keras-starter-kit-unet-train-on-full-dataset.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Keras starter kit [full training set, UNet]"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Note\n","\n","- 高い解像度でリサイズすることはprecisionの向上につながるため有効\n","- seresnextでチャネル間の相関を見れるので有効\n","- 文字の太さ・書き方に大きくバリアントがあるので、横の相関よりもdepthの相関の方が大事かもしれない\n","- 深さに関して、隣り合う深さ同士に大きな変化はない\n","- fpをfnよりも小さくしたい\n","- valid_scoreはCFG.thd依存\n","- valid_lossはCFG.loss1/loss2依存\n","- encoder内でdepthをクロップしてバッチで繋げた方が精度が良い\n","- maskに対しては有効ではないが、labelは的確なラベルを用いることで精度が向上\n","- depthは22~34 or 24 ~ 36\n","    - 固めるより2skipで広げたほうが良い\n","- 画像のサイズは大きい方が良いのか？（BUFFER / SHARED_HEIGHT）\n","    - 比率を同じにして試してみる\n","    - SHARED_HEIGHTをデカくするとデータがメモリに載らなさそうだった\n","- NetのweightsにBatchNormはない方が良い\n","- BUFFER:strideを160:96から160:80にしたら精度が落ちた\n","    - trainの精度は上がっていた\n","    - BUFFERに対してstrideが細かすぎると過学習に繋がっているのかもしれない\n","    - P.S. strideを小さくすることで、学習時のバイアスが減るので良い学習ができる\n","- 文字が見えなくなるよりも、高いthdを設定して、文字が大きく見えた方が良い（仮説）\n","- バイリニアよりもバイキュービックが良い\n","    - 計算コストが気になるところ\n","- ノイズについて\n","    - 大きすぎると良くない(intensityが(0.0001, 0.0005)くらいが良かった)\n","        - 元画像が255 * 255の範囲であることを踏まえると、1/255の変化で255のずれがある\n","- lossのαとβについて\n","    - 理論的にはαは大きい方が良い（偽陽性fpはスコアを大きく下げるので）\n","    - しかし、thdを用いて偽陽性を防ぐ手法もあるため、学習時にはあえて偽陰性に強いペナルティを与えた方が良いのかもしれない\n","    - Stacked UnetのI層目では高いβで見落としをなくして、二層目で高いαにしてノイズを除去する作戦\n","- 文字サイズとリサイズサイズについて\n","- thresholdを変えるだけでpublic lbが大きく変わる\n","    - 0.5から0.7にしたら0.05上がった（0.58→0.63）\n","- 学習時・推論時に低いRESIZED_HEIGHTで進めると、提出時にcvとlbに差が出るかもしれない。これは最終的にリサイズする際に細かい部分の見落としが発生するからだと思う\n","    - 良い学習をするために、正しいラベル・正しい入力が適切→resizeすることでデータの質が損なわれているのかも\n","- [ ] 3dcnnにbatch化したdepthを横に繋げる実装が使えるか考える\n","- [ ] Stacked Unetを3dcnnに適用してみる\n","- 解像度（BUFFER）はある程度大きくないといけない（<112）\n","- DecoderBlockのattention=scseについて\n","- 3dresnetで見る場合、最初のconv1とmaxpoolで解像度が1/4に縮小されるので、元が128x128であれば32x32をUpScaleすることになる。なので、元々ある程度高い解像度が必要\n","    - これは2次元encoderでも同様のことが言えるはず\n","- 3dresnetで見る場合、Z_LISTは2飛ばしで広く見る方が精度が高い\n","- ampを有効にする（precision=16）\n","- 3dresnetで学習時の精度が良くならない\n","    - 解像度が低い（仮説）\n","    - agumentationが難しすぎる（仮説）\n","        - augmentationの確率を下げても変化はなかった\n","        - augmentationをなくすとどうなる？\n","            - 減らすとtrainの精度が上がるが、valはlossが下がらない現象が発生\n","                - それでもtrainの精度は60%程度で停滞\n","    - KMが有効に見える\n","- pretrainedのモデルの種類：K,KM\n","- 3次元においてもStacked UNetは有効（以下、比較）\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/kylnuh3w?workspace=user-fummicc1\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/jr49frri?workspace=user-fummicc1\n","\n","- augmentationを無くしたら学習時の精度は上がった\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/rdmdfcm4?workspace=user-fummicc1\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/ry6jtkkp?workspace=user-fummicc1\n","- fragment1もaとbに分割してみる（5fold）\n","- augmentation\n","    - transposeが微妙かも？\n","        - でも、正方形の入力画像でtransposeするのはHorizontal/VerticalのFlipと同値に感じる\n","    - Flip系はOK\n","    - なぜかPadIfNeededが検証データに対して効いている\n","- BCELossだと学習が不安定になった\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/1byzvdec?workspace=user-fummicc1\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/pmqpfwcg?workspace=user-fummicc1\n","- ラベルの値が0,1の場合はSoftBCEとBCEに違いはない\n","- BCELossが有効だった\n","    - 不均一性を考慮して（あとpositiveがほしいので）、pos_weightを1以上にする\n","- Rotate TTAは学習時にRotateのaugmentationをしているなら意味がなさそう\n","- Z_DIFFは4より6が良い\n","- Pooling Attentionのattention_typeはscseとNoneのどちらが良いのか\n","    - Noneの方が良さそう\n","        - https://wandb.ai/fummicc1/lightning_logs/runs/6dewm37h?workspace=user-fummicc1\n","        - https://wandb.ai/fummicc1/lightning_logs/runs/vyj9isbf?workspace=user-fummicc1\n","\n","- 解像度は高いから良いというわけではない\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/6zchova0?workspace=user-fummicc1\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/vyj9isbf?workspace=user-fummicc1\n","\n","- CROP_DEPTHは重ねた方が良いかも\n","- MultiChannelNoiseはない方が良い\n","\n","- Z_LISTについて\n","    - 学習した結果をvalで活かすには、同じ層あたりで同じ特徴量を持つ必要がある\n","        - しかし、fragmentによっては前半に集まっている・後半に集まっているがバラバラ\n","        - より普遍的な成果を出すには中央の層を集中して使った方が良いかも\n","\n","- lr_schedulerの違い\n","- 解像度は高い方が良い\n","    - Z_LISTは意味のある階層だけ見るでOK\n","\n","- エッジ部分の識別が甘い\n","    - mask_paddingしているため\n","\n","- submission時のdenoiseはskimageのresizeを先にかけた方が良い\n","- batchsize = 24 でbatchnorm必要？\n","    - batch正規化はあった方が良い\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/l2f4pbad?workspace=user-fummicc1\n","    - https://wandb.ai/fummicc1/lightning_logs/runs/qukacva9?workspace=user-fummicc1\n","\n","- リサイズをしないでpadで高さを調整して学習させたらTest Fragmentに最適化されて精度が上がった"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Setup"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:10:40.298967Z","iopub.status.busy":"2023-03-18T23:10:40.298008Z","iopub.status.idle":"2023-03-18T23:10:49.514216Z","shell.execute_reply":"2023-03-18T23:10:49.513058Z","shell.execute_reply.started":"2023-03-18T23:10:40.298921Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Global seed set to 42\n"]}],"source":["import numpy as np\n","import torch\n","import torch.nn as nn\n","import wandb\n","import torchvision\n","import datetime\n","import imageio\n","\n","# import cupy\n","from sklearn.model_selection import KFold\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","import pytorch_lightning\n","import segmentation_models_pytorch as smp\n","import pytorch_lightning as pl\n","import pytorch_lightning.callbacks.model_checkpoint\n","import pytorch_lightning.plugins\n","from skimage.transform import resize as resize_ski\n","from pytorch_lightning.strategies.ddp import DDPStrategy\n","from pytorch_lightning.loggers import WandbLogger\n","from einops import rearrange, reduce, repeat\n","import torch.nn.functional as F\n","import segmentation_models_pytorch as smp\n","from segmentation_models_pytorch.decoders.unet.decoder import UnetDecoder, DecoderBlock\n","from timm.models.resnet import (\n","    resnet10t,\n","    resnet34d,\n","    resnet50d,\n","    resnet14t,\n","    seresnext26d_32x4d,\n","    seresnext50_32x4d,\n","    seresnext26tn_32x4d,\n",")\n","from timm.models.mvitv2 import mvitv2_base\n","import os\n","import torch.utils.data\n","from dataclasses import dataclass\n","\n","from scipy.ndimage import distance_transform_edt\n","\n","import ssl\n","\n","ssl._create_default_https_context = ssl._create_unverified_context\n","\n","import glob\n","import matplotlib.pyplot as plt\n","from tqdm import tqdm\n","import cv2\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.utils.data\n","import os, cv2\n","import gc\n","import sys\n","import matplotlib.patches as patches\n","import random\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","import matplotlib.pyplot as plt\n","import pytorch_lightning as pl\n","\n","import torch\n","import torch.optim as optim\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.cuda import amp\n","from torch.utils.data import Dataset, DataLoader\n","import segmentation_models_pytorch as smp\n","import albumentations as A\n","from