Poster Model v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import sampler\n",
    "\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as T\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/pkl_files/trimmed_inputs.pkl\", \"rb\") as fp:\n",
    "    inputs = pickle.load(fp)\n",
    "with open(\"data/pkl_files/trimmed_outputs.pkl\", \"rb\") as fp:\n",
    "    outputs = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 4, 32, 32)\n",
      "(25650, 1, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Sanity check\n",
    "print(inputs.shape)\n",
    "print(outputs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LYMTMzgD4OID3mtltABzAKQB/3M7JrAHkFogUFU2yCt+jmAQIAPnBCrXVazzTrlzhmVSWD58v1oIqs8zvr80S9z8TkT7zC7GWV2R8NiJDRUzLw9z/6m18q+feu/91cPw733mMzvnaLM/sPLJwJbW9u+9latvXH25FdmyCZzLm+rj2mYlcV9UFnmVqS5FrhLzWzUhSX36BnOcySvutGvzu/uHAMM/lFEK8IdA3/IRIFAW/EImi4BciURT8QiSKgl+IROl8uy4i6TULXKNolMK2WBZV4zwv+BjLe/LdPOtscCj8DcWlMpd4ctu5nMfakAHAUrab2mhGF4Bab/igjS5+n89HvnhZL3Jb8QJ/AtWRsP933/kBOuczf/sotT0+u5/avjd5C7XNVsPXwXIlIstl+GvW282zPvN9S9Q2XOK2kxPhLEJ7PlLUlhwuLpm/Fr3zC5EoCn4hEkXBL0SiKPiFSBQFvxCJouAXIlE6KvV5HijvDmsRHilKuRYylUhB0F4u5TQiGX/TU73B8VhfvSq1ADYVkZsix4wVdmSS6dIV/DkXJ/h7QIYrn9Gss/Jw+NLqmuCFLAcjEls+omGdXeBFRheJpBfL7GxEiq7WinyeR4qCVho81K7eNh0cP3E9n1Nj9TsjmaKXond+IRJFwS9Eoij4hUgUBb8QiaLgFyJROpvYYw4vhXdts0W+m9tYIG5Gdr0buyIZDpFCZ36B70bnSK21+jDfErcKv7/Gdu29N+L/FN9mzy6FfWxEEqfWmtiztCvSUiwf3hXvOcvX9w9vfB+1/eXxH1Lb+e391HZyYSQ4PprlSTO1Bt/Rr9cj9R9rXL2Zm+eJWs1a+Bop9fEkol0D4WKNE/n2M3v0zi9Eoij4hUgUBb8QiaLgFyJRFPxCJIqCX4hEaadd114Afw1gF4AmgIPu/jkzGwbwVQD7sNKy64PuHs5QeJWGITsdPmV2L0+ByUyH9abuc7FqfJzKILfF2h0186Q+XpnfQ3ML3JaNtORqzkXafEUSamjSUiTfo8EVKuS42oRGrAMYcX9xD9cOB8Z5C62bIzUI7xl6jtpe6tkZHP8/+ZvonGpE6luscalytsKfWzOS9DM5GZYdl+b48U6OhZveVsvtq/ftvPPXAfypu98M4D0A/sTMbgHwAICn3P0GAE+1fhdCvEFYNfjdfdTdn2v9PA/gOIA9AO4B8EjrYY8AuHeznBRCbDyX9Te/me0D8E4AzwDY6e6jwMoNAsCOjXZOCLF5tB38ZtYL4OsAPubuc5cx74CZHTKzQ43FyPdIhRAdpa3gN7M8VgL/UXf/Rmt4zMx2t+y7AYyH5rr7QXff7+77sz3hTQohROdZNfjNzAB8EcBxd//sRaYnANzX+vk+AN/eePeEEJtFO7rAHQA+AuComR1ujT0I4JMAHjez+wGcBvAHqx3IHMjUieTxIv9U0HsmPJ6tcq0ptxyrgcfveQ2u5KAWLuEHJxIgAEQUHmQr3JZhRdoALO2r8XmLYZmq+2ykTl8kESwTKUJYnOLPu3ss7GN+gWdANl85R23/EMmO/PvFG6htkrxoPVn+xHq40odcpM5gNmIbneOZhxlSv7KxxB3JzRFbo335e9Xgd/cfg7e3+522zySEeF2hb/gJkSgKfiESRcEvRKIo+IVIFAW/EInS0QKeVgeK42HhYPg4l6/mrwq7WR7hskbf6bW1/7JY9ltP2Bgr0tk9Fsnci0hKtSHuf/EMT+srTpHx6UiLsq5YIU5qQmmCy3bFsaXguC1xffNrL/2I2r63PExts/UStbE2X2/rI/oxgIUGz6b75cIuahsuhJ8zAJyqcf8blfCFYMuR9+a1JbS+Br3zC5EoCn4hEkXBL0SiKPiFSBQFvxCJouAXIlE626vPuZQ2dSvXlCojYdnLalwOm3wH10LquyPpdKwvIIAMkV5i2Xm5Re5jJLEMzYj8Vpjmx+wZCy9wtsylPs/wcy1cwfXI5RG+VvmlcHpk1xyXwyJqL74z/TZqG10eoLb+fLgC6Qvz4cKeALC9sEBte0oz3I8yz9y7Zed5anshGy6CtVDjma51lkka6f94KXrnFyJRFPxCJIqCX4hEUfALkSgKfiESpaO7/c0uYPHK8K5zs4vvUubnw/eo2gDfwbYhvpVeLPFtZevj/amqL4Z3c2N1/2Zu4c+L5JwAAPKzfAc+w/NpUB4Mr1Wmxu/zrLUWADRzsQwS/ty6Tk4Exz/9o8fpnM9P/nNq++no1dRWi7TX2jd8ITi+UOUv2vgiKdYIYP/2V6jtpdkRarumP+wHAPSXwtdcZSASnqQ4pJF6gCH0zi9Eoij4hUgUBb8QiaLgFyJRFPxCJIqCX4hEWVXqM7O9AP4awC4ATQAH3f1zZvYJAH8E4FVN50F3fzJ+MMDJGTNVLilVdxNpLqZqNCPHq/KnzeqpAUChEj5mvZs70ixG5MgS1+zqNS5F1Xr5c6t1h8djSUQxqTImR+a4KorGzsHg+M1dxEEA/ZED9hV59lQpx6Xb3nx43mKti86Znuc+HsleETlXZJEjdOfD/g/38ySoiam+4LhfRunKdnT+OoA/dffnzKwPwM/M7Pst20Pu/un2TyeEeL3QTq++UQCjrZ/nzew4gD2b7ZgQYnO5rL/5zWwfgHcCeKY19FEzO2JmD5vZ0Ab7JoTYRNoOfjPrBfB1AB9z9zkAnwdwHYDbsPLJ4DNk3gEzO2RmhxoLixvgshBiI2gr+M0sj5XAf9TdvwEA7j7m7g13bwL4AoDbQ3Pd/aC773f3/dleXplECNFZVg1+MzMAXwRw3N0/e9H47ose9gEAxzbePSHEZtHObv8dAD4C4KiZHW6NPQjgw2Z2G1YEt1MA/nj1szVh28PSyzW7Jum0WwdHg+N/+/Jb6JxqJVKLL8vlt+Yk171Y5qGzemoA8jNcOoyUIESzwI3VgYiMSTIdu2b4fT7HFaVovcC+01zaysyGDzre4H/6LTcivcEinJnhNfx+NbU7OJ67ELk+IvLmaJ5/em108+vqxb5IgUKaoRfpHbcB7bra2e3/MTlVXNMXQryu0Tf8hEgUBb8QiaLgFyJRFPxCJIqCX4hE6Wy7LgBO1IsLSyU658mJW4Lj9TKXhrzC72uZZS6/IcOlrUYpbCtM8XPl5yOnqnI/YkU1Y1mEzJfSOJ9TmuKSUmGGZx52/d9fUttfHH06OP6fx3mRzh+euZ7aKjV+qTqRygBgaNdccLy+gy/w/DTP6svM8GsuH5FTbapIbdVBUtS2EMkI7WavS/saoN75hUgUBb8QiaLgFyJRFPxCJIqCX4hEUfALkSgdlfoySxl0HwlLenPbuBTC8EGefmU1LnnkFiJZcTsjxyyG5ZX6Ti7JVOr8/tqscakvMxeRtiL92PLz4WMub+fPuSciAxZfOE9tf/X8d6ntCxd+Izh+coH3s1uucBmtu7i24piFfPg16zb+nHPb+Os5X+CSdHWJv2YWkZedSHpW4Neis3NFMhIvRe/8QiSKgl+IRFHwC5EoCn4hEkXBL0SiKPiFSJSOSn3NomPhxnAhw8wil0KaPUS/iMh5UT8izzo3Henj1x2+V9Z7eeZbtAhjnfufXYo8N+M21luv5xyXtnr+5gi1/dtjh6ntrybvpLYnT94aHB/s5dVCM5GMynKVy4DLM1wmzvcSiTCSCRjLEoxR6Of9BL2PH7PRCF9XMS/qNXJdXYbreucXIlEU/EIkioJfiERR8AuRKAp+IRJl1d1+MysCeBpAofX4r7n7x83sGgBfATAM4DkAH3H3aPZFNt/A0M5wTbXFZd4mq1gIKwRzk5HGn/lIS65BvquczUcyI8bDSR35V7rolBqpzwbEW2h1zfNt21ov958pGbHWWh9//ifU9t35t1Hbz6auorZ6JGmJUY0k9mQyfB2z03xeoxh+PSOCCa0zCQDbt/GijM2ISlCO1CBcnCdqReStOUOeV6wG5WUc/v9TAfDb7v4OrLTjvsvM3gPgUwAecvcbAEwDuL/tswohtpxVg99XWGj9mm/9cwC/DeBrrfFHANy7KR4KITaFtv7mN7Nsq0PvOIDvA3gJwIy7v/rtljMA9myOi0KIzaCt4Hf3hrvfBuBKALcDuDn0sNBcMztgZofM7FCdtG0WQnSey9rtd/cZAH8H4D0ABs3s1V2MKwGcI3MOuvt+d9+fG+DNEIQQnWXV4Dez7WY22Pq5BOB9AI4D+AGAf9F62H0Avr1ZTgohNp52Ent2A3jEzLJYuVk87u7/y8yeB/AVM/tPAP4BwBdXO1CjnsX0ZF/Qlo3UKwOR+kqDZTqlEmnl1ZyPtPkq8fvhtsNhW//LXEarDvAlzi2GnxeAqBZVGeAyWmE2nGRUOHqazrk6t0xtN5fOUtuzXVdTW3d3OMmlkOWvc7G0tjp9lT3c/1wufL7qef4pND/Hr4GxCl/7rj7uf5Mk7wBAs0yukUidwXxP+NqJSZiXsmrwu/sRAO8MjJ/Eyt//Qog3IPqGnxCJouAXIlEU/EIkioJfiERR8AuRKObefhbQuk9mNgHg5davIwAmO3Zyjvx4LfLjtbzR/Lja3be3c8COBv9rTmx2yN33b8nJ5Yf8kB/62C9Eqij4hUiUrQz+g1t47ouRH69FfryWN60fW/Y3vxBia9HHfiESZUuC38zuMrMXzOyEmT2wFT60/DhlZkfN7LCZHergeR82s3EzO3bR2LCZfd/MXmz9P7RFfnzCzM621uSwmd3dAT/2mtkPzOy4mf3CzP5da7yjaxLxo6NrYmZFM/upmf285cd/bI1fY2bPtNbjq2bGK8e2g7t39B+ALFbKgF0LoAvAzwHc0mk/Wr6cAjCyBee9E8C7ABy7aOy/AHig9fMDAD61RX58AsCfdXg9dgN4V+vnPgC/AnBLp9ck4kdH1wQrHfd6Wz/nATyDlQI6jwP4UGv8vwH4N+s5z1a8898O4IS7n/SVUt9fAXDPFvixZbj70wAuXDJ8D1YKoQIdKohK/Og47j7q7s+1fp7HSrGYPejwmkT86Ci+wqYXzd2K4N8D4JWLft/K4p8O4Htm9jMzO7BFPrzKTncfBVYuQgA7ttCXj5rZkdafBZv+58fFmNk+rNSPeAZbuCaX+AF0eE06UTR3K4I/VGtkqySHO9z9XQB+D8CfmBnvOZ0OnwdwHVZ6NIwC+EynTmxmvQC+DuBj7h7u7rI1fnR8TXwdRXPbZSuC/wyAvRf9Tot/bjbufq71/ziAb2JrKxONmdluAGj9P74VTrj7WOvCawL4Ajq0JmaWx0rAPeru32gNd3xNQn5s1Zq0zn3ZRXPbZSuC/1kAN7R2LrsAfAjAE512wsx6zKzv1Z8B/C6AY/FZm8oTWCmECmxhQdRXg63FB9CBNTEzw0oNyOPu/tmLTB1dE+ZHp9ekY0VzO7WDeclu5t1Y2Ul9CcCfb5EP12JFafg5gF900g8Aj2Hl42MNK5+E7gewDcBTAF5s/T+8RX58GcBRAEewEny7O+DHb2LlI+wRAIdb/+7u9JpE/OjomgB4O1aK4h7Byo3mLy66Zn8K4ASA/wmgsJ7z6Bt+QiSKvuEnRKIo+IVIFAW/EImi4BciURT8QiSKgl+IRFHwC5EoCn4hEuX/AYdsBTk0BnJpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sanity Check\n",
    "idx = np.random.randint(0, inputs.shape[0])\n",
    "def plot_sampled_images(idx):    \n",
    "    cropped_seg_img = outputs[idx,0,:,:]\n",
    "    plt.imshow(cropped_seg_img)\n",
    "    plt.show()\n",
    "    cropped_flair_img = inputs[idx,0,:,:]\n",
    "    plt.imshow(cropped_flair_img)\n",
    "    plt.show()\n",
    "    cropped_t1_img = inputs[idx,1,:,:]\n",
    "    plt.imshow(cropped_t1_img)\n",
    "    plt.show()\n",
    "    cropped_t1ce_img = inputs[idx,2,:,:]\n",
    "    plt.imshow(cropped_t1ce_img)\n",
    "    plt.show()\n",
    "    cropped_t2_img = inputs[idx,3,:,:]\n",
    "    plt.imshow(cropped_t2_img)\n",
    "    plt.show()\n",
    "plot_sampled_images(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "shuffled_idxs = list(range(inputs.shape[0]))\n",
    "np.random.shuffle(shuffled_idxs)\n",
    "train_cutoff = int(inputs.shape[0]*75/100)\n",
    "val_cutoff = int(inputs.shape[0]*95/100)\n",
    "\n",
    "train_inputs = []\n",
    "train_outputs = []\n",
    "val_inputs = []\n",
    "val_outputs = []\n",
    "test_inputs = []\n",
    "test_outputs = []\n",
    "\n",
    "for iteration, idx in enumerate(shuffled_idxs):\n",
    "    if iteration < train_cutoff:\n",
    "        train_inputs.append(inputs[idx])\n",
    "        train_outputs.append(outputs[idx])\n",
    "    elif iteration < val_cutoff:\n",
    "        val_inputs.append(inputs[idx])\n",
    "        val_outputs.append(outputs[idx])\n",
    "    else:\n",
    "        test_inputs.append(inputs[idx])\n",
    "        test_outputs.append(outputs[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19237\n",
      "19237\n",
      "5130\n",
      "5130\n",
      "1283\n",
      "1283\n"
     ]
    }
   ],
   "source": [
    "# sanity check\n",
    "print(len(train_inputs))\n",
    "print(len(train_outputs))\n",
    "print(len(val_inputs))\n",
    "print(len(val_outputs))\n",
    "print(len(test_inputs))\n",
    "print(len(test_outputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs = np.asarray(train_inputs)\n",
    "val_inputs = np.asarray(val_inputs)\n",
    "test_inputs = np.asarray(test_inputs)\n",
    "\n",
    "train_inputs_mean = np.mean(train_inputs, axis=0)\n",
    "train_inputs_std = np.std(train_inputs, axis=0)\n",
    "\n",
    "train_inputs -= train_inputs_mean\n",
    "train_inputs /= train_inputs_std\n",
    "val_inputs -= train_inputs_mean\n",
    "val_inputs /= train_inputs_std\n",
    "test_inputs -= train_inputs_mean\n",
    "test_inputs /= train_inputs_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19698688\n",
      "0.06718163158886521\n"
     ]
    }
   ],
   "source": [
    "train_outputs = np.asarray(train_outputs)\n",
    "n_tumor_pixels = np.count_nonzero(train_outputs)\n",
    "n_tumor1 = np.count_nonzero(train_outputs==1)\n",
    "n_tumor2 = np.count_nonzero(train_outputs==2)\n",
    "n_tumor3 = np.count_nonzero(train_outputs==3)\n",
    "n_tumor4 = np.count_nonzero(train_outputs==4)\n",
    "total_pixels = train_outputs.shape[0]*train_outputs.shape[2]*train_outputs.shape[3]\n",
    "print(total_pixels)\n",
    "n_blank_pixels = total_pixels - n_tumor_pixels\n",
    "frac_tumor = n_tumor_pixels/total_pixels\n",
    "print(frac_tumor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = []\n",
    "val_data = []\n",
    "test_data = []\n",
    "\n",
    "for idx in range(train_inputs.shape[0]):\n",
    "    train_data.append([train_inputs[idx], train_outputs[idx]])\n",
    "for idx in range(val_inputs.shape[0]):\n",
    "    val_data.append([val_inputs[idx], val_outputs[idx]])\n",
    "for idx in range(test_inputs.shape[0]):\n",
    "    test_data.append([test_inputs[idx], test_outputs[idx]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301\n",
      "19237\n"
     ]
    }
   ],
   "source": [
    "train_loader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=64)\n",
    "val_loader = torch.utils.data.DataLoader(val_data, shuffle=True, batch_size=64)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, shuffle=True, batch_size=64)\n",
    "\n",
    "print(len(train_loader))\n",
    "print(len(train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using device: cuda\n"
     ]
    }
   ],
   "source": [
    "USE_GPU = True\n",
    "\n",
    "dtype = torch.float32 # we will be using float throughout this tutorial\n",
    "\n",
    "if USE_GPU and torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "\n",
    "# Constant to control how frequently we print train loss\n",
    "print_every = 30\n",
    "\n",
    "print('using device:', device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.unet import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 5, 32, 32])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    }
   ],
   "source": [
    "def test_UNet():\n",
    "    channels_in = 4\n",
    "    n_classes = 5\n",
    "    x = torch.zeros((64, 4, 32, 32), dtype=dtype)\n",
    "    model = UNet(channels_in, n_classes)\n",
    "    scores = model(x)\n",
    "    print(scores.size())\n",
    "test_UNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 55.02574714020483, 24.78145204141908, 1.0, 74.10709158076433]\n"
     ]
    }
   ],
   "source": [
    "# Defining all parameters \n",
    "print_every = 25\n",
    "loss_history = []\n",
    "learning_rate = 1e-1\n",
    "input_channels = 4\n",
    "n_classes = 5\n",
    "model = UNet(channel_in=input_channels, classes=n_classes)\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, nesterov=True)\n",
    "# criterion = torch.nn.CrossEntropyLoss(weight=torch.FloatTensor([1.0, n_blank_pixel/float(n_tumor_pixels)]).cuda()) \n",
    "class_weights = [1.0, n_blank_pixels/float(n_tumor1), n_blank_pixels/float(n_tumor2), 1.0, n_blank_pixels/float(n_tumor4)]\n",
    "print(class_weights)\n",
    "criterion = torch.nn.CrossEntropyLoss(weight=torch.FloatTensor(class_weights).cuda()) \n",
