{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Information\n",
    "\n",
    "This notebook demonstrates how the `fastai_sparse` library can be used in semantic segmentation tasks using the example of the [ShapeNet Core55](https://shapenet.cs.stanford.edu/iccv17/) 3D semantic segmentation solution presented in [SparseConvNet example](https://github.com/facebookresearch/SparseConvNet/tree/master/examples/3d_segmentation). \n",
    "\n",
    "\n",
    "<img src=\"https://camo.githubusercontent.com/a94ad53ba6adc857323bd9ba3050805fa16d8aab/687474703a2f2f6d73617676612e6769746875622e696f2f66696c65732f73686170656e65742e706e67\" width=\"480\" />\n",
    "\n",
    "Initial data is subset of ShapeNetCore containing about 17,000 models from 16 shape categories Each category is annotated with 2 to 6 parts and there are 50 different parts annotated in total. 3D shapes are represented as point clouds uniformly sampled from 3D surfaces\n",
    "\n",
    "Evaluation metric: weighted IoU (see [https://arxiv.org/pdf/1711.10275.pdf](https://arxiv.org/pdf/1711.10275.pdf))\n",
    "\n",
    "Firstly, it is necessary to upload and prepare the initial data.  See [examples/shapenet_iccv17](examples/shapenet_iccv17)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import sparseconvnet as scn\n",
    "import time\n",
    "import os, sys\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import glob\n",
    "from IPython.display import display, HTML, FileLink\n",
    "from os.path import join, exists, basename, splitext\n",
    "from pathlib import Path\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "#from tensorboardX import SummaryWriter\n",
    "from joblib import cpu_count\n",
    "from tqdm import tqdm\n",
    "#from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "#import fastai\n",
    "\n",
    "# autoreload python modules on the fly when its source is changed\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai_sparse import utils\n",
    "from fastai_sparse.utils import log, log_dict, print_random_states\n",
    "from fastai_sparse.datasets import find_files\n",
    "from fastai_sparse.datasets import PointsDataset\n",
    "#, SparseDataBunch\n",
    "from fastai_sparse import visualize\n",
    "\n",
    "from datasets import DataSourceConfig, reader_fn\n",
    "import transform as T\n",
    "#from data import merge_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment environment / system metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'unet_24_detailed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "virtualenv:     (fastai_sparse) \n",
      "python:         3.6.8\n",
      "nvidia driver:  b'384.130'\n",
      "nvidia cuda:    9.0, V9.0.176\n",
      "cudnn:          7.1.4\n",
      "torch:          1.0.1.post2\n",
      "fastai:         1.0.48\n",
      "fastai_sparse:  0.0.3.dev0\n"
     ]
    }
   ],
   "source": [
    "utils.watermark()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:70% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.wide_notebook()\n",
    "# uncomment this lines if you want switch off interactive and save visaulisation as screenshoots:\n",
    "# For rendering run command in terminal:    `chromium-browser --remote-debugging-port=9222`\n",
    "if  True:\n",
    "    visualize.options.interactive = False\n",
    "    visualize.options.save_images = True\n",
    "    visualize.options.verbose = True\n",
    "    visualize.options.filename_pattern_image = Path('images', experiment_name, 'fig_{fig_number}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sourse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\n",
      "data/train_val\n"
     ]
    }
   ],
   "source": [
    "SOURCE_DIR = Path('data').expanduser()\n",
    "#SOURCE_DIR = Path('/home/ssd/shapenet_data').expanduser()\n",
    "assert SOURCE_DIR.exists()\n",
    "\n",
    "DIR_TRAIN_VAL = SOURCE_DIR / 'train_val'\n",
    "assert DIR_TRAIN_VAL.exists(), \"Hint: run `download_and_split_data.sh` then `convert_to_numpy.ipynb`\"\n",
    "\n",
    "print(SOURCE_DIR)\n",
    "print(DIR_TRAIN_VAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of categories: 16\n"
     ]
    }
   ],
   "source": [
    "categories = [\n",
    "    \"02691156\", \"02773838\", \"02954340\", \"02958343\", \"03001627\", \"03261776\",\n",
    "    \"03467517\", \"03624134\", \"03636649\", \"03642806\", \"03790512\", \"03797390\",\n",
    "    \"03948459\", \"04099429\", \"04225987\", \"04379243\"\n",
    "]\n",
    "\n",
    "classes = [\n",
    "    'Airplane', 'Bag', 'Cap', 'Car', 'Chair', 'Earphone', 'Guitar', 'Knife',\n",
    "    'Lamp', 'Laptop', 'Motorbike', 'Mug', 'Pistol', 'Rocket', 'Skateboard',\n",
    "    'Table'\n",
    "]\n",
    "\n",
    "num_classes_by_category = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]\n",
    "assert len(categories) == len(classes)\n",
    "\n",
    "print(\"Number of categories:\", len(categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = find_files(path=SOURCE_DIR / 'npy' / 'train', ext='.points.npy', ext_labels='.labels.npy', categories=categories)\n",
    "df_valid = find_files(path=SOURCE_DIR / 'npy' / 'valid', ext='.points.npy', ext_labels='.labels.npy', categories=categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6955\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>example_id</th>\n",
       "      <th>subdir</th>\n",
       "      <th>categ_idx</th>\n",
       "      <th>ext</th>\n",
       "      <th>ext_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000908</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>010886</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>013973</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007190</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010360</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  example_id    subdir  categ_idx          ext   ext_labels\n",
       "0     000908  02691156          0  .points.npy  .labels.npy\n",
       "1     010886  02691156          0  .points.npy  .labels.npy\n",
       "2     013973  02691156          0  .points.npy  .labels.npy\n",
       "3     007190  02691156          0  .points.npy  .labels.npy\n",
       "4     010360  02691156          0  .points.npy  .labels.npy"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(df_train))\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7052\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>example_id</th>\n",
       "      <th>subdir</th>\n",
       "      <th>categ_idx</th>\n",
       "      <th>ext</th>\n",
       "      <th>ext_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>005663</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>011957</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>009038</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>009906</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>004778</td>\n",
       "      <td>02691156</td>\n",
       "      <td>0</td>\n",
       "      <td>.points.npy</td>\n",
       "      <td>.labels.npy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  example_id    subdir  categ_idx          ext   ext_labels\n",
       "0     005663  02691156          0  .points.npy  .labels.npy\n",
       "1     011957  02691156          0  .points.npy  .labels.npy\n",
       "2     009038  02691156          0  .points.npy  .labels.npy\n",
       "3     009906  02691156          0  .points.npy  .labels.npy\n",
       "4     004778  02691156          0  .points.npy  .labels.npy"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(df_valid))\n",
    "df_valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataSets config\n",
    "You can create PointsDataset using the configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataSourceConfig;\n",
       "   root_dir: data/npy/train\n",
       "   batch_size: 16\n",
       "   num_workers: 12\n",
       "   init_numpy_random_seed: True\n",
       "   num_classes: 50\n",
