isic_skin_lesion_diagnosis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JmzrpUuFvxSs"
   },
   "source": [
    "## Copy and Unzip Data and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VCkKrLPgM2UQ"
   },
   "outputs": [],
   "source": [
    "# Python library for copying files from My Drive\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UdHrkJd7NG0P"
   },
   "outputs": [],
   "source": [
    "# Ignore if running locally\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CbepHYh9M3tu"
   },
   "outputs": [],
   "source": [
    "# Ignore if running locally\n",
    "# Otherwise change `drive/My Drive/Colab Notebooks/Data/ISIC2018_Task3_Training_*.zip` to /path/to/data\n",
    "shutil.copy2(\"drive/My Drive/Colab Notebooks/Data/ISIC2018_Task3_Training_GroundTruth.zip\", \"./\")\n",
    "shutil.copy2(\"drive/My Drive/Colab Notebooks/Data/ISIC2018_Task3_Training_Input.zip\", \"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tQE6BMXsIxS2"
   },
   "outputs": [],
   "source": [
    "!unzip -q ISIC2018_Task3_Training_GroundTruth.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WQdrc1kKLEeE"
   },
   "outputs": [],
   "source": [
    "!unzip -q ISIC2018_Task3_Training_Input.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kdKpLQ1Gv4pc"
   },
   "source": [
    "## Attribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "id": "TCcfzRS8LN2j",
    "outputId": "f4069073-5b8d-48cd-bec7-fe947ca87324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIC-2018 Challenge: Task 3: Training Ground Truth (HAM10000 Dataset: https://arxiv.org/abs/1803.10417 ) (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna\n",
      "\n",
      "ISIC-2018 Challenge: Task 3: Training Ground Truth (HAM10000 Dataset: https://arxiv.org/abs/1803.10417 ) is licensed under a\n",
      "Creative Commons Attribution-NonCommercial 4.0 International License.\n",
      "\n",
      "You should have received a copy of the license along with this\n",
      "work. If not, see <http://creativecommons.org/licenses/by-nc/4.0/>.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(open(\"ISIC2018_Task3_Training_GroundTruth/ATTRIBUTION.txt\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PUEMjxVdwGCV"
   },
   "source": [
    "## Make labels/data FAST.AI ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k_uUResxLlOm"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wzlzUNfTkSvp"
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZVDbYFGnLz18"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"ISIC2018_Task3_Training_GroundTruth/ISIC2018_Task3_Training_GroundTruth.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oYQEheKGM7pg"
   },
   "outputs": [],
   "source": [
    "for col in df.columns:\n",
    "  if col != \"image\":\n",
    "    s = df[col].sum()\n",
    "    c = df[col].count()\n",
    "    print(\"Column: {}\\t Count: {}\\t Percentage: {}\".format(col, s, round((s/c)*100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9FP5PN_ORU07"
   },
   "outputs": [],
   "source": [
    "# Image classes are in individual columns\n",
    "# Created a new column name `class` \n",
    "# which contains values from 0-6\n",
    "df[\"class\"] = df.drop(\"image\", axis=1).apply(lambda x: x.to_numpy().argmax(), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WTGRE7Bar3jF",
    "outputId": "1512050a-39a6-47af-8ce6-6b4aabff4135"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MEL': 0, 'NV': 1, 'BCC': 2, 'AKIEC': 3, 'BKL': 4, 'DF': 5, 'VASC': 6}\n"
     ]
    }
   ],
   "source": [
    "# What each value means in the `class` column\n",
    "a = list(df.columns)\n",
    "for i in [\"image\", \"class\"]:\n",
    "  a.remove(i)\n",
    "a = dict(zip(a, range(len(a))))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "im8xQ1S7sv4Y"
   },
   "outputs": [],
   "source": [
    "df.drop(a.keys(), axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2_iVNt3rws-8"
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"isic_labels.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GEcwTggBlhZH"
   },
   "source": [
    "### Extract Validation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pkwRdkJelgWY"
   },
   "outputs": [],
   "source": [
    "test_idx = []\n",
    "for c in df[\"class\"].unique():\n",
    "    # get all the image names from a particular class\n",
    "    temp = df[df['class'].apply(lambda x: True if x == c else False)]\n",
    "    # extract 10% of images from each class\n",
    "    test_idx += temp.sample(math.floor(temp.shape[0]/10)).index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7VyItTIVFeIP"
   },
   "outputs": [],
   "source": [
    "# create a test dataframe\n",
    "test_df = df.iloc[test_idx].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "75bEjfHaFd3z"
   },
   "outputs": [],
   "source": [
    "# create a train dataframe\n",
    "train_df = df.drop(labels=test_idx).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HpATIKmyFds7"
   },
   "outputs": [],
   "source": [
    "train_df.to_csv(\"isic_train_labels.csv\", index=False)\n",
    "test_df.to_csv(\"isic_test_labels.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2gVjmJ-4uQBB"
   },
   "source": [
    "### Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NlrvGcjbuBTt"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"isic_train_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bdBQNMxBG93z",
    "outputId": "ac30472a-c17d-406d-ced0-f7bd4f1983ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6035"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# class NV has highest number of images\n",
    "sum(df[\"class\"] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8WDQyWk6ulxP"
   },
   "outputs": [],
   "source": [
    "classes = df[\"class\"].unique()\n",
    "max_val = 6035"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SxNKQ-umuloG"
   },
   "outputs": [],
   "source": [
    "path = \"ISIC2018_Task3_Training_Input\"\n",
    "def cloning(img, count):\n",
    "  \"\"\"\n",
    "  img: Image Name\n",
    "  count: Number of times `img` needs to be copied.\n",
    "  \"\"\"\n",
    "  images = []\n",
    "  for i in range(count):\n",
    "    ii = img+\"_\"+str(i)\n",
    "    img_path = os.path.join(path, img+\".jpg\")\n",
    "    ii_path = os.path.join(path, ii+\".jpg\")\n",
    "    # Creating a copy of `img` in the file system\n",
    "    shutil.copy(img_path, ii_path)\n",
    "    images.append(ii)\n",
    "  return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IXa-w6irulcS"
   },
   "outputs": [],
   "source": [
    "# I know.. this method is pretty dumb(memory inefficient).\n",
    "# If you know how to do it with data_block api(without creating multiple copies in file system).\n",
    "# Please share with me.\n",
    "images, labels = [], []\n",
    "for c in classes:\n",