IPython.display import Video\n","\n","pytorch_lightning.seed_everything(seed=42)\n","torch.set_float32_matmul_precision(\"high\")\n","\n","\n","@dataclass\n","class CFG:\n","    # Data config\n","    # DATA_DIR = '/kaggle/input/vesuvius-challenge-ink-detection/'\n","    DATA_DIR = \"/home/fummicc1/codes/competitions/kaggle-ink-detection\"\n","    # DATA_DIR = '/home/fummicc1/codes/Kaggle/kaggle-ink-detection'\n","    BUFFER = 192  # Half-size of papyrus patches we'll use as model inputs\n","    CROP_SIZE = BUFFER * 2 # 384px\n","    STRIDE = 192\n","    Z_LIST = list(range(20, 40, 2)) # 10チャネル\n","    # Z_LIST = list(range(4, 16, 2)) + list(range(16, 36)) + list(range(36, 44, 2))\n","    Z_DIM = len(\n","        Z_LIST\n","    )  # Number of slices in the z direction. Max value is 64 - Z_START\n","    BATCH_Z_DIFF = 4\n","    BATCH_Z_INFO = [\n","        # (start, end)\n","        (0, 4),\n","        (2, 6),\n","        (4, 8),\n","        (6, 10),\n","    ]\n","    SHARED_HEIGHT = None  # Max height to resize all papyrii\n","\n","    # Model config\n","    BATCH_SIZE = 32\n","\n","    GPU_COUNT = 4\n","\n","    device = torch.device(\"cuda\")\n","    threshold = 0.5\n","    num_workers = 8\n","    exp = 1e-7\n","    mask_padding = (SHARED_HEIGHT // 80) if SHARED_HEIGHT is not None else 100\n","\n","    num_epochs = 40\n","    lr = 1e-3 # initial lr\n","    lr_scheduler_name = \"CosineAnnealingLR\"\n","    eta_min_lr = 1e-5\n","    WANDB_NOTE = \"data_listの順番を変更して[1, 3, 2a, 2b]になった.\"\n","    augmentation_names = [\n","        \"Horizontal\",\n","        \"Vertical\",\n","        \"RandomScale\",\n","        \"Transpose\",\n","        \"RandomRotate\",\n","        \"ShiftScaleRotate\",\n","        # \"Blur\",\n","        \"GridDistortion\",\n","        \"MultiChannelNoise\",\n","        \"PadIfNeeded\",\n","        \"Resize\",\n","        \"MixedUp\",\n","    ]\n","    \n","    mixed_up_alpha = 0.9\n","    mixed_up_probability = 0.4\n","    mixed_up_epochs = list(range(0, 7)) + list(range(num_epochs - 12, num_epochs - 7))\n","    \n","    loss1_alpha = 0.8\n","    loss1_beta = None\n","\n","    loss2a_alpha = 1.2\n","    loss2a_beta = None\n","\n","    loss2b_alpha = 1.2\n","\n","    loss1_weight = 0.5\n","    loss2_weight = 0.5\n","\n","    loss2a_weight = 0.5\n","    loss2b_weight = 0.5\n","\n","    lr_scheduler = optim.lr_scheduler.CosineAnnealingLR\n","    optimizer = \"AdamW\"\n","    # loss1 = smp.losses.TverskyLoss(\n","    #     smp.losses.BINARY_MODE,\n","    #     log_loss=False,\n","    #     from_logits=True,\n","    #     smooth=1e-7,\n","    #     alpha=loss1_alpha,\n","    #     beta=loss1_beta,\n","    # )\n","    loss1 = smp.losses.SoftBCEWithLogitsLoss(\n","        pos_weight=torch.tensor([loss1_alpha]),\n","    )\n","    # loss2a = smp.losses.TverskyLoss(\n","    #     smp.losses.BINARY_MODE,\n","    #     log_loss=False,\n","    #     from_logits=True,\n","    #     smooth=1e-7,\n","    #     alpha=loss2a_alpha,\n","    #     beta=loss2a_beta,\n","    # )\n","    loss2a = smp.losses.SoftBCEWithLogitsLoss(\n","        pos_weight=torch.tensor([loss2a_alpha]),\n","    )\n","    loss2b = nn.BCEWithLogitsLoss(\n","        pos_weight=torch.tensor([loss2b_alpha]),\n","    )\n","\n","    noise_intensity = (0.000025, 0.00005)\n","    \n","    mean = 0.42\n","    std = 0.3\n","    \n","    use_new_label_mask = True\n","    pretrained = True\n","    init_weight_bias = False\n","    SKIP_ZERO_ON_TRAIN = False\n","    model_name = \"seresnext26d_32x4d\"\n","    # MODEL_DEPTH = 34\n","    # MODEL_KIND = \"KM\"\n","    \n","    precision = \"float32\" # float16\n","\n","    interpolation = \"bicubic\" # PixelShuffling\n","    # use_pixel_shuffle = True\n","\n","    exp_name = \"123-gpu17\"\n","    prev_exp_name = \"102-gpu17\"\n","    \n","    attention_type = None\n","\n","\n","def class2dict(c):\n","    return {\n","        attr: getattr(c, attr)\n","        for attr in dir(c)\n","        if not callable(getattr(c, attr)) and not attr.startswith(\"__\")\n","    }"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Load up the training data"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:10:52.653587Z","iopub.status.busy":"2023-03-18T23:10:52.653064Z","iopub.status.idle":"2023-03-18T23:10:57.915118Z","shell.execute_reply":"2023-03-18T23:10:57.914028Z","shell.execute_reply.started":"2023-03-18T23:10:52.653544Z"},"trusted":true},"outputs":[],"source":["def resize(img):\n","    current_height, current_width = img.shape    \n","    aspect_ratio = current_width / current_height\n","    if CFG.SHARED_HEIGHT is None:\n","        return img\n","    # new_height = CFG.SHARED_HEIGHT\n","    # pad_y = new_height - current_height\n","    # if pad_y > 0:\n","    #     # 元画像が小さい場合は解像度を大きくしないでpaddingをつける\n","    #     img = np.pad(img, [(0, pad_y), (0, 0)], constant_values=0)\n","    # else:\n","    # 既に十分でかい場合はリサイズする\n","    # 本当はpaddingしたいけど、メモリサイズが大きくなる\n","    new_height = CFG.SHARED_HEIGHT\n","    new_width = int(CFG.SHARED_HEIGHT * aspect_ratio)\n","    new_size = (new_width, new_height)\n","    # (W, H)の順で渡すが結果は(H, W)になっている\n","    img = cv2.resize(img, new_size)\n","    return img\n","\n","def load_mask(split, index): \n","    if index == \"2a\" or index == \"2b\":\n","        mode = index[1]\n","        index = \"2\"\n","    img = cv2.imread(f\"{CFG.DATA_DIR}/{split}/{index}/mask.png\", 0) // 255\n","    if index == \"2\":\n","        h = 9456\n","        if mode == \"a\":\n","            img = img[h:, :]\n","        elif mode == \"b\":   \n","            img = img[:h, :]\n","    img = resize(img)\n","    img = np.pad(img, 1, constant_values=0)\n","    dist = distance_transform_edt(img)\n","    img[dist <= CFG.mask_padding] = 0\n","    img = img[1:-1, 1:-1]    \n","    return img\n","\n","\n","def load_labels(split, index):\n","    if index == \"2a\" or index == \"2b\":\n","        mode = index[1]\n","        index = \"2\"\n","    suffix = \"_new\" if CFG.use_new_label_mask else \"\"\n","    img = cv2.imread(f\"{CFG.DATA_DIR}/{split}/{index}/inklabels{suffix}.png\", 0) // 255    \n","    if index == \"2\":\n","        h = 9456\n","        if mode == \"a\":\n","            img = img[h:, :]\n","        elif mode == \"b\":   \n","            img = img[:h, :]\n","    img = resize(img)\n","    return img"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["# input shape: (H, W, C)\n","def rotate90(volume: np.ndarray, k=None, reverse=False):    \n","    if k:\n","        volume = np.rot90(volume, k)\n","    else:\n","        volume = np.rot90(volume, 1 if not reverse else 3)\n","    height = volume.shape[0]\n","    width = volume.shape[1]\n","    new_height = CFG.SHARED_HEIGHT\n","    new_width = int(new_height * width / height)\n","    if len(volume.shape) == 2:\n","        return cv2.resize(volume, (new_width, new_height))\n","    return