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Learned RELU
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"from exp.nb_06 import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ConvNet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get the data and training interface from where we left in the last notebook."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"x_train,y_train,x_valid,y_valid = get_data()\n",
"\n",
"x_train,x_valid = normalize_to(x_train,x_valid)\n",
"train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)\n",
"\n",
"nh,bs = 50,512\n",
"c = y_train.max().item()+1\n",
"loss_func = F.cross_entropy\n",
"\n",
"data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"mnist_view = view_tfm(1,28,28)\n",
"cbfs = [Recorder,\n",
" partial(AvgStatsCallback,accuracy),\n",
" CudaCallback,\n",
" partial(BatchTransformXCallback, mnist_view)]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"nfs = [8,16,32,64,64]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Learned RELU"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"param = nn.Parameter(torch.tensor(0.1))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.10000000149011612"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param.item()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class LearnedRelu(nn.Module):\n",
" def __init__(self, leak=0.1, sub=0.25, maxv=100):\n",
" super().__init__()\n",
" self.leak = nn.Parameter(torch.ones(1)*leak)\n",
" self.sub = nn.Parameter(torch.zeros(1)+sub)\n",
" self.maxv = nn.Parameter(torch.ones(1)*maxv)\n",
"\n",
" def forward(self, x): \n",
" x = F.leaky_relu(x,self.leak.item())\n",
" x.sub_(self.sub)\n",
" x.clamp_max_(self.maxv.item()) \n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Batchnorm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by building our own `BatchNorm` layer from scratch."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class BatchNorm(nn.Module):\n",
" def __init__(self, nf, mom=0.1, eps=1e-5):\n",
" super().__init__()\n",
" # NB: pytorch bn mom is opposite of what you'd expect\n",
" self.mom,self.eps = mom,eps\n",
" self.mults = nn.Parameter(torch.ones (nf,1,1))\n",
" self.adds = nn.Parameter(torch.zeros(nf,1,1))\n",
" self.register_buffer('vars', torch.ones(1,nf,1,1))\n",
" self.register_buffer('means', torch.zeros(1,nf,1,1))\n",
"\n",
" def update_stats(self, x):\n",
" m = x.mean((0,2,3), keepdim=True)\n",
" v = x.var ((0,2,3), keepdim=True)\n",
" self.means.lerp_(m, self.mom)\n",
" self.vars.lerp_ (v, self.mom)\n",
" return m,v\n",
" \n",
" def forward(self, x):\n",
" if self.training:\n",
" with torch.no_grad(): m,v = self.update_stats(x)\n",
" else: m,v = self.means,self.vars\n",
" x = (x-m) / (v+self.eps).sqrt()\n",
" return x*self.mults + self.adds"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def conv_layer_gen(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" # No bias needed if using bn\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=not bn),\n",
" GeneralRelu(**kwargs)]\n",
" if bn: layers.append(BatchNorm(nf))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def conv_layer_learn(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" # No bias needed if using bn\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=not bn),\n",
" LearnedRelu(**kwargs)]\n",
" if bn: layers.append(BatchNorm(nf))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def init_cnn_(m, f):\n",
" if isinstance(m, nn.Conv2d):\n",
" f(m.weight, a=0.1)\n",
" if getattr(m, 'bias', None) is not None: m.bias.data.zero_()\n",
" for l in m.children(): init_cnn_(l, f)\n",
"\n",
"def init_cnn(m, uniform=False):\n",
" f = init.kaiming_uniform_ if uniform else init.kaiming_normal_\n",
" init_cnn_(m, f)\n",
"\n",
"def get_learn_run(nfs, data, lr, layer, cbs=None, opt_func=None, uniform=False, **kwargs):\n",
" model = get_cnn_model(data, nfs, layer, **kwargs)\n",
" init_cnn(model, uniform=uniform)\n",
" return get_runner(model, data, lr=lr, cbs=cbs, opt_func=opt_func)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then use it in training and see how it helps keep the activations means to 0 and the std to 1."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.9, conv_layer_gen, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.2491483984375, tensor(0.9221, device='cuda:0')]\n",
"valid: [0.11270089111328126, tensor(0.9654, device='cuda:0')]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"with Hooks(learn.model, append_stats) as hooks:\n",
" run.fit(1, learn)\n",
" fig,(ax0,ax1) = plt.subplots(1,2, figsize=(10,4))\n",
" for h in hooks[:-1]:\n",
" ms,ss = h.stats\n",
" ax0.plot(ms[:10])\n",
" ax1.plot(ss[:10])\n",
" plt.legend(range(6));\n",
" \n",
" fig,(ax0,ax1) = plt.subplots(1,2, figsize=(10,4))\n",
" for h in hooks[:-1]:\n",
" ms,ss = h.stats\n",
" ax0.plot(ms)\n",
" ax1.plot(ss)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 1.0, conv_layer_gen, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.26710783203125, tensor(0.9157, device='cuda:0')]\n",
"valid: [0.17995037841796874, tensor(0.9414, device='cuda:0')]\n",
"train: [0.091767626953125, tensor(0.9712, device='cuda:0')]\n",
"valid: [0.15938792724609374, tensor(0.9472, device='cuda:0')]\n",
"train: [0.0640931494140625, tensor(0.9801, device='cuda:0')]\n",
"valid: [0.15299569091796875, tensor(0.9506, device='cuda:0')]\n",
"CPU times: user 2.73 s, sys: 272 ms, total: 3 s\n",
"Wall time: 3.06 s\n"
]
}
],
"source": [
"%time run.fit(3, learn)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.9, conv_layer_learn, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.26300181640625, tensor(0.9198, device='cuda:0')]\n",
"valid: [0.2810866943359375, tensor(0.9055, device='cuda:0')]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ww8nejjWJ+acceP2emIejvTr4CVgpxYwRobz0SzypBRWE+7ujteb5xbvxd3didhMrje3Mj8DbSikHwAkYDbxm2ykJcWLlNfVsSStiV1YphRW1lFTWMTDEi4sGdaWixsJryxKYvzUT3bA22zfYk49vHsmkPhJwtQYJvMRpyS2tZsWeXK4a2f2k/sGuTcrn47UpeLo48uB5vU98B8w2o4ujHUPDfBq97uvuRK8gDyprLY3mXh3Nx82Jt64ZRhcvl2aVdBjXsPoUGeDOPedE4eJoz4c3j+QvX2/l2UW7AfBzd+LuyT2b9XqaMirSj4fO7839X2/l94Q8JvcNYk9OKRuTC3novN4H/zsHebnwl2b8t3N1sic6wpc1SfmnPKfVCfmMCPfF/bB8riuGh/Dy0nh+iM3gwfP7sDI+jz/2FfLUZQNOqcp/a1NKzQEmAQFKqQzgCUzZCLTW72mtdyullgDbASvwP631TlvNV4jjKSivYeGObH7cmsWWtCIOHPx2drDDw9mBb2LSeXJBHApwsFfcPiGScT0D6BHoTpifmwRcrUgCL3FavtmUziu/JjAy0o+egc0/NbcpxSSzL9mZ3ezA6499BUSH+x23z9db1w7DwU41uxfYyZzKC/Nz4y/n9mZKv6CDq2khPq58+6exxGWV8m1MOhOiAvA6A0HHRYO68tyi3Xy0NplJfQL554+78HFzbHbe2NEm9ArgxSXx5JZVE+R5cluAeWU1xGWXHrN92tXblQlRAfywOZMu3i48u3A3kQHuXHOcwwFtidb6mmaMeQl4qRWmI8Qpic8p47+r9/Hj1kzqLJo+XTy5Z3IUIyP9GBzqg5eL+TW/J6eMn7ZlYbVqbpsQ2RFSAdotCbzEaUnOrwAgNrXopAKvmIbThwn7y0nKLT9hqYOC8hr25JTx8NRuxx3XN/jY0g1nilKK+89tPL+sfzcvnrxswBl7Lkd7O24cG8FLv8Tz8tJ4NiYX8tzlg/B1dzqlx5sYFciLxLM2KZ/Lh51c9f3f4k0VhbN7H1uPbGZ0d+6bs4V/zNvJhKgAXpo5uMk8OSHE6dFa81t8Lv9ZuZeUgkqq6yyUVdfj6mjPtaPCuGZ0WJPvgf26ejVa2ka0Pgm8xGlJKWgIvFKKmBV94u09MPld2zNKmDa0Gz9uzWLJzmzuOacX38dm8PSCXYwI9+Ws3oFMHxqCr7sT9RYr//xxF9D4L/+O6tpRYby5PJF3ftvL4FBvrmrG9mlTBnTzwtfNkdWJJx94Ld+9n27eLvRv5E37/P5duHBgMGN7+nP96PATFoAVQpyaP/YV8Pyi3WzLKKG7nyvn9gvC2cGeEB9XZowIPeUPZaL1SeAlTktKQSUAsWkn7pN4wPaMEmotVi4Z3I2MoioW7cjh8uGhPPnTLrp4OZNSUMlvC+J4fVkiD53fmw37Clm4I5vHL+7HwJDGyz10RL7uTlw5IpQ5G9N4etrA02rPYWenGBcVwOrEfMqq65qdg1VdZ2FVgmmV1FgOiIujPf+5fsQpz0sIcXzphZU8u3A3S3bl0M3bhReuHMQVw0PbVY9XcSQJvMQpK6mqo7CiFn93J5JyyymurMXH7cSfug4UOY0O9+XCgcE8s3A3d30Ri1VrPrllFN393IjLKuVfP8cdXOl6/OJ+3D6xQ5yUOymPXdiXmSNCGdq98QMFJ2PmiFCW7Mxh2jtr+eCGEc1qart+XwFVdRam9JNuOUK0Jq01X29K518/x6E1PHReb+44q4cUM+0AJPASpyylIb9r2tAQPlqbzOa0Is7p2+WE99uUUkivIA983Z2YOsAEXtszSnjqsgF0b2go3b+bF1/dMZpf4/ZTU2/l0iHHz+3qqDxdHE+6iXZTJvUJ4svbR3PPV5u57O219Az0oM5i5ew+gUf0pfzvqn0MDfNhZIQfy+L24+5kf0Q9MSFEy9qdXcq/F+/h94Q8xvX058UZg0+pRI1om5oVeCmlLgDeAOwxR6r/fdR1Z0yj2RFAAXCV1jql4dpg4H3AC3Mke6TWuvpMvQBhOwfyu6YN7cZn61OISTlx4GWxamJTi7hksAmkuvu5MSrSD3uljumTqJTi/AFNt9wRJ29MD38W3DuBFxbvoaSqjvzyWj5YtY8rh4fSu4sn6/bm8+yi3Xg4O/Ddn8ayfHcuE3sFNvuUqBDi5CTllvHm8iRySqsJ83OjtKqOpXH78XR24IlL+3PT2AjJnexgThh4KaXsgXeA8zCVnDcppX7SWscdNuw2oEhrHaWUuhp4AbiqofDgF8ANWuttSil/oO6MvwphE8n5FSgFfYI9GdDNi9jUE+d5Jewvo6y6nlGRh1ZxvrhtNEohby6tpKu3K69fPQyAoopaxr+wgrdXJPHG1UN5dWkCXbycAbjmv39QXFnHuf1PvIophDg5cVmlfLQ2mbmbM3BzcqBvsCerEvKoqbdy3zlR3DohslmpG6L9ac6K1yggSWu9D0Ap9TUwDTg88