{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "import schedulefree\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "#from torchinfo import summary\n",
    "import torch.optim as optim\n",
    "from functools import partial\n",
    "assert torch.cuda.is_available()\n",
    "\n",
    "# Not always necessary depending on your hardware/GPU\n",
    "#os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:512\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 512\n",
    "input_height = 512\n",
    "MODEL_SCALE = 4\n",
    "workers = 8\n",
    "batch_size = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image image_1109.png\n",
      "Image shape = (512, 512, 3)\n",
      "            name  img_width  img_height      x      y      w      l     angle  \\\n",
      "1178  image_1109        512         512  277.0  256.0  46.27  62.21 -0.705712   \n",
      "\n",
      "          h  \n",
      "1178  62.21  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f90c83bc910>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_folder = 'images_augmented/'\n",
    "labels_file = \"train.csv\"\n",
    "\n",
    "# Pick up a random image\n",
    "IMAGE = random.choice(os.listdir(dataset_folder))\n",
    "\n",
    "print(f\"Image {IMAGE}\")\n",
    "\n",
    "img = cv2.imread(os.path.join(dataset_folder, IMAGE))\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "\n",
    "# Load labels\n",
    "train_df = pd.read_csv(labels_file)\n",
    "train_df['x'] = train_df['x'].astype(float)\n",
    "train_df['y'] = train_df['y'].astype(float)\n",
    "train_df['w'] = train_df['w'].astype(float)\n",
    "train_df['h'] = train_df['l'].astype(float)\n",
    "train_df['angle'] = train_df['angle'].astype(float)\n",
    "\n",
    "print(f\"Image shape = {img.shape}\")\n",
    "\n",
    "target = train_df[train_df['name']==IMAGE[:-4]]\n",
    "print(target)\n",
    "\n",
    "# convert targets to its center.\n",
    "centers = np.array([target[\"x\"], target[\"y\"]]).T\n",
    "\n",
    "rotations = []\n",
    "for angle in target[\"angle\"]:\n",
    "    cos_angle = np.cos(angle)\n",
    "    sin_angle = np.sin(angle)\n",
    "    rotations.append(np.array([[cos_angle, sin_angle], [-sin_angle, cos_angle]]))\n",
    "\n",
    "bboxs = target[[\"x\", \"y\", \"w\", \"l\"]].to_numpy()\n",
    "\n",
    "for center, rot, box in zip(centers, rotations, bboxs):\n",
    "    bottom_right = np.dot(rot, np.array([box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "    top_right = np.dot(rot, np.array([box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "    top_left = np.dot(rot, np.array([-box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "    bottom_left = np.dot(rot, np.array([-box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "    \n",
    "    br = (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1]))\n",
    "    tr = (int(center[0]+top_right[0]), int(center[1]+top_right[1]))\n",
    "    tl = (int(center[0]+top_left[0]), int(center[1]+top_left[1]))\n",
    "    bl = (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1]))\n",
    "\n",
    "    thickness = 3\n",
    "    cv2.line(img, br, tr, (0, 220, 0), thickness)\n",
    "    cv2.line(img, br, bl, (220, 220, 0), thickness)\n",
    "    cv2.line(img, tl, bl, (220, 220, 0), thickness)\n",
    "    cv2.line(img, tl, tr, (220, 220, 0), thickness)\n",
    "\n",
    "#plt.figure(figsize=(14,14))\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_msra_gaussian(heatmap, center, sigma=6):\n",
    "    tmp_size = sigma * 6\n",
    "    mu_x = int(center[0])\n",
    "    mu_y = int(center[1])\n",
    "    w, h = heatmap.shape[0], heatmap.shape[1]\n",
    "    ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]\n",
    "    br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]\n",
    "    if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0:\n",
    "        return heatmap\n",
    "    size = br[0] - ul[0]\n",
    "    x = np.arange(0, size, 1, np.float32)\n",
    "    y = x[:, np.newaxis]\n",
    "    x0 = y0 = size // 2\n",
    "    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))\n",
    "    g_x = max(0, -ul[0]), min(br[0], h) - ul[0]\n",
    "    g_y = max(0, -ul[1]), min(br[1], w) - ul[1]\n",
    "    img_x = max(0, ul[0]), min(br[0], h)\n",
    "    img_y = max(0, ul[1]), min(br[1], w)\n",
    "    heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum(\n",
    "      heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]],\n",
    "      g[g_y[0]:g_y[1], g_x[0]:g_x[1]])\n",
    "    \n",
    "    assert heatmap[mu_y, mu_x] == 1 # Center value must be 1\n",
    "    return heatmap\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_gaussian(heatmap, center, radius, k=1):\n",
    "    diameter = 2 * radius + 1\n",
    "    gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)\n",
    "\n",
    "    x, y = int(center[0]), int(center[1])\n",
    "\n",
    "    height, width = heatmap.shape[0:2]\n",
    "\n",
    "    left, right = min(x, radius), min(width - x, radius + 1)\n",
    "    top, bottom = min(y, radius), min(height - y, radius + 1)\n",
    "\n",
    "    masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]\n",
    "    masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right]\n",
    "    if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:  # TODO debug\n",
    "        np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)\n",
    "    return heatmap\n",
    "\n",
    "def gaussian2D(shape, sigma=1):\n",
    "    m, n = [(ss - 1.) / 2. for ss in shape]\n",
    "    y, x = np.ogrid[-m:m + 1, -n:n + 1]\n",
    "\n",
    "    h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))\n",
    "    h[h < np.finfo(h.dtype).eps * h.max()] = 0\n",
    "    return h\n",
    "\n",
    "def gaussian_radius(det_size, min_overlap=0.7):\n",
    "    height, width = det_size\n",
    "\n",
    "    a1 = 1\n",
    "    b1 = (height + width)\n",
    "    c1 = width * height * (1 - min_overlap) / (1 + min_overlap)\n",
    "    sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1)\n",
    "    r1 = (b1 + sq1) / 2\n",
    "\n",
    "    a2 = 4\n",
    "    b2 = 2 * (height + width)\n",
    "    c2 = (1 - min_overlap) * width * height\n",
    "    sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)\n",
    "    r2 = (b2 + sq2) / 2\n",
    "\n",
    "    a3 = 4 * min_overlap\n",
    "    b3 = -2 * min_overlap * (height + width)\n",
    "    c3 = (min_overlap - 1) * width * height\n",
    "    sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3)\n",
    "    r3 = (b3 + sq3) / 2\n",
    "    return min(r1, r2, r3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Offsets along x and y are between 0 and 1 and correct quantization errors from center heatmap\n",
    "def draw_offset(offset, x, y):\n",
    "    offset[0, int(y), int(x)] = x - int(x)\n",
    "    offset[1, int(y), int(x)] = y - int(y)\n",
    "    return offset\n",
    "    \n",
    "def make_hm_offset_regr_angle(target):\n",
    "    hm = np.zeros([2,input_height//MODEL_SCALE, input_width//MODEL_SCALE])\n",
    "    offset = np.zeros([2, input_height//MODEL_SCALE, input_width//MODEL_SCALE])\n",
    "    regr = np.zeros([2, input_height//MODEL_SCALE, input_width//MODEL_SCALE])\n",
    "    cos_sin_hm = np.zeros([2, input_height//MODEL_SCALE, input_width//MODEL_SCALE])\n",
    "    mask  = np.zeros((input_height//MODEL_SCALE, input_width//MODEL_SCALE), dtype=np.float32)\n",
    "    if len(target) == 0:\n",
    "        return hm, offset, regr, cos_sin_hm\n",
    "    \n",
    "    # choice of sigma is important here, if you are working with very tiny objects, you should decrease the sigma value\n",
    "    for i, c in target.iterrows():\n",
    "        w,h = c[\"w\"],c[\"l\"]\n",
    "        \n",
    "        #4 points\n",
    "        angle = c[\"angle\"]\n",
    "        cos_angle = np.cos(angle)\n",
    "        sin_angle = np.sin(angle)\n",
    "        rot = np.array([[cos_angle, sin_angle], [-sin_angle, cos_angle]])\n",
    "        box = c[[\"x\", \"y\", \"w\", \"l\"]].to_numpy()\n",
    "        center = np.array([c[\"x\"], c[\"y\"]]).T\n",
    "        bottom_right = np.dot(rot, np.array([box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_right = np.dot(rot, np.array([box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_left = np.dot(rot, np.array([-box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        bottom_left = np.dot(rot, np.array([-box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        \n",
    "        br = (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1])) \n",
    "        tr = (int(center[0]+top_right[0]), int(center[1]+top_right[1]))\n",
    "        tl = (int(center[0]+top_left[0]), int(center[1]+top_left[1]))\n",
    "        bl = (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1]))\n",
    "        \n",
    "        br = int(br[0]/MODEL_SCALE),int(br[1]/MODEL_SCALE)\n",
    "        tr = int(tr[0]/MODEL_SCALE),int(tr[1]/MODEL_SCALE)\n",
    "        tl = int(tl[0]/MODEL_SCALE),int(tl[1]/MODEL_SCALE)\n",
    "        bl = int(bl[0]/MODEL_SCALE),int(bl[1]/MODEL_SCALE)\n",
    "        #\n",
    "        \n",
    "        radius = gaussian_radius((math.ceil(h), math.ceil(w)))\n",
    "        radius = max(0, int(radius))\n",
    "        #hm = draw_msra_gaussian(hm, [int(c[\"x\"]/MODEL_SCALE), int(c[\"y\"]/MODEL_SCALE)], \n",
    "        #                        sigma=6)\n",
    "        ct_int = (int(c[\"x\"]/MODEL_SCALE), int(c[\"y\"]/MODEL_SCALE))\n",
    "        hm[0] = draw_gaussian(hm[0], ct_int, 4)\n",
    "        hm[1] = draw_gaussian(hm[1], br, 4)\n",
    "        hm[1] = draw_gaussian(hm[1], tr, 4)\n",
    "        hm[1] = draw_gaussian(hm[1], tl, 4)\n",
    "        hm[1] = draw_gaussian(hm[1], bl, 4)\n",
    "        assert 0 <= c[\"x\"] < 1280\n",
    "        assert 0 <= c[\"y\"] < 720\n",
    "        offset = draw_offset(offset, c[\"x\"]/MODEL_SCALE, c[\"y\"]/MODEL_SCALE)\n",
    "        regr[0, int(c[\"y\"]/MODEL_SCALE), int(c[\"x\"]/MODEL_SCALE)] = c[\"w\"]/MODEL_SCALE\n",
    "        regr[1, int(c[\"y\"]/MODEL_SCALE), int(c[\"x\"]/MODEL_SCALE)] = c[\"l\"]/MODEL_SCALE\n",
    "        cos_sin_hm[0, int(c[\"y\"]/MODEL_SCALE), int(c[\"x\"]/MODEL_SCALE)] = np.cos(c[\"angle\"])\n",
    "        cos_sin_hm[1, int(c[\"y\"]/MODEL_SCALE), int(c[\"x\"]/MODEL_SCALE)] = np.sin(c[\"angle\"])\n",
    "        mask[ct_int[1], ct_int[0]] = 1\n",
    "        # for i in range(-1, 2):\n",
    "        #     for j in range(-1, 2):\n",
    "        #         try:\n",
    "        #             regr[0, int(c[\"y\"]/MODEL_SCALE)+i, int(c[\"x\"]/MODEL_SCALE)+j] = c[\"w\"]/MODEL_SCALE\n",
    "        #             regr[1, int(c[\"y\"]/MODEL_SCALE)+i, int(c[\"x\"]/MODEL_SCALE)+j] = c[\"l\"]/MODEL_SCALE\n",
    "        #             cos_sin_hm[0, int(c[\"y\"]/MODEL_SCALE)+i, int(c[\"x\"]/MODEL_SCALE)+j] = np.cos(c[\"angle\"])\n",
    "        #             cos_sin_hm[1, int(c[\"y\"]/MODEL_SCALE)+i, int(c[\"x\"]/MODEL_SCALE)+j] = np.sin(c[\"angle\"])\n",
    "        #         except:\n",
    "        #             pass\n",
    "        #angles= target[\"angle\"]\n",
    "        \n",
    "                \n",
    "    #regr[0] = regr[0].T; regr[1] = regr[1].T;\n",
    "    #angle = angle.T\n",
    "    return hm, offset, regr, cos_sin_hm,mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select(hm, threshold):\n",
    "    \"\"\"\n",
    "    Keep only local maxima (kind of NMS).\n",
    "    We make sure to have no adjacent detection in the heatmap.\n",
    "    \"\"\"\n",
    "\n",
    "    pred = hm > threshold\n",
    "    pred_centers = np.argwhere(pred)\n",
    "\n",
    "    for i, ci in enumerate(pred_centers):\n",
    "        for j in range(i + 1, len(pred_centers)):\n",
    "            cj = pred_centers[j]\n",
    "            if np.linalg.norm(ci - cj) <= 2:\n",
    "                score_i = hm[ci[0], ci[1]]\n",
    "                score_j = hm[cj[0], cj[1]]\n",
    "                if score_i > score_j:\n",
    "                    hm[cj[0], cj[1]] = 0\n",
    "                else:\n",
    "                    hm[ci[0], ci[1]] = 0\n",
    "\n",
    "    return hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred2box(hm, offset, regr, cos_sin_hm, thresh=0.99):\n",
    "    # make binding box from heatmaps\n",
    "    # thresh: threshold for logits.\n",
    "        \n",
    "    # get center\n",
    "    pred = hm > thresh\n",
    "    pred_center = np.where(hm>thresh)\n",
    "    \n",
    "    # get regressions\n",
    "    pred_r = regr[:,pred].T\n",
    "    pred_angles = cos_sin_hm[:, pred].T\n",
    "    \n",
    "    #print(\"pred_angle\", pred_angle)\n",
    "\n",
    "    # wrap as boxes\n",
    "    # [xmin, ymin, width, height]\n",
    "    # size as original image.\n",
    "    boxes = []\n",
    "    scores = hm[pred]\n",
    "    \n",
    "    pred_center = np.asarray(pred_center).T\n",
    "    #print(pred_r.shape)\n",
    "    #print(pred_angles)\n",
    "    #print(pred_angles.shape)\n",
    "    \n",
    "    for (center, b, pred_angle) in zip(pred_center, pred_r, pred_angles):\n",
    "        #print(b)\n",
    "        print(\"Center is:\",center)\n",
    "        offset_xy = offset[:, center[0], center[1]]\n",
    "        print(\"Offset is:\",offset_xy)\n",
    "        angle = np.arctan2(pred_angle[1], pred_angle[0])\n",
    "        print(\"Angle is:\",angle)\n",
    "        print(\"b is:\",b)\n",
    "        arr = np.array([(center[1]+offset_xy[0])*MODEL_SCALE, (center[0]+offset_xy[1])*MODEL_SCALE, \n",
    "                        b[0]*MODEL_SCALE, b[1]*MODEL_SCALE, angle])\n",
    "        print(\"Arr is:\",arr)\n",
    "        # Clip values between 0 and input_size\n",
    "        #arr = np.clip(arr, 0, input_size)\n",
    "        #print(\"Pred angle\", i, pred_angle[i])\n",
    "        # filter \n",
    "        #if arr[0]<0 or arr[1]<0 or arr[0]>input_size or arr[1]>input_size:\n",
    "            #pass\n",
    "        boxes.append(arr)\n",
    "    return np.asarray(boxes), scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions for plotting results\n",
    "def showbox(img, hm, offset, regr, cos_sin_hm, thresh=0.9):\n",
    "    boxes, _ = pred2box(hm, offset, regr, cos_sin_hm, thresh=thresh)\n",
    "    \n",
    "    sample = img\n",
    "\n",
    "    for box in boxes:\n",
    "        center = [int(box[0]), int(box[1])]\n",
    "        cos_angle = np.cos(box[4])\n",
    "        sin_angle = np.sin(box[4])\n",
    "        rot = np.array([[cos_angle, sin_angle], [-sin_angle, cos_angle]])\n",
    "        \n",
    "        bottom_right = np.dot(rot, np.array([box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_right = np.dot(rot, np.array([box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_left = np.dot(rot, np.array([-box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        bottom_left = np.dot(rot, np.array([-box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        \n",
    "        thickness = 3\n",
    "        cv2.line(sample, (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1])),\n",
    "                      (int(center[0]+top_right[0]), int(center[1]+top_right[1])),\n",
    "                      (0, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1])),\n",
    "                      (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+top_left[0]), int(center[1]+top_left[1])),\n",
    "                      (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+top_left[0]), int(center[1]+top_left[1])),\n",
    "                      (int(center[0]+top_right[0]), int(center[1]+top_right[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "    return sample\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model, model_path, optimizer=None, resume=False, \n",
    "               lr=None, lr_step=None):\n",
    "  start_epoch = 0\n",
    "  checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)\n",
    "  print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch']))\n",
    "  state_dict_ = checkpoint['state_dict']\n",
    "  state_dict = {}\n",
    "  \n",
    "  # convert data_parallal to model\n",
    "  for k in state_dict_:\n",
    "    if k.startswith('module') and not k.startswith('module_list'):\n",
    "      state_dict[k[7:]] = state_dict_[k]\n",