    "    if c == 1:\n",
    "        continue\n",
    "    data = df[df[\"class\"].apply(lambda x: True if x == c else False)]\n",
    "    l = data.image.shape[0]\n",
    "    rest = max_val - (math.floor(max_val/l)*l)\n",
    "    ns = [j+1 if i < rest else j for i, j in enumerate([math.floor(max_val/l)]*l)]\n",
    "    for i, n in zip(data.image.values, ns):\n",
    "        images += cloning(i, n)\n",
    "        labels += [c]*n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "irVZTpHIxCGl"
   },
   "outputs": [],
   "source": [
    "# `oversampled` do not contain any class `1` image\n",
    "oversampled = pd.DataFrame({\"image\": images, \"class\": labels})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BMlcsVw4xaYr"
   },
   "outputs": [],
   "source": [
    "df_1 = df[df[\"class\"].apply(lambda x: True if x == 1 else False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1E-sr1dNyLY7"
   },
   "outputs": [],
   "source": [
    "balanced = pd.concat([oversampled, df_1]).reset_index(drop=True) # New balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eq_INxjGOsMq"
   },
   "outputs": [],
   "source": [
    "# For data_block api to understand which image is for training\n",
    "# and which one is for validation\n",
    "balanced[\"is_valid\"] = False\n",
    "test_df[\"is_valid\"] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RHCd_Nd4O5Jf"
   },
   "outputs": [],
   "source": [
    "balanced = pd.concat([balanced, test_df]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "adEzmPe-PMxb"
   },
   "outputs": [],
   "source": [
    "# balanced[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-qjiv_TxzGTu"
   },
   "outputs": [],
   "source": [
    "balanced.to_csv(\"isic_labels_balanced.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZpR6SAQ3wOw_"
   },
   "source": [
    "## Start Coding FAST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl https://course.fast.ai/setup/colab | bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ykSN9COyt6xd"
   },
   "outputs": [],
   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F9gDMNln0eZU"
   },
   "outputs": [],
   "source": [
    "# Copied from lesson3-planet.ipynb\n",
    "tfms = get_transforms(max_rotate=20, max_zoom=1.5, max_lighting=0.5, max_warp=0.4, p_affine=1., p_lighting=1., flip_vert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5GcUcmdR0cbn"
   },
   "outputs": [],
   "source": [
    "src = (ImageList.from_csv(\"./\", \"isic_labels_balanced.csv\", folder=\"ISIC2018_Task3_Training_Input\", suffix=\".jpg\")\n",
    "                .split_from_df() # uses `is_valid` column\n",
    "                .label_from_df())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6hBvYCizPcKV"
   },
   "source": [
    "After this, I did a bunch of training using various Resnet models and training sizes.\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "```\n",
    "Recall(average=\"macro\") == Balanced Accuracy\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1YRp-_JtOCTr"
   },
   "source": [
    "### Resnet 34 with size 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0YtS22Y0OLjK"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "           .databunch()\n",
    "           .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "colab_type": "code",
    "id": "z_JIBSjpOPLe",
    "outputId": "e46dd99e-54fc-4da2-ce7b-1c5445246842"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.torch/models/resnet34-333f7ec4.pth\n",
      "87306240it [00:00, 140655574.56it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.052865</td>\n",
       "      <td>0.821396</td>\n",
       "      <td>0.679653</td>\n",
       "      <td>17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.785176</td>\n",
       "      <td>0.668422</td>\n",
       "      <td>0.721265</td>\n",
       "      <td>17:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.639008</td>\n",
       "      <td>0.551742</td>\n",
       "      <td>0.792149</td>\n",
       "      <td>17:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.544116</td>\n",
       "      <td>0.577696</td>\n",
       "      <td>0.801285</td>\n",
       "      <td>17:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.551528</td>\n",
       "      <td>0.556584</td>\n",
       "      <td>0.781129</td>\n",
       "      <td>17:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet34, metrics=Recall(average=\"macro\"))\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "p4U7076sOWEf",
    "outputId": "69ec38d4-bf7f-45a5-eb14-d46cc1de4d64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz224-rn34.pth'"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After training every stage I copy it to my drive.\n",
    "learn.save('stage1-sz224-rn34')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "hp5gj5PnOcHN",
    "outputId": "83a6c7eb-f14d-4ad9-d61a-7c15da9ead7e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.519597</td>\n",
       "      <td>0.557518</td>\n",
       "      <td>0.783658</td>\n",
       "      <td>17:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.446808</td>\n",
       "      <td>0.512407</td>\n",
       "      <td>0.780379</td>\n",
       "      <td>17:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.424493</td>\n",
       "      <td>0.490918</td>\n",
       "      <td>0.817858</td>\n",
       "      <td>17:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.379019</td>\n",
       "      <td>0.455817</td>\n",
       "      <td>0.819809</td>\n",
       "      <td>17:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.369598</td>\n",
       "      <td>0.473230</td>\n",
       "      <td>0.822619</td>\n",
       "      <td>17:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uBDQrw6nOghz",
    "outputId": "85243641-f4e5-4a15-f609-712933131220"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz224-rn34.pth'"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz224-rn34')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i9bAenl_khCD"
   },
   "source": [
    "### Resnet 34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DjtZv2ASlw4O"
   },
   "source": [
    "#### Size 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JEUXTOjj1tgl"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=64)\n",
    "           .databunch()\n",
    "           .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "23Ltj1CkQfdh"
   },
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W3AYNWig3IIM"
   },
   "outputs": [],
   "source": [
    "data.show_batch(rows=2, figsize=(9,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5_uN13vE3NrF",
    "outputId": "de3ab29a-89e0-48ad-b877-aff5e3b7c17f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6], 7)"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.classes, data.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uqpE5YqY3mLi"