    "# could try dice loss? not sure how it would work nor how it expects its parameters though lol\n",
    "\n",
    "if USE_GPU and torch.cuda.is_available():\n",
    "    model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_masks(x):\n",
    "    mask = np.zeros((n_classes, x.shape[0], x.shape[1]))\n",
    "    for c in range(n_classes):\n",
    "        mask[c] = x == c\n",
    "    return mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_iou(scores, output):  \n",
    "    scores = scores.astype(np.bool)\n",
    "    output = output.astype(np.bool)\n",
    "    overlap = scores*output\n",
    "    union = scores+output # Logical OR\n",
    "    IOU = (overlap.sum())/(float(union.sum())+1e-10) \n",
    "    return IOU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou_metric(preds, labels):\n",
    "    # preds and labels are of size (64, 32, 32)\n",
    "    # return average iou value for each class\n",
    "    numpy_preds = preds.cpu().numpy()  \n",
    "    numpy_labels = labels.cpu().numpy()\n",
    "    \n",
    "    iou_tracker = np.zeros((numpy_preds.shape[0], n_classes))\n",
    "    # get the masks\n",
    "    for idx in range(numpy_preds.shape[0]):\n",
    "        \"\"\"plt.title('Segmented Output')\n",
    "        plt.imshow(numpy_preds[idx,:,:])\n",
    "        plt.show()\n",
    "        plt.title('Ground Truth')\n",
    "        plt.imshow(numpy_labels[idx,:,:])\n",
    "        plt.show()\"\"\"\n",
    "        pred_mask = create_masks(numpy_preds[idx])\n",
    "        label_mask = create_masks(numpy_labels[idx])\n",
    "        for c in range(n_classes):\n",
    "            iou_tracker[idx, c] = compute_iou(pred_mask[c], label_mask[c])\n",
    "        # print(iou_tracker[idx])\n",
    "    # print(np.mean(iou_tracker, axis=0))\n",
    "    return np.mean(iou_tracker, axis=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_iou(loader, model):\n",
    "    all_iou = np.zeros((len(loader),n_classes))\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for t, (x,y) in enumerate(loader):\n",
    "            x = x.to(device=device, dtype=dtype)  # move to device, e.g. GPU\n",
    "            y = y.to(device=device, dtype=torch.float).view(y.shape[0], 32, 32)\n",
    "            scores = model(x)\n",
    "            _, preds = scores.max(1)\n",
    "            \n",
    "            all_iou[t] = iou_metric(preds, y)\n",
    "    # print(np.mean(all_iou, axis=0))\n",
    "    return np.mean(all_iou, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92123506, 0.        , 0.        , 0.        , 0.00092325])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_iou(val_loader, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_output(scores, y):\n",
    "    with torch.no_grad():\n",
    "        _, preds = scores.max(1)\n",
    "        numpy_scores = preds.cpu().numpy().astype(np.uint8)\n",
    "        numpy_truth = y.cpu().numpy().astype(np.uint8)\n",
    "        idx = np.random.randint(1, y.shape[0])\n",
    "        while(np.count_nonzero(numpy_truth[idx]) < int(0.10*32*32)):\n",
    "            idx = np.random.randint(1, y.shape[0])\n",
    "        plt.title('Segmented Output')\n",
    "        plt.imshow(numpy_scores[idx,:,:])\n",
    "        plt.show()\n",
    "        plt.title('Ground Truth')\n",
    "        plt.imshow(numpy_truth[idx,:,:])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, Iteration 0, loss = 1.5924\n",
      "Validation IOU:  [9.24706401e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 6.85945336e-04]\n",
      "Training IOU:  [9.23728138e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 6.52824604e-04]\n",
      "Epoch 0, Iteration 25, loss = 1.5636\n",
      "Validation IOU:  [0.86649699 0.         0.         0.         0.00862104]\n",
      "Training IOU:  [0.86708698 0.         0.         0.         0.00834109]\n",
      "Epoch 0, Iteration 50, loss = 1.5617\n",
      "Validation IOU:  [0.84961706 0.         0.         0.         0.01096786]\n",
      "Training IOU:  [0.84924782 0.         0.         0.         0.01060798]\n",
      "Epoch 0, Iteration 75, loss = 1.5335\n",
      "Validation IOU:  [0.81967457 0.         0.         0.         0.01380913]\n",
      "Training IOU:  [0.82082343 0.         0.         0.         0.01317643]\n",
      "Epoch 0, Iteration 100, loss = 1.5064\n",
      "Validation IOU:  [0.6494453  0.         0.01637469 0.         0.01312736]\n",
      "Training IOU:  [0.65254603 0.         0.01769473 0.         0.01309369]\n",
      "Epoch 0, Iteration 125, loss = 1.5272\n",
      "Validation IOU:  [0.55435358 0.         0.03058474 0.         0.01345122]\n",
      "Training IOU:  [0.55684129 0.         0.03092253 0.         0.01260594]\n",
      "Epoch 0, Iteration 150, loss = 1.5205\n",
      "Validation IOU:  [0.5889851  0.         0.02516125 0.         0.01280378]\n",
      "Training IOU:  [0.59325653 0.         0.02544793 0.         0.01236643]\n",
      "Epoch 0, Iteration 175, loss = 1.4691\n",
      "Validation IOU:  [0.49489735 0.         0.02944551 0.         0.0184722 ]\n",
      "Training IOU:  [0.49844333 0.         0.02968281 0.         0.01750337]\n",
      "Epoch 0, Iteration 200, loss = 1.3951\n",
      "Validation IOU:  [5.36213845e-01 3.04999352e-04 2.88835242e-02 0.00000000e+00\n",
      " 1.79168857e-02]\n",
      "Training IOU:  [5.38832637e-01 2.36276309e-04 2.92007160e-02 0.00000000e+00\n",
      " 1.75682883e-02]\n",
      "Epoch 0, Iteration 225, loss = 1.4460\n",
      "Validation IOU:  [0.5409242  0.         0.03121737 0.         0.01736895]\n",
      "Training IOU:  [0.54367832 0.         0.03270628 0.         0.01799225]\n",
      "Epoch 0, Iteration 250, loss = 1.4900\n",
      "Validation IOU:  [0.42932006 0.         0.03577766 0.         0.01759318]\n",
      "Training IOU:  [0.42848836 0.         0.03476147 0.         0.01578119]\n",
      "Epoch 0, Iteration 275, loss = 1.5094\n",
      "Validation IOU:  [0.5112027  0.         0.03648471 0.         0.01651357]\n",
      "Training IOU:  [0.51292078 0.         0.03591669 0.         0.01557851]\n",
      "Epoch 0, Iteration 300, loss = 1.3817\n",
      "Validation IOU:  [0.53857606 0.         0.03148867 0.         0.01783616]\n",
      "Training IOU:  [0.54249078 0.         0.032157   0.         0.01771578]\n",
      "Epoch 1, Iteration 0, loss = 1.4966\n",
      "Validation IOU:  [0.54154942 0.         0.03011525 0.         0.01802349]\n",
      "Training IOU:  [0.54358848 0.         0.03169513 0.         0.01806277]\n",
      "Epoch 1, Iteration 25, loss = 1.4419\n",
      "Validation IOU:  [0.49771863 0.         0.03519687 0.         0.02067451]\n",
      "Training IOU:  [0.50144244 0.         0.03511066 0.         0.01954624]\n",
      "Epoch 1, Iteration 50, loss = 1.4991\n",
      "Validation IOU:  [0.5222579  0.00670144 0.03773071 0.         0.01805268]\n",
      "Training IOU:  [0.52567028 0.00633354 0.03640773 0.         0.01762759]\n",
      "Epoch 1, Iteration 75, loss = 1.5201\n",
      "Validation IOU:  [0.54053635 0.         0.03753732 0.         0.01797543]\n",
      "Training IOU:  [0.54260146 0.         0.03760241 0.         0.01679326]\n",
      "Epoch 1, Iteration 100, loss = 1.3921\n",
      "Validation IOU:  [0.52151527 0.0043977  0.02868744 0.         0.01882247]\n",
      "Training IOU:  [0.52740766 0.00436945 0.02884987 0.         0.01897574]\n",
      "Epoch 1, Iteration 125, loss = 1.4166\n",
      "Validation IOU:  [0.51271032 0.00588314 0.03422291 0.         0.01767126]\n",
      "Training IOU:  [0.51602003 0.00628182 0.03426756 0.         0.01745495]\n",
      "Epoch 1, Iteration 150, loss = 1.4393\n",
      "Validation IOU:  [0.55197911 0.00630918 0.0384541  0.         0.01557852]\n",
      "Training IOU:  [0.55364247 0.00590199 0.03905575 0.         0.01431713]\n",
      "Epoch 1, Iteration 175, loss = 1.4297\n",
      "Validation IOU:  [0.57610762 0.         0.03540971 0.         0.01689313]\n",
      "Training IOU:  [0.57678208 0.         0.034959   0.         0.01712331]\n",
      "Epoch 1, Iteration 200, loss = 1.3415\n",
      "Validation IOU:  [0.5645046  0.         0.03634085 0.         0.01824759]\n",
      "Training IOU:  [0.56588509 0.         0.03637256 0.         0.01873604]\n",
      "Epoch 1, Iteration 225, loss = 1.4468\n",
      "Validation IOU:  [0.59707337 0.00733413 0.02647133 0.         0.01282488]\n",
      "Training IOU:  [0.59871452 0.00784199 0.02774448 0.         0.01267126]\n",
      "Epoch 1, Iteration 250, loss = 1.4277\n",
      "Validation IOU:  [0.56637558 0.         0.03719463 0.         0.02026687]\n",
      "Training IOU:  [0.56988774 0.         0.03740123 0.         0.02012177]\n",
      "Epoch 1, Iteration 275, loss = 1.4552\n",
      "Validation IOU:  [0.56862787 0.         0.03643752 0.         0.01863761]\n",
      "Training IOU:  [0.57192069 0.         0.03707445 0.         0.01865856]\n",
      "Epoch 1, Iteration 300, loss = 1.4476\n",
      "Validation IOU:  [0.60945351 0.0097109  0.03668077 0.         0.01367699]\n",
      "Training IOU:  [0.61051927 0.00993422 0.03663335 0.         0.01237105]\n",
      "Epoch 2, Iteration 0, loss = 1.3746\n",
      "Validation IOU:  [0.60615778 0.01112903 0.03595549 0.         0.01283306]\n",
      "Training IOU:  [0.60704704 0.0106877  0.03646477 0.         0.01153928]\n",
      "Epoch 2, Iteration 25, loss = 1.4967\n",
      "Validation IOU:  [0.485345   0.00328955 0.0393533  0.         0.01893647]\n",
      "Training IOU:  [0.49068407 0.003093   0.03966861 0.         0.01829554]\n",
      "Epoch 2, Iteration 50, loss = 1.3905\n",
      "Validation IOU:  [0.56413745 0.00940918 0.03658553 0.         0.01700811]\n",
      "Training IOU:  [0.56407374 0.00965494 0.0381702  0.         0.01605799]\n",
      "Epoch 2, Iteration 75, loss = 1.4274\n",
      "Validation IOU:  [0.56115013 0.00910374 0.03689318 0.         0.01855704]\n",
      "Training IOU:  [0.56502999 0.00923504 0.03857066 0.         0.01781447]\n",
      "Epoch 2, Iteration 100, loss = 1.4025\n",
      "Validation IOU:  [0.58831795 0.00499173 0.03704073 0.         0.0207659 ]\n",
      "Training IOU:  [0.59061928 0.00483337 0.03832263 0.         0.02024623]\n",
      "Epoch 2, Iteration 125, loss = 1.2759\n",
      "Validation IOU:  [0.58783896 0.0019235  0.03571479 0.         0.0204808 ]\n",
      "Training IOU:  [0.59212155 0.00192431 0.03736179 0.         0.0200717 ]\n",
      "Epoch 2, Iteration 150, loss = 1.3735\n",
      "Validation IOU:  [0.6137849  0.00849444 0.03252577 0.         0.01894485]\n",
      "Training IOU:  [0.61579685 0.00957115 0.03374101 0.         0.01898262]\n",
      "Epoch 2, Iteration 175, loss = 1.3860\n",
      "Validation IOU:  [0.60913949 0.         0.03336313 0.         0.0204398 ]\n",
      "Training IOU:  [0.61151396 0.         0.03303629 0.         0.0199896 ]\n",
      "Epoch 2, Iteration 200, loss = 1.3406\n",
      "Validation IOU:  [0.56574246 0.00519562 0.03730504 0.         0.01973345]\n",
      "Training IOU:  [0.56763137 0.00561517 0.03710095 0.         0.01999514]\n",
      "Epoch 2, Iteration 225, loss = 1.3815\n",
      "Validation IOU:  [0.58253539 0.00846445 0.03535172 0.         0.02077299]\n",
      "Training IOU:  [0.58171892 0.00970838 0.03650202 0.         0.01940912]\n",
      "Epoch 2, Iteration 250, loss = 1.4216\n",
      "Validation IOU:  [0.54617414 0.01021516 0.03237302 0.         0.01969545]\n",
      "Training IOU:  [0.54965018 0.01065331 0.03438998 0.         0.01844443]\n",
      "Epoch 2, Iteration 275, loss = 1.5703\n",
      "Validation IOU:  [0.57149351 0.0076811  0.03264799 0.         0.01904231]\n",
      "Training IOU:  [0.57516713 0.00771148 0.03497385 0.         0.01942291]\n",
      "Epoch 2, Iteration 300, loss = 1.4965\n",
      "Validation IOU:  [0.56668473 0.00733111 0.03410531 0.         0.02014465]\n",
      "Training IOU:  [0.56834051 0.00837583 0.03621755 0.         0.01966534]\n",
      "Epoch 3, Iteration 0, loss = 1.3393\n",
      "Validation IOU:  [0.57221612 0.00761277 0.03591178 0.         0.01971652]\n",