JoGPNnw/feYas8KOB/YrrXeBqC1LjhD8xYtpKC8hnd+28sjF/Q5YS5BakEl3bxdcXG0Z3i4L3M2plFnsR436fNQfpffwduk/IDt+Lo7cePYCN5ftZdhYT7EpBbxr+kDGRrqw6z316MUTO7TeU6SCtHS/thXwL8X72FrejFODnbcOj6SuydH4SenEjuN5vzGCwHSD/s5o+G2RsdoreuBEsAf6A1opdQvSqnNSqlHTn/KoiX9Grefj9Yms7KhdtPxJOdXEO5v8g5GhPtSXWclLqu0yfFaa36IzSAywJ1QX9czNmdxem6fGImzgx1PLYgjxMeVq6K7MyjUm09uGclzlw/C38PZ1lMUokOYuzmDGz7cQEFFDf93SX82/n0Kj1/SX4KuTqY5gVdj+z+6mWMcgAnAdQ1/Xq6UmnLMEyg1WykVo5SKycvLa8aUREs5UB6iOe1lUgoqiAhwBw6tYC3akd3k+A3JhWzLKOHWCZHSnqINCfBw5vrRJr/uvilRB1cgR/fwbzdV6IVoy6xWzevLEnjw221Eh/vx870TuU22Ejut5gReGcDhlRtDgaymxjTkdXkDhQ23/661ztdaVwKLgOFHP4HW+gOtdbTWOjowULY1bCm1IWF+XdLxd4WLK2sprqwj0t8EXsHeLlwxPIT/rUlmZ2ZJo/d5//e9+Ls7MXPEyRXwFC3vvnN78cz0gVw5XP7fCHEmFVXUctunm3h9WSJXDA/h01tH4e3a9nuZipbTnMBrE9BLKRWplHICrgZ+OmrMT8BNDd/PAFZorTXwCzBYKeXWEJCdzZG5YaKNObDitS+/gsziqibHHWgVdGDFC+CfDUvmD3+/nYqaej5YtZcZ/1nHD7EZ7M4u5bf4PG4eFyF1aNogLxfTB9JBijIKcUZYrZqF27O5+M3VrE0q4F/TBvDKzCGS0ypOnFyvta5XSt2DCaLsgY+01ruUUk8DMVrrn4APgc+VUkmYla6rG+5bpJR6FRO8aWCR1nphC70WcZq01qQWVDA+yp+1SQWsTcpvsg1QakOAFhlwqLaMj5sTz0wfyJ2fxzLmueWU1dTT1duFh77bhoezA25O9tww9tSaPAshRHvxe0Ie/168h93ZpUQFefD9DSMYHHr6RZBFx9CsOl5a60WYbcLDb/vnYd9XAzObuO8XmJISoo3LK6+hstbCef26EJ9TftzA60ApiQMFTw+YOiCYa0aFsSOzmEem9mVCVADfx2bw4i97uGZUmOQ0CCE6rLyyGp7+OY4F27KI8Hfj9auGcumQbqfV7kt0PFK5XhyU1rCKFRHgzoQof9Yk5aO1bjQRPqWggm7ero0W1nz+ikFH/DxrZHdmRkvukBCi49qWXswtn2yirLqOB87txV2TekrhYdEo2WwWBx3I74rwd2d8VAD55bXE7y87ZlxJZR0xKUX0CHQ/5lpTlFJyklEI0SFt2FfAdf/bgJuTPQvvm8gD5/aWoEs0SVa8xEGpBRXY2ylCfF1xcggAYHVCPn2DvQ6OKauu48aPN5JXVsOLMwbbaqpCCGEzOSXVPD5/B4m55Xi5OJKwv4xQX1e+vH0Mwd4utp6eaONkxUsclFJQSYiPK472dnTzcWVIdx8+/yOVOosVgOo6C7d+soldmSW8c91wxkcF2HjGok3IiIXaSlvPQohWsSxuPxe+sYp1ewsYHOpDgIcT5w8I5ps7x0rQJZpFVrzEQakFhyrRA9w7OYrbP4th3pZMZkV357VlCWxKKeKta4ZxnvTvEwCp6+DjC2Hq8zD2blvPRogWYbFqfo3bz4dr9rEppYgB3bx485ph9Az0sPXURDskgZcATCmJ5PwKpg891A1qSr8gBoZ48faKJHoEuPPfVfu4ZlR3Lh3SzYYzFW2G1Qq//N18n7XFtnMR4gyrrK1nVUIev8blsmLPfooq6wj1deX/LunP9WPCJIdLnDIJvAQAxZV1lFXXH7HipZTigSm9uf2zGG76aCPBXi78/aJ+NpylaFN2fGcCLhdv2L/T1rM5aUqpj4BLgFyt9cBGrk8CfgSSG26aq7V+uvVmKGwhKbeMF5fEsyoxj+o6K14uDpzTN4gLBnbl3H5BUmRYnDYJvARgykOAOdF4uAOrXjszS3n3+hF4ukiri07NaoXaMqgph+VPQbdh0PMcWPM61FWDY7vKcfkEeBv47DhjVmutL2md6Qhb+yE2g8fn78TZ0Y6rorszdUAwIyP9cJRgS5xBEni1ceU19bg62rd4Ab7UgzW8jiyIqpTirWuGsyurhLN7Sx/Ndq2uGkozwb/nqd3faoXPp0Py74duu/J/UL4ftAXy9kC3oWdmrq1Aa71KKRVh63kI28ooqmTFnlyW7trPmqR8Rkf68eY1w+ji1a4+RIh2RAKvNkxrzRXvrsXd2YE5d4xp0R6HKQWmEn2or9sx1yID3IkMaH7NLtGI356D+MXg2RW6DoGzHwH7Flg93Py52fa74N9weN206lL44krIjIW71kFQ35N/7D0LTNA18nYI6gcBfSB8HBTsNddzdrSrwKuZxiqltgFZwF+11rsaG6SUmg3MBggLC2vF6YlTtTOzhHdXJrF4Zw5am/e5h6f24c6zesh2omhREni1YbGpRSTsLwfg7/N28MrMISddhLSwopZnF+7m0Qv7EORpPsFtTS/ms3UpvDBj8MEl9JR8U4leGlgfpigFlD34NN42qdmqisxWnE93KEmHxF/APwqGXHVGpnlQ3I/w072Ahl7nQdS55vbqEhN0ZW0BB2dY+RzMOt7uWiOsFhM8BvSGC18Eu8P+nvhGgqN7u8zzOoHNQLjWulwpdREwH+jV2ECt9QfABwDR0dG69aYoTlZOSTX/WhjHwu3ZeLo4cPeknswY0V0+XIpWI2F9GzZvSyYujnbceVYP5m7O5MM1ySe+01FeXhrPD5szWLkn7+BtP2/LYu6WTFYnmtvqLFZWJeYztLs0cT0oIwb+Mx6+mgX6NH+P7vwBLDVw5YdmtSmwL6x/++Qe12qFTf+D8tym5zt3NoRGg08YrHjGPH5dFXwxwwRdMz+BcfeaAC1725H3j/0E3hljVq2aeg15e2Dy348MugDs7KDLgKbv205prUu11uUN3y8CHJVSUryuHfvij1SmvLKSZXH7eeDcXqz92zk8PLWvBF2iVUng1UbV1lv5eXs2UwcE8+gFfZk6oAvPL95Dbmn1ce+XXVJFZW09ALuzS/l6YxoAibmHWv8k5ppVtB82ZwKwMj6PwoparhgegsAEJV9cAdZ6yI0z23OnY8sX0GWg2WJUCsbcDTnbIWVN8x8j+XdY+JAJro4O2PKTYM7V4BkM13wNZz1iAq09C2HenZCxCWZ8BP0uhbF/BhcfE5iBCeiWPg4L7of8BPjqKijLMdfqqk1x1N0LzGpXl0HQb1rj8wseCDk7Tz9IbUOUUsGqYYlZKTUK835ZYNtZiVP1/u97eXz+ToaH+/LrX87mgXN74yWHhYQNSODVRq2Mz6Wkqo7pw0Kws1M8PLUPFqtmwfbsJu9TXlPP+a+uYurrq4hNLeTpBXF4uToS7u92MNgCSGr4/te4/ZRU1TF3cwb+7k6cJcnzUFkIn18Ozl5wx2/g6AZbPj/1x9u/ywRBw64/lHM1eBa4BcD6d8zpwMWPwg93mO28pmz5ApQd7PuJllOOAAAgAElEQVQNYj8+dHtJhkl41xqu+x7cA2DINeDXA3643axunf8M9G8ImFy8Yfz9kLgUPpgErw+EdW+ZvK3bl0FVsQniVjwDr/WH/50D31xvnue8J83qVmOCB0FNCRSnQUkmfHsjlO0/9f9urUApNQdYD/RRSmUopW5TSv1JKfWnhiEzgJ0NOV5vAldr3YEiyw4sp6San7dn8fHaZH7ZlcNbyxN5fvEeLh3SjY9vHkmY/7G5rEK0FsnxaqPmb83E392JiQ1teaKCPBkY4sX8LZncNiGy0fss3pFNWU09zo52zHhvPVrD09MGEJtaRExKEWCCs8ziKqYO6MIvu/bz9cY0lu/O5boxYXJkGiDhF6gsgNu+hi79of902PGDqczudApv1lu+BDtHGDTr0G2OribQ+f3f8O4Yk/cFJuF94kPHPkZVkVl1ir4NChLhl8fN6lNZNqz4l8nhuvlnCGhIP7J3gEmPwdw7zH3G/vnIxxt9J6RvNNuf/r2gxyQYeq0JDGd8CHOugayt0OdCGHI1+EaAd3dw82v6dXYZZP7M2mwCytzdUFMKnm23w4HW+poTXH8bU25CtBPFlbXc9PEmtqUXH3Pt4kFdeW3WEEmcFzYngVcbVFJVx7LduVw7KuyIN4npQ0N4ZuFuknLLiQo6tlXFvC2ZhPu7seDeCTz7826ySqq4dlQYpVV1/Lg1i4qaevY2rHZdMTyUxNxyXlmaQK3FypXDQ1vt9bVpCUvAIxhCos3Pw66HbV/B7p9MEHIyKgpg+9cmgHH3P/LayNvMSpO9E9yyBDa8Z7bzek459mTgju9NkDT8BnD1hXfHwYcNifPOXnDtN2Yb83CDZppALHjwkacbAZzc4dqvG59znwvhtqVm5cyvR/Nfa5f+gIKFf4XKfJj56aFAUIhWUFNvYfZnsezOKuWxC/sytqc/3XxcySquoqy6ntGRfhJ0iTZBAq82aP3efGrrrVwyuOsRt182pBvPLdrNj1szeej8Pkdcyy6pYv2+Au47pxdeLo68MGPwwWtRQZ4A7M0rP7jl2CvIgyuHh/LSL/H06eLJgG5eLfyq2oH6Wti7AgZMP7SlFj7OBCBbvji5wCtljdnqqykzOV1H8wiCe2NNIOXoAoF9IH2DWaU6/xmTrO4VYoKmLV+YrbwDwdW1X5stzJARJnessaKlSpnipqei+6iTv4+Tu6kPVpBkXu+A6af23EKcAq01j3y/nY0phbxx9VCmHdb6LMDD2YYzE+JYEv63QRuTi3B2sGNw6JGnDIO8XBgfFcD8rZkcnWry49YstIbLhx2bIN+ri1kdS9xfTmJuGU72doT5uTF9WAhO9nbMGtn9pMtUtGs5O+DLmabmVX3todvT1pntsd4XHLpNKRh6HaSshtWvmlyq8lyYc605LdhYys+O7+HTS00wcvsyCB/b+Dy8uh4Kmtz84PL3TI7UV7PgtQHw2kCTTJ+9FYbdcOh+ERPMdmFodNuqFN/3ElPC4jzpqiNa15vLk/hxaxYPT+1zRNAlRFskK15tUExqIUO7++DkcGxcPH1oCA99t43Y1CKiI0zOjdaaeZszGR7mQ0Qjx6LD/dxwsrcjMbecpP3l9Ah0x8HejhAfV1Y/OpnAjviJMHU9LHvSFCqNmnLo9tw98Nk0U1A0canZ3rv4Zeh7scnvsnc2OU+HG/tns8K0/CmzkpWzHSoaynNkbz1yZamuypwS7DoUbloAzsduCTepxyT4a6I5SZmzA/atNLldTh5m67CtO+8pW89AdEKLd2Tz2rIErhgewt2TTrErgxCtSFa82piKmnp2ZZUyKrLxROapA4PxdHY4oqbXrqxS4veXcXkTeVoO9nb0CHQnKbeMhNyyI/LDuni5YNfC7YhsYtWLkP6HKQsx/25zum/7d/DZZSbZ/c8b4Pq5Jpfpu5shdZ2pLB95llmpOpyjqynHcP6zJhhyD4JbfwEHF7MNeLiN/zVJ7+c/c3JB1wEuXhA2BkbdAVd/CY8kw/3bjp/YLkQntT2jmAe/3cawMB+eu3xQ51q5F+2WrHi1MVvSirFY9cHVrKN5ODtwy/gI3lyRxJ6cUvp08eS5RbvxdHHg0qNywg4XFeTBppRCcstqmDH8NCuxtwUH6k8lLDYnAYdcc6iwZ8Fek6s18SGzFbj2Ddj6pbnmFgC3LDL5SP49zWrVh+fDl7NM8+ejTwAeoBSMu8fkLrkHgYOTqYu14zsTkDm6mNOFa141220R48/M63R0aVvbiUK0Aev25vPRmmSW78kl2MuF928YIV03RLshK15tzKaUQuwUDA9ruor8rRMicXey560VSfy0LYt1ewt45IK++Lg5NXmfXkGe7C+tQetDOV/tlqUefroH/njHFDn98c/w3gSzjQgQ8xHYOcCo2XDuE/CXXaZi/F3r4L4tJpH9ADc/uO4700oHoPfU4z+3d6gJusDkflWXQPxC8/PaN0zphyn/PLOvV4hOKKu4ivd/38v0d9bytx+2U1FTj9aad35L4tr/bmBLWjH3To7ip3smHGyHJkR7ICteNqa15tEftnNO3y5cMDCYmNRC+nX1wvM4FZV93Jy4aVwE//l9L+uS8hkc6s21o47fmPfwYKtXI6Uo2g2rxZz82zUXJv3d5HDFzYdFj5hCojf+ZLb/+l1qKrmDSWL3ano1EL9Ik4+VvdW022muyLNNfauYj81W5ab/wcAZx5Z2EEKclN/ic7nj0xjqrZq+wZ58G5NOTGoRIyP8mLMxjWlDu/HClYNllUu0S7Li1cqeWrCLf/54qJlwVkk138Zk8NC3W0nKLWdzajEjm9hmPNztE3vg6mhPcVUdz0wfiP0J8rQOBFsOdopw/3bal0xrWPKYCbrOexomPWq2AAdcDjfOh7pKU429utgUDj0ZXfqbIqInw87O3CdlNWz6EMbeA9Ok3qYQpyOtoJL752yhVxdPVv51EkseOIvPbxtNUUUtczamcfO4CF6bNVSCLtFuyYpXM3wfmwHAjBHNKzL6y64cHOwUk/sEHZG4nl9ew+frU3FxtOeJSwdgb6fYkmYqytdarNzw4Qaq6ixER/ie8Dn83J144crBlNfUH1N2ojHh/u442CkiAtwbPS3ZLqx/Gza+bwKc8fcfea3LANMy57Nppgl1xITWmVP0babHYfRtEDmxdZ5TiA6qus7Cn74wvVHfu374wQ+J46MCWHz/RHZmlTC5T5Ak0Yt2TQKvE9Ba88rSeOyUalbg9fXGNP42dwcAPQLdue+cXkxvqK01b3Mm9VZNeU09u7NLGRjizZa0Ypwd7HhxxmDu/3orQLNWvAAuHdKt2a/DycGOASHeRAW2oW3G+tpD+VInkrbBJNMPuBzO+1fjY7qPgjtXmWrwrfXG7NkFZn7SOs8lRAemteaxuTuIyy7l45tHHrMyH+Tlwjlekssl2r92uvTRejKKqsguqSazuIrc0urjjl24PZvH5u3g7N6BvHH1UFwd7Xngm638Fp+L1pqvN6UR2VBna2NyIQCb04oYHOrNtKEh3DQ2nOFhPnRpoTeXz28bxTPTB7bIY5+0vb/BCxGmhENzbP4UnDxh2jtNN2oG06bGN/yMTFEI0Xr+8/te5m3J5MHzejO5b5CtpyNEi5HA6wQ2pRQe/H5zw7ZgY/bklPLAN1sYEebLe9ePYNrQEObePY5eQR489sMOVsbnsTevgrsm9STEx5VNKYXU1FvYlVnKsDCztfjUtIH8cNe4FnstXi6OuDq1gbyIsv0mQb6uAla/cuLxtZWmDteAacfW2BJCtHtLd+Xw0i/xXDqkG/eeE2Xr6QjRoiTwOoGNyYV4ujjgZG/H5rRjO94f8P7v+3Cyt+O/N0YfDG6cHex5eeYQcsuquevLWDycHbh4UFdGRfqxKaWQXVml1FqsDOt+KEerTeQuVBaeeExjCvbCH++Zmlg/3WtOIB7twKnEmnIYeTtkbIL0jcd/3D0Lobbc1OoSQnQoW9KKuP/rrQwO8ealGYPbxnugEC1IAq8T2JhSyKgIPwaEeLE5tfEVr+ySKhZsy+KqkWH4uh+ZszSkuw93nt2T6jorlw7piruzAyMj/Mgvr2Xe5kwAhoefOJm+1ez4Hl6KgszNzRtvqYNt38BHF8Jbw2HJo6a9zubP4Nej6lnV18LChyD5d7jwBTj3KXDxhvXvHP85ts0B7zAIa7nVQCFE60vKLefWTzYR6OnMf2+KlpOKolOQ5PrjyCurYV9eBVdFdye3rIbP/0iltt56zKnAT9amoIFbxkc0+jj3T+mFVWuuH21yj0ZFmkDru9h0unm7tFhO10mzWmDl86AtsO7NEyeNW+rg25tMAVG/HiaQ6j/N1MVa9Ig5hegbYfoMVuTD/D+ZFa5x98HwG00C/IibYd1bpjl0YzW0ynJg328w4cHj53YJIdqVzOIqbvxwA/Z2is9vGyVFUEWnIYHXcRzI7xoZ6Ud2cTUfrkkmLruUod19WLFnP+5ODvQN9uKrDWlcNKgr3f3cGn0cF0d7Hruw38GfewZ64OfuRGFF7cH8rjZh51woSDIFQON+hKLUIxPVa8pN4+nQaFOg9Kf7TNA19XkY/acjA6Opz0FRMiz6q/kCkxw/81PTdueAUbNh3dsw708