       "   num_classes_by_category: [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]\n",
       "   class_offsets: [ 0  4  6  8 12 16 19 22 24 28 30 36 38 41 44 47 50]\n",
       " Items count: 6955"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_source_config = DataSourceConfig(                                       \n",
    "                                       root_dir=SOURCE_DIR / 'npy' / 'train',                                       \n",
    "                                       df=df_train,\n",
    "                                       batch_size=16,\n",
    "                                       num_workers=12,\n",
    "                                       num_classes=50,\n",
    "                                       num_classes_by_category=num_classes_by_category,\n",
    "                                      )\n",
    "train_source_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataSourceConfig;\n",
       "   root_dir: data/npy/valid\n",
       "   batch_size: 16\n",
       "   num_workers: 12\n",
       "   init_numpy_random_seed: False\n",
       "   num_classes: 50\n",
       "   num_classes_by_category: [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]\n",
       "   class_offsets: [ 0  4  6  8 12 16 19 22 24 28 30 36 38 41 44 47 50]\n",
       " Items count: 7052"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_source_config = DataSourceConfig(                                       \n",
    "                                       root_dir=SOURCE_DIR / 'npy' / 'valid',\n",
    "                                       df=df_valid,\n",
    "                                       batch_size=16,\n",
    "                                       num_workers=12,\n",
    "                                       num_classes=50,\n",
    "                                       num_classes_by_category=num_classes_by_category,\n",
    "                                       init_numpy_random_seed=False,\n",
    "                                      )\n",
    "valid_source_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_source_config.check_accordance(valid_source_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Load file names: 100%|██████████| 6955/6955 [00:01<00:00, 6525.49it/s]\n",
      "Load file names: 100%|██████████| 7052/7052 [00:01<00:00, 6516.53it/s]\n"
     ]
    }
   ],
   "source": [
    "train_items = PointsDataset.from_source_config(train_source_config, reader_fn=reader_fn)\n",
    "valid_items = PointsDataset.from_source_config(valid_source_config, reader_fn=reader_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Check files exist: 100%|██████████| 6955/6955 [00:00<00:00, 188045.98it/s]\n",
      "Check files exist: 100%|██████████| 7052/7052 [00:00<00:00, 184664.28it/s]\n"
     ]
    }
   ],
   "source": [
    "train_items.check()\n",
    "valid_items.check()\n",
    "#train_items.check_num_classes(max_num_examples=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointsDataset (6955 items)\n",
       "('000908', n: 2460),('010886', n: 2407),('013973', n: 2463),('007190', n: 2690),('010360', n: 2438)\n",
       "Path: data/npy/train"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointsItem ('002337', n: 2712)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = train_items.get(5)\n",
    "o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we've done with one example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PointsItem (002337)\n",
      "points                   shape: (2712, 3)             dtype: float32        min:   -0.35228,  max:    0.35657,  mean:   -0.01345\n",
      "labels                   shape: (2712,)               dtype: int64          min:          1,  max:          4,  mean:    1.74410\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Saved to file: <a href='images/unet_24_detailed/fig_1.png' target='_blank'>images/unet_24_detailed/fig_1.png</a>"
      ],
      "text/plain": [
       "images/unet_24_detailed/fig_1.png"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o.describe()\n",
    "o.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define transforms\n",
    "\n",
    "In order to reproduce the [example of SparseConvNet](https://github.com/facebookresearch/SparseConvNet/tree/master/examples/3d_segmentation), the same transformations have been redone, but in the manner of fast.ai transformations.\n",
    "\n",
    "The following cells define the transformations: preprocessing (PRE_TFMS); augmentation (AUGS_); and transformation to convert the points cloud to a sparse representation (SPARSE_TFMS). Sparse  representation is the input format for the SparseConvNet model and contains a list of voxels and their features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution = 24\n",
    "\n",
    "PRE_TFMS = [\n",
    "           T.fit_to_sphere(center=False),\n",
    "           T.shift_labels(offset=-1)\n",
    "           ]\n",
    "\n",
    "AUGS_TRAIN = [\n",
    "    T.rotate(),\n",
    "    T.flip_x(p=0.5),\n",
    "]\n",
    "\n",
    "AUGS_VALID = [\n",
    "    T.rotate(),\n",
    "    T.flip_x(p=0.5),\n",
    "]\n",
    "\n",
    "SPARSE_TFMS = [\n",
    "    T.translate(offset=2),  # segment [-1, 1] ---> segment [1, 3]\n",
    "    T.scale(scale=resolution),\n",
    "    T.merge_features(ones=True),\n",
    "    T.to_sparse_voxels(),\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we got in results of train and valid tranformations for the first example:\n",
    "\n",
    "initial representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PointsItem (012843)\n",
      "points                   shape: (2640, 3)             dtype: float32        min:   -0.45134,  max:    0.44987,  mean:    0.01532\n",
      "labels                   shape: (2640,)               dtype: int64          min:         48,  max:         49,  mean:   48.21932\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Saved to file: <a href='images/unet_24_detailed/fig_2.png' target='_blank'>images/unet_24_detailed/fig_2.png</a>"
      ],
      "text/plain": [
       "images/unet_24_detailed/fig_2.png"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = train_items[-1]\n",
    "o.describe()\n",
    "o.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "transformed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id: 012843\n",
      "coords                   shape: (2640, 3)             dtype: int64          min:         27,  max:         70,  mean:   47.64508\n",
      "features                 shape: (2640, 1)             dtype: float32        min:    1.00000,  max:    1.00000,  mean:    1.00000\n",
      "x                        shape: (2640,)               dtype: int64          min:         27,  max:         70,  mean:   47.19735\n",
      "y                        shape: (2640,)               dtype: int64          min:         34,  max:         59,  mean:   44.41023\n",
      "z                        shape: (2640,)               dtype: int64          min:         40,  max:         59,  mean:   51.32765\n",
      "labels                   shape: (2640,)               dtype: int64          min:         47,  max:         48,  mean:   47.21932\n",
      "voxels: 1011\n",
      "points / voxels: 2.6112759643916914\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Saved to file: <a href='images/unet_24_detailed/fig_3.png' target='_blank'>images/unet_24_detailed/fig_3.png</a>"
      ],
      "text/plain": [
       "images/unet_24_detailed/fig_3.png"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "b = o.copy().apply_tfms(PRE_TFMS  + AUGS_TRAIN + SPARSE_TFMS)\n",
    "b.describe()\n",
    "b.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply transforms to datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = (\n",
    "    PRE_TFMS + AUGS_TRAIN + SPARSE_TFMS,\n",
    "    PRE_TFMS + AUGS_VALID + SPARSE_TFMS,\n",
    ")\n",
    "\n",
    "train_items.transform(tfms[0])\n",
    "pass\n",
    "\n",
