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Dive-into-DL-PyTorch/code/chapter09_computer-vision/9.4_anchor.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 9.4 锚框"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2.0\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from PIL import Image\n",
"import numpy as np\n",
"import math\n",
"import torch\n",
"\n",
"import sys\n",
"sys.path.append(\"..\") \n",
"import d2lzh_pytorch as d2l\n",
"print(torch.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.1 生成多个锚框"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w = 728, h = 561\n"
]
}
],
"source": [
"d2l.set_figsize()\n",
"img = Image.open('../../img/catdog.jpg')\n",
"w, h = img.size\n",
"print(\"w = %d, h = %d\" % (w, h))\n",
"\n",
"# d2l.plt.imshow(img); # 加分号只显示图"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def MultiBoxPrior(feature_map, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5]):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" feature_map: torch tensor, Shape: [N, C, H, W].\n",
" sizes: List of sizes (0~1) of generated MultiBoxPriores. \n",
" ratios: List of aspect ratios (non-negative) of generated MultiBoxPriores. \n",
" Returns:\n",
" anchors of shape (1, num_anchors, 4). 由于batch里每个都一样, 所以第一维为1\n",
" \"\"\"\n",
" pairs = [] # pair of (size, sqrt(ration))\n",
" for r in ratios:\n",
" pairs.append([sizes[0], math.sqrt(r)])\n",
" for s in sizes[1:]:\n",
" pairs.append([s, math.sqrt(ratios[0])])\n",
" \n",
" pairs = np.array(pairs)\n",
" \n",
" ss1 = pairs[:, 0] * pairs[:, 1] # size * sqrt(ration)\n",
" ss2 = pairs[:, 0] / pairs[:, 1] # size / sqrt(ration)\n",
" \n",
" base_anchors = np.stack([-ss1, -ss2, ss1, ss2], axis=1) / 2\n",
" \n",
" h, w = feature_map.shape[-2:]\n",
" shifts_x = np.arange(0, w) / w\n",
" shifts_y = np.arange(0, h) / h\n",
" shift_x, shift_y = np.meshgrid(shifts_x, shifts_y)\n",
" shift_x = shift_x.reshape(-1)\n",
" shift_y = shift_y.reshape(-1)\n",
" shifts = np.stack((shift_x, shift_y, shift_x, shift_y), axis=1)\n",
" \n",
" anchors = shifts.reshape((-1, 1, 4)) + base_anchors.reshape((1, -1, 4))\n",
" \n",
" return torch.tensor(anchors, dtype=torch.float32).view(1, -1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 2042040, 4])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = torch.Tensor(1, 3, h, w) # 构造输入数据\n",
"Y = MultiBoxPrior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])\n",
"Y.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-0.0316, 0.0706, 0.7184, 0.8206])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boxes = Y.reshape((h, w, 5, 4))\n",
"boxes[250, 250, 0, :]# * torch.tensor([w, h, w, h], dtype=torch.float32)\n",
"# 第一个size和ratio分别为0.75和1, 则宽高均为0.75 = 0.7184 + 0.0316 = 0.8206 - 0.0706"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# 本函数已保存在dd2lzh_pytorch包中方便以后使用\n",
"def show_bboxes(axes, bboxes, labels=None, colors=None):\n",
" def _make_list(obj, default_values=None):\n",
" if obj is None:\n",
" obj = default_values\n",
" elif not isinstance(obj, (list, tuple)):\n",
" obj = [obj]\n",
" return obj\n",
"\n",
" labels = _make_list(labels)\n",
" colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])\n",
" for i, bbox in enumerate(bboxes):\n",
" color = colors[i % len(colors)]\n",
" rect = d2l.bbox_to_rect(bbox.detach().cpu().numpy(), color)\n",
" axes.add_patch(rect)\n",
" if labels and len(labels) > i:\n",
" text_color = 'k' if color == 'w' else 'w'\n",
" axes.text(rect.xy[0], rect.xy[1], labels[i],\n",
" va='center', ha='center', fontsize=6, color=text_color,\n",
" bbox=dict(facecolor=color, lw=0))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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},
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}
],
"source": [
"d2l.set_figsize()\n",
"fig = d2l.plt.imshow(img)\n",
"bbox_scale = torch.tensor([[w, h, w, h]], dtype=torch.float32)\n",
"show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,\n",
" ['s=0.75, r=1', 's=0.75, r=2', 's=0.55, r=0.5', 's=0.5, r=1', 's=0.25, r=1'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.2 交并比\n",
"代码来自https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/utils.py#L356"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def compute_intersection(set_1, set_2):\n",
" \"\"\"\n",
" 计算anchor之间的交集\n",
" Args:\n",
" set_1: a tensor of dimensions (n1, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" set_2: a tensor of dimensions (n2, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" Returns:\n",
" intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2)\n",
" \"\"\"\n",
" # PyTorch auto-broadcasts singleton dimensions\n",
" lower_bounds = torch.max(set_1[:, :2].unsqueeze(1), set_2[:, :2].unsqueeze(0)) # (n1, n2, 2)\n",
" upper_bounds = torch.min(set_1[:, 2:].unsqueeze(1), set_2[:, 2:].unsqueeze(0)) # (n1, n2, 2)\n",
" intersection_dims = torch.clamp(upper_bounds - lower_bounds, min=0) # (n1, n2, 2)\n",
" return intersection_dims[:, :, 0] * intersection_dims[:, :, 1] # (n1, n2)\n",
"\n",
"\n",
"def compute_jaccard(set_1, set_2):\n",
" \"\"\"\n",
" 计算anchor之间的Jaccard系数(IoU)\n",
" Args:\n",
" set_1: a tensor of dimensions (n1, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" set_2: a tensor of dimensions (n2, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" Returns:\n",
" Jaccard Overlap of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2)\n",
" \"\"\"\n",
" # Find intersections\n",
" intersection = compute_intersection(set_1, set_2) # (n1, n2)\n",
"\n",
" # Find areas of each box in both sets\n",
" areas_set_1 = (set_1[:, 2] - set_1[:, 0]) * (set_1[:, 3] - set_1[:, 1]) # (n1)\n",
" areas_set_2 = (set_2[:, 2] - set_2[:, 0]) * (set_2[:, 3] - set_2[:, 1]) # (n2)\n",
"\n",
" # Find the union\n",
" # PyTorch auto-broadcasts singleton dimensions\n",
