abide2_fader.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import copy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pydicom\n",
    "import nibabel as nib\n",
    "\n",
    "from scipy import ndimage as nd\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torchvision.transforms as transforms\n",
    "import torch.utils.data as data\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold, RepeatedStratifiedKFold, ShuffleSplit\n",
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "from sklearn.metrics import roc_auc_score, r2_score, accuracy_score\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%pylab inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              total        used        free      shared  buff/cache   available\n",
      "Mem:           125G         61G         56G        294M        7.7G         63G\n",
      "Swap:           14G         14G        298M\n"
     ]
    }
   ],
   "source": [
    "!free -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Oct  8 22:23:03 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.64       Driver Version: 440.64       CUDA Version: 10.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla P100-PCIE...  Off  | 00000000:83:00.0 Off |                    0 |\n",
      "| N/A   30C    P0    27W / 250W |     10MiB / 16280MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla P100-PCIE...  Off  | 00000000:89:00.0 Off |                    0 |\n",
      "| N/A   61C    P0   168W / 250W |  13999MiB / 16280MiB |    100%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  Tesla P100-PCIE...  Off  | 00000000:8E:00.0 Off |                    0 |\n",
      "| N/A   25C    P0    33W / 250W |   7787MiB / 16280MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  Tesla P100-PCIE...  Off  | 00000000:91:00.0 Off |                    0 |\n",
      "| N/A   25C    P0    26W / 250W |     10MiB / 16280MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch version: 1.5.0\n",
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "print(\"Torch version:\", torch.__version__)\n",
    "if use_cuda:\n",
    "    print(\"Using GPU\")\n",
    "else:\n",
    "    print(\"Not using GPU\")\n",
    "\n",
    "device = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.configs & utils\n",
    "from utils import *\n",
    "\n",
    "# 2.models\n",
    "from models import Flatten, Reshape\n",
    "\n",
    "# 3.loss\n",
    "# from loss import compute_preds, compute_probs\n",
    "\n",
    "# 4.dataloader & transforms\n",
    "from data_loader_abide import ABIDEMRI, CPACMRI\n",
    "from transforms import *\n",
    "\n",
    "torch.backends.cudnn.enabled = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import rgb2hex\n",
    "\n",
    "def get_colors(cmap, breaks):\n",
    "    return [rgb2hex(cmap(bb)) for bb in breaks]\n",
    "\n",
    "def display_brain(dataset, i):\n",
    "    n = dataset[i]\n",
    "    print(n.shape)\n",
    "    if len(n.shape) == 4:\n",
    "        n = n[0]\n",
    "    elif len(n.shape) == 5:\n",
    "        n = n[0, 0]\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.subplot(131)\n",
    "    plt.imshow(n[n.shape[0] // 2, :, :])\n",
    "    plt.subplot(132)\n",
    "    plt.imshow(n[:, n.shape[1] // 2, :])\n",
    "    plt.subplot(133)\n",
    "    plt.imshow(n[:, :, n.shape[2] // 2])\n",
    "    plt.show()\n",
    "    \n",
    "def display_brain_img(img, vmin=None, vmax=None, cmap=\"viridis\", site=\"\"):\n",
    "    n = img[0]\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(131)\n",
    "    plt.imshow(n[n.shape[0] // 2, :, :].T[::-1], vmin=vmin, vmax=vmax, cmap=cmap)\n",
    "    plt.ylabel(site, fontdict={\"size\" : 20})\n",
    "    plt.yticks([], [])\n",
    "    plt.xticks([], [])\n",
    "    plt.subplot(132)\n",
    "    plt.imshow(n[:, n.shape[1] // 2, :].T[::-1], vmin=vmin, vmax=vmax, cmap=cmap)\n",
    "    plt.axis('off')\n",
    "    plt.subplot(133)\n",
    "    plt.imshow(n[:, :, n.shape[2] // 2], vmin=vmin, vmax=vmax, cmap=cmap)\n",
    "    plt.axis('off')\n",
    "    plt.subplots_adjust(wspace=0, hspace=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder3d(nn.Module):\n",
    "    def __init__(self, \n",
    "                 img_shape=(1, 64, 64, 64), \n",
    "                 conv_model=[32, 64, 128, 256],\n",
    "                 lrelu_slope=0.2,\n",
    "                 noises=[0, 0, 0, 0]):\n",
    "        super(self.__class__, self).__init__()\n",
    "        img_shape = np.array(img_shape)\n",
    "        self.n_layers = len(conv_model)\n",
    "        \n",
    "        self.model = []\n",
    "        C_in = img_shape[0]\n",
    "        for i, C_out in enumerate(conv_model):\n",
    "            self.model += [\n",
    "                nn.Dropout3d(noises[i]),\n",
    "                nn.Conv3d(C_in, C_out, 4, 2, 1, bias=False),\n",
    "                nn.BatchNorm3d(C_out),\n",
    "                nn.LeakyReLU(lrelu_slope, inplace=True),\n",
    "            ]\n",
    "            C_in = C_out\n",
    "            \n",
    "        print(C_out, \"activations of size:\", \n",
    "              list(img_shape[1:] // (2 ** self.n_layers)))\n",
    "        self.model = nn.Sequential(*self.model)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder3d(nn.Module):\n",
    "    def __init__(self, \n",
    "                 latent_shape=(256, 4, 4, 4), \n",
    "#                  img_shape=(1, 64, 64, 64),\n",
    "                 conv_model=[128, 64, 32, 1]):\n",
    "        super(self.__class__, self).__init__()\n",
    "        self.latent_shape = np.array(latent_shape)\n",
    "#         img_shape = np.array(img_shape)\n",
    "        self.n_layers = len(conv_model)\n",
    "        \n",
    "        self.model = []\n",
    "        C_in = latent_shape[0]\n",
    "        for C_out in conv_model:\n",
    "            self.model += [\n",
    "                nn.ConvTranspose3d(C_in, C_out, 4, 2, 1, bias=False),\n",
    "                nn.BatchNorm3d(C_out),\n",
    "                nn.ReLU(inplace=True),\n",
    "            ]\n",
    "            C_in = C_out\n",
    "            \n",
    "        print(C_out, \"activations of size:\", \n",
    "              list(self.latent_shape[1:] * (2 ** self.n_layers)))\n",
    "        self.model = nn.Sequential(*self.model)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoder + decoder or aenc architecture\n",
    "\n",
    "class Autoencoder3d(nn.Module):\n",
    "    def __init__(self, \n",
    "                 img_shape=(1, 64, 64, 64), \n",
    "                 enc_conv_model=[32, 64, 128, 256],\n",
    "                 lrelu_slope=0.2,\n",
    "                 noises=[0, 0, 0, 0]):\n",
    "        super(self.__class__, self).__init__()\n",
    "        self.img_shape = np.array(img_shape)\n",
    "        self.enc_conv_model = enc_conv_model\n",
    "        self.dec_conv_model = enc_conv_model[-2::-1] + [img_shape[0]]\n",
    "        self.n_layers = len(enc_conv_model)\n",
    "        self.latent_shape = np.array([enc_conv_model[-1]] + list(self.img_shape[1:] // (2 ** self.n_layers)))\n",
    "        self.lrelu_slope = lrelu_slope\n",
    "        \n",
    "        self.encoder = nn.Sequential(\n",
    "            Encoder3d(img_shape,\n",
    "                      self.enc_conv_model,\n",
    "                      lrelu_slope,\n",
    "                      noises),\n",
    "            nn.Conv3d(self.latent_shape[0], self.latent_shape[0], 3, 1, 1),\n",
    "        )\n",
    "        \n",
    "        self.decoder = nn.Sequential(\n",
    "            Decoder3d(self.latent_shape, \n",
    "                      self.dec_conv_model),\n",
    "            nn.Conv3d(self.dec_conv_model[-1], 1, 3, 1, 1, bias=True),\n",
    "#             nn.ReLU(inplace=True)\n",
    "#             nn.Sigmoid() # -> normalize images to (0, 1)\n",
    "        )\n",
    "        \n",
    "    def encode(self, x):\n",
    "        z = self.encoder(x) # z repr is 4d tensor \n",
    "        return z\n",
    "    \n",
    "    def decode(self, z):\n",
    "        x_rec = self.decoder(z)\n",
    "        return x_rec\n",
    "\n",
    "    def forward(self, x):        \n",
    "        z = self.encode(x)\n",
    "        x_rec = self.decode(z)\n",
    "        return x_rec, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_enc(train_loader, val_loader, args,\n",
    "              AE, AE_opt, device,\n",
    "              AE_crit=nn.MSELoss()):\n",
    "    \n",
    "    def plot_results(epoch):\n",
    "        clear_output(True)\n",
    "        print(\"EPOCH {}\".format(epoch))\n",
    "        \n",
    "        plt.figure(figsize=(15, 6))\n",
    "        plt.subplot(111)\n",
    "        plt.plot(train_stats[\"rec_loss\"], label=\"train_rec_loss\")\n",
    "        plt.plot(train_stats[\"val rec_loss\"], label=\"val rec_loss\")\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"Train recon\")\n",
    "        i = np.random.choice(len(train_loader.dataset))\n",
    "        img = train_loader.dataset[i]\n",
    "        display_brain_img(img.detach().numpy(), vmin=0., vmax=1.)\n",
    "        img_recon, _ = AE(img.unsqueeze(dim=0).to(device))\n",
    "        display_brain_img(img_recon.cpu().detach().numpy()[0], vmin=0., vmax=1.)\n",
    "        print(\"Val recon\")\n",
    "        i = np.random.choice(len(val_loader.dataset))\n",
    "        img = val_loader.dataset[i]\n",
    "        display_brain_img(img.detach().numpy(), vmin=0., vmax=1.)\n",
    "        img_recon, _ = AE(img.unsqueeze(dim=0).to(device))\n",
    "        display_brain_img(img_recon.cpu().detach().numpy()[0], vmin=0., vmax=1.)\n",
    "        \n",
    "        print(\"  rec loss (in-iteration): \\t{:.6f}\".format(train_stats[\"rec_loss\"][-1]))\n",
    "        print(\"  val rec loss: \\t{:.6f}\".format(train_stats[\"val rec_loss\"][-1]))\n",
    "        \n",
    "    train_stats = {\n",
    "        \"rec_loss\" : [],\n",
    "        \"val rec_loss\" : [],\n",
    "    }\n",
    "    \n",
    "    for epoch in range(args[\"n_epochs\"]):\n",
    "        print(\"TRAIN EPOCH {}...\".format(epoch))\n",
    "        epoch_train_stats = {\n",
    "            \"rec_loss\" : [],\n",
    "            \"val rec_loss\" : [],\n",
    "        }\n",
    "        \n",
    "        AE.train(True)\n",
    "        # here train_loader returns x, y, a \n",
    "        for inputs_batch in tqdm(train_loader):\n",
    "            inputs_batch = inputs_batch.to(device)\n",
    "            \n",
    "            # train AE\n",
    "            recons_batch, latents_batch = AE(inputs_batch)\n",
    "            rec_loss = AE_crit(recons_batch, inputs_batch)\n",
    "            AE_opt.zero_grad()\n",
    "            rec_loss.backward()\n",
    "            AE_opt.step()\n",
    "            epoch_train_stats[\"rec_loss\"].append(rec_loss.item())\n",
    "            \n",
    "        \n",
    "        AE.train(False)\n",
    "        for inputs_batch in tqdm(val_loader):\n",
    "            inputs_batch = inputs_batch.to(device)\n",
    "\n",
    "            recons_batch, latents_batch = AE(inputs_batch)\n",
    "            rec_loss = AE_crit(recons_batch, inputs_batch)\n",
    "            epoch_train_stats[\"val rec_loss\"].append(rec_loss.item())\n",
    "            \n",
    "        # add mean stats\n",
    "        for stat_name in epoch_train_stats:\n",
    "            if stat_name in train_stats:\n",
    "                train_stats[stat_name].append(np.mean(epoch_train_stats[stat_name]))\n",
    "        \n",
    "        plot_results(epoch)\n",
    "        \n",
    "    return train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LatentDiscriminator(nn.Module):\n",
    "    def __init__(self,\n",
    "                 latent_shape, # takes decoder.latent_shape\n",
    "                 n_attrs_outputs,\n",
    "                 conv_model=[512],\n",
    "                 fc_model=[],\n",
    "                 dropout=0,\n",
    "                 batchnorm=True):\n",
    "        super().__init__()\n",
    "        self.latent_shape = np.array(latent_shape)\n",
    "        self.n_attrs_outputs = n_attrs_outputs\n",
    "        self.n_outputs = sum(n_attrs_outputs)\n",
    "        self.n_layers = len(conv_model)\n",
    "        \n",
    "        self.model = []\n",
    "        C_in = latent_shape[0]\n",
    "        for C_out in conv_model:\n",
    "            self.model += [\n",
    "                nn.Conv3d(C_in, C_out, 3, 2, 1, bias=False),\n",
    "                nn.BatchNorm3d(C_out),\n",
    "                nn.ReLU(inplace=True),\n",
    "            ]\n",
    "            C_in = C_out\n",
    "            \n",
    "        self.model += [\n",
    "            nn.AdaptiveAvgPool3d((1, 1, 1)),\n",
    "            Flatten()\n",
    "        ]\n",
    "        \n",
    "        n_in = C_in\n",
    "        for n_out in fc_model:\n",
    "            self.model += [nn.Dropout(dropout),\n",
    "                           nn.Linear(n_in, n_out)]\n",
    "            if batchnorm:\n",
    "                self.model += [nn.BatchNorm1d(n_out)]\n",
    "            self.model += [\n",
    "                nn.ReLU(True),\n",
    "            ]\n",
    "            n_in = n_out\n",
    "        self.model += [nn.Dropout(dropout),\n",
    "                       nn.Linear(n_in, self.n_outputs)]\n",
    "        self.model = nn.Sequential(*self.model)\n",
    "\n",
    "    def forward(self, z):\n",
    "        z = self.model(z)\n",
    "        return z\n",
    "    \n",
    "###############################################################################    \n",
    "    \n",
    "class FaderDecoder3d(nn.Module):\n",
    "    def __init__(self, \n",
    "                 latent_shape=(256, 4, 4, 4), \n",
    "#                  img_shape=(1, 64, 64, 64),\n",
    "                 conv_model=[128, 64, 32, 1],\n",
    "                 n_attrs_outputs=[],\n",
    "                ):\n",
    "        super(self.__class__, self).__init__()\n",
    "        self.latent_shape = np.array(latent_shape)\n",
    "#         img_shape = np.array(img_shape)\n",
    "        self.n_layers = len(conv_model)\n",
    "        self.n_attrs_outputs = n_attrs_outputs # list of n_outputs for each attr\n",
    "        self.n_attrs_channels = sum(n_attrs_outputs)\n",
    "        \n",
    "        self.model = []\n",
    "        C_in = latent_shape[0]\n",
    "        for C_out in conv_model:\n",
    "            self.model += [\n",
    "                nn.ConvTranspose3d(C_in + self.n_attrs_channels, C_out, 4, 2, 1, bias=False),\n",
    "                nn.BatchNorm3d(C_out),\n",
    "                nn.ReLU(inplace=True),\n",
    "            ]\n",
    "            C_in = C_out\n",
    "            \n",
    "        print(C_out, \"activations of size:\", \n",
    "              list(self.latent_shape[1:] * (2 ** self.n_layers)))\n",
    "        self.model = nn.Sequential(*self.model)\n",
    "    \n",
    "    def forward(self, x, attrs):\n",
    "        for i in range(len(self.model)):\n",
    "            # we should add attrs channels to each x before apply conv\n",
    "            # assume attrs here are already one-hot-encoded vectors\n",
    "            # so just create 1s-channels and multiply them object-wise\n",
    "            if i % 3 == 0:\n",
    "                bs, C, h, w, d = x.size()\n",
    "                attrs_C = torch.ones(bs, self.n_attrs_channels, h, w, d).to(x)\n",
    "                attrs_C *= attrs.view(bs, self.n_attrs_channels, 1, 1, 1).float()\n",
    "                x = torch.cat((x, attrs_C), dim=1) # channelwise\n",
    "            x = self.model[i](x)\n",
    "        return x\n",
    "    \n",
    "###############################################################################        \n",
    "\n",
    "# encoder + decoder or aenc architecture\n",
    "\n",
    "class FaderNet3d(nn.Module):\n",
    "    def __init__(self, \n",
    "                 img_shape=(1, 64, 64, 64), \n",
    "                 enc_conv_model=[32, 64, 128, 256],\n",
    "                 lrelu_slope=0.2,\n",
    "                 noises=[0, 0, 0, 0, 0],\n",
    "                 n_attrs_outputs=[],\n",
    "                ):\n",
    "        super(self.__class__, self).__init__()\n",
    "        self.img_shape = np.array(img_shape)\n",
    "        self.enc_conv_model = enc_conv_model\n",
    "        self.dec_conv_model = enc_conv_model[-2::-1] + [img_shape[0]]\n",
    "        self.n_layers = len(enc_conv_model)\n",
    "        self.latent_shape = np.array([enc_conv_model[-1]] + list(self.img_shape[1:] // (2 ** self.n_layers)))\n",
    "        self.lrelu_slope = lrelu_slope\n",
    "        self.n_attrs_outputs = n_attrs_outputs\n",
    "        \n",
    "        self.encoder = nn.Sequential(\n",
    "            Encoder3d(img_shape,\n",