resize_ski(volume, (new_height, new_width, volume.shape[2]))"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:11:02.854397Z","iopub.status.busy":"2023-03-18T23:11:02.85384Z","iopub.status.idle":"2023-03-18T23:11:02.867203Z","shell.execute_reply":"2023-03-18T23:11:02.866021Z","shell.execute_reply.started":"2023-03-18T23:11:02.854351Z"},"trusted":true},"outputs":[],"source":["def load_volume(split, index):\n","    if index == \"2a\" or index == \"2b\":\n","        mode = index[1]\n","        index = \"2\"\n","    # Load the 3d x-ray scan, one slice at a time\n","    all = sorted(glob.glob(f\"{CFG.DATA_DIR}/{split}/{index}/surface_volume/*.tif\"))\n","    z_slices_fnames = [all[i] for i in range(len(all)) if i in CFG.Z_LIST]\n","    assert len(z_slices_fnames) == CFG.Z_DIM\n","    z_slices = []\n","    for z, filename in  tqdm(enumerate(z_slices_fnames)):\n","        img = cv2.imread(filename, -1)\n","        if index == \"2\":\n","            h = 9456\n","            if mode == \"a\":\n","                img = img[h:, :]\n","            elif mode == \"b\":\n","                img = img[:h, :]\n","        img = resize(img)\n","        # img = (img / (2 ** 8)).astype(np.uint8)\n","        if CFG.precision == \"float16\":\n","            img = img.astype(np.float32) // 255\n","        elif CFG.precision == \"float32\":\n","            img = img.astype(np.float32) / 255\n","        else:\n","            assert False\n","        z_slices.append(img)\n","    return np.stack(z_slices, axis=-1)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Create a dataset in the input volume\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["def is_in_masked_zone(location, mask):\n","    return mask[location[0], location[1]] > 0\n"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:14:17.958942Z","iopub.status.busy":"2023-03-18T23:14:17.958495Z","iopub.status.idle":"2023-03-18T23:14:18.45515Z","shell.execute_reply":"2023-03-18T23:14:18.454126Z","shell.execute_reply.started":"2023-03-18T23:14:17.958902Z"},"trusted":true},"outputs":[],"source":["def generate_locations_ds(volume, mask, label=None, skip_zero=False):\n","    is_in_mask_train = lambda x: is_in_masked_zone(x, mask)\n","\n","    # Create a list to store train locations\n","    locations = []\n","\n","    # Generate train locations\n","    volume_height, volume_width = volume.shape[:-1]\n","\n","    for y in range(CFG.BUFFER, volume_height - CFG.BUFFER, CFG.STRIDE):\n","        for x in range(CFG.BUFFER, volume_width - CFG.BUFFER, CFG.STRIDE):\n","            if skip_zero and CFG.SKIP_ZERO_ON_TRAIN and label is not None and np.all(label[y - CFG.BUFFER : y + CFG.BUFFER, x - CFG.BUFFER : x + CFG.BUFFER] == 0):\n","                # print(f\"skip location at (y: {y}, x: {x})\")\n","                continue\n","            if is_in_mask_train((y, x)):\n","                locations.append((y, x))\n","\n","    # Convert the list of train locations to a PyTorch tensor\n","    locations_ds = np.stack(locations, axis=0)\n","    return locations_ds"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Visualize some training patches\n","\n","Sanity check visually that our patches are where they should be."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["def extract_subvolume(location, volume):\n","    global printed\n","    y = location[0]\n","    x = location[1]\n","    subvolume = volume[y-CFG.BUFFER:y+CFG.BUFFER, x-CFG.BUFFER:x+CFG.BUFFER, :].astype(np.float32)\n","    \n","    return subvolume"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## SubvolumeDataset"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["import torch\n","import numpy as np\n","\n","def mixup_data(x, y):\n","    alpha = CFG.mixed_up_alpha\n","    if alpha > 0.:\n","        lam = np.random.beta(alpha, alpha)\n","    else:\n","        lam = 1.\n","\n","    batch_size = x.size()[0]\n","    index = torch.randperm(batch_size)\n","\n","    mixed_x = lam * x + (1 - lam) * x[index]    \n","    y_a, y_b = y, y[index]\n","    \n","    mixed_x = mixed_x.float()\n","    y_a = y_a.float()\n","    y_b = y_b.float()\n","    \n","    y = y_a * lam + y_b * (1 - lam)\n","\n","    return mixed_x, y, y_a, y_b, lam\n"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["import numpy as np\n","from albumentations.core.transforms_interface import ImageOnlyTransform\n","\n","class MultichannelNoise(ImageOnlyTransform):\n","\n","    def __init__(self, intensity=CFG.noise_intensity, always_apply=False, p=0.5):\n","        super().__init__(always_apply, p)\n","        self.intensity = intensity\n","\n","    def apply(self, img, **params):\n","        intensity = np.random.uniform(*self.intensity)\n","        noise = np.random.normal(loc=0, scale=intensity, size=img.shape)\n","        # print(\"img\", img)\n","        img = img + noise        \n","        return np.clip(img, 0, 1).astype(np.float32) # クリップして0から255の範囲に保つ\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["def smooth_labels(labels, alpha=0.1):\n","    labels = labels * (1 - alpha) + alpha * 0.5\n","    return labels"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["\n","import torch\n","from torch.utils.data import Dataset, DataLoader\n","from sklearn.preprocessing import OneHotEncoder\n","\n","from albumentations.core.transforms_interface import ImageOnlyTransform\n","\n","class SubvolumeDataset(Dataset):\n","    def __init__(self, locations, volume, labels, buffer, is_train: bool, return_location: bool = False):\n","        self.locations = locations\n","        self.volume = volume\n","        self.labels = labels        \n","        self.buffer = buffer\n","        self.is_train = is_train\n","        self.return_location = return_location\n","\n","    def __len__(self):\n","        return len(self.locations)\n","\n","    def __getitem__(self, idx):\n","        label = None\n","        location = np.array(self.locations[idx])\n","        y, x = location[0], location[1]\n","\n","        subvolume = extract_subvolume(location, self.volume)\n","        \n","        if self.labels is not None:\n","            label = self.labels[y - self.buffer:y + self.buffer, x - self.buffer:x + self.buffer]            \n","            label = np.stack([label], axis=-1)            \n","            \n","        # 段々meanは小さくなる\n","        mean = np.array([CFG.mean for i in range(0, CFG.Z_DIM)]).reshape(-1, 1, 1)\n","        # 段々stdは小さくなる\n","        std = np.array([CFG.std for i in range(0, CFG.Z_DIM)]).reshape(-1, 1, 1)\n","        \n","        if self.is_train and label is not None:    \n","            # label = smooth_labels(label, alpha=0.1)                  \n","            transformed = A.Compose([\n","                A.ToFloat(max_value=255),\n","                A.HorizontalFlip(p=0.5) if \"Horizontal\" in CFG.augmentation_names else A.Compose([]),\n","                A.VerticalFlip(p=0.5) if \"Vertical\" in CFG.augmentation_names else A.Compose([]),  \n","                A.RandomScale(p=0.4, scale_limit=0.3) if \"RandomScale\" in