576522Z",
     "iopub.status.busy": "2022-08-05T09:52:49.576239Z",
     "iopub.status.idle": "2022-08-05T09:52:49.584207Z",
     "shell.execute_reply": "2022-08-05T09:52:49.582191Z"
    },
    "papermill": {
     "duration": 0.027642,
     "end_time": "2022-08-05T09:52:49.588477",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.560835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path = \"./model_weights_effunet_no_sch_tiled_bin_class\"\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)\n",
    "    \n",
    "def save_model(model, optimizer, criterion, epoch):\n",
    "    \"\"\"\n",
    "    Function to save the trained model to disk.\n",
    "    \"\"\"\n",
    "    print(f\"\\n Saving model at {epoch}th epoch\")\n",
    "    fname = f'{model_path}/{CONFIG[\"model_name\"]}-{CONFIG[\"LEARNING_RATE\"]}-{CONFIG[\"AUG\"]}-{epoch}-{CONFIG[\"BATCH_SIZE\"]}.pth'\n",
    "    torch.save({\n",
    "                'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'loss': criterion,\n",
    "                }, fname )\n",
    "    return fname"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e66eb8b",
   "metadata": {
    "papermill": {
     "duration": 0.014229,
     "end_time": "2022-08-05T09:52:49.618481",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.604252",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Train & Validation Function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "67d5b161",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.648689Z",
     "iopub.status.busy": "2022-08-05T09:52:49.648389Z",
     "iopub.status.idle": "2022-08-05T09:52:49.660422Z",
     "shell.execute_reply": "2022-08-05T09:52:49.655188Z"
    },
    "papermill": {
     "duration": 0.030525,
     "end_time": "2022-08-05T09:52:49.663403",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.632878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/code/debarshichanda/pytorch-w-b-birdclef-22-starter#Training-Function\n",
    "def train_fn(loader, model, optimizer, loss_fn,epoch):\n",
    "    bar = tqdm(enumerate(loader), total=len(loader))\n",
    "    \n",
    "    \n",
    "    running_loss = 0.0\n",
    "    dataset_size = 0\n",
    "    \n",
    "    for batch_idx, (data, targets) in bar:\n",
    "        data = data.float().to(device=CONFIG[\"DEVICE\"])\n",
    "        targets = targets.float().to(device=CONFIG[\"DEVICE\"])\n",
    "\n",
    "        # forward\n",
    "        predictions = model(data)\n",
    "\n",
    "        loss = loss_fn(predictions, targets)\n",
    "\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        # backward\n",
    "        loss.backward()\n",
    "        # Update Weights\n",
    "        optimizer.step()\n",
    "        \n",
    "#         if scheduler is not None:\n",
    "#             scheduler.step()\n",
    "            \n",
    "        # Calculate Loss\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        epoch_loss = running_loss / len(loader)\n",
    "#         running_loss += (loss.item() * BATCH_SIZE)\n",
    "#         dataset_size += BATCH_SIZE\n",
    "        \n",
    "#         epoch_loss = running_loss / dataset_size\n",
    "        # update tqdm loop\n",
    "        bar.set_postfix(Epoch=epoch,loss=epoch_loss)\n",
    "    \n",
    "        \n",
    "    gc.collect()\n",
    "    \n",
    "    return epoch_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bc39b315",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.696364Z",
     "iopub.status.busy": "2022-08-05T09:52:49.694987Z",
     "iopub.status.idle": "2022-08-05T09:52:49.708749Z",
     "shell.execute_reply": "2022-08-05T09:52:49.706200Z"
    },
    "papermill": {
     "duration": 0.032129,
     "end_time": "2022-08-05T09:52:49.711566",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.679437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def valid_one_epoch(model, dataloader, device, epoch, loss_fn):\n",
    "    model.eval()\n",
    "    \n",
    "    dataset_size = 0\n",
    "    running_loss = 0.0\n",
    "#     criterion = losses[CFG.loss]\n",
    "    \n",
    "    val_scores = []\n",
    "    \n",
    "    pbar = tqdm(enumerate(dataloader), total=len(dataloader))\n",
    "    for step, (images, masks) in pbar:\n",
    "        images  = images.to(CONFIG[\"DEVICE\"], dtype=torch.float)\n",
    "        masks   = masks.to(CONFIG[\"DEVICE\"], dtype=torch.float)\n",
    "        \n",
    "        \n",
    "        y_pred  = model(images)\n",
    "        loss    = loss_fn(y_pred, masks)\n",
    "        \n",
    "        running_loss += (loss.item() * CONFIG[\"BATCH_SIZE\"])\n",
    "        dataset_size += CONFIG[\"BATCH_SIZE\"]\n",
    "        \n",
    "        epoch_loss = running_loss / dataset_size\n",
    "        \n",
    "        y_pred = nn.Sigmoid()(y_pred)\n",
    "        val_dice = dice_coef(masks, y_pred).cpu().detach().numpy()\n",
    "        val_jaccard = iou_coef(masks, y_pred).cpu().detach().numpy()\n",
    "        val_scores.append([val_dice, val_jaccard])\n",
    "        \n",
    "        mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0\n",
    "        current_lr = optimizer.param_groups[0]['lr']\n",
    "        pbar.set_postfix(valid_loss=f'{epoch_loss:0.4f}',\n",
    "                        lr=f'{current_lr:0.5f}',\n",
    "                        gpu_memory=f'{mem:0.2f} GB')\n",
    "    val_scores  = np.mean(val_scores, axis=0)\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()\n",
    "    \n",
    "    return epoch_loss, val_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93abfac",
   "metadata": {
    "papermill": {
     "duration": 0.014254,
     "end_time": "2022-08-05T09:52:49.740931",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.726677",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ee5e74b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T09:52:49.778064Z",
     "iopub.status.busy": "2022-08-05T09:52:49.777776Z",
     "iopub.status.idle": "2022-08-05T12:31:24.751217Z",
     "shell.execute_reply": "2022-08-05T12:31:24.750210Z"
    },
    "papermill": {
     "duration": 9514.993823,
     "end_time": "2022-08-05T12:31:24.753406",
     "exception": false,
     "start_time": "2022-08-05T09:52:49.759583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m===================== Epoch: [1/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [09:42<00:00,  3.69s/it, Epoch=1, loss=0.572]\n",
      "100%|██████████| 79/79 [03:01<00:00,  2.29s/it, gpu_memory=3.34 GB, lr=0.00010, valid_loss=0.3423]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 1th epoch\n",
      "\n",
      " Final Train Loss: 0.5725  |  Final Val Loss: 0.3423\n",
      "Valid Dice: 0.2740 | Valid Jaccard: 0.2118\n",
      "\u001b[34mval dice increased: -inf ---> 0.2740319073200226\n",
      "\u001b[34mval jaccard increased: -inf ---> 0.21177852153778076\n",
      "\u001b[0m===================== Epoch: [2/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:47<00:00,  3.31it/s, Epoch=2, loss=0.16] \n",
      "100%|██████████| 79/79 [00:08<00:00,  8.86it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1487]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 2th epoch\n",
      "\n",
      " Final Train Loss: 0.1599  |  Final Val Loss: 0.1487\n",
      "Valid Dice: 0.4604 | Valid Jaccard: 0.4063\n",
      "\u001b[34mval dice increased: 0.2740319073200226 ---> 0.46039271354675293\n",
      "\u001b[34mval jaccard increased: 0.21177852153778076 ---> 0.40630924701690674\n",
      "\u001b[0m===================== Epoch: [3/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.40it/s, Epoch=3, loss=0.129] \n",
      "100%|██████████| 79/79 [00:08<00:00,  8.89it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1121]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 3th epoch\n",
      "\n",
      " Final Train Loss: 0.1287  |  Final Val Loss: 0.1121\n",
      "Valid Dice: 0.5449 | Valid Jaccard: 0.4904\n",
      "\u001b[34mval dice increased: 0.46039271354675293 ---> 0.5448552370071411\n",
      "\u001b[34mval jaccard increased: 0.40630924701690674 ---> 0.49039173126220703\n",
      "\u001b[0m===================== Epoch: [4/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.44it/s, Epoch=4, loss=0.113] \n",
      "100%|██████████| 79/79 [00:08<00:00,  8.80it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1037]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 4th epoch\n",
      "\n",
      " Final Train Loss: 0.1132  |  Final Val Loss: 0.1037\n",
      "Valid Dice: 0.6238 | Valid Jaccard: 0.5693\n",
      "\u001b[34mval dice increased: 0.5448552370071411 ---> 0.6238093972206116\n",
      "\u001b[34mval jaccard increased: 0.49039173126220703 ---> 0.5693173408508301\n",
      "\u001b[0m===================== Epoch: [5/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.51it/s, Epoch=5, loss=0.104] \n",
      "100%|██████████| 79/79 [00:08<00:00,  8.93it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1021]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 5th epoch\n",
      "\n",
      " Final Train Loss: 0.1040  |  Final Val Loss: 0.1021\n",
      "Valid Dice: 0.6718 | Valid Jaccard: 0.6192\n",
      "\u001b[34mval dice increased: 0.6238093972206116 ---> 0.6717789769172668\n",
      "\u001b[34mval jaccard increased: 0.5693173408508301 ---> 0.6192241907119751\n",