   },
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.resnet34, metrics=Recall(average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hYhlUKCuUeug"
   },
   "outputs": [],
   "source": [
    "# learn.lr_find()\n",
    "# learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "e5hZNws43vua",
    "outputId": "40911f1c-d44c-4b44-afd1-5b4286040961"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.332112</td>\n",
       "      <td>1.139046</td>\n",
       "      <td>0.586971</td>\n",
       "      <td>09:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.113166</td>\n",
       "      <td>0.986617</td>\n",
       "      <td>0.583628</td>\n",
       "      <td>08:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.971651</td>\n",
       "      <td>0.877294</td>\n",
       "      <td>0.632187</td>\n",
       "      <td>09:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.908795</td>\n",
       "      <td>0.867847</td>\n",
       "      <td>0.668636</td>\n",
       "      <td>09:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.900284</td>\n",
       "      <td>0.822514</td>\n",
       "      <td>0.649331</td>\n",
       "      <td>09:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1LigeJNqZZJU",
    "outputId": "61aff127-a273-460a-8b10-68eaec0da335"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz64-rn34.pth'"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz64-rn34')\n",
    "shutil.copy2(\"./models/stage1-sz64-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f7i36MoiXmhP"
   },
   "outputs": [],
   "source": [
    "learn.unfreeze()\n",
    "# learn.lr_find()\n",
    "# learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "__rSJF4LXvos",
    "outputId": "1ea627e4-3164-46bc-fcb6-f17b6d2ffbbc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.863984</td>\n",
       "      <td>0.818419</td>\n",
       "      <td>0.677289</td>\n",
       "      <td>09:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.813371</td>\n",
       "      <td>0.819260</td>\n",
       "      <td>0.693993</td>\n",
       "      <td>08:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.785273</td>\n",
       "      <td>0.701954</td>\n",
       "      <td>08:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.725196</td>\n",
       "      <td>0.753805</td>\n",
       "      <td>0.717991</td>\n",
       "      <td>08:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.721513</td>\n",
       "      <td>0.770385</td>\n",
       "      <td>0.706379</td>\n",
       "      <td>09:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ncSSzRnAgt-i",
    "outputId": "352f3939-263d-49f9-800b-c900bd5b213a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz64-rn34.pth'"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz64-rn34')\n",
    "shutil.copy2(\"./models/stage2-sz64-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_x37y4A-l27W"
   },
   "source": [
    "#### Size 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "LsWxT1qxg024",
    "outputId": "43825c81-defd-4258-c0c4-2c2b8fc39e45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 128, 128])"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (src.transform(tfms, size=128)\n",
    "        .databunch().normalize(imagenet_stats))\n",
    "\n",
    "learn.data = data # Take the model(trained with size 64) and retrain on size 128\n",
    "data.train_ds[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_8UbYmTohA6r"
   },
   "outputs": [],
   "source": [
    "learn.freeze()\n",
    "# learn.lr_find()\n",
    "# learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "haYc9PBIMi1g",
    "outputId": "cb6525a3-a42a-45de-e1a9-654e9b2e183a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.745451</td>\n",
       "      <td>0.705414</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>11:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.668169</td>\n",
       "      <td>0.635699</td>\n",
       "      <td>0.737007</td>\n",
       "      <td>11:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.609389</td>\n",
       "      <td>0.660066</td>\n",
       "      <td>0.765177</td>\n",
       "      <td>11:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.547385</td>\n",
       "      <td>0.609776</td>\n",
       "      <td>0.765725</td>\n",
       "      <td>11:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.534062</td>\n",
       "      <td>0.589395</td>\n",
       "      <td>0.766407</td>\n",
       "      <td>11:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yjb83zk9iGxy",
    "outputId": "20475a04-5c0a-477f-be89-6509217b5155"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz128-rn34.pth'"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz128-rn34')\n",
    "shutil.copy2(\"./models/stage1-sz128-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r7ITDaqBlCka"
   },
   "outputs": [],
   "source": [
    "learn.unfreeze()\n",
    "# learn.lr_find()\n",
    "# learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "_3gHv2-AlKyz",
    "outputId": "37cb2438-3bb7-4cea-9881-211f7b53f685"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.506721</td>\n",
       "      <td>0.623814</td>\n",
       "      <td>0.771984</td>\n",
       "      <td>11:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.464688</td>\n",
       "      <td>0.560413</td>\n",
       "      <td>0.788222</td>\n",
       "      <td>11:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.454082</td>\n",
       "      <td>0.592337</td>\n",
       "      <td>0.790315</td>\n",
       "      <td>11:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(3, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "V9MR_ghnl9bs",
    "outputId": "87abd555-cc83-494b-a216-e4c6983b3934"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz128-rn34.pth'"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz128-rn34')\n",
    "shutil.copy2(\"./models/stage2-sz128-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8DfKO91Tl8UL"
   },
   "source": [
    "#### Size 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uIFKLGRmcVtX"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=256)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "sL-4hlU-mNbn",
    "outputId": "7573796c-2242-496d-d552-3e9cded54245"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 256, 256])"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.data = data # Take the model(trained with size 128) and retrain on size 256\n",
    "data.train_ds[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rsqEXffLmVpw"
   },
   "outputs": [],
   "source": [
    "learn.freeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "zIVSRIJsmZAe",
    "outputId": "43d6639f-d3df-43df-81a8-b985425ca572"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.535596</td>\n",