CFG.augmentation_names else A.Compose([]),\n","                A.Transpose(p=0.5) if \"Transpose\" in CFG.augmentation_names else A.Compose([]), \n","                A.RandomRotate90(p=0.5,) if \"RandomRotate\" in CFG.augmentation_names else A.Compose([]), \n","                A.ShiftScaleRotate(p=0.7, scale_limit=0.35, rotate_limit=45) if \"ShiftScaleRotate\" in CFG.augmentation_names else A.Compose([]),\n","                A.MotionBlur(p=0.2) if \"Blur\" in CFG.augmentation_names else A.Compose([]),\n","                A.GridDistortion(num_steps=5, distort_limit=0.15, p=0.3) if \"GridDistortion\" in CFG.augmentation_names else A.Compose([]),\n","                MultichannelNoise(\n","                    p=0.2,\n","                ) if \"MultichannelNoise\" in CFG.augmentation_names else A.Compose([]),\n","                A.PadIfNeeded(min_height=self.buffer * 2, min_width=self.buffer * 2) if \"PadIfNeeded\" in CFG.augmentation_names else A.Compose([]),\n","                A.Resize(height=self.buffer * 2, width=self.buffer * 2) if \"Resize\" in CFG.augmentation_names else A.Compose([]),\n","            ])(image=subvolume, mask=label)            \n","            subvolume = transformed[\"image\"]\n","            label = transformed[\"mask\"]\n","            subvolume = np.transpose(subvolume, (2, 0, 1))\n","            label = np.transpose(label, (2, 0, 1))\n","            subvolume = (subvolume - mean) / std            \n","        else:\n","            if label is None:\n","                subvolume = np.transpose(subvolume, (2, 0, 1))\n","                subvolume /= 255.\n","                subvolume = (subvolume - mean) / std\n","            else:\n","                # print(\"subvolume in val dataset (before aug)\", subvolume, file=open(\"before-val-aug.log\", \"w\")) \n","                subvolume = np.transpose(subvolume, (2, 0, 1))\n","                label = np.transpose(label, (2, 0, 1))\n","                subvolume /= 255.\n","                subvolume = (subvolume - mean) / std\n","        # print(\"subvolume\", subvolume)\n","        if self.return_location:\n","            return subvolume, location\n","        return subvolume, label        "]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Visualize validation dataset patches\n","\n","Note that they are partially overlapping, since the stride is half the patch size."]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:14:31.265448Z","iopub.status.busy":"2023-03-18T23:14:31.264916Z","iopub.status.idle":"2023-03-18T23:14:35.752995Z","shell.execute_reply":"2023-03-18T23:14:35.751869Z","shell.execute_reply.started":"2023-03-18T23:14:31.265399Z"},"trusted":true},"outputs":[],"source":["def visualize_dataset_patches(locations_ds, labels, mode: str, fold = 0):\n","    fig, ax = plt.subplots()\n","    ax.imshow(labels)\n","\n","    for y, x in locations_ds:\n","        patch = patches.Rectangle([x - CFG.BUFFER, y - CFG.BUFFER], 2 * CFG.BUFFER, 2 * CFG.BUFFER, linewidth=2, edgecolor='g', facecolor='none')\n","        ax.add_patch(patch)\n","    plt.savefig(f\"fold-{fold}-{mode}.png\")\n","    plt.show()    "]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Compute a trivial baseline\n","\n","This is the highest validation score you can reach without looking at the inputs.\n","The model can be considered to have statistical power only if it can beat this baseline."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Dataset check"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Model"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["# ref - https://www.kaggle.com/competitions/vesuvius-challenge-ink-detection/discussion/397288\n","def fbeta_score(preds, targets, threshold, beta=0.5, smooth=1e-5):\n","    preds_t = torch.where(preds > threshold, 1.0, 0.0).float()\n","    y_true_count = targets.sum()\n","    \n","    ctp = preds_t[targets==1].sum()\n","    cfp = preds_t[targets==0].sum()\n","    beta_squared = beta * beta\n","\n","    c_precision = ctp / (ctp + cfp + smooth)\n","    c_recall = ctp / (y_true_count + smooth)\n","    dice = (1 + beta_squared) * (c_precision * c_recall) / (beta_squared * c_precision + c_recall + smooth)\n","\n","    return dice"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["def init_subpixel(weight):\n","    co, ci, h, w = weight.shape\n","    co2 = co // 4\n","    # initialize sub kernel\n","    k = torch.empty([co2, ci, h, w])\n","    nn.init.kaiming_uniform_(k)\n","    # repeat 4 times\n","    k = k.repeat_interleave(4, dim=0)\n","    weight.data.copy_(k)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["def init_linear(m, relu=True):\n","    if relu: nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n","    else: nn.init.xavier_uniform_(m.weight)\n","    if m.bias is not None: nn.init.zeros_(m.bias)"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["def initialize_weights(model: nn.Module):\n","    for module in model.modules():\n","        if isinstance(module, (nn.Conv2d, nn.Linear)):\n","            nn.init.kaiming_normal_(module.weight, nonlinearity='relu')\n","            if module.bias is not None:\n","                nn.init.constant_(module.bias, 0)\n","        elif isinstance(module, nn.BatchNorm2d):\n","            nn.init.constant_(module.weight, 1)\n","            nn.init.constant_(module.bias, 0)"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["class SmpUnetDecoder(nn.Module):\n","\tdef __init__(\n","\t\tself,\n","\t\tin_channel,\n","\t\tskip_channel,\n","\t\tout_channel,\n","\t):\n","\t\tsuper().__init__()\n","\t\tself.center = nn.Identity()\n","\n","\t\ti_channel = [\n","\t\t\tin_channel,\n","\t\t] + out_channel[:-1]\n","\t\ts_channel = skip_channel\n","\t\to_channel = out_channel\n","\t\t# print(\"i:\", i_channel)\n","\t\t# print(\"s:\", s_channel)\n","\t\t# print(\"o:\", o_channel)\n","\t\tblock = [\n","\t\t\tDecoderBlock(i, s, o, use_batchnorm=True, attention_type=CFG.attention_type)\n","\t\t\tfor i, s, o in zip(i_channel, s_channel, o_channel)\n","\t\t]\n","\t\tself.block = nn.ModuleList(block)\n","\n","\tdef forward(self, feature, skip):\n","\t\t# for s in skip:\n","\t\t\t# print(\"feature\", feature.shape, \"skip\", s.shape)\n","\t\td = self.center(feature)\n","\t\tdecode = []\n","\t\tfor i, block in enumerate(self.block):\n","\t\t\ts = skip[i]\n","\t\t\td = block(d, s)\n","\t\t\tdecode.append(d)\n","\n","\t\tlast = d\n","\t\treturn last, decode\n","\n","class Net(nn.Module):\n","\tdef __init__(\n","\t\tself,\n","\t):\n","\t\tsuper().