Localization_Model_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Nbz2xj6UT42"
   },
   "source": [
    "On final dataset, using only labeled images ; Resnet18 -> Linear -> LeakyRelu -> Linear\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_FsPlBygKHxW"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from torch.utils.data import Dataset\n",
    "from torch.optim import lr_scheduler\n",
    "from torchvision import datasets, models, transforms\n",
    "from PIL import Image\n",
    "import random\n",
    "import cv2\n",
    "import progressbar\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "1RC2tw5lKHxb",
    "outputId": "6ad997aa-b34c-4b3d-90db-269b1476fac6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8090 entries, 0 to 8089\n",
      "Data columns (total 3 columns):\n",
      "image      8090 non-null object\n",
      "points     8090 non-null object\n",
      "labeled    8090 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 189.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\"data/dataset.pkl\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "GjxrbTL_FxuN",
    "outputId": "ad073d6d-0ac5-439c-ba78-e863b56520cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>points</th>\n",
       "      <th>labeled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/dactar/1300_img.png</td>\n",
       "      <td>[177.16228095049055, 184.83182101741053, 244.7...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/dactar/1766_img.png</td>\n",
       "      <td>[409.7109787838158, 362.74050003943523, 536.55...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/dactar/130_img.png</td>\n",
       "      <td>[386.66894459252546, 342.1963080587535, 503.59...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/dactar/1785_img.png</td>\n",
       "      <td>[197.75577726950075, 171.98786970581278, 250.1...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/dactar/617_img.png</td>\n",
       "      <td>[180.14505945291972, 298.14972872833135, 288.5...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      image  \\\n",
       "0  data/dactar/1300_img.png   \n",
       "1  data/dactar/1766_img.png   \n",
       "2   data/dactar/130_img.png   \n",
       "3  data/dactar/1785_img.png   \n",
       "4   data/dactar/617_img.png   \n",
       "\n",
       "                                              points  labeled  \n",
       "0  [177.16228095049055, 184.83182101741053, 244.7...        1  \n",
       "1  [409.7109787838158, 362.74050003943523, 536.55...        1  \n",
       "2  [386.66894459252546, 342.1963080587535, 503.59...        1  \n",
       "3  [197.75577726950075, 171.98786970581278, 250.1...        1  \n",
       "4  [180.14505945291972, 298.14972872833135, 288.5...        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "FZ5asiMGFzvo",
    "outputId": "c22bd406-cdb3-4a01-ae66-37f804aba3ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5576 entries, 0 to 7440\n",
      "Data columns (total 3 columns):\n",
      "image      5576 non-null object\n",
      "points     5576 non-null object\n",
      "labeled    5576 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 174.2+ KB\n"
     ]
    }
   ],
   "source": [
    "todrop = []\n",
    "for i in range(len(df)):\n",
    "  if df.iloc[i].labeled == 0:\n",
    "    todrop.append(i)\n",
    "df = df.drop(index=todrop)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "KXUL_4UwGSfj",
    "outputId": "993e335c-949e-4dd5-dab3-8fc8c2a6ea28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5576 entries, 0 to 5575\n",
      "Data columns (total 2 columns):\n",
      "image     5576 non-null object\n",
      "points    5576 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(columns=['labeled'])\n",
    "df = df.reset_index(drop=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "INMko7t7KHxk"
   },
   "source": [
    "### Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3TywrFzPKHxl"
   },
   "outputs": [],
   "source": [
    "class AverageMeter(object):\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.cnt = 0\n",
    "        \n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.cnt += n\n",
    "        self.avg = self.sum / self.cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5-mKB_zRKHxo"
   },
   "source": [