    "    else:\n",
    "      state_dict[k] = state_dict_[k]\n",
    "  model_state_dict = model.state_dict()\n",
    "\n",
    "  # check loaded parameters and created model parameters\n",
    "  msg = 'If you see this, your model does not fully load the ' + \\\n",
    "        'pre-trained weight. Please make sure ' + \\\n",
    "        'you have correctly specified --arch xxx ' + \\\n",
    "        'or set the correct --num_classes for your own dataset.'\n",
    "  for k in state_dict:\n",
    "    if k in model_state_dict:\n",
    "      if state_dict[k].shape != model_state_dict[k].shape:\n",
    "        print('Skip loading parameter {}, required shape{}, '\\\n",
    "              'loaded shape{}. {}'.format(\n",
    "          k, model_state_dict[k].shape, state_dict[k].shape, msg))\n",
    "        state_dict[k] = model_state_dict[k]\n",
    "    else:\n",
    "      print('Drop parameter {}.'.format(k) + msg)\n",
    "  for k in model_state_dict:\n",
    "    if not (k in state_dict):\n",
    "      print('No param {}.'.format(k) + msg)\n",
    "      state_dict[k] = model_state_dict[k]\n",
    "  model.load_state_dict(state_dict, strict=False)\n",
    "\n",
    "  # resume optimizer parameters\n",
    "  if optimizer is not None and resume:\n",
    "    if 'optimizer' in checkpoint:\n",
    "      optimizer.load_state_dict(checkpoint['optimizer'])\n",
    "      start_epoch = checkpoint['epoch']\n",
    "      start_lr = lr\n",
    "      for step in lr_step:\n",
    "        if start_epoch >= step:\n",
    "          start_lr *= 0.1\n",
    "      for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = start_lr\n",
    "      print('Resumed optimizer with start lr', start_lr)\n",
    "    else:\n",
    "      print('No optimizer parameters in checkpoint.')\n",
    "  if optimizer is not None:\n",
    "    return model, optimizer, start_epoch\n",
    "  else:\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train-test by unique image ids, corresponding to image paths\n",
    "train_id, test_id = train_test_split(sorted(os.listdir(dataset_folder)), test_size=0.1, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resnet-18 expect normalized channels in input\n",
    "class Normalize(object):\n",
    "    def __init__(self):\n",
    "        self.mean=[0.485, 0.456, 0.406]\n",
    "        self.std=[0.229, 0.224, 0.225]\n",
    "        self.norm = transforms.Normalize(self.mean, self.std)\n",
    "    def __call__(self, image):\n",
    "        image = image.astype(np.float32)/255\n",
    "        axis = (0,1)\n",
    "        image -= self.mean\n",
    "        image /= self.std\n",
    "        return image\n",
    "    \n",
    "\n",
    "class CarDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, img_id, labels, transform=None):\n",
    "        self.img_id = img_id\n",
    "        self.labels = labels\n",
    "        if transform:\n",
    "            self.transform = transform\n",
    "        self.normalize = Normalize()\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.img_id)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img = cv2.imread(os.path.join(dataset_folder, self.img_id[idx]))\n",
    "        img = self.normalize(img)\n",
    "        img = img.transpose([2,0,1])\n",
    "        target = self.labels[self.labels['name']==self.img_id[idx][:-4]]\n",
    "        hm, offset, regr, cos_sin_hm,mask = make_hm_offset_regr_angle(target)\n",
    "        #assert(hm.shape == (128, 128))\n",
    "        #assert(regr.shape == (2, 128, 128))\n",
    "        #assert(angle.shape == (128, 128))\n",
    "        return img, hm, offset, regr, cos_sin_hm,mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traindataset = CarDataset(train_id, train_df)\n",
    "valdataset = CarDataset(test_id, train_df)\n",
    "\n",
    "# Your angle histogram should be as flat as possible to reduce overfitting!\n",
    "target = train_df[\"angle\"]\n",
    "plt.hist(target, bins=50)\n",
    "plt.title(\"Angle histogram\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 128, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check traindataset\n",
    "\n",
    "k = random.randint(0, len(train_id))\n",
    "# Test dataset\n",
    "img, hm, offset, regr, cos_sin_hm,mask = traindataset[k]\n",
    "\n",
    "plt.imshow(hm[0])\n",
    "plt.title(\"Main heatmap\")\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(hm[1])\n",
    "plt.title(\"4 Corner heatmap\")\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(offset[0])\n",
    "plt.title(\"Offset x heatmap\")\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(regr[0])\n",
    "plt.title(\"Width heatmap\")\n",
    "plt.show()\n",
    "\n",
    "print(cos_sin_hm.shape)\n",
    "\n",
    "plt.imshow(cos_sin_hm[0])\n",
    "plt.title(\"Cos heatmap\")\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(cos_sin_hm[1])\n",
    "plt.title(\"Sin heatmap\")\n",
    "plt.show()\n",
    "\n",
    "# Recover ball detection from heatmaps\n",
    "img = cv2.imread(os.path.join(dataset_folder, train_id[k]))\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "img = showbox(img, hm[0], offset, regr, cos_sin_hm, 0.99)\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.title(\"Recovered postions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(traindataset,batch_size=batch_size,shuffle=True,num_workers=workers)\n",
    "val_loader = torch.utils.data.DataLoader(valdataset,batch_size=batch_size,shuffle=False,num_workers=workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 x 3 x 3 x 48\n",
      "3 x 3 x 48 x 96\n",
      "3 x 3 x 96 x 24\n",
      "3 x 3 x 120 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 160 x 70\n",
      "3 x 3 x 70 x 24\n",
      "3 x 3 x 94 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 230 x 118\n",
      "Blk out = 214\n",
      "1 x 1 x 214 x 192\n",
      "3 x 3 x 192 x 24\n",
      "3 x 3 x 216 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 256 x 70\n",
      "3 x 3 x 70 x 24\n",
      "3 x 3 x 94 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 326 x 118\n",
      "3 x 3 x 118 x 24\n",
      "3 x 3 x 142 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 182 x 70\n",
      "3 x 3 x 70 x 24\n",
      "3 x 3 x 94 x 40\n",
      "3 x 3 x 40 x 24\n",
      "3 x 3 x 444 x 200\n",
      "Blk out = 392\n",
      "1 x 1 x 392 x 256\n",
      "3 x 3 x 256 x 28\n",
      "3 x 3 x 284 x 48\n",
      "3 x 3 x 48 x 28\n",
      "3 x 3 x 332 x 80\n",
      "3 x 3 x 80 x 28\n",
      "3 x 3 x 108 x 48\n",
      "3 x 3 x 48 x 28\n",
      "3 x 3 x 412 x 138\n",
      "3 x 3 x 138 x 28\n",
      "3 x 3 x 166 x 48\n",
      "3 x 3 x 48 x 28\n",
      "3 x 3 x 214 x 80\n",
      "3 x 3 x 80 x 28\n",
      "3 x 3 x 108 x 48\n",
      "3 x 3 x 48 x 28\n",
      "3 x 3 x 550 x 234\n",
      "Blk out = 458\n",
      "1 x 1 x 458 x 320\n",
      "3 x 3 x 320 x 36\n",
      "3 x 3 x 356 x 62\n",
      "3 x 3 x 62 x 36\n",
      "3 x 3 x 418 x 104\n",
      "3 x 3 x 104 x 36\n",
      "3 x 3 x 140 x 62\n",
      "3 x 3 x 62 x 36\n",
      "3 x 3 x 522 x 176\n",
      "3 x 3 x 176 x 36\n",
      "3 x 3 x 212 x 62\n",
      "3 x 3 x 62 x 36\n",
      "3 x 3 x 274 x 104\n",
      "3 x 3 x 104 x 36\n",
      "3 x 3 x 140 x 62\n",
      "3 x 3 x 62 x 36\n",
      "3 x 3 x 698 x 300\n",
      "Blk out = 588\n",
      "1 x 1 x 588 x 480\n",
      "3 x 3 x 480 x 48\n",
      "3 x 3 x 528 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 610 x 138\n",
      "3 x 3 x 138 x 48\n",
      "3 x 3 x 186 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 748 x 236\n",
      "3 x 3 x 236 x 48\n",
      "3 x 3 x 284 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 366 x 138\n",
      "3 x 3 x 138 x 48\n",
      "3 x 3 x 186 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 984 x 400\n",
      "Blk out = 784\n",
      "1 x 1 x 784 x 256\n",
      "3 x 3 x 768 x 80\n",
      "3 x 3 x 848 x 136\n",
      "3 x 3 x 136 x 80\n",
      "3 x 3 x 984 x 232\n",
      "3 x 3 x 232 x 80\n",
      "3 x 3 x 312 x 136\n",
      "3 x 3 x 136 x 80\n",
      "3 x 3 x 1216 x 394\n",
      "Blk out = 714\n",
      "1 x 1 x 1498 x 256\n",
      "3 x 3 x 672 x 64\n",
      "3 x 3 x 736 x 108\n",
      "3 x 3 x 108 x 64\n",
      "3 x 3 x 844 x 184\n",
      "3 x 3 x 184 x 64\n",
      "3 x 3 x 248 x 108\n",
      "3 x 3 x 108 x 64\n",
      "3 x 3 x 1028 x 314\n",
      "Blk out = 570\n",
      "1 x 1 x 1028 x 192\n",
      "3 x 3 x 480 x 48\n",
      "3 x 3 x 528 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 610 x 138\n",
      "3 x 3 x 138 x 48\n",
      "3 x 3 x 186 x 82\n",
      "3 x 3 x 82 x 48\n",
      "3 x 3 x 748 x 236\n",
      "Blk out = 428\n",
      "1 x 1 x 642 x 96\n",
      "3 x 3 x 288 x 28\n",
      "3 x 3 x 316 x 48\n",
      "3 x 3 x 48 x 28\n",
      "3 x 3 x 364 x 80\n",
      "Blk out = 136\n",
      "Parameters= 37365088\n",
      "loaded centernet_hardnet85_coco.pth, epoch 300\n",
      "Skip loading parameter hm.0.weight, required shapetorch.Size([128, 200, 3, 3]), loaded shapetorch.Size([320, 200, 3, 3]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "Skip loading parameter hm.0.bias, required shapetorch.Size([128]), loaded shapetorch.Size([320]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "Skip loading parameter hm.2.weight, required shapetorch.Size([2, 128, 1, 1]), loaded shapetorch.Size([80, 320, 1, 1]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "Skip loading parameter hm.2.bias, required shapetorch.Size([2]), loaded shapetorch.Size([80]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "Skip loading parameter wh.2.weight, required shapetorch.Size([2, 128, 1, 1]), loaded shapetorch.Size([4, 128, 1, 1]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "Skip loading parameter wh.2.bias, required shapetorch.Size([2]), loaded shapetorch.Size([4]). If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param offset.0.weight.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param offset.0.bias.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param offset.2.weight.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param offset.2.bias.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param angle.0.weight.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param angle.0.bias.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param angle.2.weight.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n",
      "No param angle.2.bias.If you see this, your model does not fully load the pre-trained weight. Please make sure you have correctly specified --arch xxx or set the correct --num_classes for your own dataset.\n"
     ]
    }
   ],
   "source": [
    "from hardnet import get_pose_net\n",
    "model = get_pose_net(85,{\"hm\":2,\"offset\":2,\"wh\":2,\"angle\":2})\n",
    "model = load_model(model,\"centernet_hardnet85_coco.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 2, 128, 128])\n",
      "torch.Size([1, 2, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "#model = centernet()\n",
    "# Check if it runs correctly\n",
    "output = model(torch.rand(1,3,input_height,input_width))\n",
    "print(output[0].size())\n",
    "print(output[1].size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#summary(model, (1, 3, input_height, input_width))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def focal_loss(pred_mask, gt,gamma=2):\n",
    "#     \"\"\"\n",
    "#     Focal loss was introduced to address the class imbalance problem\n",
    "#     For example here, most of the pixels in the heatmap are negative.\n",
    "    \n",
    "#     We split the focal loss between positive and negative losses.\n",
    "    \n",
    "#     https://arxiv.org/pdf/1708.02002.pdf\n",
    "    \n",
    "#     Code taken from the CenterNet repo.\n",
    "#     \"\"\"\n",
    "#     gt = gt.unsqueeze(1).float()\n",
    "\n",
    "#     pos_inds = gt.ge(1.0).float()\n",
    "#     neg_inds = gt.lt(1.0).float()\n",
    "    \n",
    "#     neg_weights = torch.pow(1 - gt, 4)\n",
    "    \n",
    "#     # add 1e-12 to avoid nan values\n",
    "#     pos_loss = - torch.pow(1 - pred_mask, gamma) * torch.log(pred_mask + 1e-12) * pos_inds # [2, 1, 128, 128]\n",
    "#     neg_loss = - torch.pow(pred_mask, gamma) * torch.log(1 - pred_mask + 1e-12) * neg_inds * neg_weights # [2, 1, 128, 128]\n",
    "    \n",
    "#     loss = 0\n",
    "    \n",
    "#     num_pos  = pos_inds.float().sum() # scalar number of positives\n",
    "#     pos_loss = pos_loss.sum()\n",
    "#     neg_loss = neg_loss.sum()\n",
    "    \n",
    "#     if num_pos == 0:\n",
    "#         loss = neg_loss\n",
    "#     else:\n",
    "#         pos_loss /= num_pos\n",
    "#         neg_loss /= num_pos\n",
    "#         loss = pos_loss + neg_loss\n",
    "        \n",
    "#     assert not torch.isnan(pos_loss)\n",
    "#     assert not torch.isnan(neg_loss)\n",
    "        \n",
    "#     return loss, pos_loss, neg_loss\n",
    "\n",
    "def focal_loss(pred_mask, gt,gamma=2):\n",
    "    \"\"\"\n",
    "    Focal loss was introduced to address the class imbalance problem\n",
    "    For example here, most of the pixels in the heatmap are negative.\n",
    "\n",
    "    We split the focal loss between positive and negative losses.\n",
    "\n",
    "    https://arxiv.org/pdf/1708.02002.pdf\n",
    "\n",
    "    Code taken from the CenterNet repo.\n",
    "    \"\"\"\n",
    "    gt = gt.unsqueeze(1).float()\n",
    "\n",
    "    pos_inds = gt.eq(1).float()\n",
    "    neg_inds = gt.lt(1).float()\n",
    "\n",
    "    neg_weights = torch.pow(1 - gt, 4)\n",
    "\n",
    "    #with torch.cuda.amp.autocast(enabled=False):\n",
    "    # add 1e-12 to avoid nan values\n",
    "    #pos_loss = - torch.pow(1 - pred_mask, gamma) * torch.log(pred_mask + 1e-12) * pos_inds # [2, 1, 128, 128]\n",
    "    pos_loss = torch.log(pred_mask) * torch.pow(1 - pred_mask, 2) * pos_inds\n",
    "    neg_loss = torch.log(1 - pred_mask) * torch.pow(pred_mask, 2) * neg_weights * neg_inds\n",
    "    #neg_loss = - torch.pow(pred_mask, gamma) * torch.log(1 - pred_mask + 1e-12) * neg_inds * neg_weights # [2, 1, 128, 128]\n",
    "\n",
    "    loss = 0\n",
    "\n",
    "    num_pos  = pos_inds.float().sum() # scalar number of positives\n",
    "    pos_loss = pos_loss.sum()\n",
    "    neg_loss = neg_loss.sum()\n",
    "\n",
    "\n",
    "    # if num_pos == 0:\n",
    "    #     loss = neg_loss\n",
    "    # else:\n",
    "    #     pos_loss /= num_pos\n",
    "    #     neg_loss /= num_pos\n",
    "    #     loss = pos_loss + neg_loss\n",
    "\n",
    "    if num_pos == 0:\n",
    "        loss = -neg_loss\n",
    "    else:\n",
    "        loss = -(pos_loss + neg_loss) / num_pos\n",
    "\n",
    "    assert not torch.isnan(pos_loss)\n",
    "    assert not torch.isnan(neg_loss)\n",
    "\n",
    "    return loss, pos_loss, neg_loss\n",
    "\n",
    "\n",
    "def _regr_loss(off_pred, off_gt, wh_pred, wh_gt, angle_pred, angle_gt, mask):\n",
    "    ''' L1 regression loss\n",
    "    We compute l1 loss over mask of positive pixels for offset, width/height and sine/cosine angle\n",
    "    '''\n",
    "    #mask = hm_gt[0].float().ge(1.0).float()\n",
    "    \n",
    "    \n",
    "    #mask = mask.expand_as(off_gt).float()\n",
    "    mask = torch.unsqueeze(mask,-1).repeat(1,1,1,2).permute(0,3,1,2)\n",
    "    num = mask.float().sum() \n",
    "    assert off_pred.size() == mask.size()\n",
    "    off_pred = off_pred * mask\n",
    "    off_gt = off_gt * mask\n",
    "    wh_pred = wh_pred * mask\n",
    "    wh_gt = wh_gt * mask\n",
    "    angle_pred = angle_pred * mask\n",
    "    angle_gt = angle_gt * mask\n",
    "    \n",
    "\n",
    "    off_loss = nn.functional.l1_loss(off_pred, off_gt, reduction='sum') / (num + 1e-4)\n",
    "    \n",
    "    # Scale with 0.1 width and height loss (change it you need)\n",
    "    wh_loss = 0.1 * nn.functional.l1_loss(wh_pred, wh_gt, reduction='sum') / (num + 1e-4)\n",
    "    \n",
    "    angle_loss = nn.functional.l1_loss(angle_pred, angle_gt, reduction='sum') / (num + 1e-4)\n",
    "    \n",
    "    return off_loss, wh_loss, angle_loss\n",
    "\n",
    "def global_loss(hm_pred, hm_gt, off_pred, off_gt, wh_pred, wh_gt, angle_pred, angle_gt,mask):\n",
    "    \"\"\"\n",
    "    Global loss is the sum of the focal loss and of the offset loss\n",