      "Training IOU:  [0.57184057 0.00858329 0.03674757 0.         0.01942171]\n",
      "Epoch 3, Iteration 25, loss = 1.4132\n",
      "Validation IOU:  [0.57363824 0.00596671 0.03396624 0.         0.02161604]\n",
      "Training IOU:  [0.57789314 0.00629944 0.03358888 0.         0.02081098]\n",
      "Epoch 3, Iteration 50, loss = 1.2889\n",
      "Validation IOU:  [0.54251139 0.00615063 0.03887292 0.         0.01880056]\n",
      "Training IOU:  [0.54597808 0.00583399 0.0397642  0.         0.01852004]\n",
      "Epoch 3, Iteration 75, loss = 1.4664\n",
      "Validation IOU:  [0.59461176 0.00890822 0.02989157 0.         0.01891968]\n",
      "Training IOU:  [0.59581352 0.01016045 0.03127209 0.         0.01892812]\n",
      "Epoch 3, Iteration 100, loss = 1.2353\n",
      "Validation IOU:  [0.56678043 0.00598057 0.03367333 0.         0.02005442]\n",
      "Training IOU:  [0.56849394 0.0068125  0.0353296  0.         0.0200673 ]\n",
      "Epoch 3, Iteration 125, loss = 1.4101\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation IOU:  [0.54882103 0.01077653 0.03606421 0.         0.01790459]\n",
      "Training IOU:  [0.54829334 0.0106276  0.03837941 0.         0.01757763]\n",
      "Epoch 3, Iteration 150, loss = 1.3320\n",
      "Validation IOU:  [0.59094784 0.0051832  0.0339392  0.         0.01951392]\n",
      "Training IOU:  [0.59311665 0.00591721 0.0366094  0.         0.01977497]\n",
      "Epoch 3, Iteration 175, loss = 1.3548\n",
      "Validation IOU:  [0.59962044 0.00480606 0.03242611 0.         0.0198914 ]\n",
      "Training IOU:  [0.60243792 0.00561353 0.03519671 0.         0.02000127]\n",
      "Epoch 3, Iteration 200, loss = 1.2430\n",
      "Validation IOU:  [0.58039552 0.00660902 0.03625626 0.         0.01995707]\n",
      "Training IOU:  [0.58256292 0.00706843 0.03854596 0.         0.01962768]\n",
      "Epoch 3, Iteration 225, loss = 1.4018\n",
      "Validation IOU:  [0.58196492 0.00957957 0.0365573  0.         0.02056741]\n",
      "Training IOU:  [0.58619092 0.0103261  0.03879355 0.         0.01869258]\n",
      "Epoch 3, Iteration 250, loss = 1.3009\n",
      "Validation IOU:  [0.56023499 0.01225691 0.03716022 0.         0.01535466]\n",
      "Training IOU:  [0.56252712 0.0136688  0.03817051 0.         0.01462097]\n",
      "Epoch 3, Iteration 275, loss = 1.3462\n",
      "Validation IOU:  [0.59256428 0.0101529  0.03587537 0.         0.01789583]\n",
      "Training IOU:  [0.59667945 0.01154099 0.0379255  0.         0.01811735]\n",
      "Epoch 3, Iteration 300, loss = 1.4660\n",
      "Validation IOU:  [0.62077481 0.01037548 0.03616341 0.         0.01775756]\n",
      "Training IOU:  [0.62414409 0.01187716 0.03763431 0.         0.01788327]\n",
      "Epoch 4, Iteration 0, loss = 1.3191\n",
      "Validation IOU:  [0.61123775 0.01126564 0.03591552 0.         0.01737751]\n",
      "Training IOU:  [0.61606431 0.0123137  0.03792157 0.         0.01729011]\n",
      "Epoch 4, Iteration 25, loss = 1.3943\n",
      "Validation IOU:  [0.56709557 0.01141851 0.03049249 0.         0.01632821]\n",
      "Training IOU:  [0.56976146 0.0129817  0.03267594 0.         0.01645359]\n",
      "Epoch 4, Iteration 50, loss = 1.2326\n",
      "Validation IOU:  [0.58387897 0.01038662 0.03559843 0.         0.01814346]\n",
      "Training IOU:  [0.58592726 0.01157    0.03876068 0.         0.01847789]\n",
      "Epoch 4, Iteration 75, loss = 1.3601\n",
      "Validation IOU:  [0.57318588 0.00743327 0.03523519 0.         0.02098041]\n",
      "Training IOU:  [0.5764553  0.00747616 0.0375378  0.         0.0203964 ]\n",
      "Epoch 4, Iteration 100, loss = 1.4668\n",
      "Validation IOU:  [0.56588343 0.00646198 0.03819343 0.         0.02229587]\n",
      "Training IOU:  [0.56993609 0.00697193 0.04084823 0.         0.020634  ]\n",
      "Epoch 4, Iteration 125, loss = 1.3703\n",
      "Validation IOU:  [0.59239408 0.00325446 0.03684179 0.         0.02029158]\n",
      "Training IOU:  [0.59578314 0.00376688 0.0386399  0.         0.02065094]\n",
      "Epoch 4, Iteration 150, loss = 1.3775\n",
      "Validation IOU:  [0.59842079 0.00880301 0.03410586 0.         0.01997352]\n",
      "Training IOU:  [0.60145331 0.00959209 0.03665137 0.         0.01984272]\n",
      "Epoch 4, Iteration 175, loss = 1.3752\n",
      "Validation IOU:  [0.55827524 0.01263407 0.03201057 0.         0.01653299]\n",
      "Training IOU:  [0.55801859 0.01333288 0.03497933 0.         0.01569624]\n",
      "Epoch 4, Iteration 200, loss = 1.4224\n",
      "Validation IOU:  [0.59162899 0.01017464 0.03407325 0.         0.02012894]\n",
      "Training IOU:  [0.59475227 0.01159818 0.03593496 0.         0.01902798]\n",
      "Epoch 4, Iteration 225, loss = 1.4035\n",
      "Validation IOU:  [0.58591426 0.00738415 0.03556369 0.         0.01949162]\n",
      "Training IOU:  [0.58825567 0.0084288  0.03793726 0.         0.0199942 ]\n",
      "Epoch 4, Iteration 250, loss = 1.2630\n",
      "Validation IOU:  [0.61377327 0.00742294 0.03596928 0.         0.01840635]\n",
      "Training IOU:  [0.61432505 0.0079113  0.03783411 0.         0.01860281]\n",
      "Epoch 4, Iteration 275, loss = 1.4525\n",
      "Validation IOU:  [0.65149599 0.00750887 0.03564727 0.         0.01936552]\n",
      "Training IOU:  [0.65200175 0.00806379 0.03715972 0.         0.01896063]\n",
      "Epoch 4, Iteration 300, loss = 1.4521\n",
      "Validation IOU:  [0.60916226 0.01185336 0.03624465 0.         0.01655047]\n",
      "Training IOU:  [0.61240443 0.01255629 0.03803978 0.         0.0165162 ]\n",
      "Epoch 5, Iteration 0, loss = 1.3617\n",
      "Validation IOU:  [0.6056597  0.01110031 0.03738114 0.         0.01734386]\n",
      "Training IOU:  [0.60814731 0.01193313 0.03847457 0.         0.01734562]\n",
      "Epoch 5, Iteration 25, loss = 1.3977\n",
      "Validation IOU:  [0.6357851  0.00478505 0.03504088 0.         0.01842409]\n",
      "Training IOU:  [0.63852672 0.0051076  0.03665825 0.         0.01927118]\n",
      "Epoch 5, Iteration 50, loss = 1.4780\n",
      "Validation IOU:  [0.61514589 0.00827275 0.03229823 0.         0.02114944]\n",
      "Training IOU:  [0.62022747 0.00885153 0.03462051 0.         0.0207655 ]\n",
      "Epoch 5, Iteration 75, loss = 1.2871\n",
      "Validation IOU:  [0.57448591 0.00447191 0.03523498 0.         0.02129383]\n",
      "Training IOU:  [0.57940704 0.00492655 0.03785038 0.         0.02137501]\n",
      "Epoch 5, Iteration 100, loss = 1.3701\n",
      "Validation IOU:  [0.58045111 0.01150153 0.03250916 0.         0.01880982]\n",
      "Training IOU:  [0.58158863 0.01252175 0.03431008 0.         0.01876245]\n",
      "Epoch 5, Iteration 125, loss = 1.3859\n",
      "Validation IOU:  [0.53352904 0.01185857 0.03612933 0.         0.02055977]\n",
      "Training IOU:  [0.53409626 0.01337862 0.03685598 0.         0.02060142]\n",
      "Epoch 5, Iteration 150, loss = 1.3791\n",
      "Validation IOU:  [0.54661107 0.00883723 0.03712252 0.         0.02145428]\n",
      "Training IOU:  [0.55278157 0.00984442 0.03828097 0.         0.02120162]\n",
      "Epoch 5, Iteration 175, loss = 1.3291\n",
      "Validation IOU:  [0.57001303 0.01087798 0.03574791 0.         0.01928243]\n",
      "Training IOU:  [0.572915   0.01239096 0.03797137 0.         0.01942962]\n",
      "Epoch 5, Iteration 200, loss = 1.3904\n",
      "Validation IOU:  [0.54074362 0.01144887 0.03640043 0.         0.01830814]\n",
      "Training IOU:  [0.54229829 0.01277256 0.03771698 0.         0.01834652]\n",
      "Epoch 5, Iteration 225, loss = 1.3043\n",
      "Validation IOU:  [0.61575648 0.00802519 0.03491751 0.         0.0197251 ]\n",
      "Training IOU:  [0.6201722  0.00925866 0.03746336 0.         0.02041928]\n",
      "Epoch 5, Iteration 250, loss = 1.4622\n",
      "Validation IOU:  [0.59594399 0.01040873 0.035085   0.         0.02009604]\n",
      "Training IOU:  [0.59842895 0.01174532 0.03802458 0.         0.02008207]\n",
      "Epoch 5, Iteration 275, loss = 1.3760\n",
      "Validation IOU:  [0.60715334 0.00970972 0.03398874 0.         0.01797437]\n",
      "Training IOU:  [0.60888752 0.01072077 0.03684131 0.         0.01807152]\n",
      "Epoch 5, Iteration 300, loss = 1.3823\n",
      "Validation IOU:  [0.60794169 0.00767813 0.03685626 0.         0.01655573]\n",
      "Training IOU:  [0.60854511 0.00842778 0.03971027 0.         0.01516241]\n",
      "Epoch 6, Iteration 0, loss = 1.4029\n",
      "Validation IOU:  [0.60362016 0.00763242 0.03626893 0.         0.01561417]\n",
      "Training IOU:  [0.60624798 0.00838847 0.03998378 0.         0.01542343]\n",
      "Epoch 6, Iteration 25, loss = 1.4219\n",
      "Validation IOU:  [0.51166797 0.01135047 0.0359609  0.         0.01857876]\n",
      "Training IOU:  [0.51439205 0.01293296 0.03875613 0.         0.01828402]\n",
      "Epoch 6, Iteration 50, loss = 1.3453\n",
      "Validation IOU:  [0.5883994  0.01096936 0.03612097 0.         0.0182385 ]\n",
      "Training IOU:  [0.59044655 0.0125842  0.03853836 0.         0.01898953]\n",
      "Epoch 6, Iteration 75, loss = 1.3126\n",
      "Validation IOU:  [0.62415802 0.00438051 0.03748253 0.         0.02128897]\n",
      "Training IOU:  [0.62767989 0.00527556 0.03971301 0.         0.0209244 ]\n",
      "Epoch 6, Iteration 100, loss = 1.3853\n",
      "Validation IOU:  [0.55529867 0.01199664 0.03637221 0.         0.01907713]\n",
      "Training IOU:  [0.55976345 0.01284282 0.03784459 0.         0.01852512]\n",
      "Epoch 6, Iteration 125, loss = 1.4083\n",
      "Validation IOU:  [0.58357256 0.00789634 0.03529724 0.         0.02029898]\n",
      "Training IOU:  [0.58638713 0.00921412 0.03736951 0.         0.01989493]\n",
      "Epoch 6, Iteration 150, loss = 1.4381\n",
      "Validation IOU:  [0.56862272 0.01005403 0.03574131 0.         0.01982834]\n",
      "Training IOU:  [0.56942553 0.01079014 0.03765219 0.         0.02043003]\n",
      "Epoch 6, Iteration 175, loss = 1.4173\n",
      "Validation IOU:  [0.58159974 0.00933959 0.03111294 0.         0.02016352]\n",
      "Training IOU:  [0.5832658  0.01029408 0.03470635 0.         0.02014584]\n",
      "Epoch 6, Iteration 200, loss = 1.4624\n",
      "Validation IOU:  [0.55591719 0.00829424 0.03405344 0.         0.02072624]\n",
      "Training IOU:  [0.55986043 0.00933882 0.03815245 0.         0.02160143]\n",
      "Epoch 6, Iteration 225, loss = 1.3262\n",
      "Validation IOU:  [0.5981056  0.00843424 0.03770086 0.         0.01826445]\n",
      "Training IOU:  [0.59942553 0.01016404 0.04141716 0.         0.01897934]\n",
      "Epoch 6, Iteration 250, loss = 1.3714\n",
      "Validation IOU:  [0.62594325 0.007248   0.03861884 0.         0.02061309]\n",
      "Training IOU:  [0.62872805 0.00912611 0.04121588 0.         0.02124897]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6, Iteration 275, loss = 1.4337\n",
      "Validation IOU:  [0.61245213 0.0054677  0.03941061 0.         0.02081356]\n",
      "Training IOU:  [0.61496298 0.00631514 0.04222879 0.         0.02101571]\n",
      "Epoch 6, Iteration 300, loss = 1.5451\n",
      "Validation IOU:  [0.61704276 0.01017335 0.03621951 0.         0.01985321]\n",
      "Training IOU:  [0.61932558 0.01265648 0.03719089 0.         0.0192082 ]\n",
      "Epoch 7, Iteration 0, loss = 1.3091\n",
      "Validation IOU:  [0.61627506 0.01059624 0.03495238 0.         0.01935335]\n",
      "Training IOU:  [0.6193865  0.01254563 0.03758875 0.         0.0193001 ]\n",
      "Epoch 7, Iteration 25, loss = 1.3268\n",
      "Validation IOU:  [0.58508485 0.00813317 0.03679842 0.         0.02152462]\n",
      "Training IOU:  [0.58895875 0.00925741 0.04033695 0.         0.02217053]\n",
      "Epoch 7, Iteration 50, loss = 1.3601\n",
      "Validation IOU:  [0.58501627 0.01130972 0.03626844 0.         0.02007128]\n",
      "Training IOU:  [0.58628942 0.01271214 0.03788049 0.         0.02078256]\n",
      "Epoch 7, Iteration 75, loss = 1.3427\n",
      "Validation IOU:  [0.63854008 0.00923112 0.03600064 0.         0.01946931]\n",
      "Training IOU:  [0.639789   0.01107678 0.03793359 0.         0.01953841]\n",
      "Epoch 7, Iteration 100, loss = 1.3932\n",
      "Validation IOU:  [0.59068339 0.00888021 0.03984842 0.         0.02178456]\n",