      "\u001b[0m===================== Epoch: [6/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.44it/s, Epoch=6, loss=0.0995]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.79it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0944]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 6th epoch\n",
      "\n",
      " Final Train Loss: 0.0995  |  Final Val Loss: 0.0944\n",
      "Valid Dice: 0.6656 | Valid Jaccard: 0.6120\n",
      "\u001b[0m===================== Epoch: [7/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.38it/s, Epoch=7, loss=0.0931]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.80it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0989]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 7th epoch\n",
      "\n",
      " Final Train Loss: 0.0931  |  Final Val Loss: 0.0989\n",
      "Valid Dice: 0.7026 | Valid Jaccard: 0.6528\n",
      "\u001b[34mval dice increased: 0.6717789769172668 ---> 0.702570915222168\n",
      "\u001b[34mval jaccard increased: 0.6192241907119751 ---> 0.6528330445289612\n",
      "\u001b[0m===================== Epoch: [8/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.39it/s, Epoch=8, loss=0.086] \n",
      "100%|██████████| 79/79 [00:08<00:00,  8.83it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1023]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 8th epoch\n",
      "\n",
      " Final Train Loss: 0.0860  |  Final Val Loss: 0.1023\n",
      "Valid Dice: 0.7172 | Valid Jaccard: 0.6675\n",
      "\u001b[34mval dice increased: 0.702570915222168 ---> 0.7171646356582642\n",
      "\u001b[34mval jaccard increased: 0.6528330445289612 ---> 0.6674961447715759\n",
      "\u001b[0m===================== Epoch: [9/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=9, loss=0.0822]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.82it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0999]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 9th epoch\n",
      "\n",
      " Final Train Loss: 0.0822  |  Final Val Loss: 0.0999\n",
      "Valid Dice: 0.7237 | Valid Jaccard: 0.6771\n",
      "\u001b[34mval dice increased: 0.7171646356582642 ---> 0.723724901676178\n",
      "\u001b[34mval jaccard increased: 0.6674961447715759 ---> 0.6770637631416321\n",
      "\u001b[0m===================== Epoch: [10/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.41it/s, Epoch=10, loss=0.0774]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.97it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0953]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 10th epoch\n",
      "\n",
      " Final Train Loss: 0.0774  |  Final Val Loss: 0.0953\n",
      "Valid Dice: 0.7311 | Valid Jaccard: 0.6841\n",
      "\u001b[34mval dice increased: 0.723724901676178 ---> 0.7311299443244934\n",
      "\u001b[34mval jaccard increased: 0.6770637631416321 ---> 0.6841458678245544\n",
      "\u001b[0m===================== Epoch: [11/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.37it/s, Epoch=11, loss=0.0757]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.92it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0994]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 11th epoch\n",
      "\n",
      " Final Train Loss: 0.0757  |  Final Val Loss: 0.0994\n",
      "Valid Dice: 0.7293 | Valid Jaccard: 0.6821\n",
      "\u001b[0m===================== Epoch: [12/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.38it/s, Epoch=12, loss=0.0747]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.91it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0945]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 12th epoch\n",
      "\n",
      " Final Train Loss: 0.0747  |  Final Val Loss: 0.0945\n",
      "Valid Dice: 0.7239 | Valid Jaccard: 0.6764\n",
      "\u001b[0m===================== Epoch: [13/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=13, loss=0.0699]\n",
      "100%|██████████| 79/79 [00:09<00:00,  8.76it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0854]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 13th epoch\n",
      "\n",
      " Final Train Loss: 0.0699  |  Final Val Loss: 0.0854\n",
      "Valid Dice: 0.7215 | Valid Jaccard: 0.6754\n",
      "\u001b[0m===================== Epoch: [14/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:50<00:00,  3.10it/s, Epoch=14, loss=0.0702]\n",
      "100%|██████████| 79/79 [00:09<00:00,  8.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0962]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 14th epoch\n",
      "\n",
      " Final Train Loss: 0.0702  |  Final Val Loss: 0.0962\n",
      "Valid Dice: 0.7240 | Valid Jaccard: 0.6781\n",
      "\u001b[0m===================== Epoch: [15/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.42it/s, Epoch=15, loss=0.0651]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0950]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 15th epoch\n",
      "\n",
      " Final Train Loss: 0.0651  |  Final Val Loss: 0.0950\n",
      "Valid Dice: 0.6812 | Valid Jaccard: 0.6332\n",
      "\u001b[0m===================== Epoch: [16/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.41it/s, Epoch=16, loss=0.0667]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1127]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 16th epoch\n",
      "\n",
      " Final Train Loss: 0.0667  |  Final Val Loss: 0.1127\n",
      "Valid Dice: 0.7130 | Valid Jaccard: 0.6677\n",
      "\u001b[0m===================== Epoch: [17/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=17, loss=0.0631]\n",
      "100%|██████████| 79/79 [00:10<00:00,  7.86it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0823]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 17th epoch\n",
      "\n",
      " Final Train Loss: 0.0631  |  Final Val Loss: 0.0823\n",
      "Valid Dice: 0.7389 | Valid Jaccard: 0.6917\n",
      "\u001b[34mval dice increased: 0.7311299443244934 ---> 0.7388610243797302\n",
      "\u001b[34mval jaccard increased: 0.6841458678245544 ---> 0.6917158365249634\n",
      "\u001b[0m===================== Epoch: [18/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.46it/s, Epoch=18, loss=0.0595]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0863]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 18th epoch\n",
      "\n",
      " Final Train Loss: 0.0595  |  Final Val Loss: 0.0863\n",
      "Valid Dice: 0.7394 | Valid Jaccard: 0.6929\n",
      "\u001b[34mval dice increased: 0.7388610243797302 ---> 0.7393980622291565\n",
      "\u001b[34mval jaccard increased: 0.6917158365249634 ---> 0.6928930878639221\n",
      "\u001b[0m===================== Epoch: [19/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=19, loss=0.0612]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0847]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 19th epoch\n",
      "\n",
      " Final Train Loss: 0.0612  |  Final Val Loss: 0.0847\n",
      "Valid Dice: 0.7430 | Valid Jaccard: 0.6986\n",
      "\u001b[34mval dice increased: 0.7393980622291565 ---> 0.7430151700973511\n",
      "\u001b[34mval jaccard increased: 0.6928930878639221 ---> 0.6985777616500854\n",
      "\u001b[0m===================== Epoch: [20/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=20, loss=0.0575]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0945]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 20th epoch\n",
      "\n",
      " Final Train Loss: 0.0575  |  Final Val Loss: 0.0945\n",
      "Valid Dice: 0.7496 | Valid Jaccard: 0.7042\n",
      "\u001b[34mval dice increased: 0.7430151700973511 ---> 0.7496224641799927\n",
      "\u001b[34mval jaccard increased: 0.6985777616500854 ---> 0.704227089881897\n",
      "\u001b[0m===================== Epoch: [21/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=21, loss=0.061] \n",
      "100%|██████████| 79/79 [00:08<00:00,  9.12it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0782]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 21th epoch\n",
      "\n",
      " Final Train Loss: 0.0610  |  Final Val Loss: 0.0782\n",
      "Valid Dice: 0.7461 | Valid Jaccard: 0.7012\n",
      "\u001b[0m===================== Epoch: [22/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.55it/s, Epoch=22, loss=0.0561]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.83it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0836]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 22th epoch\n",
      "\n",
      " Final Train Loss: 0.0561  |  Final Val Loss: 0.0836\n",
      "Valid Dice: 0.7406 | Valid Jaccard: 0.6958\n",
      "\u001b[0m===================== Epoch: [23/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:43<00:00,  3.61it/s, Epoch=23, loss=0.0544]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0817]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 23th epoch\n",
      "\n",
      " Final Train Loss: 0.0544  |  Final Val Loss: 0.0817\n",
      "Valid Dice: 0.7450 | Valid Jaccard: 0.7002\n",
      "\u001b[0m===================== Epoch: [24/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=24, loss=0.0544]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0807]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 24th epoch\n",
      "\n",
      " Final Train Loss: 0.0544  |  Final Val Loss: 0.0807\n",