    "        \n",
    "    Focal loss is the sum of pos_loss and neg_loss, we extract them just for the record\n",
    "    \"\"\"\n",
    "    \n",
    "    #pred_mask = torch.clamp(torch.sigmoid(hm_pred[:, 0]),1e-4,1- 1e-4)\n",
    "    pred_mask = torch.clamp(torch.sigmoid(hm_pred), 1e-4, 1 - 1e-4)\n",
    "  \n",
    "    pred_mask = pred_mask.unsqueeze(1).float()\n",
    "    \n",
    "    foc_loss, pos_loss, neg_loss = focal_loss(pred_mask, hm_gt)\n",
    "     \n",
    "    off_loss, wh_loss, angle_loss = _regr_loss(off_pred, off_gt, wh_pred, wh_gt, angle_pred, angle_gt, mask)\n",
    "    \n",
    "    assert not torch.isnan(off_loss)\n",
    "        \n",
    "    return foc_loss, pos_loss, neg_loss, off_loss, wh_loss, angle_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch):\n",
    "    print(f'epochs {epoch+1}/{epochs}')\n",
    "    print(f'Learning rate : {optimizer.param_groups[0][\"lr\"]}')\n",
    "    train_loss = 0.0\n",
    "    train_pos_loss = 0.0\n",
    "    train_neg_loss = 0.0\n",
    "    train_off_loss = 0.0\n",
    "    train_wh_loss = 0.0\n",
    "    train_angle_loss = 0.0\n",
    "    t = tqdm(train_loader)\n",
    "    rd = np.random.rand()\n",
    "    \n",
    "    # Training\n",
    "    model.train()\n",
    "    #optimizer.train()\n",
    "    for idx, (img, hm, offset, regr, angle,mask) in enumerate(t):       \n",
    "        # send to gpu\n",
    "        img = img.to(device)\n",
    "        hm_gt = hm.to(device)\n",
    "        offset_gt = offset.to(device)\n",
    "        wh_gt = regr.to(device)\n",
    "        angle_gt = angle.to(device)\n",
    "        mask = mask.to(device)\n",
    "        # set opt\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # run model\n",
    "        with torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16):\n",
    "            preds_hm, preds_offset, preds_wh, preds_angle = model(img)\n",
    "            #assert not np.isnan(preds_hm.cpu().detach().numpy()[0, 0, 0, 0])\n",
    "            \n",
    "            foc_loss, pos_loss, neg_loss, off_loss, wh_loss, angle_loss = global_loss(preds_hm, hm_gt, preds_offset, offset_gt, preds_wh, wh_gt, preds_angle, angle_gt,mask)\n",
    "            loss = foc_loss + off_loss + wh_loss + angle_loss\n",
    "        \n",
    "        if torch.isnan(loss):\n",
    "            print(\"NAN loss\")\n",
    "            continue\n",
    "            \n",
    "        # misc\n",
    "        train_loss += float(loss) # TRICK to avoid GPU memory increasing\n",
    "        train_pos_loss += float(pos_loss)\n",
    "        train_neg_loss += float(neg_loss)\n",
    "        train_off_loss += float(off_loss)\n",
    "        train_wh_loss += float(wh_loss)\n",
    "        train_angle_loss += float(angle_loss)\n",
    "        \n",
    "        loss.backward()\n",
    "        # NB: I tried to use gradient clipping to avoid NaN values but it didnt work as expected\n",
    "        optimizer.step()\n",
    "        \n",
    "        t.set_description(f'(l={train_loss/(idx+1):.2f})  (off={train_off_loss/(idx+1):.3f}) (wh={train_wh_loss/(idx+1):.3f}) (a={train_angle_loss/(idx+1):.3f})')\n",
    "        #(pos={train_pos_loss/(idx+1):.3f}) (neg={train_neg_loss/(idx+1):.3f})\n",
    "    # Validation\n",
    "    val_loss = 0.0\n",
    "    val_pos_loss = 0.0\n",
    "    val_neg_loss = 0.0\n",
    "    val_off_loss = 0.0\n",
    "    val_wh_loss = 0.0\n",
    "    val_angle_loss = 0.0\n",
    "    \n",
    "    model.eval()\n",
    "    #optimizer.eval()\n",
    "    with torch.no_grad():\n",
    "        for idx, (img, hm, offset, regr, angle,mask) in enumerate(tqdm(val_loader)):       \n",
    "            # send to gpu\n",
    "            img = img.to(device)\n",
    "            hm_gt = hm.to(device)\n",
    "            offset_gt = offset.to(device)\n",
    "            wh_gt = regr.to(device)\n",
    "            angle_gt = angle.to(device)\n",
    "            mask = mask.to(device)\n",
    "            # run model\n",
    "            preds_hm, preds_offset, preds_wh, preds_angle = model(img)\n",
    "\n",
    "            foc_loss, pos_loss, neg_loss, off_loss, wh_loss, angle_loss = global_loss(preds_hm, hm_gt, preds_offset, offset_gt, preds_wh, wh_gt, preds_angle, angle_gt,mask)\n",
    "            \n",
    "            loss = foc_loss + off_loss + wh_loss + angle_loss\n",
    "            # misc\n",
    "            val_loss += float(loss)\n",
    "            val_pos_loss += float(pos_loss)\n",
    "            val_neg_loss += float(neg_loss)\n",
    "            val_off_loss += float(off_loss)\n",
    "            val_wh_loss += float(wh_loss)\n",
    "            val_angle_loss += float(angle_loss)\n",
    "\n",
    "\n",
    "    print(f'train loss : {train_loss/len(train_loader):.4f}')\n",
    "    print(f'Pos loss : {train_pos_loss/len(train_loader):.4f}')\n",
    "    print(f'Neg loss : {train_neg_loss/len(train_loader):.4f}')\n",
    "    print(f'Off loss : {train_off_loss/len(train_loader):.4f}')\n",
    "    print(f'Wh loss : {train_wh_loss/len(train_loader):.4f}')\n",
    "    print(f'Angle loss : {train_angle_loss/len(train_loader):.4f}')\n",
    "    \n",
    "    print(\"\")\n",
    "    print(f'Val loss : {val_loss/len(val_loader):.4f}')\n",
    "    print(f'Val Pos loss : {val_pos_loss/len(val_loader):.4f}')\n",
    "    print(f'Val Neg loss : {val_neg_loss/len(val_loader):.4f}')\n",
    "    print(f'Val Off loss : {val_off_loss/len(val_loader):.4f}')\n",
    "    print(f'Val Wh loss : {val_wh_loss/len(val_loader):.4f}')\n",
    "    print(f'Val Angle loss : {val_angle_loss/len(val_loader):.4f}')\n",
    "    \n",
    "    # save logs\n",
    "    log_epoch = {'epoch': epoch+1, 'lr': optimizer.state_dict()['param_groups'][0]['lr'],\n",
    "                'train_loss': train_loss/len(train_loader), 'train_pos_loss': train_pos_loss/len(train_loader),\n",
    "                'train_neg_loss': train_neg_loss/len(train_loader), 'train_off_loss': train_off_loss/len(train_loader), \n",
    "                'val_loss': val_loss/len(val_loader), 'val_pos_loss': val_pos_loss/len(val_loader),\n",
    "                'val_neg_loss': val_neg_loss/len(val_loader), 'val_off_loss': val_off_loss/len(val_loader)}\n",
    "    logs.append(log_epoch)\n",
    "    \n",
    "    return val_loss/len(val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = centernet()\n",
    "#model.load_state_dict(torch.load(\"centernet-oriented-bbox.pth\"))\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "optimizer_type = \"adam\"\n",
    "Init_lr = 5e-4\n",
    "Min_lr  = Init_lr * 0.01\n",
    "weight_decay = 0\n",
    "momentum = 0.9\n",
    "nbs             = 64\n",
    "lr_limit_max    = 5e-4 if optimizer_type == 'adam' else 5e-2\n",
    "lr_limit_min    = 2.5e-4 if optimizer_type == 'adam' else 5e-4\n",
    "Init_lr_fit     = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)\n",
    "Min_lr_fit      = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)\n",
    "lr_decay_type = \"cos\"\n",
    "# Optimizer\n",
    "#optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
    "#optimizer = schedulefree.AdamWScheduleFree(model.parameters(), lr=Init_lr_fit,weight_decay = weight_decay,betas = (momentum, 0.999))\n",
    "optimizer = optim.Adam(model.parameters(), Init_lr_fit, betas = (momentum, 0.999), weight_decay = weight_decay)\n",
    "logs = []\n",
    "best_loss = float('inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):\n",
    "    def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):\n",
    "        if iters <= warmup_total_iters:\n",
    "            # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start\n",
    "            lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_start\n",
    "        elif iters >= total_iters - no_aug_iter:\n",
    "            lr = min_lr\n",
    "        else:\n",
    "            lr = min_lr + 0.5 * (lr - min_lr) * (\n",
    "                1.0 + math.cos(math.pi* (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter))\n",
    "            )\n",
    "        return lr\n",
    "\n",
    "    def step_lr(lr, decay_rate, step_size, iters):\n",
    "        if step_size < 1:\n",
    "            raise ValueError(\"step_size must above 1.\")\n",
    "        n       = iters // step_size\n",
    "        out_lr  = lr * decay_rate ** n\n",
    "        return out_lr\n",
    "\n",
    "    if lr_decay_type == \"cos\":\n",
    "        warmup_total_iters  = min(max(warmup_iters_ratio * total_iters, 1), 3)\n",
    "        warmup_lr_start     = max(warmup_lr_ratio * lr, 1e-6)\n",
    "        no_aug_iter         = min(max(no_aug_iter_ratio * total_iters, 1), 15)\n",
    "        func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)\n",
    "    else:\n",
    "        decay_rate  = (min_lr / lr) ** (1 / (step_num - 1))\n",
    "        step_size   = total_iters / step_num\n",
    "        func = partial(step_lr, lr, decay_rate, step_size)\n",
    "\n",
    "    return func\n",
    "\n",
    "def set_optimizer_lr(optimizer, lr_scheduler_func, epoch):\n",
    "    lr = lr_scheduler_func(epoch)\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "\n",
    "def get_lr(optimizer):\n",
    "    for param_group in optimizer.param_groups:\n",
    "        return param_group['lr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "counter = 0\n",
    "lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs 1/100\n",
      "Learning rate : 2.5e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=3.89)  (off=0.287) (wh=1.198) (a=0.526): 100%|█| 404/404 [03:47<00:00,  1.78i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:11<00:00,  3.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 3.8885\n",
      "Pos loss : -64.4510\n",
      "Neg loss : -19.0878\n",
      "Off loss : 0.2866\n",
      "Wh loss : 1.1983\n",
      "Angle loss : 0.5261\n",
      "\n",
      "Val loss : 1.9164\n",
      "Val Pos loss : -34.5785\n",
      "Val Neg loss : -9.1629\n",
      "Val Off loss : 0.2398\n",
      "Val Wh loss : 0.3653\n",
      "Val Angle loss : 0.3964\n",
      "Improved validation loss to 1.916\n",
      "epochs 2/100\n",
      "Learning rate : 4.9999999999999996e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=1.66)  (off=0.236) (wh=0.371) (a=0.348): 100%|█| 404/404 [03:43<00:00,  1.80i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 1.6572\n",
      "Pos loss : -22.4293\n",
      "Neg loss : -9.3708\n",
      "Off loss : 0.2359\n",
      "Wh loss : 0.3706\n",
      "Angle loss : 0.3479\n",
      "\n",
      "Val loss : 1.3253\n",
      "Val Pos loss : -14.1836\n",
      "Val Neg loss : -9.9143\n",
      "Val Off loss : 0.2271\n",
      "Val Wh loss : 0.3254\n",
      "Val Angle loss : 0.2691\n",
      "Improved validation loss to 1.325\n",
      "epochs 3/100\n",
      "Learning rate : 0.000125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=1.29)  (off=0.232) (wh=0.280) (a=0.270): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 1.2949\n",
      "Pos loss : -15.9162\n",
      "Neg loss : -7.3045\n",
      "Off loss : 0.2324\n",
      "Wh loss : 0.2804\n",
      "Angle loss : 0.2697\n",
      "\n",
      "Val loss : 1.0553\n",
      "Val Pos loss : -12.6517\n",
      "Val Neg loss : -6.6354\n",
      "Val Off loss : 0.2241\n",
      "Val Wh loss : 0.2157\n",
      "Val Angle loss : 0.2103\n",
      "Improved validation loss to 1.055\n",
      "epochs 4/100\n",
      "Learning rate : 0.00025\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=1.18)  (off=0.220) (wh=0.244) (a=0.226): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 1.1772\n",
      "Pos loss : -15.2058\n",
      "Neg loss : -6.9881\n",
      "Off loss : 0.2203\n",
      "Wh loss : 0.2437\n",
      "Angle loss : 0.2262\n",
      "\n",
      "Val loss : 1.0998\n",
      "Val Pos loss : -19.6157\n",
      "Val Neg loss : -4.6478\n",
      "Val Off loss : 0.2151\n",
      "Val Wh loss : 0.2001\n",
      "Val Angle loss : 0.1974\n",
      "epochs 5/100\n",
      "Learning rate : 0.000249927856517814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.83)  (off=0.200) (wh=0.186) (a=0.147): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.8271\n",
      "Pos loss : -8.4159\n",
      "Neg loss : -4.7805\n",
      "Off loss : 0.2003\n",
      "Wh loss : 0.1861\n",
      "Angle loss : 0.1469\n",
      "\n",
      "Val loss : 0.8731\n",
      "Val Pos loss : -15.2379\n",
      "Val Neg loss : -3.0033\n",
      "Val Off loss : 0.1957\n",
      "Val Wh loss : 0.1520\n",
      "Val Angle loss : 0.1336\n",
      "Improved validation loss to 0.873\n",
      "epochs 6/100\n",
      "Learning rate : 0.00024971151018732924\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.68)  (off=0.186) (wh=0.163) (a=0.109): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.6842\n",
      "Pos loss : -6.0988\n",
      "Neg loss : -3.9986\n",
      "Off loss : 0.1856\n",
      "Wh loss : 0.1629\n",
      "Angle loss : 0.1086\n",
      "\n",
      "Val loss : 0.8610\n",
      "Val Pos loss : -16.6608\n",
      "Val Neg loss : -2.5267\n",
      "Val Off loss : 0.1976\n",
      "Val Wh loss : 0.1410\n",
      "Val Angle loss : 0.1138\n",
      "Improved validation loss to 0.861\n",
      "epochs 7/100\n",
      "Learning rate : 0.0002493512132586892\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.62)  (off=0.179) (wh=0.149) (a=0.099): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.6244\n",
      "Pos loss : -5.3199\n",
      "Neg loss : -3.5418\n",
      "Off loss : 0.1791\n",
      "Wh loss : 0.1492\n",
      "Angle loss : 0.0987\n",
      "\n",
      "Val loss : 0.7369\n",
      "Val Pos loss : -9.3303\n",
      "Val Neg loss : -4.9840\n",
      "Val Off loss : 0.1934\n",
      "Val Wh loss : 0.1320\n",
      "Val Angle loss : 0.1055\n",
      "Improved validation loss to 0.737\n",
      "epochs 8/100\n",
      "Learning rate : 0.00024884738582199595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.54)  (off=0.168) (wh=0.140) (a=0.083): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.5396\n",
      "Pos loss : -3.7580\n",
      "Neg loss : -2.8199\n",
      "Off loss : 0.1679\n",
      "Wh loss : 0.1399\n",
      "Angle loss : 0.0831\n",
      "\n",
      "Val loss : 0.6549\n",
      "Val Pos loss : -8.2674\n",
      "Val Neg loss : -3.7745\n",
      "Val Off loss : 0.1840\n",
      "Val Wh loss : 0.1286\n",
      "Val Angle loss : 0.0875\n",
      "Improved validation loss to 0.655\n",
      "epochs 9/100\n",
      "Learning rate : 0.00024820061531750313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.48)  (off=0.162) (wh=0.127) (a=0.072): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.4841\n",
      "Pos loss : -3.0365\n",
      "Neg loss : -2.4617\n",
      "Off loss : 0.1617\n",
      "Wh loss : 0.1268\n",
      "Angle loss : 0.0718\n",
      "\n",
      "Val loss : 0.9334\n",
      "Val Pos loss : -17.4925\n",
      "Val Neg loss : -6.4560\n",
      "Val Off loss : 0.1934\n",
      "Val Wh loss : 0.1333\n",
      "Val Angle loss : 0.1065\n",
      "epochs 10/100\n",
      "Learning rate : 0.00024741165585068746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.50)  (off=0.161) (wh=0.126) (a=0.072): 100%|█| 404/404 [03:43<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.4998\n",
      "Pos loss : -3.6482\n",
      "Neg loss : -2.7259\n",
      "Off loss : 0.1605\n",
      "Wh loss : 0.1260\n",
      "Angle loss : 0.0722\n",
      "\n",
      "Val loss : 0.7174\n",
      "Val Pos loss : -11.2269\n",
      "Val Neg loss : -3.8052\n",
      "Val Off loss : 0.1770\n",
      "Val Wh loss : 0.1228\n",
      "Val Angle loss : 0.1021\n",
      "epochs 11/100\n",
      "Learning rate : 0.0002464814273129952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.51)  (off=0.159) (wh=0.126) (a=0.072): 100%|█| 404/404 [03:42<00:00,  1.81i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.5120\n",
      "Pos loss : -4.0506\n",
      "Neg loss : -2.8893\n",
      "Off loss : 0.1591\n",
      "Wh loss : 0.1257\n",
      "Angle loss : 0.0719\n",
      "\n",
      "Val loss : 0.6577\n",
      "Val Pos loss : -9.4825\n",
      "Val Neg loss : -4.2660\n",
      "Val Off loss : 0.1768\n",
      "Val Wh loss : 0.1055\n",
      "Val Angle loss : 0.0783\n",
      "epochs 12/100\n",