    "valid_items.transform(tfms[1])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataBunch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fast.ai the data is represented DataBunch which contains train, valid and optionally test data loaders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai_sparse.data import SparseDataBunch\n",
    "from data import merge_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  6955, shuffle:  True, batch_size: 16, num_workers: 12, num_batches: 434, drop_last: True\n",
      "Valid:  7052, shuffle: False, batch_size: 16, num_workers: 12, num_batches: 441, drop_last: False\n"
     ]
    }
   ],
   "source": [
    "data = SparseDataBunch.create(train_ds=train_items,\n",
    "                              valid_ds=valid_items,\n",
    "                              collate_fn=merge_fn,)\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader idle run speed measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_cpus: 16\n",
      "Model name:            AMD Ryzen 7 1700 Eight-Core Processor\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from fastai_sparse.core import num_cpus\n",
    "print(\"num_cpus:\", num_cpus())\n",
    "!lscpu | grep \"Model\"\n",
    "print()\n",
    "\n",
    "#data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 434/434 [00:04<00:00, 106.94it/s]\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "t = tqdm(enumerate(data.train_dl), total=len(data.train_dl))\n",
    "for i, batch in t:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 441/441 [00:02<00:00, 184.60it/s]\n"
     ]
    }
   ],
   "source": [
    "# valid\n",
    "t = tqdm(enumerate(data.valid_dl), total=len(data.valid_dl))\n",
    "for i, batch in t:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "U-Net SparseConvNet implemenation ([link](https://github.com/facebookresearch/SparseConvNet/blob/master/examples/3d_segmentation/unet.py)): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SparseModelConfig;\n",
       "   spatial_size: 192\n",
       "   dimension: 3\n",
       "   block_reps: 1\n",
       "   m: 32\n",
       "   num_planes: [32, 64, 96, 128, 160]\n",
       "   residual_blocks: False\n",
       "   num_classes: 50\n",
       "   num_input_features: 1\n",
       "   mode: 3\n",
       "   downsample: [2, 2]\n",
       "   bias: False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fastai_sparse.learner import SparseModelConfig\n",
    "\n",
    "\n",
    "model_config = SparseModelConfig(spatial_size=24 * 8, num_input_features=1)\n",
    "model_config.check_accordance(data.train_ds.source_config, sparse_item=data.train_ds[0])\n",
    "model_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        nn.Module.__init__(self)\n",
    "        \n",
    "        spatial_size = torch.LongTensor([cfg.spatial_size]*3)\n",
    "        \n",
    "        self.sparseModel = scn.Sequential(\n",
    "            scn.InputLayer(cfg.dimension, spatial_size, mode=cfg.mode),\n",
    "            scn.SubmanifoldConvolution(cfg.dimension, nIn=cfg.num_input_features, nOut=cfg.m, filter_size=3, bias=cfg.bias),\n",
    "            scn.UNet(cfg.dimension, cfg.block_reps, cfg.num_planes, residual_blocks=cfg.residual_blocks, downsample=cfg.downsample),\n",
    "            scn.BatchNormReLU(cfg.m),\n",
    "            scn.OutputLayer(cfg.dimension),\n",
    "        )\n",
    "        self.linear = nn.Linear(cfg.m, cfg.num_classes)\n",
    "\n",
    "    def forward(self, xb):\n",
    "        coords = xb['coords']\n",
    "        features = xb['features']\n",
    "        x = [coords, features]\n",
    "\n",
    "        x = self.sparseModel(x)\n",
    "        x = self.linear(x)\n",
    "        return x\n",
    "\n",
    "model = Model(model_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(\n",
       "  (sparseModel): Sequential(\n",
       "    (0): InputLayer()\n",
       "    (1): SubmanifoldConvolution 1->32 C3\n",
       "    (2): Sequential(\n",
       "      (0): Sequential(\n",
       "        (0): BatchNormLeakyReLU(32,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "        (1): SubmanifoldConvolution 32->32 C3\n",
       "      )\n",
       "      (1): ConcatTable(\n",
       "        (0): Identity()\n",
       "        (1): Sequential(\n",
       "          (0): BatchNormLeakyReLU(32,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "          (1): Convolution 32->64 C2/2\n",
       "          (2): Sequential(\n",
       "            (0): Sequential(\n",
       "              (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "              (1): SubmanifoldConvolution 64->64 C3\n",
       "            )\n",
       "            (1): ConcatTable(\n",
       "              (0): Identity()\n",
       "              (1): Sequential(\n",
       "                (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                (1): Convolution 64->96 C2/2\n",
       "                (2): Sequential(\n",
       "                  (0): Sequential(\n",
       "                    (0): BatchNormLeakyReLU(96,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                    (1): SubmanifoldConvolution 96->96 C3\n",
       "                  )\n",
       "                  (1): ConcatTable(\n",
       "                    (0): Identity()\n",
       "                    (1): Sequential(\n",
       "                      (0): BatchNormLeakyReLU(96,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                      (1): Convolution 96->128 C2/2\n",
       "                      (2): Sequential(\n",
       "                        (0): Sequential(\n",
       "                          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                          (1): SubmanifoldConvolution 128->128 C3\n",
       "                        )\n",
       "                        (1): ConcatTable(\n",
       "                          (0): Identity()\n",
       "                          (1): Sequential(\n",
       "                            (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                            (1): Convolution 128->160 C2/2\n",
       "                            (2): Sequential(\n",
       "                              (0): Sequential(\n",
       "                                (0): BatchNormLeakyReLU(160,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                                (1): SubmanifoldConvolution 160->160 C3\n",
       "                              )\n",
       "                            )\n",
       "                            (3): BatchNormLeakyReLU(160,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                            (4): Deconvolution 160->128 C2/2\n",
       "                          )\n",
       "                        )\n",
       "                        (2): JoinTable()\n",
       "                        (3): Sequential(\n",
       "                          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                          (1): SubmanifoldConvolution 256->128 C3\n",
       "                        )\n",
       "                      )\n",
       "                      (3): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                      (4): Deconvolution 128->96 C2/2\n",
       "                    )\n",
       "                  )\n",
       "                  (2): JoinTable()\n",
       "                  (3): Sequential(\n",
       "                    (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                    (1): SubmanifoldConvolution 192->96 C3\n",
       "                  )\n",
       "                )\n",
       "                (3): BatchNormLeakyReLU(96,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "                (4): Deconvolution 96->64 C2/2\n",
       "              )\n",
       "            )\n",
       "            (2): JoinTable()\n",
       "            (3): Sequential(\n",