waAZEnAUBh+V4bP0StBWGXN2iL10I0Xr25ZVz/f82UFZdz5zZY9pvbUEhTkGnDLwqauqpqrMQ4OF83HEbkwtxcbRjYDdvunqbT2ObU4sorKjh1k9iAFOmobbeyh0TI5v9/EoposN9WRq3n2HHaQ3UqqwWWPUSBPWHq76EN4fChvfggucPjdnxLWz6r/n66V6w1MK5T8LYu499PHsHmPExbP8G6mtMC59e55qVscN5h8KU/zPbjT//xdw28g7Tdmfj+7DiWegxyZxWFEK0e1vSirjjsxi0hjmzxzAwxNvWUxKiVXXKwOu5RbvZkFzIsgfPPu64jcmFDA/zxcnBjq7ernTzdmH5nv3E55TRN9iTe86JYlncfnzdnZpVxPRwoyL92lbgFfcj5MebYMmnOwy4wuRpnf0ouDbMccuXJjC78AXzfZf+xxYyPZyzB4xsRgX5CX+B8Q+YFbIN75uvrV+aSvSDZsGlb5yZ1yiEsJklO3P4aG0yG5ML6ertwhe3j6ZnW6orKEQr6ZSB1968cpJyyymtrsOrkZ6IpdV1zNucye6cUu4759BKy7BwXxZuz8bJwY4vbh9N32AvLhnc/CKmh5s1sjvODnYM697CW41VRaaX4MjbwcWr8TFaw5pXIaAP9G/YBhx3j1nh2viB6YeYFw+ZMXD+sxB5lvk6k5Qyq2EXvgADr4TlT0O/y2DUHa1XDFUIccZprXltWSJvLk+ku58rf7+oL1dFh+Ht1nQ/WiE6sk4ZeOWW1gCQkFNG9FFV4pfF7ee+r7dQWWthSKg3V43sfvDaiDATeD16QV/6BjcRxDSTl4sjN4yNOK3HOKGqYvhsumn+7OAMY//c+LiU1ZCzAy57+1CeVtchJvBZ9ZLJ59o2B5Q9DJ7VsnMGU4H+5p9b/nlEp6aU+gi4BMjVWjdZWVgpNRL4A7hKa/19a82vI9Ba89qvCby5IolZ0aE8f8XgE/aVFaKj63SBl9aanIYK9HuOCry01rywZA9dvV147aqhx2wfzhrZnQBPZy4Z1LVV53xKqkvg88tNaQePLrB7QdOB1/p3wD3QnD483MWvQupak/helgO9p4KHVJQWHcYnwNvAZ00NUErZAy8Av7TSnDqU91ft480VSVwV3Z3nrxh0RO9aITqrTnc+v6ymnspaU9gzPqfsiGtrkvJJzC3n7klRjeZseTg7cNmQbu3jzWPBA2YV66rPIfpWSPsDynOPHZefCAlLzFak41HHuT0C4ZLXzIpZeQ4MvbZ15i5EK9BarwJOVC34XuAHoJF/POJ4Vsbn8sKSPVw8uKsEXUIcptMFXvtLDvVbPDrw+mhNMgEezlwypB2saB1PRgzsmmuS1vtcaLYK0aYCPJgWPAlLIXs7rH0d7J0huokk+P7TTMV4rxDoNbXVXoIQtqaUCgEuB95rxtjZSqkYpVRMXl5ey0+ujUvJr+C+OVvo08WTl2YMlqBLiMM0K/BSSl2glIpXSiUppf7WyHVnpdQ3Ddc3KKUijroeppQqV0r99cxM+9Ttb8jv6hXkwZ6cUrTWgEm4/y0+jxvGhOPs0I6rJ2sNS//PbB2Ov8/cFtQffCNhz89gtcK3N8JXM+H9ibDlCxhylVndasq0d+CeTeDg1PQYITqe14FHtdaN9L46ktb6A611tNY6OjDwOP+WOgGLVfPnrzZjZ6f4743RuDl1uowWIY7rhP8iGnIc3gHOAzKATUqpn7TWcYcNuw0o0lpHKaWuxuREXHXY9deAxWdu2qfuQH7X2b0D+d+aZHJKq+nq7crHa5NxsrfjujGNVE9vT+IXQ9o6uPgVcPY0tyllVr3++A+sfA6SfoXJj5tCpcXpJy5OamcvzalFZxQNfN3QOzAAuEgpVa+1nm/babVtczdnsCurlDevGdZkUWkhOrPmrHiNApK01vu01rXA18C0o8ZMAz5t+P57YIpqeLdSSk0H9gG7zsyUT8/+hsDrrN7mU+me7DJKqur4ITaTy4Z2O2FR1TatthJ+/T/w7wXDbzryWr9LwVrXcErxMjjrrzDgcrMqJgnzQhxDax2ptY7QWkdg3tfulqDr+KrrLLz6awJDQr3bxyEkIWygOWvAIUD6YT9nAKObGqO1rldKlQD+Sqkq4FHMalmT24xKqdnAbICwsJZdccopqcbHzZEhDcnze3LK2JdfQVWdhZvHRbToc7coreHnB6BgL9wwF+yPqpETEg2e3UxF+cvektpYotNTSs0BJgEBSqkM4AnAEUBrfcK8LnGsT9alkF1SzauzhkpelxBNaE7g1di/Ht3MMU8Br2mty9VxftFrrT8APgCIjo4++rHPqJzSarp4uuDt5khXbxf25JSyLb2YEeG+7bt1xab/mfY8k/8BPc859rqdHdwwz2wZuraRavlC2JDW+pqTGHtzC06lXauzWNmcWsSOzBLe/S2JyX0CGdvT39bTEqLNak7glQF0P+znUCCriTEZSikHwBtzTHs0MEMp9SLgA1iVUtVa67dPe+anKLe0mi4NfRf7BHuyZGcONfVW/nJeb1tNqXkSfjFNrKe9fexqVtYWWPKYOXU48TjnF4L6tuwchRCdisWqufWTTaxOzAcg3N+Nf1zc38azEqJta07gtQnopZSKBDKBq4GjCzr9BNwErAdmACu0OS448cAApdSTQLktgy4wK159gk3SeZ9gT1bG5xHg4cyFA9twPkJeAnx/K9SWmyKmA684dM1qMc2l3fzhivcPVZ4XQogW9vqyBFYn5vPoBX25ckQIQZ4uJ76TEJ3cCX9La63rgXswlZt3A99qrXcppZ5WSl3WMOxDTE5XEvAgcEzJibag3mIlr6yGYC/z5tCvoe3PtaO64+TQRgOWmnL49gbT8se7u+mdeLjYT8yK19RnwbWF+z4KIUSDFXv281ZDK6C7JvWUoEuIZmpWgRWt9SJg0VG3/fOw76uBmUff76jxT57C/M6o/PJarBqCGgKvs3sHMmNEKDe15aT6RQ+bBtU3zDPtf5b+wxQ+7ToYKvJNM+mIiaaxtBBCtIIft2by97k7GNDNi6enNdnmUgjRiDa6zNMyDtTwOrDi5evuxMszh+DfVktIpG2AbV/BxAeh52QYdh04uplVr8pCmHen2X686CU5pSiEaHHVdRYe+nYb93+9lX5dvfjwppG4OLbjgtNC2ECnKil8oIZXsHc7WBK3WuGXx8CzK0x8yNzm6guDZ8G2ryHxV6gsgAv+DUH9bDtXIUSn8PqyROZuyeC+Kb2475woHOw71Wd30VFZ6qEgyXxVF0N1qdlVipjQIk/XKQOvLl7tIPDaNRcyY2Hau0dWjR91J2z+zARh131n/nIIIUQLyymp5uO1yUwfGsKDbf0UuBCNKc2C1HWmn3H2NqgqNIXHy3PAUnvk2DF3S+B1JuSUVONgp/B3b+M9B+uqYdlTEDzINKg+XJf+8OdN4B0Kju0ggBRCdAivL0vAqrUEXaJt0xqKUyFnJ+TtMWk5VYXmEFreHjPGwdUsWgT0AicP09u4ywAI6A1ufuDiDc5eLTbFzhV4lVYT5Onc9ioqr37V5HB1G2Z+TlgCJWlwyQ+Nl4cIiGrd+QkhOrWk3HK+jUnnpnER0n9RtD1WK+TGQdx82PEdFKUcuubkAS4+ENgbhl4HkROhy8Bj62G2ok4VeOWW1hwsntpm5OyA5U9Bxia4Zo65LXGp+YvSY5ItZyaEEGiteX7RbtycHLhnsnzoE22Apc5sFSavgtS1kL4JakpA2UHkWTD2HrOQEdTvyFSdNqJTBV45pdX0CvKw7SQWPAAVeXD1l+bnrV+ZP5OWm4Q+Jw9TpT7qXNNXUQghbGjelkyW78nl8Yv7td0T4KLjqasyuVhpf5gtQkut+SpMhsJ9YK0z4wL7wsDLIXQURE0Bz2DbzrsZOs1vdq01+0uqmRAVYMtJQNyPZr85eTV0H236K/r1MH+REn4Bv0iozDcV6oUQwoayS6p44qddRIf7csv4SFtPR3RUVmtDXtYOk4uVus4cLrPWAQp8wsDR1WwPBvSCvhdB8GBTw9Ij0NazP2mdJvD6ckMaZTX19O/WcglzJ1Sw1wRdACufN6cmKgtg2jum7U/cfAjqb5ZLo8613TyFEJ2e1pq//bCDeovm5ZlDsG9rubGifarIN9uD+QmQnwT58aYtXl2Fua7szTbh2LshfDx0H9XhurJ0isBrZ2YJT/8cZyrVDw+13UTSN5g/o2+DmA/N0Vb3IIg6D/pdBps/NX8Zu482JyuEEMJG5mxM5/eEPJ66bAARAW0vT0a0YVqbRYXSLCjLhuI0k/CevsFsH6LNOK8Qc5Jw+A1my7DrYLP44Ohqy9m3uA4feJVV13HPV5vxc3Pi1VlDbHuiMX2DSZqf+izEL4aiZBh3r8nl6j8NNr5vAq8pT9hujkKITi+toJJnFsYxPsqfG8aE23o6oq2rrzWrWIlLzVZhbhxUlxw5xt7ZlGyY9DfoOcUkvjvbOOfaRjp84LVgWzYpBZV8dcdo2yeGpm80y6aOruYv38IHYej15lrYGPDoAuX7ofcFtp2nEKLTslo1f/1+G/ZK8eIMG39YFW1TZaFZSEjfCJkxkLnZtK9zcIGuQ03v4IDe4NUNPLuZHC33wMbLI3VCHT7wSiusxNFeMSbS37YTqSqCvN0wqKGZ9YiboO/F4N6Q7G9nb2qMJC6VFkBCCJv5dH0KG5MLeWnGYEJ8OvaWj2gGSx3smmcOhpXnmsWB4lRzzc7hUKHvqHNNKQcnqfN2Ih0+8MosrqKbj6vtP7VlxJg/u48+dJv7UScsp/wTzpVtRiGEbeSWVvPK0gSTDzvChvmwwvZKs8yp+00fQkm6WbXyjYTQaLNwEDbWJMF38HysltDhA6+Mosq28aktfUPDaY3hTY9RsqQvhLCd5xbtprbeylOXDUDJ+1HnUrDX5GnlxUPWVvM9GsInwEUvQ6/zZavwDOnwgVdmURVn924DdT7SN0DwwE6bTChEW6OU+gi4BMjVWg9s5Po04F+AFagHHtBar2ndWbaeP/YVMH9rFvedEyWnGDuDigJIW29qZiX9ag52gcnTCugNZz8Cg68C/562nWcH1KEDr5p6C7llNYT4tvKKV0mG+ct7YCuxqggyYmHYda07DyHE8XwCvA181sT15cBPWmutlBoMfAv0baW5taqaegv/N38nob6u3DVJ2gJ1CBUFUJBkyjgUp4GlBlBQlmWS4g8EWvbOED4WRt5uThv6RZqcY9FiOnTglV1cDdC6W40Jv8B3N4PVAkOuAv8oWPMa1FWaZHohRJugtV6llIo4zvXyw35052DxoY7nzeWJJOaW8/EtI3F1kl+67UZdlanwvmcR7PsN6qtNwntFnvnAfwQFaFOMtPtoGHI1hI2DkOHgIK2gWlOHDrwyi6sACPVtpVMWmz8zvRiDB5mkw21zzD+EHpPg/GfNVqMQot1QSl0OPA8EAR3yk9P2jGLe+30fM0eEMrlPkK2nI5pSXwMpq01D6MxYk4tVkg5os2oVOdHUibTWges4s13o3wt8Ixpa7rjY+hWIBh078Co6EHi18IpXSSYsfRx2zTVLtbM+M7lc5zxuToYED5LEeSHaIa31PGCeUuosTL5Xo728lFKzgdkAYWFhrTfB01RdZ+Gv320j0MOZxy/pb+vpiMZkbYHNn8POH6C6GFCm5FDYGPC/Hrr0Nx/unT1tPFHRXB068MooqsROQbB3C0X6ddXwx7uw6mXQFpj0GEx8yDTyBJPjdXTJCCFEu9OwLdlTKRWgtc5v5PoHwAcA0dHR7WJLss5i5d45W0jYb7YYvV0dbT2lzs1qNW12KnLNn8XpsOVzkwDv4Ar9LoFBs0w+lgRZ7VrHDryKq+ji5YKjfQscgY1fAosfMYXk+lwEU58zSYlCiA5BKRUF7G1Irh8OOAEFNp7WGWG1ah7+bhu/xu3nqcsGyBajLVgtkL3VtI+LXwx5e8Baf+QYn3CY+rw5mOXibZt5ijOuQwdemUVVLZNYnxELc642TT1vmAc9zznzzyGEaFFKqTnAJCBAKZUBPAE4Amit3wOuBG5UStUBVcBVWut2sZp1PFW1Fv42dzs/bs3i4al9uGlchK2n1HlUl8DOubBnoSkxVFMKys4kuY+7Fzy7mtY67gHgFgCBfeSEYQfUsQOv4ipGhPue2Qe11MFPDf9AblsKLl5n9vGFEK1Ca33NCa6/ALzQStNpFfvyyrnri80k5Jbx8NQ+/HmylI5ocXXVsHeFyQHe/TPUV4FfTxh4BYSPN3nB7jZuaSdaVYcNvCxWTU5J9ZlPrF/3JuTugqu/kqBLCNFuZJdUcfm767BT8OktozirLRSW7qgqCyFhiflKWgG1ZebE4b1VuRsAACAASURBVJCrYfgNpoOJHLjqtDps4LW/tJp6qybE5yRLSRTug5Q1MPzGQ7etfcPswbv5Q9Iy6HeZ1OQSQrQbWmv+9sMOauutLLxvAj0CpYNGi0hZCzEfwu4FYKk1OyMDr4D+l0Hk2YcOXolOrcMGXhkNpSROumr9pg9h/dumAWhAL7NM/PtL4OoD1aXQdShc+GILzFgIIVrGd7EZ/J6Qx5OX9pegqyVkb4dlT5gtRRdvGHELDL3G/L6QlS1xlA4beGUWVwKnULW+KMX8ueN7mPwY7F1ulolnfQpRU87sJIUQooXllFTzr5/jGB3px41jI2w9nY6hON1Uik/fAJlbIDfOfDif+jxE3wKOrdymTrQrHTfwOrDidbKBV3Ga+XPn9zDpb7BrHrj6QeRZZ3iGQgjR8r7ckEpFTT0vzhiMnZ2svpy0kgxIXQ8526BgH+TtNikpYH43hAw324kjbzfBlxAn0HEDr+IqAjycTr7vWHGqSYIsSDKfZuIXw8ArZW9eCNEuLd6Zw6hIP8L93W09lfahosBUiU//wzSTLkk3t9s7g18PCOoPI+/g/9m77zCpyrPx498zZ3rbne2dZeksHZSiUhQs2HvXvBoTk/iamMQkRhOTN01NXn8aY29Rg93oC4oQBAtIbyJ9d4Htfaf3Oef5/XFWpAu4C+xyPtfFBbtz5px7htnZe57nfu6HftO0lkL6VKLuCPXaxKvuaHp4RX1an5XT74Klj8GcH0MiBOWXdk+QOp1O140qW4JUtoS4cUL58Q7lxCYEtG2H1S/C2pcgGQFXARSfAhN/pNX85g4Dudf+ytQdQ732VdQajB/55thfTTPmj9J6q1TM15rYlZ7R9QHqdDpdN/vwyyYAzinPO86RnGDaq7QV6sEmCNRrqxEDdWAwatvyTPpvbQ9Ena4b9NrEyxdJMqLoCKcHfdXa354+MPwKLfEaepH+KUen0/VIH25sYkxJevftV9uTCKEtllr+FFQu0L5nMIIzFwrHwuSfwYBzIK3w+Map6/V6ZUYhhKAjksBjNx/ZHb8a8UrvA1kDtdquU7/f9QHqdDpdN6tpj7C5McC9M4cc71COj2RUG9mKB8G7E5Y9Ac1faonW1Htg1HXgLgJDN+zlq9MdQq9MvKJJhURKxeM4wsTLWw1mF9g8WsHkFS90T4A6nU7XzT7c2AjAucNOomnGVAK2zNZWo1ct0mq1vpI1EC5+AoZfCcYj/N2g03WhXpl4dYQTAGQczYiXp4++SkWn0/VoVa0hXvh8J8ML0yjOOMJa154oFoAVT8OqZyHUrBXGj7pO2wvR5tH+5I3QR7d0J4RemXh5w0kA0u1HUePlKe36gHQ6ne4Y2dTg56bnVyJJ8ODlI453ON1LVWHDG7DgtxBugf7TYcITUHamnmTpTli9M/GKdI54HclUoxDaiFffKd0UlU6n03UvbzjBtc8sx2Ex8q/vjqdfb9weSEnBrsVQ8R/YPh86qrTi+Ote1/7W6U5wh5V4SZJ0LvAoIAPPCSEe2Od2C/AyMBZoB64WQuySJGkG8ABgBhLA3UKIRV0Y/wF9lXgdUY1XpEPr2ZVe0k1R6XQ6XfdasbODQCzFszeN69lJVywAa17UGln760A2a7MRSlKr4Qq3ag1NS0+HyXfDiKv1ES5dj/GNiZckSTLwODADqANWSZI0WwixeY/DbgW8Qoj+kiRdAzwIXA20ARcKIRokSRoGzAe6fa2ut7PG64hWNfp2aX97+nR9QDqdTncMrKnuwCwbGFXSg7eu2fU5vHs7+GvAkQNpRaAkYOdiEAoMPAeGXaFNK5pPgvo1Xa9zOCNepwKVQogdAJIkvQ5cDOyZeF0M/K7z328D/5AkSRJCrNvjmE2AVZIkixAi/q0jP4SOSBJJgjTbEdR47dlKQqfT6XqgNdVehhelYTEe4VZpJ4KYHz7+C6x4ShvduvUjrXP8V4QAVdH7Kup6vMMZmy0Eavf4uo79R612HyOESAF+IHOfYy4H1nV30gXaiFeazYR8JBvCejubp+pTjTqdrgeKJRU21gcY18dzvEM5MqoK62bBY2O1pGvcLXD7kr2TLtBWm+tJl64XOJxX8YGyF3Ekx0iSVI42/Xj2AS8gSd8DvgdQUvLtEx9vJHF4rSRat8Oq52D897URL5sHrO5vfX2dTqc71jY1+EkoKmN6UuK141P4z33QtAGKToHr34KC0cc7Kp2uWx1O4lUHFO/xdRHQcJBj6iRJMgJpQAeAJElFwLvATUKIqgNdQAjxDPAMwLhx4/ZN6o6YN5I4dGG9EFrh5rxfQyoK62eBLUOfZtTpdD3WmmovAGNKekDi1V6lJVzb5kJaMVz2nLZTiF4grzsJHM6rfBUwQJKkvpIkmYFrgNn7HDMbuLnz31cAi4QQQpKkdOAD4B4hxOddFfQ36Qgn8Ryqh9fC/4H374KSCfDdRZBbrhVy6tOMOt1JQ5KkFyRJapEkaeNBbr9ekqQNnX+WSpI08ljHeCRW7/LSJ9NOtstyvEM5uMYvYM5P4PHxWrH8WffDHathxJV60qU7aXzjiJcQIiVJ0h1oKxJl4AUhxCZJkv4HWC2EmA08D7wiSVIl2kjXNZ13vwPoD/xGkqTfdH7vbCFES1c/kD35IgmGFRxkyjDUCsuf0FbFXPas9sP+nQ9g9QtQNK47w9LpdCeWfwL/QGuFcyA7gSlCCK8kSeehjcqPP0axHREhBGtrvEwekH28Qzmwqo9h0R+gfg0YrTD6epj6a3DlHu/IdLpj7rAqFYUQc4G5+3zvt3v8OwZceYD7/RH447eM8Yh1hA8x1bjiKUjFYcovv/6EJZu0Oi+dTnfSEEJ8JklS6SFuX7rHl8vRyixOSDUdEdpCCcaWnmDTjK3bYf6voXKBNqNw3kMw4iqtnlanO0n1uiUi0YRCPKUeuIdXLKDt5TXkAsgeeOyD0+l0PdWtwIfHO4iDWbmzA4CxJ0phfSIMnz4Eyx4Hkx1m/EH7cGs8gadBdSetUCJEbbCW5kgzAAbJQJGriLK0sm65Xq9LvDp2bxd0gBqvNf/UesWcdtexDUqn0/VYkiRNQ0u8Tj/EMV26MvtILNzSzO/nbKYw3caAHNcxvfYBtVXCq1dCxw4YdT1M/z04T9ApUF2vI4QgnAzjjXnxxr34434CiQDemJe2aBstkRbqQnXUh+qJJqMk1ARxZf8uVzcPvZmfn/Lzbomx1yVeX3WtT993xEtVtdquvpOhSN/PS6fTfTNJkkYAzwHnCSHaD3ZcV6/MPlzPLd7Bn+ZuobzAzTM3jjuy3oXdoWY5vHat1nPr5veh7xnHNx5dr5VSUzRHmmkKN+GL+WiPtfNl25esaFxBY7jxgPcxSkay7FkUOYuYmD8Rl9mFSTaRZk6jxF1CviMfCQlFKGTZsrot9t6XeB1sg2x/LQQbYcovjkNUOp2up5EkqQT4N3CjEGL78Y5nX/M2NvLHD7Ywc3ge/3vlKGzm49StPtQCm96Fyo+0Ivr0ErjhbcjonmkaXe8nhKA+VE91oJr6UD2t0VYiyQjhZJjGcCM1gRqawk2kRGqv+6VZ0jg171SuGXwNmdZMPFYPbrMbt8VNhiUDt8WNQTr+q2d7XeLVsXufxn2mGts63zezBx/jiHQ63YlIkqTXgKlAliRJdcD9gAlACPEU8Fu0HTiekCQJICWEOCGWPu9sC3P3WxsYWZzO/7t61PHZIigR1mq4ljwCybCWaJ3yXe3DrT3j2Mej6zFiqRitkVaaI820RltpibTQFm2jI9ZBc6SZrR1b8cf9u4+XkLAardiMNvId+QzLGsa5fc+lyFlEvjOfDGsG6ZZ0cuw5J0Ri9U16XeLliySBA2yQ3bpN+ztr0DGOSKfTnYiEENd+w+3fBb57jMI5bLGkwg/+tQZZlnj8utHHPukSAja/pzWgDjbAkItg2r2Qo3+oPdkllSS+uI9YKoY/4ac+VE9TuIm4EkcRCvXBeja0bWCnf+d+97XIFjKsGWRaM5leMp2hmUPpl96PQmch2bZsZEMP3H/0IHpd4tURThx4g+y27WDPBMe+W0jqdDpdz/HYogq2NgV58b9OochjP7YX99XCnDuhahHkDYcrXoA+E49tDLrjLqkmqQ3WUhOo2T31t6FtA1vat5BUkwe9n8fiYUT2CM4rPY98Zz459hxy7blk27NxmVx0jiz3er0u8fJGEritJozyPsONbdv10S6dTtejVbaEeOazHVw2ppBpg3KO8cU/gnduAyWp9eMad6u+aXUvJoSgNdpKhbeCCm8FO/w7aI400xhupDZYS0r9ur7KIlsozyznhiE3UOQqwmq04jK5KHAWkOfIw2a0YZAMyJJ80iRXh9Lrfmq8keT+hfWgTTUOvejYB6TT6XRdQAjB/bM3YjXJ3HPekGN5YVjyMCz8A+QMhatfgcx+x+76um6jCpX6YD1bvVvZ0r6FumAdoWSIYCLIzsDOveqsMq2ZFDgLKEsrY1rxNPql96PUXUqBs4AMa0aPqK06UfS+xCucIH3fwvpwG0Q7IEtvmqrT6Xqm9zc08nllO3+4uPzY7ceoqlrn+RVPwvAr4cK/g/kYT2/qjlpKTdEUbqIh1EBHrANv3EtrpJXGcCN1wToqfBWEk2EAZEmm0FmI0+zEYXIwvWQ6AzwDGOgZyID0AaRb04/zo+k9el/iFUmQ57bu/U29sF6n0/Vwr66ooSzbwXXj+xybCypJmH0nfPEqTPghnP0nfSPrE0BKTdEYaqQ2VEt9qJ7aQC07/DuoCdYghMAkm0gqSfxxP/6EH1Woe91flmRy7bkUOAu4sOxCBmUMYkjGEPp7+mOR9Z0FjoXel3iFEwzJ32eD7LbOxEvfJkin0/VA8ZTC2hov140vOTZNUmN+ePNm2PGxtmJx8t1aU1TdMZVUk3zR8gXrW9ezvWM7Fb4KqgPVexWwmwwmStNK6Z/eH1mSSSgJTLKJdEs6aZY0Cp2FFDgLyLZlk2ZJI92SjtHQ63719yi97tnviCT27+HVul3bL8x9wu5xq9PpdAe1oc5PPKUyvu8xWJXtr4NZV2kfWC9+HEbf0P3XPEn5Yj5qgjXUBGtoDjfTHmvHF/MRU2IEE0G+bPty91RggaOAgRkDmVw0mVJ3KcWuYopcRb2u1cLJoFclXtGEQiyp4tm3uL5tG2QN0IfJdTpdj7Rih7Zb0al9u7kxaeMGePUqrTnq9W9Dv2nde72TgC/moyXaQlukjdpgLTsDO6nyVVHhraA9tvcuVDajjQxrBjajDatsZWbfmZxWcBrj8saRZkk7To9A19V6VeL11XZB+zdP3Q59Jh2HiHQ6ne7bW7Gzg0G5rgOv2O4qVYvgjZvA6oZb5kFuefddq5cSQrC1Yyuf1H7C2pa1bPdupyPWsdcxdqOdvml9Ob3wdAZ4BtDH3YdiVzF5jjwcJsdxilx3LPWqxCsQ0+a992qeGg9BoE6v79LpdD1SUlFZU+3lirHdVCohBCx/Ev5zH+QMgevfAndB91yrlxFC0B5rZ0PrBhbXL2Zx3WKaI81ISAzOGMzkosn0T+9PriOXbFs2Rc4icuw5ei+rk1yvSryCMa2hm8u6x8P6ao9GfUWjTqfrgTbW+4kklO6p70qE4YOfaysXB18Alz4FFlfXX6eHiitxWsItNEeaaYm00BRpojms7S/YGmmlOlCNN+4FwGFyMCF/AncU38EZhWeQadN3SdEdWK9KvEKdiZfTssfDatmi/Z2tJ146na7nWbFTm6rq8vquqo9hzo/BVw1T74HJvzip62CFEFT6Kvmo5iOWNyynLlhHS7Rlv+OcJifZ9myybdlMK5nGgPQBDM4YzMjskZhk0wHOrNPtrVclXl9NNbqse7z4KxeAIwcyBxynqHQ6ne7ordjRTr9sR9c0Td21BCoWQO1KqFkKGf3gO3Oh9LRvf+4eQAhBTbCGL1q/oC5Yh9PkxGaysaltE8sbl1MfqkdCYnjWcCYVTqLQWUieI2/3noJ6HZauK/SqxCsU32eqMZWAio9g2KUn9Sc5nU7XM6mqYPUuLxeO6oKaq+VPwrxfgcEIeSNg6q/htDvBZPv25z7BxJU4m9o2scO/A1/cR3u0ne3e7Wzt2EogEdjveJfJxSl5p3DLsFs4s+RMsmxZxyFq3cmidyVe+0417loMiSAMOv84RqXT6XRHp84bJRhPMbLoW7YS+PzvsOA3MORCuPSZXrftTzgZZnXTata2rGVdyzo2tm3cq8mozWijX1o/zi49m/LMckZmj6RvWl8iqQihRIgce47eVFR3zPSqV1owlsIggd3c2Uxu21ytcWrZlOMbmE6n0x2FipYgAP1zvkXB+/rXtKSr/FK47Fno4XVISSVJe6ydbR3b2Ny+mVXNq1jXvI6USGE0GBmaOZQbhtzA6JzRDM4YjMfqwWq0HvBcbrMbt9l9wNt0uu7SqxKvUDyF02LUluoKAds+hH5n9sqhdJ1O1/tVtoQA6J/jPLoTBJtg3i+hZBJc9hzIJ/5bfjQVZVHNIj6u/ZhQIkRMiRFJRggmgvgTfoKJ4O5jJSQGZQzipvKbOK3gNEZkjzhokqXTnShO/J/CIxCIJb8urG9cD4F6bZ8xnU6n64EqWkLkuCx79yY8XELABz+DVBwueuyETroUVWFV8yrm7pjLguoFhJIhcmw55DpyscgWcuw5lKWX4Ta7ybBmkGHNoH96fwZnDMZu6l3Tprre78T9STwKoVjq68L6bR+CZICB5x7foHQ6ne4oVbaEjn60a9O7sPV9mP57yOrftYF1geZwM4vrF7OicQUrm1bSEevAYXJwVslZXNL/EsbmjsUg6YuidL1P70q8OqcaAahcCEWngENvYqfT6XoeIQSVLSEuH1N4pHeElc/A/HuhYDRMvKN7AjwKO/w7WFi9kIU1C9nUvgmAbFs2kwomMa14GpOLJutThbper1clXsFYiiynGVQFmjfBuP863iHpdLoTlCRJLwAXAC1CiGEHuH0w8CIwBrhXCPG3YxlfcyBOKJ46shEvJQn/vk0b7Rp4HlzyxHGdYvyqKel/qv/Dgl0LqPJXATAiawQ/HvNjphZNpV96P30LHd1JpVclXqF4itIsB3TshFRU3+RVp9Mdyj+BfwAvH+T2DuBO4JJjFdCejmpF47pXtKTrzN/AGT+D45DQfLVR9ILqBSyoXsCuwC4kJMbmjuWeQfdwZsmZ5DnyjnlcOh1or081FCLW3Eq0sY1Yq49YMEo8opBKCowmCbPVgGdIKVkTRnVLDL0q8QrGklqNV/OX2jdy9/sQq9PpdAAIIT6TJKn0ELe3AC2SJB2XRoBHvKIxEYFPHoTiCcc86RJCsM27jYU1C5m3cx67AruQJZlxeeO4YcgNnNXnLL0pqa5LqYkEibZ2gjsbCOxqItLsJ9IWIhFRkVIGJGFAlkwYJAMGScZoMGEymDEbzBgNZkwGc+eZbFixse8E97b16/TE63AEYylcFqM2zSgZIHvwEZ9DCEFKTel7bul0uuOqoiVEms2klU8cjpXPQKgJrnyx25MuIQRbOrawvmU9m9s3s7JpJY3hRiQkTs07lZvLb+askrPwWD3dGoeu51JjMRSvl1iHH9+uVoLNfuLeEIlgnFQkhUgIJNWAARkZGQkDsmTAaDBhNJgwGyyYDVYMkgELbiy48QBfZVAJJUZSJFBUBQWFlJokooQIKgqqQSBkwGhAMskYzEYMFgMGowE1paLGFdKHl3bbY+81iVcipRJPqVpxffMmbW9G05EXac7aMovnNz7PvMvnYZG7YG80nU7X60mS9D3gewAlJSVdcs7KlhADcpyHV/8U9cGS/wf9Z0CfSV1y/QPxxXy8v+N93ql4h0pfJQAZ1gxGZI/g9pG3M7losj6ydRIRnaNO3soGAjsbCDd6SUUSKCkFNaGiJAQoBiQhaYmTwYRZtmKRrdoIlGTGLFuRADdpQOcODQbACoqaIqHGSYoEqlBQhEJSJImlYiiSD9UgwGTAYDciO204irPIHFJCZv88TLbD/MByHPSaxGuvfRqbNkLxKfsd44v5cFvch1yi/EndJ7RF21jfsp7x+eO7LV6dTtd7CCGeAZ4BGDdunOiKc1a2hDh7aO43H7hzMXx0P8R8cNZvuuLS+1nVtIrXtr7GJ7WfkFSTDMscxm8n/pYzCs8g156rF8f3Amo0qo1AeQME69vxVzcTbg6QDCZQ4loCZRDy7mk6s8GM2WDFItswSAbspGMn/esTGtmdYShCQRUKSTVOTIkSVaOk1BCqJBAIJLOEbDdictmxZLhwleaRMagQZ/a33CrrBNU7Eq81L+Fc+iRmfkW6HAN/DYz7zl6H1AZruXz25UwumsxfJ//1gG8USSXJFy1fALC0YameeOl0uuOiPRSnI5w4dH2Xvw7evwsq/gOuAm07oPyRXRpHla+Kh9c8zGd1n+GxeLh60NVc0v8SBmUM6tLr6L4dkUyiBAIIRUEyGLQkqqODRIeXSEeYcFuIqD9GPJIkGU6S+iqRwojRYMYiW7HKdiyybXftk4tsXGSDBFi16eW4GiWhxkkocSJqhKAaRFEVMBuQHWZMHiemNAdGpw2zy05aSRbuogwsdr1FyJ56R+K16V3M7Vs4x7CK/FjnQ9qnsP5vq/5GXIkzf9d8BnoG8r0R39vvNJs7NhNTYpgNZpY1LOOusXcdi+h1Ot1xIEnSa8BUIEuSpDrgfsAEIIR4SpKkPGA14AZUSZJ+AgwVQgS6O7ZDFtYLAV+8Bh/+Umudc/Yf4ZTbjqq04mA6Yh08sf4J3tr+Fg6jg7vG3sX1Q67Xyy+OISEESns7iZpa/FU1tO9oJ+yNkYyqiJSMJDoLyA0mLEYbFtmGbJAxSDIG5N1F5bLkxor76+JxGehs9p9QYsTVGHE1TkDxkxIdWu2TScLosmLJdOHok0N63zzSS7MxmfXa567Q8xMvJQV1qwC43riQ7LBWX9DkziUZrKXYVcyyhmUsql3EnaPvpMpfxWPrHgMglorREevgrrF3kWZJY03zGgCuGnQV/9ryL9qj7WTa9AasOl1vJIS49htubwKKjlE4e6lsPUjilYxpo1xfvAp9ToOLH4eMvl123VAixGtbX+PFjS8SSUW4etDV/GDkD/Qi+W9BCIEaCJBq70Dx+Yi2tuPd1UaoyUvMFyMVF6iqDEIrIJckGYPBiGwwYTXacZhcWOUM0snQJvI6tx7Wpu6SpESCmBIlLEKoKYEqqSCBMEggS2CUMViNyE4zJrcDa04aaSXZZJTlYHHq+xgfDz0/8WraAIkQ/ozhTOj4klDtXBRrOt9deh+1oVpm9p3JlvYtFDmLuKn8JoQQVPureWzdY8iSjCIUcu25/GDUD1jdtJqytDLOLzuff235F8sbl3N+2fl80foFq5tW4zK7KHIVMTF/ol7ToNPpuk1VSxibSaYgbY9fjIFGeON6qF8DU++Byb8AQ9dsqdMaaeXN7W8ya8ssgokgU4qm8NOxP6UsvaxLzt+bCSG0hKqqitb1VbRVthEPJEE1Y5StmGUbZtmi/TFYMRksWCnESiFY0P50UoWKIlKk1AQpkSKmxmhT2lDNBowZDuzFWbiLs/D0y8GVk37QmHQntp6feFUvBWD1iN9x+sdX42xayaI+Y6gOVjOlaAofVX9ETInxyLRHdg+TvzzzZWqDtRQ7i/nJJz/h9W2vc3P5zaxrWcd5fc9jSMYQ0i3pLG1YSv/0/tw6/1biSnz3Jf+r/L+4a+xdevKl0+m6xY62EGXZDgyGzvcYIeCtm6FlK1w9C4Zc8K2vkVSSLGtcxuyq2SysXkhKpJhaPJXbR95OeebJ23w61dFBvKISNRwiGQgTavESbgsTDcRJxAVKQqCoUucIlRmz0YbN6MRpTsNsKKCAAm1yGoimQkTVKAk1QUSJoiIQRgOSzYQpw4Ulw4U91427KBNnbhpmpxVZlo/vE6Drdj0/8apZxqqsPrzkW4BXPYUr5GW8bFEpMBfwyLRHaF3+GY3v/5u8bYuoT83Hfe55uM6cRllaGSKV4jvyZG6Jfsojax8hlAwxNncsskFmQv4EljYsZX3LetxmN6/MfAWjZOTZL5/lxU0vogiFn4/7uZ586XS6LlfVGmJ08R7Te5vehdoVcNFj3yrpqg3UsqxxGWua1/B5w+f4437SLGlcP+R6rhp0FSXurmmFcSJSAgEStbUk6+rxV9bhaw0h22wY7RYC9QGi/iQoJkwmJzaTE6vRhlV2IUvpuAAX7DdCpQilM7mK0Kw0gd2ErSQLz6AicoeXYHXrU3m6/fXsxEsIUjXL+F1+JjX+uVRmlzEkZGZNsoP/MV5N/XduJbJyJXabjbDbjUilCMyeg3vmTOwTxtP+3PM4a2r43TAXDyReBTOMqEjS+sE/mJ6TzceBVhx+Aw97bsL45Kukmlu4ORplQP4Ynva9RJWvijtG38GgjEGsbV7LmuY1eFrjlM7/ksL0PmSdfxG2UaP05Eyn0x22WFKhzhvlijHF2jeSMa1dRO4wGHX9UZ2zylfFk188yfxd8wHIsmVxeuHpnFd6HpMKJvXohtFqIkGispJ41Q7ijU20VvsJeeMkYiqqMCHJVgxGC2ajHavRgd3kxCoP0JptxgBvZ615mjbVF1FCRJUIPhFAkfxIViMGhxWTy4YpzY4104Etw4Uj04mzMEMvONcdscNKvCRJOhd4FG09xHNCiAf2ud2Ctt/ZWKAduFoIsavztnuAWwEFuFMIMb/Lom/bzvuGGHWpKGMCg2lUtvI3Uxn3vhVhcOUsEtnZ5N7zK9KvugqDzYZIJGh77jnannyKwNy5WIcOxXXLLQz55z/5YyMoVjOh+nsIASXAK9AZ9vN4zWaMubkgSYxYWMPjQKtnMUvzP2NOlhk1lSDXC8O3ChQZ/GI54X+9jsGuLR8RQmA/9RTSL74Yy4ABJBsaiFdWEV6xnOiatZj79CHt4otwz5yJMTu7y54inU7Xs+xsCyMElGU7V7KnhQAAIABJREFUtG+seAp8NXDje2A4smmolJriH+v+wQsbX8BmtHHb8Nu4uP/FlLhKTtgPhEJRSOzYQWzrNkQ8hkgpqNEoajBA3B/EH5IIRw0kYgIlKSHJNiyWdOxmNy5TGWkGE2ludk/3Abu7lsfUGG2iHWENIKfbESkFkUxhyU4jY0gReSNL9IJzXbf7xsRLkiQZeByYAdQBqyRJmi2E2LzHYbcCXiFEf0mSrgEeBK6WJGkocA1QDhQAH0mSNFAIoXRF8Mldn/GyJY2HXjdTUr2p87sBknYzOXf/HM/119O6o40Vf34fKa4iqxKm8iEMnTObVGsrtnHjUJMKVeFC+kUykTDQMaKNVJoRNR5HxKJ4+tgZcMlpmPr02f1GFa2spGX+ZxRtWkXaulWYN4cRsgHZ4cRx44WsmJbHgxse4SHDFQxuNiHJMiKZJLhoEfU//RkAbf0mIxePxdQB2Wefibq9iua/PEDzAw9iGzMG55QpSCYTajhMqqWFZH09qfZ2bRUnErZRo3Cdew62YcMQioIaDBLbvp1EZSXG3DwckyZicLqIrFxBbPMW3DPPw1LW9YWyKxpX8OLGF3l46sPYTfYuP79Od7Kp6lzR2C/bCYkwLP5fGHgu9Jt2ROdpibTwi89+wZrmNVw24DJ+MuYnJ9TqRCEEqZYWEjt3kdi1k/CW7TRWh4jErUiOLCzWNGTJiEGSMclurHIeFtlGmmQgzc7ulghCCMKpAGERodHQiiHTjjXXhT0nDVdhJmklmdjSHMf1sep0ezqcEa9TgUohxA4ASZJeBy4G9ky8LgZ+1/nvt4F/SFqWcjHwuhAiDuyUJKmy83zLuiL4OR9/yvfnmCn2JVl6zo1sjkmcMzrEKZfdjienmObNdXQ8v45CYw5xEQUJLFsNrG1Yx9i7L2D984swbPFTYiqmXWkhJqfwyOnYY51LuC1AE2z++xpiphXYkiacshOzwYosDSBmzyMybRrJAjepcBwRV4jme5hWUM7SL9ezbacRi9ofW9KIXbITGDsINVOQak9QatQSOcU9gJWJDXTcUMS1ZX8gvvhjgh99ROvDD+9+nAmXFWtRCfaCIi2JSyTwf/ABvrfeOvQTJMugaDlu21NP4bn2Wpynn0aysQk1FsVWXo512DAMtgN/whNCaEW9AJK03ydkVag8tOohtnu38+rWV/nu8O8e1f+jTqf7WlVLGEmCvlkOqPwA4gGY8MPDvn9CSTBryyye2fAMilD48+l/5sJ+F3ZjxN8s1dFBfPt2WtdspaWyg1gYJKyYzY7OLWRsOEyTyc/TRvRUoRJOBUmJJCqChKQQMfhRzUEMTgvmTAe2HDfOfA9ZA/Mp1hMrXQ9yOIlXIVC7x9d1wL4t3XcfI4RISZLkBzI7v798n/sWHnW0e4gGfAxruQ77aS5iSpTxSpTR9iRqhcrWJ1bgGLAL41ovNtlB8BQz5VdMIdwWYMtf5lEUzKfm3o/Jlm1EJBsNOX7G3nExstmIoiiEmv0YDAYiHSF2vL2C9KQFt+omIAVoNXgRZgOYZMx+hQIlB7lOBjp/8LdBZNs27uJqAJJqgiB+fATJMKRhDdjBBHU0kH/hKBreWcNE8xhYAYEV7QSTfWg/4yYevfRZquM1qBYzCVIIdjC5qIhHpv4vJtmEGosRXrKERG0dksmEwWbDWFZKULixG6Kk1q5G8ftxTJyIkpFLeNY/8c6ahfeVV/Z+Io1G0i66iOw7/xtjbi7RNWsIfrSQ6MYviW3egohEAJDT0nCdPQPX2edgLu2D7HazfM6zXPvyZgY0wtY+j9JwlYHMM6ZhLi1FkiTURIJUczPG3FwM5v33zRJCHHS6Q6RSCFVFkmUwGE7YaRGdrqtVtYYoTLdhM8uw9X2webSeXYdha8dWfvrJT6kN1jKlaAp3n3I3fdx9ujnir6mJBPGKCkJfbqZ+fQ0Br4qEA7vFg9viwSb3o8DSDyxaYXokFSIu4kSkGH5jHGOui/QhBRSPH6gXput6rcNJvA70G2/fvcgOdszh3PeoNpi1OJxsjnyJ2ZhOhi0dNSmwSEbs2HDH0uFLSMlOgqMtDL9iIgCOLDej/3IZK//wLuYkUAbDbp7GwD22M5BlmbSCDABceenk/vbQ/RO9NW20bavHXZiJ1W2j+rMthLc3I5lllqZv5C3HHJ47/wVOzSonEArw8mOPEbJE+PGd95FUk1xT/Rhn75xAkT+TAea+ZCUdlLZm8buG79Ng89HnnNG4x+Tw+tbX+XjJfD5b+Cr5cQ9hKYZhYAYmZ38iG+pxJ404VviRDWE61DjtqoOU0U7aWy04jXF2yQMZ9/4chM+HqaAAyWgkunEj4cVL8L35JoEPPsCYmUmyoQFhMmIrH0b6pZciZ2hTE4mduwh8MBffW2/vfuwZAB4jpnOnkL1kIf4//xU/f0XOzkJ2p5HYtUsbcZMkTAUFyGlpYDQiEglSra0ogQD2UaNwzZiBMSeHZH0d8Z07iW3aTLyiAlLa/pvmsjLy7rsXxyRt89+U14vq9yNUARIYbDYkiwU1GETx+TDm5GDKy/v6BZdMosYTndO0IJlMSGYzkrFnry3R9U5VrSFtmjGVgG3ztFWM8je/VhdUL+DeJffiNrt5esbTTCrons2yhRAk6+qIbdpMqrmJjlofLU0JUgkLZpMbpzkNlzmLLCmPLA8oIkUg6aNd6UA4gthKs8gqL6ZgZB+9MF13UpKEOPR+rpIkTQR+J4Q4p/PrewCEEH/Z45j5nccskyTJCDQB2cCv9jx2z+MOdr1x48aJ1atXH1bwv3x7Awu3NrP6vhnMfHQxBelWnrv5FNorG9n10Zd4BhVSNu349aNpi7Zxw9wbiKaivHTuSzy06iE+b/gcIQRnlZzFoIxBPL7+cV457xW+v+D7nNv3XO4a+RP++eeHmRYdTaZZK7JPqUlUVIySCYNkoC3RglN2YZVtu29vV9pJWASSy4zki+NR3JgNFpoSTaRIUWrtS4PSwMj7L9qveDRRV0/F3/5AXd1m3i/zsWyAylWjbuLucXfvNdKkxmKEV60k3tJMxa41PO2dw2U3/YmLBl7Czz/5Gds2fMqTmT9CWr8FNRLBMqA/5qIiko1NJHbtQg2Hd+8lJrI8bI/WULLdh1K5Y/c1ZI8H69ChWIcOweBwIpJJ/O/PIVldg2PSRJLNLSSqqr7xubeOHIFtxEhimzYR+/JLRDK59wGShCk/H3NpKWo8TqK6GhGJaNceOQL3OedgHT4cgMiKFURWr8HctxTrkKGY+5bu9bwIVUXx+1Ha2zHm5CC73XtdSo1ECC9dimQ24zjtNG0U7wDUeJzYl19izMvTkuMuao4pUimQ5RN61FCSpDVCiHHHO46ucCTvYftSVUH5/fO55tRi7h/SBP+6DK55DQbPPODxiqqwomkF71W8x4e7PmRE9ggenfYoWbasb/MQ9o4pkSC6ejXeZatp3tJEKGxAsmVhtWeQZsnEYfr69R5OBQkpQRIWFTnPTeawYoomDdT36tP1akf6/nU4H/lXAQMkSeoL1KMVy1+3zzGzgZvRareuABYJIYQkSbOBVyVJehituH4AsPJwg/smA3KdvLG6lo5wglA8hdOiPZzM/vlk9s/vqssctSxbFk9Mf4Ib597I5bMvJ6Em+M2E3xBX4jy06iEW1izkjMIzGJUziul9prNg1wKKXcU8P/A9ZlxwDVTbqJv3BSKURBKCGEme87zHJZdcw4VFk9n61nJi0Shv5s/nP76F5NpzmdFnBr64j6W1SwnEfFwx9Craw22cNtfHeNNodty/kEabj6XZmxifN54iawHbCur4/dgVuCe6uaDsRjKTYV7Z/AqF69wMd5STf0o/sgcW8EVgCz+q+xXBRBCyoKRvKTP7az2FfjT6Di6rWcTP0ubx2O8fI8eec9DnJZKM8IOPfsDalg2cMfMM/t+AJxCRCKaiImSXa7/jM793G+3PPofvnXdoy7Uwd4qBNjeMzR/HhJxTWbrzYyqbt3Bq/2mcM+Jy4hUVBP/zH7yvvYatvBzPjTdizMpCkg1a3VoqhRqJkKiuIbFrF5LNinPKZAwWC9FNm/C+/Aodz7+AZcAARDKpjdztwZifj+usszBmZxNeupTo2rVfJ3ZGI/axY7GNGI7iD5BsbCSyciUirjXgNRUW4jr7bFLNTcSrdmiJ5uDBqOEwgXnzUINBACS7HVt5OfZTT8U5+QxsI7XNj0UiQcujj5KorMI2ZgzW8nJEIoEaDmMtH4qlrAwhBJFVq/C9/gaxbdtIVFdj6VtK9k9+gvPMM7stAWt+8CEia9bgmDgRx2mTsI8ahXSAKWbdwTUFYkSTijbitfV9MDkOWFTvi/l4p+Id3tz2Jg3hBlxmFzcPvZn/HvPfXbKfYsrrpeG9/1CzshZJuEi35+EyjSQ3azS5WdqoVygVIECIDnsMR78cik8fRFGfg//c63Q6zTcmXp01W3cA89HaSbwghNgkSdL/AKuFELOB54FXOovnO9CSMzqPexOtED8F/KirVjTC1/uYVbaECMaSuKwn3rB1WVoZfz/z79yx8A6+N+J7XDXoKm3bokA1b21/a/dm3TP7zmR21WweX/84Y3PHap2jM6FozNcrEYUQPDv3Q57Z8AyjskfhO1Pib6ufoM5fxy3DbmGHbwevb3sdt9nNxOKJnFt6LlOLp/Jp7afcUXcHf+v4Nbl1Zgam+jCwsQ80AsQZLTw8k/gVyXwLY847B9kiM/69QoYbBgMQ/7KKLck1LLOuI2OUh1uG3YJRMjKxYCJGg/YS6pvWl0emPcIvPv4FDz7xW842TcVldmIym2nuF2aZUdtyaVzuON7Y9gbrW9czo88MFlQvYG6fGVw65FKSSpLmUAMFzoK9nkODxUL2HT9i08Xl3LnoTs4uPY+h7j78fcOzPKqsxdHPwZBTTuX3zZ/izx7NZZOu5M3RXtY0SVw+8FIuG3gZBgxsbN9ISk0xOmc0Bungo0lKKIT/g/epfuMlZKuJPg8+gPOss0jW1hLduJHQJ5/ie+stRDyOZdAgPNddi6mwCNnjIb59O6FPPqb9+ReQPR6MmZmkX3klrunTUXw+vK++SseLL2IqLMTcvx9Khxfva6+BwYD77Bk4p09H8XqJb68gum4dbU8+Sdvjj+M65xyybv8+zX/6M5HVqzGVlBD69NP9YreWlyOZzUTXrUP2eLQVslOnEFr0MXU/ugNTQQFqIoHi8yG73Zjy8rCNG0vWD36A0eMhsWsX7c8/jzEvj4wbbsDgcOB//338b7+DZLVizMpCTnMjWW2YCgpIv+xSJJMJ3zv/puPFFzGXltL+3HO0P/00pW++gW3EiG/5E3Ry2b2iMcsOSz6AAdPB9PUI9U7/Tl7e/DJzquYQV+KMzxvPXePuYlrxtKNOuIQQJOvrafl0BbWr61FiFtKs2aSb8ylNKyCpJvAm2wmYmjHmuHCX5VA0aRDFGQfYwFun032jb5xqPNaOZJi+3hfltAcW8cdLhvH7OZv47hll/PLcwd0c4dFJqandSQpob3bNkWbyHHm7bz/rrbPoiHXwyLRHOKvkrAOeZ0n9En7w0Q92f51pzeR/p/4vY3PHAhBX4pgMpr0Si6SSZNpb05hUMImJ+RN54/9e4mZxBWERYUdgJ6NjAygU2bhN6URSIcIiTLYplx2JnSxyrmF4YgADE0V4zJn4ku2ETHFwmDA4zBgsJkCQbI9gCCVJV104jPuPWgWSPqrVBuZlrmRRwUr+OPmPXNjvQm6dfytbO7by/RHfZ9bWWbREWnjp3JcYlTNqr/tXB6q5as5VlKaV8s9z/4nNaGNN8xrWtazjyoFX4jQ5uWfJPXy480NsRhtxJU5fd1+q/FXkO/KJpWJ4414ASlwlXDnwSq4bch1mef8Rme3e7fxp+Z9Y27IWi2zh9fNfp7+n/17HqJEIaiyGMSPjgP9P37RwYM/6sq8WEhxoAYISCOCdNYu2p55GxONIFgv5f/wjaRdeQMrrJV5RgcFmRzKbiSxfhn/2HJRQkIwbbyL9issxWK27r+F/7z1Cny1GTktDTk/TRuTq6wkvX47Bbsc5dSqBefO0lbOxGAaHAzkjg2RtLeZ+/TDY7aRaW1GDQdRoFFQV68gRZH3/+9Tf9VNso0dT8vxzqJEIkZUrcU6detBp1X3pU42af36+k9/N2cza77jJeP0CuOw5GHElSTXJgysf5I1tb2A2mLmo/0VcP/j6/V6X30SkUiRqavCt30Ld2moi7XEkYcNtyybdnIVBMqCIFN5EGxFrkrRxfel/zii9t5VOdwhH+v7VoxMvIQTD7p/PRaMKeG1lLb84dxA/nHpkb0QnkifWP8FndZ8xa+Ys5IM0ShRC8OHODxEI8hx5DPQMxGXeP9HZ1x+W/YHZVbPxWD1kWjN59fxXkSSJhJLALJtRFIWNr36OWNdKpimbOkcrp/z6Yt7b8R4PrHyARCLJX1vvprDFiUN2YjLs/+k6rkTxqj4SuWbCJRBI+kkG4+Q1O3GFTGQZs5AlI+FUAJ8hhJTngCIbf257mHpzEyM8I0i1xhjTPIhT1HIkg4TBbsY9II/HbC+yrn0971787u5kdV9JNcnvlv6OaCrKj0b9iLK0MhbXL+blzS+TZctiStEUUmqKt7e/zdqWtQzLHMbDUx8m35mPKlRWNq3k1S2v8mndp7jNbm4feTvPbHiGDGsGr53/Glbj0depzNs1j5Sa4oKyo9vuJVFXR8dLL5N20YXYOmvPukq8spLmBx4kvGQJaZddRs5dPyHV0UH708+QbG4m4zs345o+fe+6NiEIzptH42/vRw0GMWZn0/fdf2PMOrraIj3x0vzmvY28t76eDeM/Rlr9PNxdSVg28rNPf8bn9Z9zw5Ab+O7w75Jpyzzsc8YrKqh+ewEtO7yoqh2nM59MSy5y5wdBLdFqJ2JJ4BxexICZY7B59NEsne5wnVSJF8DF/1hCPKWytSnIHy4u58aJpd0XXA+2tnktN8+7GYAnpz/J6YWnH/RYf0PH7pWdoG03Uh+qZ3LR5L2OCTZ4iQcioApyyotx5R+6OaO/rp2try9FboziMaRjkQ//U3RbooXqbB+TLj+P7IEFtG5vYMd7q1EDMax9syg7ZyQWt514KIo9y/WNq6UW1izkviX3YTQYGWkfTsZ6M3ERZ9mgTVw+8HJuGnoTHqtn9wjjNYOu4d4J9x52vHtqCDVw4bsXklSTPDX9KSYVds9qs29DCAHJ5BHXZCXq6ml7/HE811y9uw7taOiJl+a6Z5cTjSd4N3YbFI4lcdU/uenDm9jasZX7JtzHFQOvOKzzJOrqqH93PrUb/HicpXgsX++G4U92EDCEMfXNIGt4H/LH9tWL33W6b6E7iutPaP1zXPx7XR0ATmuPfzjdZlTOKIpdxWTbsjmt4NA9gfZMugD6pfejX3q//Y7Z97hvklaUyfifa40clUSK6mXb8Fc0EW8JIBIqSCCZZL5wVDDL+n/89NSfMVIdyudvzWGMNIix/oHEX6iiQtmAxWCjUPKgChVDhYFgxWaCnddpVaK0yV6MJekkm4MYwyqgbeWEx8KQmyZzVslZOG0SfNZMnjkPWdJeOxdvbWbSNV//cju98HR+aLmFyOxWPvHP44zpM5D3mD5TFIVdn2xGSaSQTTIFY8v2Gy14dO2jSJJEH3cffrX4V7x54ZsHHbU7XiRJgqMohDcXFVLwlz93Q0QnHyEE25qC3FbSAG1NMOwyXtz4IpvaN/Hw1IeZ0WfGIe+famuj8c3/Y+eaVuz2YrLt/SjLlPEl2qm1NuEeVkTRhIEUFR3+aJlOp+t6PT5TGZDr3N1c3WU58YrrTxQGycDL572MWTafEG0FZLORsinlMGX/dh9D4n4++HAxv9nwW6yyldiQGI+d8XfU1TKhimYkXwLVHCRr8kCKxvWjct4XhLY3AQIMBoz+JHlqNsYaE2AnKPwIBGbVgtVno+ORdexQvBSacokbM2mQW7GWZZLc1kZJrIBlv34Ta78sYvVebH640NT5QeZj2LFgAe0GP/aRBaiJFPJGHx7T19Nr9fOX0moPUHb1BOxZTqqiO5i7cy63Db+NC/pdwLXvX8vdn97N8+c8v199WbDJx6ZnF0EoiSllIGlUsQ7OYfDlE3pEM8n2ykYaVu0gb2xfMvrl7pWg6r5ZayhOezjBlORiMNmpKxzJs3OvZUafGQdMutRIhNjWrdT9ZyVNOyPYzDnk2Mooyx5IJBWigUYypwxm2HlnHIdHo9PpDqbHTzUu3NLMrS9px7/+vQlMKNM/zfUGiqqwrHEZ/674Nx6Lh/sm3HdECWOwyUf9ygoKT+m/1xTo9rnr8H6yHY/qot0aZugPz9o9cqckUqz+zTsUSl+vqgwkvfjdCbylKg0bKyhX+pJnzMVk0JKmcCqANyuJKcOJGk9i2BUi17T3aFYw6SNgiCD3cdMyXfDLFb9kuudMrtk5A1t2OkOuGE/N4q3E5+7CbUonpkSJqdHOWjozMSWKtzBJ+Q+noXQk2PjsIoz+FCYhI4TAdGo+w645vM7mhxJuC7BjwZeEKpoAMDgsWDKduIozyRpSeMgRzuZNtfhf3IjdqI32xZUoypnZDDxv9GFdW59qhE+3t3LLC8vYmvZjTP2n8t+ZTlY0rmD2JbPJc+ShhMKEl35O++KV1NaCbM0h3Z5Lmln7f4mkgrQb/LjG9WHIJacim3v852qdrkc46Wq8atojTP7rxwB8cOfplBekdVdoupOAoihseu1zjHYL+eP64SnRRrOEENy24DY2t2/mmoKrMC4MYlaNnH/Xd8jP3LtnXNVHG2hcto2WcAtqIkUxuWTI6dhkB6GUn2bJSxG5u2vcoqkwJoOZpEgQH+ti2DWn4Yv5ICLY+X/rMGz0kWnKpjXehNuYjkW2Ekz6iIsENoMNh9FFnWig4JLRFI4tQzYbadlaR/2y7cRqvcjBFIpJwlGejyMvndaPNpOVSscgyShC6+ZvlIwYpUOPhrYnW4l6AIOEsT2JERkx1MPAi8ex4y+LcMou2ooTKMEYkj/BwNun4umbe1jPu554wdOfVrFk/pu8Yn6Aj2bcw12Vs/jp2J9yQ9Z5dLzyChWf70TOH0ueq1R7vXS2eYi5IfeMwZROK9dHGXW64+CkS7wUVVB+/zxiSZXP7p5GSaa9G6PTncwqvZVcOedKVFTG5Y7ji9Yv6JfejxfOeQGH6etNelc1reIXn/2CYCLIvePv5ZL+l6AmFb544WNMFSE8piwaEvX8n3sJGQk34yODUREsmbSLn19+Dx/s+IC/rPwL+Y58npr+FCgSH/3pX0xQy2lNtdM0UuGy/9IWSoQ7Qmz421wKlVwMkoGEGkdRU9iMX8cTSYWwGKx7rGJTaFFaUEwCSe3cB10GYTJgLc2k9KxhmB1WOna0EKxtI9LkJ9kSxBUykm7SRpRDqQAgcBrTiCphLAYb7YMURt9y5lE9t3riBT95fR3Tt/8Po6xruaqokGJ3MY+LG9n00qekFZ1KljWPpBqnhXYcY4sZdNE4TDa9Qa1Od7yddMX1skGiX7aTTQ0Bvbhe1636e/rzzkXvkG5NJ8OawWd1n3Hnoju5c9Gd/Hr8rylLK+O1ra/x0KqHKHYV8/SMpxnoGQhoNW1jbp+BoihEWoMU5Z2Bo204CTXBgPQBzNoyi9fWv83Cdz6jJdrCqOxRVPmquH7u9eQ58qgYUcG4815m1vr3WVK/BFNVBjP7zsSR4WTin69i1+ItNK6oRLRGMEjQ7ozgHJBH4cSBFJVkEW4LsP29VcTbgvT7/+3deXBV5R3G8e8PkrCFSGICDWuIskigiCCborSgIMWtMIwMMyBKGbACokMHptXiMmWYEYSOQkWLjE4LFeoC1FFUkLEgWLayiAlEEQKREJAlhi3w9o97qAGSkEC4977h+czcSc65J5fnvPfe37y85z3n/Lozt7bsecn9rZOcAJ3PvzzLni93YjHVaH1LD04WnmDjzA9JPBjHvqTDdH3k/qvR7FeNmc0F+gN5zrm2JTxvwEygH1AIPOyc23C18nyTm8/tbg2jUtMw55jwZUd2HT7KDTfex49FR8lJyCdjxC9oXr/e1YogImHg/YgXhP6n+N6mfWS9cA9xMZVzfzuR8liSvYSnVz3NGXeG5FrJ5B/Pp2eTnky5fQrxcRW7FtKS7CVMXz+doW2GMixjGNmHsxn9yWj2F+5nSo8p9E/vT+HpQkYsG8GW/C2k1knlgRsfoFvDbmRcn8GmvE0szFrIGXeGqXdMJbaaPyebRGLEy8zuAAqAN0vpePUDxhDqeHUBZjrnulzqdS+nhp0sOsOEyZNpWP8tvihMZvyuoaQltMXh+D7hCJ2e6q/RLZEodc0dagT4YEsub6/bw7zhna9SKpHS5RXm8dmez1i1dxXtUtrxSNtHyrwlUUXkH88n61DWedf+On3mNMv3LGdR1iLW5K4BQmetnnVniY+Np+B0ASN/PpIxHcZUSoZwiNShRjNLA5aW0vF6FfjMOTc/WM4Eejrncst6zfLWsKJTJzl4ZD8NUpry1b6jrJzbh5q7O9K17t3UjU0k92QOjR7uRsP2aZexZyISLtfcoUaAfu1S6dcu8jfFlmtT/dr1GdRqEINaDar0106ulUxyo/OvBh9bPZY+aX3ok9aHQycOsXH/Rjbnb+aGejdwd7O7eWHNC7y+5XW6N+xOxwYdOXziMDVjapZ55f19BftYlLWI4W2Hl3knhG0Ht7HryC7SEtKIj4tn+8Ht7Di8gwdvfJDGdRtX2n5HiUbAnmLLOcG6Mjte5fWvcX8i2ZqQmXKEQy2b0DpnCC2S2nPs9GEOpBdy68jBlfHPiEiUqRIdL5FrVVLNJHo160WvZj/d23NSl0lsyNvAhJUTSKyZSNYPWSTEJTCw5UAGtx580cVbj506xmOfPEb2kWy25G9hVu9ZJR6mXJu7llGfjKLobNFFz63eu5o3+71JbLVYcgu7REdWAAAHVUlEQVRymbZ+Gs90e4aEuITK3+nwKekUzxIPEZjZSGAkQNOmTcv14rVr/IxGcenEnIyl4aZCatZLZfeJb2j/zADq6AbUIlWWJkSJVDF1YuswtcdUABJrJDKmwxi6pHZh3rZ53Pvuvazeu/r/2xadLWLCygl8d/Q7htw0hDW5a3h29bMs27WMh5Y+RO+FvXk7820yD2UyfsV40hLSWPCrBczoOYPnuj/Hgv4LmNpjKlsPbuW1za+RV5jHiGUjWL13NbkFlTIwFEk5QJNiy42BfSVt6Jyb45zr5JzrlJKSUtImF7lrxmg+aLCWDT/+mx9OHeKLEx/RfcYwdbpEqjiNeIlUQe1S2rF80PLz1uUcy2HcinE8vvxxpt05jfi4eN7Y+gar9q1icrfJDGg5gIS4BGb/dzbvZ79Ps4RmpNZJ5fk1z2MYKbVSmNVrFqnxqWQk/3THgYzrM/h87+fM2TyHJdlLOHjiIHPumkOrpFbh3u3Kthh43MwWEJpcf+RS87sq6t7h4/j7S0+SUfNFdif7MydPRC6fOl4i14jGdRszt89cRn08irErxgJQN64uT3Z8kgEtBwAwuv1orqtxHcm1kundtDfVrBrL9yzn3R3vMqbDGFLjS55LOanLJNbtX0f+8Xxm9Z7FzfVvDtt+XS4zmw/0BJLNLAf4IxAL4Jz7C/ABoTMadxK6nMTwys7QJKk21W97grEr2zHwzr6V/fIiEoWqxFmNIlJ+BacKeHnTy7RKbEXf5n2pFVM594HMLcjl+JnjpF+XXuG/vZYvoFp4qojpy7L4zR3pNEgo/QQIEYlO1+RZjSJSfvFx8UzsPLHSX7e00TApW+24GP7Qv02kY4hImGhyvYiIiEiYqOMlIiIiEibqeImIiIiEiTpeIiIiImGijpeIiIhImKjjJSIiIhIm6niJiIiIhIk6XiIiIiJhEnVXrjezA8B3FfiTZCD/KsW52nzODn7n9zk7VL38zZxz5bu7dJSrYA2rau+jT3zODn7n9zk7XGH9irqOV0WZ2TpfbzXic3bwO7/P2UH5qwrf28Hn/D5nB7/z+5wdrjy/DjWKiIiIhIk6XiIiIiJhUhU6XnMiHeAK+Jwd/M7vc3ZQ/qrC93bwOb/P2cHv/D5nhyvM7/0cLxERERFfVIURLxEREREveNvxMrO+ZpZpZjvNbGKk81yKmTUxsxVmtt3MtpnZuGB9kpl9bGY7gp+Jkc5aGjOrbmYbzWxpsNzczNYG2f9hZnGRzlgaM6tnZovM7OvgPejmS9ub2fjgM7PVzOabWc1obnszm2tmeWa2tdi6EtvaQv4cfI83m9ktkUseXj7VMNWvyPK5foFq2IW87HiZWXXgFeAeoA0w2MzaRDbVJRUBTznnbgK6Ar8NMk8EPnXOtQA+DZaj1Thge7HlqcBLQfYfgEcjkqp8ZgIfOudaA+0J7UfUt72ZNQLGAp2cc22B6sBDRHfbzwP6XrCutLa+B2gRPEYCs8OUMaI8rGGqX5HlZf0C1bASOee8ewDdgI+KLU8CJkU6VwX34X3gLiATSA3WpQKZkc5WSt7GwYftl8BSwAhdQC6mpPckmh5AAvAtwZzGYuujvu2BRsAeIAmICdq+T7S3PZAGbL1UWwOvAoNL2q4qP3yvYapfYc3ubf0KsqmGXfDwcsSLn97Ic3KCdV4wszSgA7AWaOCcywUIftaPXLIyzQB+B5wNlq8HDjvnioLlaH4P0oEDwBvBoYbXzawOHrS9c24v8CKwG8gFjgDr8aftzymtrb3+Ll8Bb/db9SvsvK1foBpWEl87XlbCOi9OzzSzeOCfwBPOuaORzlMeZtYfyHPOrS++uoRNo/U9iAFuAWY75zoAPxKlw/IXCuYR3A80BxoCdQgNbV8oWtv+Unz6HFUmL/db9SsivK1foBpWEl87XjlAk2LLjYF9EcpSbmYWS6ho/c05906wer+ZpQbPpwJ5kcpXhtuA+8xsF7CA0HD9DKCemcUE20Tze5AD5Djn1gbLiwgVMh/avjfwrXPugHPuNPAO0B1/2v6c0tray+9yJfBuv1W/Isbn+gWqYRfxteP1H6BFcFZEHKGJeosjnKlMZmbAX4HtzrnpxZ5aDAwLfh9GaO5EVHHOTXLONXbOpRFq6+XOuSHACmBgsFlUZgdwzn0P7DGzVsGqXsBXeND2hIbnu5pZ7eAzdC67F21fTGltvRgYGpwZ1BU4cm44v4rzqoapfkWO5/ULVMMuFukJbFcw8a0fkAVkA7+PdJ5y5L2d0PDjZmBT8OhHaK7Bp8CO4GdSpLNeYj96AkuD39OBL4GdwEKgRqTzlZH7ZmBd0P7vAYm+tD3wLPA1sBV4C6gRzW0PzCc0l+M0of8NPlpaWxMapn8l+B5vIXTmU8T3IUzt5E0NU/2KeG5v61eQXzWs2ENXrhcREREJE18PNYqIiIh4Rx0vERERkTBRx0tEREQkTNTxEhEREQkTdbxEREREwkQdLxEREZEwUcdLREREJEzU8RIREREJk/8BaOkgCLbF8owAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"with Hooks(learn.model, append_stats) as hooks:\n",
" run.fit(1, learn)\n",
" fig,(ax0,ax1) = plt.subplots(1,2, figsize=(10,4))\n",
" for h in hooks[:-1]:\n",
" ms,ss = h.stats\n",
" ax0.plot(ms[:10])\n",
" ax1.plot(ss[:10])\n",
" plt.legend(range(6));\n",
" \n",
" fig,(ax0,ax1) = plt.subplots(1,2, figsize=(10,4))\n",
" for h in hooks[:-1]:\n",
" ms,ss = h.stats\n",
" ax0.plot(ms)\n",
" ax1.plot(ss)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 1.0, conv_layer_learn, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.253928984375, tensor(0.9200, device='cuda:0')]\n",
"valid: [0.165762109375, tensor(0.9478, device='cuda:0')]\n",
"train: [0.088923076171875, tensor(0.9723, device='cuda:0')]\n",
"valid: [0.3850069091796875, tensor(0.9094, device='cuda:0')]\n",
"train: [0.0636737890625, tensor(0.9805, device='cuda:0')]\n",
"valid: [0.07958184814453124, tensor(0.9754, device='cuda:0')]\n",
"CPU times: user 3.47 s, sys: 208 ms, total: 3.68 s\n",
"Wall time: 3.74 s\n"
]
}
],
"source": [
"%time run.fit(3, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With scheduler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's add the usual warm-up/annealing."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"sched = combine_scheds([0.3, 0.7], [sched_lin(0.6, 2.), sched_lin(2., 0.1)]) "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.9, conv_layer_gen, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched)])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.301419296875, tensor(0.9094, device='cuda:0')]\n",
"valid: [0.2026967529296875, tensor(0.9417, device='cuda:0')]\n",
"train: [0.10052498046875, tensor(0.9687, device='cuda:0')]\n",
"valid: [0.1126065673828125, tensor(0.9666, device='cuda:0')]\n",
"train: [0.070996572265625, tensor(0.9776, device='cuda:0')]\n",
"valid: [0.13142176513671874, tensor(0.9617, device='cuda:0')]\n",
"train: [0.0482486083984375, tensor(0.9854, device='cuda:0')]\n",
"valid: [0.07715589599609375, tensor(0.9771, device='cuda:0')]\n",
"train: [0.0344202392578125, tensor(0.9894, device='cuda:0')]\n",
"valid: [0.06021231689453125, tensor(0.9828, device='cuda:0')]\n",
"train: [0.0255533837890625, tensor(0.9928, device='cuda:0')]\n",