      "Training IOU:  [0.59350112 0.00945758 0.0416954  0.         0.02165998]\n",
      "Epoch 7, Iteration 125, loss = 1.4920\n",
      "Validation IOU:  [0.59090749 0.00789105 0.03625955 0.         0.0204961 ]\n",
      "Training IOU:  [0.59252932 0.00973586 0.03893612 0.         0.0213399 ]\n",
      "Epoch 7, Iteration 150, loss = 1.4790\n",
      "Validation IOU:  [0.59167131 0.00594549 0.03665739 0.         0.02152435]\n",
      "Training IOU:  [0.59392492 0.00778639 0.03937509 0.         0.02214523]\n",
      "Epoch 7, Iteration 175, loss = 1.4176\n",
      "Validation IOU:  [0.55230164 0.01155529 0.0392604  0.         0.01972174]\n",
      "Training IOU:  [0.55391149 0.01249549 0.04136709 0.         0.01950574]\n",
      "Epoch 7, Iteration 200, loss = 1.3436\n",
      "Validation IOU:  [0.5559989  0.00859838 0.03793323 0.         0.02217141]\n",
      "Training IOU:  [0.55937372 0.01018186 0.04049543 0.         0.02179959]\n",
      "Epoch 7, Iteration 225, loss = 1.3233\n",
      "Validation IOU:  [0.59550256 0.00846243 0.03681652 0.         0.02232503]\n",
      "Training IOU:  [0.59864079 0.00914147 0.03996969 0.         0.02216411]\n",
      "Epoch 7, Iteration 250, loss = 1.3223\n",
      "Validation IOU:  [0.62738729 0.01001138 0.03788898 0.         0.02052612]\n",
      "Training IOU:  [0.62826308 0.00964866 0.04068335 0.         0.02036291]\n",
      "Epoch 7, Iteration 275, loss = 1.4154\n",
      "Validation IOU:  [0.63603567 0.0034003  0.03714405 0.         0.02212154]\n",
      "Training IOU:  [0.64107355 0.00401751 0.03971305 0.         0.02101108]\n",
      "Epoch 7, Iteration 300, loss = 1.2783\n",
      "Validation IOU:  [0.60648748 0.01051416 0.03616179 0.         0.01941849]\n",
      "Training IOU:  [0.60797447 0.01144196 0.03821071 0.         0.0191868 ]\n",
      "Epoch 8, Iteration 0, loss = 1.2252\n",
      "Validation IOU:  [0.61146546 0.00976229 0.03558801 0.         0.0194239 ]\n",
      "Training IOU:  [0.61140911 0.0109463  0.03810814 0.         0.0191058 ]\n",
      "Epoch 8, Iteration 25, loss = 1.2238\n",
      "Validation IOU:  [0.6059904  0.00889447 0.03710474 0.         0.02173676]\n",
      "Training IOU:  [0.60850002 0.01018748 0.04028759 0.         0.02165712]\n",
      "Epoch 8, Iteration 50, loss = 1.3006\n",
      "Validation IOU:  [0.63539515 0.00899808 0.03602761 0.         0.01981446]\n",
      "Training IOU:  [0.6395879  0.01005443 0.03937195 0.         0.0202206 ]\n",
      "Epoch 8, Iteration 75, loss = 1.3659\n",
      "Validation IOU:  [0.62993492 0.00842343 0.03885036 0.         0.02078665]\n",
      "Training IOU:  [0.63464342 0.00983826 0.04156252 0.         0.02114875]\n",
      "Epoch 8, Iteration 100, loss = 1.3339\n",
      "Validation IOU:  [0.60786858 0.01045693 0.03153011 0.         0.02086823]\n",
      "Training IOU:  [0.6113515  0.01168193 0.03542865 0.         0.02182533]\n",
      "Epoch 8, Iteration 125, loss = 1.1729\n",
      "Validation IOU:  [0.60074244 0.01075005 0.03585042 0.         0.02113324]\n",
      "Training IOU:  [0.6038813  0.01305584 0.03912009 0.         0.02142712]\n",
      "Epoch 8, Iteration 150, loss = 1.4044\n",
      "Validation IOU:  [0.59659996 0.00712086 0.03868676 0.         0.0203974 ]\n",
      "Training IOU:  [0.59778658 0.00899591 0.04014971 0.         0.02078322]\n",
      "Epoch 8, Iteration 175, loss = 1.2283\n",
      "Validation IOU:  [0.55915433 0.0100982  0.03855896 0.         0.02151274]\n",
      "Training IOU:  [0.56014452 0.01190594 0.04083977 0.         0.02183992]\n",
      "Epoch 8, Iteration 200, loss = 1.2616\n",
      "Validation IOU:  [0.57630559 0.01097846 0.03691858 0.         0.02059601]\n",
      "Training IOU:  [0.57957861 0.01312953 0.03994135 0.         0.0204294 ]\n",
      "Epoch 8, Iteration 225, loss = 1.3959\n",
      "Validation IOU:  [0.59964568 0.00876873 0.03802658 0.         0.02147795]\n",
      "Training IOU:  [0.60332065 0.00991738 0.04031502 0.         0.02180938]\n",
      "Epoch 8, Iteration 250, loss = 1.3723\n",
      "Validation IOU:  [0.62555309 0.00677335 0.03545167 0.         0.02329706]\n",
      "Training IOU:  [0.62826405 0.0078005  0.03826013 0.         0.02286445]\n",
      "Epoch 8, Iteration 275, loss = 1.4567\n",
      "Validation IOU:  [0.622611   0.00887267 0.03734493 0.         0.02273155]\n",
      "Training IOU:  [0.62458819 0.01003116 0.04005949 0.         0.02298444]\n",
      "Epoch 8, Iteration 300, loss = 1.2854\n",
      "Validation IOU:  [0.59156387 0.01088062 0.03522897 0.         0.02193221]\n",
      "Training IOU:  [0.59509824 0.01235285 0.03907526 0.         0.02252636]\n",
      "Epoch 9, Iteration 0, loss = 1.2338\n",
      "Validation IOU:  [0.59283434 0.01063539 0.03620463 0.         0.02222987]\n",
      "Training IOU:  [0.59626331 0.01219997 0.03882872 0.         0.02250927]\n",
      "Epoch 9, Iteration 25, loss = 1.3528\n",
      "Validation IOU:  [0.58761628 0.01142464 0.03592749 0.         0.02147737]\n",
      "Training IOU:  [0.58875401 0.01340532 0.03863661 0.         0.02164294]\n",
      "Epoch 9, Iteration 50, loss = 1.2871\n",
      "Validation IOU:  [0.59703987 0.01038287 0.03138151 0.         0.02270582]\n",
      "Training IOU:  [0.60299441 0.0125758  0.03395348 0.         0.0235688 ]\n",
      "Epoch 9, Iteration 75, loss = 1.3393\n",
      "Validation IOU:  [0.57344285 0.01047816 0.03850721 0.         0.02290007]\n",
      "Training IOU:  [0.57574231 0.01199006 0.03981974 0.         0.02224726]\n",
      "Epoch 9, Iteration 100, loss = 1.2678\n",
      "Validation IOU:  [0.59075515 0.0117545  0.03736671 0.         0.02074891]\n",
      "Training IOU:  [0.59519424 0.01401631 0.0404721  0.         0.02140012]\n",
      "Epoch 9, Iteration 125, loss = 1.3925\n",
      "Validation IOU:  [0.5538635  0.0087572  0.03713989 0.         0.0223745 ]\n",
      "Training IOU:  [0.5596678  0.00987234 0.04039262 0.         0.02242408]\n",
      "Epoch 9, Iteration 150, loss = 1.2349\n",
      "Validation IOU:  [0.56907323 0.00764449 0.03698436 0.         0.02114403]\n",
      "Training IOU:  [0.57364269 0.00868813 0.0395209  0.         0.02194218]\n",
      "Epoch 9, Iteration 175, loss = 1.3236\n",
      "Validation IOU:  [0.6237151  0.00858817 0.03866458 0.         0.02215417]\n",
      "Training IOU:  [0.62710647 0.01013475 0.0416504  0.         0.02257874]\n",
      "Epoch 9, Iteration 200, loss = 1.3753\n",
      "Validation IOU:  [0.60638285 0.01015745 0.03489672 0.         0.02239545]\n",
      "Training IOU:  [0.60770778 0.01195899 0.03816267 0.         0.02249114]\n",
      "Epoch 9, Iteration 225, loss = 1.3655\n",
      "Validation IOU:  [0.61042285 0.00851501 0.03741187 0.         0.02300443]\n",
      "Training IOU:  [0.61363409 0.01055615 0.04209489 0.         0.02293786]\n",
      "Epoch 9, Iteration 250, loss = 1.3785\n",
      "Validation IOU:  [0.59721735 0.01251279 0.03588429 0.         0.01906717]\n",
      "Training IOU:  [0.59843452 0.01425359 0.03842455 0.         0.01999722]\n",
      "Epoch 9, Iteration 275, loss = 1.4646\n",
      "Validation IOU:  [0.59538689 0.00745819 0.03762296 0.         0.02042494]\n",
      "Training IOU:  [0.59838591 0.00864917 0.04007308 0.         0.0216283 ]\n",
      "Epoch 9, Iteration 300, loss = 1.4528\n",
      "Validation IOU:  [0.64425442 0.01094091 0.03748102 0.         0.01985259]\n",
      "Training IOU:  [0.64484282 0.01220004 0.03978986 0.         0.02137869]\n",
      "Epoch 10, Iteration 0, loss = 1.3704\n",
      "Validation IOU:  [0.64199912 0.01033026 0.03724853 0.         0.02101176]\n",
      "Training IOU:  [0.643665   0.01225824 0.03969923 0.         0.02171943]\n",
      "Epoch 10, Iteration 25, loss = 1.3230\n",
      "Validation IOU:  [0.62633449 0.0103153  0.0389455  0.         0.02154287]\n",
      "Training IOU:  [0.62935775 0.01184204 0.04147838 0.         0.02227487]\n",
      "Epoch 10, Iteration 50, loss = 1.2933\n",
      "Validation IOU:  [0.56990142 0.01249844 0.03286642 0.         0.0202395 ]\n",
      "Training IOU:  [0.57150774 0.01420081 0.03620086 0.         0.01989589]\n",
      "Epoch 10, Iteration 75, loss = 1.4543\n",
      "Validation IOU:  [0.59280315 0.00672605 0.0390761  0.         0.02240311]\n",
      "Training IOU:  [0.59560437 0.00822977 0.04208792 0.         0.02292103]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10, Iteration 100, loss = 1.5284\n",
      "Validation IOU:  [0.62905156 0.01085343 0.03950067 0.         0.0209944 ]\n",
      "Training IOU:  [0.62956172 0.01201718 0.04261039 0.         0.02140673]\n",
      "Epoch 10, Iteration 125, loss = 1.4451\n",
      "Validation IOU:  [0.61338258 0.00991275 0.0393076  0.         0.0213864 ]\n",
      "Training IOU:  [0.61393061 0.01200203 0.04241686 0.         0.0231996 ]\n",
      "Epoch 10, Iteration 150, loss = 1.3729\n",
      "Validation IOU:  [0.59127399 0.0094791  0.03873441 0.         0.02125538]\n",
      "Training IOU:  [0.59555823 0.01103959 0.04130255 0.         0.02225641]\n",
      "Epoch 10, Iteration 175, loss = 1.3875\n",
      "Validation IOU:  [0.59593892 0.01215828 0.03625992 0.         0.01983489]\n",
      "Training IOU:  [0.59781552 0.01427058 0.03940889 0.         0.02125883]\n",
      "Epoch 10, Iteration 200, loss = 1.3641\n",
      "Validation IOU:  [0.57902737 0.01000047 0.03726261 0.         0.02224192]\n",
      "Training IOU:  [0.58179475 0.01125561 0.04018249 0.         0.0231873 ]\n",
      "Epoch 10, Iteration 225, loss = 1.3324\n",
      "Validation IOU:  [0.6020598  0.01076822 0.03776381 0.         0.02133812]\n",
      "Training IOU:  [0.60530056 0.01274225 0.04052566 0.         0.02190235]\n",
      "Epoch 10, Iteration 250, loss = 1.3899\n",
      "Validation IOU:  [0.59774533 0.01214753 0.03801034 0.         0.02029656]\n",
      "Training IOU:  [0.59970488 0.01345405 0.04054759 0.         0.02138273]\n",
      "Epoch 10, Iteration 275, loss = 1.2894\n",
      "Validation IOU:  [0.59681511 0.01152601 0.03588081 0.         0.02114206]\n",
      "Training IOU:  [0.59864323 0.01274458 0.03782251 0.         0.02199582]\n",
      "Epoch 10, Iteration 300, loss = 1.4446\n",
      "Validation IOU:  [0.61077617 0.01073449 0.03904162 0.         0.02101955]\n",
      "Training IOU:  [0.61418256 0.011806   0.04125902 0.         0.02156475]\n",
      "Epoch 11, Iteration 0, loss = 1.3600\n",
      "Validation IOU:  [0.61186427 0.01089884 0.03742492 0.         0.0208297 ]\n",
      "Training IOU:  [0.61541163 0.01200629 0.0414768  0.         0.02186533]\n",
      "Epoch 11, Iteration 25, loss = 1.3031\n",
      "Validation IOU:  [0.64790441 0.00668052 0.03828995 0.         0.02224107]\n",
      "Training IOU:  [0.65161215 0.00876182 0.04210914 0.         0.02270695]\n",
      "Epoch 11, Iteration 50, loss = 1.3530\n",
      "Validation IOU:  [0.611055   0.01154788 0.03271708 0.         0.02194241]\n",
      "Training IOU:  [0.61561715 0.01377404 0.03634891 0.         0.0224344 ]\n",
      "Epoch 11, Iteration 75, loss = 1.3784\n",
      "Validation IOU:  [0.59384333 0.01072673 0.03498103 0.         0.02344103]\n",
      "Training IOU:  [0.5960412  0.01296442 0.03929979 0.         0.02366515]\n",
      "Epoch 11, Iteration 100, loss = 1.3181\n",
      "Validation IOU:  [0.62020219 0.01140055 0.0344158  0.         0.02246146]\n",
      "Training IOU:  [0.62254429 0.01376569 0.03717476 0.         0.02327435]\n",
      "Epoch 11, Iteration 125, loss = 1.3620\n",
      "Validation IOU:  [0.62914147 0.01016175 0.03767349 0.         0.02176311]\n",
      "Training IOU:  [0.63184701 0.01220256 0.04140699 0.         0.0228209 ]\n",
      "Epoch 11, Iteration 150, loss = 1.2191\n",
      "Validation IOU:  [0.59928128 0.01070288 0.03623607 0.         0.02163379]\n",
      "Training IOU:  [0.60192855 0.01285081 0.03943138 0.         0.02302756]\n",
      "Epoch 11, Iteration 175, loss = 1.5409\n",
      "Validation IOU:  [0.61209591 0.01118241 0.03773407 0.         0.02038973]\n",
      "Training IOU:  [0.61623531 0.01383562 0.0405465  0.         0.02177697]\n",
      "Epoch 11, Iteration 200, loss = 1.4621\n",
      "Validation IOU:  [0.61974118 0.0105806  0.03605329 0.         0.02099041]\n",