" union = areas_set_1.unsqueeze(1) + areas_set_2.unsqueeze(0) - intersection # (n1, n2)\n",
"\n",
" return intersection / union # (n1, n2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.3 标注训练集的锚框"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
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5eu0Sx0cLnnn+Ni888wI//bGPoTyPsizvCx3u1Zs0D211n6c95ajPEmFdUak3tmnC+jpV4r0z2Tth3zaIa2HR2nmU/cldiqLAsp2zaTfLC5RSJHmOY+goVSFEM42XZYllWSRLxWQyZqXbQqnq7EZ5nkdVlMyPj7HjpClEqIKW7zdJhhB0Qo0sOSJezDD0HFRGHCuWsctkmvNPful3qK2rrFxZYWfDxXEcVFVh2xbKFuR5TnelTcfR0bXGo5wCeZzUZKVkd/+A0XTG8cEtNNPBb3UxLJtAd+6noix1BmjDMHAN6wzAAJXKiA9u83Mfe4LxnSO80COKIjqDDngBpufTGazzub94mQtXrqGpmtt3bvAf/gd/n/l8jkGN5/kMh0NKIMsyPv7xj3PhwkX6/T5ZluG6LmVZ8vSzLxGGITYFbVOyvb7Kl/78z1hZ32Y5m6JpGpPllLW1Nqu9kMO7uzimIktrdl95iY1eyHh0gDPYJosXVIV5/2yjaaDroGmoSiCMN2CjWWZTmZUnFVopKKsK27YROghNoWSF4zgUeYEwm/BNCEFVVd+U2vtW7NsudkiaCWX56mcxj19iKeXZieSFJM8SRFWiqZI0zc88VVmWZNEMVaakcQRVgS6aGPJ0Cj/1AnXVAN42TUoUVuAhdI2qKjGERplkZElCy/aYaS3+k//+V6idVdqtc6yteniWQGjQCQNQGZ6uMV9m5HnOZDIhz3NsXTAcDimKAsMwyKpGk5znOUVRUGIxL3KmacYsztCUefaw6rqOptdnA20YBjrivgHZ9CVX+hZPXFsjmcdYroXlWFx57GF0Z0glJGbgEyUWM+lQFhkHuzfZGPZZu/pDRLMJr71ynSKN2bnyCL7X4nc/+Yc8+eSHuPDwDr1eD4A7d+7A8ja3XtvjM5/+Y9ptC1+YrPdDSmXQWdvmT//kszz5kSdZjI6oC/jBJ9+H0Epu3Tzg+ks3+fGP/Cit/oCpCDAc7+yaTuWs9wunzPs44UI2Y1YUTShZSc6ksJZlYegOrus22JESzXdwHAfTNLEs6x0B8bftiQUSE4N29yoH0avo0qaSGRoKw9DAtYnnMZqS1AbUoqKSBUKV6HpJmeYYukZZQKEKPM870w57nodhGEghqGpBlMTNDUozLMsilRkdUzAeLbH8Lq/HOv/VP/tXtHpXubq1huHUBEGAZVm0HAehaoq0phV6yDSCPGMlNCkdKIoCnYJuy2liP1lTVRA4LSaTGa4t0IWOrUw6muA4kyRpRipLas2iFtqJR8kQQuAiqFWBrQs816bf7zJoWRhIpospZmrg+RYqy4jzXcIwRE9LzDThvRuP0LJ9XtdbBOubTKycjfPrtFodnn/6RazKYHY85ckf+gFanRZJmeDnIX3foljeoi3H6PmEtX6fVsenTpbEuaSuS+7eeIFC1bz6/Ct0Q59eEPD0F75Gf63HtSce5dHve5wsLTmazShVjER/A4CGgWaZZyBWSqEJkyzLKIqC6XTK3YNbHO0ecGvvgJvjY+KoQMmcS+e22Nu/y3ueeD8H+2NqZaIJk8cfWec973s/H/qRj9DfGGB7AaCoMHir/MVbLnYYJ1ONqJv+tVopEIIiS7FMnSovMIQgWs6xdY2ySLCMk78bBqYmGC8iRqMRvV6PJEnOkqZTMj5PM+q6xvM8siSlyuckmk5Z1MySGf/LJz5Je+Ucl3bO07E0Wr3W2ZTedl1UVaJsnenkCNu0qB3FoigJ/QARCHzfpygKPMclS2IC12OxiGm3Q6I0wrMtVN0Mnm9bDR2YF0glKCtFVckmnNANTA1MYeJZJv1eB9+x6Pe6zKdzZJEji5QL568yGR3R6qww3z/Atm329/fZu3uISQ61xrpjki/nGK1VpLT5yE9+mDJP2D8coVkuYauD5mr4ms7n/vQPUOk+RtsHNGoFruvT6fU4Pj5u9BUqZa3j03Z0fKMmSsZcuHiRoNPi9u7rmHaAbflEy4L94yma0YDX9/2m8GG84YmrqqKSAillUy4vClA1ZV7QabXpVxJbLmmHfTZ6fX74A9/HdDrl4a1tyqLmmWeeZXN9DV0X7O/tIhydFc3AdNy3CsO3B2JOwaY0LN2gyDPSLMbUBaauM50leEGAMHTKIqHKU+KkBCWJpkss3aDf7jBVU5AVtm6QLiPyOCEMQ2bTGW3fZTabMR9P6Ha7yCQmygWpPeRX/u/fRV99lEcuXsLVSgJbIFSJpZtARbyYoKTE9yyyaIFrGpiixrMMVFXSbreRsqDtuywWC9qBT1GUdFsBSZJhG4IozXAMAzybPCkxqHEMnyhOqY1GE1GWJb5n4psatmGg15LQ0VBlRraM0QHX1AjDNvF8giotimjJysoK2WyBjKYUImew3sewLFBznHiB59TIymV2XCNlwfrqOo7bQTNdKpHz+l89i0jGaNmYWNeppMA0XW7dvEPv8ffghzX9fp/t7W3moz1EVbKcTXGHAVIpah1sv8XxfsTdu69hez667ZImU6qqIppbTSXUeCOhVUqha/ckarVib3cXIRWDbo/PfflLfP8jT1CXOWutkLVWSJ3EnF/rc3C4z+XzXVq+QxZFJNGMuj7XcOqqQgkNXby1sOKtg1g02b2WVSynM5AFrqujqpLZeIzrGKTxEtsySNIYW4f5YkmZ5aRRTC0rXCegKgqU0CmLAl3XWUYLijglCAKSxZK6lDiGyfH+AZrQSUSL333qabpXfpC1Xg+lIgb9Lrbr4Go6aZpSliWuaZCkS9pel43hgMn4ENPQWJbpSaxWYxoGmqjo91pkSqEKhaHVpFLi6Dp64BKlBXWW0TJdYlVQ1jW1paObTWycZTU72+vk0RxbF9QSXKFohS77d++y3h8wXOnjBy5S5nTaPpbvEOdLgnbAQ/0dbuweMR9PWVvfRFMassqYHNyg0+uz2H0Fp9UBY4nMVlC1zcGdF3j52VdZXRuiuauI2mQ8HtFp9zgeH3Fnb4RhGHz5q09x7do1yiJi0GvjWxae6bIx3ODgeMFnn/oSeVyQ5zmO7yFFM+OIk7GVeYawmtjYtu0T1qVhJ2ogWi7ZvXkbU2kMNANdaBxODtnZ3qLSFAeTY5578Qbv//4ncVsBnUGXzbUBjt+i2+sxGo0YDtfeMgTfMog1DBQS9AWVNkCv72KaJlVdMJvNcB2HdrdLvJhgaiWjgwMG3R5pnKCLppnTtUykUuzvvk673UZzdGbjA7pbl8jrmCxOmEdLRGKhqhzPMtAriIqUX//aHp2VIV0rwzIKut0ejuMQBAF5lDPo9NB0haAi9HRcwyKnIrUEuq7hrPZwXZcoj7Etl6Ko0DQbrYrwLIGuC5Sn4/k+h6MZlVAI16QoC3RRIZVG6JiIWuG6DqLt0rYg1muEgNrQqeuawLeplcOkTKn2CpztHlWdoGkdbKHjBCGFqDEClyeunaOUilkU4RlgBhsYCpRy8B0HnTZG7WIJycuvvkhSR2xcWqfb2cDQfD7/1Gcoa4nMM1zboqImmo947NpDOBbMEsl8fES4OQRvhdru8G9+7zeIoybXcBwHw7UxhaAq87NEqyxLPEPD1XXsExZGGiaGYTKbRjz3/A063SFHR0e4SczlzS2SJEaUFYNOjzu3b1FVFb/+a7/Jj/3Yj1EVNkWlsdFpUcRTam2ALEqMWqCZBm81KH6LnlhDEzaW6VNrFpajKGtFv2+iqooii09i2hLbtomiCA2BwCDPEsq8ZHY8pd8bIoRgdPI70TGX13scjybUtUahL6kNl8p0ubV3wIs3SjrrO7Q9HZcM+yQR1DSNLMvIshzXt5GqIpdg2iFeO0CzTc67TSXu1VdfBd6oIq2srDCfxdi2Ta2gLGtM02ziYN9HaCa+aZLnOaWskbVOLiuEqM++u5IFnmsyHo8Jw5BaFURxTN82SaIFTjvEcS38lo9tWwhDx3RduitDMlWjuV0cIVgXgjReUpPjt3rolo9h+ygMDMskTmN2LmyjDIckLnnl5VvcvvUMuuGQFwkHB0eYpslsNsMyII5jRA2+Z9Fb6dJa6dDv93nqqaeIogjLdCnzglYQUmQ5KysrVGXTTW7bNt1uF9tzcV33jK2ohIZhmPzB7/8xeSZpdwL6vTbHR/t4nsfGxiVEJdnb22M+n/PwlQ329/dBLPn0Zz7FBz/4JEqmrK2tYZwWkt6mvQUQK0BD4GBoIYWuI3DQbUWejBC80dlxb5tMmqbs7x2iI6iKEt8PybKCoihOCPAYQxfsHS1JSoN2f8jrLx5yc3/KUmak0kaaLiqJGLR6GIbXcKgnWgXDMAi6bfx2C92oMTUXzzGJoyPCUMcLuxRFweWHH2I8HqM7FkUuKcuyoXtcm7KQRNEMag0hGukiwiCrKkyhELqGIQSu4xLHSzRVYxnWibC9phME1CfXneclli3wWhbtjg9ULJcRtu+hBx2E5aFMD8/roAfrIAtUHuFgspztczxd0l1fJZIOju8iVY3r+Rzs3uVLf9mo1jY3tjEMi09/5rP4flOs8cMQ6hpdVETREs9x6Q9aWI5NbXscHBwwn89xHIeV/hCVNffftgxc00KZTSJn23bjoS0XWYFpmaha4LgOmqZTFAV5XhLPJZ1OB88yQZakacrh3V2eeORKwzzNR2ys9mj5Fh/9yA+zXC7RREUqC4Zu0/doGAbqbQiC3npMbPi0wgGTuU2eLzD0hlssi4LRaEQndN9IBkTz2u0OON4/JPACyiTDNHQ00ZR9Xdflz67f5dbdXZaVhumNSZYZlmGg6xq6pePqFRoKDcHa+YdZWekhpWR1dbVJNGwXTa+John6fIlIl/QdiZIRh4cJpmmyv3+3obcwMA2bsmwegKLIUFVNu91muYibx/WkD1DoOq5pUgmB0kxkVdP2fbIso0zTk9ar4qRs3ZD/aIKd89tM7rzMdDrB7wzodEIMQyMuc7rtPpVuo+sBRVljAjLLGO/tguUxnpf8xqd+m0R5tFdCnrh0hXg0RWUFZWVwdLCgFaZkWcIjjz7B008/jWm5mJYkXs54/NErzEZNESnwLIKVHna3x9Nfe5a6run1epimiVCiuY68aIoSgXNWitc0DWE2/H1egWHoOELw6quvEkURhm7TClxkkaJkE4ZIKen1ekRRhOc6tFvNbPlXT79Av9/nfY9fJRyskWsug8Hgu+OJBVoTuuga9Ndx5qs4ZUxapFhGC6qETqeHUBlSabhei2i+IE1LluNDyiKiNEtc3yavJ+hmn7sTi7/43KtMspS6Fvi+hyhqfKNCCEW/20cIwbmLF7Btm7W1tQZsWYYjbJJsjmVZEEfUZUYRz9HqlG67xXKxYKXTI01ug+uzOuii6zaVaVFVFa7bMCDTcSOvzPMcy9ZQlYlvm5hCYzweU7s+xklxoyxL9hcFRaEwTZtCFiBMyqLAqHM8z2EZG8zKirDn40pFGqW0Oz0ENqbeQiiTYpmiVwsSOSGeT/Fcm6AVIouSz33tFX7vqetoEnS9Rv/3TS5tbzCajKmUYJkkvPzq6/i+z539IyQ6phtQ6xZhGHLjxg221hoBvNUKsLwAoWos3UcWI+pK4Tomuq2TlVHDlpQRVqowbAulJJbrYZgCL2gxmcdYpkUZJewfHeO7Np7j0jJKbK/dCP41g2oxbYpJdNBq6FqCZZ6yTBPOnd+gTpZky5jS0rHCPko30AzjbXUsv+3ODr2/Q5bMIH2NNK1QVdmUGNMcTTXtSkLVUCk8O8A0XZI4o6wgqra4/tptdg/mpBLOb68hhGA6neJ7Nq7TZTAY0O/38TwPPwyoqoqWZ3O0dwe73W0SujxHVZIyTaAu6bfazBbZG5XCLGNr5yK27RBHKUppCNNskkpNo9frceBOWF9f5/nnnyeKIrJUUkqJpgs63YAkr3Adm+Vyied52EaFUBV5ltDrdlFKkOcpi9mcuhB02zb7R0s+8v3XkElEmucIQ6eqFWmaYrs+tueddVy0vHVkmbOYHPPqK7f53Oe/SKE8DCUJgkZZFgQBqtdjNo8wTfNsoZitzXVsy2AWrWvHAAAgAElEQVQ+m3D79m0u72yxtTFkMplgGSYrG12ytEAmJTdffQUhBP12C12AUiWdtk+tCqgVRRZR5SaV1mjHXU3HdWzOb62xTApGxzPOnz/Pc1/9GqPRiOO65qM//jim5SBQ5HXFcDhkOBxi2zZtNyTNMqI8pb+ywrgseGi4w7lzl7+hCvA7DmJN03C6G9RlQhEdYJQJspYkSYIqC2Sas1gsGB8eIbMCXekUqiarBHf2DzmeR5SlYnv7HFJJDC3HcRwubl/GcRyyIqXf77OyskJZlkRpQlmWLOczPM+jTCMimZ8AOaEuM2SZcbgYYzr6WRm73+8zj9Om0yHocHw8IbAbQMZxTBAEDIc9ZrMRa2srmOY6R0cjlFLcvXuXoswIvQDX1TCERV2X7Kx3z8IN27aJo4xEmLiize7uLpqm8Vqec/niCud6DmbgoQTojoXltjFN8yRxbGLqLEuIowXz2Yw4g/E0IpICS0jEXDGbzZBSMhqN2Ns/AjhjZYw0IbFNsmjBE49cRdDkJKurqwxWBo0IRxg89enPgipwPY/VYY84jlGFoj6p0NV1jWdY1LVCrwUyz8HNWcwmTc7huiwsi+VJx0hVSqJS8fkvfpmOZ+PZOoPtbeK8YBbFjG7eQpWKrfPnGKyv0VodcmHjIkF3iOGG75jG+G17YkGFM9imXD5Mtf8SWd1UsWSuUFWFpek4hsVCJiSLjETCcZSTKptuWACCfqiRRCX91R4XL14kz5v4qtJazUNwdBfDMOj21lkul0RFimcblLqGY5t02iG7u3N67YB4LgnbXezQ5/jwgDAMeeWVV1jZPIfp+CRxztrmFsvFhKqq6HQ67O3t0W63sR2bw8NJM/jDDqurq1y6fJ6DgwOyZUZZlnRbLnEcY5rWSYfHSZWxiGkHHkVusdK+yKvHM/YOc5K60SZbjk2UJlimiXESb85mMwzTJ69r2r5LWTSAdpwuy0SiHB2hNef42GOPNSHVuXOMxjPKsuT69etomsbqoIPnWqxeu4xSCstyaAUOyALLsjg6HPPsiy8RhB2KaEK/3aYuSzzLwnabosZkMsF1XXQFSZ4RRTFhu02WxNQiZaQUvZUh29vb3JKSixcv8vyzz2EkE5Q0uX28YGNri/GLdwmCgNdvjAjDkKBl4nd6uO0OSjep0VBFijB1IHz7COYttCc9aDUCrbLQVE6tcjIZU+VLVCYR1RwlC7Ispy4Fk6xiHi1phx5tz2B1MEDUFe3QZ211QH+wQpqmZ3X65WTC6nCN/cMRthOwtjbglddeZ+vCDk7YYrZc4Pk+lm2ziJaYlklSlCSyJs0SilKCqjFtkyTJaLda6DpkaUSRw9rqBjdu3ETXTGoJlmGDEtimQxwvaYUtyqLEMi1WegGua9Ju+9i2jq4qQt+hFbiEvkM78DF1CDyDukqoC0nL1Xn5hTtsDXtoOgRdA8Mx0ARNs4DQ0HUNw+uCblDKgiKe8Mkv7nPj7pgag1YQ4gc6hwcHaEJQFJL5dIphGGciGsNomIPnn3+Rfn9Apxsynk4ZrG/ypae/RpzVaKLi+PAWW2trtFsh1IokjnBckzRLyfKMbq9DXhVIJfE8F0GNqsBEo4hStKLG0g3avQ6Kmv1bt8krQVFKNGpCz6Xbcllb7aOqHEOvGfRXAAh8n/XVNdI0ZzSZMhiu4gbtd6RN6W2DWFADAt1zUEWKVBUyi6ilQlGR5BVKCUzLoLcyZDDo47g2w+EKspL0+h2C0EOImjiN6fU6WFYjnO8NBpRK0V9dZbC6yv7hIZs7O1y4dJXD0QTXtQnDkKIoGhH3Sd9bq9XCtg0s08BzbDzXJj0JbXzfP1FP6SwWc9rtFmmasL6+ytHxIVvbmyyjBZbVcMPT6RQhBMtFA5wkSRods+2eSTGrqjpTfmmaRrfbbUROeU7guty+e8ja9hbd1TXSSkMTOobpoNsBTtChMhwC1yKPM2aJ4H//xO8Tl4Kg3cLVa/I8JXBdHjp3jod2dlBS8tJLL2EYBtPplDt7B8RpxnBtnel8QVYWlFXNV776NMfjKUoq4mjGI49coRWEZ2q8uq6paTjhIAhI07Q5d8NguVwShiEIjSRNoYbJZMwyWuJ3QnrdDq88/zx5WeB5Djvnt/E9h26nw/Xr1zl37hxhGNLv9+n0e+hmo8ew3QDb9UiznN5w/W0D+B0BMafKfs3A9FoUeQ5VDnnerBesmeiWjuXamJpJnmcYho5tWximQRB45EVGv98lKzJc10bXBYahkcoKzTSpNQ03DDkaTRmubzOeRyg0Qr/pzDVNkzAMiZaLpthQ14Shx3w2xXNslJLoJ50ZSZIwm81ot1vE8ZLBcAU/8Njfu0sQeBRFTpom2LZNlmUsl0uEEFiG1shDbbuhoIymPDufz7FtmzzPm9ugaY2ewjWxTIMiTxGloChi8nzJxZ2HqDQN3XKphI7phLjdHsV8ynyW8PHf/hwv704oa51KFmyttNEtk5VuF9+2GR8fE0cNwM6+W8H+4SGW47CME4KwxXg647XXb9LrD/BtG8+12N+7Q+D5ACdhh0WcLHFdlzRtmgZO1WuWZeH7PopmwZlKKSzLpMwLbt+9Q5Gl7L52o8kpPJfjwwO67RZHxyNs22ZlZQUpGw45aIUUstGr2G6AH7aIk5Th+tbbg96Jvf3UsAaEokKj1gL87jpu0MUP2ggrwPJD/HYHYWhoump622wNw2wWy3Eci8GgjxA1rVaAZRmUZY4QNbks0U0D3TIZjceUVc18GWGYFqbjkiQJWZYBTTElDEOSJMEwDG7cuIHrukynU8IwJM/zsxb2nZ0domhBGPooJZnNJtRU6Iag020RhB5KKdwTMt7zPFzXxfMare14PEYpRRzHjY46bxJSx3He0MhS4do6O+c22e6GrAYO0dEeo1uvU1YK3bQIWz003WS+iKhkyehoxF+9dIdaN2m1u3TbbVxTZ3t7m16vx3K5pNPp0O12mU6nZ61QcZKjGzalrMmLiqPRmCTLuXzlKkI3uH79OnneJMCnxaHxeMydO3cwTZPFYkFRFLTbbcIwPNP7xnGMEBC2fNrtkLzIWM7HzMdHHO3dIfRtup0Wmqi5sHMO02iaenu9Hpqm0Wq12NraYrFYcHh4eMZG/LuWBngr9vYXTxHAKXeMht1aI46OMPMSV0C0nFPMC4TuUOtzHM/CslwqWeP5xomkr5nqXdcliioM08FtdUjKMf1un8PjBX7QRfbdpq2llri6Rmn7J5XBxrNXRdwor4DhoIOUJbpjscxLACzLahLDKCLwbRxbZ3S8j6oqtKqmG7QYHRw1lSodhKiRWcJsJOm1QvaORriuy7DbJylznNCl5/WxbRtZqjOv7bVCqjhGE817c81G2BZt9zyzHILMwLQ1sirB8Qxaps6ycPi1P/gcs7qgFwQEgYtSCru1QmCYuIaG57l0uiGT4xzdskmihOPDY2aLGTs7O0gl2djaoC6aAszxwT5VVbFzYRvf91nOJ6z2VnA9m/7KOeaLMSv9zbNeuntb6oMgaMrvJy1mKSm6UVFXFevdPgUm2C36TgP6vb091tfX2RwOcRyH/f19JpMJe5NjLpw7TzcMkdESd20T6oqdnZ23Db1Te0cXFKwBIQza/XMsoyVmOsZxHLTCReUxjueRJjlSSmrVKKVOdcSu69Lv97mzf0CNztHREZ1+j1JV9FYH6KaLjPSzkvZpYnNvr1ae51iWhZQS07RxHI8sy9A0A9AwDJ35fMn6+jpZGrNcTs8+47ou8qRLBcC27aaZ8qScu5xOzipRcRwT9jrNNdeNhkI3NGzX4fj4mOVyiaNpOIHfnF8FWZFTFBmOCs60um7YRiqN4/Fd/vKrt7m5O6fjDmkF5ll3hK5DGIZ02wHb60NM08B1G4YkyzJarRaPPfEYk8mE2WyGbdvc3r3F5cuX6fV6HB0dEYYhVVWxvb3NysqQ+WxKmqYkScLGhiBNC6I4pdVqoes6k8kEx3Gah7NuZpxOp0MQBBxaR4StDtM4RTc1qlLx0ksvMRgMyPMcoSleu/EyW1tbhGHI3uERVSU5mky4+ugTLNOc7dUtdNd/x3D3DsTEb5jQSmphoJkBlUzQshFVJSnTGA2FzGKkrACNPG90E6di68Fg0NzwdhvTtFld3yBXsmlVF8ZJV+39ax+ccrSnvVpKFnQ6DbiKLMXQTYTQKQqJKhXLZYxtOaRpThxFbG5s4Toet2/daap7y+WZML8omq6TLMtOYmKd4+Njer0eQgjkSav66XJQmWwWXEnzHC8IaAc+SZrgeh6aXmM6NhcuX0KzDDCavsRCVmR5yTzK+ef/8rdQVp/ANdFFzfbmJhtrq6AUt27dotdtI4ucsiiYTaccHo3IioIsLzg8OmQ2m50t4uJaJlVVsVgsePjhhymyhHYroCwyZK64e0IphmHI4eGIyWSOEAZHR2OkLKnrGtd1qaqKyXRKp9M5kxAoWZKXOW4Y4Pkuk/GcIAh4/PHHMQyDKF5y7dpVLMukKHLWBz38Vodrj78PpZkMt87RXV0HoZ+N59u1d3hpV4nCpKrA761SH5tn8Y+sS+rMRBNGo1fQLcqyEYy0221Go9GZjvXwaIxuOxiezdF4RFkZtDorZ02nhmGcdX+cAq7dbiNznSiKSNMUmZcIoTedzY6DJnTck6f/lVde4dFHrrJYxCe9fU0V7dSr27Z9JpJxXbfhvbOKzc3Ns6zdbzWgXy6XSClpDQYopRisDpspOc9xAh/TshCVwnFd0ARJkePes0SWaTr81r/8HTr9h9C9Duv9mocffoyiKHjqqafI85z1zXOkacqlnW00AUf7B1iWxc7OgMUyYREtME3zRJSTUwt1xj68+OKLDLoh43GK71rEVYqumSyXCdPplL2707PGzSzLuHxljV6vx2KxoKoqhutrSCmZTCYsFgs8y8JxPWazCZph0u12ieOYW7duIYRga3uNOJnT7/dpd3xc3eD8lceJpE67P8TxAppU7J1r3X+HQdwcrtIAvY1p6mhajdAUpUzR/IAqzahkSppEaJVOXSmiNKHSBdsPXWU2j1nZClG1Rq+1gpo107dtmBSyBCFOBEUVkhrDsiikxHIcyrJJ5EzTxHR8KprlZmut0TvHRYbv+zz2vvew+9pN2u020TKh113BdrQGyGgIrcbzHYoyw3Gb8KQ77DZSy16Ipmksl7MTWk3H82yKrAlL6ko1D5UfnHV+uKfANgzWt4bUbh9dSEzD4pf/n9/gINFxQof/7p/8PE6rw/jmi3zqU5/C90PqWuC6ISuDDrKuSKM5huPQ7TfTu5IZR/sz4lqnNizyvOTc+oDj0Yhut8tkPOFoHOEaBoFrYTtNjOv0+lRCcBA3SelsdMDFC+dISoe7L91lOp3y0EMPMV++jlKqWcbAcSkqQRmXyLLEssQJo6HR7/fxfZ+Dg6btytIsdF2nt7XNLFrS7q1giALftfm6f6DyNu0dXsniJJ4UNZTJWYx5uj7DWfyoNy0vp+3yp/qF09+DIDhrHh0MBmxsbJDnOV6rg+F4JIXkeDrH9/2znrr5fE5Zlmdx3Wk2fKoLBlhfXz+piDl0ugFFmRCEDrpRM51OSZIEx3FIkqQJGU7Ov9PpkKYpnU4HpRR5nhO2W9iug+O5CL1hKHRdR0pJmqbMoiWGY6NZJspuEQy2sDurdNZ38L2QG3sx//C//mWC4L2cv/Yo/9ev/RprG2u8fP05/uiP/oi7d+9SVVXjiQc9Qs/GFIpu6LG20sMUNbdvvIqBYqXT4olHrqApiWtqHB8fEwQB7Xb7RApbsb4xxLI0Ll26xMbGxtmCgud3Ntm5sMWjj13FMCBJEjRNo91uc+PGDV64fsQ8MnnuxQP+6vk9xrMlRQXCsDk4ntDpdNje3mY0GgEQdvq0ewPcoM3K6sbZ7PnFL36R2WzGaDQiT5J3FHXvaExcc1LBUwUiOiQZ36TIs2aRbdV40TIvSJOUqpQkUYrredgnQFgsEzTdxA8brXGr3aZSkOYFpayw/Da10NAMAwU4lsHx8TGe5zGbzRAoer1GnmmcUF+nvG5dybN/u5CmKbqQ1HVFUebE8RLfdRvPdkKtOY5zVjkEmrUrlMI7Ee1IDdA1lGhWPOr3BkjZlNx1XaeoK/wwwAsDNF1HF2DqAl3A7t0lv/A//K9o7S3Cdo9f+uV/htA00iSirjKuP/f8WYLV7XZZzGac2xz+/8y9eYxl2X3f9zl33959+6tXW3dPb9PTs28UKZISaZmiSJkMEIuybMeJgSBBEsWWkCABAiSAEiiIk8CJbcUM5ERy4iRwpFik5I2iKEriRBxKMyRnn+m9u6prr3r7u/ty8set94YUJUQiewiffwpoVFd1n3veub/l+/t8sXWFTsun4de5fOkCVy5fouY6GJoBUiIUQZZlpGlGmlY5h6qqWLaB5xh02w0uXLhQCXPqdQC6KytE8RxTF2ys95nNomW8bJom06wkLUvCNGUWRbQaPmlWEEYJNb9RoXyTCs1Qr9dJpYJpOyRZTpJlxEnEcDTmoQsXMS2b9soqXr15GhM/mPVAD/ECaaSIgny0Tzo/IM9SNAooskoQDaRJgioEFJCkKaqu4fk1EBqW5VTJkeOh6hphGJOXkiwvqDV7ZFmObpgMR2OavkuWZe8W5x2Lw8NDLMtiODj5NpKN61R1yUUzxNCqaecFrsnUq1Co3++jKApBUE2nLL6/2WyQ5zlBENBut8mpNLnD0YgkTUmiSr1Xq9VwXRe3XgMBlm1TxHMsXaHmmIxPDnn5mzvkRo3m2hl++qf/OrWVDfIsZnfnHm+9/hqObnL//n3OnTuHbdus93r8wHNPMTg5IA6nSKHQaNYpy4J+f4VHLl9hdX2dGzdvMZ/PQFQjUrqu0263abbqnDuzSafV4JFHrtJsNpdVl6zMaTR8uu0GeZZgmd4yQXRdl3mRsrN3nzRPaHWapHHIdDZD1TVqvk8czKnX6yRJwuHhIaNZWGlPul1M0yTNEiSCeRhx5uw5FMPC9WqgPLhI9oHGxCoZoIM00bz+8hDFeQYKmKrKZDZDKUs0RUFrOGhZQZikWGlJEM0pVIFmuRSyYBoG1Btdghi0UjAYjVlbW+Po6IjuSp9wNkEVAlMXDIcTXLuzHCWP4xzDsHBci7LMSZIKaBdFMxApcVRQcwxqhoVixkyHw6qEtHWPdruNaWo4js1sNsFxquZHVuS4rTrTNMLUdAZHx6z3V5lOpxiWiW5rpApYjouWl1WaW+tg9y7iipS3Xvp9arU6L755nVJR+aEnH8OodSHOCCczZBDjKSrv3LnL/s4uNcdlPp/zyQ9/mH/wP/8yYRgiyxK/6fNjH//zFGmI2oTcgVrL5+N/4RP85hd/i8HJnGefeZKb77xJv11n40yX1dXVqh3v19FOqzl5nlewFE0lDuZEs4gknzGPQubz6nD2PIeVJ58gTHKu3byFLHMeOnuOLIXZIMCwde5u79Lv92m0PTTVJEtS4iwlzTLmQXXJ2IbBYH8fq96pyFGa+cDO3YONieXiFZEhZfQt/C65fL3leeV9EUVRNedlGHieB4DrukRRNY3caDQqOqWUy1JavV4nCAJ2d3eXHasoipb0oEV2XhQFzWaTZrNJURTLKsZiYjfPc2zbwHYM0jRECGi36xiGgqaBopTkRYyqSUxLpdmqOoFHR0fLVmoYhnieRxAEy989HA4BljXbRqO6vUU2441v/gFpOKfIJYPBgFarxTPPPEOr1VpqIMqyXPIiXNfl7bffZm1tjf/kv/yvuX5nh0Gcsy/gnZv32N075vP/9IscHU/IogRRSjZW1/jJz3yGf/vf+IuIdMoj59fY7Pus9rqYmoquiGWMuigP+q0mmmWelgAv4DjOkr18+/ZtSOfUTUHL1fnIDz7H5ubmco+TJGEymSxDk0XLPcsyDg8Pl3nPt7JEJpMJURQ90GP3QMMJEFVuJxKy4JB0tEeRZwiZo1CQhhHT6RRZlFiGSZrnnAyGtDpdilIilCokKVFI0gxV0xmNpqxtnOPg8IggTsiyjLW1tUq/IMDzHIJgVk16zGa0223CMKTdbDOfzyrdhKoQzoNTJlyCV3OxdJXDw12ajRrD4RGKKCjK7HS0PkVqnGopYkbjAUiNVruNYVfEoCLLGZ2qyWzbRtVUpBCgaBimRZEXJGlOrd5genCP2fE+Td9nOA25P4Yz5x7ixz75abxGExAIWXC4u82dm9cx9Eq3EUURFy9e5OnHr/KI1+Zn/+q/xWq9wc29YzrtLtv3ttna3uXseg8Q1P0GlmmhOTZnz57F8z10w8C1dGSZU+Qpptdc6iN0XQe9iuGlrDTNsqguk9XV1Souti3iTKIYNoPxjKIsKfIcz3G5v7VNp9eqkm7HqYYTygqs43guiqIwGU+WF0ej0aDW6eHWfHTTfmCg7QdbYqsEbUhZMBge4es6SVwJdILTSQvTNCErlg8/yYrq9Z/muLUKtZokCaalVvAT12Vra4tmq0tUyCWadTFKZAqNOI6/ze95Pp/juz5QtZrLMq/i2DzHtn2SNCQJ5/T7K0yHxyhK9W8UouKzTadTSrP6uZ7nVW3XIMPza4ynU4SioMh3saXD4RC/UafVbpNLlbyUNBoN0rx64xzsbpNFM7RGnaIoWVtbW04+CEUhOWXWNZtN+v0+N67dRNM0giDg2WefpamWXLv7RV771c9zOD5BIhiMZqi6xeHRgG++/HUuX32UyTTk/IWLYDsYlkNdKDiNBDOdcnx8jH6alC6IPqZpAiWFWk1sT0bVG0/KatYwjmOu39sijDIyCTW/SZHn1Ot1BicDOp0Ovu9jGAbT6bSqr4/HtLqdJYx80XxqNBrLCtXCT3DBg/5e1wM9xCU5Akk+n1FXSpK0RFV15oMjdFmSF3NsHeKiIMokwlDw6g7j0TGtZg9VUdB1gzgKMV2NWRBRb7iM0yHhTJAJDd9bI5nnyLxAtVSGwyNUJI6ukp1653W7XcJwSpLESFmFK1ER4TgmMk9RygzTM5AyQVNKzCIDoeG6PtvbO3hugyIJKARkcUImS1TLoxSgKdXNZTs1XK+OYRgUpahGstIURbdPH5yBkgTMBrtkSUx3/RxJqaBrKfXeBg9degxh6CAllqUTxjPSvCQrxVK4VK/XuX79OtN3rlGfpliahl73+QG9ZJjk7A/G1B2dV9+6j1m6PPGcRRIe0uleZnB8jMxSDEXF8Os0ipTR4X00x0Ftd9HsBrFQUdOYIssQElqdPlkzZzqeMDg+YTgcokoTkWfUbZtsNmNjpcXRcIjQDVLN4GgaoTHH0E/RBXWfo5NjTNMkixNqDZ9cCjJFQ63Vl0zoB8kpfsDhhERSIvKYIjhCplVTIw2nZElAHgeoioZpWpQFIFNUIVBk9TqNwpQkSgGFPCuot1pMZwF5KYmiGMd3iKMcy9bZ3bvLan8FIUsMXUMgcf161a3Lcwytukmr2wYsQ63A12mKbWiMT04wNYU8y3A8G9vWieKQoqgsFtrtNmmeoxsGWZGjqOaSoCmEwLKdpU+Hqqo4joWiaRSyQtze37pHOB2jlBlJntDp9tAMA90w6T/6Q3RWVuisnrZfRVWc1A0VoUI4mS5VZ8PhkDDP+Xf+8l/nNz7/z7i7v0+4e8TTq5tM9w7Q3Rp7wZjdk2OCQUAxK9BMlV67g26bZGUBQkEIia5oBNMBZZYihKTI3gWPL+JW07KQZYmpG3iOy2Q2YzobMxoNMC0dS9epN5sUEmzHZX+/+pA2Gj6WoZPLasLaNE3a7TYIaLbahHGKqmmkWUG720MzrAcWTrwnvkyLJsFCHbX4unhla5pWvaYVSZEnJPGcNIkxVQ1NKMgsR0NU4zvfom3N85RazWNnZ5uVlR7Hx8eUZdUhm06nxHG8bKTEcdWdW+gqsjBGlSCznOPDIzzDYj6dYRgG3fVVZvMxaRZhO1XP//j4uKr3nh7cxWT0dDpd/h/H4/EyKV2gTeM4rlRylkGn1SCLAvxGA1SFvCxBVfBbTdbObAJQUImDZnGI0DW6K9WQ5crKCu9///spy5IPf/rH+Uef/zWOp1OCJGVYJojpgAuex3znhGQSkqomL12/w7XtE97+xqvcuX27+je7DkI30G0X03WpGQrj410mx/toMl5yQTRNWyoAF8/r5OQETRNsbKxSqzmYprZsoJimyWAwQFFOP/BpuhxeXXwoFk2ju3fv4vt+hYJNK1LUg1wP9BDLUgUMNN1C0y0KJaeQJappAw6ZZpICsygkyyMMRcVUdMgLBgdHxEVEUqSopoVq2uSooBlIVWMSxJRRgaPn9Nse05NjyCuNsOvX6a1vLmOu6XSKofu4TpOikMyDWXXIpIKiGdi2S14m2HUXwzUY3N+GeUnbrEOQUNc15rMpOmAJBSVKEEVaxdtRgtvokMQZ3dU+oUzwV1topkORS3zHpuFYrPQqWJ5UM3LFRSgFtpkhNBuv3sP1mnCKMzUNu2p9Gw6uW0e1VdBKtrfvoFEw2zuiv3ebK7okFZUwn7xgvd1irelgCJ2jvQleq8sL33iRV2/tcbR1QjCckGQhju6g6z6K28G366y0emyudRFyThyNKGVMqZSEZYHIBY7p4tUabD50gbbfRKYFD5+/xMPnL9Fc6WG7DnE4xdQKaprK5GRIp9HFtuvkUVKhqUwD3bYYTiqtSRrNiKfV9LVn2YgHJP6BBx1OyKo4kecReTBEpjPKPEfIyhY3SeaYuoFpmEzHY8osIc8KoijGc32cep0giHHcOrphMY1ibMelkJI0zfEcm52dnSVZXspiKQZalLfKshLzaIpBWRaVUEWBmmOjKJDnCVkSQZkhNIUsS9DKgiSsqgFJknBwcIDh2Mxm09Na8RTb84iiBK9WtbUNXSPNMxRdrfQcVFisxe0jEKRJiK6qCL2GoUoUoWDV13DXHse2383OpYSyyBBIkriyDRZU8fe5M2d589p1zN19GrqLprl4TYnhmj4AACAASURBVI9Nz8c0TIK8JMwLEikYRRNqTZftW3fotBtsnF3DrtkUWYlhaghRUsiEUlUZDobUTIdS01FElYuYpoXMy2raWa3IPJ7joKgKruswHo+QojJ7XF9fr0a9tKqM1mg0lsma5djkpy4ArlfpjRf6C6fWRNMMXL/xoERsD1iKCZVPh6ETRxOMPDr1RIvJkymiMidGALqqMRsPKQtJkqQ4jkeYZZiWi246BGGEU2+QJBl5WVKr1YnDynlpoX4bDI4rC4Nmc6lpgEoLLKTK7u4OuqHi1Vw0pWA2HVMWCa1mDVnkuL6HogBpSpHmy9qv67rUmnVAniaHJYqm02j4FHkBSNIswrRtEAauWyeaz8iySnzvOA6lLEniCEpJs7OKIkryUqD7Z1m7/Ny3x4MCijzj6OCAcD5j7/5dppMJo8GQLEm4/PjjJNt7WGlJeHJMrOvMB8fUanXyspKFGjWfRMlpdmrYQmd/cMjqWpeaZVFrNrAsDVWDYTChkBJd0RkeDUiTFMcySOME3/PIi4I4ibBti7IssE0Tz3MBiWWZCFWrOn2n+C9TN7h69SrT6XRZz988e4YwqabDk7SauK5QujlSNen1+pim/Z0+2N/lem9iYhRqrT6qYoPUiOMIw1CWJoYL98uFseECsg0sY2jLsr6tSVEJ3St/tQXXwfd9VldXGQ6HzOdzVlZWaDQaS37uQrADkBcxpqVimCqqWpWXytNqxmw2qySMshIC2bZNUabYjoGqgWGqGCrkSUQazSBP0PQKsu26PnlWsS0WpjVpmjIPxgghqXkNKMAyHXSrju51/tg9U1WVdrtdOS/lOZ7nsbm5SbPZ5NY713nyk59g5CroDQWnVad/9SKZrVHqKn67wVqvQ0NRcNOMes2n3muzsraKVkIczwnCKXmeYmQqju6xsnGWxvoGmizYvXOH+cmAZDZlMhlgmipRNCPPY4oyQZKhapJOt0Gj0WA2m9FsNpcai8V8nmmay8rK4kJYlCjn8/kyX6n5/gM7wPCgJzuUEoGCJiWIkswSpJFAVQyKQGKqGqPJMZamk8xDWp5PKQXD6Zxa3WOGjWLXmKUJtmFgWw6yiIiCFNt2iLMCQym5eGYDSUGsqgThjE63haqqTKfT5exWEIUkWczqSocsntDu1hgcn9Bp1hkNjmjXLYYnR2RhDEXB0dERGxsbDIcVRWd9o4/tuUi1IC1ybM1AUw0OpyfkiWT1wiXSQsEymli2y2S6TbLI9k2LpuNjkJIrFmZekpdQ6Dam4X3HviVlThIGFGkGik6jv8roeEC77tFrtQmyL3Pxhx7HP9/mq7/3Aj/+7PP80i/+Az71sR9j54tf4umnn+bm3TtkRZ2rjz3K+tlzrF3aJDNKwiymburkYUQUzui5OqmtkxDTtE2KxGGGZBYNmY4Fe7e2K5/B08S8ZvsEgyE1v8loMiNXTEzTXF4swhHcuncXIQTr6+somkohFFbXHyIrSmRZkGegKhqO7XPx8qXKwFH5VzUmRr4bsJcpWThEZhlKEUERE0dB5Wak68RBSByGhHGCYblIRcX2W+SFRDUMhKIyCxMsy8YwrMqgRjeQZUWyLLIc07GWYu5vB5rkhMGcPEvQlByKFEGBKiRpGmIbGqLIMVSNYDYnT1IURVvKKVutFodHB3R7PbI8R9U0ojglSTNQVTTdRDHBb/oUpSSMQqRMl+JyXdcxdIMyiynFqfTUcjH8LrXOWTS39W27JoSCgqRMIg62t2h5Jrosiecz6r7H7s5d7t/bQhWC8XDEKJhx4fIlSk1h9cwm+5MRZx++yJUnH6fR67B2/gyNRg2RZehlyfxoD0dTGB4c8vaNW1iOy2Q65dVvvsL1d97h5u3buDUf23E4Ph4wHo85ODg8vUEDVMOg2e4gNI34tFG1qNJYVuXgVKvVqgaWZRFEMa7nk2Y5lmksByM0TaPR7WHY7gNVsT1gUXw1eoRUQPUxrTZxECF0i1Tmy8A/TVI8z0M6DrN5yCSIaHS6zOdzhGZVckfVoJRV+KCpGsPhkLWzFxmfHBKFIf1ehywLl0mDEAJDqoRBiCIl45Ndmr5beWloBRQ5s9GIfrdTwbLDlCgMEXlJ3XYr4cop72x7e5tOt8n29jb99TVKAWGS4DoGjqZjmC5S5GRZTC6h3mgiZNX21jQNVTFQVYFQVTTTRLcsMlx0Zw3Nb33nrpVgCpU0nPF3fu4/45lHLtJZ6fH0008zPb7Pkw9dZjabcevGTSbBjK2TA1zX5Yc++hHGkwnvu3IWVAWha0hR2XIVacTJnTvs3r3LxuYK24cn/MFLr3A4jXj1zdtoSsHmhXW6587Rabe5fOEiOzs7mIbLwf5WpeSbJ+iOQd2vobs2zZqHagfM53Nsp8JwxVFl73V4eFhNU6cJtUabsqxksVEwX3bvFnoNHlB9eLEe8CEu3/0idDS7gaINyYVGcXrQXNclKiWGUEmLkjJMqTVbhFGG0Csmbp7nZHGG114lCCKCOKDf73M8GtFf6XO4W8FBdE2rFFGGwXg8pldrE4cRQRBQZCGe18TUJKosMDUT09AosggoGB2foCkqaRhhqzqz2QyoYtO1tTUsW2cwHlW2VbqGYZlotkmU5WiqwNIb5JmO3+yiCJM8D7Ftd9nSjaI5ZRzR9DtkqsBy2girhWZ9ZzihK/Dm66/yC//Vf0HHtTDThNH9LT53421sz8VTPcbBjP/gZ/8m/+TXPw+jjA//+Q9WYinbIp3NKRWBXXMrPkQpCYMpTt1h4+I5du/vc3A85c7+nIwSkSv8uQ8+y8aVDVTXx1J1btzZIgsigiBmNguRUtJu90jyDNO2KhgiAs9zmM0m1OvV8Gk1iFv1AC5fvoyqCCbzaFljVxRliTHo9/uVPODBHroHe4gF5rf91NxZQzVHmEIl1H00E9JkiGKVBLOE8fGMvJR4DRfTM8FpsXNwyNqZc+hCJZcGXr3SDCdJQtPzScMI13RQS0Eep6c10xJD0UiyklbdYevG1+mf28QwNEzDQJGVL7Rtedi2RhRN8GzB3t4eSI1iWlLGEtfzuH9/n/7GOvN4wspqnzBNqLkmulZiGCqaWVFsMsAwNERZYChQqDGaYoHUKxcjQyExeuRmveqYaS6W20Sif0dlaXb/Hv/53/yPWW36PPXMU6w9tInbbvH13/8qrTyjvrHGWlnyuV/5v9jb2WE4PuZw+xJpmp6SgCxsTaNV93AsE3kKeomiiGvXrnH/oGpqrLZ8Wi2DC5cu0t/sIzVIjo+4tz9mZ3dIWgSEwRQpJb1eD0VX8Rs9SsUgzQrQVXzXptfr47d77B8dYcUKQZTQ668RJRlCM9Ett1Ir5jm2pWA6Jm6jiVlvICyfohQ8SL/y96Q6sViiBL/ZJVccTMvF0B1kqVAWkCQZJQJF08nykpPBEM20WN3Y5ODwmHkUL03+VFWl2WyialX71DQ1hFIuYSaLysbR0R6zYM7qmbO0Oj0arTaHJwOSrEI1jUYjDg4OTjtRVddw2UH0XUzPwq45REmI69VJS7A8v/rquAjDIAdQDQynjus30R2LpMgpcnnaHSxI0hDVcHA9H6FqCMNBmDam61LK8jv26c7N16k3HBJZ4Pb7GKVFGSY8+vhVug8/zJXHn+RffunLnH/4EV742h/ywx/5Ud56+xZvvnWT6SzBq3eYxinTOOXu7t5SjLMw4Xn4kQ0++iPv4+Of+BCPXzmPqeSQxhzeu8/24Yjb9/a4fecucRyzublJ75Qd4bouzWZzqXdYSGMNo2I7L6Sx0+mUyWSyRCiYprnEebn1FmGUoKg6mlFdcg+Khrk8Z39WR9E/05KATBnc/kPE5Bbzg/sk4YAwHJEFJaruIFEI05y8EGSGT63RrGLovCSKKq7CQoMsUTBUyKI5sijxXJPhcEiapliWxWw0wnJMbM+m5rtMRyNqjkESziGJUESBoebU6zb3336b+Tyk0+4ThjFoGqbrUMjKf8Q2nQoZUK8jNYUcge02MKw6puPjeD2KIkfTFPIihbykKDPKsjid9atj1toUukvptnDsHnZjA77F5mrB0PjiZ3+OX/l/fpNRWHIwOuSvfOozZNkx3dUWW3tDVjpdut0un/vc5/jUpz7FC7/9Wzz66KNLkuXBYEIUzui3G1iGxuMPn1nqejVNw++1abUqlOu9G9fICoFqOOzsHfH63V2UQmG912et59JfXVkmYZZloTseVs0FIciFxNE04qTAb/cohWD3zn329/dRVbUi/pw9/25uoKpIQ8eyXdbPnscwLbxGe6k/eVDrAcfEf2RJQBg4jTWi8b1T0bwgChOCSYTpaNhORbV0bYdprmFYHvM4RfKuo32e59UAZxyhCbXCX9kmw0GFZ3VPrQdcz6TR6ZICRSlptNvMRgPCMMZTqnbi0dER4zGsr65ycHC01ETM8xShngp74pgsh/76JkmRo+oajbrPPMhxak3CKKWMCpotn1ImFFGMoZooKgihohsS065hOR5BqaNYPqbXoHrxlSxegIvB2en1t/jw1SucJCbX93b49Rdf5FJXpdN0ONkZsHc4JAi+yeaFh/mNL3yJzW6HICkYz2OSQnD/aEi9ZnMwmNLrthkMBks74uFwSFiAqljMZnPm0uLwZMArb3wTTa2R6gq+VSPNJOPJiG6vs1SZSSmZTCYMpmNcz6PWapxid6ubWLesJclnf3+fXq/Hzs4OmqbR6XQqqaVl4zXaWJ6P7/tIlGUd/0GtB1xi+yNLZBRCRZgWs8EOebCNVBQyqSANHR2dvCiwXY9CAlYNgYKhGcisrPD5SUzNtEjmE3RTEM2myLwgi1KUIkZSof/zEnrrlyhRK+FPnmGYNkVZUPd9bENDMwxM18V0a8QUCJkSBzOk6aEpOnleglDRVJ36Sh8UBUVRsUyLLCsoSommWVimh6obFHmBphhYhodml6gIZJxR011UxyOROprXptTWsJ0FxvTdh7eQI55EMyIZM5sdUlBi+DUs2yEPIx5aqXNyss/a6hpxJrlzb4ezGz1UBa5cPI+uST7wvufY7HVI0XiqGVE7+wRhkLN9e4+V5hr1Vp3jw2PSOOX+YcJ4HFHmkmbDp9tqEUdTonDM2tommgJRGKEIhTTNiMuEYDYnS1Js1ULz2sSzCNfSQC3I8oI0zylKSDOJZmo0W010Q0dRFRzXpddbwXUcVEVBWXiaPMD13t7Ei19yarOaSA0kqIqJqpTM4hjbqxEXEkU3MUwbReioqgnkyCIhDSsyZRwFWJqD4zikUUyR50ihYJg2UrPo9Pooiobv14mTilWx8Io2dJU0mjIaDaj5duXvoeZEp/XNXNWZz+c0Wy3QVdy6z/R0fH8xIY1po5suqm6imxa65SzNyqWUUAiEomM4KpplkWIiNBfFrNFsNv/YfVl0EzurZ2k6DjXHhZs7XGj6vPyNV3n7MOOGPse0m8QnU1xT4cmHz/DYE0/zBy/9Ibe2X0YRGi+8+A1+8H1P8swHPkxtcpNREuI2XIy6xUk0Zm835t69e6iqys7u0anvSSX+H4/Hp9LSCo3lmAbJNCSMqrayYhmnktOQ45ND2oaNSGPiuYKuOdh2i0YDilxiWS5hkYFmgGbgeB7tTiW71CzngQ6Hftv5ek9+6h9Zguph65pFWSTIUqHIQXVbtFc3mAQRiYSa5SDQkaWC0FVEWcW60WxWGY87Fp5pM0hS8rIkA9xanV6jA4qKUspqolotWV1dZTKZMBmNmMoCS6a0Wi1qvs1sNqY8neiN05hUVAdXGBpJkaPLYsl7W+oxbA/d8DBtH9V81xmqmhwpyeMKXmiYJqqhYyo9sBuo/sqfvC+nN9JDjz7LN3/vC1iOz0qvy9XL6ziq4Hd+/+vMk5QUqHkWnf4a+7tb/Nx/89/x6BPPcTAOcf02T166yvHhETe+8hI/cHmDk/0t5mHAJJxzd2sLz/SXM262o2EYKqVM0A2QhYFhOkSzCXEcM5hUBKUrK2sMBgM6TpdXXn+Zbq8BayUbhmA2mjMrIxouFEa9arf7zcoXu+Yvc5Tzly5VumHdBKXCLDzYlK5a35dDnGbp