__init__()\n","\t\tself.output_type = [\"inference\", \"loss\"]\n","\n","\t\tconv_dim = 64\n","\t\tencoder1_dim = [\n","\t\t\tconv_dim,\n","\t\t\t256,\n","\t\t\t512,\n","\t\t\t1024,\n","\t\t\t2048,\n","\t\t]\n","\t\tdecoder1_dim = [\n","\t\t\t1024,\n","\t\t\t512,\n","\t\t\t256,\n","\t\t\t64,\n","\t\t]\n","\t\tif CFG.model_name == \"seresnext50_32x4d\":\n","\t\t\tself.encoder1 = seresnext50_32x4d(pretrained=CFG.pretrained, in_chans=CFG.BATCH_Z_DIFF)\n","\t\telif CFG.model_name == \"seresnext26d_32x4d\":\n","\t\t\tself.encoder1 = seresnext26d_32x4d(pretrained=CFG.pretrained, in_chans=CFG.BATCH_Z_DIFF)\n","\t\telif CFG.model_name == \"seresnext26tn_32x4d\":\n","\t\t\tself.encoder1 = seresnext26tn_32x4d(pretrained=CFG.pretrained, in_chans=CFG.BATCH_Z_DIFF)\n","\n","\t\tself.decoder1 = SmpUnetDecoder(\n","\t\t\tin_channel=encoder1_dim[-1],\n","\t\t\tskip_channel=encoder1_dim[:-1][::-1],\n","\t\t\tout_channel=decoder1_dim,\n","\t\t)\n","\t\t# -- pool attention weight  \n","\t\tself.weight1 = nn.ModuleList(\n","\t\t\t[\n","\t\t\t\tnn.Sequential(\n","\t\t\t\t\tnn.Conv2d(dim, dim, kernel_size=3, padding=1),\n","\t\t\t\t\tnn.BatchNorm2d(dim),\n","\t\t\t\t\tnn.ReLU(inplace=True),\n","\t\t\t\t)\n","\t\t\t\tfor dim in encoder1_dim\n","\t\t\t]\n","\t\t)\n","\t\tself.logit1 = nn.Conv2d(decoder1_dim[-1], 1, kernel_size=1)\n","\n","\t\t# --------------------------------\t\t\n","\t\tencoder2_dim = [64, 64, 128, 256, 512]\n","\t\tdecoder2_dim = [\n","\t\t\t256,\n","\t\t\t128,\n","\t\t\t64,\n","\t\t\t64,\n","\t\t]\n","\t\tself.encoder2 = resnet10t(pretrained=CFG.pretrained, in_chans=decoder1_dim[-1])\n","\n","\t\tself.decoder2 = SmpUnetDecoder(\n","\t\t\tin_channel=encoder2_dim[-1],\n","\t\t\tskip_channel=encoder2_dim[:-1][::-1],\n","\t\t\tout_channel=decoder2_dim,\n","\t\t)\n","\t\tself.logit2 = nn.Conv2d(decoder2_dim[-1], 1, kernel_size=1)\n","\n","\t\tif CFG.init_weight_bias:\n","\t\t\tinitialize_weights(self.logit1)\n","\t\t\tinitialize_weights(self.logit2)\n","\t\t\tinitialize_weights(self.weight1)\n","\n","\tdef forward(self, batch):\n","\t\tv = batch\n","\t\tB, C, H, W = v.shape\n","\t\tvv = [\n","\t\t\tv[:, a: b]\n","\t\t\tfor a, b in CFG.BATCH_Z_INFO\n","\t\t]\n","\t\t# print(list(map(lambda a: a.shape, vv)))\n","\t\tK = len(vv)\n","\t\tx = torch.cat(vv, 0)\n","\t\t# x = v\n","\n","\t\t# ----------------------\n","\t\tencoder = []\n","\t\te = self.encoder1\n","\t\tx = e.conv1(x)\n","\t\tx = e.bn1(x)\n","\t\tx = e.act1(x)\n","\t\tencoder.append(x)\n","\t\tx = F.avg_pool2d(x, kernel_size=2, stride=2)\n","\t\tx = e.layer1(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer2(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer3(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer4(x)\n","\t\tencoder.append(x)\n","\t\t# print('encoder', [f.shape for f in encoder])\n","\n","\t\tfor i in range(len(encoder)):\n","\t\t\te = encoder[i]\n","\t\t\te = F.avg_pool2d(e, 2, 2)\n","\t\t\tf = self.weight1[i](e)\n","\t\t\t_, c, h, w = e.shape\n","\t\t\tf = rearrange(f, \"(K B) c h w -> B K c h w\", K=K, B=B, h=h, w=w)  #\n","\t\t\te = rearrange(e, \"(K B) c h w -> B K c h w\", K=K, B=B, h=h, w=w)  #\n","\t\t\tw = F.softmax(f, 1)\n","\t\t\te = (w * e).sum(1)\n","\t\t\tencoder[i] = e\n","\n","\t\tfeature = encoder[-1]\n","\t\tskip = encoder[:-1][::-1]\n","\t\t# print('encoder after weighted', [f.shape for f in encoder])\n","\t\t# print('skip after weighted', [f.shape for f in skip])\n","\t\tlast, decoder = self.decoder1(feature, skip)\n","\t\tlogit1 = self.logit1(last)\n","\t\t# print(\"last.shape\", last.shape)\n","\t\t# print(\"logit1.shape\", logit1.shape)\n","\t\t# print(\"encoder1 shapes\", list(map(lambda a: a.shape, encoder)))\n","  \n","\t\tlogit1 = F.interpolate(\n","\t\t\tlogit1, size=(H, W), mode=CFG.interpolation, align_corners=False, antialias=True\n","\t\t)\n","\n","\t\t# ----------------------\n","\t\tx = last  # .detach()\n","\t\t# x = F.avg_pool2d(x,kernel_size=2,stride=2)\n","\t\tencoder = []\n","\t\te = self.encoder2\n","\t\tx = e.conv1(x)\n","\t\tx = e.bn1(x)\n","\t\tx = e.act1(x)\n","\t\tencoder.append(x)\n","\t\tx = F.avg_pool2d(x, kernel_size=2, stride=2)\n","\t\tx = e.layer1(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer2(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer3(x)\n","\t\tencoder.append(x)\n","\t\tx = e.layer4(x)\n","\t\tencoder.append(x)\n","\t\t\n","\t\tfeature = encoder[-1]\n","\t\tskip = encoder[:-1][::-1]\n","\n","\t\t# print(\"feature.shape\", list(map(lambda a: a.shape, feature)))\n","\t\t# print(\"skip.shape\", list(map(lambda a: a.shape, skip)))\n","\n","\t\tlast, decoder = self.decoder2(feature, skip)\n","\t\tlogit2 = self.logit2(last)\n","\t\t# print(\"logit2.shape\", logit2.shape)\n","\t\tlogit2 = F.interpolate(\n","\t\t\tlogit2, size=(H, W), mode=CFG.interpolation, align_corners=False, antialias=True\n","\t\t)\n","\t\treturn logit1, logit2\n","\t\t# return logit1"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["net_check_input = torch.from_numpy(np.random.randn(12, CFG.Z_DIM, CFG.CROP_SIZE, CFG.CROP_SIZE).astype(np.float32))\n","out = Net()(net_check_input)"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["tc = torch\n","def TTA(x:tc.Tensor,model:nn.Module):\n","    #x.shape=(batch,c,h,w)\n","    shape=x.shape\n","    x=[x,*[tc.rot90(x,k=i,dims=(-2,-1)) for i in range(1,4)]]\n","    x=tc.cat(x,dim=0)\n","    _, x = model(x)\n","    x=x.reshape(4,shape[0], 1 ,*shape[2:])\n","    x=[tc.rot90(x[i],k=-i,dims=(-2,-1)) for i in range(4)]\n","    x=tc.stack(x,dim=0)\n","    return x.mean(0)"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[],"source":["\n","\n","class Model(pl.LightningModule):\n","\ttraining_step_outputs = []\n","\tvalidation_step_outputs = []\n","\ttest_step_outputs = [[], []]\n","\n","\tdef __init__(self, **kwargs):\n","\t\tsuper().__init__()\n","\n","\t\tself.model = Net()        \n","\n","\t\tself.loss1 = CFG.loss1\n","\t\tself.loss2 = CFG.loss2a\n","\t\tself.loss3 = CFG.loss2b\n","\n","\tdef forward(self, image, stage):\n","\t\tif stage != \"train\":\n","\t\t\t# mask = TTA(image, self.model)\n","\t\t\t_, mask = self.model(image)\n","\t\telse:\n","\t\t\tmask = self.model(image)\n","\t\treturn mask\n","\n","\tdef shared_step(self, batch, stage):\n","\t\tsubvolumes, labels = batch\n","\n","\t\timage, labels = subvolumes.float(), labels.float()        \n","\t\tassert image.ndim == 4\n","\t\t\n","\t\th, w = image.shape[2:]\n","\t\tassert h % 32 == 0 and w % 32 == 0\n","\t\t\n","\t\t# print(\"labels\", labels.max(), labels.min())\n","\n","\t\tassert labels.max() <= 1.0 and labels.min() >= 0\n","\n","\t\tif stage == \"train\":\n","\t\t\t\n","\t\t\t# decide if perform mixed-up augmentation\n","\t\t\tshould_mixed_up = \"MixedUp\" in CFG.augmentation_names and  np.random.rand(1)[0] <= CFG.mixed_up_probability and self.current_epoch in CFG.mixed_up_epochs\n","\t\t\tif should_mixed_up:\n","\t\t\t\t# perform mixed-up\n","\t\t\t\timage, labels, labels_a, labels_b, lam = mixup_data(subvolumes, labels)\n","\t\t\t\n","\t\t\t\n","\t\t\tlogit1, logit2 = self.forward(image, stage)\n","\t\t\tif should_mixed_up:\n","\t\t\t\tloss1 = self.loss1(logit1, labels)\n","\t\t\t\tloss2a = self.loss2(logit2, labels)\n","\t\t\t\tloss2b = self.loss3(logit2, labels_a) * lam + self.loss3(logit2, labels_b) * (1 - lam)\n","\t\t\t\t# loss2b = self.loss3(logit2, labels)\n","\t\t\t\tloss = CFG.loss1_weight * loss1 + CFG.loss2_weight * (loss2a * CFG.loss2a_weight + loss2b * CFG.loss2b_weight)\n","\t\t\telse:\n","\t\t\t\tloss = self.loss1(logit1, labels) * CFG.loss1_weight + CFG.loss2_weight * (self.loss2(logit2, labels) * CFG.loss2a_weight + self.loss3(logit2, labels) * CFG.loss2b_weight)\n","\t\t\t# loss = self.loss2(logit, labels) * CFG.loss2a_weight + self.loss3(logit, labels) * CFG.loss2b_weight\n","\t\t\tlogit = logit2\n","\t\telse:\n","\t\t\tlogit = self.forward(image, stage)\n","\t\t\tloss = self.loss2(logit, labels) * CFG.loss2a_weight + self.loss3(logit, labels) * CFG.loss2b_weight\n","\t\t\n","\t\tprob2 = torch.sigmoid(logit)\n","\n","\t\tpred_mask = (prob2 > CFG.threshold).float()\n","\t\t\n","\t\t# print(\"pred_mask\", pred_mask)\n","\t\t\n","\t\tscore = fbeta_score(pred_mask, labels, threshold=CFG.threshold)\n","\n","\t\ttp, fp, fn, tn = smp.metrics.get_stats(\n","\t\t\tpred_mask.long(), labels.long(), mode=\"binary\"\n","\t\t)\n","\t\t# cur_lr = self.lr_schedulers().get_last_lr()\n","\n","\t\treturn {\n","\t\t\t\"loss\": loss,\n","\t\t\t\"tp\": tp,\n","\t\t\t\"fp\": fp,\n","\t\t\t\"fn\": fn,\n","\t\t\t\"tn\": tn,\n","\t\t\t\"score\": score,\n","\t\t\t# \"lr\": cur_lr,\n","\t\t}\n","\n","\tdef shared_epoch_end(self, outputs, stage):\n","\t\t# aggregate step metics\n","\t\ttp = torch.cat([x[\"tp\"] for x in outputs])\n","\t\tfp = torch.cat([x[\"fp\"] for x in outputs])\n","\t\tfn = torch.cat([x[\"fn\"] for x in outputs])\n","\t\ttn = torch.cat([x[\"tn\"] for x in outputs])\n","\t\t# lr = outputs[0][\"lr\"]\n","\t\tloss = torch.mean(torch.Tensor([x[\"loss\"] for x in outputs]))\n","\t\tfbeta_score = torch.mean(torch.Tensor([x[\"score\"] for x in outputs]))\n","\n","\t\tper_image_iou = smp.metrics.iou_score(\n","\t\t\ttp, fp, fn, tn, reduction=\"micro-imagewise\"\n","\t\t)\n","\n","\t\tdataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n","\n","\t\tmetrics = {\n","\t\t\tf\"{stage}_per_image_iou\": per_image_iou,\n","\t\t\tf\"{stage}_dataset_iou\": dataset_iou,\n","\t\t\tf\"{stage}_loss\": 10000 if loss.item() == 0 else loss.item(),\n","\t\t\tf\"{stage}_tp\": tp.sum().int().item(),\n","\t\t\tf\"{stage}_fp\": fp.sum().int().item(),\n","\t\t\tf\"{stage}_fn\": fn.sum().int().item(),\n","\t\t\tf\"{stage}_tn\": tn.sum().int().item(),\n","\t\t\tf\"{stage}_score\": fbeta_score.item(),\n","\t\t\t# \"lr\": lr,\n","\t\t}\n","\n","\t\tself.log_dict(metrics, prog_bar=True, sync_dist=True)\n","\n","\tdef training_step(self, batch, batch_idx):\n","\t\tout = self.shared_step(batch, \"train\")\n","\t\tself.training_step_outputs.append(out)\n","\t\treturn out\n","\n","\tdef on_train_epoch_end(self):\n","\t\tout = self.shared_epoch_end(self.training_step_outputs, \"train\")\n","\t\tself.training_step_outputs.clear()\n","\t\treturn out\n","\n","\tdef validation_step(self, batch, batch_idx):\n","\t\tout = self.shared_step(batch, \"valid\")\n","\t\tself.validation_step_outputs.append(out)\n","\t\treturn out\n","\n","\tdef on_validation_epoch_end(self):\n","\t\tout = self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n","\t\tself.validation_step_outputs.clear()\n","\t\treturn out\n","\n","\tdef test_step(self, batch, batch_idx):\n","\t\tglobal predictions_map, predictions_map_counts\n","\n","\t\tpatch_batch, loc_batch = batch\n","\n","\t\tloc_batch = loc_batch.long()\n","\t\tpatch_batch = patch_batch.float()\n","\t\tpredictions = self.forward(patch_batch, \"test\")\n","\t\tpredictions = predictions.sigmoid()\n"," \n","\t\tpredictions = torch.permute(predictions, (0, 2, 3, 1)).squeeze(dim=-1)\n","\t\tpredictions = (\n","\t\t\tpredictions.cpu().numpy()\n","\t\t)\n","\t\tloc_batch = loc_batch.cpu().numpy()\n","\t\t\n","\t\tself.test_step_outputs[0].extend(loc_batch)\n","\t\tself.test_step_outputs[1].extend(predictions)        \n","\t\treturn loc_batch, predictions\n","\n","\tdef on_test_epoch_end(self):\n","\t\tglobal predictions_map, predictions_map_counts\n","\n","\t\tlocs = np.array(self.test_step_outputs[0])\n","\t\tpreds = np.array(self.test_step_outputs[1])\n","\t\tprint(\"locs\", locs.shape)\n","\t\tprint(\"preds\", preds.shape)\n","\n","\n","\t\tfor (y, x), pred in zip(locs, preds):\n","\t\t\tpredictions_map[\n","\t\t\t\ty - CFG.BUFFER : y + CFG.BUFFER, x - CFG.BUFFER : x + CFG.BUFFER\n","\t\t\t] += pred\n","\t\t\tpredictions_map_counts[\n","\t\t\t\ty - CFG.BUFFER : y + CFG.BUFFER, x - CFG.BUFFER : x + CFG.BUFFER\n","\t\t\t] += 1\n","\t\t\n","\t\tpredictions_map /= predictions_map_counts + CFG.exp\n","\n","\tdef configure_optimizers(self):\n","\t\t# optimizer = optim.SGD(self.parameters(), lr=CFG.lr)\n","\t\tif CFG.optimizer == \"Adam\":\n","\t\t\toptimizer = optim.Adam(self.parameters(), lr=CFG.lr)\n","\t\telif CFG.optimizer == \"AdamW\":\n","\t\t\toptimizer = optim.AdamW(self.parameters(), lr=CFG.lr)\n","\t\t\n","\t\tif CFG.lr_scheduler_name == \"ReduceLROnPlateau\":\n","\t\t\tscheduler = CFG.lr_scheduler(\n","\t\t\t\toptimizer, mode=\"min\", factor=0.75, patience=5,\n","\t\t\t)\n","\t\t\treturn {\n","\t\t\t\t\"optimizer\": optimizer,\n","\t\t\t\t\"lr_scheduler\": {\"scheduler\": scheduler, \"monitor\": \"valid_loss\"},\n","\t\t\t}\n","\t\telif CFG.lr_scheduler_name == \"CosineAnnealingLR\":\n","\t\t\tscheduler = CFG.lr_scheduler(\n","\t\t\t\toptimizer, T_max=CFG.num_epochs, eta_min=CFG.eta_min_lr,\n","\t\t\t)\n","\t\t\treturn {\n","\t\t\t\t\"optimizer\": optimizer,\n","\t\t\t\t\"lr_scheduler\": scheduler,\n","\t\t\t}"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["Global seed set to 42\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjYAAAFlCAYAAAD4RKLxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACiCUlEQVR4nOydeXxM9/f/X7Nn32SzJCJS0UglSImdCpFWLaXVlCIfpUJV1VafT1HtRyldtGqrKqU+tNRasa9BEMS+RoIgC1kmkWUyy/n94Zf7NcnMZGYyyU0m7+fjcR7Mve/lvO9ycu57OW8BEREYDAaDwWAwrAAh3wowGAwGg8FgWArm2DAYDAaDwbAamGPDYDAYDAbDamCODYPBYDAYDKuBOTYMBoPBYDCsBubYMBgMBoPBsBqYY8NgMBgMBsNqYI4Ng8FgMBgMq4E5NgwGg8FgMKwG5tgwagVr166FQCDAuXPn+FaFwag3lL139+7dMytfZe/rF198AYFAYJZufn5+6Nevn1l59SEQCPDFF19YtExG7YM5NgwGg8FgMKwG5tgwGAxGPeX9999HcXExmjZtyrcqDIbFEPOtAIPBYDD4QSQSQSQS8a0Gg2FRWI9NPadsDPz27dsYPnw4nJ2d4eHhgVmzZoGIkJaWhgEDBsDJyQne3t747rvvuLylpaWYPXs22rVrB2dnZ9jb26Nr1644cuRIhXo2bdqEdu3awdHREU5OTnjllVfw448/GtQtNzcX7du3R5MmTXDr1i296crG+48fP44PP/wQDRo0gJOTE0aMGIHc3FyttGXj9idOnED79u1hY2MDf39/rFu3rkK5ly9fRvfu3WFra4smTZrgv//9L9asWWPWnAQGozZSfo6NKe9HeYx9X9esWYPXXnsNnp6ekMlkCAoKwvLly/Wm379/P0JDQ2FjY4OgoCBs3bq1Qpq8vDx88skn8PHxgUwmQ0BAAL755htoNBqDOhcUFOCTTz6Bn58fZDIZPD090bt3b1y4cMFgvjK7efPmTbzzzjtwcnJCgwYNMGnSJJSUlGilFQgE+Oijj7B9+3YEBwdDJpOhVatW2Lt3b4Vyjx49irCwMNjY2KB58+ZYuXJlleYp1VdYjw0DADB06FC8/PLLWLBgAXbv3o3//ve/cHNzw8qVK/Haa6/hm2++wYYNGzB16lS8+uqr6NatG/Lz8/Hrr78iOjoaY8aMQUFBAVavXo3IyEicPXsWoaGhAIADBw4gOjoavXr1wjfffAMAuHHjBk6ePIlJkybp1Ofp06fo3bs3cnJycOzYMTRv3rzSNnz00UdwcXHBF198gVu3bmH58uW4f/8+jh49qmUYkpOTMWTIEIwePRojR47Eb7/9hlGjRqFdu3Zo1aoVAODRo0fo2bMnBAIBZs6cCXt7e/z666+QyWRVvNIMRu3GmPejPKa8r8uXL0erVq3Qv39/iMVi7Nq1C+PHj4dGo8GECRO00t65cwdDhw7FuHHjMHLkSKxZswZvv/029u7di969ewMAioqK0L17