"valid: [0.055064190673828124, tensor(0.9840, device='cuda:0')]\n",
"train: [0.01967509033203125, tensor(0.9944, device='cuda:0')]\n",
"valid: [0.04997901000976562, tensor(0.9847, device='cuda:0')]\n",
"train: [0.016732589111328126, tensor(0.9958, device='cuda:0')]\n",
"valid: [0.047377349853515625, tensor(0.9860, device='cuda:0')]\n"
]
}
],
"source": [
"run.fit(8, learn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.9, conv_layer_learn, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched)])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.3009943359375, tensor(0.9079, device='cuda:0')]\n",
"valid: [0.2146206298828125, tensor(0.9312, device='cuda:0')]\n",
"train: [0.100962646484375, tensor(0.9694, device='cuda:0')]\n",
"valid: [0.3427990234375, tensor(0.8986, device='cuda:0')]\n",
"train: [0.20606509765625, tensor(0.9405, device='cuda:0')]\n",
"valid: [0.09719484252929687, tensor(0.9709, device='cuda:0')]\n",
"train: [0.06491736328125, tensor(0.9801, device='cuda:0')]\n",
"valid: [0.17026732177734374, tensor(0.9478, device='cuda:0')]\n",
"train: [0.045457685546875, tensor(0.9863, device='cuda:0')]\n",
"valid: [0.0574800048828125, tensor(0.9833, device='cuda:0')]\n",
"train: [0.0349907958984375, tensor(0.9900, device='cuda:0')]\n",
"valid: [0.05554625244140625, tensor(0.9841, device='cuda:0')]\n",
"train: [0.02864280029296875, tensor(0.9917, device='cuda:0')]\n",
"valid: [0.045075537109375, tensor(0.9876, device='cuda:0')]\n",
"train: [0.02446511474609375, tensor(0.9931, device='cuda:0')]\n",
"valid: [0.042788345336914065, tensor(0.9875, device='cuda:0')]\n"
]
}
],
"source": [
"run.fit(8, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More norms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Layer norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From [the paper](https://arxiv.org/abs/1607.06450): \"*batch normalization cannot be applied to online learning tasks or to extremely large distributed models where the minibatches have to be small*\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"General equation for a norm layer with learnable affine:\n",
"\n",
"$$y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta$$\n",
"\n",
"The difference with BatchNorm is\n",
"1. we don't keep a moving average\n",
"2. we don't average over the batches dimension but over the hidden dimension, so it's independent of the batch size"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"class LayerNorm(nn.Module):\n",
" __constants__ = ['eps']\n",
" def __init__(self, eps=1e-5):\n",
" super().__init__()\n",
" self.eps = eps\n",
" self.mult = nn.Parameter(tensor(1.))\n",
" self.add = nn.Parameter(tensor(0.))\n",
"\n",
" def forward(self, x):\n",
" m = x.mean((1,2,3), keepdim=True)\n",
" v = x.var ((1,2,3), keepdim=True)\n",
" x = (x-m) / ((v+self.eps).sqrt())\n",
" return x*self.mult + self.add"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def conv_ln(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=True),\n",
" GeneralRelu(**kwargs)]\n",
" if bn: layers.append(LayerNorm())\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.8, conv_ln, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [nan, tensor(0.1355, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"train: [nan, tensor(0.0986, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"train: [nan, tensor(0.0986, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"CPU times: user 3.84 s, sys: 221 ms, total: 4.07 s\n",
"Wall time: 4.09 s\n"
]
}
],
"source": [
"%time run.fit(3, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Thought experiment*: can this distinguish foggy days from sunny days (assuming you're using it before the first conv)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instance norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From [the paper](https://arxiv.org/abs/1607.08022): "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The key difference between **contrast** and batch normalization is that the latter applies the normalization to a whole batch of images instead for single ones:\n",
"\n",
"\\begin{equation}\\label{eq:bnorm}\n",
" y_{tijk} = \\frac{x_{tijk} - \\mu_{i}}{\\sqrt{\\sigma_i^2 + \\epsilon}},\n",
" \\quad\n",
" \\mu_i = \\frac{1}{HWT}\\sum_{t=1}^T\\sum_{l=1}^W \\sum_{m=1}^H x_{tilm},\n",
" \\quad\n",
" \\sigma_i^2 = \\frac{1}{HWT}\\sum_{t=1}^T\\sum_{l=1}^W \\sum_{m=1}^H (x_{tilm} - mu_i)^2.\n",
"\\end{equation}\n",
"\n",
"In order to combine the effects of instance-specific normalization and batch normalization, we propose to replace the latter by the *instance normalization* (also known as *contrast normalization*) layer:\n",
"\n",
"\\begin{equation}\\label{eq:inorm}\n",
" y_{tijk} = \\frac{x_{tijk} - \\mu_{ti}}{\\sqrt{\\sigma_{ti}^2 + \\epsilon}},\n",
" \\quad\n",
" \\mu_{ti} = \\frac{1}{HW}\\sum_{l=1}^W \\sum_{m=1}^H x_{tilm},\n",
" \\quad\n",
" \\sigma_{ti}^2 = \\frac{1}{HW}\\sum_{l=1}^W \\sum_{m=1}^H (x_{tilm} - mu_{ti})^2.\n",
"\\end{equation}"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"class InstanceNorm(nn.Module):\n",
" __constants__ = ['eps']\n",
" def __init__(self, nf, eps=1e-0):\n",
" super().__init__()\n",
" self.eps = eps\n",
" self.mults = nn.Parameter(torch.ones (nf,1,1))\n",
" self.adds = nn.Parameter(torch.zeros(nf,1,1))\n",
"\n",
" def forward(self, x):\n",
" m = x.mean((2,3), keepdim=True)\n",
" v = x.var ((2,3), keepdim=True)\n",
" res = (x-m) / ((v+self.eps).sqrt())\n",
" return res*self.mults + self.adds"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def conv_in(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=True),\n",
" GeneralRelu(**kwargs)]\n",
" if bn: layers.append(InstanceNorm(nf))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.1, conv_in, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [nan, tensor(0.0986, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"train: [nan, tensor(0.0986, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"train: [nan, tensor(0.0986, device='cuda:0')]\n",
"valid: [nan, tensor(0.0991, device='cuda:0')]\n",
"CPU times: user 3.78 s, sys: 233 ms, total: 4.02 s\n",
"Wall time: 4.05 s\n"
]
}
],
"source": [
"%time run.fit(3, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Question*: why can't this classify anything?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lost in all those norms? The authors from the [group norm paper](https://arxiv.org/pdf/1803.08494.pdf) have you covered:\n",
"\n",
"![Various norms](images/norms.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Group norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*From the PyTorch docs:*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`GroupNorm(num_groups, num_channels, eps=1e-5, affine=True)`\n",
"\n",
"The input channels are separated into `num_groups` groups, each containing\n",
"``num_channels / num_groups`` channels. The mean and standard-deviation are calculated\n",
"separately over the each group. $\\gamma$ and $\\beta$ are learnable\n",
"per-channel affine transform parameter vectors of size `num_channels` if\n",
"`affine` is `True`.\n",
"\n",
"This layer uses statistics computed from input data in both training and\n",
"evaluation modes.\n",
"\n",
"Args:\n",
"- `num_groups (int)`: number of groups to separate the channels into\n",
"- `num_channels (int)`: number of channels expected in input\n",
"- `eps`: a value added to the denominator for numerical stability. Default: `1e-5`\n",
"- `affine`: a boolean value that when set to ``True``, this module\n",
" has learnable per-channel affine parameters initialized to ones (for weights)\n",
" and zeros (for biases). Default: ``True``.\n",
"\n",
"Shape:\n",
"- Input: `(N, num_channels, *)`\n",
"- Output: `(N, num_channels, *)` (same shape as input)\n",
"\n",
"Examples::\n",
"\n",
" >>> input = torch.randn(20, 6, 10, 10)\n",
" >>> # Separate 6 channels into 3 groups\n",
" >>> m = nn.GroupNorm(3, 6)\n",
" >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)\n",
" >>> m = nn.GroupNorm(6, 6)\n",
" >>> # Put all 6 channels into a single group (equivalent with LayerNorm)\n",
" >>> m = nn.GroupNorm(1, 6)\n",
" >>> # Activating the module\n",