    "                      self.enc_conv_model,\n",
    "                      lrelu_slope,\n",
    "                      noises),\n",
    "            nn.Conv3d(self.latent_shape[0], self.latent_shape[0], 3, 1, 1),\n",
    "        )\n",
    "        \n",
    "        self.decoder = FaderDecoder3d(self.latent_shape, \n",
    "                                      self.dec_conv_model,\n",
    "                                      n_attrs_outputs)\n",
    "        self.decoder_output = nn.Sequential(\n",
    "            nn.Conv3d(self.dec_conv_model[-1], 1, 3, 1, 1, bias=True),\n",
    "#             nn.ReLU(inplace=True)\n",
    "#             nn.Sigmoid() # -> normalize images to (0, 1)\n",
    "        )\n",
    "        \n",
    "    def encode(self, x):\n",
    "        z = self.encoder(x) # z repr is 4d tensor \n",
    "        return z\n",
    "    \n",
    "    def decode(self, z, attrs):\n",
    "        x_rec = self.decoder(z, attrs)\n",
    "        x_rec = self.decoder_output(x_rec)\n",
    "        return x_rec\n",
    "\n",
    "    def forward(self, x, attrs):        \n",
    "        z = self.encode(x)\n",
    "        x_rec = self.decode(z, attrs)\n",
    "        return x_rec, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_fadernet(train_loader, val_loader, args,\n",
    "                   AE, D, AE_opt, D_opt, device,\n",
    "                   AE_crit=nn.MSELoss(), D_crit=nn.BCEWithLogitsLoss(),\n",
    "                   freq=1, cmap=\"viridis\",\n",
    "                   site_labels=['BNI', 'EMC', 'GU', 'IP', 'KKI', 'NYU', 'OHSU', 'SDSU'],\n",
    "                   subgroup_labels=[],\n",
    "                   train_stats=None):\n",
    "    \n",
    "    def plot_results(epoch, freq):\n",
    "        clear_output(True)\n",
    "        print(\"EPOCH {}\".format(epoch))\n",
    "        \n",
    "        plt.figure(figsize=(20, 6))\n",
    "        plt.subplot(121)\n",
    "        plt.plot(train_stats[\"AE_loss\"], label=\"AE_loss\")\n",
    "        plt.plot(train_stats[\"rec_loss\"], label=\"train_rec_loss\")\n",
    "        plt.plot(train_stats[\"val rec_loss\"], label=\"val rec_loss\")\n",
    "        plt.subplot(122)\n",
    "        plt.plot(train_stats[\"D_loss\"], label=\"D_loss\")\n",
    "        plt.plot(train_stats[\"LR_auc_CV\"], label=\"LR_auc_CV\")\n",
    "        plt.plot(train_stats[\"LR_auc_LSO\"], label=\"LR_auc_LSO\")\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"Train recon\")\n",
    "        i = np.random.choice(len(train_loader.dataset))\n",
    "        img, _, attrs = train_loader.dataset[i]\n",
    "        print(torch.argmax(attrs))\n",
    "        display_brain_img(img.detach().numpy(), vmin=0., vmax=1.)\n",
    "        img, attrs = img.unsqueeze(dim=0).to(device), attrs.unsqueeze(dim=0).to(device)\n",
    "        img_recon, _ = AE(img, attrs)\n",
    "        display_brain_img(img_recon.cpu().detach().numpy()[0], vmin=0., vmax=1.)\n",
    "        print(\"Val recon\")\n",
    "        i = np.random.choice(len(val_loader.dataset))\n",
    "        img, _, attrs = val_loader.dataset[i]\n",
    "        print(torch.argmax(attrs))\n",
    "        display_brain_img(img.detach().numpy(), vmin=0., vmax=1.)\n",
    "        img, attrs = img.unsqueeze(dim=0).to(device), attrs.unsqueeze(dim=0).to(device)\n",
    "        img_recon, _ = AE(img, attrs)\n",
    "        display_brain_img(img_recon.cpu().detach().numpy()[0], vmin=0., vmax=1.)\n",
    "        \n",
    "        print(\"  AE loss: \\t\\t\\t{:.6f}\".format(train_stats[\"AE_loss\"][-1]))\n",
    "        print(\"  rec loss (in-iteration): \\t{:.6f}\".format(train_stats[\"rec_loss\"][-1]))\n",
    "        print(\"  D loss: \\t\\t\\t{:.6f}\".format(train_stats[\"D_loss\"][-1]))\n",
    "        print(\"  val rec loss: \\t{:.6f}\".format(train_stats[\"val rec_loss\"][-1]))\n",
    "        print(\"  LR auc CV: \\t{:.6f}\".format(train_stats[\"LR_auc_CV\"][-1]))\n",
    "        print(\"  LR auc LSO: \\t{:.6f}\".format(train_stats[\"LR_auc_LSO\"][-1]))\n",
    "        \n",
    "        if (epoch + 1) % freq == 0:\n",
    "            Z = epoch_train_stats[\"Z_val\"]\n",
    "            sites = epoch_train_stats[\"sites_val\"]\n",
    "            pca = PCA(n_components=50)\n",
    "            Z_red = pca.fit_transform(Z)\n",
    "            tsne = TSNE(n_components=2)\n",
    "            Z_2d = tsne.fit_transform(Z_red)\n",
    "\n",
    "            plt.figure(figsize=(10, 6))\n",
    "            colors = get_colors(plt.get_cmap(cmap), np.linspace(0.05, 0.9, len(site_labels)))\n",
    "\n",
    "            if len(subgroup_labels) > 0:\n",
    "                for sg in np.unique(subgroup_labels):\n",
    "                    idx_sg = (subgroup_labels == sg)\n",
    "                    for i in range(len(site_labels)):\n",
    "                        plt.scatter(Z_2d[(sites == i) * idx_sg, 0], Z_2d[(sites == i) * idx_sg, 1], \n",
    "                                    s=30, c=colors[i], marker=\"${}$\".format(sg),\n",
    "                                    alpha=0.8, label=site_labels[i] if sg == 0 else None)\n",
    "            else:\n",
    "                for i in range(len(site_labels)):\n",
    "                    plt.scatter(Z_2d[sites == i, 0], Z_2d[sites == i, 1], \n",
    "                                s=30, c=colors[i], alpha=1, label=site_labels[i])\n",
    "            plt.legend()\n",
    "    #         plt.xticks([],[])\n",
    "    #         plt.yticks([],[])\n",
    "            plt.show()\n",
    "        \n",
    "    train_stats = {\n",
    "        \"D_loss\" : [],\n",
    "        \"rec_loss\" : [],\n",
    "        \"AE_loss\" : [],\n",
    "        \"val rec_loss\" : [],\n",
    "        \"LR_auc_CV\" : [],\n",
    "        \"LR_auc_LSO\" : [],\n",
    "    } if train_stats is None else train_stats\n",
    "    \n",
    "    for epoch in range(args[\"n_epochs\"]):\n",
    "        print(\"TRAIN EPOCH {}...\".format(epoch))\n",
    "        epoch_train_stats = {\n",
    "            \"D_loss\" : [],\n",
    "            \"rec_loss\" : [],\n",
    "            \"AE_loss\" : [],\n",
    "            \"val rec_loss\" : [],\n",
    "            \"Z_val\" : [],\n",
    "            \"y_val\" : [],\n",
    "            \"sites_val\" : [],\n",
    "            \"LR_auc_CV\" : [],\n",
    "            \"LR_auc_LSO\" : [],\n",
    "        }\n",
    "        \n",
    "        D_n_upd = args[\"D_n_upd\"] if args[\"n_pretr_epochs\"] <= epoch < args[\"n_pretr_epochs\"] + args[\"D_loop_epochs\"] else 1\n",
    "        \n",
    "        if args[\"lambda_scheduler\"] == \"linear\":\n",
    "            if epoch <= args[\"n_pretr_epochs\"]:\n",
    "                cur_lambda = args[\"min_lambda\"]\n",
    "            elif cur_lambda < args[\"max_lambda\"]:\n",
    "                cur_lambda += args[\"inc_lambda\"]\n",
    "            else: \n",
    "                cur_lambda = args[\"max_lambda\"]\n",
    "        elif args[\"lambda_scheduler\"] == \"exp\":\n",
    "            p = epoch / args[\"n_epochs\"]\n",
    "            cur_lambda = (2 / (1 + np.exp(-args[\"lambda_gamma\"] * p)) - 1) * args[\"max_lambda\"]\n",
    "        print(cur_lambda)\n",
    "        \n",
    "        D.train(True)\n",
    "        AE.train(True)\n",
    "        # here train_loader returns x, y, a \n",
    "        for inputs_batch, _, attrs_batch in tqdm(train_loader):\n",
    "            inputs_batch, attrs_batch = inputs_batch.to(device), attrs_batch.to(device)\n",
    "            \n",
    "            # train D\n",
    "            # use D to predict attrs from latent reprs\n",
    "            # encode inputs & attrs with AE\n",
    "            recons_batch, latents_batch = AE(inputs_batch, attrs_batch)\n",
    "            for _ in range(D_n_upd):\n",
    "                outputs_batch = D(latents_batch.detach())\n",
    "                D_loss = D_crit(outputs_batch, attrs_batch)\n",
    "                if epoch > args[\"n_pretr_epochs\"]:\n",
    "                    D_opt.zero_grad()\n",
    "                    D_loss.backward()\n",
    "                    D_opt.step()\n",
    "            epoch_train_stats[\"D_loss\"].append(D_loss.item())\n",
    "            \n",
    "            # train AE\n",
    "            recons_batch, latents_batch = AE(inputs_batch, attrs_batch)\n",
    "            rec_loss = AE_crit(recons_batch, inputs_batch)\n",
    "            outputs_batch = D(latents_batch)\n",
    "            D_loss = D_crit(outputs_batch, 1 - attrs_batch)   # D_loss = D_crit(outputs_batch, (1 - attrs_batch))\n",
    "            AE_loss = rec_loss + cur_lambda * D_loss      # AE_loss = rec_loss + cur_lambda * D_loss\n",
    "            AE_opt.zero_grad()\n",
    "            AE_loss.backward()\n",
    "            AE_opt.step()\n",
    "            epoch_train_stats[\"rec_loss\"].append(rec_loss.item())\n",
    "            epoch_train_stats[\"AE_loss\"].append(AE_loss.item())\n",
    "            \n",
    "        \n",
    "        AE.train(False)\n",
    "        for inputs_batch, targets_batch, attrs_batch in tqdm(val_loader):\n",
    "            inputs_batch, attrs_batch = inputs_batch.to(device), attrs_batch.to(device)\n",
    "\n",
    "            recons_batch, latents_batch = AE(inputs_batch, attrs_batch)\n",
    "            rec_loss = AE_crit(recons_batch, inputs_batch)\n",
    "            epoch_train_stats[\"val rec_loss\"].append(rec_loss.item())\n",
    "            epoch_train_stats[\"Z_val\"] += list(latents_batch.cpu().detach().numpy().reshape(len(latents_batch), -1))\n",
    "            epoch_train_stats[\"y_val\"] += list(targets_batch.cpu().detach().numpy())\n",
    "            epoch_train_stats[\"sites_val\"] += list(attrs_batch.argmax(dim=1).cpu().detach().numpy())\n",
    "            \n",
    "        epoch_train_stats[\"Z_val\"] = np.array(epoch_train_stats[\"Z_val\"])\n",
    "        epoch_train_stats[\"y_val\"] = np.array(epoch_train_stats[\"y_val\"])\n",
    "        epoch_train_stats[\"sites_val\"] = np.array(epoch_train_stats[\"sites_val\"])\n",
    "        \n",
    "        clf = LogisticRegression()\n",
    "        # CV\n",
    "        cv = StratifiedKFold(n_splits=10, random_state=42, shuffle=True)\n",
    "        epoch_train_stats[\"LR_auc_CV\"] = cross_val_score(clf, epoch_train_stats[\"Z_val\"], epoch_train_stats[\"y_val\"], scoring=\"roc_auc\", cv=cv)\n",
    "        # LSO\n",
    "        cv = LeaveOneGroupOut()\n",
    "        epoch_train_stats[\"LR_auc_LSO\"] = cross_val_score(clf, epoch_train_stats[\"Z_val\"], epoch_train_stats[\"y_val\"], \n",
    "                                                          groups=epoch_train_stats[\"sites_val\"], scoring=\"roc_auc\", cv=cv)\n",
    "        \n",
    "        # add mean stats\n",
    "        for stat_name in epoch_train_stats:\n",
    "            if stat_name in train_stats:\n",
    "                train_stats[stat_name].append(np.mean(epoch_train_stats[stat_name]))\n",
    "        \n",
    "        plot_results(epoch, freq)\n",
    "        \n",
    "    return train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "class RescaleIntensity(object):\n",
    "    \"\"\"Rescale intensity values to a certain range.\n",
    "\n",
    "    Args:\n",
    "        out_min_max: Range :math:`(n_{min}, n_{max})` of output intensities.\n",
    "        percentiles: Percentile values of the input image that will be mapped\n",
    "            to :math:`(n_{min}, n_{max})`. They can be used for contrast\n",
    "            stretching, as in `this scikit-image example`_. For example,\n",
    "            Isensee et al. use ``(0.05, 99.5)`` in their `nn-UNet paper`_.\n",
    "        masking_method: See\n",
    "            :py:class:`~torchio.transforms.preprocessing.normalization_transform.NormalizationTransform`.\n",
    "        p: Probability that this transform will be applied.\n",
    "    \"\"\"\n",
    "    def __init__(self, out_min_max, percentiles=(0, 100), masking_method=None):\n",
    "#             masking_method: TypeMaskingMethod=None,\n",
    "        super().__init__()\n",
    "        self.out_min, self.out_max = out_min_max\n",
    "        self.percentiles = percentiles\n",
    "        self.masking_method = masking_method\n",
    "        \n",
    "    def __call__(self, sample):\n",
    "        if self.masking_method is None:\n",
    "            mask = torch.ones_like(sample)\n",
    "        else:\n",
    "            mask = self.masking_method(sample)\n",
    "        return self.rescale(sample, mask)\n",
    "\n",
    "#     def apply_normalization(self, sample, mask):\n",
    "#         sample = self.rescale(sample, mask)\n",
    "\n",
    "    def rescale(self, tensor, mask):\n",
    "        array = tensor.numpy()\n",
    "        mask = mask.numpy()\n",
    "        values = array[mask]\n",
    "        cutoff = np.percentile(values, self.percentiles)\n",
    "        np.clip(array, *cutoff, out=array)\n",
    "        array -= array.min()  # [0, max]\n",
    "        array_max = array.max()\n",
    "        if array_max == 0:\n",
    "            message = (f'Rescaling image not possible due to division by zero')\n",
    "            warnings.warn(message)\n",
    "            return tensor\n",
    "        array /= array.max()  # [0, 1]\n",
    "        out_range = self.out_max - self.out_min\n",
    "        array *= out_range  # [0, out_range]\n",
    "        array += self.out_min  # [out_min, out_max]\n",
    "        return torch.from_numpy(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# пока не придумали ничего лучше, такие скейлы \n",
    "# + клипать все, что вышло за пределы\n",
    "sources_boundaries = {\n",
    "    \"OHSU\" : [(8, 0, 0), (48, 64, 36)],\n",
    "    \"KKI\" : [(24, 16, 8), (48, 64, 36)],\n",
    "    \"NYU\" : [(8, 8, 0), (48, 64, 36)],\n",
    "    \"SDSU\" : [(8, 0, 4), (48, 64, 36)],\n",
    "    \"USM\" : [(8, 0, 2), (48, 64, 36)],\n",
    "    \"UCLA\" : [(8, 0, 0), (48, 64, 36)],\n",
    "    #\n",
    "    \"IP\" : [(8, 0, 0), (48, 64, 36)],\n",
    "    \"OILH\" : [(16, 4, 7), (48, 64, 36)],\n",
    "    \"GU\" : [(8, 0, 2), (48, 64, 36)],\n",
    "    \"BNI\" : [(8, 0, 8), (48, 64, 36)],\n",
    "    \"EMC\" : [(8, 0, 0), (48, 64, 36)],\n",
    "    \"TCD\" : [(16, 4, 1), (48, 64, 36)],\n",
    "    \"IU\" : [(8, 0, 4), (48, 64, 36)],\n",
    "    \"ETH\" : [(16, 8, 2), (48, 64, 36)],\n",
    "    \"UCD\" : [(8, 0, 0), (48, 64, 36)],\n",
    "    \"all\" : [(0, 0, 0), (48, 64, 36)]\n",
    "}\n",
    "sources_scales={\n",
    "#     'KKI': 2500.,\n",
    "#     'NYU': 300.,\n",
    "#     'OHSU': 2000.,\n",
    "#     'SDSU': 10000.,\n",
    "#     'UCLA': 2000.,\n",
    "#     'USM': 1800.,\n",
    "#     #\n",
    "#     'IP': 4000.,\n",
    "#     'OILH': 7500.,\n",
    "#     'GU': 1800.,\n",
    "#     'BNI': 2500.,\n",
    "#     'EMC': 10000.,\n",
    "#     'TCD': 1200.,\n",
    "#     'IU': 1400.,\n",
    "#     'ETH': 40000.,\n",
    "#     'UCD': 2000.,\n",
    "    'all': 1.,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1006/1006 [00:03<00:00, 313.54it/s]\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose([\n",
    "    Pad(padding=(8, 0, 14), img_type=\"fMRI_tr\"),\n",
    "    ToTensor(),\n",
    "    RescaleIntensity((0, 1), masking_method=lambda x: x > 0),\n",
    "])\n",
    "\n",
    "dataset_abide = ABIDEMRI(paths='../../pominova/adibe2/Outputs/denoise/denoise/',\n",
    "                  labels_path='../../pominova/ABIDEII_Composite_Phenotypic_150220.csv', # modified targets\n",
    "                  target=None,\n",
    "                  encode_target=False,\n",
    "                  load_online=True, \n",
    "                  sub_path=\"ses-1\",\n",
    "                  mri_type=\"fMRI\", \n",
    "                  mri_file_suffix='run-1_bold_denoised_0-16.npy',\n",
    "                  transform=transform,\n",
    "                  use_sources=[\"NYU\", \"OHSU\", \"KKI\", \"GU\"],\n",
    "                  sources_boundaries=sources_boundaries,\n",
    "                  sources_scales=sources_scales,\n",
    "                  clip=False,\n",
    "                  start_pos=0, \n",
    "                  seq_len=4,\n",
    "                  domain_target=None,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['KKI', 'NYU', 'OHSU', 'GU'], dtype='object')\n",
      "484\n"
     ]
    }
   ],
   "source": [
    "sources = dataset_abide.labels[\"SOURCE\"].value_counts().index\n",
    "dataset_abide.set_target(\"DX_GROUP\")\n",
    "idx = dataset_abide.target.notnull()\n",
    "idx =  dataset_abide.labels.index[idx]\n",
    "sources = dataset_abide.labels[\"SOURCE\"][idx].value_counts().index\n",
    "dataset_abide.set_target()\n",
    "print(sources)\n",