dzx69AgffvghfH19cerUKcycORPp6elYvHixXl3GjRuHLVu24KOPPkJQUBCys7Nx4sQJ3LhxA23btq30Wr3zzjvw8/PD/Pnzcfr0afz000/Izc2t4AieOHECW7duxfjx4+Ho6IiffvoJgwcPxoMHD9CgQQMAQFJSEvr27YuGDRti7ty5UKvV+PLLL+Hh4VGpHoxyEKNeM2fOHAJAY8eO5Y6pVCpq0qQJCQQCWrBgAXc8NzeXbG1taeTIkVw6hUKhVV5ubi55eXnRv/71L+7YpEmTyMnJiVQqlV491qxZQwAoMTGR0tPTqVWrVuTv70/37t2rtA1ledu1a0elpaXc8YULFxIA2rFjB3esadOmBICOHz/OHcvKyiKZTEZTpkzhjk2cOJEEAgElJSVxx7Kzs8nNzY0AUGpqaqV6MRi1nbJ3p+x5Nvb9MPZ9LbMvL1JUVFRBj8jISPL399c6VqbL33//zR2Ty+XUsGFDatOmDXfsq6++Int7e7p9+7ZW/s8++4xEIhE9ePCAOwaA5syZw/12dnamCRMm6Ls8eilrV//+/bWOjx8/ngDQpUuXtOqUSqWUnJzMHbt06RIBoCVLlnDH3nzzTbKzs6NHjx5xx+7cuUNisbjCNWQYhg1FMQAAH3zwAfd/kUiEsLAwEBFGjx7NHXdxcUFgYCBSUlK4dFKpFACg0WiQk5MDlUqFsLAwra5cFxcXFBYW4sCBA5Xq8fDhQ3Tv3h1KpRLHjx83aVLj2LFjIZFIuN+xsbEQi8WIi4vTShcUFISuXbtyvz08PLTaBQB79+5Fx44duV4nAHBzc8OwYcOM1ofBqIsY836UYc77amtry/1fLpfj6dOn6N69O1JSUiCXy7XSNmrUCIMGDeJ+lw0xJyUlISMjAwCwefNmdO3aFa6urnj69CknERERUKvVOH78uF5dXFxccObMGTx+/LhSvXVRvodp4sSJAFDB5kRERGj1YrVu3RpOTk7cNVWr1Th48CAGDhyIRo0acekCAgIQFRVllm71GTYUxQAA+Pr6av12dnaGjY0N3N3dKxzPzs7mfv/+++/47rvvcPPmTSiVSu54s2bNuP+PHz8ef/31F6KiotC4cWP06dMH77zzDvr27VtBj/fffx9isRg3btyAt7e3SW146aWXtH47ODigYcOGFebDlG8rALi6umrNx7l//z46duxYIV1AQIBJOjEYdQ1j3o8yzHlfT548iTlz5iAhIQFFRUVa5+RyOZydnbnfAQEBFeaXtGjRAgBw7949eHt7486dO7h8+bLeIZusrCy9uixcuBAjR46Ej48P2rVrh9dffx0jRoyAv7+/UW0pb3OaN28OoVBoss3JyspCcXGxTvvCbI7psB4bBgDoXBmhb7UEEQEA/vjjD4waNQrNmzfH6tWrsXfvXhw4cACvvfaa1qQ9T09PXLx4ETt37kT//v1x5MgRREVFYeTIkRXKfuutt5CXl1fpxOKqUFm7GIz6jCnvh6nv6927d9GrVy88ffoU33//PXbv3o0DBw5g8uTJAFDpZF9daDQa9O7dGwcOHNApgwcP1pv3nXfeQUpKCpYsWYJGjRph0aJFaNWqFfbs2WOyHgD0TvJlNqdmYT02DLPZsmUL/P39sXXrVq0Xes6cORXSSqVSvPnmm3jzzTeh0Wgwfvx4rFy5ErNmzdL6Ipk4cSICAgIwe/ZsODs747PPPjNanzt37qBnz57c72fPniE9PR2vv/66yW1r2rQpkpOTKxzXdYzBqK+Y+r7u2rULCoUCO3fu1OrF0LWSEnj+vhGRln25ffs2gOcruIDnvSTPnj1DRESEWW1o2LAhxo8fj/HjxyMrKwtt27bFvHnzjBoCunPnjlbvdHJyMjQaDaebsXh6esLGxobZHAvBemwYZlP2FfLiV8eZM2eQkJCgle7FoSsAEAqFaN26NQBAoVBUKHfWrFmYOnUqZs6cWWEZqFwux82bNyuMxQPAL7/8ojUctnz5cqhUKrPGqCMjI5GQkICLFy9yx3JycrBhwwaTy2IwrBlD72t5dNkMuVyONWvW6Ez/+PFjbNu2jfudn5+PdevWITQ0lBv6euedd5CQkIB9+/ZVyJ+XlweVSqWzbLVaXcGOeHp6olGjRlp26enTp7h582aFYTMAWLp0qdbvJUuWAIDJNkckEiEiIgLbt2/Xmu+TnJxsdu9RfYb12DDMpl+/fti6dSsGDRqEN954A6mpqVixYgWCgoLw7NkzLt0HH3yAnJwcvPbaa2jSpAnu37+PJUuWIDQ0FC+//LLOshctWgS5XI4JEybA0dERw4cPBwBs27YNMTExWLNmDUaNGqWVp7S0FL169cI777yDW7duYdmyZejSpQv69+9vctumT5+OP/74A71798bEiRO55d6+vr7IyclhcSUYjBfQ976Wp0+fPlzv7Ycffohnz55h1apV8PT0RHp6eoX0LVq0wOjRo5GYmAgvLy/89ttvyMzM1HKEpk2bhp07d6Jfv37csvTCwkJcuXIFW7Zswb179yrMFQSex7Bp0qQJhgwZgpCQEDg4OODgwYNITEzUitf1888/Y+7cuThy5Ah69OihVUZqair69++Pvn37IiEhAX/88Qfee+89hISEmHwNv/jiC+zfvx+dO3dGbGws1Go1fv75ZwQHB2t9YDEqhzk2DLMZNWoUMjIysHLlSuzbtw9BQUH4448/sHnzZhw9epRLN3z4cPzyyy9YtmwZ8vLy4O3tjaFDh+KLL76AUKi/03DFihV49uwZYmJi4OjoiAEDBhjU5+eff8aGDRswe/ZsKJVKREdH46effjLLCfHx8cGRI0fw8ccf4+uvv4aHhwcmTJgAe3t7fPzxx7CxsTG5TAbDmjHmfQ0MDMSWLVvw+eefY+rUqfD29kZsbCw8PDzwr3/9q0L6l156CUuWLMG0adNw69YtNGvWDH/++SciIyO5NHZ2djh27Bi+/vprbN68GevWrYOTkxNatGiBuXPnak1GfhE7OzuMHz8e+/fvx9atW6HRaBAQEIBly5YhNjbWqDb/+eefmD17Nj777DOIxWJ89NFHWLRokZFXTJt27dphz549mDp1KmbNmgUfHx98+eWXuHHjBm7evGlWmfUVAbHZS4w6ztq1axETE4PExESEhYVVa12ffPIJVq5ciWfPnrFQ9AxGPeWLL77A3Llz8eTJE529QZZk4MCBuHbtGu7cuVOt9VgTbI4Ng6GH4uJird/Z2dlYv349unTpwpwaBoNhccrbnDt37iAuLq7CEBjDMGwoisHQQ8eOHdGjRw+8/PLLyMzMxOrVq5Gfn49Zs2bxrRqDwbBC/P39MWrUKPj7++P+/ftYvnw5pFIppk+fzrdqdQrm2DAYenj99dexZcsW/PLLLxAIBGjbti1Wr16Nbt268a0ag8GwQvr27YuNGzciIyMDMpkMHTt2xNdff10hECDDMLV6js3SpUuxaNEiZGRkICQkBEuWLEH79u35VovBYNRymO1gMOovtXaOzZ9//olPP/0Uc+bMwYULFxASEoLIyEiD4bEZDAaD2Q4Go35Ta3tsOnTogFdffRU///wzgOdhs318fDBx4kSTotEyGIz6BbMdDEb9plbOsSktLcX58+cxc+ZM7phQKERERESFqLZlKBQKrWiRZbtNN2jQgAVTYzB4gIhQUFCARo0aGYxXZElMtR3MbjAYtY+q2o5a6dg8ffoUarUaXl5eWse9vLz0BiqaP38+5s6dWxPqMRgME0hLS0OTJk1qpC5TbQezGwxG7cVc21Fr59iYysyZMyGXyzl58OAB3yoxGAwAjo6OfKugF2Y3GIzai7m2o1b22Li7u0MkEiEzM1PreGZmJrfxWXlkMhlkMllNqMdgMEygJod0TLUdzG4wrAWZTAY7OzsAzwP9lZSU8KxR1THXdtTKHhupVIp27drh0KFD3DGNRoNDhw6hY8eOPGrGYNQsYrEYDg4OsLGxgYODA0JCQhAcHIxx48YhNjYWvXr1YnNBXoDZDkZ9w87ODjNnzkR8fDySkpKQlJSEhIQErFy5Ei1btuRbPX6gWsqmTZtIJpPR2rVr6fr16zR27FhycXGhjIwMo/LL5XICwIRJnRFHR0eyt7enkJAQ6tGjB02YMIGOHDlC169fpwsXLtCNGzeoqKiICgsLuedcqVTShg0bKCgoiKRSKYnFYvLx8eG9LS+KXC6vLjOhk6rYDmY3mNQ1mT9/vt7nediwYbzrVxUx13bUyqEoABg6dCiePHmC2bNnIyMjA6Ghodi7d2+FSYEMRl1BIBDA0dERMpkMDRs2RKNGjeDp6YnQ0FDY29uje/fuUKvVaNasGSQSCcTiyl9PsViM9957D/3798eVK1egVCrRqlUr+Pn54dmzZzXQqtoHsx2M+sSff/6pN4yBr69vDWtTO6i1cWyqSn5+vt7t6hmMmkYsFuOPP/5A27ZtYW9vDxcXF9jY2FTbMmgPDw88ffq0Wso2FblcDicnJ77VMApmNxh1kXv37qFx48YAACKCUqlEcXExQkND8fDhQ561Mx9zbUet7bFhMKwFqVSKHTt2oG/fvjVS36pVq2qNU8NgMKqftm3b4pVXXgEAqFQqbpVfXXZqqgLrsWEwqgmJRAJ/f3+sWrUKXbt2rbF6xWIx1Gp1jdVXGazHhsFgmAPrsWEwahESiQRHjx7FK6+8UuNxXGqTU8NgMBg1Ta1c7s1g1GUEAgF27dqFTp068RKcrqai/DIYDEZthDk2DIYFsLe3h6OjI4KCgrBu3TpERkbypsuJEydgY2PDW/0MBoPBJ2woisEwEkdHRxARmjdvDltbWwQGBqJFixZo1KgROnToAKFQiMaNG8PBwYFXPZs2bYpjx45h6NChuHfvXoXzEokEAKBUKmtYMwaDwah+mGPDYLyARCKBVCqFi4sL7O3t4efnh6ioKNjY2KBnz57QaDTw8/ODWCzmHITaSPv27XH+/HksXLgQJ06cgFQqRd++faFWq9G5c2cIBALMmTMHly9fRnZ2tt5yhEIhHB0dIRAIEBQUBIVCgbZt20IsFiMpKQmnT5+uwVYxGAxG5bBVUYx6iY2NDaRSKV555RWIRCLOeQkLC4OLiwu8vLxgY2MDOzs7iEQivtWtEkqlEgKBoELAP5VKhcePH2P+/PmIi4tDSUkJAgIC4OnpiWbNmiEkJAQuLi4IDg6GUChEkyZNoNFouL2VFAoFevXqhZMnTxqsn62KYtQVmjZtim7dukEqlWLLli2Qy+V8q1SvMdd2MMeGYbWU/QH28fGBs7MzXn75Zbi4uCAkJARhYWFcj4yuP/r1jZycHCgUCjRo0AASicTo/aeKi4vRo0cPJCYmQp8pYY4Noy7QqVMn7Nq1C25ubgCA69evo0+fPnj06JFFyi97rkQiEXJzc/W+L4z/gy33ZtR7BAIB+vTpgw4dOsDOzg69evUCESEwMJDt4lwJZcbcVGxtbXH8+HHExcXhrbfesrBWDEbNER0drfUeBAUF4fTp0/Dx8TG5LIFAgAYNGuDVV19F9+7d4eLigq5du0Imk0EqleLcuXP45JNP8ODBA0s2gfH/YT02DKtAIBBg+fLl+OCDD+r80FFdRV8vD+uxYdQFHjx4gIYNG1bovTW29xIAYmNjERISAjc3N7Rv3x4+Pj4oKSnBJ598gqSkJMyePRtvvvkmACArK4vtX1YJbCiqHMxA1R9kMhl+/fVXDB8+nG9V6jXMsWHUZTw9PRESEoL9+/drHTfWsWnevDmSk5MrHA8NDcWlS5cgEonQrVs3HD582OSy6ytsKIpRp5HJZFAoFEaltbW15Sa4+vn5ITY2Fu3bt69mDRmG0GXQGYyaRCwWQ6VSITg4GB06dEC3bt1QUlKCr7/+Gvfv39eZx97eHoWFhQCe96AcOHDA7Po3bNig8/ilS5fQrVs3zJo1C927d+eOX7161ey6GIZhPTYM3lm0aBE6dOiAQYMGcUuPhUIhbGxs4O/vDzs7O7z22mtQq9Vo0aIFXn31Vfj4+MDJyaneT/qtLbi7u+tdNs56bBiWxNHREa1atcLLL7+MBw8e4NGjR5g4cSJeeeUV3Lt3D4MHD4adnR2XPjs7Gx4eHjon6167dg2tWrXifjs5OVVYCfXzzz/j448/rnSyb0REhE7HKCYmBitWrNCa45eZmYnAwEC26qoSzLYdZKXI5XICwKQWi0AgoMmTJ3P37P79+7Rs2TKaP38+7d69m65cuUKFhYVUUlLC45PEqIzVq1cbvM9yuZxvFY2G2Y3aLa6urnTr1i1SKBRERKRSqaigoKDS++ri4qKzPCIib29v7ne/fv105l+1ahUJBAKDujk4OBj1jC1cuJB8fHwsdk0kEgm1aNGCWrZsScHBweTq6sr7fbKUmGs7mGPDpFolLCyMVqxYQb6+vmRra0suLi7Uq1cvGjBgAP3vf//j+zFhWABPT89qMU58wOxG7ZQOHTrQunXr6N69e2bd17t379KXX35Zodz4+HhKTEwkAPTZZ5/R48eP9Zbx+++/G3QaLl++bJQulrwuvr6+FB8fT/n5+VRQUECFhYWUmppKdnZ2vN8zSwhzbMrBDBS/4uDgQLNnz6bi4mIiInr69CldvHiR7t69SyqVijQaDc9PCMNShIWFVYtx4gNmN2qf9OzZkxQKBeXl5em8ZxkZGXT37l06evQoXbx40eD9tbe31yrb1taWJk+eTABo7dq1dPr0aYP5U1NTqV+/fhQUFER2dnYkkUgoLCyMvvrqK6Oerz179ljsugQEBFBWVpbOeiIiIni/b5YQ5tiUgxmomhE3Nzf66KOPqEuXLtS3b1+aMGECLV26lK5fv873I8CoIf7+++9qMU58wOxG7ROlUklERB9//HGF+xUXF0fe3t7k4uJCYrGY7Ozs6LffftN7f9evX2+wLplMRh988IHBZ0StVlNBQQFdvnyZ4uPjqaioyOjny9nZ2WLX5enTpxXK37t3Ly