    "### Image Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3Xe7PAzKKHxo"
   },
   "outputs": [],
   "source": [
    "class ResizeAspect(object):\n",
    "    def __init__(self, h, w):\n",
    "        self.rescale_factor=None\n",
    "        self.shift_w=None\n",
    "        self.shift_h=None\n",
    "        self.hw = (h, w)\n",
    "        \n",
    "    def do_image(self,img):\n",
    "        h, w = self.hw\n",
    "        img_h, img_w = img.shape[0], img.shape[1]\n",
    "        rescale_factor = min(w/img_w, h/img_h)\n",
    "        new_w = int(img_w * rescale_factor)\n",
    "        new_h = int(img_h * rescale_factor)\n",
    "        resized_image = cv2.resize(img, (new_w,new_h), interpolation = cv2.INTER_CUBIC)\n",
    "\n",
    "        canvas = np.full((h, w, 3), 128, dtype=np.uint8)\n",
    "        shift_h = (h-new_h)//2\n",
    "        shift_w = (w-new_w)//2\n",
    "        canvas[(h-new_h)//2:(h-new_h)//2 + new_h,(w-new_w)//2:(w-new_w)//2 + new_w,  :] = resized_image\n",
    "        img = canvas.copy()\n",
    "        self.rescale_factor=rescale_factor\n",
    "        self.shift_h = shift_h\n",
    "        self.shift_w = shift_w\n",
    "        return img\n",
    "    \n",
    "    def do_box(self, box):\n",
    "#         if self.rescale_factor is None:\n",
    "#             print('The image is not scaled, do_image first!!')\n",
    "#             return\n",
    "        box = box.reshape(-1,2)\n",
    "        box *=self.rescale_factor\n",
    "        box[: ,0] += self.shift_w\n",
    "        box[: ,1] += self.shift_h\n",
    "        box = box.reshape(-1)\n",
    "        return box\n",
    "    \n",
    "    def undo_box(self, box):\n",
    "        box = box.reshape(-1,2)\n",
    "        box[: ,0] -= self.shift_w\n",
    "        box[: ,1] -= self.shift_h\n",
    "        box /=self.rescale_factor\n",
    "        box = box.reshape(-1)\n",
    "        return box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9szOaV5wKHxs"
   },
   "outputs": [],
   "source": [
    "class FinalTransform:\n",
    "    def __init__(self):\n",
    "        self.transform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])])\n",
    "    \n",
    "    def transform_inv(self,img):\n",
    "        inp = img.numpy().transpose((1, 2, 0))\n",
    "        mean = np.array([0.485, 0.456, 0.406])\n",
    "        std = np.array([0.229, 0.224, 0.225])\n",
    "        inp = std * inp + mean\n",
    "        inp = np.clip(inp, 0, 1)\n",
    "        return inp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "niEfDCspKHxv"
   },
   "source": [
    "### Dataset Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jsd-VuCkKHxv"
   },
   "outputs": [],
   "source": [
    "class LicenseDataset(Dataset):\n",
    "    def __init__(self, df):\n",
    "        \n",
    "        self.imgs = list(df.image)\n",
    "        self.boxes = df.points.tolist()\n",
    "#         self.labeled = df.labeled.tolist()\n",
    "        self.final_transform = FinalTransform()\n",
    "        self.transform = self.final_transform.transform\n",
    "#         self.transform = transforms.Compose([\n",
    "# #                 transforms.Resize((224, 224)),\n",
    "#             transforms.ToTensor(),\n",
    "#             transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "#                                  std=[0.229, 0.224, 0.225])\n",
    "#         ])\n",
    "        \n",
    "    def transform_inv(self,img):\n",
    "        inp = img.numpy().transpose((1, 2, 0))\n",
    "        mean = np.array([0.485, 0.456, 0.406])\n",
    "        std = np.array([0.229, 0.224, 0.225])\n",
    "        inp = std * inp + mean\n",
    "        inp = np.clip(inp, 0, 1)\n",
    "        return inp\n",
    "            \n",
    "    def __getitem__(self, index):\n",
    "        path= self.imgs[index]\n",
    "        box = self.boxes[index]\n",
    "        \n",
    "        img = Image.open(path).convert('RGB')\n",
    "        resizer = ResizeAspect(h=224, w=224)\n",
    "        img = resizer.do_image(np.array(img))\n",
    "        img = self.final_transform.transform(img)\n",
    "        box = resizer.do_box(box)\n",
    "        box = np.array(box, dtype=np.float32)\n",
    "        \n",
    "#         return img, box , resizer\n",
    "        return img, box\n",