       "              (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "              (1): SubmanifoldConvolution 128->64 C3\n",
       "            )\n",
       "          )\n",
       "          (3): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "          (4): Deconvolution 64->32 C2/2\n",
       "        )\n",
       "      )\n",
       "      (2): JoinTable()\n",
       "      (3): Sequential(\n",
       "        (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.9,affine=True,leakiness=0)\n",
       "        (1): SubmanifoldConvolution 64->32 C3\n",
       "      )\n",
       "    )\n",
       "    (3): BatchNormReLU(32,eps=0.0001,momentum=0.9,affine=True)\n",
       "    (4): OutputLayer()\n",
       "  )\n",
       "  (linear): Linear(in_features=32, out_features=50, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 3,841,234\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>name</th>\n",
       "      <th>number</th>\n",
       "      <th>shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sparseModel.1.weight</td>\n",
       "      <td>864</td>\n",
       "      <td>27 x 1 x 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sparseModel.2.0.0.weight</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sparseModel.2.0.0.bias</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sparseModel.2.0.1.weight</td>\n",
       "      <td>27648</td>\n",
       "      <td>27 x 32 x 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sparseModel.2.1.1.0.weight</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sparseModel.2.1.1.0.bias</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sparseModel.2.1.1.1.weight</td>\n",
       "      <td>16384</td>\n",
       "      <td>8 x 32 x 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sparseModel.2.1.1.2.0.0.weight</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sparseModel.2.1.1.2.0.0.bias</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sparseModel.2.1.1.2.0.1.weight</td>\n",
       "      <td>110592</td>\n",
       "      <td>27 x 64 x 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.0.weight</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.0.bias</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.1.weight</td>\n",
       "      <td>49152</td>\n",
       "      <td>8 x 64 x 96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.0.0.weight</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.0.0.bias</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.0.1.weight</td>\n",
       "      <td>248832</td>\n",
       "      <td>27 x 96 x 96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.0.weight</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.0.bias</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.1.weight</td>\n",
       "      <td>98304</td>\n",
       "      <td>8 x 96 x 128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.0.0.weight</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.0.0.bias</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.0.1.weight</td>\n",
       "      <td>442368</td>\n",
       "      <td>27 x 128 x 128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.0.weight</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.0.bias</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.1.weight</td>\n",
       "      <td>163840</td>\n",
       "      <td>8 x 128 x 160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.0.weight</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.0.bias</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.1.weight</td>\n",
       "      <td>691200</td>\n",
       "      <td>27 x 160 x 160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.3.weight</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.3.bias</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.4.weight</td>\n",
       "      <td>163840</td>\n",
       "      <td>8 x 160 x 128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.3.0.weight</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.3.0.bias</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.2.3.1.weight</td>\n",
       "      <td>884736</td>\n",
       "      <td>27 x 256 x 128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.3.weight</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.3.bias</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.1.1.4.weight</td>\n",
       "      <td>98304</td>\n",
       "      <td>8 x 128 x 96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.3.0.weight</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.3.0.bias</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.2.3.1.weight</td>\n",
       "      <td>497664</td>\n",
       "      <td>27 x 192 x 96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.3.weight</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.3.bias</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>sparseModel.2.1.1.2.1.1.4.weight</td>\n",
       "      <td>49152</td>\n",
       "      <td>8 x 96 x 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>sparseModel.2.1.1.2.3.0.weight</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>sparseModel.2.1.1.2.3.0.bias</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>sparseModel.2.1.1.2.3.1.weight</td>\n",
       "      <td>221184</td>\n",
       "      <td>27 x 128 x 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>sparseModel.2.1.1.3.weight</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>sparseModel.2.1.1.3.bias</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>sparseModel.2.1.1.4.weight</td>\n",
       "      <td>16384</td>\n",
       "      <td>8 x 64 x 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sparseModel.2.3.0.weight</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>sparseModel.2.3.0.bias</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>sparseModel.2.3.1.weight</td>\n",
       "      <td>55296</td>\n",
       "      <td>27 x 64 x 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>sparseModel.3.weight</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>sparseModel.3.bias</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>linear.weight</td>\n",
       "      <td>1600</td>\n",
       "      <td>50 x 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>linear.bias</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  number           shape\n",
       "0                               sparseModel.1.weight     864     27 x 1 x 32\n",
       "1                           sparseModel.2.0.0.weight      32              32\n",
       "2                             sparseModel.2.0.0.bias      32              32\n",
       "3                           sparseModel.2.0.1.weight   27648    27 x 32 x 32\n",
       "4                         sparseModel.2.1.1.0.weight      32              32\n",
       "5                           sparseModel.2.1.1.0.bias      32              32\n",
       "6                         sparseModel.2.1.1.1.weight   16384     8 x 32 x 64\n",
       "7                     sparseModel.2.1.1.2.0.0.weight      64              64\n",
       "8                       sparseModel.2.1.1.2.0.0.bias      64              64\n",
       "9                     sparseModel.2.1.1.2.0.1.weight  110592    27 x 64 x 64\n",
       "10                  sparseModel.2.1.1.2.1.1.0.weight      64              64\n",
       "11                    sparseModel.2.1.1.2.1.1.0.bias      64              64\n",
       "12                  sparseModel.2.1.1.2.1.1.1.weight   49152     8 x 64 x 96\n",
       "13              sparseModel.2.1.1.2.1.1.2.0.0.weight      96              96\n",
       "14                sparseModel.2.1.1.2.1.1.2.0.0.bias      96              96\n",
       "15              sparseModel.2.1.1.2.1.1.2.0.1.weight  248832    27 x 96 x 96\n",
       "16            sparseModel.2.1.1.2.1.1.2.1.1.0.weight      96              96\n",
       "17              sparseModel.2.1.1.2.1.1.2.1.1.0.bias      96              96\n",