    "print(len(idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KKI 211 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYU 105 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OHSU 93 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GU 75 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = \"viridis\"\n",
    "\n",
    "for s in sources:\n",
    "    s_idx = idx[dataset_abide.labels.loc[idx, \"SOURCE\"] == s]\n",
    "    i = s_idx[0]\n",
    "#     img = load_nii_to_array(dataset_abide.mri_files[i])\n",
    "    img = dataset_abide[i]\n",
    "    print(s, len(s_idx), img.shape)\n",
    "#     display_brain_img([img], site=dataset_abide.labels[\"SOURCE\"][i], cmap=cmap, vmin=0., vmax=None, axis=True)\n",
    "    display_brain_img(img[0].numpy(), site=dataset_abide.labels[\"SOURCE\"][i], cmap=cmap, vmin=0., vmax=1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KKI 211 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYU 105 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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h0WSBG7fKLdlq5DzmEuFkI2mwXcjIOwrWUdBWqTsbWLKB1yzQmIsSEQ5ld0Eh9cYPW9uu9t3Lk+XVr1opVeO5a3GfO3SxM2gvg8TgctRMGbZlUBqRLS1Q2eCh6zlHNmwXVu1ON+N4ObxsyzaeiM+7gtwB2x09jDDrG7ezHB6h3I+SMgnoA5xpzeyHh86sPHSP0gvOlJeSJGJbzum6GPs7ljSMQKbE55CwWsPzZZmjuWY8z0BXORpDUN7EtnLYj67/F7bsiVuxb7qXrtrtQI5TkCQOE2WHYXuoDHRCCCGEEEJMjybDQgghhBCismgyLIQQQgghKsvx1AxPayVFOkLUnmRkg+aevDJZ3L1k9Vat9SioGa7Y/KbbF6PQb8AWaah/oiuJOibWA+Wgo0JdrnOksWI5EmiHcrazgeOxroj1SXbDuNhftdez1oHUtX37dxPqHfl4eO6sFRwntFGovWQ7ujufiAJI/1Fr04TXfuaevS6nPv1aPDbZNHmHlduD7dqU6X73n8Z5ytTMzqX7yjHRE6PVmaecoQFtnxJayhqlO8+fvDRZXnufLdt+LPE3PlyuEWWm9QntodHBJ1Kac/tmXaQ5Hup7Z0lbiZo+skGrgQy+qPOHZWrCqOHN83LdZSAdJEpCA52PHdvIcg5uLeul0Y6Lx6fu6Vh2jfTEy4+9d7J86tOv2rrg9wJsSwYX+0gtFZ1LphJHLTz3FdRyOtIFJ20IL52bLHYuW319+068ob1V23hHYO/nyb7QfNuSSBHsqO0aeTF/9wK3pbFbbsnGfRP19tz/rPUZW53BMtsXok6Xh1HsO4nvepgsYeVmvgOg56np76wZBvs2X6M+DXXhccjUk+5DDVJMj5bsyb/5Q9GG9tL1u3bD7e24/6GtqPmGq/BNTOIjo9TzNPVtmdIxCyGEEEIIMT2aDAshhBBCiMpyPGUSQMqGo5B5CkNMZLeUz8Q4wcwNa7/lhzFmsX35pCnD7GoFOzMIddQSIclA7l452K4VsulkibA3kLnyMGfKJoatZtBeprFJ4bVUpAEjFCnnLArjokVOIaSUSPpjXNASlwits5xzbuuT0Qpm8fN2XSObSGQs3FPGnNR6OcbJpsxGxPvhzY6JFCJFMiwN58Zh4exMtHpa+x4rjdm5ABkerUrC0Ni2vz1aDBWuZVwcWbWUCfPXbcI008YLtoLQ9zlUiTKNQpg2ETZNWVBhGBWtnJxzzkFissI4Y6LJnCkvLvO1NhaLbO0E16JgRwfh84KcA+VgtN3aB3HgedyUrfzKN+KPVJbK40xqbMnjzU71KX72bT8VrUM3nrANrXku/m7u8LMh/mb7LczmxlZnqcxuSMquc+1jttHXFuL989esZ+DC1ViXOiXfw0x5QxpzMcscZ9HLhuV2ZihbSsmUCsAhav3y7HQsncTxxlNWOzweP0+tHSTL6x5arQdFaH9K2Sg75+MBux+7bMrav/1i3AcNBh5kqwW702kz0BXWS5RJJiGEEEIIIcT0aDIshBBCCCEqiybDQgghhBCishytZvib1hhZrXydgvUM6KZI55mBViqsWu2v74LmKLd6ks7lqKnafJysn1J/LqRkn5hllvQ6KXux1O5NOmbS/mKaVNY/Geh8POifQpO2A9lP3rAboo6p3iE9GdaF3YFAf8XaX6N3JK1ZUjOMukXScPWWwR7uE4+ZsvmvRh3T+NYdOh4epFy3XtC0j1I2MSjUYt882E/BQmafuuCDsHk7YAp2UQ2wXXvsvCm6/qmVyXLnPGnlQDvHqWKx4/i8vAPn1N4zTksKjGbiumyViLr7WkIHyfZiY7BTQ/2ic1anyHriMZRlZF2FOl3WeWJZYVxLpHrHsYXPHa994Lqg1nix3K6N086bPsypfmHdtQ/Z7erdJyfLS5/+uj3eMdUMe9Zuw+9AY4RPfGfgZ6IAfXhxxZRtPB5v4HCOtgO9b510rDYFMlXTlWtqjaY9YT3G7Xr3ffHG/5/f//Om7BOt+J3PVdIo/5mv/GTc/2+eMGV1sGjz3JZAm4vf8Thn+wrbmSWfUzAuFb4xgmvB3xY0N2E7fkbjNSwIfOMi901OnV62HdufOlNPexKjxXhzb33SHvDx5+L8a3ybbNdSbRfbfOLbHe4rAa/9ATzf9GZYCCGEEEJUFk2GhRBCCCFEZTlamUTJq3PMHhfG5bZWnl7vG0uZIadFi/sZrdoMdBtPYfo2u5kJFXHIZ0p3LA5X1lBGkAj5MzZrDIUMMgyzcBluRzvFcMmgPCRZCAdhiInsj/CacVjHZNaiW4Rw6MZDWImtrUx2JDo/lKSsP2Wb+875i5Pl1S8vmTL/QsxcZ7KkOZvNisFsa2lLsfKiPZHM0HM8pBHYn/2MtUXykFnu1vdYadPu+fL7iqFYlClwGbcVY5+WsBBrWPdF19hNWIH1yvveGO3TqJ9gm64lGoSnkKqRDHGGx4TiDPslWzslgaqxVV0wPky2bADDbMGCaowDD5Vhs01l4aSn19rT8eQXXr1oyvw3rsUfvb4pC/kjzkiHz7BmuWaOs3i5BtzcjC726SiN2HzS+gIO7ePOgBaCQ7LRMs8+HkrGiTJcjU8P5THcVmE/v7LxEVP0+uyNyfKbAztO/MhjURLzr5/4dlO2/LXYSFjO0dyIy0lJAbfPhGwB+wM/J1KSS7PqHoZts0/aDiVbnJ1uBFLKpMsnj7swlxmRtGtwJVph1tc3TBk+Q1lamA8GpWUoUTzszJF6MyyEEEIIISqLJsNCCCGEEKKyaDIshBBCCCEqy7FIx4yaQuecyxM2OCblMumt8oUZXj2WteKp3vuw1VT1QYLEesARpB/NybKM0yYiaG9WSLmMLlpcVk/lJIb1+NDmeNPt48HKU5al/mxiTRXa9exSil3QDo7pJHKwaWKdJFq8FFK7jsr1h3it+d5iqu17H7XCulPuPXG7F6/afZLm0BaWC758A7pbQv8UUtKoA0g7+ahBiyhP9d38RLRT23rSFLlxO67LFj9jY/dF/RJTqVIfxT5csCKqlWt/0T6Q0w4H0ONxuuL+SbCCbHO+Yjcd3J2NZjGhNR7TNbsXO0Nzi/3TYDtuUqlM5TiW0dMkx3Sw/KRplOvBk2NSIhU13utAFn64G7b3C4/Ydc1YSdE4gJrJDOzSnLO6y2zOPsN2r8RvHjrnylNt8xiIfWA4X+4FVmgTOORyO07YduHxuU00b8aB/f/1HzNlrzwZ9aivrFnruBz7OH8zAlOCgj3cqLzMs/VlCSEhuC1YpCWGanM07poJTS8ev0a6fGzXnp6Z2Df5VI3tIdW5Ds/a8Yzd8Pr3xzb5nteXTdn47j1XBn5nk3yeTZu2eZ/ozbAQQgghhKgsmgwLIYQQQojKcsTWag/m4myflspIYn5TiKm2uRvXI+lF77EYBh8sU7YZCC9wGAlDHdke/LAwdFoIHzbKJRQuYTlkQpmFMgwNl5cVMtdhGCtlu+amJ8D5cWYtlDgUpBBjXE5JUOxvD/cvIwUD2q5x6BvPr7diy+58IraXU83HTVn9+dcny/kue3eVEwbY0A7o79BjmGWOwcxZvm1Dv7tn480ct2gcgPAnyySMVddueVyR20MN9sl9COU9HHLMhuU9APvwaCaR9S3he5Zw63uI9RheF1oVnc7omjXi8GjtD53NnJUK575d3ZDmJqxWkIqB7IkvC2Yp4zI4pRGp4kzWsB5dGHxWsC3ZIwalERlZDRoJFofgwR50fOGUKdo9Ex8ybGdmJABsdQkSI5ZJjFv43KB9YhtJyABTmURZdoh1a123z++vrUfZWk7PFMwc2d6ylUEpBt91cwr83J/WNzVBTnZmqWetT2S1sysmilg2hMvcT7dRUmSLzL1OKS5ZhgWyie4Hzpmy9heiV2Xe5TSd+3xuTbvdlM9avRkWQgghhBCVRZNhIYQQQghRWTQZFkIIIYQQleXoNMPeT2xkUmn2Cun5QCdSKIP0lWHRWs8MFuK8n1ODmt3TnwdZQstjtMbkT4L61IKtEJax5Qm6byX0tmNO7YqWQyzFAp0w6pWds/relCWbZw0VpuMclWtxA+nCWO9lC2EXQ7JGSqTfzRugoeS0rwktdcpCBnXldz4+Z8pWZqIHWOt3X7V12YnCTE43mrRMm5ZjqgtOAhrNsGi9x3YuQfsj6zGTobTJqcLhd+K6sm62BhZpdUo/i82fdZfjZrl+El8pjOaovaP9UKrps36yCfeZ+qXfjYNEbc1WhlO/I72VuJ/6LOmJtxP6UOzrKZu11DcHtM/cWONRGehYC+l88Xg0ls1ehzEevh9xzupDC2mOHzWgYWSbRmP7xrZroC/efo/tR6j3LTzDEtZxqD8vPBvwuUU3N5Uy2zwnWc6PeuLUty1kMdrYiSc1Jv1+St5rnr38mMLf3Kf38pFMyfHG9vMIMzbwnCCfiRejddc+OFpr5cczqdn5WidedZpvBgrzBfjB/Tahex7BY/LeM7bjXnpxMW5G1rn4HVjKarDggTit1dqUz0y9GRZCCCGEEJVFk2EhhBBCCFFZjtBaLZS+vk5m6EF7LLaeqYNN04KNUYwhlF4jZ496L76mb+7YmEFtEOs4atm/HYYQamR7kmxUblVUkE0AaM1Uu1O+He/TyDkScoDRDFnPQCYaDkli+HLcotBUuUuUCcv5UeLvrULotDyknLKlMZnDyM7GSFk4A1EivI7XcEwWTnc+Fi/aiYWnTNn8Z1+aLOfb2+X7b3CcDENFtDL2k3dhBjrTT2scg4fFPoX8exks02aYbYzlPWitRvccw4M1csVD+7SC1Mj4FHEZWrlROPlmbLjc7zGMOpq1BxzNo2Ue1ROOwRKl0UJc+S/+0f9gyr5r5pXJ8o//y79oytp3U2F2zNpnjzeE0ChLvrB/78WpCs+PZU84RgSydpq9BRdqxJ5e6Dn3zm2zDgyqi8nWSM83vxTtHjur9iZhWypYbCVC/tg/eOw09UpIYJIZBBkj50vI6xJSv8LzFKR3Yz537HPcN1OZ8kyWR66LK2UE6sz+Kg0+M7FNzixYecyVlaiFeOW3L9u6QDsfWfWnlWIULBjjcqFvlqzH+ylauMJiohtxJs78BGR4vXu/vC78XIT5H8tijc2uMtAJIYQQQgixf6Z6M+y9f+xtVsmdcxshhJ23WU8IIYQQQohjw7QyidfdFN9Xeu9fd879vHPu50LgT/+EEEIIIYQ4Xkw7Gb7m0pPhzDl30jn3Hufc/+Sc+wHv/Y+EUG4m5X0WrWLIXgZ1wqwhQd2In7UCGtSlbDxlhZ6758r1vR3Q3QxO2yrPnIzCwsdPWa3L7jCKajc69njGXYaEN/h7RClad19aniwvWtcu1zkbz6G5ZcuszZst663E5f4q2VehpVMq1SOnw0U9J+n68PqOZmxl0EKmAOrJ2K4N9YdkAYTb1bqkpcN0uCmrGbbZQt0za7ChSd57xrbPceO9k+Wl33jNVnNto7wCQEEbhdZWrDHMj3865hSjBejr81ZjNxqAnVLXXpNaB2y0SJqXTD+L1k4FjTyMEQmNJGt/sb8tfP9tU/bfPf7rk+W//8oPmLK7X48pdZsbZCWI7Y81i9D+P/IHXzRl/+jyL0+Wf7Vz1pT92Gf+/GS5TfaEgyXYf+Hc43/0T9o2li/Gi+0b5e0v8LcDqPfN6YB4vlwXOERjy7aJ+RtxIAo9EpmbfezTN+uAKFiCYpnRNtP3HctRiDlul6f6LaTzTWhATV/h25eyHnPlZalvYnAsTaV4LuiCG+Vl4xPxJPK6LWxcjd938HcvGTxjCnaribqkxoLh43G+8Bc++llT9mTr1mR5MbPt8+9d/9RkubVGaY7NtwX2eKP58raM5xSonnjtCymzzfdH01vq2RWpngvxPtT5Wy/z3GLxNtgQJix4k0yZjnmqyXAI4cpUx/T+I865/8U590POuZ9yzv3DqWohhBBCCCHEEXCgH9CFEJ51zv0R59wbzrk/fZD7FkIIIYQQ4qA5cGu1EMLAe/8rzrk/+bYr52+9HqfwbtZuPWTlB/hmjJeM32PDgOtPxzDS9hUKNUCIpBAuGUC4pGNf0/d34z5fJwunzk6sZ3bXxlwxi1LBmgUtxCjz1NwTUf+wHRZN2ekvxjABZl1zzrnhXPy99iG7zz/+B/hK8FsAACAASURBVP7zZPlTC8+Zso08xl1uD5dN2Re3osXLne6CKWvWykMWb27GmOv28ydN2cx1sL8jCQXaS3F4xoSxfHmIsOAEk8gyh/edpR4YKmILNgwxjSjL0P1nYmG9e8WUzX0uhrTzLoVxQf7AlkphFGPmRhbxbgHtonasn1lzPUoFUBbhnHONXbgmnP0RZDspy7xC9q1UCB67JScpw/BuYtS8/3unze9fO/GByfIvfvAXTNn198ex5WzNZkz769d+dLL85eceN2Uf/2DUT20NbAP8+G/89GS59qqVbs12yq8ntv9CJis4X87aNwLvKs42iWRj7s/lmSFToXQjcaPDDRZjXdqLdrwKkPWKpXdhmEhJetiwZCNhb4ah5oKUZTq1WwHsK0U7s+n2UXBIw0dfyj6TtkObz7xgVVpetrwSv9v/a0//R1P2f5yN0qSrr9q+WdvFrLTl7TOVsZbPod6MF/SH5p83ZR9uxr762tB6DXztxrnJ8iwdr7eKGWRtmbGV5OuZkn3hdlxori9n1oVdkHQSpY2cUXPnItiRvkAPTWx4OQ0GTRhfuikv1Hf+XDysJ+t959z8264lhBBCCCHEEXJYk+GzzrnNQ9q3EEIIIYQQB8KBT4a990vOuT/qnPvKQe9bCCGEEEKIg+TANMPe+xXn3Cedcz/rnDvlnPuF5AYhTFLtTSzWvglaaLANx+moMdx4n1Vi7J5PpGgFWB/E2lUD2Bh1d60uGNOBssWKsf9iaxb8E4S0NVkWV/7O733BlH3hUtTwjm/ba9a+FLXG333uuim704/6uZ+7/1+asldfjzqq5k0rSEJNENvRob43nLX6108+ES3FfvpP/AtT9tIg6rz/5y//IVPW/Ho8px5Z3I1W4PeQ/obDa0jWOg4tnUi3iHZxbB2H2sj6ri1LaekwhfXtb7eN4srNi/F4L1jbtZRtjK83plovqZs6Sts1tJIiYeLMrXhtu99rdbOjIaQy7tsG6EHbjzZ/zlk9LNvwjefidfBtshmEdlXbtMdrboLFD7v+ob6c2tFv/9qHJss/DPph55wLs3HDWtPen0unY3pWT+32K194EnZi64L6Xk7xjG0zs26Wbjwu1xfi+DhatNfMz6APk93OwxjM1mqjHlhkjstVroFtKRM5YLeuxH3OXbPPhmwrpkYPRygRds45DynJA/WHAPmE2YItb8TtCjrrlOvUPtNim/2zvWQtUZYYhjLzzKT2icMEPVM8aHHZig+fmX/35R82ZXfuxO9XGst2n+M5OOAbVsda7zyk8t88PDwm+fujwUbUxv7f699hyhon4rc7v7r7IVM2uhefffxNgnnW8owNJcOFewTblbv5FfpYBh25YLsGq7KlpYPf45a9R70V2GfbXut8uzxXm8/KGxPajIaUXeKUz75pM9DtxeDNO+f+RQjhn+9hGyGEEEIIIR45074Zfru/J4N7oBF+1jn3CyGEf/qOaiWEEEIIIcQjYNqkGwf/oZ33MXzKdhqpLDxL0Qqsd5LC13A2Wd+W1ayjk91ntzwsgBZpYcO+3vfGwontvsrDqjmEbj1loOuALdkLZF/2bZfenCx/8AM3TVkfdBq/u37JlF1bOzFZfnL1nin78Y9/YbL8jR1rPfN7V+N+8m0roWjei/VuPGclG1948+nJ8nMfsPZ3/+CZX5wsv/wDP2/KfurJ75ksf+6XP2LKhksQXmtT6BSykYUGNVX8WQgt+ocuO2fvZ41CysiYspjVILPXYMke8P6Ho1Xe6utkLwNWa75lrQXDdgzxFrPToSaATzB7+HJ5YsjDYQjWcLs2/rj6ldgxX6GskWjV06D+nMqEhCFBTm5mLNJ6JL3A9kAZENG+rc42fHA5a+SYh/ZG2YCzK4EMhOQcN147P1leumXr0jlXbrWEDm2FMRCzVHLWvqR1HKbcotA9SFTYWg3D4Hw964kMgkjBWm3KrGhZlzz1hpApr3ZY341PSYZyB9tnAzZm9jqzKxowfM33Fu9nMhNZIpqcmgEkM95xJjk4Po+5fhw3bM7Y+3dlJcqGLszab/NPNmOjR0mgc87l0GA+cuqGKfuuxVcmy//xipUtfPlr0c5w7qq9aDN3YE5QyKIX1/13rz1jyk40Yj2v90+4MrhPo6xzXC9vE0kJTMFzFPZPfRP7Y61XXsa2lcZylCVaaBlI0lfzTGNZ7LRZ51LPvimZagvvvWzShBBCCCHEtxzTTp+f9d5/8lBrIoQQQgghxCNm2snwY8653/De/6z3PvFdohBCCCGEEO8epv2A7rudc//MOfc3nHN/0Hv/EyGEl9/x0b+pnRpa8Ymfi7pgTKPpnHNZJwrfCvonlLaRrtSkA02knWS7F6M/JC2PYdZqVlAPZVIzO7I8mbWamAzsnmYaVvz1gye/Plk+Wbd2JL+z/cRk+ep9mwK5tx01qP0T9qL9xInfmSz/euu9puxeN6pj1uatnrO3HMWyXbJNCnAjBiP7t9OvbqOO6vdN2T+59FuT5b/1Y1bb/E//7Q9Olgua4TNR1FvIOgm665xSGaM13pj0nHkL9Zxk3YVpJ0nrmdLgbUQptVv52gW7z+eifi0MrCg1m439Ybxj7ceMVop0UsZ6Zlrt1SHgIb16GNk2XduJ59okTf7gSryvo4L4NxKozIO9Xr1pjzfTLL8O43G8fpja1znnurOxvde69jqjppzTiGNaUh4/zPcIPO5A2eCMLWstRWHyaJtS1/fBHo7qmaP+j1OTY1+g8Wq0GFcOM2St1ohlGadjBiFrTnaIo1q8vgVrNVy1MFbH/2BLvfZ9OPQ2eWPhM6Wb+IDkUUPfAPA3AUhjM9732sB+rDBog96dpenjhy87R+MVXWscLova2IcvP1h5yu0K3+fAdiSA7Y7KH743e/FbjG+s2+9e7q/FZ9jztTOmbASVw2edc845aNfdM/YE8XlQp+EYz284tDfiTD1qnX904aum7NVnomXsq2/a9OvmoUbXugZ6Yk5TbVIuJ76XYc1+fQe+1Sh3PTP3i3/z3Ky9BmNdp9y3jlOl5/C8S34vk2JK/fBUa4UQvuic+zbn3D92zn27c+73vPf/9XQ1EUIIIYQQ4ngy9Sd3IYRuCOGnnXN/2Dm365z7B977f+u9f5/3/rGH/Tu0WgshhBBCCHEA7DkDXQjhV7z3H3TO/V/uwcT4D5etOvX+OVQ0PxeXSSbh7m9MFpsbVg4QwL5jzKFTtEFLWPmwP4lP2MSYVckSxMgr6hQ6xauS0at/WPX6jg0N/W9bUSowuD1ryuo7GLqxx2tClpxX7lvbtR+9/dOT5Uur66bsZDuGKHK6Lmhu0x/Y2zzsx9+dO3Om7DPz75sst8mb5c44Zs77b0/abN5f+8FoNfXsf3jalPWGMVSck+xkjPEajrRjWDBhrcagfRZmqnOOJDcNW9aHrHr3PmLDcqdfBilBP+HlRmDoiLPwmN9HaK2GWbWyRWt9FG7cnSyf+aJtK7eyeE2GJ9ijKS6yNRBKDoZt27+GHAs2+4RQc9du19iGsGIhWyHUhYYr7NAFmzAYF8YUBR6DFGi4bM997mxsH/0tK5Norsd68zhg6paw5qpndrsaSIhGsyS9aISHLj/YKSxTk2tug7Vaoq9xPfEY9Y7dbumlOF6hHaFzzrkGxXSPkDAsfwCZMDE9U7JrdybLC+dtX6nvxgu8ddme63AeZV18wFRFpyxL3PZC9lW08EvY5tXrtsFcWYjWaot1K3O5Oyg3u2rPRhnWVtfKsD5756nJcu2W7UctGONT1qjDhXILxiZJtN7finao723Y+/dLT/77yfKHPvxTpmzmS/FZz45s2AdG82xtWG4ZWwPbRZZ6YPY9lkKMyREUGUGmSj7e/A2Q/nH7h3YeevTsw7nhIWdf3W865g+/9c87524556Z/egshhBBCCHFM2NNk2HvfcM79befcz7gHmaj/inPu7wROsC6EEEIIIcS7gKknw29JI37RPXgj/Lxz7idCCM8eVsWEEEIIIYQ4bKaaDHvvf8Y597eccy3n3N93zv3lEMI7kkaEECYWUp5T8KFOuE5WGxtRrTp/02pPBssgiKJdcnpAJCUjTOU4NDYxLIOBfY7J/WgM2ppxi3Sec3FHFy6umbL5ZrzkL25a8U4AGyXep0vY5yB/8/F/b35/sh2P1wv2BBsuanleG1ktz3ODmIL5H137flP2xr3lyfKrJ06Zsh4I2u6OFk3ZnzodLeA+/+QTpqx9LQqbBqdss2zPRK0SNzOMZ7Dt2hB00IOBLRuAntQn2kdosM41rts5S1qzhP1Yjqmas5S+MqGN2kd6ygMDrRM59Tr09bkvvG6KrtyN7eilH7ca+ea5KHQbkX1fsxHb49Kc1RcOwT5tSKnQ0c6p27GddtAGKzCyEhztgp0ZpWM24wd1PiwKCc1wdtJa7X354/9qsvyL2yum7H/81R+L9ZqjbwfWcSCwx0ONH1urDRfBFmnets0MrOpqZK3m4XzRts4554Y+9llMN+uc/d6CQVvKxo5dMRtA3RLtHfvTkUN91uiJua+DJdX8V2+aosFjMJZyRlt4VKRs0Fj7myUyvafSM5v9pNajemK68J27VlP72lxs53VKc78xsGODOQQcY/e6/V4Bv7PxpPU31mMJqWrBOg7W7Vy1z7CfzP/sZHmubfv0Myvxfv4PH/1Ppuxvb8TPspaft/MhM7fYoX4Eq/K3DKgTrg3IShFs+lgzbFJtF9oLXDNyT2tsxD7Hzze8STlb6TbjwJjnexAgTKkTRqZ9M/x3nXM3nXM/GUL4T2+3shBCCCGEEO8Gpn1d9G+cc89oIiyEEEIIIb6VmOrNcAjhjx3K0d967V2wWNmFd+wz5OUBYda5Z6+bosFitDbePWN3msGrf7ZWYxsQW1heZDLeUZizczHu9PxTd03ZcjuGbjd6NrPbdi/GPa7ftD4qM2C/dfIuZdiD6AJfT6wbh077/Xj8Pzf8s6bs8vmY0ulEy8Y9RhDLXOvaMFV3GJvV2l0bKsLsYF+6bW3enl6J5/dmz557C28aWWm1wBFueNO2l+4qXIyMwiwmREghJjwGhcU9ZNPisKMN+VI4fSm2iYJsB+VAbCED4VJfs3ErzFZXyNCDYaV9hI0OCgyL5Ztbpiw7HcO7YWPTlNXWYizvwmdtP7nxB2IYlZUqvYV4PJZJdKB/9XYoBoj3lbKbNTpQxhmbwMKszhF4vOys08Hxg0ZitOXrD22b/rFXfmiy/L0nbSJQtJWbvUb2cJhNiiOO8Lu/QlaQcE75mCrq4+9xvVyeVduh67kFbZq7JVS78GwAmcTCNdumsx7cmBr12TpIm7ifuEcM9kW2Fa2XP5JRSmVkEc65tQ/ENtI7SZZsqB7h8QrLUs5V9BvvSyrLXPLismwNfte27HW59vWYPe5qe9VuiPKcvq1MHfbT7NN1gWcmn7uRRiQy1jLYltu3afzfjs/CdRp6PluPMpDfvGAz0L3v/XGec/XWZVPWhCGzYZUXyfvg4dkwblJ/T2TaxfMrKATheE07lLvaOti0slSuVt43p+YAnm/Taob3JTgM4QifwEIIIYQQQrwN02qG2Up+GqZPuiGEEEIIIcQRMO1k9Q03fTRp3jm38rZrCSGEEEIIccRMqxm+8nbrvJWQ4y845/7GW//1enJ955x/S9fFOTsCaCY92Wn4ZhTbhO0dUzZ7O4pmOqetzg4tQti6B8tytgea0nYtsF4O9DSnZ21q0E8sX5ss3x9aC5mvrF+cLPeeWzZlLdDh5KTzGYFsd2TllcYaabhiX/Ivno7X8Nys1Vf2RrF5vDmwddntxYvW61gBVN4FGyqyJQutWO/tHVvR5/JopfWeE/dN2dmZeA2zWSvaHM5FkRPfr7mleE4zTXvuI7B7GpO1Gu5mQOmmR6CJHtP55ZjSt25VQq2ZePzBiOyAatMpkVAj/GA7SMfMaS5RpXSE6ZhRBxlYD92H8zltdZDbH4q/7z1jO20O19JxnwVt+E7PWqRdXol2hXfbtu+hnng4pDTOuyCko9Tdw0X4HoE0i2gXxRZiRqfIKV+hyeUt245evHd6svzty1dNWeNEtBasfcO2Wz8GizTSL29+MFZm9pwdV9FpbdC3gsIx6OkDaesd9A0eH0fgclXQYGKzZXkh6D5rA1s4PBX7VHPH9q+wHgfPVDrkR8I+bQ59Oz7Tti/bdj2agetCpqdJ7WhiKEA9MWvacbuC5rvcTfBBqq6SutSgX3Gae6tjpQf4lFZubJ+GuuCCNSq2wWnTUtNvGpaM9RgP/3h+/kWbXvrNr8TfzcS95Xtp5k6s703cIxyz+L6buRNrxWE/zR2a08F3YGFs+62pGtkJFmzYDpEDMR/13v9x59wLzrn/1T04t7/snHv6IPYthBBCCCHEYfGONL3e++92zv0d59x3ugd/8/0959zPhhDWkxsKIYQQQghxDNjXZNh7/4Rz7uecc/+Ve/Am+F875/5aCOGVPezEueytF9MUtvINCLPPU7gLpBEcMm69EC1IZs+8x5TtXoQQxQl7vMWvQYyCX9MnrNWMvKJuVxycj8fojW1o8Xc3o6XYl5+zNionno1xiNO3bTih1o+/G5sU8p+L16x7yt7W/okYABhs2bp0ISNcL7dSCISlJRiSmVuz5z5/I9aTQ6DDmVi30Zyty2A+hq2fb1trtW904jEWrTuXCTnVO7Yu3S7IObok5xgnbi5mB6Pwb8ixkLbD0PCQrHUgIx1bTbkwpSSfQ6wsOyhb95gYuxSyTeJ537d/Qy9+BeRS49Om7Ca099Epin9uxnbV2bZ94RvbkOlvWB7W91TWAPs0tmZE+UMq7Bw45JjIDIm2T+3btvP1d5Ymy//w5g+YstbdeJDGtt3pzHpsA+tPkaUXyA86N22YNiTixJiBsb5rr9nM7VjWoLApjpccih2Deoptnlqgnpq7Zu0e69ehkPqFyTp3TPrCQ8H+wH2lFcevcYPKUtKEkvWco/bKceJQsp6jtszHw+GRj4fr7qGvpDKfGSs+Ol6GMiW2T4MyluNMbQ+3BzKQODRY0Yb2mQVpENar3B40dd9zmtfYuYtd1xx/SmtZrsvsbTsmh06UK3qSBOb9eIIog3XuIbK6Mvi5eIgZ6B4cz/uTzrm/6Zz7b5xzTefc551z/30I4T/v+chCCCGEEEIcMdP6DDedcz/jnPurzrll59wrzrm/GkL4fw6xbkIIIYQQQhwq074ZftE595hzbs09mBT/7yE84k/ShRBCCCGEOGCmnQxfdg8UId4595ecc3+poP0rEkIIlxOFpXoQtN4oHAXKsgXStoEuZflLt01ZXovpHNf+iPUn6ZyPl6Fg6cKWaQisOpy3651Yidrmrb61eXvpC/GyzN+1x6t3435y0uk2u3DuI6uJmXkjCmlnv0qi2gz0NC3Szc7Gug1XrT5753xcd7Bo64kpHGt9sk2C1Nezd0gPPo4n1d6w57B9CWzCSOM0XIjH6J4h/eFSPIanlMtLc1ErmGXlOqLA6Zjh93BM6VvhEGzB5WbjdjMzVtOONRt2SAs/F4WSfsvaJjmwlynorXZBi0XnZ9IxH6G1Wj6I+jHP2rUtsMwjazW045n/7ddM2aVu7EM3vtcKS0dPxHs+Jo2868f71Vi048AILfSo2w9baBeY0GuSDh2bVWjb615fiNel3rBl2K7CbTt+zF+L9/LUs6TTvRvPqf3iTapnrExr7QyVQb027HUJrViX/gnbNnfPxrLu6fJnQkHLOYRxjr63MDpI0lbimJF12CsL9Ppku3mc4D48NZDGNqkLZnBdbroJbWzSIg2aa+F7koQtWWqfZj88VKdSdMOY73N7gqYubJ+WsIdLanET1xOf2XxdyurlHKWG5nMPJctvA17rnNI/o/1qUp+dqAtvh9rm9nXqf8N4gmytZp5NrIme9rsXLtvH9zJ70Qx759zJt/4JIYQQQgjxrmfapBsH4kcshBBCCCHEceId+Qy/M0K5LRS8Uncjuw7aqfkTS6bMgUwi3L5nilY+F2Mk3VOXTFkOkb+CNZKJ69giDDUMF+yr+I03ok1Z556Nl8zGJFiu0aHsWbCb2sCWNdZj+Ld2d8NWE8MQlLUlQJgaQ23OOechg1nrvg3dN69Dhq46SQUytKGi+BPe1+1dUzSH1ilUl9lb0T5r+7INDXdXwB5u2d6Ivo/NOJ+zN3DjXpTScEa4DKzOxn1OS5iQAWWJWBWEyYddG6LPmrFubYrw+u0oCci7NhNgSNktQQgoFGx3jp+FVKGO0J/HN620CW12fNNey5nff3OyfHrmMVN2O4ttZ3yKpAnrsa1kt+zwB4nyXH23PNxaeDWA4UhSZYxn4vmO62S1txXPL6Mx4uTLcXn5G9ZCrLYFVkTc90CGkq8s2mp24natqzbDI/bFMGv7ngcbx9qWjbc2N+IguPCGvZ6hVt6H6p14X0azZPOGWbw69vwa9+N44tetHCxANsNCpkaU5xznr11wTGxQY4J7nY3K9QfcPs2QkbI6S0kTuJ6p7crWc3boTL5i4yRzWO/E8FuwcoO6sbWaT8gPkjZlmB2SbpGRRiTqwtnwvJEmTG+flrouAeRHnPFuTEq8MthGEo8xbtoDtsBiNdu0Mgnj1paQCYURX5js4cvOSu4O4tmnN75CCCGEEKKyaDIshBBCCCEqiybDQgghhBCishydZjg8ROfxTcB6o6D9QjDFpnPOzYDWjax18rWosb3wa1ZAc+v7okFGbzVhjUQ6pnEr1r/esX9X1MCijS1dRiDFHc3S8WA3nFY5r0f96+K21REanTWTsDUJKArqW0slD7ZXftZeswyswAqAzpvvcdiO+3QtK1xqvBKtoJZ3V0zZ+NuiPpxt3kydm1YY1pyJ594g+6o6WJGNZijlMtz4nPTDGQjfuMyDqCsjbfEOaLKXXqX7sBn1j5ySEnW1hftX1oecOz7pmPHYpPvC+nsSxKXsG/OdqB1dePaWKRvOnZ8sbz5u+9BgCS29yqs8OGmvVx3SZ7dJbjsAaW7esueA2uPFl+25z96F9Nw9siCE3WRdW5btxL4feAxEvf5Fa5/WeW+0rmvs2n3W10GnTk1lfDr29dq2HSMab8RvMxr8XQGMEYH00tlWPIcmfTsQOvGcQo/OD9pEnng28HcT2O6SfeYREFKp16FuWZOeDbuxzTd27T66drjcQ2VKlt8Go3FlXemU+t6k9pdtyRK6Z7sujSEwjvP3QAXbMCClC8ay1PcDBVJDNVzD1C7YhhAt02pdewC0PK31qC2lUlhDXfj8zLWgMmyToUf2jNDmA33ngN8tFdIqJzD9+ACeb3ozLIQQQgghKosmw0IIIYQQorIcobWai6+2+dU4vvLmcBe8bs/Xrb2YybRGoTcMr4VrN0zR2d+I+7z/cRtv6pwFqyyb8M41diALG0fzEuEntGQbLdrC4VKM5XA2Kw/pbWbuLJuyZh9kEhlZkLSjfASz9D34D5SkWKmFCTWivME5l2+BrVGZRZ4rhvyNNR7bQkG4JLu9ZopOfT5ul33CZirr9OL59jpWejFYjHGdfiqb4F5ChHBfQi3lHWTvX2M9nt+JL9rsYDle6xpnvCsPB3FGN7PdMclAl5RrJMrwvH1mr0k2H9tVoPD8/PUYoqv3bPvrL8Tjsd0QhlHbNLTUBvH+9Bft8ZrQFWbu2Ws791Jsx36XLPN2opSL+15h/ALGeF1qbAkI27101RTNfB3kSxyqR0kK7bOGEqncnl/O9cZdboLMqk62a9jeE+MHg+NHQQqR2g6OwX3mUSuIMEzsG/QIxrpRGUpiWuv2umcX4j3L9/mKiy29TFbOVIg/kaWsYAWGTlm0nRnm+BzQ2pCb/D6zviFsNTZGB1CSSaReIZohn07eSBP6tg3i/CGwPAy247rgXILtQFEmUWcLV5QB8vlhnfnxYtKo2sLFqyBv6pCME4/NbX7a/r+XjpqyIy1Bb4aFEEIIIURl0WRYCCGEEEJUFk2GhRBCCCFEZTlazfAUeEpJiZZKBesl1MuRbjapJ74fBYJLr1oh4eZTUZTTP086ra14+VA/7JzV2uTklDVaifuZO2l1hA2QunTfXLDHwyzVlMK0sRDr7dc2XRl+0Qqf0XKuAGrIOLUqWmKxHhDsv3KyWMkgrW5BJ9kG+6Mtq1HOQAN08kt2s9H3rU6W+ydtmR+Vp9MOM6AjrLOADRZ7pKHcjr/ZrgctbNiCq70G+uxNm0oW0w2jxts553K0xrO7nN4m6phaq9l6sXchXNzVE7ZoBq7X2F6DtffH6zdYsje9DtnBU5rh5g6lQt+Ovxdfsx8INNaiPo77nrEXozaNmle2G0LtaMFqD/sUp61FHS3pglNWdUZDzFpcsPRiPbHp+5xmFfs3a92hjee7pC9MjOM4tqRSsGZk24jndNTWagbSS3qsN9cTrn3zuh23s/dFG708kQZ7L9rfZApyXI8ln6nUwlC35D65Ljge07rjeWgTDfru4H75OJE3Yl0KmmH4Hehbk2QaaVwvYQHHdTHptFkTDVOgUC8v43mGOR5dT5xLsJUiHiOwrRwcY/YmpWN++U5cbw/fARwKU+qEEb0ZFkIIIYQQlUWTYSGEEEIIUVmOpUwCQ0VJ+xy2oIJ1PUsh4Hch7Ij7oLfrmEGqP7R/O+TzMRQwoAxLoRWP11y2UgHXibGHxmeWTNHq78aQZH3tnilDKzIOueaYcS8VouBrhjY/XAb7CSMracB18z7JHRJZZHLMcsfZyCBcinIK52wo1VObOPPL8dyXnr5gynYuxHs9WLTHG87Hc+CQecjK44knX4hlSy+QnAMyAxastMAWKmeZxAyE0zdsqD1lX5WUP+D1fRdYqxXC15hh7PU3TVF24Wzp4c7+f/Ge9J44bcp2z0Pf27WdffZubFcz13dNWcDwLssB7oAN4CxJXJZAvjRLsdiXr8XllEUeSY2MHSL1dWMhxvuBPlvIApU6Ph6DrdyQLrVTlLj1bLa4vfLiqAAADbNJREFUHGzCWGbl5yFFJ58ftgluxtiWxuX94qit1cw4y3aCCcs5c13uWg1WayO289E5ypiJyhm6zShjYBs0DOVnibBzQQqBP1kqAF2HpQnThvXZxrT5kTiW5uQrV3s+Pl9ZzpHDmD9us4Ti4XV+sCOstC0y6/IFxaIRPftQxcnTE//w9QplLJnC3wV93cP3wXCWzhoMRWc+b6U6+d04X2HrRm8yRyaeZ8y0nTMpv5sOvRkWQgghhBCVRZNhIYQQQghRWTQZFkIIIYQQleVYaIZZw2U1VVZ7kmFqUNKVOrBd4+1QM1zQIa9GP65bn7Ti0c5jIDRibQ1YrrTPW43haBjPof15a2f2ns9FjVP22mumDFNu5my3FBK2QkBRE4fpW0s3K+gPcT8sMy1N9Uv1TOmHU7CuyOiEh6Q5Ar1t+4Xrpqj9AlRr2VrV9c8tTpZ3z9m21F+K9V5+xeodZ96ENLNspTUCnXW/T2Wgzybrp9CN+mK+t+mUy6ncoO/8Phw4iXpMrVt1zrkbt+N2Vy7adSE9c+v2jim7/6GYbr1v3dpc50xsA60LVst/4iVI8bxBGt7Tcfxgu8Ds1Zj6vdAeuH+bwun6uhsPSosK2+XlxzNjBN8HuGcFG0XUM/N2mHqdNKf43UZ472NUl7hcu2W1sWY/ifZS0OIeIzs18/0F9QePOtMmp62FNkFtZ/HlOCZ1T9m2i/rQjMeW/OHrOUd2ZnuQYJrt2JGz+fBlXrdGQzxqhjNq8r1nY0dmPXED0hCz3hb1sMm0wwWLtGntLO3PDGw+azSEoEZ5aKcLhXNKHcMU4fnyegkdsrku1ARP/068Ef41+6w1h+DU3smKJp5Tqe9NyvaxT47JE1IIIYQQQohHjybDQgghhBCishytTOKtV+AcwjL2W10bI8jQXoYtOhKWYmj1wRERD/tpbtq6dAawNluedGI4du43bQh+9bdiNhZ3y0oh0FYo30M4D8OX6dB5+T6MNdGDHeGGtJ8Say5e96BC8IlQR8oNLIA8ppDZDSQ3nspat6IVTPt1G1oMDegaG3a7ADZ2OV3PZDh26kxsXDSdNdlhh5H2zZTHLtxjOLeszrFKCH9eu2HLLkd7PU/h5Au/9Gr8wTIrkLj0n7LWbWgz5VmCdW891oXamMmYtpesTIdxv6bss0WZDpwDj7k4JrHtGkga/KyVn3mwoAsd24c82LDlnLUvJORZZsUjbO9vB8pOUuM4S8XaIK2izHzZK9F6cOHinCnbvhDHsizhasVOYBg+L9inmQ3pJ1z6cdMWmoxpNPtA67OCBVz50d3cdeibfNsxEyxl5kPLuVq/vJ6F+QlkritULC8vqnXi//B9MNelRdJQkFewPZz5zaqoevl21gLOlo3BHXLuDbvT2S+9HvfBzz60MzwomVKOMtW9Z5XbC3ozLIQQQgghKosmw0IIIYQQorJoMiyEEEIIISrL0WqG39KVZPPkJYI2aGRH5GoJHSuk/yxoYzG1MGviNqMubfbeKVPWuwuaU9IjnfutaIfVeOGaKTM6VtYY4rHJYqu2GLXHYcFqvwJoHAvpplEbCRpG55zLsS6jPWjpUnodc0771Kqm9K+JdQsWcAm7OLMdp6DFtN/b1hrPrYLvFmn3QqItJUlpBT203ZS29ADSTj5yptU1J2A7RJNSna32roLlz0Wr/c3PRBs0f9OmO3cw1jR//3Vbhvec0iPn+9UF252Ul6X6XkpHt5f+NS1sg5lK0wsaV0/67IBpq7kPpSzncP+s+cZ7xNpK1D0ftc2aGcuoDIS7hZT0MI7zdc8HcbuF37lqyrKPRuu64YIdLMdwCNb3poS6Rl+cSPVbSBGMj+9pbcFoP7xPU0/Wv8I5sZUbPs9r5Q6Fzo2ozae6uEf9st0Oj8FzidT1zBvxP2qjct1zxk6scJ1y6iop7fbM7Xi8M5+x9mn5dpwrFVKeT2sHuac5weHqhBG9GRZCCCGEEJVFk2EhhBBCCFFZfCqEf5gs+pPhO/2njuTYQgghxJGAod+E7KlgVQe/C5IvtIKkEHW2CDLEE9ZCcnwqyvJ2L86Yss5qrNu4RVIBiGZnQzoeqJaGC3a7EahjWApRtn/nnMtAmpCRcrLeLZ/DjNvx+KPZ0tWcJ4kBSioKsgyAzwElFAUpBJSxLAP3wxno8ibIJHokk4D9sBQCs8fVurasAY6FS1etzGzuK1EaMb5nM0AaSBI2tfzoiKV9nx7/q4dqL/RmWAghhBBCVBZNhoUQQgghRGXRZFgIIYQQQlSWo7VWE0IIIapKwv6uYAFaw9Ws7BE1xKw1zrdi+njf7Zmy+k5nsjxbO2e3q0eBb3+R6g2Hr9lduhpqiAu2a2B1RhpX3CdreI0t2ahcm5pT2nbU4rK+Fy3FWBaMNmVjfmWIh6CqJDXDsG7OmcuhjK9ngHvdP2l32tiOZfNv2O2WXwKryJs2TTzayebbO6YsRy0wtcEwSuXzfucWmkeJ3gwLIYQQQojKosmwEEIIIYSoLJJJCCGEEMeBRHjZSCEa9OjOIa6fkl6QJRuGyOvrHVM2B3KA9qw93rgZ95mRbKGxHUPpedO+b9s5Hz3LhnMkaQDpQEbWYzWIzgcyxkrZoJmMd3xpYT9jyk5nZBmUcc7IOxIJ0jizm3n1mJBQsJyjDrZoq8/aa73w1dvxx701U4YZ4gJlrEVL3ULWzJRF2kFksTymfOuemRBCCCGEEG+DJsNCCCGEEKKyaDIshBBCCCEqizTDQgghxHEHtMCFdMyYqpk0oD6DFM91+8gPI9Ah37xryhpudbJc27U+aH4Yj+H7VuDrB1HgG2bbpmzexfTPw3mqi9EMl+tWh3P2HV6/AZpozm6NGuKEDRqmPHbOuRyEyWzzhq8Q8zqnoo7b1ShtdHM7rtvYsduhJrretfreuVc24o83b5kyo/0dkNA6of31tenegxb0xNNapiXSjB9X9GZYCCGEEEJUFk2GhRBCCCFEZZFMQgghhHgXg+FszkCXClH7ZvQUC32K679xc7KYtch7LGA6NcpShqH1nt1n00cZQX2LUtBBNT2F50MzTlX8ipVeoGyikPUtEZ232eKsR9q4Fc8p85zVLpY1t2zZiRejpqJ138oWmq+DDCXQNevFtHNhQFnemnCd+N6iNILlMfXy6R3arqWs+AqZDvPE+9OQ2Oe7AL0ZFkIIIYQQlUWTYSGEEEIIUVk0GRZCCCGEEJVFmmEhhBDi3UQqbfO4tMi5sd0ua4ImtNWy+wENceizbRfYvI3Kbd7czi4dL2qPaz2afsAxWDebgUVbi/S2w/l4vHHLvt8L9Xh+nB6ZbdjKaNhTcLVu3OfK8z1T1no5pkcOuza9dQDtr2+QVV07nh/quHk/Oem6PeqZyc4sx2uY0gWzDtnohG2Zd7FxFWzX3uXozbAQQgghhKgsmgwLIYQQQojKIpmEEEIIcRxACQBZepWux+smQuIc2saweyFcDlIIs+xs5jPezti8sS1ZtxvLRmSthhZiJAfI12IWtqxjpQmLvSjTaJ+eM2Wj2Vg3T1H9Wh/+ozxZm2u9ZjPzhXaUk4Qbt20Z2pmRLZmfm40/SAaSb23HfZAdnckWx5Zz5dW29yVLTPU4Ux3+pnMw1m68XcpOjdsrYtpuol2n9plab0r0ZlgIIYQQQlQWTYaFEEIIIURl0WRYCCGEEEJUFmmGhRBCiONABprMlAaTNZJoq7WXVLig+wyuPJ0v26eZ7XIqg+MHsvtynagZDjVr14b64jAgKzewIgsda1nmr8d1W2szpqyFaaRZj4pWbnx+QN61GmWXgS1Z26aGdsaOzu4z4H5Ygw2Wd6zrTlmYcbrk0u1GiZTcrBXHe8a6YDheUSuOP1i3PqWmd786+QNAb4aFEEIIIURl0WRYCCGEEEJUFskkhBBCiOPAXiQOZduxNCG12QgsvhLbFULiII0w1l/OuQAhed+wU4wwhO0ozJ2jVKFg2xUlDYW6wD7D2KaL82DlVmDK68QZ4dDeLN/asmVY7/3eSz4+SBMCXRf+Xbad843S9Qr1TNTbe5DOpIzd+NpOey1S2x2yhEJvhoUQQgghRGXRZFgIIYQQQlQWTYaFEEIIIURlkWZYCCGEeFSkUtMeyP7Lrbj2sl2AdMWpGodhoixhWRaGg9KyFId89Y4dByEjPyhS9/NgDrDPtlvYz95bid4MCyGEEEKIyqLJsBBCCCGEqCyaDAshhBBCiMqiybAQQgghhKgsmgwLIYQQQojKosmwEEIIIYSoLJoMCyGEEEKIyqLJsBBCCCGEqCyaDAshhBBCiMqiybAQQgghhKgsmgwLIYQQQojKosmwEEIIIYSoLJoMCyGEEEKIyqLJsBBCCCGEqCyaDAshhBBCiMqiybAQQgghhKgsmgwLIYQQQojKosmwEEIIIYSoLJoMCyGEEEKIyqLJsBBCCCGEqCyaDAshhBBCiMqiybAQQgghhKgsmgwLIYQQQojKosmwEEIIIYSoLJoMCyGEEEKIyqLJsBBCCCGEqCyaDAshhBBCiMriQwhHc2Dv7zrnrh7JwYWoNpdDCKtHXQkhhBDiOHBkk2EhhBBCCCGOGskkhBBCCCFEZdFkWAghhBBCVBZNhoUQQgghRGXRZFgIIYQQQlQWTYaFEEIIIURl0WRYCCGEEEJUFk2GhRBCCCFEZdFkWAghhBBCVBZNhoUQQgghRGX5/wFo3iDkBHUwPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OHSU 93 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GU 75 torch.Size([4, 1, 64, 64, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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urweHY6bo6xN1wqgRDsGneE6ojpztG90zo5TrRYu2DD1j5QtgrVbyzwRou7bd8fNud2j3yam8nwfvn7b75C+88zdc2Y232jPW//jbf92VnfsXZt0YBtR35+AYPM+3IR0z64f7UL8H/C5Ob4aFEEIIIcTEoodhIYQQQggxsRyetVp2MXl77XtfPons+PBoyFBYFV6bR00f6nNZvRa8RRpmK0p6Pixw6SfMbqn7Oh/qq1Rsuw+f/oYrW4EMUlsDH4K/1rXwUJ4s0qo5kApQ2KoxtFDRheaCK9tsV0bLNcoas9u1EEyPMtFgWGlAoSJct5CjLF8QkinlfFinnLMw1oCyZ2EYd6nUGFs2m/fthyHYOmWuQ2s1tHwLwUsoytne2DK2k8Lf2F4hhPC19ROj5eaXllzZyU9bu2fO+1C0CzVGJPGpWvvtC4GiDdq8P5do02QTnLnOrTfrt3NWU1fMzma4kWJldQvIVCrjCzGLEEkoIhjD+7JjgSVVRNZqz/7MmdHyRz/4BVfWAskB24ThWHyp5S29jpesDY4UfQasR7Ysk9a5KR/Wxz7GmeTyIJfa6HtJzfWOzXONvh8LZ6rWftM53x9We7YdyrFCCGG3D7IMyvCFdohF2g5lEit5yv6VsfGVobkMZROdxMs5pjPj+/Hf/fWfHC2f+eUXfCFagU156UUC94No6ubZTUMIIdnwdnTDvRRbw1tAdg7uTZSNy80LZLvmstWRzAqvfZ8dKWb0o2yaCVit1d/lM8Kt/U3b57F53+4Xrtj4WFnxZSiFKNB9A6VwfZID4L0pR9ZqKxW7j1zc8ff2AeyHx/QArNa6HbLi27TfSYHqGmQMSZeeQWDV/MZ4+Vl/ieb4LNq1jZc77MuGh7ZoJIuIcvY7UyCJAYzxuE/36AJKKHx/mZ+2cVumjLWYrY7rugbPNVz2gfmnbP9Z/0zws09+n5X9735MV79u91fO6Bp2QDbB91O8D5NE5A+3/oWs1YQQQgghhED0MCyEEEIIISYWPQwLIYQQQoiJ5RDTMS8lD8389ZsXguYpInsqtExL6mRrtThvP/qkIQELp6s/eKcres0Pm55lOu+1bGjV9cDUdVeGKZgvtLy+l9MQI6h/jUlvW8jaebMtWQfSFe/1fDrHLdATD2MviWmBZc2Q9EiFoh2PU2DiedZK3sZoAVI+dwf+WnuQcnmu6DXYJbi+2YLXgWI65n7sdVqtgZVxnWVAk12ga6iChniK2ha1kah9DMFrPdd6vg9+8Zpp67qP+HY/9Qdgg3aR9MTu4F7/hJaBTDxnOqrMurddS9pQh6RRdlqpI6Z7Hj757PjzugU4zXDk+2aEqa3nvRYwKVqbRzveVgd1kBd/3I/nj/zQn46Wt/peO4o62l48foxy2vIi9Nsp0uk+tmX68mNVr59cBKs1tjNDLncoDXzbtL+lnO+b99RM/90gbf1q17ZbLPr58QbokNNsFO+d8ulS5yCt87G873+oPd5nlQga4h6lq18Gm7lKxs8taHH3Q7//t/25/RLofW9suLKoZm3NKVjjU2YJlbnk0wcP18m+7RbjNMMEWp0lZLsWVaGsTZpr1BezfhJs2KKa1++/9CPHR8srD/v5CjW91y77ee7YSdOtc7tvNe0YZbJIw+9Q+vRtC/alhDTteG9iizRcN6Jz6bRtXh00SZ8NWtzstNfzo8Y2Ib1tgN8RW7LBvTfJk3Yb9L2BH7sKsC4fD9fldMx51IPT8aAu+Bpw3XLNj5UcWNXxfX8O0jPzs5L/dse3O96juWw+Z/PLvnvt//q20fLi5y67sqQE98xtP+9i2nH+RkzpmIUQQgghhCD0MCyEEEIIISaWw0tLVciH5NSREEIImS0fAnUhLpJxOGspsqVJ9mw/LiNPCGHn3Wa31H7Yhw/ROujru8dd2XzRXuE/3TjiytDSJaawzgDCggOykGlDyJ/DQa+ZMynGldasK1ttWbj8zhlvj4XWagMK8VYg1JHPUuY6DHOSxQpa5HT6fp+ZsrULb9cAyQZf33KFwt3Adte2Q7nIy+eZGVu2CG3E8gq0WmMbO2yXjk855MLi58o+rHr6rNX9pePzruyP33KPneen73dlRz9tbRuvk70ZWoVVfdafzJ5JTZI5yi4IyxwadvYy1/w13Faim0alQgje2onXijrWdgllJtr4brNDfNtHvu7KMCMc24RhH8jsi1Uas3kv79npW9/sx156gTaDLK9oglVihWz/sK9uUubEtLAicrXj54ivXz82Wv7wnd4K8kjJLMQ4y9wWjD22RZrPNsM4YnifUo182LQe25w0JGnT1b5JBY6Q9GIB5CS/8qFfdWX/y/0ftH3+/GlXlv/GhdGysyELIWQuUhbJQyRBezi6T+H9LZqmDJYN65NsR5pgX2a7tgXrI8/9mJc7nH6zhZ63Wn7e2VqzuWbl+HYYx27bX0OvB5kj2X4Lw+709JGDe0W7R9cAcP9kK1EkTrEly9dgfqH7VA6sx/otf6K5Otzbp/y8lG3afoZVf7/JV20cu+x3ITh5RWC5A0gbo54/TzztJC2DLE11aN8WV/z8kgNpC0tSsO5jmrFf3DO7vTPT/v6Gz1hscToDFoyL9HxQ/LtfGi1/bvAOVzb/WcgqWfPzJ2clPQh6MyyEEEIIISYWPQwLIYQQQoiJRQ/DQgghhBBiYjk8zXC/P0oRmyx63SXaLaEO+OXC8frDCCxkencuu7K1N9l2777jJVeGqVdZ89camPasTnZmqJUdki4Yy9hC7GTV9FdXSRf8je2jo+X757zO7f5p05xyutgG2KfNlL127+oVqF+yZjlyyrQ8bJGDyh7KaBjW2+PTnWIKTtZLoyXcetvrFps9q+vTMz5lag5SMO92vbbtxW3Twb1x+aorq2XHp4jMgi6zGHltKdrm7Q798Way1p73V7wd0bl7zZbqk/OvcWXPnjk9Wj79u77d898w/VPS8Joxpx0ku6VkGuqQdLWhj5pbslv6NiGDdljUyaKm9ePua0+5suUfvzBafuP0RVf2Utts5Ppk6cW/3blAf2jHXnPaB81rTH0a++Z6x/dp1NWlaZTZ5g1tFVkHf6VtelseX8vT9j0E2wUirEPOke5/HJzGGfV/XLe9xK6pFHm99NbQ6umpXa+tXy7YnP9Q9XlX9nOnf3e0/I//4fe4ssHP3jFazj1zyZVFZRjDlL473Ob05E7v2/f1Es3ANwH0DQDrhB04dqZ9H3wedMJHX+/vKa2+aXObbW/TV5m1eW6v5e99qAXOZMb365htPrs2rvg7myGkTmYNbxvSqLMbLK87roytx6oVq99601/fsG99JD/r26Gfs3OZP+ItvbYv2NiMmn5M5+esPgf9FHu4LF0gzEv7ahrvaWmWbAyUdZt+ris5zbCvW5yX+P5dgVTN83n/ncHuwN9D3XaQpv7Bsp/LMaX7M//ZiiurX7Lvuwrn/TcxaFHovp1JQW+GhRBCCCHExKKHYSGEEEIIMbEcnkwiCfb6emfPF01BCJ6yakUQ4kr6FDKG7VrLZK0DUeL5gn+FfyJrsoVLWS/ZyEUHCx9ymHEmbyGRPO3jRsfC3rW8D8FgVpcNslu61poZLV/c8VmMMNMP25fVzo6XCqAshG1U0mQgmImm2aeQMuynnB8fjp0v+nZfjaxeWAoxU7Tjnah6K6Y3zVtIlK3xkKWCr5f1noUk2YIL7dQ6iR8mQwh9tyicvgSZtX7i2Jdc2R+8x8o+O3ufKzvzW2YVVnrUy3iSJth8TY2Xp0RUluyBbVk8PmR+y8mANRBbSUEGPZQ5hRBc5qwL3+utlv7eytdGy5d63i7K9XEKFaLlIWeSQ5qUqXE6Z2Noq+ezeKF8imULO9CPOWtkJWfhQc4Ih2XMCowbtjfa7dnxWPqT9uoD5x3MBBlCCK3YwudTZM1YCNavOMsc2tjVYz+eL3dsnl2mcYk2bL9y/b2u7O8c+/Ro+b8+9Yeu7J/8w+8aLUcf8xaETm6XTbGgug0kkB01qvi+lDTg3pTx5+ns4lj2NGNz58Xv9+Hk2QcsUx9nGd3YsXoakkUZKhKz1O4DGB/dLtUnhPx7NB5Ccfw8hFKBIYXnE9gP27WN28fLf7DFiOQcKI0YktXZ9Ly1A1uH7sG57O75OTeatXEb0xzSaVn78bmg7du+60PZBG2H+0lyvizpQZ11WEIBmfJ6KRaTLLls2DMISl5CCOHMnMmNrndmXBlmf52meRflaY+373BlPzNv98L1xadc2T/9kN0z7/xNb0MY7cE4omyv49CbYSGEEEIIMbHoYVgIIYQQQkwsehgWQgghhBATy+FphkMY6YGjPKVehLSTgdJqok1GVPV6q+4Js6vKkg6m9IDpTB+oePutz27fO1p+bNVrTl+zZFY0rONr9E2LwnrADqRc5vTBqJtljeEAbJT4eAXQF9+z6K1E0KaGdc5NOM8+pUXNgy0Ub5cBPTEl+g2Nnu2zlDu4TRPqOXd6Xkd4qmba7ed2llzZRsva+s0rl13ZC01bF9Nnh+Cvb0j/9zsC2svFnE/RnUYLUuxmqc7qQ+sHrLP7zvknRsvN1/h+/ZXO3aPls8MzrqzwGKSdrFNq3Knx1mSojUyS8RrU2wpZSYU81AN5Ju299cRo+Sff9zlXhjrh1tDX5d7Aj0UEteFTWa9dw/1Uc77Hu9TrpNNFjR/3fdQwdskiDXX4rEvEbw6YnYGNheut6bHr8XmiFhfHBcO2cqiZn6J1O4nNc5xyGcfGdp/TTVudFckCbj5rY/HEgrc9+3LLdIJLOf/Nwf9w9t+Olj/64z/tyu7836w+E/bmus2gTjgim7ekB+2ScFpe67uclv3C37A5MP8WnzoZrS63KeVye8/GytS8n1vaLZvnel3/qJDN2XlmqO+i1Rpbq7Wbts+kTVpj0AlHaXZpbD2GOlouw+0GtE/oruU5P976oJ+uN8fbgjFoi5bJH7xenEUaF6GeOMUvLWGtMaZqzpPWGE+NzmWvbtdbb/hrz+WtL01X/fy5DSndi/TMM1+wZzq21ENLxpjmkF/Zseexn5zxdolfeO8zo+WXnrnHlc0+af01e30jHAS9GRZCCCGEEBOLHoaFEEIIIcTEcrgyiVfCQEmXQqcxSCHK462Yhid9KD2/ZWU7Z32GrwdXTBqxPvDBPpQmoCwihBAu1s3CjK3H5sv26p9DhGhZxqFFlE1wyACzWe31/bVvdyxksQDHDsFLKjBrXgghzJXGhygw/NshSyXMDDNf8qHMAlj08PEGMYSDUsJd0wUfZtkEK7kz0z4D3SyEjTmsGuchhM1ZjeD3Tt+HfI6k2Pxg1jnOrLXPsgqPd8D/X37syOfd753X2vHON3y2tTu69jv3NGXWGkJ2Igq5urHTOzxrNZc5i2ytogqcI2WXXHsTyAhIjrLZtexHLP1BazDO7Hak5EPr42B7sebAwrsFsuHDsdCkzFKYPY6lEMhK2duLYWZKli+9c+GF0TLbw51vmnyE++1O38KYDRqzCI+hq12bA7MUpp2BDFEomQghhC7UIcqHQvCZ63A5hBCqMO9UM16ugln1Vvt+jkd5xXs+9Jgru/jbZ+0aDhg2vVWgRZqzTQzB2QnuS/sJWbWufsjf+3IgjSjkfH22e9YOu3teWpjJQwbBvh8rEfTXbErm1wFJKELDjhf1xksTQoHGA/6mskwO5rmYbdfAJoyzsPFvBO5T3TZJNVFiELMsCsr2WcDB+KDtnDSCrdVAwrFP0oDrptnKkZWbyzZLx0MbtqhAkilYtUD3fZSEDbkdYN7gezvCUrJHtmxsvn/xGVf2TNsy8r5Q8dkof+nU74+W3/yh065s+kWQSRzQSlFvhoUQQgghxMSih2EhhBBCCDGx6GFYCCGEEEJMLIenGY6C6QcptWRUAvuVure8So4u23p90hyBZc6w7PUsT24cGS0fJd0g2oCgPUgIISyVzW6m3vdp/UrZ8drRDmiGe2SphOkHWT+Dul3WJrJOGEFrtwLZJnGqVwStTNIs0djyBB2A2B6uDbZyZbJdw2t6cXvRleVAe1ma8ttda5uV0FLJ9wm0icqQprELmlHWQqJWkTWNaLM1l/eWQ/14/LDB9Mxx4u2PZrK2nxuJ1zv+0LE/Hy3/wuu8DdX1Xaun491jrt+uxwgAACAASURBVCxz0TTu+9IZI8PxVlq3Gpd+lgshVfPGw/7a3v2Br4+Wdwde741jaJZs0J7dsXS0tYIv283aflATH0IIm2D/db3t04lutm1e4NTkqKOrUPrxNJ0wflewS9aMKyXTEJezfp/Y/3jsHSvvjpZrZB03zONY9/3hCmqU6ZxRP43a6RC8VR1r+d2x6b0Lbsfa5iaModmsH3tHcnZ9rBlGffFPLH7Rlf2n73vDaPnE/zd+Hr0tcCrlMUSk073+QdNP9t7hNeY10AmXqQ9e27S+zPpXd1qkd0fN8KBH95Bd6xMZ0gVjcyb8ui0z3kIMf6elXM7lx89lA0phjRLewJpauB/ElI7Z2bz16fpyKdZ8eH10v0H9dFKla8Dj0XkmrC92hbB/Op6zWuNHABjirLPGo/XIqs5ZIuZ9P97r2NwwW/RWdWjzOJP1ZW+ete9gpjK+7C01sxX9cue0K/vo1PXR8l1H113ZsGjPiUnNP9ONQ2+GhRBCCCHExKKHYSGEEEIIMbEcnkwiSUL4y/Bp0YfeMHTKWakGCxbmzF/dcWXNe81uJkMZ6LauWaiofdTbqKA9EdsYXWjMj5ZnCv4VPloqNUhCgTYjbIeCUohpCmWiRRqHQNFWqDf0TYfhiw6VJSn2ZpylypXBdmmhYZZloDSiRCFelJa8fslnAixn7drZBi3OjP9/Ww7kDkWSluA+WSbRia0d2LprJmdtjRnnQvDtwKFopEHSixc71j+zwR/vpxYsrJs591lX9vOrHxkt713yRn1zWyAZadO5gNwoDMeH62850HbRlD//JG99deMNfpw8ULP+8VLbW0lhf99ng9ZDqYpvc5Q7PJ2suDK0oOLAJI6harE3tqwz8GOvBNm/OCtTP7LYJdsaNkHWst0bnwFrj2QLaCV3rrzqyp5oWzanEyU/dx4BWQaPvQHEuut0vOt9kwLx3IlzywJlhkS5FI+F3Yy10am8t1jcGpql3h0FHxrdhLI78z5z3et/4Buj5ad2HnBlCy+cD7eTBMci26fB7+59Phvq3rtsfJcou2WzY31+m+zTnDQiJfPZoEXWapAhbp8UAqbjuECh9C7IcWhKyqAEgBPJwfBonaSsrTM25oZDfy/AuSAiSUGmZOMobrNsAfbTpfsL3rO5qJfyDjFNBoJShX3tYIucSS7CzHlpyRNZCoGH4GcAyI4XsRSCrdZws4K1S476YB6eh/i5AiVhN3pePohSxqcaR13Za6fsHpAmw3rLwkX3+0+nTHJXWJe1mhBCCCGEEKnoYVgIIYQQQkwsehgWQgghhBATyyFqhkNIXtHFZbJk7dEyvWZy6ogryzZAAzrltVGXPmIalrOnfVrlc2XTVnL6zy2wVBqStgatuljXh1qlWt5bOHUiTAk7XrPSGeTHlrHuBs9lkIzXTbE1Eup10tIjs+avCHpHLkNYo4zHa/V92levAPSg5mgq78VmM5DekVPs5uHcMimWPNzuqDW93vFWWvMFs186W/baRNzPWt/rny61TWPONn3ToDn/20c+48qe7ZsVzLvKF1zZB1779Gj5K0886Pc5azrJDNkQhjxYtLE28TaC6Wf32UrBOS7d69PkXuiYpdyA7LcKkBL7iW1vyYZ6dtbwdiHlbKfrxx5qKzOshwM7py6lrZ0pW9/sk7Yebf/6NA+ghrhLY2i7Y33nyo7vmw/eZTq6z14858rmatZv12q+b25BuvOHZ19wZZ/dune0zJaOMZx32jxQIE30ZseON03jebFgfRX1+SGEcKa4Nlr+3Z3Xu7JPX7Lz/C/u8SnNcezfS3rinz/2B6Pln/yb864s/B/h9pJAHZJ9WgQply990Ouzl+atXrgPlgump1zf9rr8AWhsE54ewVIsGqZ8P1IcP69m2r7PZ1FST6/bBmXQ99J+UBrLutxBHb7vqPl+lgFbuXw25T5Fv2PQ7Ec0vzidLktonb6XiqAOOa2y2w/Vtat7doxFK7c0W7cU27y0dfkaMMV0xO03sHrabfhvCwqgJ+7RvIt2q3xvr8A3Rvxcw/ds5PrQ5o0PTD3pyj5Te6ftPy/NsBBCCCGEEKnoYVgIIYQQQkwsh5iBLgrRX9oqUagIQ7q9BR9qLmzZq/Hde30YsDpv9kBsg7bTtVf6GL4LwYcrWe6Ar+35lf0uWB71KctbCV79p4XuOSyA8D7T1sXwJWe9wutjmYSzT6P9b3fGWzqhZVSerNXwN5dlUizZMMzKdYbylVrOW1uhNctCxksF0D6tS/KKNmS6Wiz67S40F0bL39j2di/zJbOJWij6bFYzeet391a9VOe+koW3CxQLK0UQ5ox9ePTnj35qtPz+5de5smEVwofTPjwaWhaOShbn7O+ra+F2EuXBsqzv7XE6x+2c7557zpW9tnJltHydso1hhkCWBWEWOJTehBBCIzPedg3h/lfIHSyDH1sQNnrWlpihKYQQ7po3WQgfrwthxpNz3gYNM+W98dgVV4ZyhOcby67set3q+g8z3l4MJWDHK7uubJAi80JJBUso8Pdq2/dNzKp3Z2l8f/zw7GPuN9bTlZ6XO9xXvjZafqnvy95YNIHW/3zu467s74W3jj3+LSEL4Xm69209ZHNN6T7f7j0IUXMYGmUSbD3mYCkEWGwlWb4Pp9iEgYyB5QB4+Axlb0txx3KPAZxNLYDt24AypsULdg0Yqg/Bn3aeyrqwH84qh1nfIpYfYDfnrG9oU0a3fXeMfd6NsFj24yhTBhtCkoGgnIsfo2I4b84giHaQGbJWQ5kZZ/urle35aLo03la01fcStAbYXbapDOdonj9bU7Yd25jWYV46m/dZhfs1kIFwvx6D3gwLIYQQQoiJRQ/DQgghhBBiYtHDsBBCCCGEmFgOVTM8SsPMfi+L8/vXf4W4BGn93um3e82CpeBk7S/aceVIr4lpjzmtMup2WdeHaYdxOYQQmmQpNm47TrmM9kusn0GNI+uCe2ANkyOBF+t2EdQOsYYym2JRtdM0PXGaRo11TNjUrH/idRHUFb31qE+9iBZpLzZ92t4Hpy+Pll9oeQ0lWrKtdb2msUK6ZORY2fRJb6j5c5nNtnj1m5Z1El+fnWS8xd5sxtZdebPXIbe/ZteU3/J9LgKLwhAfXjrmpAra813fF5tH7LrfNuPT4j7ePDlanqa8rnN5022/Y/ElV3atY/piTh88Bxrv3LSvE7b8QVA32xz6er6wa/PVYsWnHV5rmvXdypTXpeN8Uu/680QddJc0ds/tWZtzqnW0cdztes3/UtXOrUnzHLLb92nEMfU726fF8J0BW0HimEXtdAghvLBntnnrnZorWyl7/R+C6dbb1A6rObOga5Lu/ljOdNBvKo6fm28HUdnqN8l5LefaW2z5eNn3x62m/34GqYMefd+cC78xPXEIISSQHjlhuy/QknIZ6onjKbI6K8IxSGs/6Nv1xg3fr7MNuNeSAyN2rVyd7pl5a09Ox5xL0/pDxeyzlYOpIWFNbUA9asq8mnI/Yzu6uAj2oCV/8Vm4hgxdH95D+X7aBevImDTDEWrF6RKyBfvD0rSfs6aL1if52QXnM35Wwu+W2GYR540bTf8d2Hbf+jx/03R5YPP8gwWffr29CG3bPdj3HnozLIQQQgghJhY9DAshhBBCiInlcK3Vcjc//GDBrIP6U/71fnvJXv2v3OWzDH3nkmUheWTnTr9PeKXPr9uzEC7ncF6tYHKLNmWLQ1kBZ22aApkGhw97w/FWReUUS7YcWLvFdA3NgYWKdjs+zNnugb1Y19d5HI///1AGQrUcnkE4VEul7heG8Pr97NgyDvkMIBXO4xvHXdlds2ZRdWHPS2z2IOT7vsVnXNlX906NltESLQQfus2lZa4jy6+prO1nOVt3ZWifxmBZNfJ9KR9Zm/3snb/nyv7LMx+z9eo+xFQBa7Wom+JpdItJqtYGUd3LCFrLIP0h+dJG19qA+3slYzKWFoXLcSzwWMP9ZEjadL1tYfb7pr0cBW351rs+rI9WRCyPwuxfCyV/7d9YNRsttoA7t2Bz26XunCvLgmyGxx6GGRtdfy4ok9jt+jkC5zKWfG1BNrzliu/TOZBgcdgUf7MECyVgqy1fnzjPcp1lwTJziqQzfZCKHc/7XJcdKBtyXPg2k5TtHhOXfBthuHyn7dsImSIJRRPami8P+yfP486mjKQCTr04oDkepQNkPRZ3rK6TnO8TEVqBVfw8h4eP9sbfI1m2kK2D5Vzw9+9BdbwtWQ7kCAPKeOdkE3SLTFLuhQ7uZrAflpZkyzaOcnnWiMB6bK0G59Jp+77Ub4+X3uFtOaKqrpRsbkVZRAghzBdtPLKkdKNt4xifm0II4VR1e7S8VPBzCD6PZeh5oZaz/czn/FwwlbG5gO9uvVmwxusd7N73qg/DURT96KusEocQdkIIjydJcvlV1hVCCCGEEOLbhoO8Gf61sN8i+qZEUfS5EMLHkiQ5/6orCyGEEEIIccgc5GH4X4b0h+FMCGExhPDmEML7Qgifj6Lo9UmSbKVsI4QQQgghxKHzqg/DSZL8+EF2FEVRNoTwcyGE/y6E8DMhhJ9N3SATOe0U0lmyv3fmvKClecy0PN9z5EVXhpox1rMgRbIH2gP9HGtkUAPI6ZF3hmZdxJq/KdDMsJYuSbFIwxSmGUpvinZImFo1hBBaLStLOH0k6oNY/wRlrCdL4LwHbD3jdGj+PLOg6YpI95ymL8ZUmpxiF+uw3vZtez5jOmFMxRtCCC9uWFrleu9BV/ZdR78xWn6qccyVoTVfa+C1WKiTZI3TztD07gtZb0vj9KrUthkQmJVIo9yHOlumfYZ3WMrWjeD1yyeumtYz0xpvG3arGVYhdfKs1zW3HjTd11LOa8kwZe8a6XSf3DkyWubxfEfN/h++XCLLQ7Bamy94DdpWz6dpR1AnPlvw9nk4R7BFWgXS5PKchHPGVMlr7Epw7XfP+XTFz++YfeC52fWxZTxfXd01TfRizV97H8bXqap/j5GWBj41VTNcXyHrNflpemKkl7L/EuX25TGF4L0hPlig89aRgWuveF1nZcXapVr09o6bOzYGBgVfLwNI1czzfxZstEplv89Gxx4BogLZrvF9BMEi1iiDNVfCaYBhnxk6Xiih/twX5UAXzDZocQHuNx3+RgCsQ3O0U9wPdwno8hHrpZObrxdCCAkcI8PXjvfFqr/2IZw330/L0Gb9AX1D1bK5Z9imsQK2eXx9WUjxXKQ5Mg9Wbvyd1F5kzzxsP3puxuYi/q4CLWP7NKZrWZv7zlY3XBl+G3Kp678HOle07zouDHxDwGYh6o/XYLttDrTWAUiSZJgkyd8PITwaQvieb9V+hRBCCCGEuFXcCmu1z4cQzt6C/QohhBBCCPEt5VZYq3VCCK+e4ieTMcslSpnTWsKQiN+sN23v+88UfYhwtW9hQA6vYdh9u+MzM82ANGKPLIdQxsByB9wnhyQ5vIBwaH0cg8Tv89qehZibe/48MfzEWXgyufHHy4M0obvj91k5b9eQ81HO4E6Ndo9R1aGv6lB5q4VBjk75sDjWIYdm98AujqUXuy07SDvn6326Ym17dcPLCH69+dbR8o/d9WVX9iTIJtjiDjPz5clSb6Nv8pXZrA9FL+Qt3F2kjh1n7Dw71O598Ep6fdFnofo79352tPzfr33YlfVWLOxfOn94MolBzdpkWJpxZW85a9/aNmM/bWD2uLtrXipQhRDdattLhtB678TUjivrgNzhL26cdGX3LNoxWEKBbV6kjI4o0+hm/ZSKPYdtG1GqwFKPyw2zU2Ors51HVkbLjzVWXFnjTju3U+dWXVm9O35axhkYM9yFQNZH0fi55FRl2/0+VrS63+x7CQrLoBAMbedpnGA2zeW8z1SHVkvn8j4j1dbQ5o94n+fVbQayQeav+TorfMb67sbDfi5bnLP5MkvtUMeMe6xog3VbDd8H8V6RdCnMDveNiKzHEszsxioCsGtj6QVmtYtJ0hBwO5ZC5OFcqAu6LkKv9yLI9JYUKMNe337nGnQ8GMbDmu8vCd5POTFfy66pvEr2p2dtHOfyNP9DmL9a8ZKpNmSS69ZJ/ol1kZIlsDTn5/97lm2uY3nRTMHWHZKECee+NFnS2ZqXO6AUYv+Ytrlvhu6nWbjAO0v+HvC2oo2dx3teljGNFg6Dw8tAd3cIYe1V1xJCCCGEEOKQ+ZY+DEdR9JoQwodDCF/6Vu5XCCGEEEKIW8FBkm68+1VWyYQQFkIID4UQPvbKPv/5f/ipCSGEEEIIcWs5iGb4T8LBkm5EIYRhCOGnkyR55NVWTqIoJIWbH74/ZTqVyipZUPXH272s90w7WCBd327PNGPHa7uubLvrdZjIRsO0bqxz66dohrHCSjlKr5tF3Y2vWkwle2XLa1y7q3aeSYm0WGCfkwxJ3wV1MTPjbaHaj5r12MIVfy6bb7HzPnbaa4DSEjBvN0HDu+tFwxGkhmZrlgfmro+Wr3R8Ctr6GBu+EEL46iXTfrLFXQeONzPlhc/be1afv/CV97mylSOmd3z7ygVXhjZb2/3xdlxf2L3X/wHkspyaGX8vkQ1VH2r7xb63VqtmQIs1TbZJx+36Si8cTDd1K4jzdv6DqfHWR63YtzHaDGKdh+DHycmq110er9j4Rt1xCD6l510n/DcHp4pmKVYfev18PdjvLn2PgHNNmhaWNbuYHjntWwXm4e9+fLT8uRfu9tvBfMIpUVt9sIcrktUZ6IK7Qz8vY7p6/m4Cx/A0fVhwo2ffOBRJC4j6v27s27YF2nG0VmKw74cQQj22ueZPWudcGdocvq7g57LbTgw6XUrHXN60uq7+W993d8/aeD751y74fYJj4Y2Bty9EO8uYLKiyReu7+3oc6on5FonfpfA3KvmDabKzlI4ZLcUSTu29a/WUbfjxh9JV+vwoxKh77lK6aShLMr5s6gKcZ5fvp3C8lO9lBvS9jFuPrg/rbG/PbxiD/d2+A6KWm78Ngt/da/4+9fjmHaPls3f51PPPb4I9Y9/XdRG+MWJ7Rkyxvlj296ljZZuTeS5YhXmiTON9mVI3Iz/y4vfbOX/ptCu74+lW+GY5yMPwF0L6w3AcQtgNITweQvj1JEleTFlXCCGEEEKIbxsOknTjO27DeQghhBBCCHHbuRXWagcjCiHOv/xaPYr9i+cBRAzZWi3ftHDG7tDHbjCUytZmKEfgTFCNnoVgBiR3QFsalkIE+J1w9GJgVcsyCQwnrLf9NWxumUVIZpXCxrCbfiElFMXZZsDGZfuqt7Y68pztZ/cuH7qprVioY6tO1khgrVOg61uasvBJNO3DJXjtVxv+XJ5as6xiK9M+PPLQonmlcMaqe+43C6krbS+v+LOrFg7q9n13n5+x8+xUfFkL5BVfuHqnK3vD8tXRMod80DZmhsLGmJ3ues9LYHC7VvmqK0MJxZuLPjsYZuFiS55eDdqM44e3kXzD6ii/6zvnpT1rr61pb4+D9mlD+tYXpQlNkkKg5c9ej+wCYZ/3Vr31GIbn2QoMLYZisv1LsxhCWUG14KUx9V5KlkwIGWPmuJePZ3X49jPnXVkHJA4swTo17eUk7jxBWsXWjyj94LGH7dAia7x7KhZ+RduzEELYgFD+fM7PEUs5s0zrkc1gJ7FjTGW8XdSRnIVi5ylTYyex8Zw9xLEQQghhaHUWdf0NrrBnv/dO+zmpcsPaZfNX73Bla++zfl2q+VBzr2v7QVlECCEMO+PLXHbS0vgyrk68Zw5JlpG04HgNX1YEK7LyGkkvhva7edQfsLOIcgfaDrLHcRnKRwZkn9ZesX5Xu+y3y7ft96Doz6W9ZL87KzQvgK3csO+vHS3n9mX+w3E8YO84WCT7u9wUWKSRlVu0bnPPdsvLMtB+L27656h41vZZnPH3vgIcYyrv70U4Z7aHfp5AWVaDsr2ug2XabN7PIbMF+42ylhBCyLb9XHsQDvQwHEXR3wovKx7/pyR52fQ0iqKfDiH89E1W/3ySJD/xTZ+JEEIIIYQQt5lXtVaLouiNIYRfDCHU/vJB+BVmQwinb/LvR6Moev239jSFEEIIIYT41nMQn+EfDiH0Qgj/7CZlSXj57XL+lX/Lr6z7H3+rTlAIIYQQQohbxUFkEu8KITySJMlN/WjobfFGFEV//Mo26URhJDaKhl5bMyyDJqfk9TN5kIJd73kt3VdXT4yWdza9/vANd10cLV/c9brSLlgOcarfLuitcjlKy4hpQ/OsCwbt0CBHZfZ/kHrL6waTtmmVsuQqNP+knVv9FFlNnYEfpCcewDXMfZ00eHN2nt17vCanDNc3W/NWJb2B7YdcaUITNNjzZb9dA3TW69s+jW6haHVY7/p6eWrP9MRzBX+eqI3sxr6uc1mw5+r4OuuAhrjb9WXnVsx263UzXsP72y88OFqOT/qL/4HFPx8t/+vVh1zZVN70Vt8//xeu7CToHWMyrsuDJpXVqWugvWRNdLUDfXl4MLujW0FuF/RjsT+P1Yvzo+X+cd83MSUy64Jb8E0Aa3jRdo370dF506Ou9X3/Q/039yP8HoF14lvD8daMnM7U7RNt/8pe/7rTNh3ffUte24y63em8326ra/U5X/Rj78KelbE9UCVvGju2T8M5sUDWheWsbcd6YrRMYxu0+0s2pq72/Xz8bPfoaPlEwWvkT0Ga5QyNBrSA4z4xHdnxrwy+eT3ht5II08NSqljU12doPLtGo25Vfcb6efNuSjsM96247ue5/K6Nuf40vRuDFMj7Jh5s66zvTRGkJC5ukS4YmrO05Xda2oF2oQ7ambN9kuTUW52R3hZPk8uiIYqifdmwZGXtZd4O7pkL9L1TNcXCEtMlU9O61NQ8ZRwwNVpC1moxjOOTK/57gelTft5A7l0w20NOE5/27QR+r8BWmGhti+meQ/DfKBwt+RTrM2AzOpX1252dtXnxH3xkyZV1L9qzYanl555xHKSaz4UQvn6Tv0fh5nazF0IId97k70IIIYQQQnxbcZCH4akQws2cj//PEMJ7b/L3nVe2EUIIIYQQ4tuag8gk6iGEef5jkiQXQwgX968e5kMIzZv8fR+jrC8DCnd17IVz42TwZfDG+0rL21Mh1VkfSr8GNl4xhQE7bYu7FEvjMx7FFGbJQgi+mPPhEZQRbOx6ycYAs7qwDdoUhB2nfDhvrWqhhqU/93W28CSEL3K+WSPwwWlTS2683Y6R4dgpMKQ6K4EshEOnKFuYK/lQ7RJkplnb8pmSihCqffPyZVeWA+uxPPntYTiWwzO1knWYFklShmAnFVPWvqNlC9c8unXKlWE/+L6Fr7myzYG1NYaQQwjhDbVLo+V+8JKAZ/vLo+V78muubB5OrUMefrtDC6fnqQ/mWxjmPDyZBI7vqO/DbrXz1ldX3+z7wx0lC4lziGw2b/1qp+9lCpdaFnbn0H01Z/2Bs5v1ycZrHGxZhtZA/Xj8+wWWH2RhwLGUqla08+R+hGHF2ZwfX4UMzHN07Xje+8pgn5xFL0mReuA+B1R/aLU2QyHVpzrHR8ssdztXtvBnPfbWeBd6i6NltEtj1nq+Ly2Cvo7nj9sOSJaSaW/h98JH7ZruOnfFlaG157kZnz3xQsMm9ht7/l0UZhGLKr4vxfPWtrnheHkM95d8fnwdJtCc8RG/3QC2a/I4SrHwazatH8TbXifhLNpSXMkSlnNgGd370GqtP0vbQRZclEyEEELpBsgHqYpa99rclyuQxR2cW9xiDQUcg7P7oUUbFQ33rC9dTrwUaWba5g2UEoYQQpLY2GHZ6EzJ5mG2YER53xxJtOp9P46REkgxdvre5m2ja/fTHFVoPrLtfursl1zZP337R0bLd2ylpAIEDvJm+EII4a0H2tvLvPWVbYQQQgghhPi25iAPw58PIbwxiqK3v9qKURQ9FEJ4Uwjhc/+hJyaEEEIIIcSt5iAPw78cXg7m/2YURfeOWymKontCCL8RQhiGEH7lW3N6QgghhBBC3DpeVTOcJMnzURT9oxDCz4cQvhZF0W+Fl9/8/qU3zrEQwvtDCD8QQiiGEP5+kiTPH+jorzyKR6RnxPSHm6/zm6Dc69mNZV8G+pYs6d52m6YbGZLOJ18w7QnriQug7eG0w6jh2trx2i9MUTk/61ODVgumVWR9HmqluqQj7EH64NWq10sXboAFXHu8vUz7jNdJZiAFZ7LjtVgt0JC1Ml5vmwcbtBMLO64M7VhY+3W+btq2WtXrQN+8YjphTsuIKbRZf4T1xHrOese2i6iu0U6tXPb18sVLZ229Va9J/ZF3fXG0XIm8bcvHN98yWsZ0wiGEcCxv9jYdSl2LKWgZdCAakvYSU9KyPVfUBO3g8JB1kq8wnPH6rcZp6yvf2Drqyq6UrI+z3nava33g7Ix3fXzTrGmzn24ccWWoSWNrINS8bfd8m+fAtqtI2jW0Epwp+jbYhfPE7whCoLS1KVrj5aL/fvmRNfNR/JO1c67sbzzw2Gj5yV1fn2ma4V6MqeVJB5kznel6y89zC0X4PITljKAh3hiwjZ3t8/PX7/Jlx61PsK57N7F2Yds1tFY7VvVWUvcWTIf827tvDIcK2HjtPuC1nEfusL6M95cQQujC70fBRjSEEDpg0zck7W8GtOlsAdoH+7Z+h/og9IMsa1zhHsr3TPyWhr/F6MDvYZc0+vA706Z9wndEOXLGQ6l6wpeAjmV03x9WQLtdpvkR0h5HXapPOD6eVwghlLbA/vRM8EA7cBsVS2BtSN9+DEHzHXM65lzKtyDQfgltt9eweTBHx0ML2cWa//yLrdbGsdnx8wTeo/m5Buel6aJ/zjhXW7vpeiGEcK5o6d4/s/eA3ye0UdQ72DkfKB1zkiT/IHr56e6/DS8n1PgorRKFEAbh5Qfhf3igIwshhBBCCHHIHOhhOITRA/G/DCH8JyGEd4QQ/vKVy40QwpdCCL+WJMlL3/pTFEIIIYQQ4tZw4IfhEEJIkuR8COHnvtUnEVGGkOnzZovWOOHDla3TKu35DwAAIABJREFU9v67ddHb52RXbDsOe2N4KE+OPB3ITFar+HPZhkx2yXP+FT5Guqtv8GG5KbD06lAmoZ2WhSjYugTh0PAAwqz5GtlCgf9Wv+bjSEWw0xlvcBJCdNRvVy7aMTjLHEo9OMS7NwAbnH0hV2iHDGeQsrqYL5AlW8GkJmilFoIPfb+ws+jKsG2HFKZOIGzWuuH7UmnDrun+D553ZQ9VXxgtf2Lbh1wxy9w7p71aqBVb/2F7Jwwp18kyajayui5Fvs7mc1YvO23fukc2rA8mg4OFim4JWavn8x/xNoMfeJPl81kpeqnIcw2TQS1WfLhufs5+Z6lOUO7A/Wija+G7VooUpx/7vnJ2ymzeXtjzfQylTiiLCCGEFoSv+zQP9CAzZES+hu2ybfeVcIcre8389dHynSQRQWkEnwuOUx6XePRSbnxfaXV9naGVYYEy8zUgayBbcc1lrP3+8zs/78pW++bNhXKKEEJ4ou7lAQiOoYdrz7myLFzhJ6/e78pmwgvhtpKxdtg77fvZnRUbz+3BeOu4WsHP/xuJ9Wu2nMN7TK/n+6CTMbCDXprVJsyl3Hdxn3x/w7IM2YTFMB8nOc4IB+tRtTj5A10DdvPhtO+DUcl+Z7b9TjO98XaCwzLIQAr++vYgYy4fD+UHXC8Dlj/gdiBRSVKy4O5rrhRLxATs2vi+iLT6vl5QfsqyBbZaG8c+mz6wZq3l/PPXED5rW8h5uRj28z++do8rW/yGzUVJ4WCPuQdM9CeEEEIIIcRfPfQwLIQQQgghJhY9DAshhBBCiInlm9IM3yqiDul7t037e+QrXs+y3jUdWuNBb2OUXDJ9cX3FX1q+bNqzLKUfzEIqxJ0L3rIsC6kXW3d7PcvUnOkROR1zG7SCRbKzQacP1uIOQFM1IC0PpnEe9Ej/WrGdVqf8eaI2cbBOquFZ8qkB2juwLul8NtFyruz3MVuz9qvl/blUwG6sF5PFCmiOWFNbBD1ikbSJj26YjnD9Ga/nrF0C/Rq552AW2M6S7xNnQCf8nUtPubJfW33naJk1qUdA97o19PrYUgTabdJCrpP1FNJPrJ5W8t7G7lTO7KXqN/w+jjdBgxsd3v9746Kd/9IbV13Z3VWzx3mm4a3AlovekhBZ7ZrGe6ngtWQ7A5sHWPuLFmmXGt7Waqth283XfLuiTniq4Ps02rxt1r2lUAY0djzvJJBWtnjUa6JRU7i25/vRVtPOk+cW1PRyilm0c+q1vPa3NmvXWw/+24ijU1a/7bbfbrVlfe5kzX83cb1jA+x40ffbP1w3K6QPLvrxVQeB6O7AW/G9cfpiGMcutPt81vedJ7rHRstbX19yZbddMwy+m92F8SltWTM8W7J5td7zbeSt1UgPHqO+d7zmlLW/mK+YNa5Z0K6yHrzfG/9YgTZvg7ZfLwu6Xcq+7s+ZJK5sp4YM5+AbFbovDtatb3Gq5sE06HtjvkljOmY63hT8gTeD682W/bjF1Ow8T6D1GauAUX+LabdDCCGG845oQ2xPtlLE382OH+99eCbhb5rS9lmCeapI3ySg9phTz2fBr3GGUs9vDmxe3Hnc3/cXLtpclOTHa6LdeRxoLSGEEEIIIf4KoodhIYQQQggxsXxbyCQC2T5FTQsHFa/5kMEKvBofVH0ILQ/R0sxFH0ZqHbPX/d3TPgaDYYmpU97eqZAbn4EOpRCNtj8ehgwwhMVwOAEt4Dg0VQK7uE7k99kHSUOGQmFTIFsoUDa8BmRoa7f8NQTOygNkoV7KxfFSiymSScznLRzM1izIcsG3w3bfws+PbPjUPltfNQuueM6fc+1DlsEGrzWEEDp7ECbr+f8XXty2EPpnKAv5StnOrTv0QwgzxKEsIoQQnmpZqPbpXZ8Zbalk7cKZ61AWkq/6Prg+MLnA7BP+XKIh1C/rcW4jgxpmaNt0ZZgF7vldH76ugRxhj2zCsAztvZgOtc9uz9p8u+nnDxxvyxUvvWiC7RpnUFoHaUTuK16q0rzD+uOpu71E5PKs1Uscjw8rZot+PBfBiogzbeJ8whnvMONigebHftX63JFZP/YG0EZH5ml+hGx8HWqH3a7V70sF37bnptZHy5ydbj5nc8SFzoIr+60rbxotv2fFWxfeWfT1i/y7umW5q1w7vLEQQggBMq4OC+PnQG7bnY7VJ1vqrdftvjik/olHSCirXQKH2BcuB7uvDMkIUDaX5tq4z3YNLcS649/FxXQrwlPbd9uAaxjM+pMpzti9ni3EMgs2hww7NHc2YV0amwlYwiVs89a3a0py42UnfLwEZIecnc7JFek+FeAYUcH3lwikLJkMy1zG39txXe4TXbCH5OeTHPSRaonkkXm7F/J9H+1IZ/NeCtFnTQzwXMdkdbXLvizqwPG2/Fw+Dr0ZFkIIIYQQE4sehoUQQgghxMSih2EhhBBCCDGxHKpmOBq8rB1JYtK6DMHWpEX63j3T2U2/5IVF6x8yncq542uubLPlLY/cPkGblSOdVg80VpyKFG2NksTrQ1G3y7qbtLSoCFvUFCBVdI7SMg7AAq55YcaVxVN2nqVpr+XpbHrdpAN1RnSaQ9AH9rK+HUqz1mbtGRJVwc/jRW/FtNYz/etTjWOu7Ks3zD4t+qy3xBreAfZVs15vWwed8Hyl7cqOgGVUlbTNaPu20fZ9By2PFkreEuvru8dHy48lPnXsexYtRez9lWuu7E+2LZ0k94m/Nvf4aPlCz2svf/3iW0fLy4+SFdkALHkKvu/eToZF6++ssUYbrT5Z9eA4qZPeG3W7bDOFtcfyQtSNx3S8U/PWH4+VvTa2XwLLQ7JrO1bdHS3vfthrm2cKNhae3PQ68eqUldU3fB9b37X9ZKt+bsHrK1HaebRj7Lb92MsVbB7on/Vj4RTohK8+6sde9bIdcecBr8ksL5vGr1nzfWwI/fjp3RVXtlS2vjqT9+dyV8XmbrZR/METj46WX2r7sYCOcF9tn/ZFsJ/he73NW/jn4fbStTYrbvk+iP26nPPtvtowXfBm1/cX1G9myJoLNa8J9Xn8liBh7W8fdcGU4rkIKYJpvsJ7X5+0sWHH+ki+5bdjmzJfaIt8yxxWQDdb8jvhc3PbwbNEoCpzVmt5P4tEYK3GumDUE+8TN6e8eoy74zXKEeiEE95nmvw9ueni/tX4eNB/+FsG/j0O1gVX89bnOeVyMQtjkyqpC/fhtf60K3uhaeO/dp3avWxtG9X9PXrsOR9oLSGEEEIIIf4KoodhIYQQQggxsRyaTCIaxCG39fLr64jTo6BsgiUGEIrbfK0vW5i30BtnnsKsJ8MU2QKHY5E+WbOgdRHbofQh6xvb0mAWoGLRx6ZwP8WSD5O1OuND3SXIYNW56jNWZSCk1W3SPjDzzS5lhDthIVDOXJdUbJ8ZCk116hCKprp+17RJBX5/60FX9sePWlaq3B5lDmvbfuhwIdeAMNKLXvZRP2H7adb9NZTBTurojA+L58Eyim22BmD3stPzx1sp27qY7SyEED69ev9oud33YccPH39itHyisOXK/q8blvHu0p6XiNT/1Gzl5te89CKegnMjKdLtJMlBxi22JetZX+XQWh4sxGbKXi51rGbShBe3ffYhZ2vY98dDaUSF7H9Q8lKkDIFdyITWp5RXmDXpzpq3vHqxYee2ftm3XXHe5AGzS17igtmdpunatyFTXmOz4soitH2iLJXVGbAtInXUCvbxN/l+VH6bXd8HZ33ZY1uQ/bHpQ/dzIEuqd8nKDeRnmZpv98ud+dFyLevb6HrPMoTOkQ1TC/y4piiF2amijal/9JpPuLJfDneF2wqkBpx/hjKXvsvmhSNVPyc1CnZ9zT5lEIS+vBf7eS7C7JNZfzyfpcz36wHYhEUtsmQDqQCHzhOQV2AGVyYm+UGAS4qG47cbTNMNAO5v/CiBWVvjFj3u4OH5tWCKrsBJI1Js3gK5gqH8YH8qOfgDSSCjEjwjsOzD+eZx5kHMLkibpWQedFkCB35DzNZboOyXOEdX8l6+dbS8G8YxnQNZ5dD362NF246ztt5ommyiet7fozO7II3IKgOdEEIIIYQQqehhWAghhBBCTCx6GBZCCCGEEBPL4VmrxUmIOq/oSvJkv5WMF+z0p+CUWT4DOhjUfIYQQn0I6VT749NVMqjhzVMKwwGUVctkWQb6ILZIG3TsetneCW1pSqQZxnMpFLxeJ/tl089MkUXOXsWOn5/y59nvmL4sOdKhMki9yP9tQo3TutcDZldsP0+ve0ul/+rf/choubTuG/Dkc3bixU1vt5QZgBYyJbXw3lkvhtyFNLrDsm/p5rJpic53fB8sQorphZrXJmIq4A2y7HtxzTSiWbI4Or1gusW3rFx0ZVe7poX85PUHXNnFy6CJ7fuGOHoejjEke5k86DLbvt1vJ1Fs9X5tbdaVvW3pwmg5S5rhCqSlns77vtmDbwLumPEa6wa0eYNs/1CXXKT06rjP802fBniQkhYUjbq+snOHK0MdXaZGOmTQ7/P8gTPGkOYI1GiePOnTW++2bTzvXfdpjvGbg96ur5ftGqQDJ73fbMHGIn8D8Lq5q6PlqyXftpgKm7XiaFl5peG3awzs3FZKXguIx8/RHL+RNf15JeOvAdv9jsJ6OEySjvWJmUdvuLJn3mEpZk+93VtP4jXstOkbDqgX/n6F5yEEv9fh267Tn+f9PiOc//mTnyJul2LFxffvfXmWAdgnfgMTQggxaOOTtj/PNGs1vxP6jbvh08Jdkt424PdBfH3xeJ3uvv3gdsPx7yyTFM2ws6OL/f5TzwXgZ5BZmM8Wy/47hxJYpPHYxLm9TGMzD556RfoOYBXsVtmWs/H/2FipdGhMD1GffUA7uAOtJYQQQgghxF9B9DAshBBCCCEmlsOTSSRJCP1XXqsXSCYBGXrYEiozsNft+YZ//d3q2n6KufEWaVnKMoc2Rv19Vkx2jH1yBwjPdDLeEgTt1DhLlNtH34dfhy3Yj48ehngLQv5LVC8PgXUJXV8RzrtHWakwPBM3qQwtXjiqU7aQSOGq3y6pm1ShsejrBd1YFp/wIZjyixbyTYpUn9tmMxTP+Uw0GchSOEUSijhn59JepExJYG/Wm/Ft2561PuED0SGUZ+28X7tw3ZWdOmEh+wrZQu0OzAZrteuv4ZGrp+2cv+YzCJ7+czte6YYPTWU2oN0pzhmDTCJahH1euRpuJ/2K1eXSH/l6Pn/a5AjNnu9HaCXVoCxzW02rSx7P7bb1nYVZX1+zJQv5s5UbZpbrkTUjWjVyyB/3Uy36sY7nluz4Ph1NWbsW9kmwYD3O5gSSiu2WlwVhBs2o6mUgK7MmORj8trdku3jj5Gj55DuvuLK/uGJldyx428a5koU1MzRJYPa9rY6XE6Ft3i6F/LEfsIVYswfSkoKva7QynCp4Wc2JiolZrvW9xd1tB0LWSd33z7s+bvX5pVlv+XbPaZtruM/jtV8nWQ3OemwPin2Lo8kYPk8olI4ReczI9vJvsGSj+wZmduPsbU5iwPZiUBZ3SLKE0jGyMeX9jCUtsxpdO8pH9m0XjZdJYF2zfCNBK7mDSjtC8PIO3g6qad/x4Hex4O/DKO3KZ8dn9IvJYrIKsjaWqeLcsDfwc9Z8AWzQEt9ez+9ZlrkrXzjpys580TJVcqbipG3zfMQy3DHozbAQQgghhJhY9DAshBBCCCEmFj0MCyGEEEKIieXwNMNRFELuFVELe7qkpI4trJsWZPolf/pbVdNhrp1mbZQts9UM/mZ9XgblSHmvg8mDvVmftL99SEncIC1uVID0kaRxSkDjNOj668st2rWj7VkIISTnTZPXX/QaILSiSdopTc7Vjjom+m8TprYclvw1FHbA5qfo66W0aWXlyz7daNgz/RyrppKu6SSjnr++UDHNYa7udbol0GmihjaEEOIC2DRRlup+BWzJSDNWzdsxWHd6qW2pZNtD3+6YuvnpF4+5smOfsuNNveRTV2a3QFfY8deX9EA3maE+j+NqcEDt3C1g5ikwH+t6nWf3S9Zvq6/zIvlnv8PqEu2aQqB03dRZ0LlnreL1aZvHTJN5cnHHlU1Bu3I6942Gnefent9nDGM/X/HX198BPSz1ozzMA7tNv0+v5fT9qNcb3zfbA+vIWdJLXrlhWtnk3fTNQdcGOF5rCF4nyPZGqy2zb1uhtOW7Pbv2Ytbrl3E/OZqPcUyxrdxM0bSBrJut5cfbB15rm2b+7bUXx653W8DvGga+XnIXVkfLpz9+ypVd+ilrv5Nz1HdBI72d932p0bZ7UZrNWrJP/wqLZdKOdsHOjJ25sE+yjhUOn+mQDRpqlMmSLQGdbkRa4wRPgFMLkw2b2w7ub/vsxVJkuzl4DmAbO6xDrs+0soBpj1lojavydrgq66XdHEL1Av2gRGmVp2GMFVj7G43vP3WwRJzJe2vUIVzELNmn4X3ykRtnXFn8Cfum5Owf+W9dkizUGY2jBO4z0gwLIYQQQgjxKuhhWAghhBBCTCzfFtZqSdnbJkVZCFFSVq1M017h16767bozFpZrDXyor7dkr9Hz0z6UiWFAljtkUkINaIvGFmmZkh2Ps7dhljnO6JSFcFSGwoAJhAzPHN9wZWvTZnnUX6cwJ4RyoiGFbjLjrWBQJpFQCMbZ53AkCvZTu+R3OvcshDJJDhOBJiVhOcAQpSwUEtk1uUWU91166lEL3U5lKaSVg1BfxfelYdV+d5a9ndT5kxbOf4bt7yAiw/VSgiY7+4y/vuIVKNz14eYEZSHD8WG/QJKU7J6NlSQ/PoParSbJWh8YHPG2cfl1k4CsP0i2hjCGVr5ImRr7MC5TEleRUiUMnrKxceF+by+WzFs9J2TflN+y3+VdspKCJsl1vN6mcRJCvzSGBpAZksOtaVmhMMS6b+jBqaWozfaFVHMt27D1dW89lkAdXlr3IfiZkybpmSqMlymwRdNW2+p+Y83bDEZ1qxcMj4cQQmXFbJiWppquDEO6HZJzPAiZ8v5o22d4DKERbit4QyArKcxOV3n8siurfNJCyGvf6+fA/Ixd+6lpn7nupaGFmll20gXbzeFg/Lsx7o+Zan/Mml6KkSPLQOznbFWK9xu+FUUp96kIjpdh2WGKTVkGM+xRGZ73gOzo8F6/L7kZnmaa3IGtztIs2fCaWAoB9VIskzwyM34CKIGd2nzZyxZwHJVytE+4QJYIlrO27oBkZg/Uro2WP7V6vyt7/qnjo+Wjf+ovfvbLZvOY0L096tsYiBt+LsDJj58lxqE3w0IIIYQQYmLRw7AQQgghhJhY9DAshBBCCCEmlkPUDMemjyJbDPQzS6peo4ZakMK614nMPYdeMF4DWrlhwrf2sr/s3hnTacWcHhksh+Jp0s+gDo4zS6IeKSUV4j6LFdhPTP9XyYKO6fyVJVfm3GXKvj7jHtpQjdciRmwFw/okXLePemJfhpdb2CNd0fOWQtHlnA3Bp+Vue2uWCMRZCfUXLAub3nIoWTa93GCBUsJeA23d1TVXloOUqbWnycIJNO37bFvgdzLlNalRB7Tq7fHpI5P++PGQxj6dHRwvLlbDYRFdt4TWBbLtCnCtZz6+7ooGs1B/nGl0D6z2WEeN6W6LZEFYtPYZFqZcWfOk6X2Lm/6ApS3bZ7br+8P0S5DimSzsooHpzffO+dNc/Jwdb/MNpMlftv4x7JPWHXSXmQJpMsHyMCK9bfnZ8Snq0UFp5kX6TmNg59Za9IO9vmP64ieOem390hHTE+82/Dye/bqtu3yZ9KigB0eLwxBCaK+Y5vziKd+X1hZN+/i6o9dc2e/83kOj5cGUP95d4c/CbSUHfbLfG7taQnPgymdM93z+2AlXtvewjYezC/57ku2ujaMbu77PozVfvujnHdTK7vucBL8ZIT2xS2ve8vNjqQbXy7eXFH1vjGOApbBYRFJmpyem8ZBJsVSNcYyR9jaGGx5Jvt3YTChNdUhLuVywHWXJDs4/Z4yvo5mq7y/8PRIyVbT+UiVLwhLYIA4o5XIZNMQ5sllDyzQ+9q888S479p/4++LdXzXNfvayvw/j/TRq+/OM9+DbmpQPJNCWNQ29GRZCCCGEEBOLHoaFEEIIIcTEcngyiRDMl4ReY2PAIqIQcf/08mg5f3XLlRXXbd3pkg+vNY7Z7+VHfRhiu24hvNZxet0OP4cFymAGVl1RlkMp40Mizlqtn2J5xfIKOESu5ONB/V0LgcZd1i3gwdMOR3ErCN2EPlvBQNGcr8/itnWr8haF/MEmzGVPC8GHD/nc9qU5gjKUTXDI/LKFS7NX/DW4FuP9o70fh2DQ6owlDXkoozAn2sPtO088HssiUkJAUQGsvHgcQYfJgiyDzvj2QhnoUBoTtbx0JDdMuW6sd5bb4HpNtjeyPnD0c/547VMWgkdpQAgh9KZBfhD7slyKhd3Kn5lUoLPkffjmfvTSaHnzGsuewGaK7aJQBkJWidipWYLVOmN9M7/lx9rs07Y8/bS35sJrKm56qcX8M7Z87WEf/tzaNonS4tf8adauWp3ldn2/jaC/x2UfZu/esONvt31ZZ8MkAF8PPsNjDKd9zy+uurIUs8JbQoQWj9mSK0NrNfbkTJo2n5z4jJcIvnSf9d0jVZ/Z83jVpGPbLS9X6fatHyzP+H1eXbf+GnM/S8nQhtZqccO30bCMWT/Zdm28bZezM8vweEA/QX9i+dL42Q4lIim3l31ZHmMcfzFLmMafi7tp0phGaUSl6uelCtigsTVeE6wcUfoQQggDWJfrc6bg700IZoucz/r5ujm041Vz/nh58Jj8zcff4sru+C07l+oz1/0B4d6UzLHNokkvkjpZIKbZjKJ0cp//3c3Rm2EhhBBCCDGx6GFYCCGEEEJMLHoYFkIIIYQQE8vhaob/UqhDGryAafc4HXPH9DP94/N+s0tmKVMhnUjzqGmqejX/f4Dlr5r2Zbvh06l2Z20/vZ7XPw0WTFvDti2om9qnRwLtUCZHWmPUWJFW6Z4zpnXb7Xqt2bUmnDenYcQ0m2yXlpKyFXVMya7XCqLtWm6P9Nn3WX0uP+r3mdRAV7hOuiWXSzbF1i1i/TL8Zh0R6m/ThGF8DKfrI10YpHdM+HigX46KZGWF++E0rKCf5pTSCWqG6Rqc7prKXPbPb+Lav9VEJasHtouKj5g1V3bTp6GOQF+cFP24dLpt0lQnqCHOsKYW6qHl05CWt0zfG5F+veTmJBo0kNab+wrOZdWrvg1e+ouTttoZr4cbYgpYlmui1rHtzxPTRvcXSS8JY3ZY8OcyfQn6Uc9/jxDB9WZzdH0w9o58xWsdMx1rh9y617FG2EasI8d9lPwYytZt3st0vZXb1n22br/ltYd3fcK+L+HU67ebGLSPUdnP4yFv/dzph4O3EMw9763jZj9312h574f9PuO8tdFM2e9zDyxI+2n6V/5+Bb4hyZRJ+wv3m/yM15VmoQ/2upReFyW1eb/PAdiD7vsmBsYD64nRMo0tTodwfawLzsJ9ech66RTLMvd9DmuGU8DzRI1wCCHMlsB2M+XY2SjtGYSsIkEXzKnLh7AuaoRDCOF+SKu8O/DfCPzff/zO0fI9v+nn8szFG/Zjyo/bZNp+Rzt+O5dmOU0jzGTwe66DvfPVm2EhhBBCCDGx6GFYCCGEEEJMLIcnk4gTF252YEiXwuXZVbOJGdy14ne5YKGxqOtDDXNPW6hh5xxlQ2pbyGDxMR9a7M9YmKAz58Mz/ZpVX3fOSyiaD1g4qlzz19natPBCRNmlQslCARFJKPZAGrHd8CGKgPuh7QKHeQDMUsXHGzYxm5Vvh+KG1cXp3/FZ3zLbFupIKOSPWWQSlkKAxCA1rE/7dP0lzSKtT+mJUELBoXbsm1kOy6VILzBTHme+gTIOj0ZRmuddgiuOL+PNUELBNna3EazLfRn7cL0KhYwhlD6c9f09i7aGDcpWiBks97VBSj1DGG6f/AVD1tRvI5RwsFQFzmXpC35umXvG5qudu33oEOVZnUVq4/L4Np8Dq7ONN/h+W71s5zL3vD+X4kWwqUyRLWR3fRgT7QKLKeHIhKQl7go4jRe2UcNLWSLoS2Wy4juyCdn+7vGZ1gYLNucP8/48x/fIWwT2M8pEifNCVKLxEEOfpCycK580m77zR+5wZfd/8Dk7NEkhttasD+arvt1R+sc2fQlYsnFW0wx0u+F1yiB71MZqpUr3xaZJNliagBldhxTyx/tWvuDrJU0qEMM18bSKsoX9TpfO/JU2hGWSTqJFYrYwPuSPdnchhFCo2rqlrL+HdUHiwNeHmeUGnCYW95/153K0ZJKmxbwf7//m0htGy9GvL7qyez5vfXDfvDtr/SzhCt2wuSem8ZBmK+oahhsQxlh8wHuf3gwLIYQQQoiJRQ/DQgghhBBiYtHDsBBCCCGEmFiiw7Jcmo7mk7dF7z+UYwshhBCHQYa1wK4QtPDVqi8DbXVCum6nW5/1tnLnP3p0tHzP+150ZfW+6XSvbc+4si6kuy6TvrddH38NhYqd22Dgtap5sEyLSGuMGt5+j78LgfV6fp8Z0N9yimc8XvzvaXXG4Llhmmjejj9PwJTWxbLX/pbATq2Y97rn+bLp5qcLXlMbg054EI9/t9mLfX3eM2U2rcsFrwv+3auvHS03f++IKzv6KbBIW9/0BwEr0ajqv/FImqD9J12w+z4jzRr1m/heJk1r/KnWv7ppR9CbYSGEEEIIMbHoYVgIIYQQQkwsh5uBTgghhBAvA+HdpE4ZGWemeW0DswbWm67o7L+yrGGX1s66su4HzEarRJnPMENcp+0zkaEUgiUNA7AGK5a8nCOGUP5g4N/FJVCWzXEGOrD5JAvQQgmyfkbjM9BxlD2XHR9Kz4HdGCexRLO/tIxwhWJ/bNlU2ctOZoomHchSBtlCBqQedLweWKvFZPN2qrI9Wj5Z2nJln7z+wGh561PHXNmJT5gUYnr7WX/iYJEYTXn7QmeNuuszToY+lLEUAuQOUYEyjeJqZCfopBBs1/Yem+1cAAAE5ElEQVTvgd4MCyGEEEKIiUUPw0IIIYQQYmLRw7AQQgghhJhYpBkWQgghbhNoZxqxkBUhHWRSb1jRsk+FG4PNVYb3CTZsR/7fF1xR75mTo+Wr7/F2ackZSo2Lu9wzG63CFOmCh3befbIey6HeN/HXh/remGzCkj78JkctPEaOtMYZEPyyvncA55nJsM2blRVIS13Ijb++UsnWXap57fZGw6zyOOVyL2/7KZNmOJcZn475ZNl0wefKq67sE6sPjpY/+4k3ubLTH18bLVc2SReMLMy5nxGkQE8a/vqc9V/f63ujLNQTW6JBf2VdMOqLo1zK42qKldpB7YP1ZlgIIYQQQkwsehgWQgghxP/f3v28xllFYQD+JpNJJm1SU0otbcCI7hSELvyP3blwK7pVkC67EVdFhUqxFabG/Gom40KYe84dHRoXiXieZ/WVO/lmMnMDL52Xc6EsJ9ABwDUZhZO6VtbW1Sbi4/oRVPGe/VfN563GsLG3m39uMgkPzFng6PHB8vrFp7kOcPYgjDOb5mrC5nZbiyfA/fUU7ffrqwkboR5w3o1rOz9uv+/mNFcF8ql2aSlVLybjf34t/Ui2nVCNiGPPhiGPN5ud5WrJ0Wn7HHaneXza/VutVrC/dZzWHk7bKLK9cX6+7Y32Ws4uJ2nty+cfLa9/+yafFvf+F606M5odpbW0R6Z5Py7iCXEreym89/2ItPDm9/sz7sEV4T59Ho1/D+uyaqphDF29otsUTqADAICOMAwAQFnCMAAAZRmtBgDX5TJ0H/uzfoO1/cn+aNr4764/OQqd0MVpNy5tzZi32989W15/+LTrxn7ycHn96uO8dn6n3fN0pxtZtt86p7v7J2ltPo9HNXcd0HCbeb8WxqBNtvL7Mh63H7zoxrWNQ094qxvJFt+Jk4vc041d43HXNT54Z7a8/mDvVVo7mU/+9noYhuHJy8Pl9YtZPub47KfW837wJC0N+0/bMcu37uYe8iJ2eLt+b9o/s67PGzu83T5L48128uc+bLTPZd0+WzcG7SrSfu3vGf/Gxm/Xw/c/wwAAlCUMAwBQltFqAHBNRpMwdmpNTeIqp9PFr4lX6hXhq+2VkWzxhLbzPLJs7Ylf4bSx0bQ7ue7e/vL6zd2dtPb7YXvsy8f5lqNH7av1O3v5K/9xGMM27k5oi7WFeVeF2Azj1A52Z2nt3nYbddbXHebhnn2l4eKy1QH2JrkO8PMf7cS27589ys/3bbvP7i+5frDzvL2W0XEeyfbm3VabOHovv9dbr9t7cfuHl2lteB3GqfW1mlAjWKlCbLc90u+BtLf6sWun4XX3Y9fe0r8drXYVX518ZrQaAABEwjAAAGUJwwAAlKUzDADA/97Xl5/rDAMAQCQMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZwjAAAGUJwwAAlCUMAwBQljAMAEBZo8VicTNPPBr9OgzDjzfy5FDb4WKxuH/TLwIA/gtuLAwDAMBNU5MAAKAsYRgAgLKEYQAAyhKGAQAoSxgGAKAsYRgAgLKEYQAAyhKGAQAoSxgGAKCsPwH1jA+AW3E5igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = \"viridis\"\n",