      "Valid Dice: 0.7540 | Valid Jaccard: 0.7082\n",
      "\u001b[34mval dice increased: 0.7496224641799927 ---> 0.7539520263671875\n",
      "\u001b[34mval jaccard increased: 0.704227089881897 ---> 0.7081528902053833\n",
      "\u001b[0m===================== Epoch: [25/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=25, loss=0.0537]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.00it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0869]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 25th epoch\n",
      "\n",
      " Final Train Loss: 0.0537  |  Final Val Loss: 0.0869\n",
      "Valid Dice: 0.7437 | Valid Jaccard: 0.6990\n",
      "\u001b[0m===================== Epoch: [26/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=26, loss=0.0509]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.92it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0959]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 26th epoch\n",
      "\n",
      " Final Train Loss: 0.0509  |  Final Val Loss: 0.0959\n",
      "Valid Dice: 0.7505 | Valid Jaccard: 0.7050\n",
      "\u001b[0m===================== Epoch: [27/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=27, loss=0.0507]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0885]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 27th epoch\n",
      "\n",
      " Final Train Loss: 0.0507  |  Final Val Loss: 0.0885\n",
      "Valid Dice: 0.7390 | Valid Jaccard: 0.6948\n",
      "\u001b[0m===================== Epoch: [28/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=28, loss=0.0533]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.03it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0856]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 28th epoch\n",
      "\n",
      " Final Train Loss: 0.0533  |  Final Val Loss: 0.0856\n",
      "Valid Dice: 0.7596 | Valid Jaccard: 0.7146\n",
      "\u001b[34mval dice increased: 0.7539520263671875 ---> 0.7596228122711182\n",
      "\u001b[34mval jaccard increased: 0.7081528902053833 ---> 0.7146256566047668\n",
      "\u001b[0m===================== Epoch: [29/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=29, loss=0.0553]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.04it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0873]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 29th epoch\n",
      "\n",
      " Final Train Loss: 0.0553  |  Final Val Loss: 0.0873\n",
      "Valid Dice: 0.7491 | Valid Jaccard: 0.7049\n",
      "\u001b[0m===================== Epoch: [30/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=30, loss=0.0535]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0908]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 30th epoch\n",
      "\n",
      " Final Train Loss: 0.0535  |  Final Val Loss: 0.0908\n",
      "Valid Dice: 0.7605 | Valid Jaccard: 0.7179\n",
      "\u001b[34mval dice increased: 0.7596228122711182 ---> 0.7605427503585815\n",
      "\u001b[34mval jaccard increased: 0.7146256566047668 ---> 0.717865526676178\n",
      "\u001b[0m===================== Epoch: [31/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.51it/s, Epoch=31, loss=0.048] \n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0848]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 31th epoch\n",
      "\n",
      " Final Train Loss: 0.0480  |  Final Val Loss: 0.0848\n",
      "Valid Dice: 0.7558 | Valid Jaccard: 0.7121\n",
      "\u001b[0m===================== Epoch: [32/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.51it/s, Epoch=32, loss=0.0492]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.06it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0836]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 32th epoch\n",
      "\n",
      " Final Train Loss: 0.0492  |  Final Val Loss: 0.0836\n",
      "Valid Dice: 0.7668 | Valid Jaccard: 0.7247\n",
      "\u001b[34mval dice increased: 0.7605427503585815 ---> 0.7668046355247498\n",
      "\u001b[34mval jaccard increased: 0.717865526676178 ---> 0.7246951460838318\n",
      "\u001b[0m===================== Epoch: [33/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=33, loss=0.049] \n",
      "100%|██████████| 79/79 [00:08<00:00,  9.06it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 33th epoch\n",
      "\n",
      " Final Train Loss: 0.0490  |  Final Val Loss: 0.0814\n",
      "Valid Dice: 0.7677 | Valid Jaccard: 0.7245\n",
      "\u001b[34mval dice increased: 0.7668046355247498 ---> 0.767668604850769\n",
      "\u001b[0m===================== Epoch: [34/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.54it/s, Epoch=34, loss=0.0478]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0815]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 34th epoch\n",
      "\n",
      " Final Train Loss: 0.0478  |  Final Val Loss: 0.0815\n",
      "Valid Dice: 0.7630 | Valid Jaccard: 0.7203\n",
      "\u001b[0m===================== Epoch: [35/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=35, loss=0.0502]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.02it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0935]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 35th epoch\n",
      "\n",
      " Final Train Loss: 0.0502  |  Final Val Loss: 0.0935\n",
      "Valid Dice: 0.7444 | Valid Jaccard: 0.7002\n",
      "\u001b[0m===================== Epoch: [36/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.45it/s, Epoch=36, loss=0.0487]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0953]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 36th epoch\n",
      "\n",
      " Final Train Loss: 0.0487  |  Final Val Loss: 0.0953\n",
      "Valid Dice: 0.7615 | Valid Jaccard: 0.7187\n",
      "\u001b[0m===================== Epoch: [37/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=37, loss=0.0453]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0942]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 37th epoch\n",
      "\n",
      " Final Train Loss: 0.0453  |  Final Val Loss: 0.0942\n",
      "Valid Dice: 0.7674 | Valid Jaccard: 0.7240\n",
      "\u001b[0m===================== Epoch: [38/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=38, loss=0.0438]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.03it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0883]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 38th epoch\n",
      "\n",
      " Final Train Loss: 0.0438  |  Final Val Loss: 0.0883\n",
      "Valid Dice: 0.7553 | Valid Jaccard: 0.7119\n",
      "\u001b[0m===================== Epoch: [39/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.44it/s, Epoch=39, loss=0.0447]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0836]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 39th epoch\n",
      "\n",
      " Final Train Loss: 0.0447  |  Final Val Loss: 0.0836\n",
      "Valid Dice: 0.7597 | Valid Jaccard: 0.7153\n",
      "\u001b[0m===================== Epoch: [40/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.46it/s, Epoch=40, loss=0.0434]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0930]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 40th epoch\n",
      "\n",
      " Final Train Loss: 0.0434  |  Final Val Loss: 0.0930\n",
      "Valid Dice: 0.7650 | Valid Jaccard: 0.7208\n",
      "\u001b[0m===================== Epoch: [41/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=41, loss=0.0435]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0944]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 41th epoch\n",
      "\n",
      " Final Train Loss: 0.0435  |  Final Val Loss: 0.0944\n",
      "Valid Dice: 0.7718 | Valid Jaccard: 0.7295\n",
      "\u001b[34mval dice increased: 0.767668604850769 ---> 0.7718429565429688\n",
      "\u001b[34mval jaccard increased: 0.7246951460838318 ---> 0.7295495867729187\n",
      "\u001b[0m===================== Epoch: [42/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.58it/s, Epoch=42, loss=0.0432]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0898]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 42th epoch\n",
      "\n",
      " Final Train Loss: 0.0432  |  Final Val Loss: 0.0898\n",
      "Valid Dice: 0.7673 | Valid Jaccard: 0.7249\n",
      "\u001b[0m===================== Epoch: [43/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.45it/s, Epoch=43, loss=0.0432]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0862]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 43th epoch\n",
      "\n",
      " Final Train Loss: 0.0432  |  Final Val Loss: 0.0862\n",
      "Valid Dice: 0.7605 | Valid Jaccard: 0.7189\n",
      "\u001b[0m===================== Epoch: [44/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=44, loss=0.0452]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.11it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0857]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 44th epoch\n",
      "\n",
      " Final Train Loss: 0.0452  |  Final Val Loss: 0.0857\n",
      "Valid Dice: 0.7618 | Valid Jaccard: 0.7198\n",
      "\u001b[0m===================== Epoch: [45/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=45, loss=0.0423]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.02it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0853]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 45th epoch\n",