      "Training IOU:  [0.62373876 0.0124706  0.04056509 0.         0.02188908]\n",
      "Epoch 11, Iteration 225, loss = 1.4216\n",
      "Validation IOU:  [0.60584356 0.01036561 0.0386498  0.         0.02236076]\n",
      "Training IOU:  [0.61110826 0.01199447 0.04169699 0.         0.02306461]\n",
      "Epoch 11, Iteration 250, loss = 1.4316\n",
      "Validation IOU:  [0.6457238  0.01025064 0.03652646 0.         0.02209181]\n",
      "Training IOU:  [0.65118713 0.01167557 0.0413071  0.         0.02247492]\n",
      "Epoch 11, Iteration 275, loss = 1.2836\n",
      "Validation IOU:  [0.62260619 0.00964991 0.0388505  0.         0.02198993]\n",
      "Training IOU:  [0.62545872 0.01149598 0.04185725 0.         0.02276095]\n",
      "Epoch 11, Iteration 300, loss = 1.4401\n",
      "Validation IOU:  [0.6462285  0.0087903  0.03751954 0.         0.02213256]\n",
      "Training IOU:  [0.65202735 0.0115811  0.04128814 0.         0.02317781]\n",
      "Epoch 12, Iteration 0, loss = 1.5895\n",
      "Validation IOU:  [0.64693237 0.00866727 0.03669494 0.         0.02202833]\n",
      "Training IOU:  [0.65135729 0.01137922 0.04110914 0.         0.02305895]\n",
      "Epoch 12, Iteration 25, loss = 1.4116\n",
      "Validation IOU:  [0.60319621 0.01039712 0.03931249 0.         0.02362191]\n",
      "Training IOU:  [0.60634359 0.01243871 0.04172314 0.         0.02324101]\n",
      "Epoch 12, Iteration 50, loss = 1.3729\n",
      "Validation IOU:  [0.60979826 0.01046062 0.03849649 0.         0.02294429]\n",
      "Training IOU:  [0.6162126  0.01235087 0.04207117 0.         0.02320862]\n",
      "Epoch 12, Iteration 75, loss = 1.3279\n",
      "Validation IOU:  [0.64814524 0.00948814 0.03495493 0.         0.02250383]\n",
      "Training IOU:  [0.65311715 0.0113436  0.04016984 0.         0.02313416]\n",
      "Epoch 12, Iteration 100, loss = 1.2351\n",
      "Validation IOU:  [0.5844269  0.01237625 0.03667544 0.         0.02276665]\n",
      "Training IOU:  [0.58651368 0.01407901 0.03956326 0.         0.02291345]\n",
      "Epoch 12, Iteration 125, loss = 1.5065\n",
      "Validation IOU:  [0.57068301 0.01260322 0.03499383 0.         0.02162512]\n",
      "Training IOU:  [0.57286632 0.0149626  0.03921814 0.         0.02258426]\n",
      "Epoch 12, Iteration 150, loss = 1.4831\n",
      "Validation IOU:  [0.61892254 0.00877101 0.03765273 0.         0.02224814]\n",
      "Training IOU:  [0.62305754 0.01088342 0.04198746 0.         0.02298489]\n",
      "Epoch 12, Iteration 175, loss = 1.4667\n",
      "Validation IOU:  [0.6230585  0.01157671 0.0379045  0.         0.02063028]\n",
      "Training IOU:  [0.62696074 0.01402682 0.04027849 0.         0.02234787]\n",
      "Epoch 12, Iteration 200, loss = 1.2340\n",
      "Validation IOU:  [0.63104463 0.01149071 0.03414138 0.         0.02150146]\n",
      "Training IOU:  [0.63533516 0.01421395 0.03779925 0.         0.0229371 ]\n",
      "Epoch 12, Iteration 225, loss = 1.4008\n",
      "Validation IOU:  [0.62994916 0.00967122 0.03693053 0.         0.02260098]\n",
      "Training IOU:  [0.63381771 0.01280414 0.04040445 0.         0.02330854]\n",
      "Epoch 12, Iteration 250, loss = 1.2272\n",
      "Validation IOU:  [0.62857364 0.00987478 0.03640274 0.         0.02136259]\n",
      "Training IOU:  [0.63350103 0.01213057 0.03890438 0.         0.02221445]\n",
      "Epoch 12, Iteration 275, loss = 1.2123\n",
      "Validation IOU:  [0.60963365 0.01014467 0.03938888 0.         0.020881  ]\n",
      "Training IOU:  [0.61442092 0.01250376 0.04313972 0.         0.0224895 ]\n",
      "Epoch 12, Iteration 300, loss = 1.2672\n",
      "Validation IOU:  [0.62592112 0.00912228 0.03740623 0.         0.02131663]\n",
      "Training IOU:  [0.6317817  0.01126969 0.04203045 0.         0.02343046]\n",
      "Epoch 13, Iteration 0, loss = 1.3457\n",
      "Validation IOU:  [0.62645173 0.00862399 0.03660229 0.         0.02163234]\n",
      "Training IOU:  [0.63233958 0.01116748 0.04195748 0.         0.02335095]\n",
      "Epoch 13, Iteration 25, loss = 1.2855\n",
      "Validation IOU:  [0.62366177 0.01201091 0.03656502 0.         0.01961029]\n",
      "Training IOU:  [0.62760661 0.01404248 0.04081735 0.         0.0211986 ]\n",
      "Epoch 13, Iteration 50, loss = 1.3463\n",
      "Validation IOU:  [0.5957927  0.01018895 0.03751578 0.         0.02228825]\n",
      "Training IOU:  [0.59848706 0.01243705 0.04012191 0.         0.02277251]\n",
      "Epoch 13, Iteration 75, loss = 1.3572\n",
      "Validation IOU:  [0.61659647 0.01183665 0.03890916 0.         0.02223355]\n",
      "Training IOU:  [0.62213229 0.01418432 0.04160888 0.         0.02289469]\n",
      "Epoch 13, Iteration 100, loss = 1.2431\n",
      "Validation IOU:  [0.61748411 0.01175119 0.0378089  0.         0.02295803]\n",
      "Training IOU:  [0.62163359 0.01324364 0.04129594 0.         0.0227629 ]\n",
      "Epoch 13, Iteration 125, loss = 1.2505\n",
      "Validation IOU:  [0.63586606 0.00915424 0.03927316 0.         0.0230192 ]\n",
      "Training IOU:  [0.639821   0.01162404 0.04182929 0.         0.02323504]\n",
      "Epoch 13, Iteration 150, loss = 1.3223\n",
      "Validation IOU:  [0.64103387 0.01142118 0.03804803 0.         0.02188621]\n",
      "Training IOU:  [0.6437875  0.01374257 0.04226888 0.         0.02302976]\n",
      "Epoch 13, Iteration 175, loss = 1.2511\n",
      "Validation IOU:  [0.63513932 0.00868992 0.03841071 0.         0.02289384]\n",
      "Training IOU:  [0.63787127 0.01093436 0.04343558 0.         0.02382124]\n",
      "Epoch 13, Iteration 200, loss = 1.3027\n",
      "Validation IOU:  [0.63008311 0.00933083 0.03728642 0.         0.02220695]\n",
      "Training IOU:  [0.63323143 0.01142063 0.04116429 0.         0.02378883]\n",
      "Epoch 13, Iteration 225, loss = 1.3559\n",
      "Validation IOU:  [0.62893585 0.00925062 0.03889864 0.         0.02163928]\n",
      "Training IOU:  [0.63303347 0.01203047 0.0420116  0.         0.02349077]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13, Iteration 250, loss = 1.4160\n",
      "Validation IOU:  [0.61557672 0.00972618 0.03720505 0.         0.02249363]\n",
      "Training IOU:  [0.61939167 0.01162376 0.04318323 0.         0.02393566]\n",
      "Epoch 13, Iteration 275, loss = 1.3641\n",
      "Validation IOU:  [0.62833194 0.01014083 0.03669782 0.         0.02149043]\n",
      "Training IOU:  [0.63241532 0.01236416 0.04097559 0.         0.0235982 ]\n",
      "Epoch 13, Iteration 300, loss = 1.3504\n",
      "Validation IOU:  [0.62696618 0.01001557 0.03789181 0.         0.02316712]\n",
      "Training IOU:  [0.63232349 0.01317844 0.04149592 0.         0.0230481 ]\n",
      "Epoch 14, Iteration 0, loss = 1.3121\n",
      "Validation IOU:  [0.62996615 0.00996576 0.03766518 0.         0.02250106]\n",
      "Training IOU:  [0.63395674 0.01314207 0.04128748 0.         0.02297222]\n",
      "Epoch 14, Iteration 25, loss = 1.3815\n",
      "Validation IOU:  [0.63407306 0.00980791 0.03754824 0.         0.02254421]\n",
      "Training IOU:  [0.63873959 0.01242499 0.04154809 0.         0.0235847 ]\n",
      "Epoch 14, Iteration 50, loss = 1.2578\n",
      "Validation IOU:  [0.59979256 0.01220126 0.03794527 0.         0.02279493]\n",
      "Training IOU:  [0.60174493 0.01442288 0.04125343 0.         0.02315423]\n",
      "Epoch 14, Iteration 75, loss = 1.3208\n",
      "Validation IOU:  [0.62903821 0.0116359  0.03719839 0.         0.02018387]\n",
      "Training IOU:  [0.63573142 0.01385298 0.04099446 0.         0.02155069]\n",
      "Epoch 14, Iteration 100, loss = 1.3471\n",
      "Validation IOU:  [0.6066381  0.01080623 0.03873816 0.         0.02220039]\n",
      "Training IOU:  [0.61171863 0.01346636 0.04188754 0.         0.02300691]\n",
      "Epoch 14, Iteration 125, loss = 1.5017\n",
      "Validation IOU:  [0.64569107 0.00871459 0.03901817 0.         0.02138772]\n",
      "Training IOU:  [0.65225965 0.01037945 0.04167667 0.         0.02294349]\n",
      "Epoch 14, Iteration 150, loss = 1.1987\n",
      "Validation IOU:  [0.62786091 0.00862026 0.038736   0.         0.0228372 ]\n",
      "Training IOU:  [0.63157732 0.01171618 0.04261282 0.         0.02408697]\n",
      "Epoch 14, Iteration 175, loss = 1.4537\n",
      "Validation IOU:  [0.64802174 0.01205051 0.03386852 0.         0.02192753]\n",
      "Training IOU:  [0.65577938 0.01515545 0.03788934 0.         0.02356954]\n",
      "Epoch 14, Iteration 200, loss = 1.1828\n",
      "Validation IOU:  [0.63930507 0.01334588 0.03743088 0.         0.02199133]\n",
      "Training IOU:  [0.64502222 0.01578411 0.04073484 0.         0.02343373]\n",
      "Epoch 14, Iteration 225, loss = 1.2980\n",
      "Validation IOU:  [0.64215956 0.00960631 0.03654023 0.         0.0217041 ]\n",
      "Training IOU:  [0.64747459 0.01186087 0.04061675 0.         0.02376427]\n",
      "Epoch 14, Iteration 250, loss = 1.2071\n",
      "Validation IOU:  [0.6013023  0.01236356 0.03716814 0.         0.02019168]\n",
      "Training IOU:  [0.60351087 0.01419244 0.04017636 0.         0.02224522]\n",
      "Epoch 14, Iteration 275, loss = 1.4103\n",
      "Validation IOU:  [0.59414456 0.01165644 0.03940432 0.         0.02027973]\n",
      "Training IOU:  [0.59900677 0.01401225 0.04325974 0.         0.02272984]\n",
      "Epoch 14, Iteration 300, loss = 1.4474\n",
      "Validation IOU:  [0.62551121 0.00864774 0.03766557 0.         0.02235041]\n",
      "Training IOU:  [0.62568976 0.01084924 0.04322687 0.         0.02415034]\n",
      "Epoch 15, Iteration 0, loss = 1.2445\n",
      "Validation IOU:  [0.62563093 0.00816553 0.03800803 0.         0.02307608]\n",
      "Training IOU:  [0.62716863 0.01074671 0.04279832 0.         0.02411788]\n",
      "Epoch 15, Iteration 25, loss = 1.3166\n",
      "Validation IOU:  [0.66395504 0.00909572 0.03666417 0.         0.02225764]\n",
      "Training IOU:  [0.66705118 0.01094145 0.04219944 0.         0.02377486]\n",
      "Epoch 15, Iteration 50, loss = 1.2115\n",
      "Validation IOU:  [0.58784689 0.01231299 0.03547242 0.         0.02287435]\n",
      "Training IOU:  [0.59349563 0.01554174 0.03998379 0.         0.02373652]\n",
      "Epoch 15, Iteration 75, loss = 1.3553\n",
      "Validation IOU:  [0.62655097 0.01010889 0.03601956 0.         0.02014134]\n",
      "Training IOU:  [0.63106723 0.01316643 0.03984101 0.         0.02188928]\n",
      "Epoch 15, Iteration 100, loss = 1.3150\n",
      "Validation IOU:  [0.62280812 0.01038006 0.03928194 0.         0.02250908]\n",
      "Training IOU:  [0.62762185 0.01285383 0.04335521 0.         0.02341359]\n",
      "Epoch 15, Iteration 125, loss = 1.3890\n",
      "Validation IOU:  [0.62558867 0.01019021 0.03390386 0.         0.02222987]\n",
      "Training IOU:  [0.63307324 0.01433766 0.03833002 0.         0.02383209]\n",
      "Epoch 15, Iteration 150, loss = 1.3667\n",
      "Validation IOU:  [0.61698445 0.00807172 0.03903132 0.         0.02309795]\n",
      "Training IOU:  [0.6236536  0.01136315 0.0428631  0.         0.02389587]\n",
      "Epoch 15, Iteration 175, loss = 1.2729\n",
      "Validation IOU:  [0.61391918 0.00794008 0.03990327 0.         0.02204099]\n",
      "Training IOU:  [0.61614916 0.01051174 0.04461444 0.         0.02423574]\n",
      "Epoch 15, Iteration 200, loss = 1.2554\n",
      "Validation IOU:  [0.60992923 0.01115469 0.03795871 0.         0.02286761]\n",
      "Training IOU:  [0.61521947 0.01428994 0.04159824 0.         0.02386363]\n",
      "Epoch 15, Iteration 225, loss = 1.3646\n",
      "Validation IOU:  [0.60802662 0.01291227 0.03589143 0.         0.0222858 ]\n",
      "Training IOU:  [0.60974829 0.01599072 0.0389114  0.         0.0233447 ]\n",
      "Epoch 15, Iteration 250, loss = 1.3989\n",
      "Validation IOU:  [0.60876447 0.01131403 0.0349909  0.         0.02193719]\n",
      "Training IOU:  [0.61429609 0.01331477 0.0394731  0.         0.02365485]\n",
      "Epoch 15, Iteration 275, loss = 1.2915\n",
      "Validation IOU:  [0.61165197 0.01059428 0.03857421 0.         0.02210335]\n",
      "Training IOU:  [0.61539745 0.01404403 0.04160048 0.         0.0237719 ]\n",
      "Epoch 15, Iteration 300, loss = 1.3828\n",