      "Learning rate : 0.00024541101430929015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.42)  (off=0.148) (wh=0.116) (a=0.063): 100%|█| 404/404 [03:42<00:00,  1.81i\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train loss : 0.4225\n",
      "Pos loss : -2.2952\n",
      "Neg loss : -1.9818\n",
      "Off loss : 0.1475\n",
      "Wh loss : 0.1159\n",
      "Angle loss : 0.0630\n",
      "\n",
      "Val loss : 0.6472\n",
      "Val Pos loss : -9.1014\n",
      "Val Neg loss : -3.2731\n",
      "Val Off loss : 0.1769\n",
      "Val Wh loss : 0.1163\n",
      "Val Angle loss : 0.0846\n",
      "Improved validation loss to 0.647\n",
      "epochs 13/100\n",
      "Learning rate : 0.00024420166489325237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
      "train loss : 0.3937\n",
      "Pos loss : -1.8903\n",
      "Neg loss : -1.6784\n",
      "Off loss : 0.1405\n",
      "Wh loss : 0.1128\n",
      "Angle loss : 0.0597\n",
      "\n",
      "Val loss : 0.6850\n",
      "Val Pos loss : -10.4503\n",
      "Val Neg loss : -4.1408\n",
      "Val Off loss : 0.1841\n",
      "Val Wh loss : 0.1227\n",
      "Val Angle loss : 0.0687\n",
      "epochs 14/100\n",
      "Learning rate : 0.00024285478911220405\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
      "train loss : 0.4070\n",
      "Pos loss : -2.5089\n",
      "Neg loss : -2.0055\n",
      "Off loss : 0.1399\n",
      "Wh loss : 0.1094\n",
      "Angle loss : 0.0559\n",
      "\n",
      "Val loss : 0.6689\n",
      "Val Pos loss : -10.3275\n",
      "Val Neg loss : -4.7071\n",
      "Val Off loss : 0.1758\n",
      "Val Wh loss : 0.0964\n",
      "Val Angle loss : 0.0655\n",
      "epochs 15/100\n",
      "Learning rate : 0.00024137195736305685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train loss : 0.3455\n",
      "Pos loss : -1.4221\n",
      "Neg loss : -1.4400\n",
      "Off loss : 0.1308\n",
      "Wh loss : 0.0996\n",
      "Angle loss : 0.0511\n",
      "\n",
      "Val loss : 0.6119\n",
      "Val Pos loss : -9.5630\n",
      "Val Neg loss : -4.1359\n",
      "Val Off loss : 0.1697\n",
      "Val Wh loss : 0.0872\n",
      "Val Angle loss : 0.0620\n",
      "Improved validation loss to 0.612\n",
      "epochs 16/100\n",
      "Learning rate : 0.0002397548985612998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.36)  (off=0.129) (wh=0.100) (a=0.050): 100%|█| 404/404 [03:42<00:00,  1.82i\n",
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     ]
    },
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     "text": [
      "train loss : 0.3571\n",
      "Pos loss : -1.9919\n",
      "Neg loss : -1.6056\n",
      "Off loss : 0.1290\n",
      "Wh loss : 0.1004\n",
      "Angle loss : 0.0499\n",
      "\n",
      "Val loss : 0.8162\n",
      "Val Pos loss : -17.3411\n",
      "Val Neg loss : -3.1951\n",
      "Val Off loss : 0.1793\n",
      "Val Wh loss : 0.1150\n",
      "Val Angle loss : 0.0897\n",
      "epochs 17/100\n",
      "Learning rate : 0.00023800549812516092\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "train loss : 0.3742\n",
      "Pos loss : -2.0435\n",
      "Neg loss : -1.8213\n",
      "Off loss : 0.1323\n",
      "Wh loss : 0.1014\n",
      "Angle loss : 0.0547\n",
      "\n",
      "Val loss : 0.6630\n",
      "Val Pos loss : -12.6258\n",
      "Val Neg loss : -2.4839\n",
      "Val Off loss : 0.1720\n",
      "Val Wh loss : 0.1104\n",
      "Val Angle loss : 0.0559\n",
      "epochs 18/100\n",
      "Learning rate : 0.00023612579577729392\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "train loss : 0.3446\n",
      "Pos loss : -1.7605\n",
      "Neg loss : -1.6056\n",
      "Off loss : 0.1244\n",
      "Wh loss : 0.0973\n",
      "Angle loss : 0.0484\n",
      "\n",
      "Val loss : 0.6465\n",
      "Val Pos loss : -10.1814\n",
      "Val Neg loss : -4.3448\n",
      "Val Off loss : 0.1734\n",
      "Val Wh loss : 0.0901\n",
      "Val Angle loss : 0.0680\n",
      "epochs 19/100\n",
      "Learning rate : 0.00023411798316655297\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "train loss : 0.1518\n",
      "Pos loss : -0.2826\n",
      "Neg loss : -0.4383\n",
      "Off loss : 0.0612\n",
      "Wh loss : 0.0516\n",
      "Angle loss : 0.0227\n",
      "\n",
      "Val loss : 0.6386\n",
      "Val Pos loss : -15.0089\n",
      "Val Neg loss : -2.3984\n",
      "Val Off loss : 0.1639\n",
      "Val Wh loss : 0.0672\n",
      "Val Angle loss : 0.0342\n",
      "epochs 40/100\n",
      "Learning rate : 0.00016769135200615955\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.20)  (off=0.065) (wh=0.058) (a=0.031): 100%|█| 404/404 [03:41<00:00,  1.82i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.2018\n",
      "Pos loss : -1.1097\n",
      "Neg loss : -1.0415\n",
      "Off loss : 0.0646\n",
      "Wh loss : 0.0583\n",
      "Angle loss : 0.0308\n",
      "\n",
      "Val loss : 0.5936\n",
      "Val Pos loss : -13.2119\n",
      "Val Neg loss : -2.1398\n",
      "Val Off loss : 0.1646\n",
      "Val Wh loss : 0.0620\n",
      "Val Angle loss : 0.0381\n",
      "epochs 41/100\n",
      "Learning rate : 0.00016368617567153314\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.15)  (off=0.057) (wh=0.049) (a=0.023): 100%|█| 404/404 [03:40<00:00,  1.83i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1459\n",
      "Pos loss : -0.2854\n",
      "Neg loss : -0.4640\n",
      "Off loss : 0.0574\n",
      "Wh loss : 0.0488\n",
      "Angle loss : 0.0232\n",
      "\n",
      "Val loss : 0.6319\n",
      "Val Pos loss : -15.6791\n",
      "Val Neg loss : -1.7874\n",
      "Val Off loss : 0.1662\n",
      "Val Wh loss : 0.0595\n",
      "Val Angle loss : 0.0324\n",
      "epochs 42/100\n",
      "Learning rate : 0.00015963735043068178\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.13)  (off=0.052) (wh=0.046) (a=0.021): 100%|█| 404/404 [03:40<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1308\n",
      "Pos loss : -0.1825\n",
      "Neg loss : -0.3472\n",
      "Off loss : 0.0519\n",
      "Wh loss : 0.0456\n",
      "Angle loss : 0.0214\n",
      "\n",
      "Val loss : 0.6701\n",
      "Val Pos loss : -17.5698\n",
      "Val Neg loss : -1.7424\n",
      "Val Off loss : 0.1635\n",
      "Val Wh loss : 0.0597\n",
      "Val Angle loss : 0.0323\n",
      "epochs 43/100\n",
      "Learning rate : 0.0001555495970327228\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.12)  (off=0.050) (wh=0.044) (a=0.020): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1238\n",
      "Pos loss : -0.1524\n",
      "Neg loss : -0.3003\n",
      "Off loss : 0.0499\n",
      "Wh loss : 0.0437\n",
      "Angle loss : 0.0201\n",
      "\n",
      "Val loss : 0.6955\n",
      "Val Pos loss : -18.6528\n",
      "Val Neg loss : -1.9697\n",
      "Val Off loss : 0.1592\n",
      "Val Wh loss : 0.0622\n",
      "Val Angle loss : 0.0321\n",
      "epochs 44/100\n",
      "Learning rate : 0.00015142768161526343\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.12)  (off=0.048) (wh=0.043) (a=0.019): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1207\n",
      "Pos loss : -0.1925\n",
      "Neg loss : -0.3082\n",
      "Off loss : 0.0481\n",
      "Wh loss : 0.0429\n",
      "Angle loss : 0.0185\n",
      "\n",
      "Val loss : 0.6803\n",
      "Val Pos loss : -18.0241\n",
      "Val Neg loss : -1.7801\n",
      "Val Off loss : 0.1633\n",
      "Val Wh loss : 0.0602\n",
      "Val Angle loss : 0.0296\n",
      "epochs 45/100\n",
      "Learning rate : 0.0001472764101472975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.12)  (off=0.048) (wh=0.043) (a=0.019): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1192\n",
      "Pos loss : -0.1457\n",
      "Neg loss : -0.2910\n",
      "Off loss : 0.0482\n",
      "Wh loss : 0.0425\n",
      "Angle loss : 0.0187\n",
      "\n",
      "Val loss : 0.6909\n",
      "Val Pos loss : -18.6499\n",
      "Val Neg loss : -1.7031\n",
      "Val Off loss : 0.1658\n",
      "Val Wh loss : 0.0584\n",
      "Val Angle loss : 0.0307\n",
      "epochs 46/100\n",
      "Learning rate : 0.00014310062282566052\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.12)  (off=0.047) (wh=0.042) (a=0.019): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1187\n",
      "Pos loss : -0.1751\n",
      "Neg loss : -0.2971\n",
      "Off loss : 0.0471\n",
      "Wh loss : 0.0424\n",
      "Angle loss : 0.0186\n",
      "\n",
      "Val loss : 0.6736\n",
      "Val Pos loss : -18.1854\n",
      "Val Neg loss : -1.7134\n",
      "Val Off loss : 0.1565\n",
      "Val Wh loss : 0.0582\n",
      "Val Angle loss : 0.0302\n",
      "epochs 47/100\n",
      "Learning rate : 0.00013890518843157675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.12)  (off=0.046) (wh=0.042) (a=0.019): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1191\n",
      "Pos loss : -0.2059\n",
      "Neg loss : -0.3331\n",
      "Off loss : 0.0460\n",
      "Wh loss : 0.0422\n",
      "Angle loss : 0.0188\n",
      "\n",
      "Val loss : 0.6524\n",
      "Val Pos loss : -16.0826\n",
      "Val Neg loss : -2.4533\n",
      "Val Off loss : 0.1646\n",
      "Val Wh loss : 0.0574\n",
      "Val Angle loss : 0.0320\n",
      "epochs 48/100\n",
      "Learning rate : 0.00013469499865387808\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.11)  (off=0.045) (wh=0.040) (a=0.018): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1127\n",
      "Pos loss : -0.1625\n",
      "Neg loss : -0.3047\n",
      "Off loss : 0.0446\n",
      "Wh loss : 0.0397\n",
      "Angle loss : 0.0179\n",
      "\n",
      "Val loss : 0.6920\n",
      "Val Pos loss : -19.1239\n",
      "Val Neg loss : -1.5574\n",
      "Val Off loss : 0.1629\n",
      "Val Wh loss : 0.0598\n",
      "Val Angle loss : 0.0271\n",
      "epochs 49/100\n",
      "Learning rate : 0.00013047496238551354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.11)  (off=0.042) (wh=0.040) (a=0.017): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1086\n",
      "Pos loss : -0.1509\n",
      "Neg loss : -0.2849\n",
      "Off loss : 0.0423\n",
      "Wh loss : 0.0398\n",
      "Angle loss : 0.0168\n",
      "\n",
      "Val loss : 0.6886\n",
      "Val Pos loss : -19.0456\n",
      "Val Neg loss : -1.7055\n",
      "Val Off loss : 0.1571\n",
      "Val Wh loss : 0.0584\n",
      "Val Angle loss : 0.0258\n",
      "epochs 50/100\n",
      "Learning rate : 0.00012625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.11)  (off=0.042) (wh=0.038) (a=0.016): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1051\n",
      "Pos loss : -0.1278\n",
      "Neg loss : -0.2599\n",
      "Off loss : 0.0418\n",
      "Wh loss : 0.0384\n",
      "Angle loss : 0.0163\n",
      "\n",
      "Val loss : 0.7612\n",
      "Val Pos loss : -22.5108\n",
      "Val Neg loss : -1.6757\n",
      "Val Off loss : 0.1568\n",
      "Val Wh loss : 0.0560\n",
      "Val Angle loss : 0.0299\n",
      "epochs 51/100\n",
      "Learning rate : 0.00012202503761448647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.11)  (off=0.039) (wh=0.037) (a=0.016): 100%|█| 404/404 [03:39<00:00,  1.84i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1059\n",
      "Pos loss : -0.2354\n",
      "Neg loss : -0.3449\n",
      "Off loss : 0.0392\n",
      "Wh loss : 0.0373\n",
      "Angle loss : 0.0162\n",
      "\n",
      "Val loss : 0.6283\n",
      "Val Pos loss : -15.7905\n",
      "Val Neg loss : -2.4888\n",
      "Val Off loss : 0.1550\n",
      "Val Wh loss : 0.0557\n",
      "Val Angle loss : 0.0268\n",
      "epochs 52/100\n",
      "Learning rate : 0.00011780500134612199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.10)  (off=0.038) (wh=0.036) (a=0.016): 100%|█| 404/404 [03:44<00:00,  1.80i\n",
      "100%|███████████████████████████████████████████| 45/45 [00:10<00:00,  4.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss : 0.1005\n",
      "Pos loss : -0.1687\n",
      "Neg loss : -0.3050\n",
      "Off loss : 0.0383\n",
      "Wh loss : 0.0357\n",
      "Angle loss : 0.0158\n",
      "\n",
      "Val loss : 0.7072\n",
      "Val Pos loss : -19.6833\n",
      "Val Neg loss : -2.0069\n",
      "Val Off loss : 0.1583\n",
      "Val Wh loss : 0.0553\n",
      "Val Angle loss : 0.0265\n",
      "epochs 53/100\n",
      "Learning rate : 0.00011359481156842329\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(l=0.07)  (off=0.024) (wh=0.028) (a=0.014):   1%| | 4/404 [00:02<04:23,  1.52it/\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[1;32m      2\u001b[0m     set_optimizer_lr(optimizer, lr_scheduler_func, epoch)\n\u001b[0;32m----> 3\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     counter \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m val_loss \u001b[38;5;241m<\u001b[39m best_loss:\n",
      "Cell \u001b[0;32mIn[19], line 18\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#optimizer.train()\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, (img, hm, offset, regr, angle,mask) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(t):       \n\u001b[1;32m     17\u001b[0m     \u001b[38;5;66;03m# send to gpu\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m     hm_gt \u001b[38;5;241m=\u001b[39m hm\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     20\u001b[0m     offset_gt \u001b[38;5;241m=\u001b[39m offset\u001b[38;5;241m.\u001b[39mto(device)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    set_optimizer_lr(optimizer, lr_scheduler_func, epoch)\n",
    "    val_loss = train(epoch)\n",
    "    counter += 1\n",
    "    if val_loss < best_loss:\n",
    "        best_loss = val_loss\n",
    "        print(f\"Improved validation loss to {best_loss:.3f}\")\n",
    "        torch.save(model.state_dict(), \"4corner_hardnet.pth\") \n",
    "    if counter % 5 == 0:\n",
    "        torch.save(model.state_dict(), f\"ep_{counter}-4corner_hardnet.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PLyYjv5izOUWkln2l5RSSZvo+u4izuUWUGjV8PNy4qHsEl/SJZHS3CPy8mmYI0TRX7SI0TaOgxOCU1/bxcLPZ/zCzZs3ilVdeITY2lpCQEE6fPs2ll17Kc889h5eXF59++imTJk3i0KFDtG/fvtbnmTt3Li+99BIvv/wyCxYsYOrUqZw6dYrQ0FCbrFMIIRzJaNQ4k1HAgeRsDiblcDglh6JSA17ubni56/Hy0Juve7rrzZeFJUayCkoqfWVXuJ5fXP654eGmw8NNb/7ydNPh4a6uh/l5EhPmR4dWvnQI9aNDmC8dwnwJ8Paocb2apnEur5jkrEKSsgpJziogKauQc7nFpOcXk5lfTHpeMZn5JWTkF2O08sCqghIDv+9J4vc9SXi56xnVLZxL+0RxUfeIWtcEUFBs4MTZPI6fzeVYah7peUXMvaK3dS9uQxL4NEJBiYGeTy1zymvv/+94fD1t89f33//+l4svvtj8fWhoKP369TN//8wzz/Djjz/yyy+/1Fk7NW3aNK6//noAnn/+eebPn8+WLVuYMGGCTdYphBD2kldUysHkbPYn5XAwKZuDyTkcSs4ht6jUrq9bYtAoMRiA6r9EHwU2n0ivdnuYnyftw3zpEOqLBmVBjvoqNhiten1/L3eCfT0I8/ciIkB9hQd4ERHgrb4PVN+H+XlxKDmHP/Ym8ceeJE6dy2fZvhSW7UvB003PiC6tuKRPFFFB3hxPy+VYWh7H0nI5npZHYlYBVU8FffyS7jb7DLOWBD6CgQMHVvo+NzeXOXPm8Pvvv5OUlERpaSkFBQXEx8fX+Tx9+/Y1X/fz8yMwMNB8NIUQQriiY2m5fLj2BN/vOENxafWgwdNNT5fW/nSPDKR7ZAAB3u4UlRopKjVQVGKk2GBU35cYym434uWuJ8jHg0AfD4KqfAX7ehDo7YGbm46SUmNZ4KMeV2Io/yoqNZKaXcTJc3nEn8vn5Lk8Tp3L51xesflrZ3xmtfXqdNDK34uoIG8iA72JCvKmlb8XIX6ehPp5EuzrQaifJ6G+ngT5euDlbnnNTp92QfRpF8Rj47txICmHJXtV9ud4Wh4rDqay4mDt/94H+XjQKdyP2HB/OoX7Y7A23WRDEvg0go+HG/v/O95pr20rVbuzZs6cyfLly3nllVfo3LkzPj4+/Otf/6K4uLjO5/HwqJzq1Ol0GI3W/fYhhBD2pmkam0+k8+Ha4/x1oPzDunWgFz2iAukRpYKcHlGBdGzlh4eb68z6zSks4dS5fOLT8zl1Lh+9DqKCfWgT5E1kkDcRAd54utt3vTqdjp5tAunZJpCHL+7KkdRc/tiTxJ/7UigoMZgDnNhWfnSKUJehfp4uM7hWAp9G0Ol0TkvV2dP69euZNm0aV155JaAyQCdPnnTuooQQopFKDUb+2JvMwr+PsychC1AZkrE9WnPnhbEM7BDiMh/OtQnw9qB32yB6tw1y9lIA9TnYtXUAXVsHMGNsV2cvxyLN71NbNFqXLl344YcfmDRpEjqdjv/85z+SuRGiCSsoNrD9VAY+nnraBPsQEeCNm941PuBLDEaSMgs5nZHPmYx8zmQUcCajgNPp+aTkFDJ1cAfuHtmpUa+RW1TKV1vi+Xj9SRIyCwDwctfzr7h23Da8I7Hh/vU8g2hOJPAR1bz22mtMnz6doUOH0qpVKx5//HGys7OdvSwhhBUKSwysPpTG73uSWHEgpVInkZteR2SgN22DfWgT7E2bYB/aBPvQNtiH8zqEEORTe4eOLaw7cpaFa49zJCWH5OzCOruLXl9+mGsHRhPi59mg10rILGDy2+tJyykCVGHwzUNiuPGC9oT5139AtWh+dJpWtda6ZcvOziYoKIisrCwCAwPNtxcWFnLixAk6duyIt7e3E1fYPMj7KYTtFZYY+PuwCnb+2p9CXoVgJzJQZXlSsgsprSPS8HTTc2HXcC7v34axPSJsup2/63QmLy09yIZj5yrd7uWup12ID+1CfIkOVZftQnx4e9UxDiRl8/iE7twzqmFZn6d/3suijadoF+LD/aM7M3lA22YxhE9UV9vnd1WS8RFCiCZM0zRWHUrl191JLN+fUqn9uk2QN5f1jeKyvm3o104NMDUYNdJyikjILCCxwldCZiHHz6r2478OpPDXgRR8Pd24uGdrLu/XhhFdwhtcNHskJYdX/jzEsn0pgAqubhjcnkn92hAd6kO4v1eNtTWFJUZmfrubzzae5I4RHXG3ssg4M7+Yb7adAeDFq/syrHOrBq1fNC8S+AghRBOVkVfMzG93V2ojjgry5tI+UVzWN4r+7YLRV6nlcdPriCzrAIrrEFLtOQ8l5/DL7gR+2Z3I6fQCft6VyM+7Egn29eCS3pFM6teG89qHWJQ1OZORzxt/HeGHHWcwaqDXwVXntWPG2C60C/Gt9/ET+0Yx748DJGYVsnx/Cpf0ibLgXSm3eHM8BSUGekQFMrRTmFWPFc2XBD5CCNEEbT+VwQNf7CAxqxBPdz03DGrPpH5RDIgOqRbsWKNbZACPRnZn5rhu7DqdyS+7E/ntnyTScor4cstpvtxyGoBW/p60LduSahfsY96qahvig6+nGx+uPcEXm+PNA/XG92rNzHHd6NI6wOK1eHu4ccPg9ixYeZSPN5y0KvApLjWyaMNJAO4Y0dHlu7WE40jgI4RosQxGjeNpuexNzGJvQjZ7E7I4k1HARd0juGdUJ9oE+zh7idUYjRofrjvOS0sPUWrUiAnz5a0bzrN5e7NOp2NA+xAGtA/hyct6svn4OX7Znciyfclk5JdwNreYs7nF7D6dWefzDO0UxqPjuzGgffXskiWmDu7Au6uPseVEOvsSs+jVxrI/5y+7E0nNKaJ1oBcT+7Zp0GuL5kkCHyFEi6BpGodScvjnTBb7ErLYm5jN/sTsGs/b+2zTKb7aGs+UgdHcO7ozbV0kAKq6tTWxbxTzrupT5zlJtuCm1zG0cyuGdm7FvKv6kF1QWtZ+XsCZjHwSMgvMbegJGflkF5bSr10Qj47vzvAujauriQzy5pI+Ufy6O5FFG07y0r/61fsYTdP4cO1xAG4ZGmP3gX6iaZHARwjRrJ06l8ePOxP4cWcCp87lV/u5r6cbPaMC6d02iF5tAgn29eSjdcfZdDydxZvj+Wbbaf4VF829ozoRHVp/XYq9VN3aenpST24Y1N7hWzg6nY4gXw+CfGsfoldYYrBp59S0oTH8ujuRn3YlMuuSHoTW09q+7uhZDibn4OvpxtRBHWy2DtE8SOAjhGh2MvOL+e2fJH7cmcD2Uxnm23083OgXHUTvNkHm6bcdW/lVG+Z3cc/WbD5+jjdXHGHDsXN8uSWeb7ed5l9x7bh3VGfahzkuADIaNRauPc7Ly9TWVsdWfrx1wwCLt3ycwdbt4ue1D6ZvuyD+OZPFl1viuW905zrv/+HaEwBMGRhNkK99s2Gi6ZHARwjRLBSVGlh1MI0fd55h1cE0c1GtXgfDOrfiqvPaMr5XpMVzaQbHhvFFbBhbT6bz5l9HWHf0LF9tPc23289w1YC2zLqku90H4OUUlvDQV7tYWba1dXm/Njx/VR/8vVrWP906nY5pQ2N4+JvdfL7pFHdeGFvr+VmHU3JYczgNvQ6mD+vo4JWKpqBl/d8jhGh2cotK+WjtCT7ecILM/BLz7T2iArlqQFsu79+G1oENH5J5fkwon98+mO2n0nnjryOsPXKWb7ef4VR6Pl/dcUGjOqjqYjBq3P/FTtYcTsPTXc/cy3tx3fnRLbY76bK+UTz/xwGSsgr5c18Kl/WtucPLVNszvlekQzNzoumQwEfUa9SoUfTv35833njD2UsRwqywxMDnm07xzupjpOcVAxAR4MXkAW25ckBbekTVPrm1IeI6hPLZbYPZejKdmz/awpYT6SzeEs9NF9inhuSlpQdZczgNbw89X905hP7RwXZ5nabCy92NGwZ3YP6KI3yy4USNgU9qTiE/7UwE4PYRsY5eomgipNS9mZs0aRITJkyo8Wdr165Fp9Pxzz//OHhVQjRcqcHI11vjueiV1Tz7+wHS84rp2MqP+dcPYOPsMTxxaQ+bBz0VnR8TymMTugHwwh8HzIde2tKPO8/w/t8qc/Hyv/q1+KDH5MbB7XHX69h6MoO9ZaerV/TZxlMUG4yc1z64xuGMQoAEPs3ebbfdxvLlyzlz5ky1n3388ccMHDiQvn37OmFlQljHaNT4/Z8kxr3xN49/v4fErEIiA72Zd1Uf/vz3hVzer43DThy/ZUgMcR1CyCs28MQPe7DlkYe7T2fy+Pd7ALhvdCcm9ZMZNCYRgd7mTM8nZcMJTQqKVQYQJNsj6iaBTzM3ceJEwsPD+eSTTyrdnpuby7fffsvkyZO5/vrradu2Lb6+vvTp04cvv/zSOYsVogaaprHmcBqXv72O+77YwfG0PEJ8PXjysh6sfnQU1w9qX2uhq73o9TpevLovnu561hxO44cdCTZ53tTsQu78bBvFpUbG9ojgkYu72eR5m5NpQ2MA+GVXImdzi8y3f7fjDBn5JUSH+jC+V6STVieaAgl8GkPToDjPOV8W/obp7u7OzTffzCeffFLpt9Jvv/0Wg8HAjTfeSFxcHL///jt79+7lzjvv5KabbmLLli32eteEsFipwcjsH/Zwy/+2sDchGz9PNx4a04W/HxvN7SNinXrKducIfx4a0wWA//62n9ScwkY9X2GJgbs+305KdhFdIvx5/dr+diucbsoGtA+hX3QwxQYjX22JB1Q28H/rVAv79GEdHZb5E02TFDc3Rkk+PO+kNPQTieDpZ9Fdp0+fzssvv8yaNWsYNWoUoLa5rr76ajp06MDMmTPN933ggQdYtmwZ33zzDYMGDbLHyoWwSF5RKfd/sYNVh1Rr8q3DOnLvqE52byG3xp0XxrJkbxJ7E7J56qd9vHdTXIOeR9M0nvxpLzvjMwny8WDhzQPtPo25Kbt1aAwzvt7FZ5tOcdfITqw6mMqJs3kEerszZWC0s5cnXJxkfFqA7t27M3ToUP73v/8BcPToUdauXcttt92GwWDgmWeeoU+fPoSGhuLv78+yZcuIj4938qpFS5aWU8T1Czex6pDqanr/poH8Z2JPlwp6ADzc9Lx0dT/c9TqW7kvmjz1JDXqe/60/yXfbz6DXwVs3DCCmlWW/1LRUl/aJIjzAi5TsIpbuTebDsmzPDYM74NfCZhwJ68l/IY3h4asyL856bSvcdtttPPDAA7z99tt8/PHHdOrUiZEjR/Liiy/y5ptv8sYbb9CnTx/8/PyYMWMGxcXFdlq4EHU7cTaPW/63hfj0fEJ8Pfho2vmc18ADLh2hZ5tA7hnViQUrj/LUz3sZEhtGSD1HKlS07shZnvt9PwD/d1lPRnQJt9dSmw1Pdz1TB7fnjb+O8MKSgyRkFuCu15nrf4Soi2R8GkOnU9tNzviycojZlClT0Ov1fPHFF3z66adMnz4dnU7H+vXrueKKK7jxxhvp168fsbGxHD582E5vmBB12xmfwdXvbiA+PZ/oUB++v2eoSwc9Jvdf1JkuEf6czS3mv7/tt/hxJ8/mcd8XOzBq8K+4dkwfFmO/RTYzNwxuj4ebzjxO4PJ+bYgMavigStFySODTQvj7+3Pttdcye/ZskpKSmDZtGgBdunRh+fLlbNiwgQMHDnDXXXeRkpLi3MUKp9I0jZTsxhXqNsTy/Slcv3AT6XnF9G0XxA/3DCM23N/h62gIL3c3XvpXX/Q6+HFnAqvKjpiojaZp7E3I4o5Pt5FVUMKA9sE8d2XvFjuVuSEiAryZ2Le8xvK2EXI8hbCMBD4tyG233UZGRgbjx4+nTRv1D8aTTz7Jeeedx/jx4xk1ahSRkZFMnjzZuQsVTvXCkoMMfn4F32+vPvvJXj7fdIq7PttGYYmR0d3C+fKOCwgPcK16nvoMaB9iPhvqiR/3kFNYUunnpQYjG4+dY84v+xj+4iomLljHkdRcIgO9ef/GOLzcndeh1lTdMSIWL3c943u1dulDW4Vr0Wm2nLzVDGRnZxMUFERWVhaBgeXTXwsLCzlx4gQdO3bE21vSqY0l76drOp6Wy7jX/6bUqNE60Is1j462a8u4pmm8+udh3lp1FIBrB0bz3JW9cXfwXB5bKSg2MOHNvzl1Lp8bBrfnP5f1ZO2RNJbtS2HFwZRKZ4l5e+gZ2TWcmeO60aV1gBNX3bRlF5bg4+Hm8FlOwvXU9vldlRQ3CyHMXlp6iFKj+l0oJbuIxZvjuW24fbYQsgpKePy7f1i6LxmAGWO78NCYLk16u8fH040XrurL9Qs38cXmeH7YcYbCEqP55yG+Hozt0ZpxvSIZ3rkVPp6S5WmsQGn7F1aSwEcIAcD2U+ks3ZeMXgfThnbkf+tP8O7qo1x3frTNW4R3xmfwwJc7OZNRgIebjmcn9+ba89vb9DWcZUinMKYObs/izfEUlhhpG6wmCY/r1ZqBHUKabDZLiOZCAh8hmolDyTn8tCuBO0fEWtVODWrL6fk/DgJwTVw0sy/tzoqDKZw6l8+ijSe5d1Rnm6zRaNT4cN1xc2apfagvb90wgL7tgm3y/K7i6Um9GNQxlM4R/vSMCmzSWSwhmhv51UOIZqCg2MAdn27j3dXHuP/LHRiM1pXuLduXzPZTGXh76Hl4XFc83PTMGKuOY3h/zXGyqxTqNkR6XjG3LdrK838cpNSocVnfKH57cHizC3pAzZm5on9berUJkqBHCBcjgY+VpBbcNuR9tK35K48Qn54PwPqj55i/4ojFjy0xGHlx6SFAdcm0DlTF5pf3a0vnCH+yCkr4aO2JRq1v0/FzXPLm36w6lIaXu57nr+zDW9cPkPoMIYTDSeBjIQ8P9Q90fn6+k1fSPJjeR9P7KhruYHI2C/8+DsA1ce0AFQj9fTjNosd/uSWeE2fzCPPz5K6Rncy3u+l1PHxxVwA+WneCjDzrp3kbjBpv/nWEGxZuIiW7iE7hfvx8/zBuGNxeMiFCCKeQGh8Lubm5ERwcTGqqGkzm6+sr/3A3gKZp5Ofnk5qaSnBwMG5u0tXSGEajxuwf9lBq1BjfqzUvX9MPdzc9X26JZ8bXu/jjwRF1TrPNKSzhzb9UdmjG2C74VylintArkp5RgexPyub9v48z65LuFq8tLaeIB7/cycbj5wA1mfi/V/TC11P+2RFCOI/8C2SFyMhIAHPwIxouODjY/H6Khlu8+RQ74zPx93Jn7uW9AXh6Uk92n85kf1I293+xgy/vvKDWGScf/H2cc3nFxLby47pB1buq9Hodj4zrym2LtvHJhhNMHx5DRED9c5eSsgq4YeFmTpzNw9fTjWcn9+aq89o17g8rhBA2IIGPFXQ6HVFRUURERFBS0vhiz5bKw8NDMj02kJJdyEtltTmPju9mzux4e7jxztTzmLRgHdtOZfDKskPMvrRHtccnZxWycK3aIntsQvdag6OLukfQPzqYXaczeWfVMeZc3qvOdZ1Oz+eGDzdxOr2AtsE+LJo+iM4RTePoCSFE8yeBTwO4ubnJB7dwujm/7COnqJT+0cHceEGHSj+LaeXHy9f05e7Pd/D+38cZGBPKxT1bV7rP68sPU1hiZGCHEMb3qvyzinQ6HTPHdePGjzbzxeZ47rwwljbBPjXe9+TZPKZ+uJmEzAI6hPnyxR0X0LaW+wohhDNIcbMQjeCs7rTl+1NYsjcZd72OeVf1wU1fvd5sQu8o89lRj3yzi9Pp5YX5h5Jz+Hb7aQBmX9qj3nq1YZ3DuCA2lGKDkQUrj9Z4n6OpuVz7wUYSMgvoFO7HN3cNkaBHCOFyJPARooFeW36YvnP/ZP6KIxSXGut/gI3kFpXy1M97Abh9RCw9omo/k2bWJd0Z0D6Y7MJS7vtiB0WlBgBeWHIAowaX9I4krkNIva+p0+l4ZFw3AL7ddppT5/Iq/fxQcg7XfbCRlOwiurUO4Ks7h5jb4oUQwpVI4CNEA6RkF/Le6mPkFJby2vLDTFywlh3xGQ557Vf/PERSViHRoT48NKZLnff1dNfz1g3nEezrwT9nsnju9wNsOHqWVYfScNfreGyC5V1a58eEMrJrOKVGjTcrzAnam5DFdR9s5GxuMT2jAvnyzqZ3sroQouWQwEeIBvh4/UmKDUZiW/kR6ufJ4ZRcrn53A3N+2UduUandXvefM5ks2nASgOcm97HokMu2wT68PqU/AJ9uPMVDX+8CYOrg9nRs5WfV6z8yTs31+WlnAkdTc9h1OpMbFm4iI7+Efu2C+PKOCwi18rgMIYRwJAl8hLBSblEpizefAlR9zF8Pj+Sq89qiafDJhpOMe20NKw+m2Px1Sw1GZn2/B6MGV/Rvw4Vdwy1+7OjuEdw7Sg0nTMspwt/LnQfryRbVpG+7YMb1bI1Rg8e++4cbP9xMdmEpcR1C+Pz2wQT5ykBKIYRrk8BHCCt9tSWenMJSOoX7MaZ7BKF+nrw2pT+fTh9EdKgPiVmFTP9kGw98uZOzuUU2e92P159kf1I2QT4e/GdiT6sf//DFXbkgNhSAe0d3Isy/YdtRD4/rik4HO+IzyS0q5YLYUD6dPogAOX5CCNdSUgifXgGLJsHprc5ejcuQwEcIK5QYjHy0Tp1bdceIWPQVuqku7BrOshkXcseIjuh18OvuRMa+tobvtp9pdPfX6fR8Xlt+GIAnLu1OqwYELe5uej6eNojFtw/m7gs71f+AWnSPDGRy/7YAjOjSio+nDcLPSyZjCOFyjq9WXyf+ho/Gwvd3QNYZZ6/K6XSanBZZSXZ2NkFBQWRlZREYWHu3jGiZfthxhoe/2U14gBdrHxuNt0fNNTb/nMlk1vd72J+UDcDV57Xj+at64+Vu/fyn+HP53LZoK0dScxnUMZSv77zA6cellBiMbD2ZzsAOoXi6y+9PQrik3x6GbR9BUHRZwKOBuw8MewiGPQie1tX4uTpLP7/lXywhLKRpGh+UHQY6bWhMrUEPqFqYn+8fxqPju+Gm1/H9jjNMXbjZ6q2vzcfPccXb6ziSmktEgBcvXt3X6UEPgIebnqGdWknQI4Sr0jQ4slxdv+xVuHMVtB8CpQWw5gVYMBD++QaMjhvF4Sok41OFZHyavyV7klh9KI1HJ3Szasto9aFUpn28FV9PNzbOGmNxIe/fh9O474sd5BSW0jbYhw9vGVjn7B2Tr7fG8+RPeykxaPRpG8TCmwfWeeCoEEKYpR6EdwaDmxc8fhI8fVUwtP8n+PMpyIpX92s7ECa8ANHng6EUcpIgO0FliLLOlF/PToAOw2HC8878U9XJ0s9v2ZgXLcrKgync98UOjBqcPJfH57cPrvWMqqpM2Z7rzm9vVffShV3D+fHeYdy+aCsnz+Xzr3c38MZ1A6odIWFiMGo8/8cBcy3RZX2jeOVf/SxqXRdCuChDKZzZAoeXwtEVUJQDHr7g4VN+6elb+bZul0DM8Ia93pE/1WXHEep5AXQ66HUldL0ENr0Nf78KCdtU/U9AFOSmgFZHBihpNwy6HUJjG7YmFyEZnyok49N87U/M5pr3NpBXbDDfNn1YR56aVH+H1J4zWUx6ax1ueh1/Pza6QUcxZOYXc+/iHWw4dg6dDh6f0J27LoyttHWVU1jCg1/uZNWhNABmjO3CQ2O6uMT2lhDNjtEAGSchdT+kH4eYEdD2PNs9f0GGCnIOL1XbToWZ1j3eKwhmHgaPBmR6P5kIJ9fCJS/B4Ltqvk9OMqx4BnYtBspCAb0HBLaBoHYQ2FZdBrWFnYshcQdc/IyqD3JBkvERooKU7EJuW7SVvGIDQzuFMXVwB+77Ygf/W3+CftFBXFHWpVSb9/8+BsCkvlENPn8q2NeTRdMHMeeXfSzeHM8LSw5yOCWHeVf1wcvdrVIRs7eHnlev6c9lfaMa9FpCiAo0TW3hpO6H1AOQsl9dTzukal5MdG4w6U0476aGv9a5Y3DwNzi8DOI3gVb+ixY+IdBlnPoK7gAl+VBSUHZZ8XoBbPufysAc+RN6Xm7dGgqzIH6jut55bO33C4iEyW/DhTMh/5wKcvwiQF9DFlzTVOBz4FeXDXwsJYGPaPbyi0u5bdFWkrIK6RTux7tT4wjy9eBAUmfeWnWUx7//h84R/vRqE1Tj40+n5/PHniQA7mxEGzioouDnruxDt8gA5v66nx92JHDybB53XhjL7B/2kJFfQutALxbePJC+7YIb9VpCCFQg8sUUOFfz4bq4e0N4d7W9FL8Rfrkfsk7DqNlqa8hSRgOsfRVWz6u8XRTRE7qOh64ToN35oLdwy7okH9a/CXu+tT7wOb4ajKUQ1hnCLPg3K7Sj+qpL98vgj5lquy4nWQVNTZQEPqJZMxg1HvpqF3sTsgnz8+TjaYPM9Tn/vrgrexKyWHM4jbs/386v9w8n2Lf6cQsfrTuBUVMza3q2sc32581DYujYyo97F+9gR3wmd3++A4B+7YL44OaBcsCnELZQWgTf3aqCHp2bCgIieqqv1mWXITEqGNE0WPksrH0F1rwImadV9sfdgiNYshPhhzvV1hKoLbMel6uAJ6RDw9be5xoV+BxepjI43jX/YlYjU31Pl/ENe+2aBLZRgduZrSqjdf7ttntuB5NeVNGsvbDkAMv3p+DprueDm+NoH+Zr/pmbXseb1/Wnfagvp9MLePCrXRiMlUveMvKK+XrraQDuamS2p6oRXcL56b5h5vOyJvaN4uu75FRzIWzmr7mqINcnBGb8A/dvhSmLYNTj0GOSCoRMGRidDsb8Bya+oYKk3V/AF9dAYXbdr3FoKbw7TAU9Hn4w+T2Y9hsMvrPhQQ9A697QqhsYiuDg75Y/rmIbe5eLG/76NekxSV0e+M22z+tgEviIZmvx5lMsXKs6o165ph9xHUKr3SfY15P3bozD20PP34fTeG35oUo//2zTKQpKDPSMCmRY5zCbr7FTuD+/Pzicn+8bxoLrB9Q5G0gIYYVDS1XnEsDkd1X9iiUG3grXf6WCmOOr4eNLVEanqtIiWDILvrwWCtIhsi/c9Tf0v94269fpVNYH1HaXpZL/UbVBHn7QYaht1mLSfaK6PLkW8tNt+9wO1GwCn5MnT3LbbbfRsWNHfHx86NSpE08//TTFxcXOXppwgr8Pp/HUz/sAeOTirlzer02t9+3ZJpAXr+4LwNurjrF0bzIAhSUG80nod42MtVtnla+nO/2ig6VzSwhbyUqAn+5R1wffo9rCrdF1HNz6uyr0TdkLH46FlH3lPz97VN22+V31/QX3wu1/QavOtlm/SZ+r1eXx1ZCbatljTNtcsaPAvWHn8dUqrBNE9FL1Q4eX2fa5HajZBD4HDx7EaDTy/vvvs2/fPl5//XXee+89nnjiCWcvTTjY4ZQc7lu8A4NR46rz2nL/RfX/Y3RF/7bcNlwV9z3yzS6Opubw3fYznMsrpm2wD5f2ke4q0cTlnVMFuM2d0QA/3KGyMFH94OK5DXueNgPKgpmuanjf/yaoAGTXl/D+hSqz4hMK138NE+bZPsgANS+nbZwqlt73k2WPsdc2l0mPsqzPwaa73dVsAp8JEybw8ccfM27cOGJjY7n88suZOXMmP/zwg7OXJhwoLaeIWz/eSk5RKYM6hjLvqj4WZ1JmX9KdC2JDySs2cOdn21m4Vg0svG14R4uHHIomrqQQdnzapNP4NTr4B7zSGZbOdvZK7O/vl+HUevD0h3993LiAJKQD3PYndBgGRdnw6WT46W4oyVMFzPesh24TbLb0Glmz3ZWfroqPwY6BT1mdz9G/oDjPPq9hZ836X/OsrCxCQ6vXdVRUVFREdnZ2pS/RdP3fj3tIyCygYys/3r8xzqpDQd3d9Lx1w3lEBXlzPC2PU+fyCfLx4Nrzo+24YuFS1r4Cvzyg2pKbi5JCWPq4yhps/wTyzjp7RfZzcp3qyAKY+Lplrdz18QmBm36E3lcDGuj0MPr/4OafVaeTvfW6Ur3mmS1q2GJdjq1Uf88RvSyvabJW695qBlFpoRrOaK0dn6mCcydqtoHP0aNHWbBgAXfdVcvEyjLz5s0jKCjI/BUdLR9yTVVeUSmryyYeL7h+ACF+FrShVtHK34v3bowzH7550wUd8POSqQ8thql7JmWvc9dhS5vegcyyc5kMRSqj1RzlnYPv71Af/P2nQt8ptntudy+46kP41//gjlUw8jHL5/E0VkCkyi4B7P2+7vua29jtlO0BVXRt7u761brHZp6G3x9RW4VJ/9h+bRZy+cBn1qxZ6HS6Or8OHjxY6TEJCQlMmDCBa665hjvuuKPO5589ezZZWVnmr9OnT9vzjyPsaOOxcxQbjESH+tCrEfN2+kUH8+7U85gysB13jmzaZ9IIK2TGq2m+oI4vaA5yU2Hta+p657IPw20fN79aH02Dn++FnEQI6wKXvmz719DrVdanTX/bP3d9zNtd39V+H6NBbT+BmgxtTz3KBioeXgalVjQQrZ6ngu+YERDZxz5rs4DL/yr7yCOPMG3atDrvExtb/uGUmJjI6NGjGTp0KB988EG9z+/l5YWXlx2K0oTDrT6suh5GdY1odIfUmB6tGdOj5kNERTNVsUsl64z6B92S4XWubOWzUJyjCnWnfAqv91Snch9eBt0vdfbqbGfTu+o8LDcvuOZj8PRz9opsq8ck+P1hFZin7IPWvarfJ3GnOnbCKwiiB9l3Pe3OB//Wqm3+5N91H4thkrIfdn+pro+da91UbBtz+cAnPDyc8PBwi+6bkJDA6NGjiYuL4+OPP0Zf03kjolnSNI1VB9U216hulv33IkQlpm0CUNslmfG2b092pOS9sPMzdX38PHVC94CbYMN82Lqw+QQ+iTth+VPq+vjnnJpJsBufYJXFOfibKnKuKfAx/ffbaTS4edh3PXq9OsJi2//Udpclgc+K/6r/r3pcDu3i7Lu+ejSbyCAhIYFRo0bRvn17XnnlFdLS0khOTiY5OdnZSxMOcCwtl4TMAjzd9AzpZPtBg6KZK86HE3+r615l26RNebtL02DZE+qDpudk6DBE3X7+bYBOFcGereXsqqakIAO+mw7GEpUVacLHKNSrz7/U5Z7v1d9vVeb6Hjtvc5mYhhke/KP+rdNTG+HwEjURe8xT9l9bPZpN4LN8+XKOHj3KihUraNeuHVFRUeYv0fyZipoHx4bi6+nyiUzhak78rbpUgqKh44XqtqYc+BxeCifWgJtn5Tk2ITHlH4zbPnLK0gC1jbjnO0g/0fDnKMiAT69Qf09B0XD5Aqdun9hd1wmqRT8rHk5vqfyz3FSV+QLLsi+2EDNCnR+Wl1p9PRVpGvz1tLp+3k3Qqotj1leHZhP4TJs2DU3TavwSzZ8p8BnZVba5RAMcKavv6TKuvAW6qQY+pcXw55Pq+gX3qmCnokFlDR87FztnDsupjfDecPj+NtXdc2yV9c+Rn66CnqTd4NsKbvhGtZ03Zx4+5VmWqjN9TEXNUf0hwEG1ie6eKhiDuru7Dv0BpzeDuw+MnOWYtdWj2QQ+ouXKKyplywk1cG5Utwgnr0Y0OZoGh8u2CbpOUNNyATIakY1wpm0fqdPI/cJhxCPVf95pDIR0hKIs+Ocbx62rIAN+eRA+ngBnD6nZNEXZsPhfsH2R5c9TNei55Vd10npLYOru2vcjGErLb3f0NpeJqa394K81b78ZStVBsQBD7oVA19iBkcBHNHkV29g7hTezbg5hfyn7IPuM+o204wgVFEDTzPjkp6uWYYCLngTvGsY66PXltTBbP6z5A8uWNE1ta701CHaUBTjn3QwPH4Q+U9S5T78+CH/NAaOx7ucyBT3J/6igZ9pvLSfoAYgdqf7c+WfhxGp1m6EUjq5U1x0d+HQao/6/yYxXfydV7f5SBbk+ITDsIceurQ4S+Igmz5Zt7KIFMm1zdbxQbSeYMz6nKv9W3RSsfgEKs9R03QE31X6/AVPVB1bKXojfZL/1ZJxUGZ3vb1O1IK26wrQ/VD1OQGu46oPy7Y91r8N3t0JJQc3PVTHo8QtXQU9ED/ut3RW5eUCvyeq6aabPmS0qe+cbBm3Pc+x6PH2h8xh1/UCVs7tKCmDV8+r6iJmqHshFSOAjmjRN08z1PdLGLhrEvM01Xl0GtlXzYIwlKhPUVKQdVhkcUG3ddU0W9gkp7xLautD2azGUwvo34e0LVP2JmyeMegLuXgcxw8rvp9PB6Nlw5fug94D9P8GiSZCbVvn5qgY9t/za8oIeE9N214HfVHBhmj/VeazjpklXZBpmWLXOZ/P7aqBkULTLddtJ4COatGNpeZzJkDZ20UD56eo3ZijfJtDrywuCG9N15Gh/PgmaAbpeArGj6r+/qch5/8+QY8OxH3nnYOFoNVuntAA6DId7NsCox2s/MLTfdXDzT+AdrA7Z/HAMpB1SP8tPh08vrxD0tMBMT0XtBkFQezWY8vCyCqexO3iby6TrONC7Q9qB8hEJBRmwrmxi+OgnwMPbOWurhQQ+oklbfUhtc0kbezNWUqh+uz13zPbPffSv8kMdgyuc0xfaxOp8jq1UW3Z6dxj3rGWPieqnPkSNpdYVF9dn3WsqSPEOhsvfUltSlrQwxwyH2/9SNVaZp+DDi1VQ9unlkLynQtDT3XZrbYr0euhztbq+YQGk7lOF4p0ucs56fELKR0AcLMv6rHtdbblG9IS+1zpnXXWQwEc0adLGbqWUfbBkVtM6oXv1PPh6Kiw4D97sD7/PhENLbdOKbdomMG1zmZjqfJpC4GMohWX/p66ff4d106ZNWZ/tH4OhpPFryU9XJ8ADXP2hmttiTd1dqy5w+wqIvkDVrXxzc1nQEyFBT0W9y7YpE7apy3bng2+o89ZT8dDSrAS1zQUwdo5ztt/qIYGPaLKkjb0B1rwIm991bBtzYxiNFWaW6FSL+daF8OW18GKMqvvYsABSD1rfnWQohaNl2wS1Bj4uvNVlNKgC1/dHqDOcvIPVqeHW6HmF6hLKSSo/mb4xtn4ExbmquLqhg/T8wuDmn9WBoFAW9PwqQU9FrXtBeIXtPnuexm6JbpcBOkjYDr/NUMNA2w913vZbPSTwEXZ3NDWXX3cnYjTatm3W1MbeLkTa2C2WekBd5jaRo1wStkF2AngGwGPH4bovYeBtENweDMVwfLWqbXlnMLzZF05tsPy5z2xR6XifEPUbc0WmrS5XnOVTWgw7PoW3BqpuqdT96v25fL71v/W7e0HcLeq6qTC6oYrzVVANMPzfjZui7OENV30IN/4Ad6+VoKcqna68OB2cH2AEtIboweq6aabQxc49iLQuUhQh7OpMRj5XvbOe7MJS1h05y7yr+qDX2+Z/BnMbe7fwpt3GfnoL7Pxc1WbUNHfFVgwl5Vs3eefs9zq2tO8nddntEvWh3v1S9aVpakjfkeWqTufkOjVL5Pvb4d5Nlr2P5m6Yi6un482zfE6orJMrHHhcUqACnvXzy7vNfELUdOZBdzR8cvHA6aom4+RaFRg3tHB45+fqdPDgDup8sMbS68tbpUV1fa6Bta+qLsTIvs5eDfSYCKfLRiN0n2j/E+IbwQX+bxZOUZRbvWXUxkoNRmZ8tYvsQjUL5ettp/nPz3ttcoxIxTb20U15m0vT4JcH1GC3f76272ulH1eFrKAGoNmTpsHur+DrmxpelGw0quJWKJ9dYqLTqXqQIffCTT/Ao0dVsJKdoAbhWaK2+h5QGSWdm+pKcnZ2rCgH1r0Bb/SBJY+poMe/tQqUZ+xV21uNOa4hqB10KzupvaFZH0OJ2nIEGPYguMnv1HYX0kF1y926xDUyK90nAjpVaO0CB5HWRQKflshQAv8br7YGclPt9jILVh5l26kM/L3ceWxCN3Q6WLw5njm/7Gt08NNs2tiTdkPaQXXdHl1LFZnag0H9Zm4vmadh8TXw411w4BfLA5GqErapD3nPADUhti7egTDpTXV920f1b3llxqv229q6Ydw8VPADzi1wPrVBBTx/PQ15aaqN+bJX4aF/YOgD4OVvm9cxFTnv/goKs61//N4f1OGZfuHQf6pt1iTqF9oR/F2ksSO0I1z3BUz9FsK7OXs1dZLApyXa/oma2FqSr7p87GDLiXQWrDwCwHNX9ubeUZ156eq+6HSwaOMpnv39QKOCn2bTxl4xy2PvepKKgY89urqMRpUxeOcCVTTs5qluP/QH5KRY/3zmba4Jls0BiR1ZPq34lwdUG3xtTNme6Atqr4txdoFz+nH46gY1EyWsM1zxDjy4Qw2Ds/VclI4j1VTl4lx1zIA1NA3Wv6GuX3CPmn4tWqbulzrudPhGkMCnpSnKUWPtTXKSbP4SWfklzPhqJ0YNrjqvLVf0bwvANQOjmXdlHwA+WneCF5YebHDws+ZwM2hjN5RWPmXZ3pmFs3bM+Jw7Bosmwu+PqA/P6MFw93p1aSyFXZ9b93yVtrmutPxx455R20DnjsLfL9V+P/M2Vx1Foc6c5VOYBV9cq4KeNuepiccDpqpMlD3odKoVHmDlc+WD6Cxx5M/yAuuBt9lnfULYkAQ+Lc2GBZXrO7ITbfr0mqYx+8d/SMwqJCbMl/9e0bvSz68b1J5nJqvb3l9znFf/PGx18JNXVMrm482gjf3YSrV94V7223vGSdWibC8VMz5F2VBa1PjnNJSqYtt3h8Kp9eDhB5e8pOoOwrtC3DR1v+2L6j+AsqKE7ZZvc1XkEwKXvqKur38Tkmo4OLE4XxXyAnSpob7HxFmzfAyl8O2tcPYwBLRR2weOyKLETVOBalEWfHW9Cr4sse51dTnwVvAJttfqhLAZCXxakpxk2PCWuh7Rq+w222Z8vt56mj/2JOOu1zH/+gH4e1Xfhrrpgg7MmaROVH5r1VHeXHHEqtdoNm3s/3ylLgfcpM4pMhTbPBA1MxrhbJX3ubFZn5R98NHFsPw/am5H7Ci4dyMMvqu8S6rXlepwwsxTcHyl5c+9/yd1aek2V0U9L1fnBxlL4Zf7qx80euJvtd6g9nV3MDkr8Pnz/+DYCnWI6PVfQmCUY17X3ROmfKaCrbOH4fs76g/ET22E+I1qW/OCex2zTiEaSQKflmT1C1CSB20Hqt/OALJtF/gcTc1h7q/7AXh0fDf6tguu9b7ThnXkycvUh84bfx3h7VWWp9abRRt7YXb5wLj+N6gODbDfh2xWvOpQcvMEn7KalsbU+RxbBe+PhMQd4BWkjia46afyP4eJhw/0vU5dN030rY+mlW9zNbQt+tJXVMCVtBs2vV35Z4eXqsuu4+ruhjGf0n7S+uGIDbX1I9j8nrp+1fvQpr9jXtckoDVct1hlIY8sg1XP1X1/U21Pv+sdF6AJ0UgS+LQUZ4+oGSCg6iACVd0NObbJMBSWGHjgy10UlBgY0aUVd4yIrfcxt4+IZdYlajDZy8sO8d6a+ruaKp3G3rUJb3Pt/1llHVp1hTYDKsyNsVPgk3ZYXYZ1VjUw0LiW9l2L1enlHS+E+zbXfTSBabvr4B+WHYaZsB2yToOnf8PnuAS0hnFlH9qrni/vmNO08gFrXSfU/RzBHQCd2ha0ZxecyfE18Mej6vpFT6qpys7Q9jyYNF9dX/uq6tiqScq+siBSB8MectjyhGgsCXxair/mlJ/c3GFo+W9nNsr4vLj0IAeSsgn18+TVa/pZPKTw7pGdmDmuKwAvLDnIXZ9tIzmr9m6cim3sQzs34TZ2UzdXv+tUwGDOLtipg8hU2NyqK/i1Utfz0xv+fKYxCP1vrP83/dY9Ve2IZlBD7uqz70d12XVC42pbBtyoupVKC+HXh1TQk7JPzfpx91GHYtbFw7v8FwS7F54fVedSaQY1mG7ETPu+Xn36XQtD7lfXf76v5lqp9WXjA3peAWGdHLc2IRpJAp+WIH4zHPxNzSwZO0fdFtBGXealVq+BsNKqg6l8vP4kAK9c05eIQOtqMu6/qAuzLumOu17Hsn0pjH1tDZ9uPImhhiMumkUbe+bp8uLaPlPUpb3rSUyFzeHdwbcsYGzMVlde2fBLUxBVn7iyrdUd9RQ5V9zmqjq00Fo6nZrt4+6j3u8dn5Zvc8WOtCyockRnV0GGOnusMFNtQ1/+lmsMpBs7V804KsmHr6ZW/u8l45Q6Jwxg+AynLE+IhpLAp7nTNFheNkWz/9TyM2/8wkHvDpoRchswY6VManYhM7/dDcC0oTFc1L11g57n7pGd+O3B4QxoH0xuUSlP/byPq9/dwIGkysPUmkUb+56yA0JjRkBwtLpu/oC1U8bHHPhUzPg0IvAxZXz8Ldxu7DW5rMg5vu4i50rbXDaYBxLaES4qO7n8z/+Uf1jXNK25xsfbeZaPoQS+uUW13we2K+vgsvGMnoZyc4d//U+9B1nx8O208hPcN76lslOxo9VWrRBNiAQ+zd3B39X5Ke4+MPqJ8tv1evCPVNcb0dn1fz/t5VxeMT2iAs31Og3VPTKQ7+4eyjNX9MLfy51dpzOZtGAdLy49SGGJgfziZtDGbjrKAaDvteW3V/yAtXUhraZV2Orqpk7jhoZnfAyl5TUvfhb+PXj4qAJYgG0f134/W21zVTT4HvXhXJSlpjWD5Yc62jvjs3QWnFijxgDc8JWqTXIlPiHqYFhPf5U1W/aEOurGVC84/N/OXZ8QDSCBT3NmKIUVc9X1IfdCYJvKPzfX+TSswDkpq4C/Dqhs0RvX9sfbw62eR9TPTa/jpiEx/PXwSCb0iqTUqPHu6mOMe/1v5q842vTb2BN3qlZhd+/KxavB7QGd6rqz9TEiualqJotOr4qbG5vxyT8HaICufNvMEqYi50NLai5y1jTY/4u63thtrorc3NX2kb5sa7R1b3U+lSXsuQV5eFnZ2Vg6uPpDiOxj+9ewhYjucNVCdX3LB2pbrrRQDVbseKFz1yZEA0jg05zt/Ex9yPqE1tx1EVAW+DQw4/PLrkQ0DQZ1DKVbZEAjFlpdZJA3790Uxwc3xREZ6E18er6566tJt7Gbipq7X1b5BHF3Lwgq2/ay9Yes6SywkBi1jWKu8Wlgp5Kpvsc3zLrDKCN6qCMiNIP6b7OqhB1qS8VW