       "      <td>0.575985</td>\n",
       "      <td>0.805466</td>\n",
       "      <td>15:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.478631</td>\n",
       "      <td>0.495318</td>\n",
       "      <td>0.828277</td>\n",
       "      <td>15:21</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.413386</td>\n",
       "      <td>0.518744</td>\n",
       "      <td>0.843815</td>\n",
       "      <td>15:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.386791</td>\n",
       "      <td>0.461764</td>\n",
       "      <td>0.826640</td>\n",
       "      <td>15:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.368453</td>\n",
       "      <td>0.458687</td>\n",
       "      <td>0.836557</td>\n",
       "      <td>15:05</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "l_gXtiWcmd3j",
    "outputId": "1af94b45-7f42-490c-ed50-a38d569e4841"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz256-rn34.pth'"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz256-rn34')\n",
    "shutil.copy2(\"./models/stage1-sz256-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "iMChNs63rNZk",
    "outputId": "209bff82-b71e-48c3-a102-755c4a87e77a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.347334</td>\n",
       "      <td>0.480635</td>\n",
       "      <td>0.830765</td>\n",
       "      <td>15:36</td>\n",
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       "    <tr>\n",
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       "      <td>0.304978</td>\n",
       "      <td>0.438051</td>\n",
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       "      <td>0.865638</td>\n",
       "      <td>15:28</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.255134</td>\n",
       "      <td>0.386405</td>\n",
       "      <td>0.856789</td>\n",
       "      <td>15:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.248768</td>\n",
       "      <td>0.383055</td>\n",
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       "      <td>15:31</td>\n",
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     },
     "metadata": {
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     },
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   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8qzCNmDnrogI",
    "outputId": "88ea0ad6-448f-4ba4-9883-076cb4b6465c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz256-rn34.pth'"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz256-rn34')\n",
    "shutil.copy2(\"./models/stage2-sz256-rn34.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6aVQgeCck0SK"
   },
   "source": [
    "### Resnet 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QpahH8xkk4_Z"
   },
   "source": [
    "#### Resnet 50 with Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Vwzld5Vqv83i",
    "outputId": "6732e1a2-a8de-437c-e059-76ad49d08c9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 224, 224])"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "eLM6cnNYvjX7",
    "outputId": "d304d2f1-9c35-447a-f289-5bd04d5be1fa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.torch/models/resnet50-19c8e357.pth\n",
      "102502400it [00:04, 24326111.97it/s]\n"
     ]
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet50, metrics=Recall(average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "p5YctwDHwIKX",
    "outputId": "6ef85ee1-08a7-47f7-d86d-cbc4da302ed5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.872312</td>\n",
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       "      <td>0.696638</td>\n",
       "      <td>18:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>18:35</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>0.792648</td>\n",
       "      <td>18:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.399762</td>\n",
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       "      <td>0.823713</td>\n",
       "      <td>18:31</td>\n",
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       "    <tr>\n",
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     },
     "metadata": {
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   ],
   "source": [
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ihmwjVybw4ed",
    "outputId": "f5a3bb4a-b6ee-4778-c541-11799eccccaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz224-rn50.pth'"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz224-rn50')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn50.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "YTtH3ng2wL9P",
    "outputId": "957b80b0-6233-4950-d29e-b190c94d4119"
   },
   "outputs": [
    {
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
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       "      <th>valid_loss</th>\n",
       "      <th>recall</th>\n",
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       "      <td>19:20</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6HvwCefP0_nX",
    "outputId": "5775b195-609f-4555-b025-5acd9c6d4103"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz224-rn50.pth'"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz224-rn50')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn50.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HP5X9oFulEgw"
   },
   "source": [
    "#### Resnet 50 without Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fmj_BXgH65Ap"
   },
   "outputs": [],
   "source": [
    "# Result was already bad compared to other stages.\n",
    "# Stopped it.\n",
    "data = (src.transform(size=224)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "colab_type": "code",
    "id": "nLo58lr07FBK",
    "outputId": "66244192-cc73-4569-c71b-3d6398ff09b4"
   },
   "outputs": [
    {
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       "      20.00% [1/5 16:11<1:04:45]\n",
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       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
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       "\n",
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     "output_type": "error",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-e9038cd93c4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlearn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcnn_learner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRecall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"macro\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     20\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     21\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_it\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_div\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_dl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastprogress/fastprogress.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0;34m\"Process and returns items from `DataLoader`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproc_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcvd_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    599\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMP_STATUS_CHECK_INTERVAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmpty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    171\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet50, metrics=Recall(average=\"macro\"))\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w0QvgbBK7N58"