      "Validation IOU:  [0.62734093 0.01061938 0.03804715 0.         0.0205605 ]\n",
      "Training IOU:  [0.63124124 0.01353661 0.04307229 0.         0.02359539]\n",
      "Epoch 16, Iteration 0, loss = 1.2375\n",
      "Validation IOU:  [0.62889763 0.01042674 0.0386108  0.         0.02071551]\n",
      "Training IOU:  [0.63525625 0.01347787 0.04292873 0.         0.02357109]\n",
      "Epoch 16, Iteration 25, loss = 1.2440\n",
      "Validation IOU:  [0.65405828 0.00995723 0.03770314 0.         0.02136204]\n",
      "Training IOU:  [0.65849778 0.01314297 0.04188865 0.         0.02408771]\n",
      "Epoch 16, Iteration 50, loss = 1.3933\n",
      "Validation IOU:  [0.64286509 0.0128177  0.0368755  0.         0.02079958]\n",
      "Training IOU:  [0.64961935 0.01568548 0.04221518 0.         0.02299679]\n",
      "Epoch 16, Iteration 75, loss = 1.2773\n",
      "Validation IOU:  [0.6365823  0.00958795 0.03801348 0.         0.02185458]\n",
      "Training IOU:  [0.6414566  0.01313645 0.04266585 0.         0.02391284]\n",
      "Epoch 16, Iteration 100, loss = 1.3358\n",
      "Validation IOU:  [0.62716658 0.01065901 0.04225432 0.         0.02168135]\n",
      "Training IOU:  [0.63140972 0.01297129 0.04552336 0.         0.02282187]\n",
      "Epoch 16, Iteration 125, loss = 1.2804\n",
      "Validation IOU:  [0.61716952 0.01006003 0.04109985 0.         0.02313823]\n",
      "Training IOU:  [0.62235377 0.01259031 0.04404945 0.         0.02420586]\n",
      "Epoch 16, Iteration 150, loss = 1.2706\n",
      "Validation IOU:  [0.62976663 0.01081808 0.03869824 0.         0.02190703]\n",
      "Training IOU:  [0.63337378 0.01441417 0.04285905 0.         0.02391806]\n",
      "Epoch 16, Iteration 175, loss = 1.1656\n",
      "Validation IOU:  [0.62897649 0.01129083 0.03753205 0.         0.02098314]\n",
      "Training IOU:  [0.63558862 0.01487708 0.04189233 0.         0.02297479]\n",
      "Epoch 16, Iteration 200, loss = 1.2579\n",
      "Validation IOU:  [0.63720973 0.00719917 0.04164539 0.         0.02273543]\n",
      "Training IOU:  [0.64258329 0.00939872 0.04526305 0.         0.02359122]\n",
      "Epoch 16, Iteration 225, loss = 1.3382\n",
      "Validation IOU:  [0.62908332 0.0097376  0.0391887  0.         0.02157037]\n",
      "Training IOU:  [0.63548776 0.01344396 0.04416823 0.         0.02385164]\n",
      "Epoch 16, Iteration 250, loss = 1.3426\n",
      "Validation IOU:  [0.61983063 0.01125235 0.03798559 0.         0.02308792]\n",
      "Training IOU:  [0.62283489 0.01399268 0.04175592 0.         0.02415675]\n",
      "Epoch 16, Iteration 275, loss = 1.3263\n",
      "Validation IOU:  [0.62377307 0.01100259 0.03667687 0.         0.02280741]\n",
      "Training IOU:  [0.6296204  0.01402401 0.04074938 0.         0.02428237]\n",
      "Epoch 16, Iteration 300, loss = 1.1905\n",
      "Validation IOU:  [0.60349726 0.01005936 0.03879522 0.         0.02406167]\n",
      "Training IOU:  [0.60655932 0.01372125 0.042054   0.         0.02510436]\n",
      "Epoch 17, Iteration 0, loss = 1.2951\n",
      "Validation IOU:  [0.60476013 0.00996548 0.03829421 0.         0.02374182]\n",
      "Training IOU:  [0.60809103 0.01359367 0.04229195 0.         0.02481765]\n",
      "Epoch 17, Iteration 25, loss = 1.3588\n",
      "Validation IOU:  [0.6413928  0.01182579 0.03775365 0.         0.02063941]\n",
      "Training IOU:  [0.64576117 0.01513181 0.04214563 0.         0.02253809]\n",
      "Epoch 17, Iteration 50, loss = 1.2203\n",
      "Validation IOU:  [0.65134505 0.01149555 0.03926213 0.         0.02209897]\n",
      "Training IOU:  [0.65829629 0.01460761 0.0438715  0.         0.02352696]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17, Iteration 75, loss = 1.3807\n",
      "Validation IOU:  [0.67171884 0.01173448 0.03789069 0.         0.02098098]\n",
      "Training IOU:  [0.67529864 0.01570249 0.04284313 0.         0.02345657]\n",
      "Epoch 17, Iteration 100, loss = 1.2608\n",
      "Validation IOU:  [0.65491018 0.00938855 0.03755096 0.         0.02224254]\n",
      "Training IOU:  [0.65924166 0.01198998 0.04285387 0.         0.02409766]\n",
      "Epoch 17, Iteration 125, loss = 1.2257\n",
      "Validation IOU:  [0.64087797 0.01131144 0.03827926 0.         0.02161713]\n",
      "Training IOU:  [0.64728192 0.01439514 0.04267062 0.         0.02378951]\n",
      "Epoch 17, Iteration 150, loss = 1.3377\n",
      "Validation IOU:  [0.64095642 0.01247814 0.03867179 0.         0.02221112]\n",
      "Training IOU:  [0.64521797 0.01523522 0.04339504 0.         0.02355354]\n",
      "Epoch 17, Iteration 175, loss = 1.3916\n",
      "Validation IOU:  [0.62395903 0.00940744 0.0382912  0.         0.02309557]\n",
      "Training IOU:  [0.62947203 0.0128975  0.04353726 0.         0.02432999]\n",
      "Epoch 17, Iteration 200, loss = 1.2519\n",
      "Validation IOU:  [0.64315402 0.0117965  0.03772993 0.         0.02250616]\n",
      "Training IOU:  [0.6460933  0.01482496 0.04269164 0.         0.02409033]\n",
      "Epoch 17, Iteration 225, loss = 1.2734\n",
      "Validation IOU:  [0.62512029 0.01085566 0.03620104 0.         0.02333976]\n",
      "Training IOU:  [0.63123801 0.01478294 0.04153977 0.         0.02509756]\n",
      "Epoch 17, Iteration 250, loss = 1.2725\n",
      "Validation IOU:  [0.64299155 0.01055255 0.03672845 0.         0.02263269]\n",
      "Training IOU:  [0.6483976  0.01413233 0.04140459 0.         0.02458632]\n",
      "Epoch 17, Iteration 275, loss = 1.3255\n",
      "Validation IOU:  [0.63933978 0.00976343 0.03844007 0.         0.02365093]\n",
      "Training IOU:  [0.64321923 0.01319061 0.04456041 0.         0.02484003]\n",
      "Epoch 17, Iteration 300, loss = 1.1857\n",
      "Validation IOU:  [0.65116372 0.00862521 0.03749711 0.         0.02300685]\n",
      "Training IOU:  [0.6573511  0.01261081 0.04176866 0.         0.02446686]\n",
      "Epoch 18, Iteration 0, loss = 1.3268\n",
      "Validation IOU:  [0.65307398 0.00878265 0.03677887 0.         0.02241981]\n",
      "Training IOU:  [0.65861802 0.01218009 0.0419152  0.         0.02453852]\n",
      "Epoch 18, Iteration 25, loss = 1.3487\n",
      "Validation IOU:  [0.62123055 0.01163318 0.03938208 0.         0.0233209 ]\n",
      "Training IOU:  [0.62576741 0.01530973 0.04406738 0.         0.02532176]\n",
      "Epoch 18, Iteration 50, loss = 1.3071\n",
      "Validation IOU:  [0.64834939 0.01259806 0.04018054 0.         0.02179325]\n",
      "Training IOU:  [0.6527077  0.01506816 0.0439675  0.         0.02362055]\n",
      "Epoch 18, Iteration 75, loss = 1.2885\n",
      "Validation IOU:  [0.65099804 0.00955555 0.03808551 0.         0.02330383]\n",
      "Training IOU:  [0.6560635  0.01313676 0.04341011 0.         0.02484247]\n",
      "Epoch 18, Iteration 100, loss = 1.3385\n",
      "Validation IOU:  [0.62518353 0.01007452 0.03808656 0.         0.02212164]\n",
      "Training IOU:  [0.63008345 0.01343048 0.0441081  0.         0.02422013]\n",
      "Epoch 18, Iteration 125, loss = 1.3035\n",
      "Validation IOU:  [0.6267368  0.00884826 0.04026918 0.         0.02169253]\n",
      "Training IOU:  [0.63257764 0.01225472 0.04416643 0.         0.0227553 ]\n",
      "Epoch 18, Iteration 150, loss = 1.2684\n",
      "Validation IOU:  [0.63028372 0.01380811 0.03459157 0.         0.01970456]\n",
      "Training IOU:  [0.63683783 0.01712498 0.03955257 0.         0.02243713]\n",
      "Epoch 18, Iteration 175, loss = 1.2507\n",
      "Validation IOU:  [0.60628831 0.0125582  0.03836949 0.         0.02288592]\n",
      "Training IOU:  [0.61118096 0.01529373 0.04303982 0.         0.02390262]\n",
      "Epoch 18, Iteration 200, loss = 1.4958\n",
      "Validation IOU:  [0.63211782 0.00926588 0.03985249 0.         0.0225912 ]\n",
      "Training IOU:  [0.63639479 0.01273283 0.04481256 0.         0.02507242]\n",
      "Epoch 18, Iteration 225, loss = 1.2828\n",
      "Validation IOU:  [0.64161831 0.01023405 0.03866847 0.         0.02261223]\n",
      "Training IOU:  [0.64450967 0.01426562 0.04350901 0.         0.02487079]\n",
      "Epoch 18, Iteration 250, loss = 1.2345\n",
      "Validation IOU:  [0.63856376 0.01038141 0.03822041 0.         0.02309206]\n",
      "Training IOU:  [0.6430434  0.01415593 0.04385435 0.         0.02456915]\n",
      "Epoch 18, Iteration 275, loss = 1.2376\n",
      "Validation IOU:  [0.64667696 0.00976927 0.0361103  0.         0.0228403 ]\n",
      "Training IOU:  [0.6533081  0.01407447 0.04164288 0.         0.02554995]\n",
      "Epoch 18, Iteration 300, loss = 1.3437\n",
      "Validation IOU:  [0.65732005 0.01101486 0.03775254 0.         0.02253447]\n",
      "Training IOU:  [0.66298302 0.01557661 0.04247855 0.         0.02502397]\n",
      "Epoch 19, Iteration 0, loss = 1.1242\n",
      "Validation IOU:  [0.65780384 0.01101191 0.03807616 0.         0.02294069]\n",
      "Training IOU:  [0.66473354 0.01518564 0.04253117 0.         0.02512979]\n",
      "Epoch 19, Iteration 25, loss = 1.2002\n",
      "Validation IOU:  [0.65481432 0.00926588 0.03980913 0.         0.02267539]\n",
      "Training IOU:  [0.66112825 0.01321593 0.04532086 0.         0.02497332]\n",
      "Epoch 19, Iteration 50, loss = 1.3784\n",
      "Validation IOU:  [0.65406423 0.01173983 0.03650283 0.         0.02139211]\n",
      "Training IOU:  [0.66011677 0.01544869 0.04005344 0.         0.02349635]\n",
      "Epoch 19, Iteration 75, loss = 1.2639\n",
      "Validation IOU:  [0.63849905 0.01073159 0.03942249 0.         0.02288236]\n",
      "Training IOU:  [0.6459499  0.01386923 0.04482362 0.         0.02520519]\n",
      "Epoch 19, Iteration 100, loss = 1.2712\n",
      "Validation IOU:  [0.62952709 0.0109284  0.03830586 0.         0.02299886]\n",
      "Training IOU:  [0.6345586  0.01420472 0.0441187  0.         0.02445889]\n",
      "Epoch 19, Iteration 125, loss = 1.2508\n",
      "Validation IOU:  [0.65779905 0.01181592 0.03494474 0.         0.02328413]\n",
      "Training IOU:  [0.66633502 0.0141462  0.03998498 0.         0.0245381 ]\n",
      "Epoch 19, Iteration 150, loss = 1.3110\n",
      "Validation IOU:  [0.62003588 0.01225733 0.03676217 0.         0.02210833]\n",
      "Training IOU:  [0.62463909 0.01588353 0.04187682 0.         0.0252978 ]\n",
      "Epoch 19, Iteration 175, loss = 1.4912\n",
      "Validation IOU:  [0.58769696 0.01073683 0.03974403 0.         0.02235531]\n",
      "Training IOU:  [0.59062656 0.01349416 0.04443262 0.         0.02428895]\n",
      "Epoch 19, Iteration 200, loss = 1.2836\n",
      "Validation IOU:  [0.61303699 0.01080658 0.0374704  0.         0.02283851]\n",
      "Training IOU:  [0.61692301 0.01426491 0.04305025 0.         0.02506566]\n",
      "Epoch 19, Iteration 225, loss = 1.3757\n",
      "Validation IOU:  [0.64850906 0.01062454 0.03823796 0.         0.02378422]\n",
      "Training IOU:  [0.65513382 0.01379051 0.04401255 0.         0.02543276]\n",
      "Epoch 19, Iteration 250, loss = 1.1837\n",
      "Validation IOU:  [0.65050948 0.00981406 0.03980931 0.         0.02300429]\n",
      "Training IOU:  [0.65590088 0.01300349 0.04561666 0.         0.02547344]\n",
      "Epoch 19, Iteration 275, loss = 1.3632\n",
      "Validation IOU:  [0.62410466 0.01391268 0.03655937 0.         0.01981736]\n",
      "Training IOU:  [0.63010808 0.01742153 0.04067378 0.         0.02244216]\n",
      "Epoch 19, Iteration 300, loss = 1.4233\n",
      "Validation IOU:  [0.61193592 0.01057892 0.035702   0.         0.02205288]\n",
      "Training IOU:  [0.61883428 0.01489774 0.04155813 0.         0.02435812]\n",
      "Epoch 20, Iteration 0, loss = 1.3142\n",
      "Validation IOU:  [0.6121612  0.01071687 0.03501896 0.         0.02143427]\n",
      "Training IOU:  [0.61766134 0.01465974 0.04161819 0.         0.02444913]\n",
      "Epoch 20, Iteration 25, loss = 1.1909\n",
      "Validation IOU:  [0.63856776 0.01030664 0.03952371 0.         0.02372148]\n",
      "Training IOU:  [0.64578506 0.01453039 0.04497299 0.         0.02519723]\n",
      "Epoch 20, Iteration 50, loss = 1.2137\n",
      "Validation IOU:  [0.61554669 0.01022316 0.03952992 0.         0.02378188]\n",
      "Training IOU:  [0.62025762 0.01400369 0.04377008 0.         0.0248408 ]\n",