21wVRfaGkY+r632nWP44ewY+p7eoy37Xq1H/rqz7pTC6bMswYbu6HP5v16hFEsJKEvg0V8V5sHqeuj7yMVVfUZUpA9TAjM+POxMAuHJA2wY93hLjekXy1yMjmTY0xvxv7MU9I+32enZlKCmvMTHNtqnItK1i686us2Wt7K3KDg40BT4NbdHOs7K+pyLT/Kjtn1YfjrfftM013j6Tikc+Bg/uhCEPWP4Y05iBgnQoyLTtekz/37XqbNvntZcRM9VgSFCZw+6XOXc9QjRQE22LEfXa+I4qWg7uAAOn13yfRmR8DiRlczA5B083PZf2tu/gMn8vd+Zc3ospA6NJyCxouoXNR1eo7SW/8JpPBA/tqOo9bJ7xqVDYDI3f6so1dXQ14O+h5xWw5HGV2Tm2CrqUZXY0DfY1cmihJUwZHEt5+as6prxUFZD62LCQN/uMugy0cNvN2fR6uPI9aBunMnL6xm9tC+EMkvFpjnLTyieqjnlKbaPUpBEZn592qWzP6O7hBPna6eDEKnq2CeTini5W/GkN0xEVfa6peYvIXh1EFQuboby4OT+9YWeDmTI+DQl8KhY5b69Q5Gza5vLwgy4XW/+89mSv7S7T/3dVa+9cmaefal+P7F3vXYVwVRL4NEd/v6ROyI7qD72uqv1+Dcz4GI0av+xS/2jbc5urWSnMUpOLoXI3V0X2+oCtOMMHwLfsyAo0NUPGWta2sldVscjZNEDT3ttcjWGPvxdNKw98guT/ISEcSQKf5kbTYHdZAe2Yp1R6ujbmjE+SVS3Um0+kk5RVSKC3e9NtK3e0/T+DoUgFH1H9ar6PPY6tKMgsn9PUqou6dPMA72B1vSEt7XmN2OoC1SXUfkj5JOeK21y9rmzYc9qTPTJxBRlqMCCUDxMVQjiEBD7NTU6Smleic6t/JL8p41OSp84jstBPZUXNl/WNskkLe4tQcXZPbZ0wpuLmwszGHSdRkamwOaBN5S6yxtT5NDbjA+VZnx2flg0tdNFtLrDPcElTtsc3zHUGFgrRQkjg09yYPuhCYmqv7THx9C3v9rLwzK7CEgN/7FH3ndxfUvQWyTgFp9YDurpbqT39yodK2qqzq2phs0ljhhiaa3waEfj0vEJlnbLi4feH1W2uuM0F9hlimK1+eTCfBSaEcBgJfJqbs0fUZXg3y+5vSrNbeEr7yoOp5BSV0jbYh/NjQut/gCg/oqLjiPoH59k6u2Ca4RNeZap2Y1raTcGSfyO66yoWOSepI09sOrTQlkxbXbnJakyELUjgI4TTSODT3Jh+wzfVc9THylPaTbN7rujfxuIT2Fu0ijVXNc3uqcrW9STmGT5VMj5+DQx8jMYKNT6NrO8yzfQB8PCFzi64zQXq2AZTTZSt/l6yygIfKWwWwuEk8Gluavugq40VGZ+MvGLz6ejSzWWhhB1w7og6K63n5fXf39bbKuatrioZwIZudRVmgrFUXbf0ZPbahHeD9kPV9a7j1darqzIFpLbagmyKrexCNBMS+DQ3pq2uVhZudVmR8fl9TxIlBo2eUYF0aW3bIyqaLdMRFT0mgpcF75ktO7uK89Vp6FD9v4eGFjebCpu9g+qvIbPE+Geh05jy4yRcla1b2s1bXU1keKEQzYhMbm5OCrPLMzeWjsG3YpbPTw44oqJZ0TQ1qwag99WWPcaWmYVzRwBNndVWNTvT0IyPLQqbK2obBzf9YJvnsie7BT6S8RHC0STj05ycK8v2+EWougRLWDi9+XR6PttOZaDTweX9Xfgf6+K88iyHs507prqW3DwtP8XatNWVmwJFuY17/bSybc/wbtVb6Bta42OLVvamyJZbkBWHF0rgI4TDSeDTnFjb0QUWZ3x+LjuiYlinVrQOdOG5Iz/eDW/2Lz9B2pmOrVSX7S9QreqW8AkpD1obm/UxH1VRQ72X+YR2azM+jRxe2FSZMz4nG/9cFYcXSleXEA4ngU9zYm1HF5T/xpmbqk4Pr4GmaeZursmuvM1VnA+Hl6qJwHu+d/Zq4NgKdVnTgaR1sVVnV22FzVDhvK5zVk3tNgc+LS7jU/Z3knUaSosa91wyvFAIp5LApzmxtqML1Aeg3gPQyo82qGJvQjbH0vLw9tAzvpcLHxIavxEMxer64aXOXUtpMZxYq653GmPdY21VT1JX4GOq+TGWWDW127zVZasan6bCLxw8/QFNDaRsDPM2lwv/EiFEMyaBT3Ni7uiyIvDR6yGgbFpwLZ1dpmzPxT0jCfB2zEnsDXJiTfn19GPl74cznN6sjgLxC4fWVp5kbYvOLkOJeg+g5g4/Dx91RARYt91lzvi0sK0unc52HXfZZ9SlBD5COIUEPs1FpQ86KwIfqFDnU73AudRg5JfdppPYXbwQ88Tf6tKtrM3amVkfU31Pp4vqPii2Jrbo7Eo/oebtePjVPi26IQXO5oxPCwt8oLzAubG1V1LYLIRTSeDTXGScLPug87X+N8k6ZvmsP3aOs7lFhPp5MqKLC3/YFWRA4i51/YJ71OUhZwY+DazvAdscW3G2Qr1XbYeiNqSl3VZTm5siW21BytRmIZxKAp/mwlzf08X6DEMd05tNs3sm9Y3Cw82F/3M5uQ7QIKxL+VEI8RtVQORoeWfLz5+KHW39482FtGcaXkhb2xldFVk7xFDTKrSzu3AQbC+2CnzknC4hnMqFP8mEVdLqaF2uTy0Zn7yiUpbuTQbgClfu5gI4XlbfEztSnUwf3kN1dx1d4fi1HFulLlv3gYAGFIPbopDWPMOnjv8erD2otCgbDGWBWIvM+NiqxkcCHyGcSQKf5qIhhc0m5oxP5cBn+f4UCkoMdAjzZUB0cOPWZ2+mwuaOI9Vl1/Hq0hl1Pqb6ns4N2OYC2xTSmre66pjpZO0sn9yybS5Pf9c+V8teTBmfzHgwlDbsOWR4oRBOJ4FPc9GQVnYTc8an8lbX9ztU98nk/m3R1VYn4gqyk8r+/DqIGa5u6zpBXR5Z3vAPqYbQtMqFzQ3VmEJao9GyYZZ+FWb5WKKlDi80CWijCueNpWqeT0MUZsrwQiGcTAKf5kDTGhf4VJzeXDbMbvPxc6w9cha9Dq46z8X/gTZ1c0X1A99QdT16kJqAXJipWssdJXU/5Car09jbD2n48zSmniTrtPpw1XuUZ45qYm1xc14LPa7CRK9v/HaXqbBZhhcK4TQS+DQHuSmq/kKnh7BO1j/elHIvyYfCLIxGjWd+3w/A9YPa0yHMwuMWnMW8zVXhPCy9G3QZp647crvLVFMUM7xxp5c35gPWFASHdQa3Os4htra4uSW3sps0dgtStrmEcDoJfJoD0wddSEzDPmw9fMA7WF3PSeL7HWfYm5BNgJc7D1/cgAySI2la5cLmikzbXY4MfMz1PVZOa66qMcdWmCc21/N3Z874yFaXxcwzlk427PHm4YW1zFYSQtidBD7NQWM6ukzKfgMtPHeGl5ep57v/os6E+Tcia+EI6cfVh4neo/rWUucxoHdXgeG5Y/ZfS3E+nNqgrlt7TEVV5kLaU9bXKFlS2AwVBhhamfFpqVtd0PitLsn4COF0Evg0B+aOLisOJ62qrM5nzbbdpOYU0T7Ul2nDYhq/NnszbXNFD6p+Arp3EHQYqq4f+dP+a4nfoNq9A9s17u8CGldIW9cZXRWZurpK8lXQVh/J+DR+lo8p8JHhhUI4jQQ+zYG5sLmeD7q6lHV2HTisPjSfuLQ7Xu5ujV2Z/R2v0sZelWm769AS+6/laIU29sZ2wen1ausSrOvs0jTLAx+vwLIDarGss0syPpWnahuN1j8+S87pEsLZJPBpDhrT0WVSNsunlXaOwR1DGd8r0gYLszOjsbyjq2Jhc0WmwOfUeii04hTyhrBFG3tFDcku5KWpTjZ0qri5LjqddQXOLfm4CpOg9mr71FBUbe6VRWSrSwink8CnqSvKKZ8E24jtlfjSIAAidRn8Z2JPy+f2bP0Ivp3W8KMVGiNlLxSkq4M428bVfJ+wTuoYC2Np+flZ9pCVAGkHVGddbdknazXkzC5Ttiekgypar481Bc7mk9lbcODj5g7B7dV1a7e7NE2mNgvhAiTwaerOHVWXfuHlM2yspGkan+0rBqC7Xy692wZZ9kCjEf6aC/t+hNNbGvTaZgd/hz8eheI8yx9jqu/pMBTcPWu/n3mK87KGr68+x8uOqWhzXoP/HqppSGeXJWd0VWRpgXNxPhTnlj2mleXraY4aWudTaXihZHyEcBarA5+lS5eybt068/dvv/02/fv354YbbiAjwwkHQrZ0aY3f5vpldyIbUlX3VpTeir/Dc0ehKEtdz0lu8OsDKoDa8gGsfc3yx9TWxl5Vt0vU5ZE/wWho2PrqY5rf09g29ooa0kFk7banpUMMTcML3bxUbVBL1tBZPqZtLt8wy7JxQgi7sDrwefTRR8nOVrUSe/bs4ZFHHuHSSy/lxIkTPPzwwzZfoKhHI+t7CksMvLjkIMlaCABu+WfBUGLZgxO2lV/PTWnQ65uZPhQ2vgWZFnQxGUrKW8fr21qKHqw6vPLPwZltdd+3IYyG8oyPrep7oPwDNsOKQlpLC5tNLD2oNLfCNpcrH1/iCKYtZVN2zVKmqc2S7RHCqawOfE6cOEHPnj0B+P7775k4cSLPP/88b7/9NkuWOKBzRlTWyMDnw7XHScwqxDswHE3vAWiWZ28qBhG5jcj4FOdBcY66XloIK/5b/2MStkNJHviEQuvedd/XzQM6X6yuH7bDf6NJu6AgQ2VC2g603fMGtwedm3pPLH1/re3ws7S4OU+mNptF9lWXSf9Y9zhzfY8MLxTCmawOfDw9PcnPV/vUf/31F+PGqWMBQkNDzZkgZysqKqJ///7odDp27drl7OXYVyMCn5TsQt5ZrQb7PXZpT3QVz+yyRML28us5jcj4mAItvTuggz3fwJntdT6kvI19hGr9ro95irMd6nxM3VwdL6z7iAhruXlYV0hbmFX+d1ff1GYT8wnt9WV8pJXdLLI3oIOcRMvPOYMKgY9kfIRwJqsDn+HDh/Pwww/zzDPPsGXLFi677DIADh8+TLt2rvGbzGOPPUabNi3gHxdDaflEYks/6Cp4Zdkh8osNDGgfzOX92tR6SnuNSgpUV5VJY7a6TI8Nbg/9b1DXlz1hPjC1Rifqmd9TVecxKnuSuh8yTjV8rTU5aqNjKmpiTZ2Pqd4rIEpt7VnC4oyPDC808wooL3BO2m3546SVXQiXYHXg89Zbb+Hu7s53333Hu+++S9u2qi1zyZIlTJgwweYLtNaSJUv4888/eeWVV5y9FPvLPAXGEnUSuJXp870JWXy3Qw1TM7evW5PxSfpHtYibNCbwMWV8/CPhoifBwxdOb4L9P9d8/+K88i6y2FGWvYZvKLS/QF23ZdanMBvOlK3FlvU9JtZ0dp1twNElFhc3Syt7JVFl213JVmx3mYYXBrnGL4hCtFRW5+Xbt2/Pb7/9Vu32119/3SYLaoyUlBTuuOMOfvrpJ3x9fS16TFFREUVF5TNoXGW7ziLmM7o6W7bdU8ELSw6iaXB5vzac114VNpt/E7Uk42MqbA7pqIpvG9PVZQqaAlqrNQx7CFbPg+VPqY6sqgevxm9SAV9gu/LAwBJdx6tBhoeXwuA7G77eik6uVQFgaKfyScu2ZE0HkbWFzWB5xkdOZq8ssq8a4yAZHyGaHKszPjt27GDPnj3m73/++WcmT57ME088QXFxsU0XZw1N05g2bRp33303AwdaXmA6b948goKCzF/R0dF2XKWNNfCoisTMAtYdPYtOB4+Or/BYazI+psLm7mqrU80oKbRqHWam1/MvmxY99AG1lsxTsPn96vc3b3NdaF2HUdeytvaTa9XgR1uw9bTmqiydGZNxCnZ8qq6bim8tYcr4FGbV3c0nW12VRVlZ4KxpFQIfGV4ohDNZHfjcddddHD6sPnCPHz/Oddddh6+vL99++y2PPfaYzRc4a9YsdDpdnV8HDx5kwYIF5OTkMHv2bKuef/bs2WRlZZm/Tp+28kBIZzIfTmpdfc8fe1SgcX6HUKJDK2TGzBkfCwIfU8an81g12wXKO3+slVMh4wPqsNExT6nrf79cfRvG0vk9VbXqojIohmI4vrpha63KHvN7KjIFPhkna695Ki2Cb29RwWfbOOg7xfLn9wkGyoLH/PTa7yfFzZVF9lOX6ccsC6ILM1UXIkjGRwgnszrwOXz4MP379wfg22+/5cILL+SLL77gk08+4fvvv7f1+njkkUc4cOBAnV+xsbGsXLmSjRs34uXlhbu7O507q3OKBg4cyC233FLr83t5eREYGFjpq8kw13RYd1TFkr1qW+rSPlXO4zJnfOrZ6spNg8x4QAdtzwP/soCloZ1duRVqfEz6XgdR/aAoG1a/UH57fnr59oK1R0PodBUOLV3asLVWlH5cbfPp3SFmeOOfryYhHdRlUXbts3aWzobEneATAtd8Un1rsC56t/JJ03Vtd5nb2SXwAcA/vPz/l+S9dd8XyrM9PqEyvFAIJ7O6xkfTNIxlw9T++usvJk6cCEB0dDRnz1rR2mmh8PBwwsPrT6/Pnz+fZ5991vx9YmIi48eP5+uvv2bw4ME2X5fTaVr5VpcVNR1JWQVsP6WmM1/SJ6ryD81dXUnq+WvbRjK1sbfqqrqHAlpDVnzDZ/lUzfiAqlka9xwsmgjb/geD7lB/zpPrAE29dmBUjU9Xp24TYPO7cGSZGgpoZW1UJaZtrugLVKePPXj4qK2R7ARV4Fz1uIh/voFtHwE6uOrD8vZ3a/i2UkFVbQXOpUVqKwwk41NRZF+1TZv8D3QYUvd9TcMLg2SbSwhns/pf/YEDB/Lss8/y2WefsWbNGnM7+4kTJ2jdunU9j7af9u3b07t3b/NX165q+6dTp04u02ZvU3lpZR9GOlVYa6Ele1RwMrBDCK0DvSv/0PQbbGlB2QnftTBtc7Urq6UyZ3waGPjUlPEBNaOn22WgGeDP/6jbrG1jr6r9UDVoMC8Nzmxt2HOYmLJGnUY37nnqU1udT+oB+PUhdf3CR6HL2IY9f30Fzqb6Hr07eAc37DWaI2vqfORwUiFchtWBzxtvvMGOHTu4//77+b//+z/zltJ3333H0KFDbb5AUYtKp3B7133fCkz1PZdWzfaAyi74lHV41VXnYypsNp2Ibgp8chtQ41NapKYeAwREVv/5xf9VH7hHlqkMy4m/1e3W1veYuHtCt0vV9S01FE5b6uwROLoc0EHPyQ1/HkuYusUqBj5FOfD1TerQy9jRMGpWw5+/viGGFQubG5Mha25MReTJFnR2SWGzEC7D6q2uvn37VurqMnn55Zdxc3OzyaJsISYmBq2uAXhNXQM6upKzCtlm3uaqIcgACGijApGcRGjds/rPjUZI2KGumzI+poClIVtdplZ2N8/yoKuiVp3h/DvU9tRv/1ZFvuigwzDrX8tkyH3wz1ew7ycY83R5HY01Nr6lLrtdqtZoT+YC57JZPpoGvzwA546ov6+rP1S1Og1VX8bHdE5XSz+VvSpTxif1IJQWq6C6NjK1WQiX0eBf37Zv387nn3/O559/zo4dO/D29sbDw8OWaxN1MXd0WV7YvHSvyuLEdQghKqiWAsuKdT41MZ3I7u4NEWWBUWOKm02P8W9de03RyMfUFkvGSfV9VL/ygtyGiOqrsiSaATa9Y/3jc9Ng15fq+tAHGr4OS1Xd6trygZoho3eHKYsaH5DUN8RQCptrFtxB1bgZSyDtQN33NQU+MrxQCKezOvBJTU1l9OjRnH/++Tz44IM8+OCDDBw4kDFjxpCWlmaPNYqaNGBK7x97TN1cdRQF1zfLx1TfE9VfnSUFFba6GpLxMdX31FEf5hsKIx8v/76h21wVDXtQXe74tO427ppsXQiGInUgqWkatD1VPLbi9FZY9n/q+3HPQvSgxj9/fSe0Syt7zXQ6yw8slZPZhXAZVgc+DzzwALm5uezbt4/09HTS09PZu3cv2dnZPPjgg/ZYo6iJKeNjYUdXanYhW0+pD/hLeteyzQX1T282dXS1qzAkMqARNT6mguia6nsqOv92CCvbUjK1pDdG7GiI7KNqZLZ+ZPnjivNhy0J1fegD1g1QbCjT9Ob8c/D1jSrD0HMyDL7bNs9v3uqyoMZHVBZVNs+nrqMrZHihEC7F6sBn6dKlvPPOO/To0cN8W8+ePXn77bdZsmSJTRcnalGcB1llgxYtzPgs2ZuMpsGA9sG0Ca5jjkh9GZ+qhc1Q3o2VmwpGg0XrMcutsNVVF3dPmPYH3PIbdLBBEb1OB0PLAvUt71s+dXrXYihIV9scPSY1fh2W8A4sDzpyk1UAePkC2wVd5uLm2mp8JONTK0syPjK8UAiXYnXgYzQaa6zl8fDwMM/3EXZmyvb4hllc6/J7WTfXZXVtc0HdGZ+KJ7JXzPj4hQM6VTNTW9agNpZmfEBlljqOsO7569LrSnXeV14a7P6y/vsbDbDxbXV9yP2NKyi2linr4+4DUz5TwZCt1NvOLjU+tTIVOKfsVYX/NZHhhUK4FKsDn4suuoiHHnqIxMTyD8aEhAT+/e9/M2aMncb2i8rMhc1WbHOdLNvmqi/wqSvjYzqR3S8CgiqcaebmXv7hae0sH0szPvbg5gFD7lXXN75V+weXycHfVWeVTwgMmGr/9VXU5WLQe6hMT03ddo1hKm7OT6/5PTBlgvxlq6uasC6q0L84t/bz1EyBjwwvFMIlWB34vPXWW2RnZxMTE0OnTp3o1KkTHTt2JDs7mwULFthjjaIqcyu7ZR1dy/apba7+0cG0rWubC8ozPnlpqkW3ooQK21xVt1kqbndZw5qMjz2cd7PqzDl3FA79Ufd9N5T9933+7eo8MUca+RjMOgV9r7H9c5u2ujRDzYMrcyXjUys3d2jdS11P2lXzfbLOqEup7xHCJVg9xyc6OpodO3bw119/cfDgQQB69OjB2LENnBorrGdlR5fF21ygPgTdPNVBnrnJlY9AMNX3tIur/riA1pCyx/rOLmdmfEAdNTHwNlj3Gqx/E3pMrPl+8ZvhzBb13gy607FrNLFXsOXuqaZZm84Dq7h9aigt376U4uaaRfZVRf/J/0Cff1X/ubmwWep7hHAFVgc+ADqdjosvvpiLL77Y1usRlrCioystp4gtJ0zbXBZkVXQ6lX3JjFezfCoGPqaOrrYDqz+uIcdWGA3lHUPOyvgADL5LbXWd2QLxm2puUd8wX132u655Fvn6hqnAJ+9s5Uxi/jlAA3TlmSFRWX1HV8hxFUK4FIsCn/nz51v8hNLSbmdGg9qWAYu2upbuS8aoQb92QbQL8bXsNQLaqMCn4inteWch8xTmE9mrMs/ysWKIYV4aaEbQ6Z2bTQiIhL7Xws7PYP386oHPuWOqvgdUUXNz5NdK1S9VLXA2FTb7hqltHVFdZIWW9poO95XARwiXYtG/ZK+//rpFT6bT6STwsbeMk2obyt27coFxLf74p46zuWpT0/Rm0zaX6UT2qszHVlgR+JiyQ37hju2QqsnQB1Tgc+gPlVGrGFRufAvQ1PwgC+cmNTm1TW+WVvb6te4JOjeVHctOrF7ELMXNQrgUiwKfEydO2HsdwlKmba6wLvUGC2dzi9h8QtVnWBX4BJTVIlTM+FQ9kb2qhhxb4ez6norCu0HXS+DwElXEfHlZljPvLOz6Ql13xPEUzuJnmt5cNeMjwwvr5eGjfiFIO6CyPhUDHE2rMLVZAh8hXIEctdzUWNHRtaxsm6tvuyCiQy3c5oK6Mz5tayhshoYdW+Hsjq6qhj2kLnd/VR7Abf0QSguhzXmNOxjV1ZkzPlXmMEnGxzK11fkUZsnwQiFcjAQ+TY0VHV1/7GnANhdUn+VT8UT22gKfgAoZH02z7HVcKeMDqran3fnqHK4t76uBjVs+UD9z1PEUzlLbEENzxkcCnzrVdnSFqb5HhhcK4TIk8GlqzB1ddQc+53KL2HhM/fZuURt7RVWnN6cfKz+R3TSzpCrTHJ/SAijKsex1XC3jU/EYi60fqWxP/jnV2dbjcueuzd5qO6jUHPg08gT45q62oyvkjC4hXI4EPk2NaasrrO6trmX7UjBq0KetldtcUDnjo2nl21wVT2SvytNXzYIBywucXS3jA9D9MgiNVYP8lj+lbrvgvubf0STFzY0T2UddZsWrCdgmpuGFUtgshMuQwKcpKcyCggx1PTS2zrsu2au2qSya3VOVKfApLVSvV19hs4npw9HSWT6ulvEBVTBualnXjKqDbcCNzl2TI/jVlvGRqc0W8QlWB9cCJO8pv12GFwrhchoU+Kxdu5Ybb7yRIUOGkJCg9rA/++wz1q1bZ9PFiSoyTqlL31bg5V/r3dLzitnQ0G0uAA9vVZMAKutTX2Gzib+VLe3mjI8LBT4A/W8oz4AMvK3O97rZqJjxqVijlVu21SXndNXPVOBcsc5HtrqEcDlWBz7ff/8948ePx8fHh507d1JUVARAVlYWzz//vM0XKCrILAt8QjrUebc/9yVjMGr0ahNIh7AGHnNg+g01/UTNJ7LXJMCKIYaaVn4/V8r4gCpCnfwO9L+xvNOruTPV8BiK1IGboIrapbjZcqZBhhXrfLLlnC4hXI3Vgc+zzz7Le++9x8KFC/HwKK/3GDZsGDt27LDp4kQVGSfVZXDdgc/vDe3mqsi03XV4admJ7OH1D0w0ZW4s2eoqyFCDGMG1anxMuo6HyW+rLYyWwNMP3Mu6jkx1PgUZ6uBSkDk+ljC3tO8uv022uoRwOVYHPocOHeLCCy+sdntQUBCZmZm2WJOojWmrKySm1rtomsb2U6oOaGyPRgQUplk+phPL2w6sv53bVONjScbHFBz5hKpDMoXzmVvay4pzTdke72D5O7KEqbPr3BEozq88vDConfPWJYSoxOrAJzIykqNHj1a7fd26dcTG1l1wKxrJlPGpY6srq6CE/GL1W3qHMCu7uSoyTW82FbvWdCJ7tcdYUeOT64KFzS2d6VR20ywfc2GzZHssEhCp3ivNCCn7Kg8vDGhE9lUIYVNWBz533HEHDz30EJs3b0an05GYmMjixYuZOXMm99xzjz3WKExMNT51bHUlZBYA0MrfE2+PRpx/FVjlH+qaTmSvyppjK3JcsJW9pava0i6t7NbR6cqzPsm7y7e5fELVuAchhEuwejjJrFmzMBqNjBkzhvz8fC688EK8vLyYOXMmDzzQjM8ycjajUZ2YDnVudSVmFgLQJriRU2IDKtYk1HIie1XWHFshGR/XU3V6s5zTZb2ovnBshSpwNv2CIoXNQrgUqwMfnU7H//3f//Hoo49y9OhRcnNz6dmzJ/7+LaDl15lyU9RcHZ2+znqBxLKMT5ugRgY+FTM+tZ3IXpUpiCnIgNIicPeq/b6S8XE9kvFpvMgKLe2mXxZkeKEQLsXqra7PP/+c/Px8PD096dmzJ4MGDZKgxxFM21xB7WqfnkyFwMeWGZ/62thNfELArawI1vShWRvJ+LieqkMMZXih9UxndqXsL29GkI4uIVyK1YHPv//9byIiIrjhhhv4448/MBgM9liXqCqj/voeKK/xaRPs3bjX8w0Ft7KMjSXbXKBqHPwtnOUjGR/XUy3jI8MLrRbSETwD1Dyk46vUbRL4COFSrA58kpKS+Oqrr9DpdEyZMoWoqCjuu+8+NmzYYI/1CRMLOrqgPOPTtrEZH52u7CBUHcSMsPxxlh5bIRkf11P1oFLJ+FhPry8/tytxp7oMlFZ2IVyJ1YGPu7s7EydOZPHixaSmpvL6669z8uRJRo8eTadOneyxRgEVpjbH1Hk3mxU3A1z3JUxfCuHdLH+M+diKOgIfTZOMjyuqVtxcdinFzdYxDTI0kYyPEC6lUUdO+/r6Mn78eDIyMjh16hQHDhyw1bpEVeapzTG13qXEYCQlx4aBT3C0+rKG+diKOmp8inIqzDeRjI/LMG91nSs7UsRU3CyBj1UiqwQ+MrxQCJfSoENK8/PzWbx4MZdeeilt27bljTfe4Morr2Tfvn22Xp8wyaj/nK7krEI0DTzd9YT5OWnSriXHVpjqfzwD1FEJwjWYipuLc1S2x1BUdrtsdVmlasZHhhcK4VKszvhcd911/Pbbb/j6+jJlyhT+85//MGTIEHusTZiUFkN22ej7Omf4lNf36PX1HC9hL5YcW2EKigJkm8uleAeD3l2dzZZWlr319Jfhe9YK7666Gw3FqtNR3j8hXIrVgY+bmxvffPMN48ePx82tEZOBheWyTgMaePjWWW+RmGWjjq7GCLAi4+Mv21wuRadTBc65KZBaFvhIfY/13Dwgooc6rFQKm4VwOVYHPosXL7bHOkRdzPU97es8KNRc2NzY4YWN4W9BjY9kfFyXb6uywGe/+l6GFzZMZN+ywEcKm4VwNRYFPvPnz+fOO+/E29ub+fPn13nfBx980CYLExVY2NGVYKvhhY1hyvjkpapjNvQ1lJGZOr4k4+N6TAeVph5Ul5LxaZjOY2HnZ9DufGevRAhRhUWBz+uvv87UqVPx9vbm9ddfr/V+Op1OAh97MGd8HDTDpzH8wgGdqhPJP1dzR5CplV0yPq7H1NIuW12N02syRB+QwmYhXJBFgc+JEydqvC4cJMPCjE+GC2R83DxUnUj+WZXZqSnwkYyP6zK1tBdlqUvZ6mo42eYSwiVZ3c7+3//+l/z8/Gq3FxQU8N///tcmixJVWDC1WdO0Cud0ObG4Gcq3u2rr7JKMj+syZXzM30vGRwjRvFgd+MydO5fc3Nxqt+fn5zN37lybLEpUkVn/OV3ZBaXkFatz05ya8YHyAuecWgIfyfi4LtOxFSaS8RFCNDNWBz6apqGrobNo9+7dhIaG2mRRooLCLCjIUNfryPiYCpvD/Dzx9nDymAFzZ1cNLe0lBerPBJLxcUXVMj4S+AghmheL29lDQkLQ6XTodDq6du1aKfgxGAzk5uZy991322WRLZqpvsc3DLwCar1boit0dJkE1JHxMW1/uXmpgXnCtfhWCXwk4yOEaGYsDnzeeOMNNE1j+vTpzJ07l6CgIPPPPD09iYmJkQnO9mDBNhe4yPBCE/86anwq1vfUMZNIOEnVrS6p8RFCNDMWBz633HILAB07dmTo0KF4eHjYbVGiAks7ulwx41NT4CP1Pa6t4laXu3edWUYhhGiKrJ7cPHLkSPP1wsJCiouLK/08MDCw8asS5Szo6ILyqc1OneFjYi5urqHGRzq6XJtPhTo9v3DJygkhmh2ri5vz8/O5//77iYiIwM/Pj5CQkEpfwsYsnNrsUjU+dR1bIRkf1+bmrg7WBNnmEkI0S1YHPo8++igrV67k3XffxcvLiw8//JC5c+fSpk0bPv30U3ussWWzcmqzSwU+JXlQlFP5Z5LxcX2mAmcpbBZCNENWBz6//vor77zzDldffTXu7u6MGDGCJ598kueff14OMLU1TYPMeHW9jq2uEoORlOyyA0pdobjZyx88/dX1qp1dkvFxfaY6H8n4CCGaIasDn/T0dGJjYwFVz5Oeng7A8OHD+fvvv227upYuNwVKC0Gnh6DoWu+Wkl2IUQNPNz2t/LwcuMA61DbLx5zxkcDHZZk6uyTjI4RohqwOfGJjY83ndXXv3p1vvvkGUJmg4OBgmy6uxTNtcwW2U2dg1cJU2BwV7I1e7yLFqLUdW2HO+MhWl8uKHqwu2w1y7jqEEMIOrO7quvXWW9m9ezcjR45k1qxZTJo0ibfeeouSkhJee+01e6yx5TK3sltY3xPkAvU9JjUdW2Eohbyz6rpkfFzXsAfhvJvKi5yFEKIZsTrw+fe//22+PnbsWA4ePMj27dvp3Lkzffv2teniWrxMywIfl5rhY1LTVldeKqCBzq36hGDhWiToEUI0U1YHPlV16NCBDh3q/mAWDWTu6Iqp826mjE9bVyhsNqnp2ArTXB//CNBbvcsqhBBCNJrVgc/8+fNrvF2n0+Ht7U3nzp258MILcXNz8kGZzYGFU5tdqpXdpKZjK0zXpb5HCCGEk1gd+Lz++uukpaWRn59vHliYkZGBr68v/v7+pKamEhsby6pVq4iOrr0TSVjAyqnNLhX41HRshSnjI/U9QgghnMTq/Ybnn3+e888/nyNHjnDu3DnOnTvH4cOHGTx4MG+++Sbx8fFERkZWqgUSDVBaDNkJ6nodwws1TTPX+LQNcaHAp6ZjKyTjI4QQwsmszvg8+eSTfP/993Tq1Ml8W+fOnXnllVe4+uqrOX78OC+99BJXX321TRfa4mSdBjRw96lznkp2YSm5RaWAq3V1lWV1CtJVEOfuKRkfIYQQTmd1xicpKYnS0tJqt5eWlpKcrD7Y2rRpQ05OTrX7CCtU3Oaq46BIU31PqJ8nPp4uVFflGwr6stlDeWVndknGRwghhJNZHfiMHj2au+66i507d5pv27lzJ/fccw8XXXQRAHv27KFjx462W2VLZGplt/iMLhfq6AIVrFWd5SMZHyGEEE5mdeDz0UcfERoaSlxcHF5eXnh5eTFw4EBCQ0P56KOPAPD39+fVV1+1+WIt8fvvvzN48GB8fHwICQlh8uTJTllHo1nb0eVK21wmpi060ywfc8ZHAh8hhBDOYXWNT2RkJMuXL+fgwYMcPnwYgG7dutGtWzfzfUaPHm27FVrh+++/54477uD555/noosuorS0lL179zplLY1mYUdXgit2dJmYMjs5yWA0lgc+cjK7EEIIJ2nwAMPY2Fh0Oh2dOnXC3b3RcxAbrbS0lIceeoiXX36Z2267zXx7z549nbiqRrByq6utKwY+5unNqarI2VhWG+Ynh18KIYRwDqu3uvLz87ntttvw9fWlV69exMfHA/DAAw/wwgsv2HyBltqxYwcJCQno9XoGDBhAVFQUl1xySTPI+MTUeTeXHF5oYj6oNLm8vse3lerwEkIIIZzA6sBn9uzZ7N69m9WrV+PtXV5QO3bsWL7++mubLs4ax48fB2DOnDk8+eST/Pbbb4SEhDBq1CjS09NrfVxRURHZ2dmVvpyuMBsKMtR1Sw8odbXiZiiv8clJKa/zkcJmIYQQTmR14PPTTz/x1ltvMXz4cHQV2qx79erFsWPHbLo4gFmzZqHT6er8OnjwIEajEYD/+7//4+qrryYuLo6PP/4YnU7Ht99+W+vzz5s3j6CgIPOXS0ybNm1z+YaBV0Ctdys1GEnOVjU+rrnVVTHjI63sQgghnM/q4py0tDQiIqrXaOTl5VUKhGzlkUceYdq0aXXeJzY2lqSkJKByTY+XlxexsbHm7biazJ49m4cfftj8fXZ2tvODH/PhpHVne1JyijBq4OGmo5W/l/3XZa2ACjU+kvERQgjhAqwOfAYOHMjvv//OAw88AGAOdj788EOGDBli29UB4eHhhIeH13s/U3v9oUOHGD58OAAlJSWcPHmyztPjTS35LsXcym7ZNldUkA96ve2Dzkbzr3BeV3ZS5duEEEIIJ7A68Hn++ee55JJL2L9/P6Wlpbz55pvs37+fDRs2sGbNGnus0SKBgYHcfffdPP3000RHR9OhQwdefvllAK655hqnratBMq09ld0F63ugvHvLWAqpB9R1yfgIIYRwIqtrfIYPH86uXbsoLS2lT58+/Pnnn0RERLBx40bi4uLssUaLvfzyy1x33XXcdNNNnH/++Zw6dYqVK1eaT5FvMizc6kpw5Y4uUN1bvmHqevI/6lIyPkIIIZyoQQN4OnXqxMKFC229lkbz8PDglVde4ZVXXnH2UhrHyq0ulyxsNvGPhPxzUFTWLScZHyGEEE5kdcZH2JmmWbHV5cJTm02qniwvGR8hhBBOZHHGR6/X19u1pdPpajy5XVghNwVKC0Gnh6C6u8tcenihSdUMj2R8hBBCOJHFgc+PP/5Y6882btzI/PnzzbN0RCOYtrkC24GbR513TTBvdblocTNUzvB4BYGHCwdpQgghmj2LA58rrrii2m2HDh1i1qxZ/Prrr0ydOpX//ve/Nl1ci2Th4aTZhSXkFKrsWpQrnsxuUjHDI4eTCiGEcLIG1fgkJiZyxx130KdPH0pLS9m1axeLFi2qc16OsJCVh5MG+3rg5+X8Q2JrVbHGR+p7hBBCOJlVgU9WVhaPP/44nTt3Zt++faxYsYJff/2V3r1722t9LU+GlTN8XDnbA+XHVoDU9wghhHA6i1MFL730Ei+++CKRkZF8+eWXNW59CRuwcKsroSl0dEHlYEcyPkIIIZzM4sBn1qxZ+Pj40LlzZxYtWsSiRYtqvN8PP/xgs8W1SFZudbl0YTNUDnYk4yOEEMLJLA58br75ZrscQioqKC2GrDPquoVbXW1DXDzj4+UPHn5Qkld520sIIYRwAosDn08++cSOyxAAZJ0GNHD3qT74r4omMcPHJDga0g6qSyGEEMKJXLgdqAXKrHBURT3ZtSYxtdnkslfh9BZoN8jZKxFCCNHCSeDjSiw8nLTUYCQ5WwU+Ln1Ol0nMcPUlhBBCOJmc1eVKLDycNDWnCINRw8NNR7i/lwMWJoQQQjQPEvi4EosPJ1X1PZFB3uj1UnAuhBBCWEoCH1di4VZXQlMZXiiEEEK4GAl8XImFW12mwuYmUd8jhBBCuBAJfFyFoQQK0tX1gKg679qkWtmFEEIIFyKBj6soyCi7ogOfkDrvKoGPEEII0TAS+LiK/HPq0icE9G513tVc4+Pqx1UIIYQQLkYCH1eRd1Zd+obVe9fyc7ok4yOEEEJYQwIfV2HK+NQT+OQUlpBdWApAlAQ+QgghhFUk8HEVFgY+po6uIB8P/L1k8LYQQghhDQl8XEV+WUeXX32BjxQ2CyGEEA0lgY+rsDDjk2Cu75HCZiGEEMJaEvi4inzLipsl4yOEEEI0nAQ+rsLiGh8JfIQQQoiGksDHVZgDn1Z13s1U3CyBjxBCCGE9CXxcham4uY6MT4nByMlzeYDU+AghhBANIYGPqzAPMAyt9S5zf91Hak4RAV7udGkd4KCFCSGEEM2HBD6uoDgfSlXtTm0Zn883neLzTfHodPD6tf0J9PZw4AKFEEKI5kECH1dgqu/Re4BX9UzOpuPnmPPLPgBmjuvG2J6tHbk6IYQQotmQwMcVmAIfv1ag01X60en0fO5dvINSo8akfm24d1QnJyxQCCGEaB4k8HEFtbSy5xWVcsen20jPK6ZP2yBeurovuiqBkRBCCCEsJ4GPKzAHPuWFzUajxiPf7OZgcg6t/L344OY4fDzdnLRAIYQQonmQwMcV1JDxmb/yCEv3JePppuf9m+KICpK5PUIIIURjSeDjCqoML1yyJ4k3/joCwLNX9iauQ4izViaEEEI0KxL4uIIKGZ/9idk8/M1uAKYP68iUgdFOXJgQQgjRvEjg4wrKhhfmuQVxx6fbKCgxMKJLK564tLuTFyaEEEI0LxL4uIKy4yo+/SeHhMwCYsJ8WXD9ANzd5K9HCCGEsCX5ZHUFZVtd6xLVt+9MjSPY19OJCxJCCCGaJwl8XEFZ4JOuBeDtoadHlJzDJYQQQtiDBD7OZjRWCnzahfjKkEIhhBDCTiTwcbaiLNAMAGTiT7sQmdcjhBBC2IsEPs5WVthcrPelCE8JfIQQQgg7ksDH2cq2uXLcggBoF+LrzNUIIYQQzZoEPs5WFvhkoAqaJeMjhBBC2I8EPs5WNrww1eAPSMZHCCGEsCcJfJytLOOTXKICnmjJ+AghhBB2I4GPs5UFPue0QHw83Aj1k8GFQgghhL1I4ONsZV1dGVoA7UJ8ZIaPEEIIYUcS+DhbvqrxSSdACpuFEEIIO5PAx9lMXV1lU5uFEEIIYT8S+DibucZHMj5CCCGEvUng42wV5vhIxkcIIYSwLwl8nMlQAoVZgOmAUsn4CCGEEPYkgY8zlXV0GTUdWXJAqRBCCGF3Evg4U9k2VyZ+eHl4yAwfIYQQws4k8HGmssAnXQskOlRm+AghhBD2JoGPM5kCHylsFkIIIRyiWQU+hw8f5oorrqBVq1YEBgYyfPhwVq1a5exl1a5seGGGFDYLIYQQDtGsAp+JEydSWlrKypUr2b59O/369WPixIkkJyc7e2k1Kytulo4uIYQQwjGaTeBz9uxZjhw5wqxZs+jbty9dunThhRdeID8/n7179zp7eTWTrS4hhBDCoZpN4BMWFka3bt349NNPycvLo7S0lPfff5+IiAji4uJqfVxRURHZ2dmVvhzGXNwsGR8hhBDCEdydvQBb0el0/PXXX0yePJmAgAD0ej0REREsXbqUkJCQWh83b9485s6d68CVljPmpqFHzukSQgghHMXlMz6zZs1Cp9PV+XXw4EE0TeO+++4jIiKCtWvXsmXLFiZPnsykSZNISkqq9flnz55NVlaW+ev06dMO+7OV5Kri5nz3YEJ8PRz2ukIIIURL5fIZn0ceeYRp06bVeZ/Y2FhWrlzJb7/9RkZGBoGBgQC88847LF++nEWLFjFr1qwaH+vl5YWXl5etl20RLU9tdXkEtJIZPkIIIYQDuHzgEx4eTnh4eL33y8/PB0Cvr5zE0uv1GI1Gu6ytsdwLMwDwCW7t5JUIIYQQLYPLb3VZasiQIYSEhHDLLbewe/duDh8+zKOPPsqJEye47LLLnL286orzcDcWAhAUJoGPEEII4QjNJvBp1aoVS5cuJTc3l4suuoiBAweybt06fv75Z/r16+fs5VVX1tFVpLkTEdbKyYsRQgghWgaX3+qyxsCBA1m2bJmzl2GZssAngwDahUpHlxBCCOEIzSbj0+RUOKBUWtmFEEIIx5DAx0mKc1Qre7rmL8MLhRBCCAeRwMdJss+p2ULZ+kCCZYaPEEII4RAS+DhJfkYqAKVeoTLDRwghhHAQCXycpCg7DQDNN8zJKxFCCCFaDgl8nMSYp2p83P2llV0IIYRwFAl8nERfkA6Ad1CEk1cihBBCtBwS+DiJV7E6riIgVKY2CyGEEI4igY+T+BmyAAgNj3LySoQQQoiWQwIfJygsLiFIywEgvHVbJ69GCCGEaDkk8HGCpJQU3HXqxPjAUKnxEUIIIRxFAh8nSEtJBCAPH3Qe3k5ejRBCCNFySODjBBln1dTmfPcgJ69ECCGEaFkk8HGC3PQUAIo8Q528EiGEEKJlkcDHCQqy1HEVRh8JfIQQQghHksDHCQy5amqzm78cVyGEEEI4kgQ+TqDLPweAV6B0dAkhhBCOJIGPgxWWGPApyQTAPyTSuYsRQgghWhgJfBzsTEYBITo1vNArKNzJqxFCCCFaFgl8HOxMRj5hZYGPzldqfIQQQghHksDHwc5kFBCCCnyQwEcIIYRwKAl8HOxMRgGhOgl8hBBCCGeQwMfBEtOzCdTlq2/8Wjl3MUIIIUQLI4GPg2WXTW3W0IO3HFkhhBBCOJIEPg5WkKkCH4N3MOjdnLsYIYQQooWRwMeBCooN6AvSAenoEkIIIZxBAh8HSsjMN3d06f2lvkcIIYRwNAl8HOh0hY4uyfgIIYQQjieBjwOdySggVGb4CCGEEE4jgY8DncnINx9XIYGPEEII4XgS+DhQpeGFMsNHCCGEcDgJfBxIjqsQQgghnEsCHwdKyMgnTJetvpHARwghhHA4CXwcJL+4lLO5xRVqfEKduyAhhBCiBZLAx0ESMgoATQ4oFUIIIZxIAh8HOZNRgA9FeFOibvCV4mYhhBDC0STwcZAzGfnlM3zcvMDTz7kLEkIIIVogCXwcpFIru28Y6HTOXZAQQgjRAkng4yDVAh8hhBBCOJwEPg5yJqP8gFL8JPARQgghnEECHweRjI8QQgjhfBL4OEBhiYGcwlJCZXihEEII4VQS+DiAt4cbB56ZwPQBgeoGCXyEEEIIp5DAx0Hc9Dp8SzPVNxL4CCGEEE4hgY8j5aerSwl8hBBCCKeQwMeR8s6qSwl8hBBCCKeQwMeR8s+pSwl8hBBCCKeQwMdRjEYokK0uIYQQwpkk8HGUwkzQjOq6BD5CCCGEU0jg4yimwmavQHD3dO5ahBBCiBZKAh9HyTcVNoc6dx1CCCFECyaBj6NIYbMQQgjhdBL4OIo58Gnl3HUIIYQQLZgEPo4iGR8hhBDC6STwcZQ8qfERQgghnE0CH0eR4yqEEEIIp5PAx1Fkq0sIIYRwOgl8HMUU+PhJcbMQQgjhLBL4OIpkfIQQQginazKBz3PPPcfQoUPx9fUlODi4xvvEx8dz2WWX4evrS0REBI8++iilpaWOXWhtJPARQgghnM7d2QuwVHFxMddccw1Dhgzho48+qvZzg8HAZZddRmRkJBs2bCApKYmbb74ZDw8Pnn/+eSesuOLiSsHNA9BJ4COEEEI4kU7TNM3Zi7DGJ598wowZM8jMzKx0+5IlS5g4cSKJiYm0bt0agPfee4/HH3+ctLQ0PD0tOx8rOzuboKAgsrKyCAwMtO3ijQbQ6UGns+3zCiGEEC2cpZ/fTWarqz4bN26kT58+5qAHYPz48WRnZ7Nv375aH1dUVER2dnalL7vRu0nQI4QQQjhRswl8kpOTKwU9gPn75OTkWh83b948goKCzF/R0dF2XacQQgghnMepgc+sWbPQ6XR1fh08eNCua5g9ezZZWVnmr9OnT9v19YQQQgjhPE4tbn7kkUeYNm1anfeJjY216LkiIyPZsmVLpdtSUlLMP6uNl5cXXl5eFr2GEEIIIZo2pwY+4eHhhIeH2+S5hgwZwnPPPUdqaioREREALF++nMDAQHr27GmT1xBCCCFE09Zk2tnj4+NJT08nPj4eg8HArl27AOjcuTP+/v6MGzeOnj17ctNNN/HSSy+RnJzMk08+yX333ScZHSGEEEIATaidfdq0aSxatKja7atWrWLUqFEAnDp1invuuYfVq1fj5+fHLbfcwgsvvIC7u+XxnV3b2YUQQghhF5Z+fjeZwMdRJPARQgghmp4WN8dHCCGEEKI+EvgIIYQQosWQwEcIIYQQLYYEPkIIIYRoMSTwEUIIIUSLIYGPEEIIIVqMJjPA0FFM3f12PaVdCCGEEDZl+tyub0qPBD5V5OTkAMgp7UIIIUQTlJOTQ1BQUK0/lwGGVRiNRhITEwkICECn09nsebOzs4mOjub06dMyGNGO5H12DHmfHUfea8eQ99kx7Pk+a5pGTk4Obdq0Qa+vvZJHMj5V6PV62rVrZ7fnDwwMlP+pHEDeZ8eQ99lx5L12DHmfHcNe73NdmR4TKW4WQgghRIshgY8QQgghWgwJfBzEy8uLp59+Gi8vL2cvpVmT99kx5H12HHmvHUPeZ8dwhfdZipuFEEII0WJIxkcIIYQQLYYEPkIIIYRoMSTwEUIIIUSLIYGPEEIIIVoMCXwc5O233yYmJgZvb28GDx7Mli1bnL2kJu3vv/9m0qRJtGnTBp1Ox08//VTp55qm8dRTTxEVFYWPjw9jx47lyJEjzllsEzZv3jzOP/98AgICiIiIYPLkyRw6dKjSfQoLC7nvvvsICwvD39+fq6++mpSUFCetuGl699136du3r3mo25AhQ1iyZIn55/Ie28cLL7yATqdjxowZ5tvkvW68OXPmoNPpKn11797d/HNnv8cS+DjA119/zcMPP8zTTz/Njh076NevH+PHjyc1NdXZS2uy8vLy6NevH2+//XaNP3/ppZeYP38+7733Hps3b8bPz4/x48dTWFjo4JU2bWvWrOG+++5j06ZNLF++nJKSEsaNG0deXp75Pv/+97/59ddf+fbbb1mzZg2JiYlcddVVTlx109OuXTteeOEFtm/fzrZt27jooou44oor2LdvHyDvsT1s3bqV999/n759+1a6Xd5r2+jVqxdJSUnmr3Xr1pl/5vT3WBN2N2jQIO2+++4zf28wGLQ2bdpo8+bNc+Kqmg9A+/HHH83fG41GLTIyUnv55ZfNt2VmZmpeXl7al19+6YQVNh+pqakaoK1Zs0bTNPW+enh4aN9++635PgcOHNAAbePGjc5aZrMQEhKiffjhh/Ie20FOTo7WpUsXbfny5drIkSO1hx56SNM0+e/ZVp5++mmtX79+Nf7MFd5jyfjYWXFxMdu3b2fs2LHm2/R6PWPHjmXjxo1OXFnzdeLECZKTkyu950FBQQwePFje80bKysoCIDQ0FIDt27dTUlJS6b3u3r077du3l/e6gQwGA1999RV5eXkMGTJE3mM7uO+++7jssssqvacg/z3b0pEjR2jTpg2xsbFMnTqV+Ph4wDXeYzmk1M7Onj2LwWCgdevWlW5v3bo1Bw8edNKqmrfk5GSAGt9z08+E9YxGIzNmzGDYsGH07t0bUO+1p6cnwcHBle4r77X19uzZw5AhQygsLMTf358ff/yRnj17smvXLnmPbeirr75ix44dbN26tdrP5L9n2xg8eDCffPIJ3bp1Iykpiblz5zJixAj27t3rEu+xBD5CCIvcd9997N27t9JevbCdbt26sWvXLrKysvjuu++45ZZbWLNmjbOX1aycPn2ahx56iOXLl+Pt7e3s5TRbl1xyifl63759GTx4MB06dOCbb77Bx8fHiStTZKvLzlq1aoWbm1u1ivWUlBQiIyOdtKrmzfS+yntuO/fffz+//fYbq1atol27dubbIyMjKS4uJjMzs9L95b22nqenJ507dyYuLo558+bRr18/3nzzTXmPbWj79u2kpqZy3nnn4e7ujru7O2vWrGH+/Pm4u7vTunVrea/tIDg4mK5du3L06FGX+O9ZAh878/T0JC4ujhUrVphvMxqNrFixgiFDhjhxZc1Xx44diYyMrPSeZ2dns3nzZnnPraRpGvfffz8//vgjK1eupGPHjpV+HhcXh4eHR6X3+tChQ8THx8t73UhGo5GioiJ5j21ozJgx7Nmzh127dpm/Bg4cyNSpU83X5b22vdzcXI4dO0ZUVJRr/PfskBLqFu6rr77SvLy8tE8++UTbv3+/duedd2rBwcFacnKys5fWZOXk5Gg7d+7Udu7cqQHaa6+9pu3cuVM7deqUpmma9sILL2jBwcHazz//rP3zzz/aFVdcoXXs2FErKChw8sqblnvuuUcLCgrSVq9erSUlJZm/8vPzzfe5++67tfbt22srV67Utm3bpg0ZMkQbMmSIE1fd9MyaNUtbs2aNduLECe2ff/7RZs2apel0Ou3PP//UNE3eY3uq2NWlafJe28IjjzyirV69Wjtx4oS2fv16bezYsVqrVq201NRUTdOc/x5L4OMgCxYs0Nq3b695enpqgwYN0jZt2uTsJTVpq1at0oBqX7fccoumaaql/T//+Y/WunVrzcvLSxszZox26NAh5y66CarpPQa0jz/+2HyfgoIC7d5779VCQkI0X19f7corr9SSkpKct+gmaPr06VqHDh00T09PLTw8XBszZow56NE0eY/tqWrgI+9141177bVaVFSU5unpqbVt21a79tprtaNHj5p/7uz3WKdpmuaY3JIQQgghhHNJjY8QQgghWgwJfIQQQgjRYkjgI4QQQogWQwIfIYQQQrQYEvgIIYQQosWQwEcIIYQQLYYEPkIIIYRoMSTwEUK0eDExMbzxxhvOXoYQwgEk8BFCONS0adOYPHkyAKNGjWLGjBkOe+1PPvmE4ODgardv3bqVO++802HrEEI4j7uzFyCEEI1VXFyMp6dngx8fHh5uw9UIIVyZZHyEEE4xbdo01qxZw5tvvolOp0On03Hy5EkA9u7dyyWXXIK/vz+tW7fmpptu4uzZs+bHjho1ivvvv58ZM2bQqlUrxo8fD8Brr71Gnz598PPzIzo6mnvvvZfc3FwAVq9eza233kpWVpb59ebMmQNU3+qKj4/niiuuwN/fn8DAQKZMmUJKSor553PmzKF///589tlnxMTEEBQUxHXXXUdOTo75Pt999x19+vTBx8eHsLAwxo4dS15enp3eTSGEpSTwEUI4xZtvvsmQIUO44447SEpKIikpiejoaDIzM7nooosYMGAA27ZtY+nSpaSkpDBlypRKj1+0aBGenp6sX7+e9957DwC9Xs/8+fPZt28fixYtYuXKlTz22GMADB06lDfeeIPAwEDz682cObPauoxGI1dccQXp6emsWbOG5cuXc/z4ca699tpK9zt27Bg//fQTv/32G7/99htr1qzhhRdeACApKYnrr7+e6dOnc+DAAVavXs1VV12FHI0ohPPJVpcQwimCgoLw9PTE19eXyMhI8+1vvfUWAwYM4Pnnnzff9r///Y/o6GgOHz5M165dAejSpQsvvfRSpeesWC8UExPDs88+y913380777yDp6cnQUFB6HS6Sq9X1YoVK9izZw8nTpwgOjoagE8//ZRevXqxdetWzj//fEAFSJ988gkBAQEA3HTTTaxYsYLnnnuOpKQkSktLueqqq+jQoQMAffr0acS7JYSwFcn4CCFcyu7du1m1ahX+/v7mr+7duwMqy2ISFxdX7bF//fUXY8aMoW3btgQEBHDTTTdx7tw58vPzLX79AwcOEB0dbQ56AHr27ElwcDAHDhww3xYTE2MOegCioqJITU0FoF+/fowZM4Y+ffpwzTXXsHDhQjIyMix/E4QQdiOBjxDCpeTm5jJp0iR27dpV6evIkSNceOGF5vv5+flVetzJkyeZOHEiffv25fvvv2f79u28/fbbgCp+tjUPD49K3+t0OoxGIwBubm4sX76cJUuW0LNnTxYsWEC3bt04ceKEzdchhLCOBD5CCKfx9PTEYDBUuu28885j3759xMTE0Llz50pfVYOdirZv347RaOTVV1/lggsuoGvXriQmJtb7elX16NGD06dPc/r0afNt+/fvJzMzk549e1r8Z9PpdAwbNoy5c+eyc+dOPD09+fHHHy1+vBDCPiTwEUI4TUxMDJs3b+bkyZOcPXsWo9HIfffdR3p6Otdffz1bt27l2LFjLFu2jFtvvbXOoKVz586UlJSwYMECjh8/zmeffWYueq74erm5uaxYsYKzZ8/WuAU2duxY+vTpw9SpU9mxYwdbtmzh5ptvZuTIkQwcONCiP9fmzZt5/vnn2bZtG/Hx8fzwww+kpaXRo0cP694gIYTNSeAjhHCamTNn4ubmRs+ePQkPDyc+Pp42bdqwfv16DAYD48aNo0+fPsyYMYPg4GD0+tr/yerXrx+vvfYaL774Ir1792bx4sXMmzev0n2GDh3K3XffzbXXXkt4eHi14mhQmZqff/6ZkJAQLrzwQsaOHUtsbCxff/21xX+uwMBA/v77by699FK6du3Kk08+yauvvsoll1xi+ZsjhLALnSb9lUIIIYRoISTjI4QQQogWQwIfIYQQQrQYEvgIIYQQosWQwEcIIYQQLYYEPkIIIYRoMSTwEUIIIUSLIYGPEEIIIVoMCXyEEEII0WJI4COEEEKIFkMCHyGEEEK0GBL4CCGEEKLFkMBHCCGEEC3G/wNisbHLjAadOgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_iter = [log[\"train_loss\"] for log in logs]\n",
    "val_loss_iter = [log[\"val_loss\"] for log in logs]\n",
    "plt.plot(loss_iter[1:], label=\"Train\")\n",
    "plt.plot(val_loss_iter[1:], label=\"Val\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Train loss\")\n",
    "plt.title(\"Train loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "pos_loss_iter = [log[\"train_pos_loss\"] for log in logs]\n",
    "val_pos_loss_iter = [log[\"val_pos_loss\"] for log in logs]\n",
    "plt.plot(pos_loss_iter[1:], label=\"Train\")\n",
    "plt.plot(val_pos_loss_iter[1:], label=\"Val\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Positive loss\")\n",
    "plt.title(\"Positive loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "neg_loss_iter = [log[\"train_neg_loss\"] for log in logs]\n",
    "val_neg_loss_iter = [log[\"val_neg_loss\"] for log in logs]\n",
    "plt.plot(neg_loss_iter[1:], label=\"Train\")\n",
    "plt.plot(val_neg_loss_iter[1:], label=\"Val\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Negative loss\")\n",
    "plt.title(\"Negative loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "off_loss_iter = [log[\"train_off_loss\"] for log in logs]\n",
    "val_off_loss_iter = [log[\"val_off_loss\"] for log in logs]\n",
    "plt.plot(off_loss_iter[1:], label=\"Train\")\n",
    "plt.plot(val_off_loss_iter[1:], label=\"Val\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Offset loss\")\n",
    "plt.title(\"Offset loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modeldone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#model.load_state_dict(torch.load(\"hardnet-centernet-oriented-bbox.pth\"))\n",
    "model.eval()\n",
    "\n",
    "threshold = 0.2\n",
    "\n",
    "for id in range(40):\n",
    "    img, hm_gt, offset_gt, regr_gt, angle_gt,mask = valdataset[id]\n",
    "    img = torch.from_numpy(img)\n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(img.to(device).float().unsqueeze(0))\n",
    "\n",
    "    \n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    \n",
    "    print(hm.shape)\n",
    "\n",
    "    # show image\n",
    "    img_id = test_id[id]\n",
    "    img = cv2.imread(os.path.join(dataset_folder, img_id))\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    #img = cv2.resize(img, (input_size, input_size))\n",
    "\n",
    "    # get boxes\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()\n",
    "    plt.imshow(hm[0])\n",
    "    plt.show()\n",
    "    \n",
    "    plt.imshow(hm[1])\n",
    "    plt.show()\n",
    "    hm_4corner = select(hm[1], threshold)\n",
    "    hm = select(hm[0], threshold)\n",
    "    \n",
    "    \n",
    "    plt.imshow(hm>threshold)\n",
    "    plt.title(f\"Heatmap > {threshold}\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.imshow(hm_4corner>threshold)\n",
    "    plt.title(f\"Heatmap 4 corner> {threshold}\")\n",
    "    plt.show()\n",
    "    \n",
    "    plt.imshow(wh[0])\n",
    "    plt.title(\"Width heatmap (x)\")\n",
    "    plt.show()\n",
    "    \n",
    "    sample = showbox(img, hm, offset, wh, angle, threshold)\n",
    "    plt.imshow(offset[0])\n",
    "    plt.title(\"Offset heatmap (x)\")\n",
    "    plt.show()\n",
    "    print(sample.shape)\n",
    "    \n",
    "    # show gt\n",
    "    #fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n",
    "    plt.imshow(sample)\n",
    "    plt.title(\"Detection\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize_and_pad(image, target_size=(512, 512)):\n",
    "    original_height, original_width = image.shape[:2]\n",
    "    target_width, target_height = target_size\n",
    "\n",
    "    # Calculate the scaling factor\n",
    "    scale = min(target_width / original_width, target_height / original_height)\n",
    "    \n",
    "    # Calculate new dimensions\n",
    "    new_width = int(original_width * scale)\n",
    "    new_height = int(original_height * scale)\n",
    "    \n",
    "    # Resize the image\n",
    "    resized_image = cv2.resize(image, (new_width, new_height))\n",
    "    \n",
    "    # Pad the image to the target size\n",
    "    delta_w = target_width - new_width\n",
    "    delta_h = target_height - new_height\n",
    "    top, bottom = delta_h // 2, delta_h - (delta_h // 2)\n",
    "    left, right = delta_w // 2, delta_w - (delta_w // 2)\n",
    "    padded_image = cv2.copyMakeBorder(resized_image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0, 0, 0])\n",
    "    \n",
    "    return padded_image, scale, left, top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fps = False\n",
    "half = False\n",
    "\n",
    "model.load_state_dict(torch.load(\"4corner_hardnet_barcode_angle_great_results.pth\"))\n",
    "model.eval()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "if fps:\n",
    "    from time import time\n",
    "\n",
    "if half:\n",
    "    model.half()\n",
    "    \n",
    "cap = cv2.VideoCapture(0)\n",
    "threshold = 0.2\n",
    "while 1:\n",
    "    ret,frame = cap.read()\n",
    "    #image = cv2.resize(frame,(input_width,input_height))\n",
    "    image = resize_and_pad(frame,target_size=(input_width,input_height))[0]\n",
    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    img = image.copy()\n",
    "    img = Normalize()(img)\n",
    "    img = img.transpose([2,0,1])\n",
    "    img = torch.from_numpy(img)\n",
    "    if fps: f1 = time()\n",
    "    if half:\n",
    "        tensor = img.to(device).half().unsqueeze(0)\n",
    "    else:\n",
    "        tensor = img.to(device).float().unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(tensor)\n",
    "    if fps: f2 = time(); print(\"Fps:\",1/(f2-f1))\n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()\n",
    "    hm = select(hm[0], threshold)\n",
    "    sample = showbox(image, hm, offset, wh, angle, threshold)\n",
    "    cv2.imshow(\"output\",sample)\n",
    "    ch = cv2.waitKey(1)\n",
    "    if ch == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "test_folder = glob(\"C:/Users/John/Desktop/rotated_barcode/roboflow_barcode/test/images/*.jpg\")\n",
    "threshold = 0.2\n",
    "for i in test_folder:\n",
    "    image = cv2.imread(i)\n",
    "    #image = cv2.resize(frame,(input_width,input_height))\n",
    "    image = resize_and_pad(image,target_size=(input_width,input_height))[0]\n",
    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    img = image.copy()\n",
    "    img = Normalize()(img)\n",
    "    cv2.imshow(\"normalized\",img)\n",
    "    img = img.transpose([2,0,1])\n",
    "    img = torch.from_numpy(img)\n",
    "    if half:\n",
    "        tensor = img.to(device).half().unsqueeze(0)\n",
    "    else:\n",
    "        tensor = img.to(device).float().unsqueeze(0)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(tensor)\n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()\n",
    "    hm = select(hm[0], threshold)\n",
    "    sample = showbox(image, hm, offset, wh, angle, threshold)\n",
    "    cv2.imshow(\"output\",sample)\n",
    "    ch = cv2.waitKey(0)\n",
    "    if ch == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "#cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#torch.save(model.state_dict(), \"4corner_hardnet_barcode_angle_great_results.pth\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred4corner(hm,thresh=0.99):\n",
    "    threshold = 0.2  # Adjust this threshold as needed\n",
    "    _, thresholded_heatmap = cv2.threshold(hm, threshold, 1, cv2.THRESH_BINARY)\n",
    "    \n",