   },
   "outputs": [],
   "source": [
    "learn.save('stage1-sz224-rn50-woT')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn50-woT.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BW1F6-_a_Iwh"
   },
   "outputs": [],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fg769sqlDkkM"
   },
   "outputs": [],
   "source": [
    "learn.save('stage2-sz224-rn50-woT')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn50-woT.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1fxCpnHNlL5l"
   },
   "source": [
    "#### Resnet 50 without ImageNet Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "px_H4N9u0tiN"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "        .databunch().normalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "colab_type": "code",
    "id": "Qn31WYFf019w",
    "outputId": "3de8503c-c5cd-4ddc-ede6-7ac4dcb2dfbd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.torch/models/resnet50-19c8e357.pth\n",
      "102502400it [00:02, 38893584.93it/s]\n"
     ]
    },
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       "      <th>valid_loss</th>\n",
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   "source": [
    "learn = cnn_learner(data, models.resnet50, metrics=Recall(average=\"macro\"))\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qyhX-T1mieP1"
   },
   "outputs": [],
   "source": [
    "learn.save('stage1-sz224-rn50-woISTATS')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn50-woISTATS.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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   "outputs": [
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       "      <th>epoch</th>\n",
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    {
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     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-138c4862b27e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munfreeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     20\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     21\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_it\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_div\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_dl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastprogress/fastprogress.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0;34m\"Process and returns items from `DataLoader`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproc_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcvd_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    599\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMP_STATUS_CHECK_INTERVAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmpty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    171\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Result was already bad compared to other stages.\n",
    "# Stopped it.\n",
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GnnpNvDri7W1"
   },
   "outputs": [],
   "source": [
    "learn.save('stage2-sz224-rn50-woISTATS')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn50-woISTATS.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lxzOTRZIlSXy"
   },
   "source": [
    "### Resnet 101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rk8H17y5jJoD"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "5b9FqigHjgrV",
    "outputId": "1854d848-e9ff-4719-da71-4bafb4b97cef"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.843094</td>\n",
       "      <td>0.717341</td>\n",
       "      <td>0.763869</td>\n",
       "      <td>19:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.568740</td>\n",
       "      <td>0.566178</td>\n",
       "      <td>0.794980</td>\n",
       "      <td>18:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.427227</td>\n",
       "      <td>0.532300</td>\n",
       "      <td>0.833284</td>\n",
       "      <td>19:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.354840</td>\n",
       "      <td>0.453607</td>\n",
       "      <td>0.822989</td>\n",
       "      <td>19:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.339358</td>\n",
       "      <td>0.438968</td>\n",
       "      <td>0.855869</td>\n",
       "      <td>18:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet101, metrics=Recall(average=\"macro\"))\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Z3IzAuNnjuHG",
    "outputId": "f318cb9e-4ac6-41b6-88dc-e182aca0fe60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz224-rn101.pth'"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz224-rn101')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "jd2Jz9lwjqTw",
    "outputId": "8b0ac387-6511-4bc7-9cae-ff766ba6c7a7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.313122</td>\n",
       "      <td>0.425760</td>\n",
       "      <td>0.863088</td>\n",
       "      <td>20:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.257644</td>\n",
       "      <td>0.397432</td>\n",
       "      <td>0.873868</td>\n",
       "      <td>20:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.226868</td>\n",
       "      <td>0.357821</td>\n",
       "      <td>0.866362</td>\n",
       "      <td>20:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.204757</td>\n",
       "      <td>0.325785</td>\n",
       "      <td>0.879824</td>\n",
       "      <td>20:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.191248</td>\n",
       "      <td>0.350226</td>\n",
       "      <td>0.883955</td>\n",
       "      <td>20:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZAY82Tapjss8",
    "outputId": "8944e66c-3607-4561-fcda-23973341674c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz224-rn101.pth'"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz224-rn101')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IJkW43sQRNb7"
   },
   "source": [
    "### Resnet 152"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iSWSYLokRRUE"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "colab_type": "code",
    "id": "8XL_-xVtRTap",
    "outputId": "75ad90d2-e358-44d9-ee15-e3dd87b1bd25"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet152-b121ed2d.pth\" to /root/.torch/models/resnet152-b121ed2d.pth\n",