      "Epoch 20, Iteration 75, loss = 1.2259\n",
      "Validation IOU:  [0.60286601 0.01235218 0.03650754 0.         0.02278751]\n",
      "Training IOU:  [0.60925767 0.01661839 0.04162593 0.         0.02529225]\n",
      "Epoch 20, Iteration 100, loss = 1.2692\n",
      "Validation IOU:  [0.61414447 0.01103282 0.03879452 0.         0.02228526]\n",
      "Training IOU:  [0.61828053 0.01403835 0.04506981 0.         0.02407938]\n",
      "Epoch 20, Iteration 125, loss = 1.1595\n",
      "Validation IOU:  [0.62987685 0.01001045 0.03983536 0.         0.02240045]\n",
      "Training IOU:  [0.63740453 0.01387195 0.04536618 0.         0.02455485]\n",
      "Epoch 20, Iteration 150, loss = 1.2320\n",
      "Validation IOU:  [0.63381716 0.01044538 0.0367444  0.         0.02227197]\n",
      "Training IOU:  [0.64172161 0.01409431 0.04187785 0.         0.02456931]\n",
      "Epoch 20, Iteration 175, loss = 1.2360\n",
      "Validation IOU:  [0.64325753 0.01066631 0.03663589 0.         0.02295207]\n",
      "Training IOU:  [0.64910993 0.01473552 0.04296017 0.         0.02533236]\n",
      "Epoch 20, Iteration 200, loss = 1.3872\n",
      "Validation IOU:  [0.6547819  0.01007504 0.03707464 0.         0.02272826]\n",
      "Training IOU:  [0.66201795 0.01390189 0.04296566 0.         0.02515334]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20, Iteration 225, loss = 1.2812\n",
      "Validation IOU:  [0.66732719 0.01051389 0.03664524 0.         0.02313322]\n",
      "Training IOU:  [0.6731084  0.01499741 0.04259769 0.         0.0247203 ]\n",
      "Epoch 20, Iteration 250, loss = 1.3966\n",
      "Validation IOU:  [0.62610876 0.01238907 0.03825717 0.         0.02164572]\n",
      "Training IOU:  [0.63324756 0.01702344 0.04386244 0.         0.02394033]\n",
      "Epoch 20, Iteration 275, loss = 1.2161\n",
      "Validation IOU:  [0.63379784 0.01040719 0.03960999 0.         0.02162991]\n",
      "Training IOU:  [0.64093116 0.01449932 0.04472918 0.         0.02450253]\n",
      "Epoch 20, Iteration 300, loss = 1.2826\n",
      "Validation IOU:  [0.6472917  0.01222753 0.03750125 0.         0.02189883]\n",
      "Training IOU:  [0.6531118  0.01640959 0.04379155 0.         0.02440299]\n",
      "Epoch 21, Iteration 0, loss = 1.2167\n",
      "Validation IOU:  [0.64923465 0.0121913  0.03876339 0.         0.02152298]\n",
      "Training IOU:  [0.65508031 0.01634293 0.04399486 0.         0.02439488]\n",
      "Epoch 21, Iteration 25, loss = 1.2802\n",
      "Validation IOU:  [0.65090673 0.01194684 0.03742983 0.         0.02253538]\n",
      "Training IOU:  [0.65703679 0.01611324 0.04210802 0.         0.02563243]\n",
      "Epoch 21, Iteration 50, loss = 1.3193\n",
      "Validation IOU:  [0.65066165 0.01066448 0.03951457 0.         0.02369976]\n",
      "Training IOU:  [0.65552562 0.01297858 0.04517578 0.         0.02557836]\n",
      "Epoch 21, Iteration 75, loss = 1.1663\n",
      "Validation IOU:  [0.65992971 0.00982583 0.03883601 0.         0.02231716]\n",
      "Training IOU:  [0.66549105 0.01367592 0.0441157  0.         0.02495865]\n",
      "Epoch 21, Iteration 100, loss = 1.3441\n",
      "Validation IOU:  [0.64916585 0.01179402 0.03815939 0.         0.02202066]\n",
      "Training IOU:  [0.65638591 0.01681428 0.0427672  0.         0.02575275]\n",
      "Epoch 21, Iteration 125, loss = 1.2176\n",
      "Validation IOU:  [0.6338311  0.0095866  0.04019586 0.         0.02311341]\n",
      "Training IOU:  [0.63921724 0.01358735 0.04529385 0.         0.02590251]\n",
      "Epoch 21, Iteration 150, loss = 1.2684\n",
      "Validation IOU:  [0.65593693 0.01126832 0.03806045 0.         0.02297585]\n",
      "Training IOU:  [0.66226148 0.01529092 0.04297723 0.         0.02471889]\n",
      "Epoch 21, Iteration 175, loss = 1.3076\n",
      "Validation IOU:  [0.65605884 0.01249755 0.03840202 0.         0.02316794]\n",
      "Training IOU:  [0.66456242 0.01683361 0.04458127 0.         0.02527092]\n",
      "Epoch 21, Iteration 200, loss = 1.2337\n",
      "Validation IOU:  [0.64903244 0.01178292 0.03731151 0.         0.0210366 ]\n",
      "Training IOU:  [0.65622471 0.01618125 0.04424845 0.         0.02466863]\n",
      "Epoch 21, Iteration 225, loss = 1.2273\n",
      "Validation IOU:  [0.6505922  0.01089554 0.03929918 0.         0.02245083]\n",
      "Training IOU:  [0.65861096 0.01491151 0.04415048 0.         0.02518161]\n",
      "Epoch 21, Iteration 250, loss = 1.2771\n",
      "Validation IOU:  [0.65319284 0.0116895  0.04065743 0.         0.021607  ]\n",
      "Training IOU:  [0.65966611 0.01534441 0.04621692 0.         0.02480637]\n",
      "Epoch 21, Iteration 275, loss = 1.2708\n",
      "Validation IOU:  [0.63124122 0.01083098 0.03981987 0.         0.02399475]\n",
      "Training IOU:  [0.63472838 0.01546914 0.04420328 0.         0.02586386]\n",
      "Epoch 21, Iteration 300, loss = 1.2836\n",
      "Validation IOU:  [0.62241696 0.01255193 0.03725677 0.         0.02130123]\n",
      "Training IOU:  [0.62751805 0.01691202 0.04218073 0.         0.02363492]\n",
      "Epoch 22, Iteration 0, loss = 1.3130\n",
      "Validation IOU:  [0.61942792 0.01364116 0.03660327 0.         0.02109374]\n",
      "Training IOU:  [0.62694817 0.01692734 0.04240747 0.         0.02357942]\n",
      "Epoch 22, Iteration 25, loss = 1.2051\n",
      "Validation IOU:  [0.64676232 0.01016358 0.04070519 0.         0.02146281]\n",
      "Training IOU:  [0.65184624 0.01418559 0.04587741 0.         0.02492265]\n",
      "Epoch 22, Iteration 50, loss = 1.2172\n",
      "Validation IOU:  [0.6372879  0.00874882 0.04126163 0.         0.02360908]\n",
      "Training IOU:  [0.64613347 0.01354397 0.04669192 0.         0.02606459]\n",
      "Epoch 22, Iteration 75, loss = 1.2458\n",
      "Validation IOU:  [0.6493903  0.01075067 0.03815684 0.         0.0230491 ]\n",
      "Training IOU:  [0.6553442  0.01582528 0.04568713 0.         0.0255257 ]\n",
      "Epoch 22, Iteration 100, loss = 1.3397\n",
      "Validation IOU:  [0.64631255 0.01250089 0.03998386 0.         0.0212174 ]\n",
      "Training IOU:  [0.65525406 0.0169551  0.04576479 0.         0.02489343]\n",
      "Epoch 22, Iteration 125, loss = 1.2769\n",
      "Validation IOU:  [0.62774785 0.0099988  0.03959161 0.         0.02198934]\n",
      "Training IOU:  [0.63556609 0.0146381  0.04519546 0.         0.02514038]\n",
      "Epoch 22, Iteration 150, loss = 1.2423\n",
      "Validation IOU:  [0.65060812 0.01170066 0.04051447 0.         0.02360245]\n",
      "Training IOU:  [0.65906603 0.01577766 0.04569534 0.         0.02599837]\n",
      "Epoch 22, Iteration 175, loss = 1.3647\n",
      "Validation IOU:  [0.64327576 0.01230446 0.03853281 0.         0.02261635]\n",
      "Training IOU:  [0.65104775 0.01626572 0.04561339 0.         0.02442822]\n",
      "Epoch 22, Iteration 200, loss = 1.2272\n",
      "Validation IOU:  [0.66411616 0.01097429 0.03731486 0.         0.02400817]\n",
      "Training IOU:  [0.67160609 0.01574334 0.04379127 0.         0.02602765]\n",
      "Epoch 22, Iteration 225, loss = 1.2556\n",
      "Validation IOU:  [0.64016763 0.01193363 0.03474141 0.         0.02316915]\n",
      "Training IOU:  [0.64713188 0.01554538 0.04090821 0.         0.0252995 ]\n",
      "Epoch 22, Iteration 250, loss = 1.3834\n",
      "Validation IOU:  [0.64823904 0.01080124 0.03672286 0.         0.0224584 ]\n",
      "Training IOU:  [0.65354076 0.01490731 0.04275405 0.         0.02575635]\n",
      "Epoch 22, Iteration 275, loss = 1.1953\n",
      "Validation IOU:  [0.67199802 0.01186749 0.0393557  0.         0.02123708]\n",
      "Training IOU:  [0.67776594 0.01635662 0.04471795 0.         0.0249523 ]\n",
      "Epoch 22, Iteration 300, loss = 1.4468\n",
      "Validation IOU:  [0.65422254 0.01156427 0.03550564 0.         0.02269996]\n",
      "Training IOU:  [0.66082344 0.0155576  0.04177584 0.         0.02517047]\n",
      "Epoch 23, Iteration 0, loss = 1.3027\n",
      "Validation IOU:  [0.65178641 0.01155993 0.03646789 0.         0.0225368 ]\n",
      "Training IOU:  [0.65828885 0.01535668 0.04261446 0.         0.02535476]\n",
      "Epoch 23, Iteration 25, loss = 1.3032\n",
      "Validation IOU:  [0.64348512 0.01134793 0.03773768 0.         0.02060974]\n",
      "Training IOU:  [0.65203465 0.01537708 0.04376162 0.         0.02501104]\n",
      "Epoch 23, Iteration 50, loss = 1.2710\n",
      "Validation IOU:  [0.65623967 0.01091777 0.03631616 0.         0.02315705]\n",
      "Training IOU:  [0.66321353 0.0159542  0.04229476 0.         0.02544622]\n",
      "Epoch 23, Iteration 75, loss = 1.5043\n",
      "Validation IOU:  [0.63254428 0.01142255 0.0405734  0.         0.02143403]\n",
      "Training IOU:  [0.63846346 0.0162967  0.04627422 0.         0.02558776]\n",
      "Epoch 23, Iteration 100, loss = 1.2352\n",
      "Validation IOU:  [0.63424438 0.01178933 0.04104431 0.         0.02148431]\n",
      "Training IOU:  [0.64185129 0.01697316 0.04642777 0.         0.02479619]\n",
      "Epoch 23, Iteration 125, loss = 1.3307\n",
      "Validation IOU:  [0.67289083 0.01127331 0.03708325 0.         0.02288716]\n",
      "Training IOU:  [0.68020299 0.01560255 0.04319654 0.         0.02583424]\n",
      "Epoch 23, Iteration 150, loss = 1.2750\n",
      "Validation IOU:  [0.66124285 0.01088581 0.03947928 0.         0.02243776]\n",
      "Training IOU:  [0.66758831 0.0151889  0.0455474  0.         0.02563577]\n",
      "Epoch 23, Iteration 175, loss = 1.2798\n",
      "Validation IOU:  [0.64211662 0.01300045 0.03986276 0.         0.02097009]\n",
      "Training IOU:  [0.64813019 0.01745535 0.04468021 0.         0.0244955 ]\n",
      "Epoch 23, Iteration 200, loss = 1.2776\n",
      "Validation IOU:  [0.63633808 0.01104869 0.0366667  0.         0.02227027]\n",
      "Training IOU:  [0.64221748 0.01545486 0.04303821 0.         0.02478527]\n",
      "Epoch 23, Iteration 225, loss = 1.1610\n",
      "Validation IOU:  [0.62378087 0.0109133  0.0390657  0.         0.022508  ]\n",
      "Training IOU:  [0.631259   0.01606191 0.04429581 0.         0.02526258]\n",
      "Epoch 23, Iteration 250, loss = 1.3479\n",
      "Validation IOU:  [0.65139237 0.01099546 0.03852826 0.         0.02217425]\n",
      "Training IOU:  [0.65864978 0.01551107 0.04411147 0.         0.02515119]\n",
      "Epoch 23, Iteration 275, loss = 1.2294\n",
      "Validation IOU:  [0.64239565 0.01133412 0.04216666 0.         0.02348614]\n",
      "Training IOU:  [0.64799997 0.01560544 0.04694924 0.         0.02608153]\n",
      "Epoch 23, Iteration 300, loss = 1.2590\n",
      "Validation IOU:  [0.63477879 0.01084793 0.03638845 0.         0.02310854]\n",
      "Training IOU:  [0.64310749 0.01610046 0.04245867 0.         0.02613154]\n",
      "Epoch 24, Iteration 0, loss = 1.3686\n",
      "Validation IOU:  [0.63676897 0.01108945 0.03613616 0.         0.02331568]\n",
      "Training IOU:  [0.64301865 0.01612202 0.04193225 0.         0.02630094]\n",
      "Epoch 24, Iteration 25, loss = 1.2044\n",
      "Validation IOU:  [0.63161254 0.01134569 0.03997068 0.         0.02203187]\n",
      "Training IOU:  [0.63678099 0.01703291 0.04636943 0.         0.02593312]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24, Iteration 50, loss = 1.2948\n",
      "Validation IOU:  [0.63955298 0.01242514 0.03642313 0.         0.02181789]\n",
      "Training IOU:  [0.64941373 0.01745519 0.0422634  0.         0.02490478]\n",
      "Epoch 24, Iteration 75, loss = 1.3712\n",
      "Validation IOU:  [0.66689406 0.00895986 0.03923598 0.         0.0220317 ]\n",
      "Training IOU:  [0.67236767 0.01397638 0.04620349 0.         0.02548956]\n",
      "Epoch 24, Iteration 100, loss = 1.2892\n",
      "Validation IOU:  [0.66379527 0.01228699 0.03831337 0.         0.02142905]\n",
      "Training IOU:  [0.67330471 0.01648299 0.0439803  0.         0.02392743]\n",
      "Epoch 24, Iteration 125, loss = 1.4138\n",
      "Validation IOU:  [0.67156567 0.01137529 0.03740218 0.         0.02161711]\n",
      "Training IOU:  [0.67924302 0.01619086 0.04261686 0.         0.02501463]\n",
      "Epoch 24, Iteration 150, loss = 1.2848\n",
      "Validation IOU:  [0.6642418  0.01115063 0.04000319 0.         0.02194752]\n",