    "    # Find contours (connected components) in the thresholded heatmap\n",
    "    contours, _ = cv2.findContours(thresholded_heatmap.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    \n",
    "    keypoints = []\n",
    "    for cnt in contours:\n",
    "        # 2. Refine peak location (using contour center)\n",
    "        try:\n",
    "            M = cv2.moments(cnt)\n",
    "            cx = int(M['m10'] / M['m00'])\n",
    "            cy = int(M['m01'] / M['m00'])\n",
    "            keypoints.append((cx, cy))\n",
    "        except:\n",
    "            continue\n",
    "    return keypoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "test_folder = glob(\"C:/Users/John/Desktop/rotated_barcode/roboflow_barcode/test/images/*.jpg\")[3:]\n",
    "threshold = 0.2\n",
    "for i in test_folder:\n",
    "    image = cv2.imread(i)\n",
    "    #image = cv2.resize(frame,(input_width,input_height))\n",
    "    image = resize_and_pad(image,target_size=(input_width,input_height))[0]\n",
    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    img = image.copy()\n",
    "    img = Normalize()(img)\n",
    "    img = img.transpose([2,0,1])\n",
    "    img = torch.from_numpy(img)\n",
    "    if half:\n",
    "        tensor = img.to(device).half().unsqueeze(0)\n",
    "    else:\n",
    "        tensor = img.to(device).float().unsqueeze(0)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(tensor)\n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()[1]\n",
    "    #hm = select(hm[1], threshold)\n",
    "    #sample = showbox(image, hm, offset, wh, angle, threshold)\n",
    "    hm = cv2.resize(hm,(image.shape[1],image.shape[0]))\n",
    "    _, thresholded_heatmap = cv2.threshold(hm, threshold, 1, cv2.THRESH_BINARY)\n",
    "    cv2.imshow(\"thresh\",thresholded_heatmap)\n",
    "    corners = pred4corner(hm,0.2)\n",
    "    for kp in corners:\n",
    "        cv2.circle(image, kp, 5, (0, 0, 255), -1)\n",
    "    cv2.imshow(\"output\",image)\n",
    "    cv2.imshow(\"4corner\",cv2.resize(hm,(image.shape[1],image.shape[0])))\n",
    "    ch = cv2.waitKey(0)\n",
    "    if ch == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "#cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "threshold = 0.2\n",
    "while 1:\n",
    "    ret,image = cap.read()\n",
    "    #image = cv2.resize(frame,(input_width,input_height))\n",
    "    image = resize_and_pad(image,target_size=(input_width,input_height))[0]\n",
    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    img = image.copy()\n",
    "    img = Normalize()(img)\n",
    "    img = img.transpose([2,0,1])\n",
    "    img = torch.from_numpy(img)\n",
    "    if half:\n",
    "        tensor = img.to(device).half().unsqueeze(0)\n",
    "    else:\n",
    "        tensor = img.to(device).float().unsqueeze(0)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(tensor)\n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()[1]\n",
    "    #hm = select(hm[1], threshold)\n",
    "    #sample = showbox(image, hm, offset, wh, angle, threshold)\n",
    "    hm = cv2.resize(hm,(image.shape[1],image.shape[0]))\n",
    "    _, thresholded_heatmap = cv2.threshold(hm, threshold, 1, cv2.THRESH_BINARY)\n",
    "    cv2.imshow(\"thresh\",thresholded_heatmap)\n",
    "    corners = pred4corner(hm,0.2)\n",
    "    for kp in corners:\n",
    "        cv2.circle(image, kp, 5, (0, 0, 255), -1)\n",
    "    cv2.imshow(\"output\",image)\n",
    "    cv2.imshow(\"4corner\",cv2.resize(hm,(image.shape[1],image.shape[0])))\n",
    "    ch = cv2.waitKey(1)\n",
    "    if ch == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "#cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rand_torch = torch.rand((1,3,512,512)).cuda()\n",
    "traced = torch.jit.trace(model,rand_torch)\n",
    "torch.jit.save(traced, \"hardnet_angle_4c_centernet_jit.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the entire code to c++, implement each function\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "import schedulefree\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "#from torchinfo import summary\n",
    "import torch.optim as optim\n",
    "from functools import partial\n",
    "assert torch.cuda.is_available()\n",
    "\n",
    "# Not always necessary depending on your hardware/GPU\n",
    "#os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:512\"\n",
    "\n",
    "\n",
    "# Here we dont assume that our images are 512x512 we prefer to use HD images (more common)\n",
    "input_width = 512\n",
    "input_height = 512\n",
    "\n",
    "# Model scale is 16, meaning that in the model prediction, we have heatmaps of dimensions 80 x 45\n",
    "MODEL_SCALE = 4\n",
    "workers = 8\n",
    "# Batch size for training --> if your hardware supports it, try to increase this value\n",
    "batch_size = 8\n",
    "\n",
    "\n",
    "\n",
    "def load_model(model, model_path, optimizer=None, resume=False, \n",
    "               lr=None, lr_step=None):\n",
    "  start_epoch = 0\n",
    "  checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)\n",
    "  print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch']))\n",
    "  state_dict_ = checkpoint['state_dict']\n",
    "  state_dict = {}\n",
    "  \n",
    "  # convert data_parallal to model\n",
    "  for k in state_dict_:\n",
    "    if k.startswith('module') and not k.startswith('module_list'):\n",
    "      state_dict[k[7:]] = state_dict_[k]\n",
    "    else:\n",
    "      state_dict[k] = state_dict_[k]\n",
    "  model_state_dict = model.state_dict()\n",
    "\n",
    "  # check loaded parameters and created model parameters\n",
    "  msg = 'If you see this, your model does not fully load the ' + \\\n",
    "        'pre-trained weight. Please make sure ' + \\\n",
    "        'you have correctly specified --arch xxx ' + \\\n",
    "        'or set the correct --num_classes for your own dataset.'\n",
    "  for k in state_dict:\n",
    "    if k in model_state_dict:\n",
    "      if state_dict[k].shape != model_state_dict[k].shape:\n",
    "        print('Skip loading parameter {}, required shape{}, '\\\n",
    "              'loaded shape{}. {}'.format(\n",
    "          k, model_state_dict[k].shape, state_dict[k].shape, msg))\n",
    "        state_dict[k] = model_state_dict[k]\n",
    "    else:\n",
    "      print('Drop parameter {}.'.format(k) + msg)\n",
    "  for k in model_state_dict:\n",
    "    if not (k in state_dict):\n",
    "      print('No param {}.'.format(k) + msg)\n",
    "      state_dict[k] = model_state_dict[k]\n",
    "  model.load_state_dict(state_dict, strict=False)\n",
    "\n",
    "  # resume optimizer parameters\n",
    "  if optimizer is not None and resume:\n",
    "    if 'optimizer' in checkpoint:\n",
    "      optimizer.load_state_dict(checkpoint['optimizer'])\n",
    "      start_epoch = checkpoint['epoch']\n",
    "      start_lr = lr\n",
    "      for step in lr_step:\n",
    "        if start_epoch >= step:\n",
    "          start_lr *= 0.1\n",
    "      for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = start_lr\n",
    "      print('Resumed optimizer with start lr', start_lr)\n",
    "    else:\n",
    "      print('No optimizer parameters in checkpoint.')\n",
    "  if optimizer is not None:\n",
    "    return model, optimizer, start_epoch\n",
    "  else:\n",
    "    return model\n",
    "\n",
    "from hardnet import get_pose_net\n",
    "model = get_pose_net(85,{\"hm\":2,\"offset\":2,\"wh\":2,\"angle\":2})\n",
    "model = load_model(model,\"centernet_hardnet85_coco.pth\")\n",
    "\n",
    "\n",
    "fps = False\n",
    "half = False\n",
    "\n",
    "model.load_state_dict(torch.load(\"4corner_hardnet_barcode_angle_great_results.pth\"))\n",
    "model.eval()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Resnet-18 expect normalized channels in input\n",
    "class Normalize(object):\n",
    "    def __init__(self):\n",
    "        self.mean=[0.485, 0.456, 0.406]\n",
    "        self.std=[0.229, 0.224, 0.225]\n",
    "        self.norm = transforms.Normalize(self.mean, self.std)\n",
    "    def __call__(self, image):\n",
    "        image = image.astype(np.float32)/255\n",
    "        axis = (0,1)\n",
    "        image -= self.mean\n",
    "        image /= self.std\n",
    "        return image\n",
    "\n",
    "\n",
    "def pred2box(hm, offset, regr, cos_sin_hm, thresh=0.99):\n",
    "    # make binding box from heatmaps\n",
    "    # thresh: threshold for logits.\n",
    "        \n",
    "    # get center\n",
    "    pred = hm > thresh\n",
    "    pred_center = np.where(hm>thresh)\n",
    "    \n",
    "    # get regressions\n",
    "    pred_r = regr[:,pred].T\n",
    "    pred_angles = cos_sin_hm[:, pred].T\n",
    "    \n",
    "    #print(\"pred_angle\", pred_angle)\n",
    "\n",
    "    # wrap as boxes\n",
    "    # [xmin, ymin, width, height]\n",
    "    # size as original image.\n",
    "    boxes = []\n",
    "    scores = hm[pred]\n",
    "    \n",
    "    pred_center = np.asarray(pred_center).T\n",
    "    #print(pred_r.shape)\n",
    "    #print(pred_angles)\n",
    "    #print(pred_angles.shape)\n",
    "    \n",
    "    for (center, b, pred_angle) in zip(pred_center, pred_r, pred_angles):\n",
    "        print(b)\n",
    "        print(pred_center)\n",
    "        offset_xy = offset[:, center[0], center[1]]\n",
    "        print(offset_xy)\n",
    "        angle = np.arctan2(pred_angle[1], pred_angle[0])\n",
    "        print(angle)\n",
    "        arr = np.array([(center[1]+offset_xy[0])*MODEL_SCALE, (center[0]+offset_xy[1])*MODEL_SCALE, \n",
    "                        b[0]*MODEL_SCALE, b[1]*MODEL_SCALE, angle])\n",
    "        # Clip values between 0 and input_size\n",
    "        #arr = np.clip(arr, 0, input_size)\n",
    "        #print(\"Pred angle\", i, pred_angle[i])\n",
    "        # filter \n",
    "        #if arr[0]<0 or arr[1]<0 or arr[0]>input_size or arr[1]>input_size:\n",
    "            #pass\n",
    "        boxes.append(arr)\n",
    "    print(\"Boxes:\",boxes)\n",
    "    return np.asarray(boxes), scores\n",
    "\n",
    "\n",
    "def select(hm, threshold):\n",
    "    \"\"\"\n",
    "    Keep only local maxima (kind of NMS).\n",
    "    We make sure to have no adjacent detection in the heatmap.\n",
    "    \"\"\"\n",
    "\n",
    "    pred = hm > threshold\n",
    "    pred_centers = np.argwhere(pred)\n",
    "\n",
    "    for i, ci in enumerate(pred_centers):\n",
    "        for j in range(i + 1, len(pred_centers)):\n",
    "            cj = pred_centers[j]\n",
    "            if np.linalg.norm(ci - cj) <= 2:\n",
    "                score_i = hm[ci[0], ci[1]]\n",
    "                score_j = hm[cj[0], cj[1]]\n",
    "                if score_i > score_j:\n",
    "                    hm[cj[0], cj[1]] = 0\n",
    "                else:\n",
    "                    hm[ci[0], ci[1]] = 0\n",
    "\n",
    "    return hm\n",
    "\n",
    "def resize_and_pad(image, target_size=(512, 512)):\n",
    "    original_height, original_width = image.shape[:2]\n",
    "    target_width, target_height = target_size\n",
    "\n",
    "    # Calculate the scaling factor\n",
    "    scale = min(target_width / original_width, target_height / original_height)\n",
    "    \n",
    "    # Calculate new dimensions\n",
    "    new_width = int(original_width * scale)\n",
    "    new_height = int(original_height * scale)\n",
    "    \n",
    "    # Resize the image\n",
    "    resized_image = cv2.resize(image, (new_width, new_height))\n",
    "    \n",
    "    # Pad the image to the target size\n",
    "    delta_w = target_width - new_width\n",
    "    delta_h = target_height - new_height\n",
    "    top, bottom = delta_h // 2, delta_h - (delta_h // 2)\n",
    "    left, right = delta_w // 2, delta_w - (delta_w // 2)\n",
    "    padded_image = cv2.copyMakeBorder(resized_image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0, 0, 0])\n",
    "    \n",
    "    return padded_image, scale, left, top\n",
    "\n",
    "\n",
    "def pred4corner(hm,thresh=0.99):\n",
    "    threshold = 0.2  # Adjust this threshold as needed\n",
    "    _, thresholded_heatmap = cv2.threshold(hm, threshold, 1, cv2.THRESH_BINARY)\n",
    "    \n",
    "    # Find contours (connected components) in the thresholded heatmap\n",
    "    contours, _ = cv2.findContours(thresholded_heatmap.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    \n",
    "    keypoints = []\n",
    "    for cnt in contours:\n",
    "        # 2. Refine peak location (using contour center)\n",
    "        try:\n",
    "            M = cv2.moments(cnt)\n",
    "            cx = int(M['m10'] / M['m00'])\n",
    "            cy = int(M['m01'] / M['m00'])\n",
    "            keypoints.append((cx, cy))\n",
    "        except:\n",
    "            continue\n",
    "    return keypoints\n",
    "\n",
    "\n",
    "\n",
    "# functions for plotting results\n",
    "def showbox(img, hm, offset, regr, cos_sin_hm, thresh=0.9):\n",
    "    boxes, _ = pred2box(hm, offset, regr, cos_sin_hm, thresh=thresh)\n",
    "    \n",
    "    sample = img\n",
    "\n",
    "    for box in boxes:\n",
    "        center = [int(box[0]), int(box[1])]\n",
    "        cos_angle = np.cos(box[4])\n",
    "        sin_angle = np.sin(box[4])\n",
    "        rot = np.array([[cos_angle, sin_angle], [-sin_angle, cos_angle]])\n",
    "        \n",
    "        bottom_right = np.dot(rot, np.array([box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_right = np.dot(rot, np.array([box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        top_left = np.dot(rot, np.array([-box[2]/2, -box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        bottom_left = np.dot(rot, np.array([-box[2]/2, box[3]/2]).reshape(2, 1)).reshape(2)\n",
    "        \n",
    "        thickness = 3\n",
    "        cv2.line(sample, (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1])),\n",
    "                      (int(center[0]+top_right[0]), int(center[1]+top_right[1])),\n",
    "                      (0, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+bottom_right[0]), int(center[1]+bottom_right[1])),\n",
    "                      (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+top_left[0]), int(center[1]+top_left[1])),\n",
    "                      (int(center[0]+bottom_left[0]), int(center[1]+bottom_left[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "        cv2.line(sample, (int(center[0]+top_left[0]), int(center[1]+top_left[1])),\n",
    "                      (int(center[0]+top_right[0]), int(center[1]+top_right[1])),\n",
    "                      (220, 220, 0), thickness)\n",
    "    return sample\n",
    "\n",
    "\n",
    "print(\"Done\")\n",
    "#cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "test_folder = glob(\"C:/Users/John/Desktop/rotated_barcode/roboflow_barcode/test/images/*.jpg\")\n",
    "threshold = 0.2\n",
    "for i in test_folder:\n",
    "    image = cv2.imread(i)\n",
    "    #image = cv2.resize(frame,(input_width,input_height))\n",
    "    image = resize_and_pad(image,target_size=(input_width,input_height))[0]\n",
    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    img = image.copy()\n",
    "    img = Normalize()(img)\n",
    "    cv2.imshow(\"normalized\",img)\n",
    "    img = img.transpose([2,0,1])\n",
    "    img = torch.from_numpy(img)\n",
    "    if half:\n",
    "        tensor = img.to(device).half().unsqueeze(0)\n",
    "    else:\n",
    "        tensor = img.to(device).float().unsqueeze(0)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        hm, offset, wh, angle = model(tensor)\n",
    "    hm = hm.cpu().numpy().squeeze(0)#.squeeze(0)\n",
    "    offset = offset.cpu().numpy().squeeze(0)\n",
    "    wh = wh.cpu().numpy().squeeze(0)\n",
    "    angle = angle.cpu().numpy().squeeze(0)\n",
    "    hm = torch.sigmoid(torch.from_numpy(hm)).numpy()\n",
    "    hm = select(hm[0], threshold)\n",
    "    sample = showbox(image, hm, offset, wh, angle, threshold)\n",
    "    print(offset.mean())\n",
    "    print(angle.mean())\n",
    "    print(wh.mean())\n",
    "    cv2.imshow(\"output\",sample)\n",
    "    ch = cv2.waitKey(0)\n",
    "    if ch == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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