      "241530880it [00:10, 22546052.11it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.785708</td>\n",
       "      <td>0.709159</td>\n",
       "      <td>0.739327</td>\n",
       "      <td>21:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.519026</td>\n",
       "      <td>0.645384</td>\n",
       "      <td>0.815162</td>\n",
       "      <td>21:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.409894</td>\n",
       "      <td>0.487464</td>\n",
       "      <td>0.832022</td>\n",
       "      <td>21:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.328334</td>\n",
       "      <td>0.452459</td>\n",
       "      <td>0.847651</td>\n",
       "      <td>22:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.307490</td>\n",
       "      <td>0.468136</td>\n",
       "      <td>0.849233</td>\n",
       "      <td>21:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet152, metrics=Recall(average=\"macro\"))\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "NkHoGVHCRkvT",
    "outputId": "c34be439-d19f-4e04-a7f4-54b95dca2471"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz224-rn152.pth'"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz224-rn152')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn152.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "QU2i_3DlRqI8",
    "outputId": "69349c6e-937d-4515-83c6-1f5d28b48043"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.286909</td>\n",
       "      <td>0.395956</td>\n",
       "      <td>0.855177</td>\n",
       "      <td>23:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.256752</td>\n",
       "      <td>0.413372</td>\n",
       "      <td>0.854086</td>\n",
       "      <td>23:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.214330</td>\n",
       "      <td>0.350319</td>\n",
       "      <td>0.857564</td>\n",
       "      <td>22:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.187390</td>\n",
       "      <td>0.351069</td>\n",
       "      <td>0.856926</td>\n",
       "      <td>22:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.169617</td>\n",
       "      <td>0.335280</td>\n",
       "      <td>0.848321</td>\n",
       "      <td>23:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ucHoubVZRsjW",
    "outputId": "9fb1b989-9e19-410c-d2ee-6bcdf84162df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz224-rn152.pth'"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz224-rn152')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn152.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t9RcFY1fDRrm"
   },
   "source": [
    "###  Focal Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DEqK351KDX08"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S8E_jI1JDP0z"
   },
   "outputs": [],
   "source": [
    "# https://github.com/Hsuxu/Loss_ToolBox-PyTorch/blob/master/FocalLoss/FocalLoss.py\n",
    "class FocalLoss(nn.Module):\n",
    "    \"\"\"\n",
    "    This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in\n",
    "    'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'\n",
    "        Focal_Loss= -1*alpha*(1-pt)*log(pt)\n",
    "    :param num_class:\n",
    "    :param alpha: (tensor) 3D or 4D the scalar factor for this criterion\n",
    "    :param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more\n",
    "                    focus on hard misclassified example\n",
    "    :param smooth: (float,double) smooth value when cross entropy\n",
    "    :param balance_index: (int) balance class index, should be specific when alpha is float\n",
    "    :param size_average: (bool, optional) By default, the losses are averaged over each loss element in the batch.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, num_class, alpha=None, gamma=2, balance_index=-1, smooth=None, size_average=True):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.num_class = num_class\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "        self.smooth = smooth\n",
    "        self.size_average = size_average\n",
    "\n",
    "        if self.alpha is None:\n",
    "            self.alpha = torch.ones(self.num_class, 1)\n",
    "        elif isinstance(self.alpha, (list, np.ndarray)):\n",
    "            assert len(self.alpha) == self.num_class\n",
    "            self.alpha = torch.FloatTensor(alpha).view(self.num_class, 1)\n",
    "            self.alpha = self.alpha / self.alpha.sum()\n",
    "        elif isinstance(self.alpha, float):\n",
    "            alpha = torch.ones(self.num_class, 1)\n",
    "            alpha = alpha * (1 - self.alpha)\n",
    "            alpha[balance_index] = self.alpha\n",
    "            self.alpha = alpha\n",
    "        else:\n",
    "            raise TypeError('Not support alpha type')\n",
    "\n",
    "        if self.smooth is not None:\n",
    "            if self.smooth < 0 or self.smooth > 1.0:\n",
    "                raise ValueError('smooth value should be in [0,1]')\n",
    "\n",
    "    def forward(self, input, target):\n",
    "        logit = F.softmax(input, dim=1)\n",
    "\n",
    "        if logit.dim() > 2:\n",
    "            # N,C,d1,d2 -> N,C,m (m=d1*d2*...)\n",
    "            logit = logit.view(logit.size(0), logit.size(1), -1)\n",
    "            logit = logit.permute(0, 2, 1).contiguous()\n",
    "            logit = logit.view(-1, logit.size(-1))\n",
    "        target = target.view(-1, 1)\n",
    "\n",
    "        # N = input.size(0)\n",
    "        # alpha = torch.ones(N, self.num_class)\n",
    "        # alpha = alpha * (1 - self.alpha)\n",
    "        # alpha = alpha.scatter_(1, target.long(), self.alpha)\n",
    "        epsilon = 1e-10\n",
    "        alpha = self.alpha\n",
    "        if alpha.device != input.device:\n",
    "            alpha = alpha.to(input.device)\n",
    "\n",
    "        idx = target.cpu().long()\n",
    "\n",
    "        one_hot_key = torch.FloatTensor(target.size(0), self.num_class).zero_()\n",
    "        one_hot_key = one_hot_key.scatter_(1, idx, 1)\n",
    "        if one_hot_key.device != logit.device:\n",
    "            one_hot_key = one_hot_key.to(logit.device)\n",
    "\n",
    "        if self.smooth:\n",
    "            one_hot_key = torch.clamp(\n",
    "                one_hot_key, self.smooth, 1.0 - self.smooth)\n",
    "        pt = (one_hot_key * logit).sum(1) + epsilon\n",
    "        logpt = pt.log()\n",
    "\n",
    "        gamma = self.gamma\n",
    "\n",
    "        alpha = alpha[idx]\n",
    "        loss = -1 * alpha * torch.pow((1 - pt), gamma) * logpt\n",
    "\n",
    "        if self.size_average:\n",
    "            loss = loss.mean()\n",
    "        else:\n",
    "            loss = loss.sum()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7D0nms8_OUiD"
   },
   "source": [
    "### Resnet 101 with Focal Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NYlrduTmEi6s"