    "      \n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YI_je_5WKHx0"
   },
   "outputs": [],
   "source": [
    "def train_test_split(df, test_size=0.1):\n",
    "    if isinstance(test_size, float):\n",
    "        test_size = int(test_size * len(df))\n",
    "\n",
    "    indices = df.index.tolist()\n",
    "    test_indices = random.sample(population=indices, k=test_size)\n",
    "\n",
    "    test_df = df.loc[test_indices]\n",
    "    train_df = df.drop(test_indices)\n",
    "    return train_df, test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ElXaECDIKHx6"
   },
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(df, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q__KgwU1KHyB"
   },
   "outputs": [],
   "source": [
    "train = LicenseDataset(train_df)\n",
    "test = LicenseDataset(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KV6BKiTGKHyF"
   },
   "outputs": [],
   "source": [
    "# ind = random.choice(range(len(train_df)))\n",
    "# img, box, img_size = train[ind]\n",
    "\n",
    "# box = np.append(box, box[:2]).reshape(-1,2)\n",
    "# img = train.final_transform.transform_inv(img)\n",
    "\n",
    "# plt.imshow(img)\n",
    "# plt.plot(box[:,0], box[:,1], lw=3, c='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jKrQ6UJlKHyI"
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "                train, batch_size=32,shuffle=True,\n",
    "                num_workers=4, pin_memory=True)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "                test, batch_size=32,shuffle=False,\n",
    "                num_workers=2, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LpduTwnOKHyL"
   },
   "source": [
    "### Defining Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "48sFrjXrKHyL"
   },
   "outputs": [],
   "source": [
    "model = models.resnet18(pretrained=True)\n",
    "'''\n",
    "output of our model is :\n",
    "x1, y1,\n",
    "x2, y2,\n",
    "x3, y3,\n",
    "x4, y4,\n",
    "conf -> only when no bounding box images are taken\n",
    "'''\n",
    "num_feature = model.fc.in_features\n",
    "num_hidden = 370\n",
    "num_output = 8#9\n",
    "model.fc = nn.Sequential(nn.Linear(num_feature, num_hidden),\n",
    "                         nn.LeakyReLU(),\n",
    "                         nn.Linear(num_hidden, num_output)\n",
    "                        )\n",
    "# model.fc = nn.Linear(num_feature, num_output)\n",
    "# model.eval()\n",
    "# model.train()\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XczjvxsXKHyU"
   },
   "outputs": [],
   "source": [
    "MPATH = 'saved_states/model_state_v1.pth'\n",
    "# model.load_state_dict(torch.load(MPATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VteKJnm3KHyO"
   },
   "outputs": [],
   "source": [
    "criterion = nn.SmoothL1Loss().cuda()\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mVAZoY1gKHyR"
   },
   "outputs": [],
   "source": [
    "best_model_state = model.state_dict()\n",
    "best_epoch = -1\n",
    "best_acc = 0.0\n",
    "\n",
    "epoch_loss = {True: [], False: []}\n",
    "epoch_acc = {True: [], False: []}\n",
    "epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1717
    },
    "colab_type": "code",
    "id": "gkhuk8mCKHyW",
    "outputId": "c0561d00-f7aa-4cc9-c1e0-42cf3861acd0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:03:00 Time:  0:03:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 1/20\tLoss: 12.1433\tAcc: 0.00%\tTime: 184.635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:42 Time:  0:00:42\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 1/20\tLoss: 11.9437\tAcc: 0.00%\tTime: 48.262\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:01:02 Time:  0:01:02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 2/20\tLoss: 8.9632\tAcc: 0.00%\tTime: 63.814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 2/20\tLoss: 8.5710\tAcc: 0.00%\tTime: 6.921\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 3/20\tLoss: 5.3099\tAcc: 0.00%\tTime: 62.366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 3/20\tLoss: 5.3647\tAcc: 0.00%\tTime: 6.739\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 4/20\tLoss: 4.1719\tAcc: 0.00%\tTime: 62.958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 4/20\tLoss: 4.0917\tAcc: 0.00%\tTime: 6.957\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 