      "Training IOU:  [0.67094479 0.01606    0.04498249 0.         0.02504284]\n",
      "Epoch 24, Iteration 175, loss = 1.1807\n",
      "Validation IOU:  [0.65606427 0.01085693 0.04011017 0.         0.02348858]\n",
      "Training IOU:  [0.66091872 0.01550614 0.04636633 0.         0.02537978]\n",
      "Epoch 24, Iteration 200, loss = 1.3344\n",
      "Validation IOU:  [0.65361341 0.01155159 0.0416803  0.         0.0226579 ]\n",
      "Training IOU:  [0.65873113 0.01593572 0.04663511 0.         0.02544703]\n",
      "Epoch 24, Iteration 225, loss = 1.1879\n",
      "Validation IOU:  [0.6598471  0.0116296  0.03869768 0.         0.02311532]\n",
      "Training IOU:  [0.66759555 0.01602356 0.0459599  0.         0.02625152]\n",
      "Epoch 24, Iteration 250, loss = 1.2102\n",
      "Validation IOU:  [0.6495105  0.01313533 0.03911233 0.         0.02123147]\n",
      "Training IOU:  [0.6562181  0.01804855 0.04486807 0.         0.02509262]\n",
      "Epoch 24, Iteration 275, loss = 1.3471\n",
      "Validation IOU:  [0.64550494 0.01019424 0.0385582  0.         0.02398241]\n",
      "Training IOU:  [0.65272723 0.01444955 0.04408082 0.         0.02608593]\n",
      "Epoch 24, Iteration 300, loss = 1.3304\n",
      "Validation IOU:  [0.63689756 0.01085841 0.03491115 0.         0.02274039]\n",
      "Training IOU:  [0.64547666 0.01523649 0.04069388 0.         0.02568553]\n",
      "Epoch 25, Iteration 0, loss = 1.3283\n",
      "Validation IOU:  [0.63185642 0.01164259 0.03421893 0.         0.02311176]\n",
      "Training IOU:  [0.64186026 0.01514874 0.0400542  0.         0.02576989]\n",
      "Epoch 25, Iteration 25, loss = 1.2238\n",
      "Validation IOU:  [0.64157233 0.01072731 0.03844634 0.         0.02290985]\n",
      "Training IOU:  [0.64876322 0.01534275 0.04409527 0.         0.02592322]\n",
      "Epoch 25, Iteration 50, loss = 1.4000\n",
      "Validation IOU:  [0.64383389 0.01117066 0.03882821 0.         0.02373749]\n",
      "Training IOU:  [0.64971033 0.0161659  0.04344842 0.         0.02536054]\n",
      "Epoch 25, Iteration 75, loss = 1.3215\n",
      "Validation IOU:  [0.64168336 0.00814362 0.03963026 0.         0.0212231 ]\n",
      "Training IOU:  [0.64937242 0.01262706 0.04588145 0.         0.02452959]\n",
      "Epoch 25, Iteration 100, loss = 1.2265\n",
      "Validation IOU:  [0.63711363 0.01152757 0.03934193 0.         0.0233699 ]\n",
      "Training IOU:  [0.64391352 0.01629463 0.04454637 0.         0.02626884]\n",
      "Epoch 25, Iteration 125, loss = 1.3049\n",
      "Validation IOU:  [0.63070476 0.01264133 0.0368104  0.         0.02148592]\n",
      "Training IOU:  [0.63972145 0.01699136 0.04273548 0.         0.02531515]\n",
      "Epoch 25, Iteration 150, loss = 1.2176\n",
      "Validation IOU:  [0.62918739 0.00998505 0.03768659 0.         0.02304366]\n",
      "Training IOU:  [0.63565955 0.01416627 0.04419399 0.         0.02533953]\n",
      "Epoch 25, Iteration 175, loss = 1.2504\n",
      "Validation IOU:  [0.66495345 0.01157262 0.03666905 0.         0.0219342 ]\n",
      "Training IOU:  [0.67325601 0.01619714 0.04232018 0.         0.02518256]\n",
      "Epoch 25, Iteration 200, loss = 1.2038\n",
      "Validation IOU:  [0.64760628 0.01121085 0.03639178 0.         0.02247501]\n",
      "Training IOU:  [0.65329489 0.01613102 0.04414786 0.         0.02624861]\n",
      "Epoch 25, Iteration 225, loss = 1.3000\n",
      "Validation IOU:  [0.66382318 0.01101208 0.03783092 0.         0.02239638]\n",
      "Training IOU:  [0.67020859 0.0162586  0.04394606 0.         0.02567972]\n",
      "Epoch 25, Iteration 250, loss = 1.1876\n",
      "Validation IOU:  [0.68161871 0.01248561 0.03751192 0.         0.02160098]\n",
      "Training IOU:  [0.68694793 0.01802418 0.04498426 0.         0.02607739]\n",
      "Epoch 25, Iteration 275, loss = 1.3353\n",
      "Validation IOU:  [0.68268101 0.01079636 0.03658401 0.         0.02292498]\n",
      "Training IOU:  [0.69048788 0.01704629 0.04391105 0.         0.02575583]\n",
      "Epoch 25, Iteration 300, loss = 1.1852\n",
      "Validation IOU:  [0.64675317 0.01132993 0.03928242 0.         0.02326211]\n",
      "Training IOU:  [0.65217884 0.01600867 0.04492796 0.         0.02567801]\n",
      "Epoch 26, Iteration 0, loss = 1.2199\n",
      "Validation IOU:  [0.64812978 0.01083112 0.04100332 0.         0.024909  ]\n",
      "Training IOU:  [0.65140549 0.01588716 0.04505128 0.         0.02577437]\n",
      "Epoch 26, Iteration 25, loss = 1.2238\n",
      "Validation IOU:  [0.64456506 0.01073047 0.03718578 0.         0.02218643]\n",
      "Training IOU:  [0.6529357  0.01606645 0.04356094 0.         0.02552396]\n",
      "Epoch 26, Iteration 50, loss = 1.1821\n",
      "Validation IOU:  [0.6631952  0.01089396 0.03701739 0.         0.02234926]\n",
      "Training IOU:  [0.67061833 0.01700309 0.04466259 0.         0.02536988]\n",
      "Epoch 26, Iteration 75, loss = 1.2768\n",
      "Validation IOU:  [0.64766409 0.01224225 0.04042225 0.         0.02236475]\n",
      "Training IOU:  [0.65485501 0.01755134 0.04748477 0.         0.02596343]\n",
      "Epoch 26, Iteration 100, loss = 1.2127\n",
      "Validation IOU:  [0.66426188 0.01059397 0.04077891 0.         0.02380666]\n",
      "Training IOU:  [0.67240401 0.01567073 0.04686835 0.         0.02644596]\n",
      "Epoch 26, Iteration 125, loss = 1.2506\n",
      "Validation IOU:  [0.66656543 0.01016534 0.03805468 0.         0.02358111]\n",
      "Training IOU:  [0.67369657 0.01605442 0.04592009 0.         0.02624957]\n",
      "Epoch 26, Iteration 150, loss = 1.3930\n",
      "Validation IOU:  [0.66681026 0.012196   0.04135899 0.         0.02276919]\n",
      "Training IOU:  [0.67410011 0.01807872 0.04806843 0.         0.02651695]\n",
      "Epoch 26, Iteration 175, loss = 1.2414\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-9deccbe5882c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0;31m# if t % plot_every == 0:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m                 \u001b[0;31m# plot_output(scores, labels)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m250\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<ipython-input-46-9deccbe5882c>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, optimizer, epochs)\u001b[0m\n\u001b[1;32m     30\u001b[0m                 \u001b[0mval_iou\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_iou\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m                 \u001b[0mval_iou_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_iou\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m                 \u001b[0mtrain_iou\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_iou\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m                 \u001b[0mtrain_iou_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_iou\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Validation IOU: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_iou\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-43-1efe0f204942>\u001b[0m in \u001b[0;36mcheck_iou\u001b[0;34m(loader, model)\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m             \u001b[0mall_iou\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miou_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\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     12\u001b[0m     \u001b[0;31m# print(np.mean(all_iou, axis=0))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_iou\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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<ipython-input-42-e3f2a41f2f39>\u001b[0m in \u001b[0;36miou_metric\u001b[0;34m(preds, labels)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# preds and labels are of size (64, 32, 32)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m# return average iou value for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mnumpy_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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      5\u001b[0m     \u001b[0mnumpy_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import torch.nn.functional as F\n",
    "loss_history = []\n",
    "val_iou_history = []\n",
    "train_iou_history = []\n",
    "plot_every = 100\n",
    "def train(model, optimizer, epochs=1):\n",
    "    model = model.to(device=device)\n",
    "    loss_history.clear()\n",
    "    val_iou_history.clear()\n",
    "    train_iou_history.clear()\n",
    "    for e in range(epochs):\n",
    "        for t, (x,y) in enumerate(train_loader):\n",
    "            model.train()\n",
    "            x = x.to(device=device, dtype=dtype)\n",
    "            labels = y.to(device=device, dtype=torch.long).view(y.shape[0], 32, 32)\n",
    "            # print(labels.shape)\n",
    "\n",
    "            scores = model(x)\n",
    "            # print(scores.shape)\n",
    "\n",
    "            loss = criterion(scores, labels)\n",
    "            # loss = F.cross_entropy(scores, labels, weight=torch.FloatTensor([1/(float(26265600-1762336)), 1/(float(1762336))]).cuda())\n",
    "            loss_history.append(loss.item())\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            # print(t)\n",
    "            if t % print_every == 0:\n",
    "                print('Epoch %d, Iteration %d, loss = %.4f' % (e, t, loss.item()))\n",
    "                val_iou = check_iou(val_loader, model)\n",
    "                val_iou_history.append(val_iou)\n",
    "                train_iou = check_iou(train_loader, model)\n",
    "                train_iou_history.append(train_iou)\n",
    "                print('Validation IOU: ', val_iou)\n",
    "                print('Training IOU: ', train_iou)\n",
    "            # if t % plot_every == 0:\n",
    "                # plot_output(scores, labels)\n",
    "train(model, optimizer, epochs=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.title('Loss over time')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(range(len(loss_history))[0::100], loss_history[0::100])\n",
    "fig.savefig('Loss over time (5 classes).png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(346, 5)\n",
      "(345, 5)\n"
     ]
    }
   ],
   "source": [
    "val_hist = np.asarray(val_iou_history)\n",
    "train_hist = np.asarray(train_iou_history)\n",
    "print(val_hist.shape)\n",
    "print(train_hist.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.title('IOU for no tumor over time')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('IOU')\n",
    "plt.plot(range(val_hist.shape[0]), val_hist[:,0], label='val')\n",
    "plt.plot(range(train_hist.shape[0]), train_hist[:,0], label='train')\n",
    "plt.legend()\n",
    "fig.savefig('IOU for no tumor (5 classes) - Poster.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.title('IOU for tumor 1 over time')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('IOU')\n",
    "plt.plot(range(val_hist.shape[0]), val_hist[:,1], label='val')\n",
    "plt.plot(range(train_hist.shape[0]), train_hist[:,1], label='train')\n",
    "plt.legend()\n",
    "fig.savefig('IOU for tumor 1 (5 classes) - Poster.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.title('IOU for tumor 2 over time')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('IOU')\n",
    "plt.plot(range(val_hist.shape[0]), val_hist[:,2], label='val')\n",
    "plt.plot(range(train_hist.shape[0]), train_hist[:,2], label='train')\n",
    "plt.legend()\n",
    "fig.savefig('IOU for tumor 2 (5 classes) - Poster.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.title('IOU for tumor 4 over time')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('IOU')\n",
    "plt.plot(range(val_hist.shape[0]), val_hist[:,4], label='val')\n",
    "plt.plot(range(train_hist.shape[0]), train_hist[:,4], label='train')\n",
    "plt.legend()\n",
    "fig.savefig('IOU for tumor 4 (5 classes) - Poster.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.65621317, 0.01421017, 0.04089381, 0.        , 0.02031137])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_iou(test_loader, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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