   },
   "outputs": [],
   "source": [
    "FL = FocalLoss(num_class=7, alpha=0.25, gamma=2.0, balance_index=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1L_viqrCJjzw"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=224)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "colab_type": "code",
    "id": "gixPU_0XJocW",
    "outputId": "ade1b096-b4fb-4e9c-a682-dafacfd8403b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.torch/models/resnet101-5d3b4d8f.pth\n",
      "178728960it [00:06, 27163988.73it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.335531</td>\n",
       "      <td>0.274163</td>\n",
       "      <td>0.729967</td>\n",
       "      <td>19:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.207919</td>\n",
       "      <td>0.186859</td>\n",
       "      <td>0.782605</td>\n",
       "      <td>20:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.155136</td>\n",
       "      <td>0.167362</td>\n",
       "      <td>0.833713</td>\n",
       "      <td>20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.113781</td>\n",
       "      <td>0.142307</td>\n",
       "      <td>0.832480</td>\n",
       "      <td>19:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.108782</td>\n",
       "      <td>0.148174</td>\n",
       "      <td>0.840843</td>\n",
       "      <td>20:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet101, metrics=Recall(average=\"macro\"), loss_func=FL)\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8gAvbplLOcUl",
    "outputId": "e3eb40f1-fa3e-4d12-f9eb-25b43f88eb1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz224-rn101-fLoss.pth'"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz224-rn101-fLoss')\n",
    "shutil.copy2(\"./models/stage1-sz224-rn101-fLoss.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "W2fs5HsGOepS",
    "outputId": "54957705-a1ae-42f4-9546-c72d7e64f57a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.094407</td>\n",
       "      <td>0.135128</td>\n",
       "      <td>0.837812</td>\n",
       "      <td>21:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.087238</td>\n",
       "      <td>0.120423</td>\n",
       "      <td>0.872192</td>\n",
       "      <td>21:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.066917</td>\n",
       "      <td>0.108963</td>\n",
       "      <td>0.868991</td>\n",
       "      <td>21:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058729</td>\n",
       "      <td>0.101714</td>\n",
       "      <td>0.864691</td>\n",
       "      <td>21:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.058357</td>\n",
       "      <td>0.103694</td>\n",
       "      <td>0.874223</td>\n",
       "      <td>21:31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "nQeiwCpWOgq2",
    "outputId": "b40cfdfe-3c97-4c6c-8284-7d56852aba17"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz224-rn101-fLoss.pth'"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz224-rn101-fLoss')\n",
    "shutil.copy2(\"./models/stage2-sz224-rn101-fLoss.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vtfjXdGNGuws"
   },
   "source": [
    "### Resnet 101 with various sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F7pOx7dgGuwu"
   },
   "source": [
    "#### Size 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q8CujyGqGuwv"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=64)\n",
    "           .databunch()\n",
    "           .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "colab_type": "code",
    "id": "zwgLZNI8Guwx",
    "outputId": "7b877faf-ac8f-42e3-9d83-137cb2042fb2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ImageDataBunch;\n",
       "\n",
       "Train: LabelList (42245 items)\n",
       "x: ImageList\n",
       "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
       "y: CategoryList\n",
       "0,0,0,0,0\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList (998 items)\n",
       "x: ImageList\n",
       "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
       "y: CategoryList\n",
       "1,1,1,1,1\n",
       "Path: .;\n",
       "\n",
       "Test: None"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CbX_OszzGuw3"
   },
   "outputs": [],
   "source": [
    "data.show_batch(rows=2, figsize=(9,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "dKSQJVT2GuxF",
    "outputId": "d446e42f-5c79-4b51-f9e4-10631b80c61c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/checkpoints/resnet101-5d3b4d8f.pth\n",
      "100%|██████████| 178728960/178728960 [00:02<00:00, 69422975.62it/s]\n"
     ]
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet101, metrics=Recall(average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "sJIlwVdoGuxQ",
    "outputId": "6ea0e68e-eb65-47d6-e7f2-96722c57b50e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.205486</td>\n",
       "      <td>1.064093</td>\n",
       "      <td>0.626171</td>\n",
       "      <td>09:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.927362</td>\n",
       "      <td>0.903381</td>\n",
       "      <td>0.685573</td>\n",
       "      <td>09:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0.844265</td>\n",
       "      <td>0.689880</td>\n",
       "      <td>09:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.713742</td>\n",
       "      <td>0.716945</td>\n",
       "      <td>0.730929</td>\n",
       "      <td>09:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.685730</td>\n",
       "      <td>0.719765</td>\n",
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       "      <td>09:26</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bmONsJTQGuxV",
    "outputId": "049e93e5-2e08-491f-ee5d-9573da104609"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz64-rn101.pth'"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz64-rn101')\n",
    "shutil.copy2(\"./models/stage1-sz64-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "Be4c3OY6Guxc",
    "outputId": "26ec2c2c-aad4-4124-d378-c735bc725e60"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.671971</td>\n",
       "      <td>0.697834</td>\n",
       "      <td>0.734063</td>\n",
       "      <td>09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.633835</td>\n",
       "      <td>0.649670</td>\n",
       "      <td>0.736626</td>\n",
       "      <td>09:30</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.557029</td>\n",
       "      <td>0.615166</td>\n",
       "      <td>0.746225</td>\n",
       "      <td>09:40</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.508771</td>\n",
       "      <td>0.602678</td>\n",
       "      <td>0.746066</td>\n",
       "      <td>09:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.507367</td>\n",
       "      <td>0.604513</td>\n",
       "      <td>0.756266</td>\n",