      "\n",
      " Final Train Loss: 0.0423  |  Final Val Loss: 0.0853\n",
      "Valid Dice: 0.7679 | Valid Jaccard: 0.7257\n",
      "\u001b[0m===================== Epoch: [46/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.57it/s, Epoch=46, loss=0.0428]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.04it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0830]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 46th epoch\n",
      "\n",
      " Final Train Loss: 0.0428  |  Final Val Loss: 0.0830\n",
      "Valid Dice: 0.7712 | Valid Jaccard: 0.7280\n",
      "\u001b[0m===================== Epoch: [47/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=47, loss=0.0416]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.03it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0897]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 47th epoch\n",
      "\n",
      " Final Train Loss: 0.0416  |  Final Val Loss: 0.0897\n",
      "Valid Dice: 0.7705 | Valid Jaccard: 0.7285\n",
      "\u001b[0m===================== Epoch: [48/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=48, loss=0.0408]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.11it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0866]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 48th epoch\n",
      "\n",
      " Final Train Loss: 0.0408  |  Final Val Loss: 0.0866\n",
      "Valid Dice: 0.7685 | Valid Jaccard: 0.7267\n",
      "\u001b[0m===================== Epoch: [49/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.55it/s, Epoch=49, loss=0.0402]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0854]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 49th epoch\n",
      "\n",
      " Final Train Loss: 0.0402  |  Final Val Loss: 0.0854\n",
      "Valid Dice: 0.7648 | Valid Jaccard: 0.7239\n",
      "\u001b[0m===================== Epoch: [50/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=50, loss=0.0406]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0867]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 50th epoch\n",
      "\n",
      " Final Train Loss: 0.0406  |  Final Val Loss: 0.0867\n",
      "Valid Dice: 0.7545 | Valid Jaccard: 0.7117\n",
      "\u001b[0m===================== Epoch: [51/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=51, loss=0.0416]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0929]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 51th epoch\n",
      "\n",
      " Final Train Loss: 0.0416  |  Final Val Loss: 0.0929\n",
      "Valid Dice: 0.7653 | Valid Jaccard: 0.7242\n",
      "\u001b[0m===================== Epoch: [52/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=52, loss=0.0415]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0895]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 52th epoch\n",
      "\n",
      " Final Train Loss: 0.0415  |  Final Val Loss: 0.0895\n",
      "Valid Dice: 0.7716 | Valid Jaccard: 0.7302\n",
      "\u001b[34mval jaccard increased: 0.7295495867729187 ---> 0.7302236557006836\n",
      "\u001b[0m===================== Epoch: [53/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=53, loss=0.0386]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0972]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 53th epoch\n",
      "\n",
      " Final Train Loss: 0.0386  |  Final Val Loss: 0.0972\n",
      "Valid Dice: 0.7700 | Valid Jaccard: 0.7287\n",
      "\u001b[0m===================== Epoch: [54/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=54, loss=0.0456]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0906]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 54th epoch\n",
      "\n",
      " Final Train Loss: 0.0456  |  Final Val Loss: 0.0906\n",
      "Valid Dice: 0.7566 | Valid Jaccard: 0.7150\n",
      "\u001b[0m===================== Epoch: [55/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=55, loss=0.0438]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0844]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 55th epoch\n",
      "\n",
      " Final Train Loss: 0.0438  |  Final Val Loss: 0.0844\n",
      "Valid Dice: 0.7532 | Valid Jaccard: 0.7109\n",
      "\u001b[0m===================== Epoch: [56/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=56, loss=0.0426]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0912]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 56th epoch\n",
      "\n",
      " Final Train Loss: 0.0426  |  Final Val Loss: 0.0912\n",
      "Valid Dice: 0.7606 | Valid Jaccard: 0.7183\n",
      "\u001b[0m===================== Epoch: [57/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.55it/s, Epoch=57, loss=0.0382]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0929]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 57th epoch\n",
      "\n",
      " Final Train Loss: 0.0382  |  Final Val Loss: 0.0929\n",
      "Valid Dice: 0.7686 | Valid Jaccard: 0.7282\n",
      "\u001b[0m===================== Epoch: [58/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=58, loss=0.0372]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0885]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 58th epoch\n",
      "\n",
      " Final Train Loss: 0.0372  |  Final Val Loss: 0.0885\n",
      "Valid Dice: 0.7611 | Valid Jaccard: 0.7194\n",
      "\u001b[0m===================== Epoch: [59/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=59, loss=0.0369]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0970]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 59th epoch\n",
      "\n",
      " Final Train Loss: 0.0369  |  Final Val Loss: 0.0970\n",
      "Valid Dice: 0.7637 | Valid Jaccard: 0.7217\n",
      "\u001b[0m===================== Epoch: [60/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:43<00:00,  3.59it/s, Epoch=60, loss=0.0361]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0951]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 60th epoch\n",
      "\n",
      " Final Train Loss: 0.0361  |  Final Val Loss: 0.0951\n",
      "Valid Dice: 0.7684 | Valid Jaccard: 0.7276\n",
      "\u001b[0m===================== Epoch: [61/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=61, loss=0.0361]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.96it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0902]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 61th epoch\n",
      "\n",
      " Final Train Loss: 0.0361  |  Final Val Loss: 0.0902\n",
      "Valid Dice: 0.7627 | Valid Jaccard: 0.7200\n",
      "\u001b[0m===================== Epoch: [62/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.43it/s, Epoch=62, loss=0.0358]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0912]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 62th epoch\n",
      "\n",
      " Final Train Loss: 0.0358  |  Final Val Loss: 0.0912\n",
      "Valid Dice: 0.7663 | Valid Jaccard: 0.7254\n",
      "\u001b[0m===================== Epoch: [63/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=63, loss=0.0359]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0903]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 63th epoch\n",
      "\n",
      " Final Train Loss: 0.0359  |  Final Val Loss: 0.0903\n",
      "Valid Dice: 0.7599 | Valid Jaccard: 0.7174\n",
      "\u001b[0m===================== Epoch: [64/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=64, loss=0.0348]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0958]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 64th epoch\n",
      "\n",
      " Final Train Loss: 0.0348  |  Final Val Loss: 0.0958\n",
      "Valid Dice: 0.7740 | Valid Jaccard: 0.7327\n",
      "\u001b[34mval dice increased: 0.7718429565429688 ---> 0.7740395069122314\n",
      "\u001b[34mval jaccard increased: 0.7302236557006836 ---> 0.7326973676681519\n",
      "\u001b[0m===================== Epoch: [65/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=65, loss=0.0363]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1039]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 65th epoch\n",
      "\n",
      " Final Train Loss: 0.0363  |  Final Val Loss: 0.1039\n",
      "Valid Dice: 0.7743 | Valid Jaccard: 0.7337\n",
      "\u001b[34mval dice increased: 0.7740395069122314 ---> 0.77427738904953\n",
      "\u001b[34mval jaccard increased: 0.7326973676681519 ---> 0.7336561679840088\n",
      "\u001b[0m===================== Epoch: [66/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=66, loss=0.0472]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.88it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0838]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 66th epoch\n",
      "\n",
      " Final Train Loss: 0.0472  |  Final Val Loss: 0.0838\n",
      "Valid Dice: 0.7759 | Valid Jaccard: 0.7334\n",
      "\u001b[34mval dice increased: 0.77427738904953 ---> 0.7759214043617249\n",
      "\u001b[0m===================== Epoch: [67/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=67, loss=0.0368]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0898]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 67th epoch\n",
      "\n",
      " Final Train Loss: 0.0368  |  Final Val Loss: 0.0898\n",
      "Valid Dice: 0.7685 | Valid Jaccard: 0.7256\n",