6QiRpCxBVXUMw0IxfHKh0uytIoVGJlSQyqmJS3UTWpqNZ1loqiRTiqXQXVdU7Hody6uhGCZFCUVWYV7zPCdLqu87d+4cg8N91LSKPweDAZ5nM4/yygosl4wjgWoZKIZOHmdMplOyRBKGlc+0ruukeUGt6aHbDopmUrMr7e1CFK8Lh7jMEaaBMHQU4SIMF6n+yQjTJaDFrnHxkceQky4PPRQyPr6Hkod86KlLpEmO2uzx1Ze+SWG4/N43bmLYNfaOR+S6x927uxzcep2/+NGn+eLv/D6ieD93vv4SVr3GcD6l1Wmz2a/T61XGl8PJmOl0iq6rBEFKkhYIReL7Pu12iygvWVlfJ85z4jzn1Vfe4rXX3uL5H3iMp555BJmlnBwdYjsaTstCM/PKy0Q3CIIj/HqLsw9VWmlFMxC6iVA0KtnUe7Pe00MsZTWLppaSskhAt1EVA6UQaEJHqB66WaOQGqCjZfNlG1nLEwy7jqJpiFOr2SzOSdIMWZFLUF2fHIG6LLoLNEMliwukWpWDjo6O0BSNWrPF4eEhaRiglmD4bdQkJwpOMPwauYRwPEaVJY16nVkUYZgOIgPb8lA9F0330Y0GQoOwqBCzehkDJZnuo6o+ilZHUS2SWhNT95dzh9+5N3IJWzFKlbWLj/OFX3udftNn5yjCcTwEc3obD9E5t8Hlq4/wD3/5/6BlCUYzBc3I2D+6z8Pnn+FT/9pPs/3yl7jYMJnOodt1OXNmnXbnKkJIVuoGpuEwGs44USwsT+O1N97AMAx8u0YmS+JS487WPTrtFW7uXader5NlGYN4Cr5Noens3LqN+bDONJpjel1O9sesKSdgmeSKhubaFFkFV+/01xGKsvyvvxc38GK9p3XiRcwXnKrEhBCVvWyWoaoqnq2RhrPKHqGs9A8LmsxCg7CY7ADI8xTbsdB0aHfqWJqGKArIcwxFwTFUkBmWqSGKFFnmOKZJzTEqWzHXxfO8ShIYpzieS5hV4+6moWBbBlmSc7I3YDoYkUQxQlVAV9FUC8tyKGVOlgeIMkPIAqkaoBiYKmhWjdJbheZ5TN3jT3p0i7Gmxei8rguQJR/6yI/QWFnjzJkzS0TtYDDg/p1t2nWfv/pTP8HZjS7C0Gh2Nrly5Qk++JEP8Yv/6z9kZ/eA56+e4xPPnOOZK+foeQKHiL5vIBWdUlGRqrpsgJimie/7nJycLEfrPc/j/v37lYuS45CmKfv7hwwHE7JUkuclw5OI/d0x03HG/u6Y/f39arD2lJy0eFYLWer3Y72nh3hR1LYsa1myWXBvDcOAIsZQc2QeMjzeWdItbdteovxVVV1qI9IsYjYbYdkVtzePA3RRUiQhqszRVIlSlihlhqqARokiM4JpNea0cLvUdZ0iSkhkgVmvEY6nlNkcQ1Eo4px4llLGKbPxhDBNKDUF12mgqjpCSBS1wBQFuqZSCg1hukizjeF20Z0WeWkA5nds77faBS/EQu9ulk6tu0pn/QJf/vKXabVarK5WnhbHh8eMR0N6XZ+f/Mm/wNkVj9tvvE67ZvOr/+jvU0z2afg1nn38USZ7N6k5Ng3f4+zGBg3fI5Nw8+4We0eDZTOi0+ngeR4bGxvM53PW19eRUtLtdnnssceYTqdsbW2RJgVZKrlze4u11U1a7QZJGrG3v0NvpcPx8TFBECz3djHiVCW87+Xpene9p+FEWVbSzNFoVBXeVXUJ3sjznDyNUHSDXCokUUqShkvU/quvvkqr118WzaMoIs3i6sbTVBQVKCRlkaFrCmE4xzJ94miGyBLCJKTm2gh5yus9TS7n8ylIiWfaBOkQy3NQSx0oELLA0EwOjvZo+jq6p+J4Lo1OG1mqJHGGYgqkqBi+AtBtH91vUpprCN2FUlbKOXm6tX9MKPEdHStZVk0QqeDU2+i6zmuvvcaZzfVqajgT7Ny9y8Zmj5V2nb/0yR/mX37xJd762gucWfO5cuYCP/LnPsbNt97gA889z87efSbTAdMoQ1FhMJxgmDY3blzDbXawbfv052acOXOGOK4sIj760Y/iOj6e59HtdimKgm++dRcpwbZdkiQjPtnmE5/8CNeuXePg8C6uV19eMkIIup0uuuVWyZ39fUm53ttDrEgBakmn7SEPYJTPmQQBxzsT9rePSdWCvJQ4tQZ7B4e8cecA3/erA5um7B7+2vL1X5YlnlqyfqbLpasXqKsuaqkwm45p12vk8ZDjnSPC+YCa2WQyHWCbFVetEBpKYTKdTbFdk0zNmAQTyEsUJFEeYOV9FEtBczWE4zIeT1lfEdRcD13aBPExNaNAEQ6q5hAVBY1aH9oXKQwfFf3/fz/+JCH44s9F5Tb8r/+bf4Nf+m//U1Y7dbyV8/R6OceDE+KiIIsFjm3yH/77f5mT8YzjkyFpWGCpkocur/HSm3/Io+cvcXS4Q5n77GzvkSomW1v36K+sEEhJq9UgngU8+8STPPbs89y+fbtycp0mxAT8k3/xz/mJn/gJPrm+xqPPDfjGS19DyWMKdE4OjzBUkw8//zz//J/9Bh/6qY8xj3N0xSCaJXg9G10zcU7Diu/Hem8/KkolLxzt7zOYwBf+6dvEBVx5/Gme/OSPsrd9j939I377qy+iaBYBHscH88pNNC6Y7E4pitGyF28Ik3k+IY+v8/jVC1h1C8fQCGcjhCywLB2ZqaiioNduIvMUr15nOByia2DZGkWRYJkuwTygkDmiUHBqzapon1bkzOPjYxqmAYqOoulMowiz10TTDVxhkoxiEpEzSGe0V30idL5Tm/bdr7UnrvLsxz6Bm87Y297BWOuwvr5OvdNia3ubl/7wOj/1157EyVPWvA4No8c8mLJ1b5eyVJgEAYPJhL2jIzqdDkEQsLKywmQyoQwznv7ABzgcDTj36MMYumA8OubWrVvs7u7ysY//GOc2NhFFiYagyEOOjg4YnhxjODbHx7u8/tbbeN6nyYXB1p27XH78KQzLwjCspbnP93N919qJ4bDysFj89W+N7/K8mnDIAa1M+YWf/znu3LuPqRhcvnIJ169GWg6ODrl5+y6HJ0NmQcQkqIQ8i4TQPj28jlM1FjRF0DJyuo7CY5fPs76q0KjZqJQVSwGQRUjNbhFkUUVlTNNKy9DqVl4btVpVySgziiwhnE2wTR3L0AmjCYoweeF3X8UxbD748Q9S76+j2E0avs+tm/f4nd/+HfbvH6IEEtf2iEyTv/W//TKZfw5gyVz+XlYG6GXGGy98gXde/n0ms5CNM5t4zTqWbfP2K9f5f7/2In/jP/oZpCjZu3mN46MRhu6g6waT6ZC7d+8uWXFCiKVr6qXLj7CyssLVRx9Ft0w+/6v/N4Zh8Morr9Dtdtk7GPCZz3yGl19+mU9/+tN84/VvMo8zLj58lbWNTYokYjQ8QRQpnuvwza98hb/y7/57zBWd3DBptdao1+sPjO7zp1nfddt5AYobDodLZROwHBESQpADf/dv/Twnu7sUiseVpy5gmApbt97m4O5NVjdWuHvnJrP5BEUtiWYVXqpIY0RZMQoEEMznFHmOUk7RlJKihEyoXDlXZ6XTQpQ5yAIVECKHDOIsQiDJ0oQkjiiFPE2sJME8xDBMVE0nzzI0XUfIAk0TBPMIBZvRcMrDj1/B8Hxu3t3hf/97v8i1d26wc3REqRtEfp1ERqSMuHXvZX7wY39tWTP+nh9KCScnQ15//RucOdvi9rU71Pwa0/mMg8ND+v0OXr3J//JL/yf1+grR9ADX9RkOJoxHc67deJtut8t0NqPRbHLr1i2uXr3K2bNnGRcpTs3DM22+8dU/QNUFYRhgWSa2baEIne2tbRzbJo4i1s+u8+TTT3Pm/AXGQYKuafRX1xiNRty8dYO/9OOf4p1bt2murYNl0mp0l3yR79f6sx9iCZQlyunDcmwHmcVoBiSFhm3qFVSbnL/zt/97vvrii+zsHzEeD2EeMzwc4jp1olTy1hvvEMwTVMVGFDpBEi594YqiIM6KJbg7ywuiAmZRxiSIwLCQURUjG4aKodtk+RzLroNhk4xDLN0mDgI0RaKrCqYqyOMAQymJyxwpS1zbJYqSqskiK1qOptlM50ecPf8o/+Nn/zG/99U3KdFBs/HcJs16myzISaIMR7iUc/jwj36U0m2j5ZAr2dIU7btZhQBN5mhFgkgL+h2fW7duc/7MBdKoZDQ+otPpsdJf43Of+3VOhgPiNCOYTInigPX1HrPZhEbDRwjJ+voq9XoNx7F48vHnaDgub77+DXzP4O71G4iyRBdV8ryy3uWRJx7l8eee4eyly8wmU1Td4Ob1m5w9e4Y0GPEvvvBFLARPnF1l68Yb6L0Vuucu0a5XepOFcOv7tf7MhziaT9FNgwUXHgmaoYGQzAfHmHpFWPzyb32Rz/79z3Kwf8CZtQ0cwyQJKz3DYtK52+2wtX2fySygKCSK8S5OCSAv36XRFEVBAVAW5HnGaDwlDCTDwYBHHjmL0DIMFE4OjvjKb3+Jc2cf4v79+zQaPmE4x7BMhBB4nlfZK/RWQErGg2GVOGbFKYI2Zz7N2JoIfv4Xf5W7xxl+s4ul6+img+36HBwPcW2HIs9o1euVb0e3SWvjCqaikSsS9XuoXhZ5iaapuLZNIWE2n/LwpSv83u++wIXzD9Ff6TAajXFdhzffeoN6rUOWlPS7awwHE+I8plZv0Gi2ub+7x8OXL2JZVjVutdqnLHNW+qv0+n1812I+m+DaOh96/klee+c6mxsbXLt2g067hTBVemsrlKrC/b09Gp7D2Ycu8LUXXoA05GBvl2a3yfmHH0W1WuhGJbP8V/oQ6zoMTo5IkwjLsagK+iV5GjPcuUsWV5ayv/ml32Hrzl0uPnQepZQ8++RTpEnE0dERKysrhGHI4eEBeVFg2S5FKUmLqnS1AHGgVDy1hUFgpmHXrwAAIABJREFUIiv+rZSS2WxOWJbois25zY1K5CMtQGe1t8rx4QEXL15kPB6yttYHZWGSUg2TBkmKZZoIWflUGJpNmiWkaUYSCd7en3FnrNLqbuJQoBg6puMxms7RbRfP1LEsE0VK0izBq7s8/gM/gkAhU0D7HsggqiJQFIFhmBRSoOpGRfq0bUbHRxRpRm+lR14k9Ptt2u0OaRoRBVOSJCQuc9Y3zzALAh559DFEkS0dV9fWe8RJRCahu36GR595ns7qBr1um5s33ubyw1d46803eOSRR3jxay9y6cwm4TSAsmBzbR3HsfH8Bl//2te4dHadaD4jTac89fz70eqdSqn2fTzA8N2EE6LANg10XUPRVKoCRwmnIhuB5B//yq/yd/+nz1JzXATQ8htkcQJUxn7z+ZzxeIyqKli2QxAlCEWlVOQp5b1SruWlXJp/G4ZBkObIssRxbBzbZpIEaKWOhcql8+cxGh3KsiJZrjRrpwIdDVUVKKci78UGC73yudCEwuHhIUUqiZM5B/tHHB6MmBU694YZZS7pWhLFtLFsl2arjaJqWIqkyDMUWSIUWD3T5+r7fwSkSq6IB1D2kZQSbNvBdSxODvYZHR2ysbrC4d4hYRxSq7vUai4f/8THOT7ep+aYzGYjpGrg+3WSJOXmzZuc21xDUZTKJWk8JEkTDk5GfOWrf8Dvfu0VshIuX77M+9/3PHXXYTKdstJfRQjBwZ273HjnOg3PZzocIwwNVTdZ7XSYDQ4JZ1N8TyVTLbpnH3uQOIk/9fpTH+JFq3h3axvbsknmU/a27tLorVWFekVHs01OBhP+h7/9C4wHI4bDEf3VVWqNOsfjEUfHA9KiugXjvGAcJYyDkChNkEIynoyZz+fVzWgYWE7VJl4kig3PoOG7BLPZqXmMiS5U1lYboJYUQUivU2d1o8VksItXt5FKRl6mSCqtgtRUolyi6BaGZRPHKaByPJ4RzSPSVHAYFNzaO6Tj2UBJqtg8fm6TVs1H5AWWqhGmAfMUTBTMMuPh5z7IuSd+EEoolBzte26GCiQVtLvUPHzPZzYdYVk6Vs1nNJ3gNWpM5xMmJwHj4QShaAhFQ0XntVdegxLajRY37u6wsr5OJnP66+uU6EyGETfeuotWhsgsYWvrPr/9lReRhsIH3v9+Bns7hMcHPP2+p8jTiK07t5BpyvBkhmuozKMRt3e2mRyPWOu12D484qkP/TDfJ03Zt60/9SFexKn1msfWvXusbKzjWSaK4QASUZaQRfzMz/wsJydDGo3KpG8Bix4MBgBMplMm0ylRHBOEwbJlGUURcRJjWRb9fr8yfBHvmi2qqooiJLZl0T11cNcUqHs+gpzh6JgnH7mM6+gIMlaaNQaDk8r4RJYoSjVVouo6JQqaXhGANKEQhSEHB2Pms4BxJLl7GHIyTKh7Po6i0NAVtFN2wiKcSdIUKRR8S0WVEQ899hSbV59DSEGhlA/gELMsXxZFVQPvdtrsHR1iKTo3b9wgnk7JgwTNqgzUO52KLlSr+ZRlQa/XZTqd0O13OTk5wXVrXLt2ncODY+azkPPnz2Ga+jKZrowVY3b3D3A8n1qzwe2bb9PpdpFSsrLSYxrFjIYneJaBjqTbblIkE0zPp7t5EeePQRK81+tP/bGpKOs2u1tbrK2tsXf3Ln7NxbOjCoIShlx7+42ln5miKFy9ehVgGdOGWUKmSHTPxrZtfLtyL1pYz86C+dIB3nEcCir80RKtqgp8262GFnt94miKbZhopsPqmY1T/fAeTz1xgfFJZZYoyanVXPK8pCwr9Gq7t/b/tXdmP5Ld93X/3H2turfW7q7qdaZn4cyQHG4WJYo2RVO2JEuKEuTFQIIgTwICBMhLgASI8y8ECJAYsBBBcALHCQzEToAgTizYjLVFJEVpOCKHM5yZnul9rapbdW/d/ebhdhephbBo9WxUn8GgHxqDqeo6/bvf3/f7Peews9+nWqkwHJTLSVkhs7rdZ5TqvLcZsD4aE7BLXSl4/MwphnFCEATlDnIYltFkSYRRt3Bcl+Xl5bJU+Vkd6N8aR6WPpJZKcq01zdnnXmR/4xaX1RdRspT+zg7rG1s4jjPZyzBMlScvX6JSqZTJRnlMEhdQKAy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"source": [
"bbox_scale = torch.tensor((w, h, w, h), dtype=torch.float32)\n",
"ground_truth = torch.tensor([[0, 0.1, 0.08, 0.52, 0.92],\n",
" [1, 0.55, 0.2, 0.9, 0.88]])\n",
"anchors = torch.tensor([[0, 0.1, 0.2, 0.3], [0.15, 0.2, 0.4, 0.4],\n",
" [0.63, 0.05, 0.88, 0.98], [0.66, 0.45, 0.8, 0.8],\n",
" [0.57, 0.3, 0.92, 0.9]])\n",
"\n",
"fig = d2l.plt.imshow(img)\n",
"show_bboxes(fig.axes, ground_truth[:, 1:] * bbox_scale, ['dog', 'cat'], 'k')\n",
"show_bboxes(fig.axes, anchors * bbox_scale, ['0', '1', '2', '3', '4']);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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"text/plain": [
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" [0.1417, 0.0000],\n",
" [0.0000, 0.5657],\n",
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],
"source": [
"compute_jaccard(anchors, ground_truth[:, 1:]) # 验证一下写的compute_jaccard函数"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def assign_anchor(bb, anchor, jaccard_threshold=0.5):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」图9.3所讲为每个anchor分配真实的bb, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" bb: 真实边界框(bounding box), shape:（nb, 4）\n",
" anchor: 待分配的anchor, shape:（na, 4）\n",
" jaccard_threshold: 预先设定的阈值\n",
" Returns:\n",
" assigned_idx: shape: (na, ), 每个anchor分配的真实bb对应的索引, 若未分配任何bb则为-1\n",
" \"\"\"\n",
" na = anchor.shape[0]\n",
" nb = bb.shape[0]\n",
" jaccard = compute_jaccard(anchor, bb).detach().cpu().numpy() # shape: (na, nb)\n",
" assigned_idx = np.ones(na) * -1 # 初始全为-1\n",
" \n",
" # 先为每个bb分配一个anchor(不要求满足jaccard_threshold)\n",
" jaccard_cp = jaccard.copy()\n",
" for j in range(nb):\n",
" i = np.argmax(jaccard_cp[:, j])\n",
" assigned_idx[i] = j\n",
" jaccard_cp[i, :] = float(\"-inf\") # 赋值为负无穷, 相当于去掉这一行\n",
" \n",
" # 处理还未被分配的anchor, 要求满足jaccard_threshold\n",
" for i in range(na):\n",
" if assigned_idx[i] == -1:\n",
" j = np.argmax(jaccard[i, :])\n",
" if jaccard[i, j] >= jaccard_threshold:\n",
" assigned_idx[i] = j\n",
" \n",
" return torch.tensor(assigned_idx, dtype=torch.long)\n",
"\n",
"\n",
"def xy_to_cxcy(xy):\n",
" \"\"\"\n",
" 将(x_min, y_min, x_max, y_max)形式的anchor转换成(center_x, center_y, w, h)形式的.\n",
" https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/utils.py\n",
" Args:\n",
" xy: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)\n",
" Returns: \n",
" bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)\n",
" \"\"\"\n",
" return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2, # c_x, c_y\n",
" xy[:, 2:] - xy[:, :2]], 1) # w, h\n",
"\n",
"def MultiBoxTarget(anchor, label):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" anchor: torch tensor, 输入的锚框, 一般是通过MultiBoxPrior生成, shape:（1，锚框总数，4）\n",
" label: 真实标签, shape为(bn, 每张图片最多的真实锚框数, 5)\n",
" 第二维中，如果给定图片没有这么多锚框, 可以先用-1填充空白, 最后一维中的元素为[类别标签, 四个坐标值]\n",
" Returns:\n",
" 列表, [bbox_offset, bbox_mask, cls_labels]\n",
" bbox_offset: 每个锚框的标注偏移量，形状为(bn，锚框总数*4)\n",
" bbox_mask: 形状同bbox_offset, 每个锚框的掩码, 一一对应上面的偏移量, 负类锚框(背景)对应的掩码均为0, 正类锚框的掩码均为1\n",
" cls_labels: 每个锚框的标注类别, 其中0表示为背景, 形状为(bn，锚框总数)\n",
" \"\"\"\n",
" assert len(anchor.shape) == 3 and len(label.shape) == 3\n",
" bn = label.shape[0]\n",
" \n",
" def MultiBoxTarget_one(anc, lab, eps=1e-6):\n",
" \"\"\"\n",
" MultiBoxTarget函数的辅助函数, 处理batch中的一个\n",
" Args:\n",
" anc: shape of (锚框总数, 4)\n",
" lab: shape of (真实锚框数, 5), 5代表[类别标签, 四个坐标值]\n",
" eps: 一个极小值, 防止log0\n",
" Returns:\n",
" offset: (锚框总数*4, )\n",
" bbox_mask: (锚框总数*4, ), 0代表背景, 1代表非背景\n",
" cls_labels: (锚框总数, 4), 0代表背景\n",
" \"\"\"\n",
" an = anc.shape[0]\n",
" assigned_idx = assign_anchor(lab[:, 1:], anc) # (锚框总数, )\n",
" bbox_mask = ((assigned_idx >= 0).float().unsqueeze(-1)).repeat(1, 4) # (锚框总数, 4)\n",
"\n",
" cls_labels = torch.zeros(an, dtype=torch.long) # 0表示背景\n",
" assigned_bb = torch.zeros((an, 4), dtype=torch.float32) # 所有anchor对应的bb坐标\n",
" for i in range(an):\n",
" bb_idx = assigned_idx[i]\n",
" if bb_idx >= 0: # 即非背景\n",
" cls_labels[i] = lab[bb_idx, 0].long().item() + 1 # 注意要加一\n",
" assigned_bb[i, :] = lab[bb_idx, 1:]\n",
"\n",
" center_anc = xy_to_cxcy(anc) # (center_x, center_y, w, h)\n",
" center_assigned_bb = xy_to_cxcy(assigned_bb)\n",
"\n",
" offset_xy = 10.0 * (center_assigned_bb[:, :2] - center_anc[:, :2]) / center_anc[:, 2:]\n",
" offset_wh = 5.0 * torch.log(eps + center_assigned_bb[:, 2:] / center_anc[:, 2:])\n",
" offset = torch.cat([offset_xy, offset_wh], dim = 1) * bbox_mask # (锚框总数, 4)\n",
"\n",
" return offset.view(-1), bbox_mask.view(-1), cls_labels\n",
" \n",
" batch_offset = []\n",
" batch_mask = []\n",
" batch_cls_labels = []\n",