" >>> output = m(input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fix small batch sizes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What's the problem?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we compute the statistics (mean and std) for a BatchNorm Layer on a small batch, it is possible that we get a standard deviation very close to 0. because there aren't many samples (the variance of one thing is 0. since it's equal to its mean)."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"data = DataBunch(*get_dls(train_ds, valid_ds, 2), c)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def conv_layer(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=not bn),\n",
" GeneralRelu(**kwargs)]\n",
" if bn: layers.append(nn.BatchNorm2d(nf, eps=1e-5, momentum=0.1))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.4, conv_layer, cbs=cbfs)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [2.33690859375, tensor(0.1798, device='cuda:0')]\n",
"valid: [21556.2768, tensor(0.2378, device='cuda:0')]\n",
"CPU times: user 1min 23s, sys: 3.5 s, total: 1min 27s\n",
"Wall time: 1min 29s\n"
]
}
],
"source": [
"%time run.fit(1, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running Batch Norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To solve this problem we introduce a Running BatchNorm that uses smoother running mean and variance for the mean and std."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"class RunningBatchNorm(nn.Module):\n",
" def __init__(self, nf, mom=0.1, eps=1e-5):\n",
" super().__init__()\n",
" self.mom,self.eps = mom,eps\n",
" self.mults = nn.Parameter(torch.ones (nf,1,1))\n",
" self.adds = nn.Parameter(torch.zeros(nf,1,1))\n",
" self.register_buffer('sums', torch.zeros(1,nf,1,1))\n",
" self.register_buffer('sqrs', torch.zeros(1,nf,1,1))\n",
" self.register_buffer('batch', tensor(0.))\n",
" self.register_buffer('count', tensor(0.))\n",
" self.register_buffer('step', tensor(0.))\n",
" self.register_buffer('dbias', tensor(0.))\n",
"\n",
" def update_stats(self, x):\n",
" bs,nc,*_ = x.shape\n",
" self.sums.detach_()\n",
" self.sqrs.detach_()\n",
" dims = (0,2,3)\n",
" s = x.sum(dims, keepdim=True)\n",
" ss = (x*x).sum(dims, keepdim=True)\n",
" c = self.count.new_tensor(x.numel()/nc)\n",
" mom1 = 1 - (1-self.mom)/math.sqrt(bs-1)\n",
" self.mom1 = self.dbias.new_tensor(mom1)\n",
" self.sums.lerp_(s, self.mom1)\n",
" self.sqrs.lerp_(ss, self.mom1)\n",
" self.count.lerp_(c, self.mom1)\n",
" self.dbias = self.dbias*(1-self.mom1) + self.mom1\n",
" self.batch += bs\n",
" self.step += 1\n",
"\n",
" def forward(self, x):\n",
" if self.training: self.update_stats(x)\n",
" sums = self.sums\n",
" sqrs = self.sqrs\n",
" c = self.count\n",
" if self.step<100:\n",
" sums = sums / self.dbias\n",
" sqrs = sqrs / self.dbias\n",
" c = c / self.dbias\n",
" means = sums/c\n",
" vars = (sqrs/c).sub_(means*means)\n",
" if bool(self.batch < 20): vars.clamp_min_(0.01)\n",
" x = (x-means).div_((vars.add_(self.eps)).sqrt())\n",
" return x.mul_(self.mults).add_(self.adds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NB: the calculation of `self.dbias` in the version in the lesson video was incorrect. The correct version is in the cell above. Also, we changed how we calculated `self.mom1` to something that it more mathematically appropriate. These two changes improved the accuracy from 91% (in the video) to 97%+ (shown below)!"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"def conv_rbn_gen(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=not bn),\n",
" GeneralRelu(**kwargs)]\n",
" if bn: layers.append(RunningBatchNorm(nf))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def conv_rbn_learn(ni, nf, ks=3, stride=2, bn=True, **kwargs):\n",
" layers = [nn.Conv2d(ni, nf, ks, padding=ks//2, stride=stride, bias=not bn),\n",
" LearnedRelu(**kwargs)]\n",
" if bn: layers.append(RunningBatchNorm(nf))\n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What can we do in a single epoch?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's see with a decent batch size what result we can get."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"data = DataBunch(*get_dls(train_ds, valid_ds, 32), c)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.8, conv_rbn_gen, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched_lin(1., 0.2))])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.16527302734375, tensor(0.9498, device='cuda:0')]\n",
"valid: [0.07317039794921874, tensor(0.9797, device='cuda:0')]\n",
"CPU times: user 13.6 s, sys: 558 ms, total: 14.2 s\n",
"Wall time: 14.5 s\n"
]
}
],
"source": [
"%time run.fit(1, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try LearnedRelu"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.8, conv_rbn_learn, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched_lin(1., 0.2))])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.16582439453125, tensor(0.9488, device='cuda:0')]\n",
"valid: [0.06944829711914062, tensor(0.9790, device='cuda:0')]\n",
"CPU times: user 15.9 s, sys: 858 ms, total: 16.8 s\n",
"Wall time: 17.1 s\n"
]
}
],
"source": [
"%time run.fit(1, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simplified RunningBatchNorm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It turns out we don't actually need to debias - because, for instance, dividing a debiased sum by a debiased count is the same as dividing a *biased* sum by a *biased* count! So we can remove all the debiasing stuff and end up with a simpler class. Also, we should save `eps` as a buffer since it impacts the calculation. (Thanks to Stas Bekman for noticing these.) Also we can slightly change the final calculation in `forward` with one that uses `factor` and `offset` to reduce the amount of broadcasting required. (Thanks to Tom Viehmann for this suggestion.)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"class RunningBatchNorm(nn.Module):\n",
" def __init__(self, nf, mom=0.1, eps=1e-5):\n",
" super().__init__()\n",
" self.mom, self.eps = mom, eps\n",
" self.mults = nn.Parameter(torch.ones (nf,1,1))\n",
" self.adds = nn.Parameter(torch.zeros(nf,1,1))\n",
" self.register_buffer('sums', torch.zeros(1,nf,1,1))\n",
" self.register_buffer('sqrs', torch.zeros(1,nf,1,1))\n",
" self.register_buffer('count', tensor(0.))\n",
" self.register_buffer('factor', tensor(0.))\n",
" self.register_buffer('offset', tensor(0.))\n",
" self.batch = 0\n",
" \n",
" def update_stats(self, x):\n",
" bs,nc,*_ = x.shape\n",
" self.sums.detach_()\n",
" self.sqrs.detach_()\n",
" dims = (0,2,3)\n",
" s = x .sum(dims, keepdim=True)\n",
" ss = (x*x).sum(dims, keepdim=True)\n",
" c = s.new_tensor(x.numel()/nc)\n",
" mom1 = s.new_tensor(1 - (1-self.mom)/math.sqrt(bs-1))\n",
" self.sums .lerp_(s , mom1)\n",
" self.sqrs .lerp_(ss, mom1)\n",
" self.count.lerp_(c , mom1)\n",
" self.batch += bs\n",
" means = self.sums/self.count\n",
" varns = (self.sqrs/self.count).sub_(means*means)\n",
" if bool(self.batch < 20): varns.clamp_min_(0.01)\n",
" self.factor = self.mults / (varns+self.eps).sqrt()\n",
" self.offset = self.adds - means*self.factor\n",
" \n",
" def forward(self, x):\n",
" if self.training: self.update_stats(x)\n",
" return x*self.factor + self.offset"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.8, conv_rbn_gen, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched_lin(1., 0.2))])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.157169111328125, tensor(0.9513, device='cuda:0')]\n",
"valid: [0.10296422119140625, tensor(0.9797, device='cuda:0')]\n",
"CPU times: user 12.1 s, sys: 585 ms, total: 12.7 s\n",
"Wall time: 12.9 s\n"
]
}
],
"source": [
"%time run.fit(1, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try LearnedRelu"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"learn,run = get_learn_run(nfs, data, 0.8, conv_rbn_learn, cbs=cbfs\n",
" +[partial(ParamScheduler,'lr', sched_lin(1., 0.2))])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: [0.160661044921875, tensor(0.9506, device='cuda:0')]\n",
"valid: [0.06689930419921875, tensor(0.9810, device='cuda:0')]\n",
"CPU times: user 14.3 s, sys: 711 ms, total: 15 s\n",
"Wall time: 15.3 s\n"
]
}
],
"source": [
"%time run.fit(1, learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nb_auto_export()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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