      "\u001b[0m===================== Epoch: [68/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=68, loss=0.0392]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0843]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 68th epoch\n",
      "\n",
      " Final Train Loss: 0.0392  |  Final Val Loss: 0.0843\n",
      "Valid Dice: 0.7675 | Valid Jaccard: 0.7256\n",
      "\u001b[0m===================== Epoch: [69/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.54it/s, Epoch=69, loss=0.0362]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0992]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 69th epoch\n",
      "\n",
      " Final Train Loss: 0.0362  |  Final Val Loss: 0.0992\n",
      "Valid Dice: 0.7665 | Valid Jaccard: 0.7246\n",
      "\u001b[0m===================== Epoch: [70/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.57it/s, Epoch=70, loss=0.0389]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0880]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 70th epoch\n",
      "\n",
      " Final Train Loss: 0.0389  |  Final Val Loss: 0.0880\n",
      "Valid Dice: 0.7689 | Valid Jaccard: 0.7283\n",
      "\u001b[0m===================== Epoch: [71/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.42it/s, Epoch=71, loss=0.0351]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0950]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 71th epoch\n",
      "\n",
      " Final Train Loss: 0.0351  |  Final Val Loss: 0.0950\n",
      "Valid Dice: 0.7746 | Valid Jaccard: 0.7341\n",
      "\u001b[34mval jaccard increased: 0.7336561679840088 ---> 0.7340503931045532\n",
      "\u001b[0m===================== Epoch: [72/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.46it/s, Epoch=72, loss=0.0344]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0982]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 72th epoch\n",
      "\n",
      " Final Train Loss: 0.0344  |  Final Val Loss: 0.0982\n",
      "Valid Dice: 0.7671 | Valid Jaccard: 0.7255\n",
      "\u001b[0m===================== Epoch: [73/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.51it/s, Epoch=73, loss=0.0369]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.06it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0887]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 73th epoch\n",
      "\n",
      " Final Train Loss: 0.0369  |  Final Val Loss: 0.0887\n",
      "Valid Dice: 0.7650 | Valid Jaccard: 0.7223\n",
      "\u001b[0m===================== Epoch: [74/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=74, loss=0.0373]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0938]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 74th epoch\n",
      "\n",
      " Final Train Loss: 0.0373  |  Final Val Loss: 0.0938\n",
      "Valid Dice: 0.7547 | Valid Jaccard: 0.7131\n",
      "\u001b[0m===================== Epoch: [75/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=75, loss=0.0368]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.11it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0935]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 75th epoch\n",
      "\n",
      " Final Train Loss: 0.0368  |  Final Val Loss: 0.0935\n",
      "Valid Dice: 0.7738 | Valid Jaccard: 0.7330\n",
      "\u001b[0m===================== Epoch: [76/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=76, loss=0.0343]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0955]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 76th epoch\n",
      "\n",
      " Final Train Loss: 0.0343  |  Final Val Loss: 0.0955\n",
      "Valid Dice: 0.7708 | Valid Jaccard: 0.7307\n",
      "\u001b[0m===================== Epoch: [77/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.44it/s, Epoch=77, loss=0.034] \n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0976]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 77th epoch\n",
      "\n",
      " Final Train Loss: 0.0340  |  Final Val Loss: 0.0976\n",
      "Valid Dice: 0.7579 | Valid Jaccard: 0.7159\n",
      "\u001b[0m===================== Epoch: [78/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.51it/s, Epoch=78, loss=0.0332]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0951]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 78th epoch\n",
      "\n",
      " Final Train Loss: 0.0332  |  Final Val Loss: 0.0951\n",
      "Valid Dice: 0.7639 | Valid Jaccard: 0.7230\n",
      "\u001b[0m===================== Epoch: [79/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.53it/s, Epoch=79, loss=0.0336]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.06it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1039]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 79th epoch\n",
      "\n",
      " Final Train Loss: 0.0336  |  Final Val Loss: 0.1039\n",
      "Valid Dice: 0.7603 | Valid Jaccard: 0.7190\n",
      "\u001b[0m===================== Epoch: [80/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=80, loss=0.0336]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.09it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1009]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 80th epoch\n",
      "\n",
      " Final Train Loss: 0.0336  |  Final Val Loss: 0.1009\n",
      "Valid Dice: 0.7621 | Valid Jaccard: 0.7198\n",
      "\u001b[0m===================== Epoch: [81/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=81, loss=0.033] \n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 81th epoch\n",
      "\n",
      " Final Train Loss: 0.0330  |  Final Val Loss: 0.1000\n",
      "Valid Dice: 0.7707 | Valid Jaccard: 0.7296\n",
      "\u001b[0m===================== Epoch: [82/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.56it/s, Epoch=82, loss=0.0324]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.03it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1052]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 82th epoch\n",
      "\n",
      " Final Train Loss: 0.0324  |  Final Val Loss: 0.1052\n",
      "Valid Dice: 0.7670 | Valid Jaccard: 0.7253\n",
      "\u001b[0m===================== Epoch: [83/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.49it/s, Epoch=83, loss=0.0325]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.03it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0952]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 83th epoch\n",
      "\n",
      " Final Train Loss: 0.0325  |  Final Val Loss: 0.0952\n",
      "Valid Dice: 0.7723 | Valid Jaccard: 0.7303\n",
      "\u001b[0m===================== Epoch: [84/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=84, loss=0.0384]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0938]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 84th epoch\n",
      "\n",
      " Final Train Loss: 0.0384  |  Final Val Loss: 0.0938\n",
      "Valid Dice: 0.7631 | Valid Jaccard: 0.7223\n",
      "\u001b[0m===================== Epoch: [85/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=85, loss=0.0354]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0993]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 85th epoch\n",
      "\n",
      " Final Train Loss: 0.0354  |  Final Val Loss: 0.0993\n",
      "Valid Dice: 0.7724 | Valid Jaccard: 0.7302\n",
      "\u001b[0m===================== Epoch: [86/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.48it/s, Epoch=86, loss=0.0328]\n",
      "100%|██████████| 79/79 [00:09<00:00,  8.29it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0944]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 86th epoch\n",
      "\n",
      " Final Train Loss: 0.0328  |  Final Val Loss: 0.0944\n",
      "Valid Dice: 0.7680 | Valid Jaccard: 0.7267\n",
      "\u001b[0m===================== Epoch: [87/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.57it/s, Epoch=87, loss=0.0326]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.79it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1021]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 87th epoch\n",
      "\n",
      " Final Train Loss: 0.0326  |  Final Val Loss: 0.1021\n",
      "Valid Dice: 0.7498 | Valid Jaccard: 0.7092\n",
      "\u001b[0m===================== Epoch: [88/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.57it/s, Epoch=88, loss=0.0322]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1025]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 88th epoch\n",
      "\n",
      " Final Train Loss: 0.0322  |  Final Val Loss: 0.1025\n",
      "Valid Dice: 0.7684 | Valid Jaccard: 0.7286\n",
      "\u001b[0m===================== Epoch: [89/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.55it/s, Epoch=89, loss=0.0411]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.12it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0986]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 89th epoch\n",
      "\n",
      " Final Train Loss: 0.0411  |  Final Val Loss: 0.0986\n",
      "Valid Dice: 0.7618 | Valid Jaccard: 0.7219\n",
      "\u001b[0m===================== Epoch: [90/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=90, loss=0.0358]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.11it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0956]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 90th epoch\n",
      "\n",
      " Final Train Loss: 0.0358  |  Final Val Loss: 0.0956\n",