" for b in range(bn):\n",
" offset, bbox_mask, cls_labels = MultiBoxTarget_one(anchor[0, :, :], label[b, :, :])\n",
" \n",
" batch_offset.append(offset)\n",
" batch_mask.append(bbox_mask)\n",
" batch_cls_labels.append(cls_labels)\n",
" \n",
" bbox_offset = torch.stack(batch_offset)\n",
" bbox_mask = torch.stack(batch_mask)\n",
" cls_labels = torch.stack(batch_cls_labels)\n",
" \n",
" return [bbox_offset, bbox_mask, cls_labels]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"labels = MultiBoxTarget(anchors.unsqueeze(dim=0),\n",
" ground_truth.unsqueeze(dim=0))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0, 1, 2, 0, 2]])"
]
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"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels[2]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1.,\n",
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"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
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"source": [
"labels[1]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, 1.4000e+00,\n",
" 1.0000e+01, 2.5940e+00, 7.1754e+00, -1.2000e+00, 2.6882e-01,\n",
" 1.6824e+00, -1.5655e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,\n",
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"execution_count": 15,
"metadata": {},
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],
"source": [
"labels[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.4. 输出预测边界框"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"anchors = torch.tensor([[0.1, 0.08, 0.52, 0.92], [0.08, 0.2, 0.56, 0.95],\n",
" [0.15, 0.3, 0.62, 0.91], [0.55, 0.2, 0.9, 0.88]])\n",
"offset_preds = torch.tensor([0.0] * (4 * len(anchors)))\n",
"cls_probs = torch.tensor([[0., 0., 0., 0.,], # 背景的预测概率\n",
" [0.9, 0.8, 0.7, 0.1], # 狗的预测概率\n",
" [0.1, 0.2, 0.3, 0.9]]) # 猫的预测概率"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
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"source": [
"fig = d2l.plt.imshow(img)\n",
"show_bboxes(fig.axes, anchors * bbox_scale,\n",
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},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"from collections import namedtuple\n",
"Pred_BB_Info = namedtuple(\"Pred_BB_Info\", [\"index\", \"class_id\", \"confidence\", \"xyxy\"])\n",
"\n",
"def non_max_suppression(bb_info_list, nms_threshold = 0.5):\n",
" \"\"\"\n",
" 非极大抑制处理预测的边界框\n",
" Args:\n",
" bb_info_list: Pred_BB_Info的列表, 包含预测类别、置信度等信息\n",
" nms_threshold: 阈值\n",
" Returns:\n",
" output: Pred_BB_Info的列表, 只保留过滤后的边界框信息\n",
" \"\"\"\n",
" output = []\n",
" # 先根据置信度从高到低排序\n",
" sorted_bb_info_list = sorted(bb_info_list, key = lambda x: x.confidence, reverse=True)\n",
"\n",
" while len(sorted_bb_info_list) != 0:\n",
" best = sorted_bb_info_list.pop(0)\n",
" output.append(best)\n",
" \n",
" if len(sorted_bb_info_list) == 0:\n",
" break\n",
"\n",
" bb_xyxy = []\n",
" for bb in sorted_bb_info_list:\n",
" bb_xyxy.append(bb.xyxy)\n",
" \n",
" iou = compute_jaccard(torch.tensor([best.xyxy]), \n",
" torch.tensor(bb_xyxy))[0] # shape: (len(sorted_bb_info_list), )\n",
" \n",
" n = len(sorted_bb_info_list)\n",
" sorted_bb_info_list = [sorted_bb_info_list[i] for i in range(n) if iou[i] <= nms_threshold]\n",
" return output\n",
"\n",
"def MultiBoxDetection(cls_prob, loc_pred, anchor, nms_threshold = 0.5):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" cls_prob: 经过softmax后得到的各个锚框的预测概率, shape:(bn, 预测总类别数+1, 锚框个数)\n",
" loc_pred: 预测的各个锚框的偏移量, shape:(bn, 锚框个数*4)\n",
" anchor: MultiBoxPrior输出的默认锚框, shape: (1, 锚框个数, 4)\n",
" nms_threshold: 非极大抑制中的阈值\n",
" Returns:\n",
" 所有锚框的信息, shape: (bn, 锚框个数, 6)\n",
" 每个锚框信息由[class_id, confidence, xmin, ymin, xmax, ymax]表示\n",
" class_id=-1 表示背景或在非极大值抑制中被移除了\n",
" \"\"\"\n",
" assert len(cls_prob.shape) == 3 and len(loc_pred.shape) == 2 and len(anchor.shape) == 3\n",
" bn = cls_prob.shape[0]\n",
" \n",
" def MultiBoxDetection_one(c_p, l_p, anc, nms_threshold = 0.5):\n",
" \"\"\"\n",
" MultiBoxDetection的辅助函数, 处理batch中的一个\n",
" Args:\n",
" c_p: (预测总类别数+1, 锚框个数)\n",
" l_p: (锚框个数*4, )\n",
" anc: (锚框个数, 4)\n",
" nms_threshold: 非极大抑制中的阈值\n",
" Return:\n",
" output: (锚框个数, 6)\n",
" \"\"\"\n",
" pred_bb_num = c_p.shape[1]\n",
" anc = (anc + l_p.view(pred_bb_num, 4)).detach().cpu().numpy() # 加上偏移量\n",
" \n",
" confidence, class_id = torch.max(c_p, 0)\n",
" confidence = confidence.detach().cpu().numpy()\n",
" class_id = class_id.detach().cpu().numpy()\n",
" \n",
" pred_bb_info = [Pred_BB_Info(\n",
" index = i,\n",
" class_id = class_id[i] - 1, # 正类label从0开始\n",
" confidence = confidence[i],\n",
" xyxy=[*anc[i]]) # xyxy是个列表\n",
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" \n",
" # 正类的index\n",
" obj_bb_idx = [bb.index for bb in non_max_suppression(pred_bb_info, nms_threshold)]\n",
" \n",
" output = []\n",
" for bb in pred_bb_info:\n",
" output.append([\n",
" (bb.class_id if bb.index in obj_bb_idx else -1.0),\n",
" bb.confidence,\n",
" *bb.xyxy\n",
" ])\n",
" \n",
" return torch.tensor(output) # shape: (锚框个数, 6)\n",
" \n",
" batch_output = []\n",
" for b in range(bn):\n",
" batch_output.append(MultiBoxDetection_one(cls_prob[b], loc_pred[b], anchor[0], nms_threshold))\n",
" \n",
" return torch.stack(batch_output)"
]
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x1293d89b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = d2l.plt.imshow(img)\n",
"for i in output[0].detach().cpu().numpy():\n",
" if i[0] == -1:\n",
" continue\n",
" label = ('dog=', 'cat=')[int(i[0])] + str(i[1])\n",
" show_bboxes(fig.axes, [torch.tensor(i[2:]) * bbox_scale], label)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"language": "python",
"name": "conda-env-py36-py"
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
},
"nbformat": 4,
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}
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