    "\n",
    "for s in sources:\n",
    "    s_idx = idx[dataset_abide.labels.loc[idx, \"SOURCE\"] == s]\n",
    "    i = s_idx[0]\n",
    "#     img = load_nii_to_array(dataset_abide.mri_files[i])\n",
    "    img = dataset_abide[i]\n",
    "    print(s, len(s_idx), img.shape)\n",
    "#     display_brain_img([img], site=dataset_abide.labels[\"SOURCE\"][i], cmap=cmap, vmin=0., vmax=None, axis=True)\n",
    "    display_brain_img(img[0].numpy(), site=dataset_abide.labels[\"SOURCE\"][i], cmap=cmap, vmin=0., vmax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KKI     211\n",
       "NYU     105\n",
       "OHSU     93\n",
       "GU       75\n",
       "BNI      58\n",
       "SDSU     58\n",
       "IP       55\n",
       "EMC      54\n",
       "OILH     53\n",
       "TCD      42\n",
       "IU       40\n",
       "ETH      37\n",
       "USM      33\n",
       "UCD      32\n",
       "UCLA     32\n",
       "KUL      28\n",
       "Name: SOURCE, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_abide.labels[\"SOURCE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "484"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_abide.set_target(\"DX_GROUP\", True, domain_target=\"SOURCE\");\n",
    "select_indices = dataset_abide.labels.index[dataset_abide.target.notnull()]\n",
    "len(select_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 484/484 [00:50<00:00,  9.52it/s]\n"
     ]
    }
   ],
   "source": [
    "# create a tensor with train dataset\n",
    "tensors_abide = []\n",
    "targets_abide = []\n",
    "sources_abide = []\n",
    "for i in tqdm(select_indices):\n",
    "    x, y, a = dataset_abide[i]\n",
    "#     print(x.size())\n",
    "    tensors_abide.append(x)\n",
    "    targets_abide.append(y)\n",
    "    sources_abide.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1936, 1, 64, 64, 64])\n"
     ]
    }
   ],
   "source": [
    "tensors_abide = torch.cat(tensors_abide, dim=0)\n",
    "print(tensors_abide.size())\n",
    "torch.save(tensors_abide, \"tensors/fmri-4sl_abide2_z-norm_4-sites\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([484])\n"
     ]
    }
   ],
   "source": [
    "targets_abide = torch.Tensor(targets_abide)\n",
    "print(targets_abide.size())\n",
    "torch.save(targets_abide, \"tensors/targets_abide2_4-sites\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([484])\n"
     ]
    }
   ],
   "source": [
    "sources_abide = torch.Tensor(sources_abide)\n",
    "print(sources_abide.size())\n",
    "torch.save(sources_abide, \"tensors/sources_abide2_4-sites\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0.0, 'GU'), (1.0, 'KKI'), (2.0, 'NYU'), (3.0, 'OHSU')}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set((list(zip(sources_abide.numpy(), dataset_abide.labels.loc[select_indices, \"SOURCE\"].tolist()))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1936, 1, 64, 64, 64]) torch.Size([1936]) torch.Size([1936])\n",
      "tensor(0.) tensor(0.9894)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tensors_abide = torch.load(\"tensors/fmri-4sl_abide2_z-norm_4-sites\")\n",
    "targets_abide = torch.load(\"tensors/targets_abide2_4-sites\")\n",
    "sources_abide = torch.load(\"tensors/sources_abide2_4-sites\")\n",
    "\n",
    "targets_abide = targets_abide.repeat(4, 1).t().reshape(-1)\n",
    "sources_abide = sources_abide.repeat(4, 1).t().reshape(-1)\n",
    "\n",
    "print(tensors_abide.size(), targets_abide.size(), sources_abide.size())\n",
    "n = tensors_abide[3] # random tensor\n",
    "print(n.min(), n.max())\n",
    "display_brain_img(tensors_abide[3].numpy(), vmin=0., vmax=1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1936, 4])\n",
      "torch.Size([1936, 4])\n"
     ]
    }
   ],
   "source": [
    "sources_abide_ohe = []\n",
    "for v in sources_abide.unique():\n",
    "    sources_abide_ohe.append((sources_abide == v).float())\n",
    "sources_abide_ohe = torch.stack(sources_abide_ohe, dim=-1)\n",
    "print(sources_abide_ohe.size())\n",
    "\n",
    "\n",
    "# attrs_abide = torch.cat((sources_abide_ohe, age_groups_abide_ohe), dim=-1)\n",
    "attrs_abide = sources_abide_ohe\n",
    "print(attrs_abide.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GU', 'KKI', 'NYU', 'OHSU']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_labels = [l[1] for l in sorted({\n",
    "    (0.0, 'GU'), (1.0, 'KKI'), (2.0, 'NYU'), (3.0, 'OHSU')\n",
    "})]\n",
    "site_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "general_args = {\n",
    "    # opt\n",
    "    \"learning_rate\" : 3e-5,\n",
    "    \"weight_decay\" : 0,\n",
    "    # scheduler\n",
    "    \"step_size\" : 50,\n",
    "    \"gamma\" : 0.5, # lr *= gamma\n",
    "    # training\n",
    "    \"batch_size\" : 128,\n",
    "    \"n_epochs\" : 100,\n",
    "    \"epsilon\" : 0,\n",
    "    \"n_splits\" : 10,\n",
    "    \"n_repeats\" : 1,\n",
    "    \"mode\" : \"clf\",\n",
    "    # model\n",
    "    \"img_shape\" : (1, 64, 64, 64),\n",
    "    \"n_outputs\" : 2,\n",
    "    \"conv_model\" : [32, 64, 128, 256, 512],\n",
    "    \"lrelu_slope\" : 0.2,\n",
    "    \"noises\" : [0, 0, 0, 0, 0],\n",
    "    \"n_attrs_outputs\" : [4],\n",
    "    # disc\n",
    "    \"D_conv_model\" : [512],\n",
    "    \"D_fc_model\" : [512, 256, 128, 64, 32],\n",
    "    \"D_dropout\" : 0.3,\n",
    "    \"D_batchnorm\" : True,\n",
    "    # D_training\n",
    "    \"D_learning_rate\" : 1e-5,\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 100,\n",
    "    \"n_pretr_epochs\" : 0,\n",
    "    \"min_lambda\" : 0,\n",
    "    \"inc_lambda\" : 1e-2 / 200,\n",
    "    \"max_lambda\" : 1e-2,\n",
    "    \"lambda_scheduler\" : \"linear\",\n",
    "    \"lambda_gamma\" : 10,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(args):\n",
    "    AE = FaderNet3d(\n",
    "        args[\"img_shape\"],\n",
    "        args[\"conv_model\"],\n",
    "        args[\"lrelu_slope\"],\n",
    "        args[\"noises\"],\n",
    "        args[\"n_attrs_outputs\"],\n",
    "    )\n",
    "    AE = AE.to(device)\n",
    "    \n",
    "    D = LatentDiscriminator(\n",
    "        AE.decoder.latent_shape,\n",
    "        args[\"n_attrs_outputs\"],\n",
    "        args[\"D_conv_model\"],\n",
    "        args[\"D_fc_model\"],\n",
    "        args[\"D_dropout\"],\n",
    "        args[\"D_batchnorm\"]\n",
    "    )\n",
    "    D = D.to(device)\n",
    "    \n",
    "    AE_opt = torch.optim.Adam(AE.parameters(), lr=args[\"learning_rate\"], \n",
    "                           weight_decay=args[\"weight_decay\"])\n",
    "    D_opt = torch.optim.Adam(D.parameters(), lr=args[\"D_learning_rate\"], \n",
    "                           weight_decay=args[\"weight_decay\"])\n",
    "    return AE, D, AE_opt, D_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "484\n",
      "['GU', 'KKI', 'NYU', 'OHSU']\n",
      "512 activations of size: [2, 2, 2]\n",
      "1 activations of size: [64, 64, 64]\n"
     ]
    }
   ],
   "source": [
    "args = {k : v for k, v in general_args.items()}\n",
    "args.update({\n",
    "    \"batch_size\" : 64,\n",
    "    \"learning_rate\" : 1e-4,\n",
    "    \"D_learning_rate\" : 3e-5,\n",
    "    \"n_epochs\" : 100,\n",
    "    \"n_pretr_epochs\" : 0, # no lambda increment\n",
    "    # Fadernet\n",
    "    \"img_shape\" : (1, 64, 64, 64),\n",
    "    \"conv_model\" : [32, 64, 128, 256, 512],\n",
    "    \"lrely_slope\" : 0.2,\n",
    "    \"noises\" : [0, 0, 0, 0, 0, 0],\n",
    "    \"n_attrs_outputs\" : [4],\n",
    "    # disc\n",
    "    \"D_conv_model\" : [],\n",
    "    \"D_fc_model\" : [512, 128, 32],\n",
    "    \"D_dropout\" : 0.3,\n",
    "    \"D_batchnorm\" : True,\n",
    "    # D_training\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 100,\n",
    "    \"min_lambda\" : 0,\n",
    "    \"inc_lambda\" : 3e-3 / 100,\n",
    "    \"max_lambda\" : 3e-3,\n",
    "})\n",
    "\n",
    "tensor_dataset = data.TensorDataset(tensors_abide, targets_abide, attrs_abide)\n",
    "# train on whole, val on random subset (since we ~know that perf is same for train and val)\n",
    "idx = np.arange(len(tensor_dataset))\n",
    "train_idx = idx#[::8]\n",
    "val_idx = idx[::4]\n",
    "print(len(val_idx))\n",
    "print(site_labels)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, train_idx), batch_size=args[\"batch_size\"], shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, val_idx), batch_size=args[\"batch_size\"], shuffle=False)\n",
    "# set target for domain classification\n",
    "AE, D, AE_opt, D_opt = create_model(args)\n",
    "train_stats = {\"D_loss\" : [], \"rec_loss\" : [], \"AE_loss\" : [], \"val rec_loss\" : [], \"LR_auc\" : [],}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare\n",
    "\n",
    "# - check with constatnly growing d-loss weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "484\n",
      "['GU', 'KKI', 'NYU', 'OHSU']\n",
      "512 activations of size: [2, 2, 2]\n",
      "1 activations of size: [64, 64, 64]\n"
     ]
    }
   ],
   "source": [
    "args = {k : v for k, v in general_args.items()}\n",
    "args.update({\n",
    "    \"batch_size\" : 64,\n",
    "    \"learning_rate\" : 1e-4,\n",
    "    \"D_learning_rate\" : 3e-5,\n",
    "    \"n_epochs\" : 100,\n",
    "    \"n_pretr_epochs\" : 0, # no lambda increment\n",
    "    # Fadernet\n",
    "    \"img_shape\" : (1, 64, 64, 64),\n",
    "    \"conv_model\" : [32, 64, 128, 256, 512],\n",
    "    \"lrely_slope\" : 0.2,\n",
    "    \"n_attrs_outputs\" : [4],\n",
    "    # disc\n",
    "    \"D_conv_model\" : [],\n",
    "    \"D_fc_model\" : [512, 128, 32],\n",
    "    \"D_dropout\" : 0.3,\n",
    "    \"D_batchnorm\" : True,\n",
    "    # D_training\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 100,\n",
    "    \"min_lambda\" : 0,\n",
    "    \"inc_lambda\" : 2e-3 / 100,\n",
    "    \"max_lambda\" : 10e-3,\n",
    "})\n",
    "\n",
    "tensor_dataset = data.TensorDataset(tensors_abide, targets_abide, attrs_abide)\n",
    "# train on whole, val on random subset (since we ~know that perf is same for train and val)\n",
    "idx = np.arange(len(tensor_dataset))\n",
    "train_idx = idx#[::8]\n",
    "val_idx = idx[::4]\n",
    "print(len(val_idx))\n",
    "print(site_labels)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, train_idx), batch_size=args[\"batch_size\"], shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, val_idx), batch_size=args[\"batch_size\"], shuffle=False)\n",
    "# set target for domain classification\n",
    "AE, D, AE_opt, D_opt = create_model(args)\n",
    "train_stats = {\"D_loss\" : [], \"rec_loss\" : [], \"AE_loss\" : [], \"val rec_loss\" : [], \"LR_auc\" : [],}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "D_loss 0.5334307214906139\n",
      "rec_loss 0.0008947197122559432\n",
      "AE_loss 0.0058118738834896395\n",
      "val rec_loss 0.0009471947196288966\n",
      "LR_auc 0.613618039808516\n",
      "LR_auc_CV 0.613618039808516\n",
      "LR_auc_LSO nan\n"
     ]
    }
   ],
   "source": [
    "train_stats[\"LR_auc_CV\"] = train_stats[\"LR_auc\"]\n",
    "train_stats[\"LR_auc_LSO\"] = [np.nan for _ in range(len(train_stats[\"LR_auc\"]))]\n",
    "\n",
    "print(len(train_stats[\"LR_auc_CV\"]))\n",
    "for k in train_stats:\n",
    "    print(k, train_stats[k][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train recon\n",
      "tensor(0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val recon\n",
      "tensor(2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/31 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  AE loss: \t\t\t0.013085\n",
      "  rec loss (in-iteration): \t0.000490\n",
      "  D loss: \t\t\t0.531036\n",
      "  val rec loss: \t0.000853\n",
      "  LR auc CV: \t0.557902\n",
      "  LR auc LSO: \t0.478061\n",
      "TRAIN EPOCH 1...\n",
      "0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|████████  | 25/31 [01:30<00:21,  3.61s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-3db836ac9280>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m                              \u001b[0mAE_crit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMSELoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD_crit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBCEWithLogitsLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                              \u001b[0msite_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msite_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_stats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_stats\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                              freq=10)\n\u001b[0m",
      "\u001b[0;32m<ipython-input-39-de06d7e069b2>\u001b[0m in \u001b[0;36mtrain_fadernet\u001b[0;34m(train_loader, val_loader, args, AE, D, AE_opt, D_opt, device, AE_crit, D_crit, freq, cmap, site_labels, subgroup_labels, train_stats)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mAE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;31m# here train_loader returns x, y, a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0minputs_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m             \u001b[0minputs_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1129\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1130\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    383\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'numpy'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'string_'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "args[\"n_epochs\"] = 100\n",
    "args[\"min_lambda\"] = 10e-3\n",
    "train_stats = train_fadernet(train_loader, val_loader, args, \n",
    "                             AE, D, AE_opt, D_opt, device, \n",
    "                             AE_crit=nn.MSELoss(), D_crit=nn.BCEWithLogitsLoss(),\n",
    "                             site_labels=site_labels, train_stats=train_stats,\n",
    "                             freq=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "484\n",
      "['GU', 'KKI', 'NYU', 'OHSU']\n",
      "256 activations of size: [4, 4, 4]\n",
      "1 activations of size: [64, 64, 64]\n"
     ]
    }
   ],
   "source": [
    "args = {k : v for k, v in general_args.items()}\n",
    "args.update({\n",
    "    \"batch_size\" : 64,\n",
    "    \"learning_rate\" : 1e-4,\n",
    "    \"D_learning_rate\" : 3e-5,\n",
    "    \"n_epochs\" : 100,\n",
    "    \"n_pretr_epochs\" : 0, # no lambda increment\n",
    "    # Fadernet\n",
    "    \"img_shape\" : (1, 64, 64, 64),\n",
    "    \"conv_model\" : [32, 64, 128, 256],\n",
    "    \"lrely_slope\" : 0.2,\n",
    "    \"noises\" : [0, 0, 0, 0, 0],\n",
    "    \"n_attrs_outputs\" : [4],\n",
    "    # disc\n",
    "    \"D_conv_model\" : [256],\n",
    "    \"D_fc_model\" : [256, 64],\n",
    "    \"D_dropout\" : 0.3,\n",
    "    \"D_batchnorm\" : True,\n",
    "    # D_training\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 100,\n",
    "    \"min_lambda\" : 0,\n",
    "    \"inc_lambda\" : 1e-3 / 100,\n",
    "    \"max_lambda\" : 1e-3,\n",