      "Valid Dice: 0.7413 | Valid Jaccard: 0.6971\n",
      "\u001b[0m===================== Epoch: [91/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.46it/s, Epoch=91, loss=0.0335]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0974]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 91th epoch\n",
      "\n",
      " Final Train Loss: 0.0335  |  Final Val Loss: 0.0974\n",
      "Valid Dice: 0.7493 | Valid Jaccard: 0.7076\n",
      "\u001b[0m===================== Epoch: [92/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.57it/s, Epoch=92, loss=0.0355]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0977]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 92th epoch\n",
      "\n",
      " Final Train Loss: 0.0355  |  Final Val Loss: 0.0977\n",
      "Valid Dice: 0.7683 | Valid Jaccard: 0.7269\n",
      "\u001b[0m===================== Epoch: [93/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=93, loss=0.0336]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.08it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0910]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 93th epoch\n",
      "\n",
      " Final Train Loss: 0.0336  |  Final Val Loss: 0.0910\n",
      "Valid Dice: 0.7636 | Valid Jaccard: 0.7213\n",
      "\u001b[0m===================== Epoch: [94/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:46<00:00,  3.41it/s, Epoch=94, loss=0.0312]\n",
      "100%|██████████| 79/79 [00:09<00:00,  8.46it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0952]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 94th epoch\n",
      "\n",
      " Final Train Loss: 0.0312  |  Final Val Loss: 0.0952\n",
      "Valid Dice: 0.7641 | Valid Jaccard: 0.7224\n",
      "\u001b[0m===================== Epoch: [95/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.47it/s, Epoch=95, loss=0.0308]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.07it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0962]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 95th epoch\n",
      "\n",
      " Final Train Loss: 0.0308  |  Final Val Loss: 0.0962\n",
      "Valid Dice: 0.7562 | Valid Jaccard: 0.7137\n",
      "\u001b[0m===================== Epoch: [96/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.47it/s, Epoch=96, loss=0.0299]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.10it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.1007]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 96th epoch\n",
      "\n",
      " Final Train Loss: 0.0299  |  Final Val Loss: 0.1007\n",
      "Valid Dice: 0.7539 | Valid Jaccard: 0.7136\n",
      "\u001b[0m===================== Epoch: [97/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=97, loss=0.0307]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.11it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0986]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 97th epoch\n",
      "\n",
      " Final Train Loss: 0.0307  |  Final Val Loss: 0.0986\n",
      "Valid Dice: 0.7596 | Valid Jaccard: 0.7181\n",
      "\u001b[0m===================== Epoch: [98/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:43<00:00,  3.60it/s, Epoch=98, loss=0.0386]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.06it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0999]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 98th epoch\n",
      "\n",
      " Final Train Loss: 0.0386  |  Final Val Loss: 0.0999\n",
      "Valid Dice: 0.7588 | Valid Jaccard: 0.7171\n",
      "\u001b[0m===================== Epoch: [99/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:45<00:00,  3.50it/s, Epoch=99, loss=0.0414]\n",
      "100%|██████████| 79/79 [00:08<00:00,  8.88it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0990]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 99th epoch\n",
      "\n",
      " Final Train Loss: 0.0414  |  Final Val Loss: 0.0990\n",
      "Valid Dice: 0.7542 | Valid Jaccard: 0.7132\n",
      "\u001b[0m===================== Epoch: [100/100] =====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158/158 [00:44<00:00,  3.52it/s, Epoch=100, loss=0.0329]\n",
      "100%|██████████| 79/79 [00:08<00:00,  9.05it/s, gpu_memory=3.37 GB, lr=0.00010, valid_loss=0.0961]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Saving model at 100th epoch\n",
      "\n",
      " Final Train Loss: 0.0329  |  Final Val Loss: 0.0961\n",
      "Valid Dice: 0.7533 | Valid Jaccard: 0.7122\n",
      "\n",
      " ====================== [Training Summary] ====================== \n",
      "\n",
      "Training complete in 1h 46m 46s\n",
      "avg time per [train + val] epoch 0h 1m 4s\n",
      "Best: ~~~~~~ dice: 0.7759  || jaccard 0.7341 || val_loss 0.0782 || train_loss 0.0299 ~~~~~~\n",
      "Saved training summary...\n",
      "\n",
      " =============================================================== \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>Train_Loss</th>\n",
       "      <th>Valid_Loss</th>\n",
       "      <th>Valid_jaccard</th>\n",
       "      <th>Valid_dice</th>\n",
       "      <th>model_file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.572457</td>\n",
       "      <td>0.342285</td>\n",
       "      <td>0.211779</td>\n",
       "      <td>0.274032</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-1-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.159940</td>\n",
       "      <td>0.148667</td>\n",
       "      <td>0.406309</td>\n",
       "      <td>0.460393</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-2-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.128688</td>\n",
       "      <td>0.112099</td>\n",
       "      <td>0.490392</td>\n",
       "      <td>0.544855</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-3-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.113170</td>\n",
       "      <td>0.103722</td>\n",
       "      <td>0.569317</td>\n",
       "      <td>0.623809</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-4-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.103957</td>\n",
       "      <td>0.102096</td>\n",
       "      <td>0.619224</td>\n",
       "      <td>0.671779</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-5-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0.029933</td>\n",
       "      <td>0.100651</td>\n",
       "      <td>0.713582</td>\n",
       "      <td>0.753950</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-96-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0.030691</td>\n",
       "      <td>0.098594</td>\n",
       "      <td>0.718087</td>\n",
       "      <td>0.759593</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-97-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>0.038603</td>\n",
       "      <td>0.099853</td>\n",
       "      <td>0.717138</td>\n",
       "      <td>0.758794</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-98-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>0.041412</td>\n",
       "      <td>0.098968</td>\n",
       "      <td>0.713176</td>\n",
       "      <td>0.754186</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-99-8.pth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>0.032912</td>\n",
       "      <td>0.096082</td>\n",
       "      <td>0.712167</td>\n",
       "      <td>0.753308</td>\n",
       "      <td>./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-100-8.pth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch  Train_Loss  Valid_Loss  Valid_jaccard  Valid_dice  \\\n",
       "0       1    0.572457    0.342285       0.211779    0.274032   \n",
       "1       2    0.159940    0.148667       0.406309    0.460393   \n",
       "2       3    0.128688    0.112099       0.490392    0.544855   \n",
       "3       4    0.113170    0.103722       0.569317    0.623809   \n",
       "4       5    0.103957    0.102096       0.619224    0.671779   \n",
       "..    ...         ...         ...            ...         ...   \n",
       "95     96    0.029933    0.100651       0.713582    0.753950   \n",
       "96     97    0.030691    0.098594       0.718087    0.759593   \n",
       "97     98    0.038603    0.099853       0.717138    0.758794   \n",
       "98     99    0.041412    0.098968       0.713176    0.754186   \n",
       "99    100    0.032912    0.096082       0.712167    0.753308   \n",
       "\n",
       "                                                                         model_file  \n",
       "0     ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-1-8.pth  \n",
       "1     ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-2-8.pth  \n",
       "2     ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-3-8.pth  \n",
       "3     ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-4-8.pth  \n",
       "4     ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-5-8.pth  \n",
       "..                                                                              ...  \n",
       "95   ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-96-8.pth  \n",
       "96   ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-97-8.pth  \n",
       "97   ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-98-8.pth  \n",
       "98   ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-99-8.pth  \n",