5/20\tLoss: 3.9659\tAcc: 0.00%\tTime: 63.686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 5/20\tLoss: 3.9185\tAcc: 0.00%\tTime: 7.051\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 6/20\tLoss: 4.0040\tAcc: 0.00%\tTime: 65.332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 6/20\tLoss: 3.9394\tAcc: 0.00%\tTime: 7.120\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 7/20\tLoss: 3.7059\tAcc: 0.00%\tTime: 64.855\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 7/20\tLoss: 3.6640\tAcc: 0.00%\tTime: 7.063\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 8/20\tLoss: 3.5729\tAcc: 0.00%\tTime: 64.027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 8/20\tLoss: 3.5440\tAcc: 0.00%\tTime: 7.081\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 9/20\tLoss: 3.3037\tAcc: 0.00%\tTime: 64.136\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 9/20\tLoss: 3.2543\tAcc: 0.00%\tTime: 7.003\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 10/20\tLoss: 3.2550\tAcc: 0.00%\tTime: 64.735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 10/20\tLoss: 3.2027\tAcc: 0.00%\tTime: 6.940\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:01:02 Time:  0:01:02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 11/20\tLoss: 3.1531\tAcc: 0.00%\tTime: 63.566\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 11/20\tLoss: 3.1052\tAcc: 0.00%\tTime: 7.194\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:01:04 Time:  0:01:04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 12/20\tLoss: 3.1115\tAcc: 0.00%\tTime: 65.764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 12/20\tLoss: 3.0616\tAcc: 0.00%\tTime: 6.933\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:01:02 Time:  0:01:02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 13/20\tLoss: 3.0893\tAcc: 0.00%\tTime: 63.847\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 13/20\tLoss: 3.0323\tAcc: 0.00%\tTime: 6.986\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:01:02 Time:  0:01:02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 14/20\tLoss: 3.0691\tAcc: 0.00%\tTime: 64.430\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:05 Time:  0:00:05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 14/20\tLoss: 3.0253\tAcc: 0.00%\tTime: 6.564\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:00:59 Time:  0:00:59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 15/20\tLoss: 3.0126\tAcc: 0.00%\tTime: 61.318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 15/20\tLoss: 2.9631\tAcc: 0.00%\tTime: 6.814\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 16/20\tLoss: 2.8875\tAcc: 0.00%\tTime: 63.746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 16/20\tLoss: 2.8499\tAcc: 0.00%\tTime: 7.022\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:00:59 Time:  0:00:59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 17/20\tLoss: 2.9613\tAcc: 0.00%\tTime: 60.957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 17/20\tLoss: 2.9138\tAcc: 0.00%\tTime: 6.729\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 18/20\tLoss: 2.8708\tAcc: 0.00%\tTime: 61.197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 18/20\tLoss: 2.8321\tAcc: 0.00%\tTime: 7.323\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (157 of 157) |######################| Elapsed Time: 0:00:59 Time:  0:00:59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 19/20\tLoss: 2.9453\tAcc: 0.00%\tTime: 61.115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 19/20\tLoss: 2.9103\tAcc: 0.00%\tTime: 6.880\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100% (157 of 157) |######################| Elapsed Time: 0:00:58 Time:  0:00:58\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True]\tEpoch: 20/20\tLoss: 2.9569\tAcc: 0.00%\tTime: 60.280\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (18 of 18) |########################| Elapsed Time: 0:00:06 Time:  0:00:06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\tEpoch: 20/20\tLoss: 2.9286\tAcc: 0.00%\tTime: 6.974\n",
      "[Info] best test acc: 0.00% at -1th epoch\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    accs = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    for train_mode in (True, False):\n",
    "        if train_mode:\n",
    "            scheduler.step()\n",
    "            data_loader = train_loader\n",