    "})\n",
    "\n",
    "tensor_dataset = data.TensorDataset(tensors_abide, targets_abide, attrs_abide)\n",
    "# train on whole, val on random subset (since we ~know that perf is same for train and val)\n",
    "idx = np.arange(len(tensor_dataset))\n",
    "train_idx = idx#[::8]\n",
    "val_idx = idx[::4]\n",
    "print(len(val_idx))\n",
    "print(site_labels)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, train_idx), batch_size=args[\"batch_size\"], shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, val_idx), batch_size=args[\"batch_size\"], shuffle=False)\n",
    "# set target for domain classification\n",
    "AE, D, AE_opt, D_opt = create_model(args)\n",
    "train_stats = {\"D_loss\" : [], \"rec_loss\" : [], \"AE_loss\" : [], \n",
    "               \"val rec_loss\" : [], \"LR_auc_CV\" : [], \"LR_auc_LSO\" : [],} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 49\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train recon\n",
      "tensor(1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val recon\n",
      "tensor(2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  AE loss: \t\t\t0.003955\n",
      "  rec loss (in-iteration): \t0.001450\n",
      "  D loss: \t\t\t0.419218\n",
      "  val rec loss: \t0.001638\n",
      "  LR auc CV: \t0.603847\n",
      "  LR auc LSO: \t0.572572\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args[\"n_epochs\"] = 50\n",
    "args[\"min_lambda\"] = 1e-3\n",
    "train_stats = train_fadernet(train_loader, val_loader, args, \n",
    "                             AE, D, AE_opt, D_opt, device, \n",
    "                             AE_crit=nn.MSELoss(), D_crit=nn.BCEWithLogitsLoss(),\n",
    "                             site_labels=site_labels, train_stats=train_stats,\n",
    "                             freq=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FaderAE3d_ABIDE2-fmri-4slices-4sites_32-64-128-256_epochs(100)\n"
     ]
    }
   ],
   "source": [
    "name = \"FaderAE3d_ABIDE2-fmri-4slices-4sites_{}_epochs({})\".format(\n",
    "    \"-\".join(map(str, args[\"conv_model\"])),\n",
    "    len(train_stats[\"rec_loss\"])\n",
    ")\n",
    "print(name)\n",
    "torch.save({\n",
    "    'train_stats' : train_stats,\n",
    "    'model_state_dict': AE.state_dict(),\n",
    "    'optimizer_state_dict': AE_opt.state_dict(),\n",
    "    'disc_state_dict': D.state_dict(),\n",
    "    'disc_optimizer_state_dict': D_opt.state_dict(),\n",
    "}, \"weights/{}.pth\".format(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for next training, modify train to draw embs only each n epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "484\n",
      "['GU', 'KKI', 'NYU', 'OHSU']\n",
      "1024 activations of size: [1, 1, 1]\n",
      "1 activations of size: [64, 64, 64]\n"
     ]
    }
   ],
   "source": [
    "args = {k : v for k, v in general_args.items()}\n",
    "args.update({\n",
    "    \"batch_size\" : 64,\n",
    "    \"learning_rate\" : 1e-4,\n",
    "    \"D_learning_rate\" : 3e-5,\n",
    "    \"n_epochs\" : 100,\n",
    "    \"n_pretr_epochs\" : 0, # no lambda increment\n",
    "    # Fadernet\n",
    "    \"img_shape\" : (1, 64, 64, 64),\n",
    "    \"conv_model\" : [32, 64, 128, 256, 512, 1024],\n",
    "    \"lrely_slope\" : 0.2,\n",
    "    \"noises\" : [0, 0, 0, 0, 0, 0],\n",
    "    \"n_attrs_outputs\" : [4],\n",
    "    # disc\n",
    "    \"D_conv_model\" : [],\n",
    "    \"D_fc_model\" : [1024, 256, 64],\n",
    "    \"D_dropout\" : 0.3,\n",
    "    \"D_batchnorm\" : True,\n",
    "    # D_training\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 100,\n",
    "    \"min_lambda\" : 0,\n",
    "    \"inc_lambda\" : 3e-3 / 100,\n",
    "    \"max_lambda\" : 3e-3,\n",
    "})\n",
    "\n",
    "tensor_dataset = data.TensorDataset(tensors_abide, targets_abide, attrs_abide)\n",
    "# train on whole, val on random subset (since we ~know that perf is same for train and val)\n",
    "idx = np.arange(len(tensor_dataset))\n",
    "train_idx = idx#[::8]\n",
    "val_idx = idx[::4]\n",
    "print(len(val_idx))\n",
    "print(site_labels)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, train_idx), batch_size=args[\"batch_size\"], shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    data.Subset(tensor_dataset, val_idx), batch_size=args[\"batch_size\"], shuffle=False)\n",
    "# set target for domain classification\n",
    "AE, D, AE_opt, D_opt = create_model(args)\n",
    "train_stats = {\"D_loss\" : [], \"rec_loss\" : [], \"AE_loss\" : [], \"val rec_loss\" : [], \"LR_auc\" : [],}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 199\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train recon\n",
      "tensor(2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val recon\n",
      "tensor(0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EKLWgaw0/C5IIVUmzpGByLVz7s6/aueytF0PYdu8bv2rKrv59uBZ7P7NSn+of3lovl2dnpixGOy4+P2jrmrwAx/CI+gviSWq8m09bKQ6G1b2Qphde7SshYCmAN89gmSdnYMkHtgFL8NAWKSP7ppt2bjbHeOZGqNbhjimbf+Gl9fLxy3aenofNohG5uI1BKbB7247Z8RnYU51Rf78L/fHmPVOG5zCFe0sURdGM5gyz3eXw7FDs2etZjUHeQO1QTJvfFxa7aG9HVp1wjOTctkMCUqo4Jys6bDO6f6EUp9ZbvMxuOPGw7ZpZrdmaqyYNsys2Hxur2GotIYQQQgghngD08CqEEEIIIQaDHl6FEEIIIcRg6KZ5jYMOr6Yp9DQSLbU/NaUDruvZr5BGIgZtR/zc06bs53/41Hr50u/eMmXfvBEsmljX+uEiaFxWhT2HDDSvGZWhtnRBGrklrLvM7PXMVpjWzZ5fPgYtMNloFUXyyOUoiqIK7bBWnD6wWUtVYo5AqksMOlc+HurwuJ6Tp4NedfyS1fC880bQAk/ffMGUXf3hc+vlg59Zy5XkvTuhmo5FWkR2KW4KPexLBaXEBFuQmPSdfqpL2D/3XRxHUGejJe2SSrhtWa1iYH3EVlneqeFYT21/x2vC84CxQJs62kfWKeJ1IWFw+mLoO+/+5y+ZsoPfD2P/67u2H91fBK3b/XOr1ytBy8p9Gsf+isZXAXZV1dKWJTPoYzHrWh39V/XIxY/qiduRhV0Oll7jqdXBTV8OGv3xa/Zav/e1cF1mP7hhyp7618H2Z/fvPjBlxd2gM/Q1o81WTDHpLU3fZcurllrWWorbhjHr2b+5KTbxXGvpWlva9bVNB/tRBRqLTLrzXdunzXZsSXYPtM1U5/iLr66XH37V2kzd/3JYd/W8bbvxbtCpf+FpqzOdJKENFjTfnqzCvPDgdNeU4f0yn9vtRveDHdbee/b7imt/H3TxO99715RVJ0FkWxzb+0lyDLaaYLcVRZFps4rs35KDcA6sh8XUuPGqOZ16cmZt/7jNDGXz/G9sw7jv4HZ8b0PrMba8ctIeu+Oh5f0S0ZtXIYQQQggxGPTwKoQQQgghBkPvDFs12yBPDuCEVqK29glshwX2F+WcslGApOD4OzaLVvVbYIf19E9N2Z1VCAXMC7IMAZYUzsBwYU4WOYs8hDdyDuNDaI+3w7KyFvZrDsebMCOH+OEQycS+pjc2PLRdArY7nK2H7XsQtOipnQNICs7peJNZCDNlX7Kh4A+egpDn1w9N2bXvh/a7/IMjUxa/HyxRONtS5fVBr+96WV3aWmeNOCTfInzihfv7lhG1TEK2EH44GbZqx8d1ydopa7Z4w7HP1nd4nZPXXzFFb/+nQSJ07d+9acr2JyHU9pO7T5kyE5HuYEWXxBhOs2XmkqVlcxmN59iMZ7vPCmdIz0rNCT1nSzuXrc5h3qNxOd0P12z5q9am7p3nwpy7841XTNn17wX5xt6f/9iUFQ/DOK2WNvyZXAnhWC9zUN/sc+4+ESN7czJe1YVvrfbZZVy6wFionZvJikf3DAiPx2QPFX/5tfXy3W/a8Pi9r4dO9+qXrVTk37ocxttTEyvLuQ33WSNJi+x9l+/Bz+6G0P3JnpUWpXCPQgvKKIqiOzfCOT24Zu0xV5fD/WTntddMWQJqh/Gc5Hnj0H7Ly7bdS6haQQqoYgJzBN0TJ3DLSkglMz4N2x28v2/Kph+E9kuOKPNYDve6FVmdYfif+zX2Jb4n4X3PsYdzpaSOFKEtevMqhBBCCCEGgx5ehRBCCCHEYNDDqxBCCCGEGAwdNa/xWtvqatvYTgT1FLydsdYh6xLUQbCGB8rSF58zRXd/O2iszv4Ta3HxP73xp+vlO7nVvzzMrVYGyUGbw2khMRUkam+iqK5lbSpjmykP1LWyUqQCHU2Vsx6leuR6URRFMWhcYtL5lY5mroRjJJz2Eo5Rz8ga1i0L1plWj1wviqJodC3om3eft/rH2y8EC5i7X7PWLVd/EH5f/R7pYd/7cL3MetgIbJvqVm5Q74LtepwUsE46WqMFQm0RH7sPXayyvN2YtLWOBnBsx7OZB1hbDLYtnKIS9Xs8RxRfDGP9Z/+x1YK98K33wnpUz1snYezvz6zWMoP+yNZ3mLbZs4YreOzhpS5sOxQrSJtLM7IZ6zxHxM3tZ8Y3a2UdGy0z9mn/q0U494R0u/svwjz7oj3ez18I1/rZ/S+bsst/9rNQr3OrozX1mlJKU89Cqq89HAL2W6Yfe/e9Limii7b5np190liIZzBucupIkCq5uH3HFI2uBfuoe3/wBVN27w/CfPvrL//ElH17J8yjIxJdZ1W4Tm+dX6ey0I9PMysKxfl+QZrXNA/HOHG2m2dkqwllO4f2+5jVL4W5ZfkazUkj1H1yP0JtZ3M6db7P4n64bLnC+dEeDi/vwwf2/HZvhvbbvWPve5g2d3bHznPjD4INWnX/oa0nPm/VnufA9s8ZT64tnpNWti168yqEEEIIIQaDHl6FEEIIIcRg6G+VVbDVErwGLikkYl4fk+3CeNKwXhRFkFWlPD0zRQmEGU9+7VlTdud3w+vx//K175myvzoNWUFuL6xsAKUBHK7GjDXjxJ5DCXZHCwrXYLiSJQQYdmQrqdjY7pAVB4YwOGzrZMqKXBut0GYc/je7d8rYtguzEXHYBUOgXBZjSNLJ6HWc2UwxWLfpF61c5Pb1sO7img2tPP2vQhhm9LfWPq2ATF21MDiG7dh+BkP+JHkx9lsUPjRZqNpm4WlLT5lA1SEcaq4Jj+dRQyg2ilrbjuVfedUUvf0fBanPi99835QtYSzePbYWQCn0MU/ak2UsZYJqURjfjksvrM0hSFhmiy2U+qyonsZ5jNpoEgrLjLPZxI9eZngugd8FZQk7gaxGbJ83uxGkODd/z4Z7i0kIU1//v2375e+EjEcJZ11r25f72lCh/ZbxGHSyefF49cYN2sbReq1DsSyzw0xLbH0E65bf/popeu+3IaPhb94zZX/8pT9bLx+ObMj9H5Yhe+W/ObIZ7FaQdZLD/0YyRmMBf7PUZwmPK+ckDTDWkrRPLEtTO7eM4feIJH9WvmaKjO3lKrd9Am0g85ItAVFq5oXVSS63B9KpSzb8f/x0uBbHNC7jHJ5bHlhZ5LUfhHvi5b+l8fUOjEXKbmfssRwZTW0ex+xbfEG7ZJL7ZHedtxBCCCGEEOIxoYdXIYQQQggxGPTwKoQQQgghBkN3zevH+pyaZs3RLBjtA8kgjFZ2RHYooNNJDqwNzvKrr6yX3/1Dq1X5J9/4v9bLV1ObLu37Z8Fa5/LE2iKd5uH457mtC1p6FJTSzqSFjCzpCDSMnMoV9DYFaeTQome1Yt0dWFXQ8dDqJiY7G9TaVSxLhlPqohKz8izWGj16/x/9ATS2lIkX08qyRmk0araYWYCVD1/rKzdCisLTb1l9z81x6FsvHFufn/hHQQPLqUlR3xmTJi/GC8wnj2WUFjWOwjlUlM7vM+PZBlFZF51r4+F2rcYKrbK6WKwsfiWM2bf/0HaWL3/97fUyp20+moP9lpftln5njuUV6rjLmo67WVM+mkI/Hts+jFZZZWaPNwLtajSlyRP1bayj9Wx3zAE4XXfVWJZAvWty5rQ5RSuOxcNr9tuF298OYy/JreXh1WUYb7W0wAurv2yENareuOyTZhb3z5MqzqTehMvukfiDdYM4z9A+iwfB+ojno7P/4NfWy3f/yFqS/d6rf7VefnF235TdzEJK2LeWduzdy4KOfEFjj78ZaYJtJ3E71qJP4FuTGaVpRl0rf3eygt+rFWlCY1ym/o73Uud7Dk6Zjt+vsB2WaVz+nsPYaFE6WnhUq8b0vRHWc9/eM/D8CpvdN7ozgfmxuGrKLsF9qfrwrt0wa3lf4m+fzJxPN33zANLORk5vXoUQQgghxGDQw6sQQgghhBgM3WUDn1gc8CthtE+gmBJKDIydRxSZsGkV21BQNQ+/431rdXP0agjrf+cr3zdll0YhLPIXx6+bspMsvCovnbwObHGB4YyMQgFYVst4BaGBjCw1ypb2HhyywBAdZzhyXHdMWcXHa3CG4Q15O2PlQyeP2bcizjhkMvnQdhA+KeiaJWhnwqEckFos5vZ4GVyz6dSGnM7eCP3s1qkNnzwz+mI43D+8Y8pM5qcZWY3EzTZaaG/G2bfYVmujeLZBXOaF2UdOqBTngYT7NPQVymRWnoUxO3rN2u7c/LfDtX3xjQ9M2fEyjOd7p1amgFZ03I8K6GMjshUydlg0vlrbYXlhUx7PIA3wmogtqCqUFGROBiCWgJgyOgba25G8wdslSyiQHM53SeHC/aeCjOD2Nw9N2eGbT62Xk+9ZC7veVM3zTuMmOM49ix+v8fiCJe1Co269aAwZm7qvfsmU3fzPguzif/7qPzVl10ZBWvejpbWdfHd5ab08L62U7hjupW1lAlEURSuYp/m+h/Z2tWyVINebkF0llvE+c7j38Hi2tl001vH4zv25Jh8y2e0cWzwGypIlzUkwviuWwgAljVm0zBvP6N7yUphzH5za56vdW8FOcnJm+1kFc3XNDgvHQMKynGa5nJkWWtpm6c2rEEIIIYQYDHp4FUIIIYQQg0EPr0IIIYQQYjB00rzGcRx0NaxnMHZYVgdhZB6cTgw1REWztULx8tOm6MFXQtnvXfmBKfvxIuh2Lo+tXmMEGhfU7PDvJdltoKVHRrqZHDQ8S9LWGW0Ma2VB98n6HmQ8stfT2mjRyqhZ4x2ZlLO2yEv7ynZBZpdQlI5JxwIyUNYcmmvBcjCoC1vwYL3rtlmgBaZ9ZpwiE7h2LWi+7v2mLSsnwV/kWerzyXu3Q704HSOmgGVrkao5pSTq1mp6oj4YLxjHsos3S5q3s5ZXjgbQ0UPx9UqfDeP73m/asR7/+tF6+fLUjucPToNOkvuDl345KtEOi6oJ/b1a8jXDDtjBTgxsrhIa66jVrlvrgE6YxyEenocoXvpaG6EujfR6LbW6PF+gHpf1j9hHeC7BdQ9ff2jKPvhHQX/+4n2rxYx+8rNwbLKFqpbwXUVL2x0XTF1M861J9UyTTmwnq+b9s44Wtas8hrJQgeSKTXN9/pVgNfbOH9m6/Ndf+Yv18v3c2k6+t7oKZVb7eFqEa7sq7T0RdaZchvfP08xqZVmTimBK2ILWS7CM7sGYohXvj1Fk0zh7Y4/LKudeGjf+sJZytfEMbV2Tw5q5057DCL7hqFL61gMvfcpp31Hjbbcbg+XW+Wv2W6TTn4Z2v3R6zdblFvyY2/kYtd18/zLjg7/twLToE7JMbUBvXoUQQgghxGDQw6sQQgghhBgM3a2yPnn12zekSaGVCqQC1dxmX8Fw0P03bKgDM+ssKmu/8gHYe4xjGwrALDxe+IIZQUiBwxloFzWiV/MU9Da/MJS5YmsueN3PIc8cMwA54fBayALLCrLiwPAaO+sUzf/jlOC1U8ScvaQ5QwlG4Gv2W3Cta+FePHbpnDufA4SAOGMZhpdnuzYuePJa6FuX3rYhtYNb0A53bWaaWgY6U9gcWjfjYROyAbwQVfv9YYaXuCZpAasvxxYv3tmxm4F8Aq2xoiiKyi+EzGb3ftXu88ostMmduW0Dkw2L+imGC0vKrGMsomoh97i5DGQstYRluF1OYx0zZ1FyGbsT+h07ZXgMHiYY1mcbKxhTce6U8RiCc2BLnngGoXWWU+BPGs8LyNq0t2NDl/OnIdvSdTv/xz+BH944ce2reowvsog0e+d7G3SQusUWjKExZxtqzrQUjUNI9eyrz5ui+/9dkD/9D1/6c1M2gg7/86UNAy8h7rwsbV3QPuosbw7/czYslNnNV3afON9zqB4v0zKzc+gI7ok5jXVcN3dkbjVrSccWz9y/Gvdo79W17cYsUwDpA9cN5VkZ9SWcvlhmBxH4kmVAIFcpaFyO4PfeJWtTevJyePbau2VlOaM7UJmU7nMoB8jZTw8lDI6cRxm2hBBCCCHE5w09vAohhBBCiMGgh1chhBBCCDEYumteP9ZQxBPS6aCdCFleGVscToc5t1oLs93zz6yXH7xh9RP/43P/cr38sLBpIW9MgvZnXth63l+Fde8srI4K7T3mmd0OrTjYCmacNtvgxKDvYYAQ3qkAACAASURBVG2nSTlLVlKLeTh+zarK5HkljQtq2Fgv6mUHRR0jaXi8VJdomTOeWPsL1P6wLgjJ82atLGtevWyMqCeqpeB0tIp4jAmdQ3Qp6DTnV6z2Z/8g6C/jo2O7HfRz1oVGTnpVXLOEsVEtMX8vt6tnb+SotZztPKssA1vmwbrVDqXMZVsV4P5XDtbLu79kLZN2x6EN7lIaQ6M3J6us3LGJsbpj7hCN1aR9OAJVHrIL0J6d07Tr6FpxeMdOCtaKxxfa6bCt1Qqsg1bOPlmmCSkqYxqz0Rn0d7p+xS78geqSw1g4La11YQnbLZ6yfYnU1JakeXzZ9LAtxxFqV9nyCn+T/VvMekAEyzg98Sl8+0Ga19XXXl0vv/df2bnqj3/5u+vlo8KOk7eX19fL91e2DO+Ri6K5zkcre9VTaOglbXcGOle2rkIqtrWCudj73oGtFytzn7Xr4r2Hv72ozc0N+2S8NOW4S77vJQnaOZIu3kw8dH7wM2axbOlMIDBn8DcweA783HL2FLTtZfssNMXvOdhKMG5+1ovthaF6wkmxzrsBvXkVQgghhBCDQQ+vQgghhBBiMHSXDXzyejdrDoXGHCVwsouUGEqk9/3lYQhTjF+2NlofZCHz0dsLa/1RwDN57tgpMZjJakXhjNTZjRdeQAuRWhjE+dfB2hNRWAltd1hSgJs5EWMOQWL2iyqlUBva5yzs8YpJ2G5BtjvpNIS16pFzsNjic8AwjJMdqGZ7gstkpVJ4kXUIZeZkz1JBiDfhTEyzEE7hEGFlZDTNmUaqjHaKNFn5eDKBz7JuD/i8470gy6k8ycIXXjE/j74Ylq/PrGXS0TyEk1fLZpuzdEzXGcdbxuPEyZTl2VOZA/B4xgwyzRZUtXFpwo7N+6xVpWpYjqK6FKJhu4qHHqpTWMIA6yY0/8fYjVkyATKFctqsyahZ5i3BUmnlbMdWWZ7MxR6w3Xoc4kTw2DU7LAzZUv3BNo7tyiqwkUuuXTVl7/5ekE/88Tf+xJQtqmBldXN1yZQd52EMneRWgnG8CmXevYzD/wVc5wVlpFygbGDV/JiRjJqtpBiTMY/Gc03qBniOmNZJkNrBkaGhO6d7bA6BO/aRFUoTqU+YcUn1LKcwR8yon42aj+dm+LwR5uBsl+Rf2JeXdq7ehL0jZ6prQm9ehRBCCCHEYNDDqxBCCCGEGAx6eBVCCCGEEIOhk+a1qqpmjR5oHWp2Iqjz2LV2G8kuaORyu+/F02Hdf/zavzFlV9Ngh/Xh6NCU3c+CFQhaY0WR1bLupTYVqMc5WGctKN1djjYdtB3aQLGeCLUdtZSlS7iGK9L3oEbUy7LGFjmgu0uWVBfQsJUkcfFz46EGlbQ4cK3ZsSYHYTRbHE0maGtF6XZBh8TaGKNfYhsS2A5ts6IoilLQ+LKmEnXKCaW7S46CDrsg7Q/agngaHk4ja7bLcdnRxnq0tcpy7LxqKWBn0EFY67sXxmy5bztSci8sn71ux2z+crAFm4xs+zzA1I+UDjl2bHCqqFlnirZPEdlFVWPYD+vZ0HqGxXRQFvM3AWjxxhrbqHk7a5HTTscaRZFx2mEbLXP8mq4Vr1mzFrjmfrSDmnmad/C60FxWwjzH21UHoR+cPW37melZNL7iSVi3onHZC9T4cdpn1NfSOEGdK48hkwaadccHwTYue/mGKbv+jQ9Dtejd01ERxt5xbu+zR1n4fU5pXtHmivv0Ehp65dhTsR4Wda78TQPeJ2K2F/PuNThdcZ/Gew8dr8APHmpCbscyL26eH026aUeiWdRSx8Jm/A0Mjn3eZ9U8l1WeLZ5zf17MQz9gw9ISrmGZUt/dBUs7soislvCdi/OtUw3zzVS7d6p68yqEEEIIIQaDHl6FEEIIIcRg6G6V9UlYid/vg51ILcSJ9gmcdQS2S3ZshpVsL7xK5kxZf3P2ElSJQsvwij2h9++4bs4hErD7WJL1B4ZMCrZ0wfUo/L+CTFns2RFDuLpm/YEWTXN6bQ+r1kJtaJXBoRU4fpF6IZKIyvDYTgj8lKQPE6gLh1gxvLFj+0vO1lmAsRuryTAgHFXLetJcb2PBwvuE811csWGQ/Nlg1zZakGXIOVjAUZkXFilXGfz47LYjre2AnPVqsgeUDlE2vQjsw8qJ7Q8jsNEqJhSGy5ptd7wMOWhvszq34VDMahVTJhiTpYabA8rqNjvtriePS5PxiqU+nhwA9zmmfY5hR2wF5rhvmbHO9k44ZL3jeeOZJRM4Zp35g0O600shmHn68oEpuzYObV1lVv5VrVrKwbpkqusD3AcrtpRD70WWPy3Dea8u2T79+qW762XOLLksw9h7mFnZwK2zINPhOQ6ldFy2AMlOxjaC2KXJ4srMxdw1sb+zPSbO/dxXPNmAsY/kdsUDOLaMjBf1xmPQ+EV5XsXzlWPDl+AcVVMWhT8UtkvYezLbruHp0ljPnQxmKL04fcGud/hOGIvThR1rSRSyI3KmVZtFy9E7cvbWBvTmVQghhBBCDAY9vAohhBBCiMGgh1chhBBCCDEYOmle4yhYfng2CBWVxaBhiHetrjWegoCDbEgWl4PW4pmptWS4tQwaHtbDHq+C3uf+wuqCUK9a0zeiJpS0MSgX4VRquB2ngK3yZh1LBRoitpAxOleuJ2jPOB0c6sQSR/PK8i6st2dXwppD1DplqaNV4QOCToj1viVoQkeUqnZ/t9n6BlPx1m3Jmk8Kz31MKUaTK+F6nr24Z8rOb4V+dnDXlsVoHedcUE6nF6POEFIbs65v4zh1rFll4fimMZvvhvFczmiOAA0U26/M9sP57U/sueJYTGjsnZ2E+aSc0/Fg1Zjkw2hBVXHKUqORs0W1dMzINBwkoX47mQX943hsNd7z82D8lC/tOeB+vD5cU+6Bfq6WfhSajDV5EaSaxPOJIrL58eTYrMNHa7pml7CoonOfXgq68bN9ez3xnsJjI06by6LEEzKiphd1wWhNSCcwcfpD0mw3h9ZA1empKYoP9tfLR6/ZOfXZWbgPnhfWiu7WMqSEvbuw89Hd0/Cbp+IUU3XTfW8FllAFW2U5+lRM1Tye2P5epDBP0/0yQwsq1q6iTV1Np+7Y1Dnaztp+TCFs56WQ5t07enOjh6U5IoafJT2ZVZCGvTZf4bX3rLn4FJwy3M/iaTvYT58Pc/z4oe1nyRxMtxbWgMtNHYsp0/G7Dwe9eRVCCCGEEINBD69CCCGEEGIwdLfK+piaHQlmSOCsQROQBuT21XEFr5bjyNp7TI/Dq+S/fvCSKduF7FgHqQ0lpwmELChbTwJhEZYNFI5sAMmpDLNo5WSVhRY2nCEHM91UExsKKEZOyALCDTGVYWgn4+Ph+Xo2GUQMYSW2PUkhBJrObHjIyDDOyf7oNNQzIQuWYhpCZdkOhS6vNdsmldi2zvnVt2uWaJjsMDt2u7OnQtn+LmWTAtswto6rWYhg3TCcGG3AKqstbNeDdlhksRLvwDitWeZhaI/KwB6IZQOFYxc1AqnAiNoOw/ErjhaCVVY54bAz9GmW82DokkOQnpsSjHWWwiyhXy3nNhRsLL0WNMAwzEjnEKHEhE8Cd+OE6mt2REa2wlmhnPnDs9pDeLukebscs+mxwxHeU87OTJkrsUH7ubZWWZ7VI45tLoNjVZxiEI5VHFvZQArj6+hbNvR6fXyyXr69slnq0B6roAuGcpuS7l84vjKSBuB8WPH95BzWrUlFmuUuOP+W3LAoXyMpHUpcWApjbLS4j6FqhTNQYd1qWa1gkeypTNZQvi7Yp+l4lTNOypljVxg7Y90bb7gutXsVNY91Y/9FXSKfOQdESRlLbDw866wG9OZVCCGEEEIMBj28CiGEEEKIwaCHVyGEEEIIMRg6aV6rKKSK9Ox/WBhZYXpM0sGVR8H6YzSxwpLVXvMxPjgNtiA1XSvoPNLYailKqPd8RcfDFLCkDzE6HdKH5BnoFBdkEwaauWRJ5w6Hr6akxYGUqZwKz9O4GKsdqqfRx/I+PTsRTLdL13MEetidqbW4yCDt4JJ0Ojlam5ySPmsBZYW9notp0JamU6slnUzRjqi9nVkM/TUjrSJadWVkS1bshOFT7FnNa3w19M/4iERDqNHjVLENOqENJ66s49hhxZSuL57BuVJZMXUs9B4erZf3bt4wZbfPwmDIrpJdFPSdcUpjHfWwI2qfabPFFp4u94flCFLcsiUgWFAlpE8tsX9Q/zPaVZaZ3gv9aPKQtMAgL85oPiz2UYtp9+nafZlq07hn4TAC1lnunMQaYry+bDUG8yPrGFdLsFhkySinIMeqpM16c2OV1Tb9con6TbYiak7zajoZ9aPy/kP4YetRPgVpp8lO6Yenz62Xs9Lu8zwP14vTok9h3BSkL+RvOBC0vGKtpZkrSfdZQFm+oG9gEke/iVpZp99WtQ6BFlQsJsWD0z69NMoIf3cC51CrC+h/k0mz3VxB1nBogVlLGQ0/Y5pb8D6YUn9Bu8yCUvjivMc2aCWMRbYzK6bhd37J3vfGS7DOYssrHItsm4V2dN6zJW7Sai0hhBBCCCGeAPTwKoQQQgghBkPvDFs1ux8MmfArYQxBcvYtsAUpnr9uyo5fD8vPUF3OVyFEsks2FnvjZquUrGgOa6JlSM01w8luk0C4MtmxYaoSwlRl1XxsDhOgXU8tZIyhAadeNasbDNdwWAJ/O6GVamQL0SasntErLI8o3FvshutULm0DphBt4EgchjA4dInr5pwNxsuChufHIRK8GGxLBhGT8+ds5rgZZJeaLqg/ngRrnFpYs4dlyEaoX+jGMpQB1boKRhJ3bBtMIIw0vW3tjUb3Q6j0+AZJMCC0yONwlYcpLKeQNGZ/K+j/dJttjuQ8Tpafmg2P2SksO7Z4bG+X76Ltjt1lAfZs5ZjD0lhppy6OBZBro8VlEAquOsgGzBzlWKLxXIbjNGFrJAdPUuBKBRpSDpl7XUVjuWi23kJ5TTwm6Q1mnSRrybNXDtbLhwcPTdlbx9fWy7PUhmUx8yPbAeK9bZHZ+RbDyZ6tFWcfLGEOL0gaYOzmeCxgX+U+jfc2frXGoXu7V6gYb4er0flh1i5HGlDLXIV2lRNbluBYZ1tGPH4tq2bzu8QSs2rRUMB2mU1sn0CV5oruiWiLxpICo4ahc0cpU75j2320H+6Do4d0D4Z7W0mSAh4DbdCbVyGEEEIIMRj08CqEEEIIIQaDHl6FEEIIIcRg6CY0iOO1pQFreAyk2zM61yml0QRd0J03DkzZ1V+7s17GdLBRxPZNpCED4R1rXDEFLLsSeSlhUU/Ej/yY+nE0btZU1eRyRbOeEjU1FetRHi3N+vggDfuPoihCvaonH6ql3gPtINl7oB0Rg1oc1sMmcH4lp9ADoU7J3QysddCirAus78G0hzVtMzYa9bMCZK4nL9i6rA7CMQ7ia6Zsho328NiUmbTLrB3fJp49iafDJausEvpDtkf99nlQrud2n9MH4fgnpzZNNFKQldlsL1yv3ZmdI7zxPHL0gUfHwe6lyEnLhymdp6yRg+WaGBj6GI+vA0hxu2+LjLaONd44Fum62LSX9niJY4dl0ujyajgWeDIDG63RlLSRqN/nbw7geJz+dnc3tOc5fdgQ70IfOTmxZX2tshrSw8ZgZ8ffemAKZ7aPNBaRGdcD9vlLL5uiW98KdfzSgT23D45DSth8Ytvc+9YD71+zsa0LpiJn2yzvW4EUvmOoxqxXhu9cVs3jsH7FnfdpOE69bz14F2nz/GVstXiXMN5G1DfxG47JhL5zQQsqutfkC9A6L23ZaIH3PboyYMM3njZruuf0/Ujfbz0MfEuEKZ/n+BHY24127HcgMdghJgk1ktLDCiGEEEKIzzN6eBVCCCGEEIOhuz/BJ6ERDmk6YccK1o05m8ilECc7fcHu4/effnu9fJTZUOJT+8FuaETZczBcuKjsKeYQtsophFU6r9gjsMMqKbSC61ZUhjKCmN+UY1ifnYow3