       "99  ./model_weights_effunet_no_sch_tiled_bin_class/Vanilla_Unet-0.0001-No-100-8.pth  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = defaultdict(list)\n",
    "best_val_dice = -float('inf')\n",
    "best_val_jaccard = -float('inf')\n",
    "start = time.time()\n",
    "\n",
    "loss_fn = BCELoss\n",
    "\n",
    "for epoch in range(CONFIG[\"NUM_EPOCHS\"]):\n",
    "    \n",
    "    print(f'{sr_}===================== Epoch: [{epoch+1}/{CONFIG[\"NUM_EPOCHS\"]}] =====================')\n",
    "\n",
    "    # train and validation loop\n",
    "    train_loss = train_fn(train_loader, model, optimizer, loss_fn, epoch+1)\n",
    "    val_loss, val_scores = valid_one_epoch(model, test_loader,CONFIG[\"DEVICE\"], epoch+1, loss_fn)\n",
    "    val_dice, val_jaccard = val_scores\n",
    "    \n",
    "    \n",
    "    # save model\n",
    "    model_file = save_model(model, optimizer, BCELoss, epoch+1)\n",
    "    \n",
    "    \n",
    "    # weight and baise Log the metrics\n",
    "    wandb.log({\"Train Loss\": train_loss})\n",
    "    wandb.log({\"Valid Loss\": val_loss})\n",
    "    wandb.log({\"Valid dice\": val_dice})\n",
    "    wandb.log({\"Valid jaccard\": val_jaccard})\n",
    "\n",
    "    # logging\n",
    "    history[\"epoch\"].append(epoch+1)\n",
    "    history['Train_Loss'].append(train_loss)\n",
    "    history['Valid_Loss'].append(val_loss)\n",
    "    history['Valid_jaccard'].append(val_jaccard)\n",
    "    history['Valid_dice'].append(val_dice)\n",
    "    history[\"model_file\"].append(model_file)\n",
    "\n",
    "    \n",
    "    # print loss and scores\n",
    "    print(f\"\\n Final Train Loss: {train_loss:0.4f}  |  Final Val Loss: {val_loss:0.4f}\") \n",
    "    \n",
    "    print(f'Valid Dice: {val_dice:0.4f} | Valid Jaccard: {val_jaccard:0.4f}')\n",
    "    \n",
    "    \n",
    "    # print best val score\n",
    "    if best_val_dice < val_dice:\n",
    "        print(f\"{b_}val dice increased: {best_val_dice} ---> {val_dice}\")\n",
    "        best_val_dice = val_dice\n",
    "    \n",
    "    if best_val_jaccard < val_jaccard:\n",
    "        print(f\"{b_}val jaccard increased: {best_val_jaccard} ---> {val_jaccard}\")\n",
    "        best_val_jaccard = val_jaccard\n",
    "        \n",
    "        \n",
    "        \n",
    "# print time\n",
    "end = time.time()\n",
    "time_elapsed = end - start\n",
    "avg_time_per_epoch = time_elapsed/CONFIG[\"NUM_EPOCHS\"] \n",
    "summary_df = pd.DataFrame.from_dict(history)\n",
    "\n",
    "print(f'\\n ====================== [Training Summary] ====================== \\n')\n",
    "print('Training complete in {:.0f}h {:.0f}m {:.0f}s'.format(\n",
    "    time_elapsed // 3600, (time_elapsed % 3600) // 60, (time_elapsed % 3600) % 60))\n",
    "\n",
    "print('avg time per [train + val] epoch {:.0f}h {:.0f}m {:.0f}s'.format(\n",
    "    avg_time_per_epoch // 3600, (avg_time_per_epoch % 3600) // 60, (avg_time_per_epoch % 3600) % 60))\n",
    "\n",
    "print(\"Best: ~~~~~~ dice: {:.4f}  || jaccard {:.4f} || val_loss {:.4f} || train_loss {:.4f} ~~~~~~\".format(\n",
    "    best_val_dice, best_val_jaccard, min(history['Valid_Loss']), min(history['Train_Loss'])))\n",
    "\n",
    "\n",
    "summary_df.to_csv('training_summary.csv')\n",
    "print(\"Saved training summary...\")\n",
    "\n",
    "print(f'\\n =============================================================== \\n')\n",
    "\n",
    "display(summary_df)\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "# plotting training and validation loss\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "fig1 = plt.subplot(2,2,1)\n",
    "fig1.plot(history['epoch'], history['Train_Loss'], color='lime', marker='>')\n",
    "fig1.set_title(\"train loss [dice]\")\n",
    "fig1.set(xlabel='epoch', ylabel='train loss')\n",
    "\n",
    "fig2 = plt.subplot(2,2,2)\n",
    "fig2.plot(history['epoch'], history['Valid_Loss'], color='cyan',  marker='>')\n",
    "fig2.set_title(\"val loss [dice]\")\n",
    "fig2.set(xlabel='epoch', ylabel='val loss')\n",
    "\n",
    "fig3 = plt.subplot(2,2,3)\n",
    "fig3.plot(history['epoch'], history['Valid_dice'], color='orange',  marker='>')\n",
    "fig3.set_title(\"Val Dice\")\n",
    "fig3.set(xlabel='epoch', ylabel='val dice')\n",
    "\n",
    "fig4 = plt.subplot(2,2,4)\n",
    "fig4.plot(history['epoch'], history['Valid_jaccard'], color='lightcoral',  marker='>')\n",
    "fig4.set_title(\"val jaccard\")\n",
    "fig4.set(xlabel='epoch', ylabel='val jaccard')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "249a6fe2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4622ccf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2ae8bac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ccdd989f",
   "metadata": {
    "papermill": {
     "duration": 2.296108,
     "end_time": "2022-08-05T12:31:29.348610",
     "exception": false,
     "start_time": "2022-08-05T12:31:27.052502",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Inference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1000b7a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:33.632440Z",
     "iopub.status.busy": "2022-08-05T12:31:33.631883Z",
     "iopub.status.idle": "2022-08-05T12:31:33.642209Z",
     "shell.execute_reply": "2022-08-05T12:31:33.639177Z"
    },
    "papermill": {
     "duration": 2.15568,
     "end_time": "2022-08-05T12:31:33.645207",
     "exception": false,
     "start_time": "2022-08-05T12:31:31.489527",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from PIL import Image\n",
    "\n",
    "# def tile_image(p_img, folder, size: int = 768) -> list:\n",
    "#     w = h = size\n",
    "#     im = np.array(Image.open(p_img))\n",
    "#     # https://stackoverflow.com/a/47581978/4521646\n",
    "#     tiles = [im[i:(i + h), j:(j + w), ...] for i in range(0, im.shape[0], h) for j in range(0, im.shape[1], w)]\n",
    "#     idxs = [(i, (i + h), j, (j + w)) for i in range(0, im.shape[0], h) for j in range(0, im.shape[1], w)]\n",
    "#     name, _ = os.path.splitext(os.path.basename(p_img))\n",
    "#     files = []\n",
    "#     for k, tile in enumerate(tiles):\n",
    "#         if tile.shape[:2] != (h, w):\n",
    "#             tile_ = tile\n",
    "#             tile = np.zeros_like(tiles[0])\n",
    "#             tile[:tile_.shape[0], :tile_.shape[1], ...] = tile_\n",
    "#         p_img = os.path.join(folder, f\"{name}_{k:02}.png\")\n",
    "#         Image.fromarray(tile).save(p_img)\n",
    "#         files.append(p_img)\n",
    "#     return files, idxs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "52575b12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:38.053828Z",
     "iopub.status.busy": "2022-08-05T12:31:38.052490Z",
     "iopub.status.idle": "2022-08-05T12:31:38.063383Z",
     "shell.execute_reply": "2022-08-05T12:31:38.062320Z"
    },
    "papermill": {
     "duration": 2.282369,
     "end_time": "2022-08-05T12:31:38.068885",
     "exception": false,
     "start_time": "2022-08-05T12:31:35.786516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !mkdir -p /kaggle/temp/images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3ac0a5ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:42.525124Z",
     "iopub.status.busy": "2022-08-05T12:31:42.524537Z",
     "iopub.status.idle": "2022-08-05T12:31:42.531238Z",
     "shell.execute_reply": "2022-08-05T12:31:42.530198Z"
    },
    "papermill": {
     "duration": 2.145943,
     "end_time": "2022-08-05T12:31:42.534937",
     "exception": false,
     "start_time": "2022-08-05T12:31:40.388994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !mkdir -p frozen_packages\n",
    "# !cp ../input/starter-flash-semantic-segmentation/frozen_packages/* frozen_packages/\n",
    "# !cp ../input/ftus-segm-eda-viewer/frozen_packages/* frozen_packages/\n",
    "# !pip install -q 'kaggle-image-segmentation' --no-index --find-links frozen_packages/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c9533d3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:47.226855Z",
     "iopub.status.busy": "2022-08-05T12:31:47.226255Z",
     "iopub.status.idle": "2022-08-05T12:31:47.233967Z",
     "shell.execute_reply": "2022-08-05T12:31:47.233016Z"
    },
    "papermill": {
     "duration": 2.134014,
     "end_time": "2022-08-05T12:31:47.236560",
     "exception": false,
     "start_time": "2022-08-05T12:31:45.102546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from scipy.ndimage import binary_opening\n",
    "# from skimage.morphology import disk\n",
    "# from kaggle_imsegm.mask import rle_encode\n",
    "\n",
    "\n",
    "# DATASET_FOLDER = \"/kaggle/input/hubmap-organ-segmentation\"\n",
    "# df_test = pd.read_csv(\"../input/hubmap-organ-segmentation/test.csv\")\n",
    "# TILE_SIZE = 1024\n",
    "\n",
    "# preds = []\n",
    "# for _, row in df_test.iterrows():\n",
    "# #     print(row[\"pixel_size\"])\n",
    "#     scale = row[\"pixel_size\"] / 0.4\n",
    "#     test_img = os.path.join(DATASET_FOLDER, \"test_images\", f\"{row['id']}.tiff\")\n",
    "#     im = plt.imread(test_img)\n",
    "    \n",
    "#     # perform scaling on level tiles as the input is scaled to the CNN input size anyway\n",
    "    \n",
    "#     tiles_img, idxs = tile_image(test_img, \"/kaggle/temp/images\", size=int(TILE_SIZE / scale))\n",
    "# #     print(idxs)\n",