       "      <td>09:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0t6nGIMiGuxi",
    "outputId": "139fb4ec-e6ef-42ea-b187-dbb32c9a9f7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz64-rn101.pth'"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz64-rn101')\n",
    "shutil.copy2(\"./models/stage2-sz64-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bRptl1PqGuxk"
   },
   "source": [
    "#### Size 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_Y74QNL9Guxl",
    "outputId": "b5e10f47-9077-4ece-92b5-230a8db6584a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./models/stage1-sz128-rn101.pth'"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (src.transform(tfms, size=128)\n",
    "        .databunch().normalize(imagenet_stats))\n",
    "\n",
    "learn.data = data\n",
    "data.train_ds[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "oos37GZhGuxn",
    "outputId": "67568521-b9bb-4226-e582-12748fc81b97"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.529179</td>\n",
       "      <td>0.529846</td>\n",
       "      <td>0.772806</td>\n",
       "      <td>12:31</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.453468</td>\n",
       "      <td>0.546258</td>\n",
       "      <td>0.808928</td>\n",
       "      <td>12:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.379408</td>\n",
       "      <td>0.537282</td>\n",
       "      <td>0.808096</td>\n",
       "      <td>12:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.331285</td>\n",
       "      <td>0.467886</td>\n",
       "      <td>0.831906</td>\n",
       "      <td>12:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.319030</td>\n",
       "      <td>0.480315</td>\n",
       "      <td>0.832879</td>\n",
       "      <td>12:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.freeze()\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fKo8kavxGuxr"
   },
   "outputs": [],
   "source": [
    "learn.save('stage1-sz128-rn101')\n",
    "shutil.copy2(\"./models/stage1-sz128-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "DT0raGWrGuxt",
    "outputId": "7e43fdb4-23eb-469e-c3f8-7311fcfee6ca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.315274</td>\n",
       "      <td>0.483389</td>\n",
       "      <td>0.825892</td>\n",
       "      <td>13:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.278202</td>\n",
       "      <td>0.468720</td>\n",
       "      <td>0.831486</td>\n",
       "      <td>12:31</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.256792</td>\n",
       "      <td>0.456374</td>\n",
       "      <td>0.846680</td>\n",
       "      <td>12:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(3, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XqD7JIwiGuxy"
   },
   "outputs": [],
   "source": [
    "learn.save('stage2-sz128-rn101')\n",
    "shutil.copy2(\"./models/stage2-sz128-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "evLZhzoCGux0"
   },
   "source": [
    "#### Size 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5PcN19YPGux2"
   },
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=256)\n",
    "        .databunch().normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vg4ybR89Gux4"
   },
   "outputs": [],
   "source": [
    "learn.data = data\n",
    "data.train_ds[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "RjP2Ab2qGux7",
    "outputId": "0d22779e-6781-4687-b947-623fd09a1fa0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.317201</td>\n",
       "      <td>0.453863</td>\n",
       "      <td>0.838456</td>\n",
       "      <td>17:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.268208</td>\n",
       "      <td>0.456758</td>\n",
       "      <td>0.854131</td>\n",
       "      <td>17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.228409</td>\n",
       "      <td>0.910646</td>\n",
       "      <td>0.859474</td>\n",
       "      <td>17:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.203442</td>\n",
       "      <td>0.640575</td>\n",
       "      <td>0.878315</td>\n",
       "      <td>16:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.183151</td>\n",
       "      <td>0.610423</td>\n",
       "      <td>0.876823</td>\n",
       "      <td>16:38</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.freeze()\n",
    "learn.fit_one_cycle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4pilDKWaGux_",
    "outputId": "f967d4fe-21ab-4bd0-eefd-b7201b0a3c6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage1-sz256-rn101.pth'"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage1-sz256-rn101')\n",
    "shutil.copy2(\"./models/stage1-sz256-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "WwnS7HI_GuyD",
    "outputId": "b6c6e455-417c-4f35-cb72-19c10b2863e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>recall</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.183502</td>\n",
       "      <td>0.816586</td>\n",
       "      <td>0.852542</td>\n",
       "      <td>18:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.163206</td>\n",
       "      <td>0.384709</td>\n",
       "      <td>0.874428</td>\n",
       "      <td>18:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.144963</td>\n",
       "      <td>0.330809</td>\n",
       "      <td>0.885069</td>\n",
       "      <td>18:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.115770</td>\n",
       "      <td>0.326646</td>\n",
       "      <td>0.868896</td>\n",
       "      <td>18:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.108229</td>\n",
       "      <td>0.318335</td>\n",
       "      <td>0.885232</td>\n",
       "      <td>18:42</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(5, slice(1e-6, 1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MMFReQM4GuyG",
    "outputId": "80e31d6a-fa96-4433-f70b-7a40cf95261a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./drive/My Drive/Colab Notebooks/Data/models/stage2-sz256-rn101.pth'"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('stage2-sz256-rn101')\n",
    "shutil.copy2(\"./models/stage2-sz256-rn101.pth\", \"./drive/My Drive/Colab Notebooks/Data/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
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    "id": "irFfd5lTRh0b",
    "outputId": "dd6e903a-91c9-437f-9989-7533eb0c18be"
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    {
     "data": {
      "text/plain": [
       "[0.3183352, tensor(0.8852)]"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
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    "## To Do\n",
    "\n",
    "1.   Focal loss + Resnet101 + various train sizes\n",
    "2.   Resnet152 + various train sizes\n",
    "3.   Focal loss + Resnet152 + various train sizes"
   ]
  }
 ],
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