       "18            sparseModel.2.1.1.2.1.1.2.1.1.1.weight   98304    8 x 96 x 128\n",
       "19        sparseModel.2.1.1.2.1.1.2.1.1.2.0.0.weight     128             128\n",
       "20          sparseModel.2.1.1.2.1.1.2.1.1.2.0.0.bias     128             128\n",
       "21        sparseModel.2.1.1.2.1.1.2.1.1.2.0.1.weight  442368  27 x 128 x 128\n",
       "22      sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.0.weight     128             128\n",
       "23        sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.0.bias     128             128\n",
       "24      sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.1.weight  163840   8 x 128 x 160\n",
       "25  sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.0.weight     160             160\n",
       "26    sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.0.bias     160             160\n",
       "27  sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.2.0.1.weight  691200  27 x 160 x 160\n",
       "28      sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.3.weight     160             160\n",
       "29        sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.3.bias     160             160\n",
       "30      sparseModel.2.1.1.2.1.1.2.1.1.2.1.1.4.weight  163840   8 x 160 x 128\n",
       "31        sparseModel.2.1.1.2.1.1.2.1.1.2.3.0.weight     256             256\n",
       "32          sparseModel.2.1.1.2.1.1.2.1.1.2.3.0.bias     256             256\n",
       "33        sparseModel.2.1.1.2.1.1.2.1.1.2.3.1.weight  884736  27 x 256 x 128\n",
       "34            sparseModel.2.1.1.2.1.1.2.1.1.3.weight     128             128\n",
       "35              sparseModel.2.1.1.2.1.1.2.1.1.3.bias     128             128\n",
       "36            sparseModel.2.1.1.2.1.1.2.1.1.4.weight   98304    8 x 128 x 96\n",
       "37              sparseModel.2.1.1.2.1.1.2.3.0.weight     192             192\n",
       "38                sparseModel.2.1.1.2.1.1.2.3.0.bias     192             192\n",
       "39              sparseModel.2.1.1.2.1.1.2.3.1.weight  497664   27 x 192 x 96\n",
       "40                  sparseModel.2.1.1.2.1.1.3.weight      96              96\n",
       "41                    sparseModel.2.1.1.2.1.1.3.bias      96              96\n",
       "42                  sparseModel.2.1.1.2.1.1.4.weight   49152     8 x 96 x 64\n",
       "43                    sparseModel.2.1.1.2.3.0.weight     128             128\n",
       "44                      sparseModel.2.1.1.2.3.0.bias     128             128\n",
       "45                    sparseModel.2.1.1.2.3.1.weight  221184   27 x 128 x 64\n",
       "46                        sparseModel.2.1.1.3.weight      64              64\n",
       "47                          sparseModel.2.1.1.3.bias      64              64\n",
       "48                        sparseModel.2.1.1.4.weight   16384     8 x 64 x 32\n",
       "49                          sparseModel.2.3.0.weight      64              64\n",
       "50                            sparseModel.2.3.0.bias      64              64\n",
       "51                          sparseModel.2.3.1.weight   55296    27 x 64 x 32\n",
       "52                              sparseModel.3.weight      32              32\n",
       "53                                sparseModel.3.bias      32              32\n",
       "54                                     linear.weight    1600         50 x 32\n",
       "55                                       linear.bias      50              50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learner creation\n",
    "Learner is core fast.ai class which contains model architecture, databunch and optimizer options and implement train loop and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "from functools import partial\n",
    "from fastai.callbacks.general_sched import TrainingPhase, GeneralScheduler\n",
    "#from fastai.callbacks.csv_logger import CSVLogger\n",
    "#from fastai.callbacks.tracker import SaveModelCallback\n",
    "from fastai.callback import annealing_exp\n",
    "\n",
    "\n",
    "from fastai_sparse.learner import Learner\n",
    "from fastai_sparse.callbacks import TimeLogger, SaveModelCallback, CSVLogger, CSVLoggerIouByCategory\n",
    "\n",
    "from metrics import IouByCategories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(data, model,\n",
    "                opt_func=partial(optim.SGD, momentum=0.9),\n",
    "                wd=1e-4,\n",
    "                true_wd=False,\n",
    "                path=str(Path('results', experiment_name)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Rate finder\n",
    "We use Learning Rate Finder provided by fast.ai library to find the optimal learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "CPU times: user 27.9 s, sys: 6.98 s, total: 34.9 s\n",
      "Wall time: 28.3 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "learn.lr_find(start_lr=1e-5, end_lr=100)\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train\n",
    "To visualize the learning process, we specify some additional callbacks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.callbacks = []\n",
    "cb_iou = IouByCategories(learn, len(categories))\n",
    "learn.callbacks.append(cb_iou)\n",
    "\n",
    "learn.callbacks.append(TimeLogger(learn))\n",
    "learn.callbacks.append(CSVLogger(learn))\n",
    "learn.callbacks.append(CSVLoggerIouByCategory(learn, cb_iou, categories_names=classes))\n",
    "\n",
    "learn.callbacks.append(SaveModelCallback(learn, every='epoch', name='weights', overwrite=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 09:16 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>train_time</th>\n",
       "      <th>valid_time</th>\n",
       "      <th>train_waiou</th>\n",
       "      <th>valid_waiou</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.066369</td>\n",
       "      <td>1.948893</td>\n",
       "      <td>121.718252</td>\n",
       "      <td>63.441886</td>\n",
       "      <td>0.451465</td>\n",
       "      <td>0.518886</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.672141</td>\n",
       "      <td>1.583488</td>\n",
       "      <td>121.557564</td>\n",
       "      <td>63.407442</td>\n",
       "      <td>0.548420</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.318663</td>\n",
       "      <td>1.290712</td>\n",
       "      <td>122.697799</td>\n",
       "      <td>63.085566</td>\n",
       "      <td>0.616172</td>\n",
       "      <td>0.590881</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_annealing_exp(3, lr=0.1, lr_decay=4e-2, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# learn.fit_annealing_exp(100, lr=0.1, lr_decay=4e-2, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "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>epoch</th>\n",
       "      <th>datatype</th>\n",
       "      <th>average</th>\n",
       "      <th>Airplane</th>\n",
       "      <th>Bag</th>\n",
       "      <th>Cap</th>\n",
       "      <th>Car</th>\n",
       "      <th>Chair</th>\n",
       "      <th>Earphone</th>\n",
       "      <th>Guitar</th>\n",
       "      <th>Knife</th>\n",
       "      <th>Lamp</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Motorbike</th>\n",
       "      <th>Mug</th>\n",
       "      <th>Pistol</th>\n",
       "      <th>Rocket</th>\n",
       "      <th>Skateboard</th>\n",
       "      <th>Table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>valid</td>\n",
       "      <td>0.518886</td>\n",
       "      <td>0.379600</td>\n",
       "      <td>0.492501</td>\n",
       "      <td>0.414970</td>\n",
       "      <td>0.227367</td>\n",
       "      <td>0.509858</td>\n",
       "      <td>0.531944</td>\n",
       "      <td>0.484199</td>\n",
       "      <td>0.445833</td>\n",
       "      <td>0.539656</td>\n",
       "      <td>0.344094</td>\n",
       "      <td>0.231674</td>\n",
       "      <td>0.473616</td>\n",
       "      <td>0.243760</td>\n",