    "#     pred_list = []\n",
    "#     for img_file in tiles_img:\n",
    "#         img_org = plt.imread(img_file)\n",
    "        \n",
    "#         transformed = test_transform(image=img_org)\n",
    "#         img = transformed[\"image\"]\n",
    "#         img = img.reshape((1, *img.shape))\n",
    "#         img = img.to(CONFIG[\"DEVICE\"])\n",
    "        \n",
    "#         output = model(img)\n",
    "#         output = nn.Sigmoid()(output)\n",
    "#         msks = (output>0.5).to(torch.uint8).cpu().detach().numpy()\n",
    "        \n",
    "#         msks = msks.reshape((msks.shape[2], msks.shape[3]))\n",
    "#         msks = cv2.resize(msks, (img_org.shape[0:2]), interpolation = cv2.INTER_AREA) #cv2.resize(msks,(*img_org.shape))\n",
    "# #         print(\"msks--->\", msks.shape)\n",
    "        \n",
    "#         pred_list.append(msks)\n",
    "    \n",
    "    \n",
    "#     seg = np.zeros(im.shape[:2], dtype=np.uint8)\n",
    "#     for tile, (i1, i2, j1, j2) in zip(pred_list, idxs):\n",
    "#         i2 = min(i2, im.shape[0])\n",
    "#         j2 = min(j2, im.shape[1])\n",
    "# #         print('tile',np.array(tile, dtype=np.uint8)[:(i2 - i1), :(j2 - j1)].shape)\n",
    "# #         print(seg[i1:i2, j1:j2].shape)\n",
    "#         seg[i1:i2, j1:j2] = np.array(tile, dtype=np.uint8)[:(i2 - i1), :(j2 - j1)]\n",
    "#     # seg = resize(seg * 255, img.shape[:2], order=0) / 255\n",
    "#     seg = (seg >= 1).astype(np.uint8)  # binary mask\n",
    "#     seg = binary_opening(seg, structure=disk(6)).astype(np.uint8)\n",
    "    \n",
    "    \n",
    "#     rle = rle_encode(seg.T) if np.sum(seg) > 1 else {}\n",
    "#     name, _ = os.path.splitext(os.path.basename(test_img))\n",
    "#     preds.append({\"id\": row['id'], \"rle\": rle.get(1, \"\")})\n",
    "    \n",
    "# df_pred = pd.DataFrame(preds)\n",
    "# display(df_pred[df_pred[\"rle\"] != \"\"].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f76165b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:51.798918Z",
     "iopub.status.busy": "2022-08-05T12:31:51.798352Z",
     "iopub.status.idle": "2022-08-05T12:31:51.804521Z",
     "shell.execute_reply": "2022-08-05T12:31:51.803276Z"
    },
    "papermill": {
     "duration": 2.150654,
     "end_time": "2022-08-05T12:31:51.807724",
     "exception": false,
     "start_time": "2022-08-05T12:31:49.657070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_ssub = pd.read_csv(os.path.join(DATASET_FOLDER, \"sample_submission.csv\"))\n",
    "# del df_ssub['rle']\n",
    "# df_pred = df_ssub.merge(df_pred, on='id')\n",
    "\n",
    "# df_pred[['id', 'rle']].to_csv(\"submission.csv\", index=False)\n",
    "\n",
    "# !head submission.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9e1eb486",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:31:56.373162Z",
     "iopub.status.busy": "2022-08-05T12:31:56.372632Z",
     "iopub.status.idle": "2022-08-05T12:31:56.379691Z",
     "shell.execute_reply": "2022-08-05T12:31:56.378656Z"
    },
    "papermill": {
     "duration": 2.429302,
     "end_time": "2022-08-05T12:31:56.382912",
     "exception": false,
     "start_time": "2022-08-05T12:31:53.953610",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(seg.shape)\n",
    "# plt.imshow(seg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363aeabe",
   "metadata": {
    "papermill": {
     "duration": 2.406307,
     "end_time": "2022-08-05T12:32:00.926806",
     "exception": false,
     "start_time": "2022-08-05T12:31:58.520499",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model File Check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c54ad86c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-05T12:32:05.195880Z",
     "iopub.status.busy": "2022-08-05T12:32:05.195526Z",
     "iopub.status.idle": "2022-08-05T12:32:06.353591Z",
     "shell.execute_reply": "2022-08-05T12:32:06.352364Z"
    },
    "papermill": {
     "duration": 3.277763,
     "end_time": "2022-08-05T12:32:06.356010",
     "exception": false,
     "start_time": "2022-08-05T12:32:03.078247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: cannot access './model_weights': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!ls -la ./model_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9686579b",
   "metadata": {
    "papermill": {
     "duration": 2.136924,
     "end_time": "2022-08-05T12:32:10.621982",
     "exception": false,
     "start_time": "2022-08-05T12:32:08.485058",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17be2d99",
   "metadata": {
    "papermill": {
     "duration": 2.124861,
     "end_time": "2022-08-05T12:32:15.096416",
     "exception": false,
     "start_time": "2022-08-05T12:32:12.971555",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1192430e",
   "metadata": {
    "papermill": {
     "duration": 2.115912,
     "end_time": "2022-08-05T12:32:19.335809",
     "exception": false,
     "start_time": "2022-08-05T12:32:17.219897",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a01833",
   "metadata": {
    "papermill": {
     "duration": 2.348658,
     "end_time": "2022-08-05T12:32:23.843920",
     "exception": false,
     "start_time": "2022-08-05T12:32:21.495262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.0 ('.venv': venv)",
   "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": "3.7.0"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 9646.266578,
   "end_time": "2022-08-05T12:32:28.698541",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-08-05T09:51:42.431963",
   "version": "2.3.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "21a2a557654fc1676068684031cf9bb9dfda94e124d3623f4e9c9ed764d794ac"
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0ea51a4850c84b36bff51fdd6f2d1e82": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_ca5cfa16f1e64484a7d696dde4186d37",
       "max": 77999237,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_c5a7559f871f4fe882bb7659626c1ede",
       "value": 77999237
      }
     },
     "2e6942b0458f4b879b2ed97233ad5f7b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "342193034a2f45ce963f676283332bb7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_2e6942b0458f4b879b2ed97233ad5f7b",
       "max": 1,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_6686062741b145748e22c2bf7911c59d",
       "value": 0
      }
     },
     "37becea4b9e74366b33057289a79560d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "LabelModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "LabelView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_3b1c5a6ac9984e45bef2842279824a5a",
       "placeholder": "​",
       "style": "IPY_MODEL_d51da069d6f1435e9c2a5f4d3e8b87a4",
       "value": ""
      }
     },
     "3b1c5a6ac9984e45bef2842279824a5a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4c99be112a334536ad8d5b7457597c65": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "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": ""
      }
     },
     "64518feff0374a91b6cbc00d0d2802fb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_f3dd0beebc304c4290413850e943a6a6",
       "placeholder": "​",
       "style": "IPY_MODEL_f1f9810008bf4a29b4dae35f10b10d21",
       "value": " 74.4M/74.4M [00:06&lt;00:00, 8.26MB/s]"
      }
     },
     "6686062741b145748e22c2bf7911c59d": {
      "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,
       "description_width": ""
      }
     },
     "72a27fb4514047cc80b3308db42dfa04": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "8785b7a2028e479080db54777bd6eaf8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "VBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "VBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_37becea4b9e74366b33057289a79560d",
        "IPY_MODEL_342193034a2f45ce963f676283332bb7"
       ],
       "layout": "IPY_MODEL_bfe232db2b284f80bc9b6ef833c2c50f"
      }
     },
     "bfe232db2b284f80bc9b6ef833c2c50f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c5a7559f871f4fe882bb7659626c1ede": {
      "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,
       "description_width": ""
      }
     },
     "c9abdda77bb54d4f8edcb5c78a790883": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_72a27fb4514047cc80b3308db42dfa04",
       "placeholder": "​",
       "style": "IPY_MODEL_4c99be112a334536ad8d5b7457597c65",
       "value": "100%"
      }
     },
     "ca5cfa16f1e64484a7d696dde4186d37": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d51da069d6f1435e9c2a5f4d3e8b87a4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "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": ""
      }
     },
     "eb5c725c0c6248a7989047d847109743": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_c9abdda77bb54d4f8edcb5c78a790883",
        "IPY_MODEL_0ea51a4850c84b36bff51fdd6f2d1e82",
        "IPY_MODEL_64518feff0374a91b6cbc00d0d2802fb"
       ],
       "layout": "IPY_MODEL_fb6acd3f2e7846bf90b8bd2fdf526438"
      }
     },
     "f1f9810008bf4a29b4dae35f10b10d21": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "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": ""
      }
     },
     "f3dd0beebc304c4290413850e943a6a6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "fb6acd3f2e7846bf90b8bd2fdf526438": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}