FBzJXOsZ1q6idTCfsY+h+xEIMyJGcOiKIrKcXP4FcMSteuJxzi0thkFqEeStPmEOGtS07E/FWwGDulCyCs5t/0lPYe6UDIRE7WljC8VZtWiDFsosdl6Vi2E7bBAU1Mb2RhGZYsT2M34jNoHsgpVzz5lirLDsOHlw3NTdrbAUJg9HspRapZX8Jut0zz7LSON8ayyeFxyqM8UNixHkQn5c5apCqa9WqQUrcEcOyxOAGSy9XCiGwhlVjRfmQxAjqSgJAlDbUwhnHELi8z5ceyy+Vr3tspqAkKcbPVoylg2AGOqOLbyIGTx7K75fe3rt9fLN2anpuz2WbhfusovajyTdXLEWZ+a2wCt6Lz7Xg1cle8n2I1qNlowLjlLXeHc91B6xi5unnUVZilzLijfa1AqMEltfzPPGGzZiGOdjlGADKmkbI6jGWaPpGcoaIfVimROaDdXs5ZszqKFEiXO6IUsL1E/K8Lxxwf2mS0+Br0BPT/iWHGzt+KxWq0lhBBCCCHEE4AeXoUQQgghxGDQw6sQQgghhBgMna2y4snH+rOKdBCoBWLNAmpqEtaqgK3ErtVkvDK7u17+++JZuxloUE5X1n5rngVthdXLRdEKrBxYF4nVZo0LShq9VLE5WUlFOeg8yBIqQe0KS0JRRsP6HtSxcEo5RyZmmsxzxvD0sLwd/PZSstayj6JEKWNxXVgsHPlaEZOOceRo8lwtcPPFSObhGDsf2noevhXab3xu+0t6DlYqH1rdWnQCqVH5wuA4ukirrISuJY5T0vlVy2DJEz84MmWTh8HKJzukvM2YKnnXWmxll8O51izsQP+NqZijKIoysEtbrUjDDho91mGmYDczmZAGEG3q2NIFNN81LSmOPe5vRjdO+0QdHru/4bo8rTpadJQ8Vo5unDWHZp+sDwTtdkUDKj51biWY2pJti0yKW9IeY+pptvfqo139NBrTw8J3C6k9z3hKfRzLYNwkNJbL+Xy9nO/Y/vDy4YP18kG6MGWvXb4XtiMN6qoEfWpuxxdqXkds+Yd1dtLKjkfN13xJ12UF97qCrLJivGdx14T+GLPoGu/PTipVppw361pNqma2ktqHFOZO6mIv5XxJVll4DNabm7HAm8HctrRFps1qdzL8JoCKzBxF516i3pw06wV0+Xxmy5YHoZ4zsrcbz4J1Ft5DPjog1rPd1x568yqEEEIIIQaDHl6FEEIIIcRg6G6V9TEme0gUBTlBFNXCjCYGPravkgt4tVzM7OviN+c31stslZWBxQW/0mfrLKR0bFtGE7S1YusgJ5yGxyfZAIblKpINmKQSVGZe1XNY2wkv4DE4JMhZeGw9YZcUoisdC4/EyShWmfA/hZ7vBanH9CGFleAQtWuGbhs7VHYY+hnXqzJ+WI1VrileMONQXDbXc3LfhkHSe0EqEJ/b0J+x8qHMO6asR9aR3nAYdhzah62HTAajlfUIi3MMudtdximEUZd2n7NbYZ/Hh9Y6KIbxzFZ0sTPW7Yr0G8ZULeMa2tlwWN2zvIJ1EyeMz+OrwqhmLayJP+h4xnKIypyQoGkXKhthN6Z5Z3waNhzNTVGtrZHsECzL9qkuKGmgc8fQbNIukhhF0TYybI0euRxFkbnX1bJOwmQS37BZ9hIYNzu37dzx//3ll9bL179815Rd3Qk2clen1lLuPA/3Ug5l8z2yLTmHvQGU7GTnlAHtLJSNT2jMtlR8JJw1DrYbcTIxdNEiJUdhEz3RdjAP8HYgdyip3fHw/Dxg9sFWcCjL4QR9i+YbO85yOc1JCWS0Yzsso8phWzLHKgvlUSxbxOxfLJ1Kcs8u0CvDyazdO1W9eRVCCCGEEINBD69CCCGEEGIw6OFVCCGEEEIMho6a1yrY97D2B/EsfpZWKzs6B/VIZXWtV8ZB01M6oqp70Z75ff887KeWwg5TIyasQQWNC+vSUH9DOpYY1k0XpJ8zmk3SjqSQinFK4hFM78gpKh27ntjRyuIv3i6dN1t4GAciShVndDN3rWho9+2w4eU3rfZs952T9fLo6MyUYcrR8sAKlrJLQYu5vGy779nT4firy6bIpB9lbTVeJrwOURRFo/Pwe3xC22EG3xlr4bC/NPt9Vb5fW+N2G6cm9gVdE6WwNeOb6picBy1fek4aQJwzfvyWKXrmL0Pay/dndh7InwFrLrJaiuH/b1b1od0dzx/ZPNQtdwSV1Yy003gtuOkc6yos41TCqCmrTXPQORPqRjhOK+p+po/zHIEOfaxZWzXPA6Za/FmDJ6nEMr7U2N1p7kzeC2N/5zYdIG2+dVXe/aePxZYZk9SuWMbjvMF6K4qiqCrChZ/+/L4pe/G74VuPW8c2jfLs2x+sl1PSe6Nl5MqxbypIN4661nTUrCFfLu14Lk7D7/S+bY/JUTh31qdi3ymsy6Xpq+QSFsVweVkvmsEjQEnTDn4bwf0Utek1Szm8d7OW1NHam+5C32wUcIzaWMft+PkDbeqcp7aaHRbOLY5+mecIvFCe7n9Evl3TozC+0iMSxnvjEu8NTupnRG9ehRBCCCHEYNDDqxBCCCGEGAy9rbL4FTBmook5BAnEuzYkGIG1zv7P7bP0//HWV9fLL11+2LjP88zGCTCUnVKYsYLMOhyxzVdwOWrxBVjmmAVkCSsp9IAhEg7DmZAdW3h5oTYHzI7FdTHV5owaYH9RkrVIuQMVJclEBXYiO3dsqAqlAvs/vGfKoqMgG6jmFB/aCRVIqJEm0F/Gx/Z40wdBNlCO7fllkPkjn5LVCLTL9IGN6WKmrNGCLKMysFZbUdlpCJlU5zZ8YqQCWdZYVrPl2Sa1sGZziCcGWQfXP8btOFHbYYjtxZf2Tdnxy2GfmG0riqJoNEarrMZq1YcJhkdZQpPgdaY5AiQFHMq2IXeWATWHBGO8vhR6NlVzElCx/dYIhg2H/wu03WELqolj7QO2eByeLHahHSgcakKLnjRgac9hejP08cO3SRIC12xyYsdX8eHtWt3DhtjuPTNxtZTvYPi/lj0SsgjV7OZg/9XVS6bs7Jlw4YtftpKq5/ZCRrv7S2spl0F/z9j+DTrZYmXvl2kK14i2m0BZSXKDchZ+Fzt2rjKOfzQXG9nArLkv5lYNaPo/34Jz3A9npxpjnNuWobqiGrOWrsONFzAZr6iiMRyjnPFzEjxD1cL4zgGNfZ+TKYsllAhLA/C3YzM4ntuKjmGcxnOrKTBZtfj+Ar9d2Q+gN69CCCGEEGIw6OFVCCGEEEIMBj28CiGEEEKIwdBJ81qVVVSS1dUnGFkEa38c7V58L2hZn/tnVutwaxUsQ374W1Yr+8Xng+bpYGLrhKnwKta6gQZlMad8cAjb5xhNF50f2uCwrRVUjS1yUFfCOjRj9+Ho4DxGZLeRnjTbl5TQE/J9W5f0bmg/TguZwu+9m7b9UOdavvOeKTO6aOofMfaxe1YrlhyHC8M2U+i6wjpTlPHGZLNTwfHKc5ty0WjfJtRfMA0l6eIq0IIaXVxEmlHCXBdMD+vY7mwFtBSi8VxiamgSoaYPjtfLY2rXahLO++wVq3k9eRn2cWg7J6ZYrBbs0QTLNZspKDq125mxQZcTJYA13S5KwUiahfY2I9J2mlSuPJ6dlLMmrSzPH7BubuWPdesbIIeptDgkeydMh0y6VtS3Ryvq75iWmrV1KDk8t2U7t0PhpZ9afWfy5vthnztknxY59LHDiiKbOhZBDR7bxpnjcuNBI5A2PJqF2er89Sum6PQ/DN8D/JMv/wtT9r3TFxoPj9ZZ89zqWpd5GHusw8RUstNx85VNyB5qNgnndDax22WX4PhnLJ4Oi9W4w1zW0nKtSxmmX+Z+Wy1h3qH+nqGWlHWtOG54n/h8wOMLnwEy5y5PdXFT/3KK+JZU+E0AHS+B6TkuWPuO38c061prqc9Bpx6n7d6p6s2rEEIIIYQYDHp4FUIIIYQQg2FjVlmu1UEJYUYK/8QQPolXNrRy9Udhu/kz1r/p3d2QQmlvZsOMRyfhGNmJDfXyK3Ck8jJqwHZs94Lhf7bwwBAhb4cZcziLhZtlAiOezr8ftTL4zZlNUKbA2XNiiKSnFFWfHoV6zijEj6GBZIf9t8CSh/sLWt1kdp8l9JFqThoGCFPX7NogrF+3VMJGIgkDS2DagiFzz4bKaWeT2eoipALm4CC9oTraa2KvV3lyGta7dGDKFs+FMbu4bLdDG5zlGaXIQbs7LyTIUh/4WbE9FIzFEWVVw4w8NSsfnCJIloNjvzaGcBfcpaA7pKzKwq7JEV2UMFBZAdNs6c3yPO+gLKNsPj9PlsCM5pDB6ZxPHiz6KFyYYB9ckJ2eR1+JDcoNGuweaxKgCO49LI+DeYwlTsle8IE6etX29//+V/60sYpj8PWbjmyjH0bhGiV0I8LQck6WVyitY0kBlvFUmIDF3HRKHRC2y1hGgvZzfL/E/kj3L2NlRSF301e9vsmDz5HsxCg/ZNusVbNswNx3aymvnKphqJ7ljig34K7pnHtNwtNwPLYQw3qmZ3Yf+++Fwr337bhM76AFpmMRyWOl6DChfIzevAohhBBCiMGgh1chhBBCCDEY9PAqhBBCCCEGQyfNaxyBlnBqRZOYypL1PVb/x6kYQRdxZgWVU9AiXnr+eVN2+1rQDC0ObF3QTmf8kOx6YBm1bVEUWc0ca06cdGmoh+FTT4xuxpah1m1EWjdjtVPT4oTlkuSBBUh8+fwysMAqyaIELUvYvqSYhP9xRhmlD3wvtNHsHz60+zwKtknViry5UIPKZcaahq2RHK0s5CSsSYt66uAqEAPFEWtznRSSmC6ZrLnM8VlrxBYijzrWBetfO+l+oU3iM6t5Gi2DBnb2kHR3b+OFsJ16dRU1wrYu49NmvVcJY4HTmY4gfSpbV6FtXblPbY5jjzWhoNstKPUj6mNNnSOrkctJGj5G9yiW+cFlGi1JU+lo5FLQ+MaF7Zts/9V0PJ53UFfLc5nR6jpzWZLZgxf37sN2Hfp83/EBY8ykZsa/sxYWxnbd5qXKPwAAHfNJREFUgg/Sw5LFZHk9pIR9+BWrF31xHCwG/35h73uJI5rMoSOhNVYURVFRNr+nwhSwy6z9dqijXS5sh8jO4bfznYnn+1jxByT4k9Ope3ZRaFPnrUdl1QQF5/xM0+6bFK8utXStcIxyStrqltrVmt2X80xj7vOcGhfu8+MTu93u3fDdSXrP2tthSljWh5vvouhblioP+2ybFl1vXoUQQgghxGDQw6sQQgghhBgM/a2yavFxDK1QqB7DuWw1Mgtxsurpa6bszm+EzCN3f8OGlF589c56+WRhZQNnkDkrm9pwRgK2O+OJ3ecKQh0cHSrj5tfvaGMRUzgDVRJsu4P2QGxrhYdIySUGbXH4v48SLj3b0qB9T0kJX/CACdnnjIP7RXTwrm2/ndsQJuDwP+4+t2ECE2KL2boFLUOaJSjJLqUVwtA9h9+dUCLKDzj0h9mwvDBILYNOS1hig+EUE66/aKssT56B2VBIUoDnU5EMaHwrdKQ42zNl03sQpprb8Xz0amjz7JBsu0x2O1vNBNUGFIlCaQBHEo2khjLdGFs8KsNQPUsRUKbAdnMmixYnvDoOdRmfU19BZQBnCZuHk0/PuP3weLaio+MwhuNauzf3x/xS8OaqUntdsr0whhZXmj3Eij07V48PgsyEx0mJfatvRi0G+y7MVya7F2cDhPtXlFKZdyzMTEQh6adGYZz8XcX2YaG9Cuq4JoxPcpCipR0WywSyHO0Hm+egomh+D1YLZTuWlMaSyskOFZN8zWzHdlF4T+bBHlePWvxoVcdGy5MYmnX5FLxMYLif2vMA1JOzeJbN54fyQ56TsK1LRwbBc9L4CEL8JA1DqUzNWw3vyVV3a6xavT7zHoQQQgghhLgg9PAqhBBCCCEGgx5ehRBCCCHEYOikea2ioD2KWVNoUkhaDZLREU7IYwVIjk7N7/3399fL989tVUtHw9OQ2e/jwrCYZ5yiDFNixo1lycI+83u6Ek+XZiQujl5vRFlQZw8gLd9DutbQDqNls66kYq0i6NSSld0uPQq61tHNu3Y7SNtYLdgjB66TY39h0qBGVt9W8j7NdrRPPCfS65kUp6y3Qa0bWdqYzkTaXNR71uywkDH7CoXj1zS9Zj0ou2irLLQHSqn+eP34mmBKT9JAY49LH9pOHcPYn7xrr+Xs7o318t2vWT0sWsPN7tvrksIhSGIbZQehNqyHTefhDzz2cJyiHjWKomj2MFyX0ZzGEGhQk6Xtm+UsHK+Y2sqkp0FfNr5j50erIaM58DRoQot7D6Im0KLmo92gFpi1fM39LgX9eUxz/ATGxj6lB0etOKYWjqIoKs7AhqdDXVrj7RPTRONc4tneMWijRdaSyUk4t/G9K6ZsUYXrdzW11+R2FnTA08T2I5RSz1LbrsslfFtCusgM9Ko5aVfzHFO5OnZNOb0Hw+lqRppkz5ISyiq26cJ1SQ9rmpK/OwEbKL4/mxTSZBFpU0/zds5DBpzDaELf+MBDAFvt4TNHxXpffDYZNWtsY7pm+F1NShZ9Y7hoOZWhFV5q3bCi0QK+YaK0zUbzSnjpYTuYMa7Rm1chhBBCCDEY9PAqhBBCCCEGQ7cMW3EcQrVNmYCiqG6RgOGT2TRqohqTNAAyO5Uze7xVEV47L1Y2TFUYew86BoQ3ahEffFXP4Qwnw4XZP0dPMLLHu0gevV4UWXusdG4rilKB3Z/eM2UxhKPYnipCqycva1Jpj4f7KckSCm1k4n2KzaKlUtYcTqixgmOUrLVoabFBYYlkCn2Q+pkJfXex8Iid//0wmwh3NPzN44jtiR61zUXgWWUBcUIZ87CTU/9LjsBzjeaBCtqcJUnpedjP5OHElKH92/TIXqPJcaj39Nj29+VZ2HB1QOG007Cf/Zu2v08egITm7rEpi+YooWF/O8dKaBdspnYpxRbaL7G8ZsdeiyYSGrNoYRZPrN2cGRscIscxxeMS7aq4DK0Sz8knjDLtIAnaUFEGuxIzM/K1bWud5Up2YB9GsuOM19q8AlKsg/2oicM37e///cE31sv/+PL3TdnN7PJ6+SwnKQKEpM8z2zfOluF3ntu5MVuBHSBnYTJqK082UPOWDMuUvSlGWyZWcHn3YLSi4yb2MmDiMllzofUdl9WyaiHebQIub0LzYzICCQNJNEq4J5cVS+ngpEbcB5sttlASxc8mYxiKJqNnZOfVdMH2hHB8fsZoCWeqq3AMt8zoqDevQgghhBBiMOjhVQghhBBCDAY9vAohhBBCiMHQzSqrqtb6N7YpMik9WQ+CGga2N8LtSP9kdBeXrWZyBFqSnGw6ypaWV7U0cpByLpmzHVZYTjhlJOynZLsN0KqMSPaJmpOEHJpQZzImzesYNIDx3G5orKsonSlaWbGOsWY7hdth+lReD+yQWEtqjs/pU51UrrU0rFjmWLJFubMd1pOtn9rqf2k9o9tJnP8D+Zrhb7YMgd9Gs9xTW9QbsAqqpYBlTSNgrLI49e056BRrfRP6LfWVdD/oMg8ObNuhxdv0jtWZpneDxrbasfrA6fWwz9Wh3ef0fugr4/esprw6DtZFxcmJLcPrQrrLeAwaRO5HmIqU2/nyYdj/jL4JgN8x2W8lUJdy5aQupnmgRKs4R6frzh98flAWp9QnYFzWxj3qS537RlRtKD0s0mBNV6sjWAO5Sj1OgX0U9NLX/9pqp//Pf/Xr6+Vf+fc+MGUJKDgTutEepKHtjsgq6yQO/X9JWssia27nFFKolzVbvFCXnK2dUKPJ92C0w+LtoC7xkj8gge34dgX9qmJPyrhhOSKda0pti4fnXTopbm0K+uZ5m62y4qpZt2ssxLi9TOp6OgakwV5dtmUFSspJs4zPO6Nlh+8tcF5wnilq98se33TozasQQgghhBgMengVQgghhBCDoZNsAKmFjTDMMiELF5PdiMJGEM4r92xoL9sFWys6/vF5eOddy6iFK3Mkua0TEoUCMDzJYVNjo8X2HtWjl6OIMvtwEiOQGKQLsttYNti4RJF5VW9ClRFlgWLLFxP24xBdcyYfc3wOgeN18jJQcV9qXrNuO4U49m1G0sBhbwhzcljQSgPYDgZCOV5Yn62moJ5eqNSzqNo6WI9aphusM8mH4Hxq2ZswaxeHlrF/7NosTJgtaudtCr9CuybnlO3lGML6JzZNzOQYMhzNaL7CPsb94cql9fJoz9pMVWjf5EgratnfUDpF46TYDXNidsXaaGF2trSg+QpstRKuJ54TzQOmVXis8byA24GlF8s+zDWk8zOSAqpL5cgdKqyLkzFvI5m5cF6pzbfQH73sW85xk7feM7+f/vMvr5f/l5e+bcr+i1f/unk/cIO5MrOWZMuief49M859JJeDEHiZNc/MJowe2ftXxN3GyZSVnIfjs31TCffgckp9ZeTdF3BFKsPtvOcIZ/cJHXuEdlhe93OydHHW0BKlkSy1wIqzVRZajLKC8gCKqG3Hx2FHxYRttDCjKLU7SgzL5nvpJu5tevMqhBBCCCEGgx5ehRBCCCHEYNDDqxBCCCGEGAzdNK9VFVUf21nVUmxWjv4PrWAoxWEE6RDL1JaNz0A/96HVe50fYrpP0r+glcTK7jPB316GW9KAJKDhiWup8GA9lj6CJGREGSPROivJKLXlSfg9vWs9ttIPj8LuUWcXRSbdYlWx9gxSlrItmadBAU0Z20yZ9vTsolj8g+s6x3ZTk9bEzrAda6sxRaWTkrJmC4UpbrnvelYgjo1WPA06xtoZYBuBjVDJ6Ua3jTlv1rXCMuu/YVXuK0ZruSBvOFzv9KyxLLpz1/6GupWO5tBtc25XaK/S65srOy4TaFd3LHhw2sQx1GVC/Qiufbyi83sY9L4lW3o5aaJd+zfQsMX0XUNVwrVnbWwR5iG+Zu7Yx/tI3xSwfWnSqHqaWT4XuH61dJhoZTa3Y/vqXwR7rPevv2DK/vaPXmw8/M6oWSM8HYVruTe1bYD6Sk61biCNphn6Xvr0nPt01ViWrEBPSV0a55baazf+oATxnnISR/OKeBm+C25b0H/TdUENMdfZ6FwL597CoIXYjHSm8GyUpM369nxBFwk0ryN6NkkWzvcd+B2IKxRu/n6kLXrzKoQQQgghBoMeXoUQQgghxGDoJhuIQ1iVwz8mvDputkOpWQOBHUqS2ZBFkkPo/J59zl5cgZAZvX73XkCbJBb86O7ZaFVoD2GL8Dcf24tm2P3bn/iqPlmRtQ7Y0sRkgxNNYd1zkhRgeJRCwYlnZdW0D4bb1stchXgWLF69nLA92+6YcAZv51hsmdCpI32ohZ57hEGiyMokXFuwi6RmReRYko2csOO4uSx2bLSMBZGTfc2zf4undC0xxDuheuF2jgVaNW/OnFaTtDhjId4J1mDVpX1Tlh2G8Pziij1eCplvxg/tOEn3wj65b5p+xTZ12EY89rxxAnNSLXsa7p/D/Y6cw83Ch+HmbYwT7I8okWA7QLxGnoyI2QGrR6p/eTtIY57751ae8Rev/vJ6+Tvf/r4pmxeh7VYUdi4hSJwmbGHUfC/Ncy/zopPJ0thh0YZQt4SzaGGTW+fMqMQsWgnH8fHGzhZvntQDtmM5IG7H+4Dz5b1XFVh81qQWjtQN9llmfI/C/k4bGqkW2WrC7zS1DYFtO7pvx/rlH4flK997aI93+37URIxzqWMJuAn05lUIIYQQQgwGPbwKIYQQQojBoIdXIYQQQggxGHqnh2WrFFPGei/87VmckCZifBr0UFd/ZJ+zb++F42dPW4uQ0W7YrkzJ5gd0YvE56T7BpgP1Nfw7JmuM0SL8ZqusAmxBCuv2ZdLKpud2n5NT0NSQhVicgy6SbIUwpWLNlgb3wdYtqE9hfVlLrQpbKpl29/QvXfShqGusiZYb1ovI0on7YNxSP1fr15CCc4dSd3r6N9Q/crpM/IH61+a9XTyob2drMdA71rSWYI9VZWTXg9eB2pXXtRs6umqvnqhlJWun2KQLJk0o6ve53zp9E78D8PpGTHKyKWovyz1TlmTheKP7p7YqR2CPVbNwcnR3qFd1rJdqY89Lj4zjjepSLrFPtLew24rOtWn/eGzWtnv9z1i10bXEdqV9lmB/WP3sbVP24p9dWS//+I2nTNkrh/fWy9emNj3sCqzMckoBOxuH6z4ibWcB96iMdLSrVdgnazuzEZRldM1QKzsmje3M+4AEljkdrKdr9T69wLK2H8vwypyuFeoWO9pc59OBmnY1HuM+7brJKIypkZMmd7Wke82tICq+/jd23av/7+1Qz/dumjK0D6x9k+KMS5Me1lvP+64G0JtXIYQQQggxGPTwKoQQQgghBkPHDFsQRnPCaRFJCtwwWY7haioswqvl9MwWTo7CPrOrZEcBr50rsuKIIYTBWbSMwwZZf6AdVkJRzCR79HpRFEUJxCU48hAXj16OoigCtw2TZSeKrFUWh63wNX5NGgCv6mvSDq+NGkui3mF8zA5Uy6LlhBRwXfccOKsVSiE2FHI0IROWDWCIhO2d8Fp4koku1jubBkO9CYWbxs19DO2wOPwTQ/+ocuuDEzs2bu41whD/yoZmTd14nxgK7hD+d7fDMs5gh2PPsVWrKPNYApmypiznATlRzMfzLOY8CzuQUNTq6YXIcbyRVCsCCyeWF+ARqgmNE5TN8BzhyRSQTcsLqI+Zsc2Wa21h28L9IA8pz6zd4d7fvL9efvP/ecWUXf1OkArcmFkZCcLh/zFYZ43IZqrAMDfdwKpxaJOiIOkDZHYqeDu8t5Ftl7kJ517GRud3zbsKlx07LA6540+ui7HmouPBOdVkAwa6d8PxR/wgAaTjZunl4pT8xY5Cn9z50J7Djb8NfXnvh7dNWXUS+g9LAzBDpCdN9LIM1kUYeL+RbEAIIYQQQnzO0MOrEEIIIYQYDHp4FUIIIYQQgyHukrLrML5afSv+nY9+kL2M0Yn11Rk5VkSexVY8thpb11oH8SxyWM/mWXxtAj73JyU1qGhHS8uc3vtsu49t9CNvXNZSx6rfioGC/bzJBo/7t7G8ammPR/uv2fqh3ZynXX7muvn5k/82/P6tf/QDU7afhnvi/ZVNKX6eh/vnycpqJhd5OP4ys3VBDSynPS3Ajmu1ovssOihS+tkyQ108XTO0pHLSrNbTwzplTetFkf0Gh+2+ENJqo3a1Nh0uIDXuonmfFcvN0cFubs89PQu/927aA156K2jox3esfVpy90H4Qd9sRGjHSbpWtOcsz+0+jT3hmLSymD7b6df8TPrPjv7XRza23rwKIYQQQojBoIdXIYQQQggxGLpn2Prknb+XpWhTIVRjWWP3mYBdQ80WCS1L+HhgN8Ovp82rbLJiqVZOOozHGSrtEtJ199OcjQhhOUWXdZ0V6Tds5/UlBy+E17pe9Q3pd/Xo5S5sOsx/EX0Rx76XTc8JsdYyUHn2TV6fRvstz77PTWfTwQ6r5Zhys285cL/1+qorc2qLV68u2eZMWfu2NdZ3qWM1xaH1tlZZXTDjuTkrmAEzrnn3E7LRiuGeVZEFWgxZx2rbwXUvfvqOKXvpu5fWy3/92oum7L/5pb8M21U2M9ciD8dI2CqrdOyOYF1eC7M+lSlZQsF2FVlSlonzHAEHiVdOn3KSYUUpPw/Ej14viqxMwbsNkbwheRDafee2vTJ7H4S2nj607T67uwiHy21ZsRPaKD1emLJ4ESyvapZ5aBs6tfLK4sXQD/iajR6CHICkAdXS2vk1wTaK5rmMpJ74vBW3nCv15lUIIYQQQgwGPbwKIYQQQojBoIdXIYQQQggxGLprXj/R9bBV1iaspDro9crF4tNX6nr4llqOrdBXq8jbVT3bAbbrIpXtsm4vel6XrWji+tJF19rHHmvbePXopGFv0BRG2+lH1bbt7bZAl3679bHnHtzrE/3a1rU47Kl9d78JYDwt98fUrCVRq8c6e0zV7Vle5dRPcT/87QXsB9PIRlEU7fzw5np58mcvm7I/vfTGevnLlz40ZV88CKlB54XVIt5e7q+X78z3Tdn9s2C5NUmbx1pOmt6ybG5LTKcaT+0+PRutGPSpMaejRTGrl3KWU7lCitvRzNalWIX9jO5ZXfLlH4fjX/mJTe87fv9h+PHgyO4TfvPcNb5+LZQ9d8OUZU8drJeX12xdigl+L2DPb+dO0MpOPrB1ie4FG60SrLE+qhumum7W6NfGirGAc1JBt5wD9eZVCCGEEEIMBj28CiGEEEKIwdBdNvAJfUNyThjHtcvx4FfXWcvQm2O741uB0TN/3+xibW2Fatu1sw5yz2FT9LXmQvqe+zbYVCzWrSdawDl96UmUEGwKp4072UWhVZFnaeRVpcO841pS9e07bceQ21d6jottaA+8enqbOe3ObWTauouspa2sahPjDUOhLEnDbEMjul5ob8T2WzhudncaD/3Mv3xgfn8weWG9/E+/bjNzffP1YLm1l1rpxpVJsEl6sLSZudBWKyaLLcy+lSa2/c9XQZpQulZc9Iei2dYq5mxcTTsa8YaNuzSyAT6/+CQ8Ol35gT32jT8HWcade83VmtmsVunLoY3yG4embH41rHv2jH1sW16Ca70gacDdcA67t2wfHL9/f71c3rP9pZoHuQNLXuIZ2JSyzRvalrJVFvZzlsNgPy9llSWEEEIIIT5n6OFVCCGEEEIMBj28CiGEEEKIwRDX7AwcDuOr1bfi3/l4yw5pST9vej0hNoU3jgZo9STE5wIYl27aWtDicjrMyNP/jUFHiKnOoyiKJ2BX5d07SeNdgeVWRVaSqK8sXn3GlN1/I1hgHb9uD1G9HjSvz12zdkqvH95dLx+M7fHmRbhm95bW0utoFbS6y9zqKUvQys4zW7aCdfPc6qPzDNP0kj4aLuGILb1g3aKw7/LKs3C89KGty7Xvh51e+xfv27q8+0E43pVLpiy6fjWsd91aj82fCv1gdUAplkH/ndAnPUkeynZY13oL2uzufVPGFliNkBbdaNNZn+r0eQP1a+97hX8+/98euSO9eRVCCCGEEINBD69CCCGEEGIw9LfK8ixIaqHQZpspY4HSxWLFs4ZxtnOtbtoe293OsX26CKubbVtJbYOhnkPb/unJaDaVIe1JoXcmJMcyB+YIHr8mhOWNewpLme06zCWtrbK69OG2c01PC6qNzateBirH8rD3NfPKti2pMWPUuV5g9VRRFWPH8qeC8GrMGYXGIFOYkGQB68VSBCwjGya0Jkr+7mem6MabIYx/7dXnTBlKCt57w1pl3XktyAF2p5kpW0FYf7G051AWzX2sgAxY1RlZNGUwD0xoHsAMW0t7XZJVsx9Weh7KJpTgbfogrHz4c9tGuz8OWcnK+w9NWfp0yIBVXrtsyvJr4Vpnu3R+ME52P7TXM12E/p4+sFm7kpPwuzo5tSexCvvhzFUxylUSZ9z3tCCs3dtgPNSzb+Hc0m7uHMhTghBCCCGEEHp4FUIIIYQQA0IPr0IIIYQQYjB0t8pKfvejDb0UjrLKEqIfn+eUsEIMhQTtgDagr+2gETbpOOk+i9rAklPOtp0vuC4tt0t2reY1PgCrJ9ZFYr1Zb542f2qDusxqbu23cD/JZWtBVS1Xj1wviqy+sjw5aTz2xoC+U0s9De1e5VbXqvn+0Xy3/BNZZQkhhBBCiGGjh1chhBBCCDEYeltlse2CSzL69HWiR7xix+O1tbj6LLS1balt12xfYtiUVdaTWpeL2K4vfa3Peh+vZ3/9RQkdeZnFvM06zBGuxZZn0Yf77GIT44WX21rmfR64CEuvviHyPvtoS5f5Fqvh9LFqZUPLLNdrTdtQdkZ+UUB5fm7/wL8vkPLW4tNX+qzANUvIsszIG/hZCOaBjQ3tthaEnqUcP4dh5S7ivrPhsag3r0IIIYQQYjDo4VUIIYQQQgwGPbwKIYQQQojB0F3z+rE2ge0ujKaMtV8trUaeKOlX3zSdnnbD2+c20oJedF0uertfJNpqJoeime2ZFrfLHOFKJi96rjHpRj/n/f1Jmge88eBZRvXpIO7Ya6/frZxUmZ4m1WUTOkxPM+nMSXFKKW5Rc9tFU95Sm+vvw2lz5/zKxYY0tn11n23ndW8MbTul8qex4XuT3rwKIYQQQojBoIdXIYQQQggxGLZjldXW1iGy1h9d7LA8G5ym9boegzaEZdpHh/PdCBii6RsD2pQ9lXf8LVtgdbJWw5BJXwsxZ91aP8Nw2KZD+Y87/OPRdyx4fdrpR14fQFyrrA7bubS1yuqLZ4NzEXNQ23mniw1f2/lrU2Oo7XXaxPH6ZsvbhsRkE+fN/Rv36fT93iH+bdDl/C76+E9SdsWLtJjrid68CiGEEEKIwaCHVyGEEEIIMRj08CqEEEIIIQZDb82rm/K1g3akU5pZ3K6tVOoiLHEuWgOyCR3URdj1bPkYvdu2r4WYs+5W7GfM358MndGn0jstbj/rtr7X/bFaZfXeh3eNLiJ9dsuxcdGWgF24yHHUN4XtNuo4lPnDYxOa0Iu41n1RXTqhN69CCCGEEGIw6OFVCCGEEEIMhv6ygb62El2ydDh4Vll9y3rbtmzChqSLNcXjtOby6GBx1Ha7tpZGXdiIXVqn7TZsz/Ikh77awufQ0g6r71h/xE7Ddl2y/CCb6g+bsBerHePxWdh1wpv/+95jNm05tAnboIues73jb2q+uHC5HByv5xyI1pxR9CmZQT8PDMDyKoqiXuPjCZrFhBBCCCGE8NHDqxBCCCGEGAx6eBVCCCGEEIPh4q2yvLIONiqepKtvWW82YoPTM33gRdDX3mYD2124pdFF01aT9CSlDtwEfA497bAufKxvg23Yi21ju22zqXptenxc9Py+DR738TcN66NxGnUGfu/vHYbKUNq9Rz315lUIIYQQQgwGPbwKIYQQQojB0F028En4chM2JlHU2rblQiyTPNsWNz65hVfzj9NaZVNWOm2vZ0+LtK3Qpd3NahuyXfus6z1uUE7UwTptE5ZXfa3a2CrL1IVttNoer8t84Y31vufXtv/1HOtdxiG2J2/nlnkWZhc4HuI03CZrddqAfVP9gC2vrTdXDWW+6Evf548nSTLT5R7/eW9PpOV10ZtXIYQQQggxGPTwKoQQQgghBoMeXoUQQgghxGCIqw5aijiO70RR9M72qiOEaODlqqpuPO5KCCGEEI+bTg+vQgghhBBCPE4kGxBCCCGEEINBD69CCCGEEGIw6OFVCCGEEEIMBj28CiGEEEKIwaCHVyGEEEIIMRj08CqEEEIIIQaDHl6FEEIIIcRg0MOrEEIIIYQYDHp4FUIIIYQQg+H/B/OIC8KE+57VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  AE loss: \t\t\t0.004139\n",
      "  rec loss (in-iteration): \t0.000378\n",
      "  D loss: \t\t\t0.526339\n",
      "  val rec loss: \t0.000415\n",
      "  LR auc: \t0.548868\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.update({\n",
    "    \"n_epochs\" : 200,\n",
    "    # D_training\n",
    "    \"D_n_upd\" : 5,\n",
    "    \"D_loop_epochs\" : 200,\n",
    "    \"min_lambda\" : 3e-3,\n",
    "    \"inc_lambda\" : 3e-3 / 100,\n",
    "    \"max_lambda\" : 3e-3,\n",
    "})\n",
    "train_stats = train_fadernet(train_loader, val_loader, args, \n",
    "                             AE, D, AE_opt, D_opt, device, \n",
    "                             AE_crit=nn.MSELoss(), D_crit=nn.BCEWithLogitsLoss(),\n",
    "                             site_labels=site_labels, train_stats=train_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500, 0.5488684177374654)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_stats[\"LR_auc\"]), train_stats[\"LR_auc\"][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FaderAE3d_ABIDE2-fmri-4slices-4sites_32-64-128-256-512-1024\n"
     ]
    }
   ],
   "source": [
    "name = \"FaderAE3d_ABIDE2-fmri-4slices-4sites_{}\".format(\n",
    "    \"-\".join(map(str, args[\"conv_model\"])),\n",
    ")\n",
    "print(name)\n",
    "torch.save({\n",
    "    'train_stats' : train_stats,\n",
    "    'model_state_dict': AE.state_dict(),\n",
    "    'optimizer_state_dict': AE_opt.state_dict(),\n",
    "    'D_model_state_dict': D.state_dict(),\n",
    "    'D_optimizer_state_dict': D_opt.state_dict(),\n",
    "}, \"weights/{}.pth\".format(name))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}