       "      <td>0.283587</td>\n",
       "      <td>0.391957</td>\n",
       "      <td>0.704618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>0.548420</td>\n",
       "      <td>0.416585</td>\n",
       "      <td>0.472786</td>\n",
       "      <td>0.366650</td>\n",
       "      <td>0.240662</td>\n",
       "      <td>0.555655</td>\n",
       "      <td>0.513805</td>\n",
       "      <td>0.504931</td>\n",
       "      <td>0.574740</td>\n",
       "      <td>0.525350</td>\n",
       "      <td>0.424994</td>\n",
       "      <td>0.261024</td>\n",
       "      <td>0.484206</td>\n",
       "      <td>0.273017</td>\n",
       "      <td>0.287875</td>\n",
       "      <td>0.414793</td>\n",
       "      <td>0.719739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>valid</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>0.516312</td>\n",
       "      <td>0.492734</td>\n",
       "      <td>0.398441</td>\n",
       "      <td>0.268659</td>\n",
       "      <td>0.637381</td>\n",
       "      <td>0.516604</td>\n",
       "      <td>0.608734</td>\n",
       "      <td>0.617883</td>\n",
       "      <td>0.553338</td>\n",
       "      <td>0.431588</td>\n",
       "      <td>0.263243</td>\n",
       "      <td>0.545524</td>\n",
       "      <td>0.256332</td>\n",
       "      <td>0.275717</td>\n",
       "      <td>0.462712</td>\n",
       "      <td>0.747984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>0.616172</td>\n",
       "      <td>0.543162</td>\n",
       "      <td>0.476210</td>\n",
       "      <td>0.397841</td>\n",
       "      <td>0.248176</td>\n",
       "      <td>0.669318</td>\n",
       "      <td>0.524780</td>\n",
       "      <td>0.638997</td>\n",
       "      <td>0.636003</td>\n",
       "      <td>0.541066</td>\n",
       "      <td>0.411982</td>\n",
       "      <td>0.266059</td>\n",
       "      <td>0.573489</td>\n",
       "      <td>0.281751</td>\n",
       "      <td>0.291917</td>\n",
       "      <td>0.482149</td>\n",
       "      <td>0.755185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>valid</td>\n",
       "      <td>0.590881</td>\n",
       "      <td>0.523460</td>\n",
       "      <td>0.523732</td>\n",
       "      <td>0.392183</td>\n",
       "      <td>0.228041</td>\n",
       "      <td>0.613275</td>\n",
       "      <td>0.552095</td>\n",
       "      <td>0.596156</td>\n",
       "      <td>0.624740</td>\n",
       "      <td>0.534418</td>\n",
       "      <td>0.383885</td>\n",
       "      <td>0.270513</td>\n",
       "      <td>0.589842</td>\n",
       "      <td>0.294795</td>\n",
       "      <td>0.262375</td>\n",
       "      <td>0.492768</td>\n",
       "      <td>0.742103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch datatype   average  Airplane       Bag       Cap       Car     Chair  \\\n",
       "1      0    valid  0.518886  0.379600  0.492501  0.414970  0.227367  0.509858   \n",
       "2      1    train  0.548420  0.416585  0.472786  0.366650  0.240662  0.555655   \n",
       "3      1    valid  0.600601  0.516312  0.492734  0.398441  0.268659  0.637381   \n",
       "4      2    train  0.616172  0.543162  0.476210  0.397841  0.248176  0.669318   \n",
       "5      2    valid  0.590881  0.523460  0.523732  0.392183  0.228041  0.613275   \n",
       "\n",
       "   Earphone    Guitar     Knife      Lamp    Laptop  Motorbike       Mug  \\\n",
       "1  0.531944  0.484199  0.445833  0.539656  0.344094   0.231674  0.473616   \n",
       "2  0.513805  0.504931  0.574740  0.525350  0.424994   0.261024  0.484206   \n",
       "3  0.516604  0.608734  0.617883  0.553338  0.431588   0.263243  0.545524   \n",
       "4  0.524780  0.638997  0.636003  0.541066  0.411982   0.266059  0.573489   \n",
       "5  0.552095  0.596156  0.624740  0.534418  0.383885   0.270513  0.589842   \n",
       "\n",
       "     Pistol    Rocket  Skateboard     Table  \n",
       "1  0.243760  0.283587    0.391957  0.704618  \n",
       "2  0.273017  0.287875    0.414793  0.719739  \n",
       "3  0.256332  0.275717    0.462712  0.747984  \n",
       "4  0.281751  0.291917    0.482149  0.755185  \n",
       "5  0.294795  0.262375    0.492768  0.742103  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.find_callback('CSVLoggerIouByCategory').read_logged_file().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>train_time</th>\n",
       "      <th>valid_time</th>\n",
       "      <th>train_waiou</th>\n",
       "      <th>valid_waiou</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.066369</td>\n",
       "      <td>1.948893</td>\n",
       "      <td>121.718252</td>\n",
       "      <td>63.441886</td>\n",
       "      <td>0.451465</td>\n",
       "      <td>0.518886</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.672141</td>\n",
       "      <td>1.583488</td>\n",
       "      <td>121.557564</td>\n",
       "      <td>63.407442</td>\n",
       "      <td>0.548420</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.318663</td>\n",
       "      <td>1.290712</td>\n",
       "      <td>122.697799</td>\n",
       "      <td>63.085566</td>\n",
       "      <td>0.616172</td>\n",
       "      <td>0.590881</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  train_loss  valid_loss  train_time  valid_time  train_waiou  \\\n",
       "0      0    2.066369    1.948893  121.718252   63.441886     0.451465   \n",
       "1      1    1.672141    1.583488  121.557564   63.407442     0.548420   \n",
       "2      2    1.318663    1.290712  122.697799   63.085566     0.616172   \n",
       "\n",
       "   valid_waiou   time  \n",
       "0     0.518886  03:05  \n",
       "1     0.600601  03:05  \n",
       "2     0.590881  03:05  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.find_callback('CSVLogger').read_logged_file().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VNXd+PHPd7LvewgkhIR9kyVEFpVNcaNuKC5Ua7G1PLX1qa1P7QNqtbW1tdWfWzdr3fsoaBWVqkhdwA1kCULY1wRICCQkkIWQbeb8/rg3kwQSCCGTmTDf9+s1L+6ce+6d770k8805595zxRiDUkopBeDwdgBKKaV8hyYFpZRSbpoUlFJKuWlSUEop5aZJQSmllJsmBaWUUm6aFJRSSrlpUlBKKeWmSUEppZRboLcDOF2JiYkmIyPD22EopVS3kpOTc8gYk3Sqet0uKWRkZLBmzRpvh6GUUt2KiOxpTz3tPlJKKeWmSUEppZSbJgWllFJu3W5MQSl19qivr6egoICamhpvh3LWCA0NJS0tjaCgoA5t7/GkICIBwBqg0BhzxXHrQoBXgDFAKXCjMSbf0zEppXxDQUEBUVFRZGRkICLeDqfbM8ZQWlpKQUEBmZmZHdpHV3Qf3QVsaWPd94HDxpj+wBPAH7ogHqWUj6ipqSEhIUETQicRERISEs6o5eXRpCAiacC3gOfaqHI18LK9/CZwkehPh1J+RX/lO9eZnk9PtxSeBH4BuNpYnwrsAzDGNADlQMLxlURkjoisEZE1JSUlHQpk24FKHlm8lYqa+g5tr5RS/sBjSUFErgCKjTE5Z7ovY8yzxphsY0x2UtIpb8hr1d6yap75bBe7iqvONByl1FmitLSUUaNGMWrUKFJSUkhNTXW/r6ura9c+brvtNrZt2+bhSLuOJweazweuEpHpQCgQLSL/Z4y5pVmdQqA3UCAigUAM1oBzp8tMjAAg79BRRqfHeeIjlFLdTEJCAuvWrQPgV7/6FZGRkfz85z9vUccYgzEGh6P1v6FffPFFj8fZlTzWUjDGzDPGpBljMoCbgE+PSwgAi4Dv2ssz7TrGE/H0iA4B4FBVrSd2r5Q6i+zcuZOhQ4dy8803M2zYMIqKipgzZw7Z2dkMGzaMhx56yF33ggsuYN26dTQ0NBAbG8vcuXMZOXIkEyZMoLi42ItH0TFdfp+CiDwErDHGLAKeB/4pIjuBMqzk4RGRIYEEOoQj1TqmoJQv+vW/N7F5f0Wn7nNor2gevHJYh7bdunUrr7zyCtnZ2QA88sgjxMfH09DQwNSpU5k5cyZDhw5tsU15eTmTJ0/mkUce4e677+aFF15g7ty5Z3wcXalLkoIxZhmwzF5+oFl5DXB9V8QgIsSGB3HkmCYFpdSp9evXz50QAObPn8/zzz9PQ0MD+/fvZ/PmzSckhbCwMC6//HIAxowZwxdffNGlMXcGv7qjOSYsiHJtKSjlkzr6F72nREREuJd37NjBU089xapVq4iNjeWWW25p9V6A4OBg93JAQAANDQ1dEmtn8qu5j2LDgzlyrH1XFCilVKOKigqioqKIjo6mqKiIJUuWeDskj/GrlkJsWBBF5TrHilLq9GRlZTF06FAGDx5Mnz59OP/8870dkseIhy728Zjs7GzT0Yfs/M8b6/l6dylfzb2wk6NSSnXEli1bGDJkiLfDOOu0dl5FJMcYk93GJm5+1n0UxJFq7T5SSqm2+FdSCAviaJ2Tuoa2Zt1QSin/5l9JIdyaX1wHm5VSqnV+lRQSIq27mkurNCkopVRr/CopJEVZSaGkUqe6UEqp1vhVUogLt24sOayDzUop1Sq/SgoRIQEAVNc5vRyJUsoXTJ069YQb0Z588knuuOOONreJjIwEYP/+/cycObPVOlOmTOFUl84/+eSTVFdXu99Pnz6dI0eOtDd0j/GrpBAeZN2rp0lBKQUwa9YsFixY0KJswYIFzJo165Tb9urVizfffLPDn318Uvjggw+IjY3t8P46i18lhbBgq6VwrK77zUeilOp8M2fO5P3333c/UCc/P5/9+/czevRoLrroIrKysjjnnHN49913T9g2Pz+f4cOHA3Ds2DFuuukmhgwZwowZMzh27Ji73h133OGecvvBBx8E4Omnn2b//v1MnTqVqVOnApCRkcGhQ4cAePzxxxk+fDjDhw/nySefdH/ekCFD+MEPfsCwYcO45JJLWnxOZ/GraS6CAx0EOkRbCkr5osVz4cCGzt1nyjlw+SNtro6Pj2fs2LEsXryYq6++mgULFnDDDTcQFhbG22+/TXR0NIcOHWL8+PFcddVVbT7/+G9/+xvh4eFs2bKF3NxcsrKy3Osefvhh4uPjcTqdXHTRReTm5vKTn/yExx9/nKVLl5KYmNhiXzk5Obz44ousXLkSYwzjxo1j8uTJxMXFsWPHDubPn88//vEPbrjhBt566y1uueX4x9ScGb9qKQCEBwdoUlBKuTXvQmrsOjLGcO+99zJixAimTZtGYWEhBw8ebHMfn3/+ufvLecSIEYwYMcK97o033iArK4vRo0ezadMmNm/efNJ4vvzyS2bMmEFERASRkZFce+217im4MzMzGTVqFGBNzZ2fn38mh94qv2opAIQHB1Kt3UdK+Z6T/EXvSVdffTU/+9nPWLt2LdXV1YwZM4aXXnqJkpIScnJyCAoKIiMjo9Wpsk8lLy+Pxx57jNWrVxMXF8fs2bM7tJ9GISEh7uWAgACPdB9pS0Ep5dciIyOZOnUq3/ve99wDzOXl5SQnJxMUFMTSpUvZs2fPSfcxadIkXnvtNQA2btxIbm4uYE25HRERQUxMDAcPHmTx4sXubaKioqisrDxhXxMnTuSdd96hurqao0eP8vbbbzNx4sTOOtxT8r+WQogmBaVUS7NmzWLGjBnubqSbb76ZK6+8knPOOYfs7GwGDx580u3vuOMObrvtNoYMGcKQIUMYM2YMACNHjmT06NEMHjyY3r17t5hye86cOVx22WX06tWLpUuXusuzsrKYPXs2Y8eOBeD2229n9OjRHukqao1fTZ0NcMMzK3A4YMGcCZ0YlVKqI3TqbM/QqbNPQ1hwAMe0paCUUq3yu6SgYwpKKdU2P0wKgZoUlPIh3a0L29ed6fn0w6QQoJekKuUjQkNDKS0t1cTQSYwxlJaWEhoa2uF9+N/VR9p9pJTPSEtLo6CggJKSEm+HctYIDQ0lLS2tw9v7XVIICw6gtsGF02UIcLR+y7pSqmsEBQWRmZnp7TBUM37XfRQR3DhTqnYhKaXU8fwuKTTNlKpdSEopdTyPJQURCRWRVSKyXkQ2icivW6kzW0RKRGSd/brdU/E0Cg/WB+0opVRbPDmmUAtcaIypEpEg4EsRWWyM+fq4eq8bY+70YBwthNvdR0e1+0gppU7gsaRgrGvMquy3QfbL69edhWv3kVJKtcmjYwoiEiAi64Bi4CNjzMpWql0nIrki8qaI9PZkPAARIVYerKzRloJSSh3Po0nBGOM0xowC0oCxIjL8uCr/BjKMMSOAj4CXW9uPiMwRkTUisuZMr2dOibFu6jhQ0fE5zZVS6mzVJVcfGWOOAEuBy44rLzXG1NpvnwPGtLH9s8aYbGNMdlJS0hnF0iMqBBE4UK5JQSmljufJq4+SRCTWXg4DLga2HlenZ7O3VwFbPBVPo8AAB2FBARyt1e4jpZQ6nievPuoJvCwiAVjJ5w1jzHsi8hCwxhizCPiJiFwFNABlwGwPxuMWHhxIdb0ONCul1PE8efVRLjC6lfIHmi3PA+Z5Koa2hAcHUK0tBaWUOoHf3dEMOimeUkq1xS+TQmRIoF6SqpRSrfDLpJCZGMGO4kpvh6GUUj7HL5NCRmIEh6rqqNHBZqWUasEvk0JSZAgAh6pqT1FTKaX8i18mheRoKynsP6I3sCmlVHN+mRSG9owGYGNhuZcjUUop3+KXSSE5OpSU6FByC454OxSllPIpfpkUAM5JiyFXWwpKKdWC3yaFEakx7C45SkVNvbdDUUopn+G3SWF4agwAW4v0fgWllGrkt0lhaC9rsHnzfu1CUkqpRn6bFJKjQoiPCGaLthSUUsrNb5OCiDCkZxRbDlR4OxSllPIZfpsUAAYkR7G75CjGGG+HopRSPsGvk0KfhHCqahs4VFXn7VCUUson+HVSyEiMAGBP6VEvR6KUUr7Bv5NCgpUU8g5pUlBKKfDzpJAWF0aAQ9hTWu3tUJRSyif4dVIICnCQFhdGnnYfKaUU4OdJAaBPQoSOKSillM3vk0JmQjj5h6r1slSllEKTApmJEVTVNlBSqU9hU0opv08Kg+0H7izfVerlSJRSyvv8PimMzYgnOSqEN9bs0y4kpZTf8/uk4HAIN41NZ/muUjYX6TxISin/5vdJAWDqoCQADlbUeDkSpZTyLo8lBREJFZFVIrJeRDaJyK9bqRMiIq+LyE4RWSkiGZ6K52SSokIAOFihg81KKf/myZZCLXChMWYkMAq4TETGH1fn+8BhY0x/4AngDx6Mp009Y8JwCLy2cq83Pl4ppXyGx5KCsVTZb4Ps1/EjuVcDL9vLbwIXiYh4Kqa2BDgEl4ENheWs23ekqz9eKaV8hkfHFEQkQETWAcXAR8aYlcdVSQX2ARhjGoByIMGTMbVlbEY8ANf85StvfLxSSvkEjyYFY4zTGDMKSAPGisjwjuxHROaIyBoRWVNSUtK5Qdpe/6+mni2nSy9NVUr5py65+sgYcwRYClx23KpCoDeAiAQCMcAJd5EZY541xmQbY7KTkpI8EqOI8OjMEYA+X0Ep5b88efVRkojE2sthwMXA1uOqLQK+ay/PBD41XryDbFBKFADbD1Z6KwSllPIqT7YUegJLRSQXWI01pvCeiDwkIlfZdZ4HEkRkJ3A3MNeD8ZxS/+RIALYfrDpFTaWUOjsFemrHxphcYHQr5Q80W64BrvdUDKcrPDiQ3vFhbDugLQWllH/SO5qPMy4zgfc3FPHZds8MaCullC/TpHCcn04bAMDSrcVejkQppbqeJoXjpMWFMzglipeW57N0myYGpZR/0aTQiimDkgG47cXVXo5EKaW6liaFVtyQnebtEJRSyis0KbSib1Ik904fDEB5db2Xo1FKqa6jSaENmYnWPQt5enezUsqPaFJoQ7+kCAC26z0LSik/okmhDRkJEUSFBrK+QKfSVkr5D00KbXA4hFG9Y/lixyGdNVUp5Tc0KZzErLHp7C2rZsmmA94ORSmluoQmhZO4dFgKAD96dS05ew57ORqllPI8TQonEeAQJvS1HgSnz29WSvmDdiUFEeknIiH28hQR+UnjsxLOdi/MPpdzUmN0wFkp5Rfa21J4C3CKSH/gWaynpb3msah8SFhwAJcNT2FncRVr8su8HY5SSnlUe5OCyxjTAMwA/mSMuQfrITp+IbtPHAAzn1nh5UiUUsqz2psU6kVkFtajM9+zy4I8E5LvOTcj3r1c73R5MRKllPKs9iaF24AJwMPGmDwRyQT+6bmwfIvDIdx0bm8AfvjPHLz4GGmllPKodiUFY8xmY8xPjDHzRSQOiDLG/MHDsfmUuy8ZCMAnW4vJnPcBReXHvByRUkp1vvZefbRMRKJFJB5YC/xDRB73bGi+JTkqlM0PXep+P/sFfdaCUurs097uoxhjTAVwLfCKMWYcMM1zYfmm8OBAFt15PgC7SqqoqXd6OSKllOpc7U0KgSLSE7iBpoFmvzQiLZaXvzeWBpfh0SXbvB2OUkp1qvYmhYeAJcAuY8xqEekL7PBcWL5tYv9EhvSM5vkv89hVUuXtcJRSqtO0d6D5X8aYEcaYO+z3u40x13k2NN/lcAh/mjUKEetqpM+3l1DXoJeqKqW6v/YONKeJyNsiUmy/3hIRv36Qcf/kKP543Qh2FFdx6wurmLswF4DaBievr97Lqjy9+1kp1f0EtrPei1jTWlxvv7/FLrvYE0F1F9dlpfHnpTvZU1rNwrWFHCiv4evdpTQ+fuEnFw3g7osHejdIpZQ6De0dU0gyxrxojGmwXy8BSR6Mq1twOITP7pnKc7dmA7B8l5UQbjs/A4CnP9nBG6v3eTFCpZQ6Pe1NCqUicouIBNivW4BSTwbWnUwb2oM1909jzqS+LJ97IQ9eOYylP58CwLy3N1BcWePdAJVSqp3amxS+h3U56gGgCJgJzD7ZBiLSW0SWishmEdkkIne1UmeKiJSLyDr79cBpxu8zEiNDuHf6EHrFhgGQmRjB4rsm4nQZnvx4B7v1vgalVDfQrjEFY8we4KrmZSLyU+DJk2zWAPyPMWatiEQBOSLykTFm83H1vjDGXHE6QXcXg1OiSI0N47WVe3lt5V56RIew8l6/u+dPKdWNnMmT1+4+2UpjTJExZq29XAlsAVLP4PO6HRFh4Y/Oc78/WFHLtgOVXoxIKaVO7kySgrS7okgGMBpY2crqCSKyXkQWi8iwNrafIyJrRGRNSUlJh4L1lh7RoXx89ySG9IwG4NInP+e6vy3nq52HvByZUkqd6EySQrvmjxaRSKwnt/3Unj+pubVAH2PMSOBPwDutfpAxzxpjso0x2UlJ3e+ip/7JUSy+ayJzLx8MQM6ew/zw/3IoP1bv5ciUUqqlk44piEglrX/5CxB2qp2LSBBWQnjVGLPw+PXNk4Qx5gMR+auIJBpjzso/o384uR+DUqI4VufkR6+u5b63N/Bfk/oRHOhgYI9IRNrd+FJKKY84aVIwxkR1dMdifcM9D2wxxrQ6zbaIpAAHjTFGRMZitVzO6ktdpw5KBmDW2HTmr9rLe7lF7nU/mzaQu6YN8FZoSinV7juaO+J84DvABhFZZ5fdC6QDGGOewbq09Q4RaQCOATcZP3ms2bzpg5m/am+Lsic+3k6fhHDGZsa7L21VSqmuJN3tOzg7O9usWbPG22F0ikeXbOWTLcUsuvMCNu0vZ8Zfl7vX5dw/jdCgACJCPJm3lVL+QkRyjDHZp6ynScF3vJ9bxF0LvqHB1fR/8u6Pz2dk71gvRqWUOhtoUuim6hpcDLx/cYuy4AAHv7xyKIeP1jG+bwJjM+O9FJ1Sqrtqb1LQvgkfExzo4IXZ2RytdbJ0WzEL1xZS53Txy3c2WusDHHx892TSE8K9HKlS6mykScEHXTi4BwDThvQgIyGCaUN68JMF39AzJpQvdhxi0qNLeeuOCYzpoy0GpVTn0u6jbsLlMjgcwuur9/K/b20A4PN7pmqLQSnVLu3tPjqTO5pVF3I4rBvbbjw3nV9cNgiAxz/a5s2QlFJnIU0K3dAPJ/WjT0I4eaXVGGN4Y/U+fvOeNflsg9PFY0u2kbOnjH1l1SzdWuzlaJVS3YmOKXRDDodw2fAU/v7ZbjLnfeAuX76rlF3FVdQ5Xfx56U53ud7zoJRqL20pdFM3Zvd2L6fadz9vKaqgzukiPDigRd0xv/2YYQ8u4Yo/fcGR6roujVMp1b3oQHM35nQZBKvlsP/IMfYfOUaDy3BuRjxF5cf454o9rMwrY92+I+5tLh3Wg79/55RjTUqps4zep+AHAhxNs6r2ig1rMV9SWlw486YPAeBIdR0xYUFc/tQXLNl0kG/2HmZVXhk7iqv4zdXDCTuuZaGU8l+aFPxAbHgwAC/edi4Tfv9pizmWBqdEcfvEvt4KTSnlY3RMwY/0jAnj3umDW5T99v0trNx9Vs9WrpQ6DTqm4GeMMVTWNhAVEsjesmomP7qM7D5x/PKKoQzpGU1woP6doNTZSG9eU60SEaJDgxAR+iREcP+3hrBmz2Gu/stXDLx/MfMW5lLb4PR2mEopL9Gk4Odun9iXp24a5X4/f9U+nv8yj4LD1azJL/NiZEopb9DuIwVA3qGjvLw8n81FFazKa0oGa+6fRmJkiBcjU0p1Bu0+UqclMzGCX101jP93/cgW5b/7YAsFh6u587W1VNTUeyk6pVRX0UtSVQu948N584cTcDiEl5fns3BtIQvXFgIwpGc0P57a38sRKqU8SVsK6gTZGfFkpcdxz6WDWpS/l1vkpYiUUl1FWwqqTWlx4fzz+2P5dGsxxRW1vL+hiKv+/CUX9E9k3b4j/GBiX7YeqOTbY9P5cFMRo3rHMSglytthK6XOgA40q3bZV1bNxD8uPWW9uy4awLQhPRjcM4qgAG2IKuUrdKBZdare8eHcOqFPi7KEiOAT6j31yQ6u/POXTHv8M+av2kt3+6NDKX+nLQV12kqrajlQUcOwXjHUNjgpraqjV2wYOXsO85elO/m02YN9bhmfzk3npuMyhqO1TgalRBHfSjJRSnlWe1sKmhRUpyuurOHx/2xnZV4ZeYeOnrD+L9/O4sNNB4gMCeD3144ArOk3GlyG6lonH24q4rqsNAK1+0mpTqNJQXmdy2V49D/b+NuyXW3WiQkLIjIkkMIjxwAICXRQ2+ACYMW8C+kZE9bmtkqp9tOkoHzOvrJqVuwq5Zt9R0iJDuXpT3fgdJ385++jn01iQA+9okmpM+X1gWYR6S0iS0Vks4hsEpG7WqkjIvK0iOwUkVwRyfJUPMr7eseHc8O5vfn9tedw17QBrLlvGjNGpwLw7zsv4LXbx7nrXptllc9duEHvpFaqC3nyPoUG4H+MMWtFJArIEZGPjDGbm9W5HBhgv8YBf7P/VX4gLiKYP1w3gu9fkMnw1BgArhrZi/F9E/j2uHQu6J/IL97M5b63N/KnWaM5VufkJwu+YWCPSMKDA7l4aA8GaitCqU7VZd1HIvIu8GdjzEfNyv4OLDPGzLffbwOmGGPavHVWu4/8y2/e28zzX+aRGBlCdp84Ptx0oMX6vN9P59OtxSzfVcovrxjqpSiV8n1e7z46LpgMYDSw8rhVqcC+Zu8L7DKlANzdS4eqavlw0wESIoK5aHCye/2/1hRw+ytreP7LPJZtK25rN0qpdvL4NBciEgm8BfzUGFPRwX3MAeYApKend2J0ytcNT43hrTsmsLGwgsAAYcboVMKDA6l3ujjvkU/5xVu57rqPLN7K5IFJiIi7rLEl3LxMKdU2jyYFEQnCSgivGmMWtlKlEOjd7H2aXdaCMeZZ4Fmwuo88EKryYWP6xDOmT3yLsqAABwvmjOei//cZAOf1S2D5rlLW7j3CgB6R/GrRJvfsrmBd+vrNLy/G4Wg7OdQ1uFi4toBLh6Wwt6yaeQs3sLnI+jsmNMjBC989l/P6J56w3ZHqOqJCgwg4yb6V6i48NqYg1p9mLwNlxpiftlHnW8CdwHSsAeanjTFjT7ZfHVM4yxzYCHF9IKRjA8ZbD1RQdrSOQT2iGPPbj09a977pQ/jBpL5trn/q4x088fH2Ntc7BLb85jJCAgPcZSWVtZz78MdcOqwHyVGhVNbU0zcpkh9M7Muukir6J0cSGhTQ5j6V6irtHVPwZEvhfOA7wAYRWWeX3QukAxhjngE+wEoIO4Fq4DYPxqN8jTHw2g1QdRBSx0DfKZA5GdLOhcD2TYUxOCXavTx1UBJLt5UAMKZPHLPPyyApKgSnyzBv4QYe/mALD3+whWlDevDXm7MIDmw5pJZ3qKrF+798O4s+CeHsKa1mdX4ZLy3P5+vdZUwemERtg5NLn/ic/NJqAJZsOthi28c/akou4zLjmTQwiTmT+iKgd2orn6Y3rynvMQbyPofdyyDvM9j/DRgXBIVD+gToO9lKEikjwHHqL9Kaeie/eW8zM8ekMTo9rsW61mZ5vXpUL357zXCiQoMAuO3FVRysqGXu5YMZmxnf4i/8qtoGzvv9J5zXL5E/fXs043/3CaVH6wDrMtpF6/czLjOemWPSuOfNXNoSFCBsfugynUFWdTm9o1l1P8eOwJ6vYPdnVpIo2WqVh8VBxkQrSfSdCvF9oQMDx9sPVuJ0GS5/6osW5clRIdx2fiZv5uyjR3Qor/1gfKvbP/juRuav2sdTN43ijlfXMnlgEr+8Ygj9kiI5UFFDj6hQHA5hX1k1dy34hu+el8FdC9adsJ//mtyXeZcPOe34lToTmhRU91d5oKklsfszqCiwyqPTmloRfSdDVMpp7bauwcWWogp+/e9NrN17pMW62edl8KurhrW63de7S7np2a/d73N/dQnRdiujLUu3FbNsazEDU6K47+2NAIQFBfB/t49jTJ+4k26rVGfSpKDOLsZA2e6mrqa8z+HYYWtd4qCmJJFxAYTFtnOXhgMVNfxn00EeXLSJoADhmwcuITKk9aE2Yww3/v1rVuWXkZkYwdKfTzmtQzhYUcOB8hpmv7iKw9X1LJgznvF9E05rH0p1lCYFdXZzueDghqZWxN4VUF8N4oBeo5taEb3HQ1DoKXd3tLaB8OCAU97PUO90sWDVXmaO6U1YcMeuKmpscVwytAfP3nrK31GlOoUmBeVfGuqgYLXVitj9GRSuAVcDBIRA+jg7SUyBnqMgwPuPJr/37Q28tnIvv7pyKLPGpbe4zFUpT9CkoPxbbSXsWWEniWVw0OrPJyTG6mJq7G5KGtShQeszVXa0jqzfWNOAZSZG8PHdk/XmN+VRmhSUaq6qBPI/b7qy6XC+VR6ZApmTmpJEbO+T7qYzrckvY+YzKwD44eR+zL18cJd9tvI/mhSUOpnDe5q6mvI+g6PWTW/E9226iS5zEoTHn2wvneI7z6/kix2HmHf5YL57XgYhgQ6dq0l1Ok0KSrWXMVC8uSlB5H8FdZWAQMo5ditiCvSZAMERnf7xh4/WMeOvX7nvjgZ44IqhPPTeZganRDE4JYqw4EB+f+05nf7Zyn9oUlCqo5z11t3VjUli30pw1oEjyJqCo689aJ06BgJOfp9Ce1XW1HP9MyvYeqCyzTo/mJjJfd/SZ0aojtGkoFRnqau2Lnlt7G4qWg8YCI6EPuc1Xf6aPKxd03G0ZVVeGTc9u4I7LxzA05/sAOA/P5vEw+9v4Zu9h6moaeB752cyNjOeCwcnnzB3k1Ino0lBKU+pLoP8L5uSRKn1BU54ImRObLr8NT6zwx9R2+BscZlqg9PFrS+sYvmuUgBuGZ/Ob6/R7iTVfpoUlOoq5YUtB60r7afJxqY3JYjMSRCZfLK9nJLTZXj7m0LuXbiBOqeLJ24cyeXDe7aYuG/7wUoueeJz3v7ReSdMCqj8myYFpbzBGDi0o+n+iPwvoKbcWpc8tKmrqc/5EBp90l21ZWNhOVf86UvAmiL8wSsndLQrAAAUN0lEQVSH4jKQs+cwjyzeQr3T+p3+x63ZXDQ4mTfXFuByGa7NStMuJz+mSUEpX+ByQtG6plbE3q+hoQYkwH6GhH1/RO+xEBjSrl0aY8ic90GHwnnvvy9geGpMh7ZV3ZsmBaV8UX0NFKxqShKFa8E4ITAM0sc3JYmeI8HR9tQXxRU1VNc5mfLYshPWfT3vIn73wRYWrd/f6rbbf3u5thj8kCYFpbqDmnLrvojGMYmSLVZ5aKw9HccU65XQv9XpOA5W1LC5qIK48GD6xIdTXe8kNTaMY3VOhjzwIf2SInjl++PoFRPaonWx6t6LSI5uOVHggfIath6oYMqgMxv7UL5Jk4JS3VHlQWta8LxlsPtzKN9rlUf1avkMiehep9xVXYOLoABx3x1d1+Bi4P2L3et3PHw5h6vrSI4KxekyDHngQ+oaXMy9fDATByQyrJd2M51NNCko1d0ZA4fzrBbE7mX2MyTKrHUJA+xWROMzJNp3pVFr4xHxEcGU2Y8WbW79A5cQE945N+cp79OkoNTZxuWyZntt7Grasxzqj1rPkOg5sqkVkT4BgsLa3M3e0mqm/r9lOF0n/u5/a0RP3s+1LqnVSfrOLpoUlDrbNdRBYU7T0+gKVtvPkAiG3uOakkSvrBOeIeFyGVbnl+F0Gb793Ep+Nm0gFwxI5JzUGI7WNvDD/8thdX4Zn90zld7x4d45PtWpNCko5W9qq6zpOBqTxIENVnlItHVfROOYRPKQUz5DYl9ZNZMeXcqdU/vzP5cM8nzsyuPamxS8/wgqpVTnCImEARdbL4CjpfYzJJZZ3U3b7UHmiOSWz5CI63PCrnrHhzN5YBIvL8/HIcLN49JPuFpJnZ00KSh1topIgGEzrBfAkb1N90fs/gw2vmmVx2U2JYjMSRCRCMC1WWks21bCU5/sYEdxJX+9eYyXDkR1JU0KSvmL2HTI+o71MgZKtjZd2bRxIeS8ZNXrcQ5kTuLSlNHMyAhi0d4gPtlSTHVdA+HBgRhjqHO6ePLjHcwck0a/pMgTPqre6eKxJdu4alQvhvWKobiihqv/8hVOl+HdO8+nZ0zbA+HKu3RMQSkFzgbrGRJ5y6xEsW8VOGsBqA+J54vqdLY6+nPTjBk8uTmKV3KrAEiKCmHVvRchIpQfq+eH/8xhxe7SFrvukxDOnmYPEAL44hdTSYsL0yfMdSEdaFZKdVxDnfU0usIcTGEO29Z+zkApwCHW98VeVxLrTT/Wu6zXnBuvYUOJ0/0ciNYMSI7kmtGpPLpkGwCBDuHtH51Pv+QIwoO108LTNCkopTpNzp4yfvTi52TU7mSkYye39imjR+UmgioLAHAaYbvpzXpXX6678ipKYoaT3HcUb3xzkD2lR+mTEMH12WkEBTj4+b/W82ZOQYv9zxidyoNXDiU2PNgbh+cXvJ4UROQF4Aqg2BgzvJX1U4B3gTy7aKEx5qFT7VeTglLeUVPvZPpTX9A/OZJnb7W+WzZs285HH39IQNE3jJRdjA3OI9xZYW0QGGbdVJeaZc0Im5oFcZnUuwx7y6pZu+cw97yZ695/n4Rw7v/WUC4e2oPG7yXtXuo8vpAUJgFVwCsnSQo/N8ZccTr71aSglG+pqm3gx6+uZeKARL53XgaO8nxr9tfCtdbNdUXrrOnCwZqOo1djkhhDbY+R/PeiQoICHLy/oajV/S+feyG9YnVg+kx5PSnYQWQA72lSUMqPOeuheAvst5NE4VprvMK4rPUx6ZA6ml3Bg7h3ZQgbTCbVNN0TERkSyGf3TCEhsn3Pm1Ct6y5J4S2gANiPlSA2tbGfOcAcgPT09DF79uzxUMRKqS5RdxSK1je1Jgpz4Ij1e21wUBndj+i+43itMJFXC5KoiBrAez+9kOiwQA5U1JAUGYJDBIdDu5faqzskhWjAZYypEpHpwFPGmAGn2qe2FJQ6Sx09ZF0W25gkCnOg2rq8tdYEscn0Yb2rH+tc/Vhv+tF/0Aiemz3Wy0F3Hz6fFFqpmw9kG2MOnayeJgWl/IQxVuuhcC17N35J0eavOEfyCBfr/okjJoJcV196DTuf/qMmWwPZUSlt7u5obQMP/XszJVW1PHdrtt+1Mnx+7iMRSQEOGmOMiIwFHEDpKTZTSvkLEYjLgLgM0odfywuLNvHt5buYPyMOU5DDrm8+Y6RjFxlbnoWtz1jbRKc2u9ppDPQcBaHRNDhdTHlsGSWVVkJZX3CE4akxBAXoY0mP58mrj+YDU4BE4CDwIBAEYIx5RkTuBO4AGoBjwN3GmOWn2q+2FJRSAJU19bzwZT5/+3gDQ2UPT1zgJLF8IxGH1kPZbruWUB3djw8O97K6nVz92GrSqbf/Hg4OdFDX4OLa0ak8fuMo7x1MF/CJ7iNP0KSglGpuxa5SZv3ja/f7f35/LBNTA2D/Wsp3fs3qrz5mpGMXSWLdP+F0BLGhId2dJNabfuSZFO6aNogfT+1/1rYeNCkopfxGv3s/cD9JLjTIwaI7L2Bgjygy5r4PwOAekXx4W19r2o6CHIq3LiehcguBDUcBqDBh5Lr6Et1/PCPGXmh1PUX39NrxeILPjykopVRnufvigTy6ZBtP3TSK+9/ZyIPvbmJGVqp7/Z9vzoLYKIjtjQy7hh6XAi4nHNqOKVjDkn8vYpDZwdC8lyDveQCOBCRSlzKK5MHnWUmi12gIjfHOAXYhbSkopc4KR2sbiAgJ5LkvdvPb97e4y1+fM55xfRNOuq0xhrfWFnLfv1YzMaqIa5IOULcvh5Gyi36Opjutj4RnUNdjFBUJI0gZegGR6aMgsHvcVKfdR0opv9TgdPHnpTtZuq2EW8alc31273ZtZ4xh+tNfsqWookV5NFX89+BKyneuZITsYpRjF8lyBACXIwhHynCcPbNwpI1BUsdA4kBwdGxc4vPtJby7bj+PXT+i0+d90qSglFKnqaq2gav+/CW7S46y5KeTeOw/2/ho80H3+vu/NYTfvr+ZFMoY6djF2OB8ru1xkKAD64iUY1al4CjoNarpstjULOtS2XZ8yQ994EOq65xM6JvAgB6RbD9YyXfGZzD9nJQzThKaFJRSqhNsO1DJlX/6ktnnZ3Dv9CHUNbjYUVzJ4aP13PL8SgAEF32liNe/FURi+SbrbuwDG8BVb+0kskdTguiVZf0bFuf+DGMMt7+8hk+2FrcaQ3JUCCvmXUTAGdxwp0lBKaU6SU29k9CggBZlxhgeXLSJV1bs4bqsNN5a2/SMiPf++wKG9wiFAxubTQSYA4e2u+scDEqlKnEk/UZO5v3SFO7+wlBLMJ/dM4X1BeW8lVNAoEPciSIlOpSv5l7Y4cSgSUEppTzMGENxZS09okN5d10hP319HcZAZmIED88Yzpg+cYQENksmNeWwfx05yz+mZNsKRjp20VPKAKg3AQT0HI4jze526pUFSYOocwljf/cxR6rreeLGkcwYndahWDUpKKWUF/xrzb4WDw/a+pvLCHQIpUfrSI4KobrOybAHl7jXJ3OYkY5dPHZeAzFluVD4DdSWWyuDIqxLYVOz2OLoz+BzpyExqcd/ZLvofQpKKeUF12f35o01+1idfxiAwb/80L1uQt8EahucANw8Lp1vj0tn4dpCrsu6iphe0VYll8uapqP5bLErn2GIsw6cd8KlD3s0fm0pKKWUB5QdreO2l1azft+RE9b1ignly/+9sP0ztTbUQfEmCImGhH4dikdbCkop5UXxEcG886Pz2H6wiozEcLYdqOTFr/IZ1iuaa0annt7U3YHBVjdSF9CkoJRSHiIiDEqJAmBEWixPdIOZWM/O6QCVUkp1iCYFpZRSbpoUlFJKuWlSUEop5aZJQSmllJsmBaWUUm6aFJRSSrlpUlBKKeXW7aa5EJESYE8HN08EDnViOF1JY/eO7hp7d40bNHZP6WOMSTpVpW6XFM6EiKxpz9wfvkhj947uGnt3jRs0dm/T7iOllFJumhSUUkq5+VtSeNbbAZwBjd07umvs3TVu0Ni9yq/GFJRSSp2cv7UUlFJKnYTfJAURuUxEtonIThGZ6+14mhOR3iKyVEQ2i8gmEbnLLo8XkY9EZIf9b5xdLiLytH0suSKS5d0jABEJEJFvROQ9+32miKy0Y3xdRILt8hD7/U57fYaX444VkTdFZKuIbBGRCd3lvIvIz+yfl40iMl9EQn31vIvICyJSLCIbm5Wd9nkWke/a9XeIyHe9FPej9s9Lroi8LSKxzdbNs+PeJiKXNiv32e+fExhjzvoXEADsAvoCwcB6YKi342oWX08gy16OArYDQ4E/AnPt8rnAH+zl6cBiQIDxwEofOIa7gdeA9+z3bwA32cvPAHfYyz8CnrGXbwJe93LcLwO328vBQGx3OO9AKpAHhDU737N99bwDk4AsYGOzstM6z0A8sNv+N85ejvNC3JcAgfbyH5rFPdT+bgkBMu3vnABf//454Zi9HUAX/UBOAJY0ez8PmOftuE4S77vAxcA2oKdd1hPYZi//HZjVrL67npfiTQM+AS4E3rN/mQ81+8Vxn39gCTDBXg6064mX4o6xv1jluHKfP+92Uthnf0EG2uf9Ul8+70DGcV+up3WegVnA35uVt6jXVXEft24G8Kq93OJ7pfGcd7fvH3/pPmr8BWpUYJf5HLtZPxpYCfQwxhTZqw4APexlXzueJ4FfAC77fQJwxBjTYL9vHp87dnt9uV3fGzKBEuBFu+vrORGJoBucd2NMIfAYsBcowjqPOXSP897odM+zz5z/Zr6H1aqB7hV3m/wlKXQLIhIJvAX81BhT0Xydsf7E8LlLxUTkCqDYGJPj7Vg6IBCra+BvxpjRwFGsbgw3Hz7vccDVWImtFxABXObVoM6Ar57nkxGR+4AG4FVvx9KZ/CUpFAK9m71Ps8t8hogEYSWEV40xC+3igyLS017fEyi2y33peM4HrhKRfGABVhfSU0CsiATadZrH547dXh8DlHZlwM0UAAXGmJX2+zexkkR3OO/TgDxjTIkxph5YiPV/0R3Oe6PTPc8+c/5FZDZwBXCzndCgG8TdHv6SFFYDA+wrM4KxBtoWeTkmNxER4HlgizHm8WarFgGNV1h8F2usobH8VvsqjfFAebNmeJcyxswzxqQZYzKwzuunxpibgaXATLva8bE3HtNMu75X/kI0xhwA9onIILvoImAz3eC8Y3UbjReRcPvnpzF2nz/vzZzueV4CXCIicXZL6RK7rEuJyGVY3aVXGWOqm61aBNxkX+mVCQwAVuHj3z8n8PagRle9sK5o2I51FcB93o7nuNguwGo65wLr7Nd0rD7fT4AdwMdAvF1fgL/Yx7IByPb2MdhxTaHp6qO+WL8QO4F/ASF2eaj9fqe9vq+XYx4FrLHP/TtYV7V0i/MO/BrYCmwE/ol11YtPnndgPtbYRz1WC+37HTnPWH34O+3XbV6KeyfWGEHj7+ozzerfZ8e9Dbi8WbnPfv8c/9I7mpVSSrn5S/eRUkqpdtCkoJRSyk2TglJKKTdNCkoppdw0KSillHLTpKB8jog4RWSdiKwXkbUict4p6seKyI/asd9lItKtn5/b2UTkJRGZeeqayl9oUlC+6JgxZpQxZiTW5GG/P0X9WKxZQH1SszuMlfJ5mhSUr4sGDoM1N5SIfGK3HjaIyNV2nUeAfnbr4lG77v/addaLyCPN9ne9iKwSke0iMtGuG2DPkb/aniP/v+zyniLyub3fjY31mxORfBH5o/1Zq0Skv13+kog8IyIrgT+K9eyAd+z9fy0iI5od04v29rkicp1dfomIrLCP9V/2vFiIyCNiPXcjV0Qes8uut+NbLyKfn+KYRET+LNbc/h8DyZ35n6W6P/0LRvmiMBFZh3UXbk+s+ZQAaoAZxpgKEUkEvhaRRViT2A03xowCEJHLsSaLG2eMqRaR+Gb7DjTGjBWR6cCDWHMIfR9rKoVzRSQE+EpE/gNcizXl8cMiEgCEtxFvuTHmHBG5FWvG2Cvs8jTgPGOMU0T+BHxjjLlGRC4EXsG6m/qXjdvbscfZx3Y/MM0Yc1RE/he4W0T+gjVV82BjjJGmh7s8AFxqjClsVtbWMY0GBmHN/d8Da2qMF9r1v6L8giYF5YuONfuCnwC8IiLDsaY/+J2ITMKapjuVpumWm5sGvGjseWmMMWXN1jVONpiDNU8+WHPojGjWtx6DNW/NauAFsSYrfMcYs66NeOc3+/eJZuX/MsY47eULgOvseD4VkQQRibZjvalxA2PMYbFmnh2K9UUO1oNZVmBNd10DPC/WE+7eszf7CnhJRN5odnxtHdMkYL4d134R+bSNY1J+SpOC8mnGmBX2X85JWPPHJAFjjDH1Ys3MGnqau6y1/3XS9PMvwH8bY06YXM1OQN/C+tJ93BjzSmthtrF89DRjc38s8JExZlYr8YzFmvxuJnAncKEx5ociMs6OM0dExrR1THYLSak26ZiC8mkiMhjrcYalWH/tFtsJYSrQx65WifUY00YfAbeJSLi9j+bdR61ZAtxhtwgQkYEiEiEifYCDxph/AM9hTavdmhub/buijTpfADfb+58CHDLWMzM+An7c7HjjgK+B85uNT0TYMUUCMcaYD4CfASPt9f2MMSuNMQ9gPTSod1vHBHwO3GiPOfQEpp7i3Cg/oy0F5YsaxxTA+ov3u3a//KvAv0VkA9bMplsBjDGlIvKVWA9XX2yMuUdERgFrRKQO+AC49ySf9xxWV9JasfprSoBrsGZ9vUdE6oEq4NY2to8TkVysVsgJf93bfoXVFZULVNM0ZfRvgb/YsTuBXxtjFoo1X/98ezwArDGGSuBdEQm1z8vd9rpHRWSAXfYJ1jOAc9s4prexxmg2Y02/3VYSU35KZ0lV6gzYXVjZxphD3o5Fqc6g3UdKKaXctKWglFLKTVsKSiml3DQpKKWUctOkoJRSyk2TglJKKTdNCkoppdw0KSillHL7/wybRdlSYwXeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "1124px",
    "left": "1506px",
    "top": "370.133px",
    "width": "243px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}