    "        else:\n",
    "            data_loader = test_loader\n",
    "        model.train(mode=train_mode)\n",
    "            \n",
    "        end = time.time()\n",
    "        bar = progressbar.ProgressBar()\n",
    "        for ims, boxes in bar(data_loader):\n",
    "            \n",
    "            inputs = ims.cuda()\n",
    "            targets = boxes.cuda()\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # forward\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, targets)\n",
    "#             acc = compute_acc(outputs.data.cpu(), targets.data.cpu(), im_sizes)\n",
    "            \n",
    "            nsample = inputs.size(0)\n",
    "#             accs.update(acc, nsample)\n",
    "            losses.update(loss.data, nsample)\n",
    "            \n",
    "            if train_mode:\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "        \n",
    "#         if not train_mode and accs.avg > best_acc:\n",
    "#             best_acc = accs.avg\n",
    "#             best_epoch = epoch\n",
    "#             best_model_state = model.state_dict()\n",
    "            \n",
    "        elapsed_time = time.time() - end\n",
    "        print('[{}]\\tEpoch: {}/{}\\tLoss: {:.4f}\\tAcc: {:.2%}\\tTime: {:.3f}'.format(\n",
    "            train_mode, epoch+1, epochs, losses.avg, accs.avg, elapsed_time))\n",
    "        epoch_loss[train_mode].append(losses.avg)\n",
    "        epoch_acc[train_mode].append(accs.avg)\n",
    "        \n",
    "    print('[Info] best test acc: {:.2%} at {}th epoch'.format(best_acc, best_epoch))\n",
    "    torch.save(model.state_dict(), MPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hoxzZgW5Tt1r",
    "outputId": "064b2d4d-8b56-4776-a47a-4e0760558536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working in test mode\n"
     ]
    }
   ],
   "source": [
    "test_batch = 0\n",
    "indx = -1\n",
    "model.eval()\n",
    "print('Working in test mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "colab_type": "code",
    "id": "aRB_bJN6U7Z0",
    "outputId": "55d800a6-3371-4d8c-8a14-4669dfb01d15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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WZM4Y0WhctvzFBrNcWah1nxl65qyxqnrNPssYL221O3zfbKIDZoznZBqjEdMy4xwnGIed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z2KZ6UVdWWWWVVfbs2ZmhYXxoaLhnksSPnrl50ilWkCrXIAirholPMVSYJAqnJSH9SpPrjdz2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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if indx < 0:\n",
    "  # model.train(mode=False)\n",
    "  for i,(ims, boxes) in enumerate(test_loader):\n",
    "    if i<test_batch:\n",
    "      continue\n",
    "    inputs = ims.cuda()\n",
    "    targets = boxes.cuda()\n",
    "    outputs = model(inputs)\n",
    "    test_batch +=1\n",
    "    break\n",
    "    \n",
    "indx +=1\n",
    "img = inputs[indx].data.cpu()\n",
    "img = FinalTransform().transform_inv(img)\n",
    "out = outputs[indx].data.cpu().numpy()\n",
    "out = np.append(out, out[:2]).reshape(-1,2)\n",
    "targ = targets[indx].data.cpu().numpy()\n",
    "targ = np.append(targ, targ[:2]).reshape(-1,2)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.imshow(img)\n",
    "plt.plot(targ[:,0], targ[:,1], lw=3, c='g')\n",
    "plt.plot(out[:,0], out[:,1], lw=3, c='r')\n",
    "print(indx)\n",
    "if indx == 31:\n",
    "  indx = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1i-BSsbTTfuW"
   },
   "outputs": [],
   "source": [
    "# i+=1\n",
    "# img = inputs[i].data.cpu()\n",
    "# img = FinalTransform().transform_inv(img)\n",
    "# out = outputs[i].data.cpu().numpy()\n",
    "# out = np.append(out, out[:2]).reshape(-1,2)\n",
    "\n",
    "# plt.figure(figsize=(8, 8))\n",
    "# plt.imshow(img)\n",
    "# plt.plot(out[:,0], out[:,1], lw=3, c='g')\n",
    "# i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jC0m718jgs_F"
   },
   "outputs": [],
   "source": [
    "model_state = model.state_dict()\n",
    "torch.save(model_state, MPATH)\n",
    "# optimizer_state = optimizer.state_dict()\n",
    "# torch.save(optimizer_state, 'optimizer_state_v1.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QZDh9D0UOFG2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Localization_Model_v1.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}