1dupSaNGnC+32zhDDHphzMQFWfhIeHU69evWjy5MmUmppKRESlpaWkVqv5vekMXlAoFNVinPiA2Y2aEYFAQHZ2dhQWFka+vr560/n4+HD3Jjc3V+teXbt2jUQikc6y9fXcqFQqEovFJBAISCQSkVgs1lnv3LlzK9RnCGN6oH/66Se97fTz86MuXbpQVFRUpXN5BAIBffPNNzrr0HU96rIwx6YczEBVj/j7+5NCoSCVSsX3LWbUIgICAixunPiA2Y3qE4FAQGFhYbRu3Travn07Xb58mUpKSujo0aN68xw6dEjvvRo+fDhJJBKSyWRkY2NDAoGAhEIhASAnJye9+fbv30+bN2+mAwcO0LFjx2jdunXUs2dPnbbu+PHjVXqe1Go1JScn05gxYyqUL5FIyMnJib799lvKycnhPg47dOhg8DouW7ZMZ12rVq3i/R5bWsy1HWy5N8NoxGIxUlNT0aRJE75VYdQycnJyMGzYMOzdu7fCObbcm+Hn54d58+bhrbfego2NDXf8wIEDeO+99/D06VOd+Qz9ebp79y7S09Mhk8kgkUiQnJwMGxsbpKenY/PmzRUC7b2IWq1GYWEhHjx4gM8//xxHjx7VufRaKpVi1KhR+Ne//oX27dubHFBv+PDh2L17N/Ly8rhjQqEQ//73v9GnTx94e3vjpZde0srj6+uLtLQ0vWWWlpZCIpFUOC4Wi6FWq03Sr7bDlnuXg315WVbCwsLowIEDfN9WRi1GpVJR48aNLfbVxQfMblheJkyYQNnZ2dw1vnv3LhER7dy5s9KhE33k5+dr/Var1VRcXExZWVl05swZ2rdvH82aNUtv/qioKAoICDB69ZCNjQ1FR0fTgQMHKC0trdKhJ7lcTvHx8RXK6devH+3YscNg3sp00Ten5/vvv+f9XltaWI9NOdiXl+Xw8fHBnTt32CaSDIOUlJTA3t4eGo1G6zjrsam/LFq0CFOnTtU6VlBQAEdHR4hEogrPSnn0/XkKDAxE586dERwcjMzMTFy/fh0PHz5EVlYWnj59CrVaDbVarTe/i4uLWcHxRCIRXFxc8Nprr+Gvv/7Sm65ly5ZITU1FaWmp1nG1Wg2hUKg339SpU/Hdd98Z1KGoqAi2trYVjo8YMQLr16+vpAV1C7ZXVDmYgbIcp0+fRocOHfhWg1GLUSqViIyMxJEjRyqcY45N/cTZ2VlrCKaMPXv2YNeuXVi+fHmlZej782SMU2QovyX2aNJXtkKh0BpuMyZPbm4ufv/9d0yePLnSeo8ePaq1NUMZxl6TukSNDUUdO3aM+vXrRw0bNiQAtG3bNq3zGo2GZs2aRd7e3mRjY0O9evWi27dva6XJzs6m9957jxwdHcnZ2Zn+9a9/VYgeeenSJerSpQvJZDJq0qSJ3lng+mBdypYRoVBo6iPCqIfomnxZJnK5nNmNeij+/v4Vru/p06eNXrljyPa0bt3aqDJ0oVQqLdI+fdy4ccOkPIWFheTn52d0vTKZjKZOnUqlpaVcGWfOnOH9fleH1NiqqLi4OPrPf/5DW7duJaCigVqwYAE5OzvT9u3b6dKlS9S/f39q1qwZF6iNiKhv374UEhJCp0+fpvj4eAoICKDo6GjuvFwuJy8vLxo2bBhdvXqVNm7cSLa2trRy5Uqj9WQGyjJiyjVn1E+OHz9eqXFidqN+iUAg0Lmi6I033jCpHH1cuXKlQlqRSES2trZay6VjY2Ppgw8+oF27dtHOnTvpyy+/NOiEGyuGnC5DsXLKU1BQUGmAS33Ss2dPGj58OEVHR1tNpOHywstyb0DbQGk0GvL29qZFixZxx/Ly8kgmk9HGjRuJiOj69esEgBITE7k0e/bsIYFAQI8ePSIiomXLlpGrqyu3HwgR0YwZMygwMNBo3ZiBqrp8+eWX5j4ajHrC6dOnycbGxiTjxOyG9UtsbKzO69upUyeTyjGETCYjADRo0CD66aefaP/+/XTp0iXasmVLhQjDQqGQWwpuCXn33Xf16uXk5KQ334uTqBMTEyk4OJj3e1WbpVY4Nnfv3iUAlJSUpJWuW7duXNTI1atXk4uLi9Z5pVJJIpGItm7dSkRE77//Pg0YMEArzeHDhwkA5eTkGKUbM1BVk+nTp5v2MDDqJZU5NbqME7Mb1i/6Qv2bGkDuypUreu+Vo6MjAaDCwsIK5y5duqQ3+J4lxNAfXEP5/P39afr06TRixAij3p36LuY6NvqnZ5tBRkYGAMDLy0vruJeXF3cuIyMDnp6eWufFYjHc3Ny00ugq48U6yqNQKJCfn68lDNMRCAT4/PPP8c033/CtCqOWM3fuXJSUlFS5HGY3rA8PDw+dx02Ns9K1a1edE5CB56urAKBz584VzrVu3RoHDx40qS5jWbx4sd4JrTdu3DCYNyUlBQsXLsS6dess8u4wdCPmWwFLMX/+fMydO5dvNeosXl5eaNiwIaZNm4b33nuPb3UYdQBreN+Y3ag59AXhM0ReXh5effVVbNy4EUFBQdzKnxdX3128eBEqlQqlpaW4c+cOzpw5g8LCQhw4cMCS6gN4vtJr0qRJes937NjR4nUyTMeijo23tzcAIDMzEw0bNuSOZ2ZmIjQ0lEuTlZWllU+lUiEnJ4fL7+3tjczMTK00Zb/L0pRn5syZ+PTTT7nf+fn58PHxqVqD6hFJSUlwcXHRGR+BwdAFWShSBLMb1oW+OC1//PGHWeUlJyejS5cueOmll2BjYwOlUomrV69qpenduzeePn2Ku3fvori42Kx6jGHnzp0Gz5sTG4dheSw6FNWsWTN4e3vj0KFD3LH8/HycOXOG82Q7duyIvLw8nD9/nktz+PBhaDQaLlZKx44dcfz4cSiVSi7NgQMHEBgYCFdXV511y2QyODk5aQnDeBo2bMicGgYvMLthXfzyyy9av7OysrB+/Xr85z//MbtMhUKBq1ev4ty5c7h06VKFIa2jR4/i6tWr1erUCAQCdOvWTe/5S5cuVVvdDBMxdVJOQUEBJSUlUVJSEgGg77//npKSkuj+/ftE9HzZpouLC+3YsYMuX75MAwYM0Llss02bNnTmzBk6ceIEvfTSS1rLNvPy8sjLy4vef/99unr1Km3atIns7OzYss1qFAbDVIx9tuRyObMb9UjK06VLF951soQsXrzY4LNjaJdyJuZJja2KOnLkiE4FRo4cSUT/F2jLy8uLZDIZ9erVi27duqVVRnZ2NkVHR5ODgwM5OTlRTEyMwUBbjRs3pgULFpikJzNQpgmDYSrGLp+Vy+XMbtQjKc+IESN418kSUtkfWb71s0Zhe0WVg4VGNw0rfQwY1cjAgQOxY8eOStOxLRXqF4cPH0bPnj0BPJ8H1bx5czx48IBnraqOIRu5fv16jBgxoga1qR+YazusZlUUw3y++OILvlVg1EHYJFuGLvr06YMPP/wQRUVFSElJsQqnZsiQIXrPERFGjRpVc8owKoU5NvWcpUuXYvz48XyrwaiDVFecEEbdRqVSYenSpXyrYVGICPHx8ejatSsAoLS0FCqVCo8ePcL8+fOtbvPJug4biqrnWOntZ1Qzp0+fNjpmBxuKYlgDEokEY8eOha2tLU6cOIFnz57hwYMHLKhjNcKGohgm07JlS75VYNRRevfuzbcKDEaNolQqra4nylqxaBwbRt0hNDQUp0+f5lsNRh3l2bNnfKvAYDAYOmE9NvWUkydPws7Ojm81GAwGg8GwKKzHph4SERHBnBoGg8FgWCXMsalnDB8+HHFxcXyrwWAwGAxGtcAcm3pEv379sG7dOkgkEr5VYdRx/P39+VaBwWAwdMIcm3rC4MGDsW3bNggEAr5VYVgBTZs25VsFBoPB0AlzbOoJCxcuhFjM5oozLMOnn37KtwoMBoOhE+bY1BOaNWvGtwoMK+KNN97gWwUGg8HQCXNs6gG9e/dmQ1AMi8KeJwaDUVthjo2VI5FIsGvXLr7VYDAYDAajRmCOjZWwaNEiNG/eHDKZDCKRCCKRCH5+fti/fz9kMhnf6jGsjJKSEr5VYNRThMLnf7ZYryFDH2w2qRUwffp0TJ06FWPGjMGdO3eQnZ0NsViMtm3bwtXVlW/1GFZIZGQk3yowajnBwcHo3r072rRpA5FIBADIy8vD5cuXsW7dOqjVaqPLkslk+Ne//oVXX30V3t7eSEpKgp+fH06cOIHLly/jzJkzUKlU1dUURh2D7e5dRxGLxXB1dcWMGTMwZcoUvtVh1COIiPtqNga2u3ftpW/fvnBxccHNmzehVCohkUigUqkgl8shFAohkUiQnJxsVtkFBQVwcHDQee6ff/7B4MGDUVpaalRZwcHBuHLlis5zSqUSx48fx9ixY5GSkmKWrozaCdvdux4hFotx4MABBAQEoEmTJnyrw6hn/Pzzz3yrwLAAYrEYu3fvhkAggEKhgEqlglgshkajQXFxMYRCIUQiESIjI83aMHfq1KlYsWKFznP9+vXDrl278PbbbyM/P7/SsoKCgvSek0gk6NWrF86fP4+5c+di8eLFJutaGfb29iguLkZAQAAaNWoEAEhNTYWDgwNSUlJARNBoNFAqlbDSvoI6BeuxqWNIpVJs374dUVFRfKvCqKd4enriyZMnRqdnPTa1j5YtW2LJkiWIiIioNG1VrsmPP/6IcePGQSqV6jx/8+ZNdOjQoVLnJjk5Gc2bN9d57tSpU+jUqRP3WygUmu1cCAQC2NjYoHHjxigqKkJpaSmys7Nx8eJF3L17F7169eL22SsoKIBEIkFKSgrUajUUCgXS0tKwf/9+/PHHH2wemgUw23aQlSKXywmA1YhAIKDQ0FA6deoU35eWUc8ZNmyYSc+uXC7nW2WjsTa7oU9ycnJMui5vv/222XV16tTJoN26efNmpWVoNBq9+c+dO0dz587lfgsEArP0bNGiBW3atIkuXrxI+fn5lJWVRWlpabRjxw6jr5NaraYrV65QYmIide/enff7XNfFXNvBHJs6Ip9//jkVFxfzfVkZDHry5EmNGCc+sDa7oUsiIiJMvi6lpaUkFovNrlMmk1Fqaqre8ivLrw+NRkNOTk4EgFQqFT169Mhk3cLDw2ny5MmUl5ens46///6bbt++bdR1On36NNnb21NRURHl5+fzfq/ruphrO9hy7zqASCTCl19+CRsbG75VYTDqzLASoyLLly9HXFycyfkkEgnefPNNs+tVKBQICwvDnTt3dJ4fMGBABfuma4L6s2fPuP/n5+dj+vTp3DBWVFQU2rRpY5Jen3zyCY4dO4bvv/9e73Cbs7MzAgICjCqvffv26N+/P2xsbPQOvzGqHzbHpg7w7rvvYuPGjXyrwWBwmBJDhM2xqR34+/vj7t27Os9pNBrMmzcPTZo0QUxMjM40ubm5cHNzq5IOLi4uSEtLg4ODAw4cOIDevXsDAIgIiYmJGDt2LCQSCYDn23Y4ODggLi4Ohw8fBgDs27ePCzUQGhqKS5cuVUkfU//8ERHu3r2L+Ph4PHz4EDk5Ofjwww/h5+cHGxsbEBH3bljzs1RTsFVRVsxvv/3GtwoMBqOOc+TIEb3n3nvvPfz5558QCATo168fPDw8KqRxdXWFt7c3MjIyzNYhLy8PHTt2xJUrV9CiRQvuuEAgQPv27ZGQkMD11JQFFi0sLMTvv/+OHj16cE7NkiVLquzUREdHV5pGLpdj5syZEIlEyMvLQ2lpKfbu3as12Xn16tV46aWXMGTIEFy8eBGzZs3Cyy+/jHnz5lVJP0YVMGsAqw5gLWPlI0eO5PtSMhgVMOUZZnNs+JfPP/9cb5uLioq00oaEhOhNe+nSJbMn574okydP5sosKCioUE9SUhL3fwD0zTff0K5du4iI6PHjxxa5Jmq1Wm87nzx5QnFxcRQYGGhyua6urhQREUFCoZD3+17XpUbm2MyfPx+vvvoqHB0d4enpiYEDB+LWrVtaaUpKSjBhwgQ0aNAADg4OGDx4MDIzM7XSPHjwAG+88Qbs7Ozg6emJadOmVYgaefToUbRt2xYymQwBAQFYu3atKapaDay3hmENMNvBH3PmzMFXX33F/c7Ly9M6L5FIEBMTgwYNGiAgIACFhYV6y2rdujV++OGHKuv0ww8/ICMjA0VFRejQoQMSEhJw5coVZGVlAQBeeeUVrfQ2NjZo2LAhAGgt7TYXmUxmMMjkr7/+in79+lV4Ro0hNzcXBw8ehEajqYqKjKpgihcUGRlJa9asoatXr9LFixfp9ddfJ19fX3r27BmXZty4ceTj40OHDh2ic+fOUXh4OHXq1Ik7r1KpKDg4mCIiIigpKYni4uLI3d2dZs6cyaVJSUkhOzs7+vTTT+n69eu0ZMkSEolEtHfvXqN1tZYvLwajNtK3b1+Tvrrqiu2wFrtRJkKhsMJS6ydPnhAR0axZs7SOp6WlUX5+PveVvHr1atq4caNWmoyMDHr8+DHJZLIq6+bl5UXBwcEEgCQSCdnZ2VHDhg0r3JOy9Pb29jR9+nSLXBeBQGDwOQgJCamQRyKR8H4/65vwstw7KyuLANCxY8eIiCgvL48kEglt3ryZS3Pjxg0CQAkJCUREFBcXR0KhkDIyMrg0y5cvJycnJ1IoFERENH36dGrVqpVWXUOHDqXIyEijdbMGA9WiRQuz7w2DUZ3873//q5Jxqq22wxrsBgAaMmQIeXp60q5du2j37t0621p27XWxa9cuAkBNmzbVOr5s2TKKjY2lgICAatG7S5cuFXQZOHBgtdRliKSkJPrqq68IeO4ctmjRgo4fP04+Pj7cEJOjoyNJpVISiUQkEol4v+fWKOY6NlWaPCyXywGAmyl//vx5KJVKrWiWLVu2hK+vLxISEhAeHo6EhAS88sor8PLy4tJERkYiNjYW165dQ5s2bZCQkFAhImZkZCQ++eQTvbooFAooFArutzFhums7J06c4FsFBkMnXbt2rVL+2mI7rNFuxMTEYPXq1Xj06JHeLVdKS0sRHh6O/Px8FBYWQqFQ4PHjxygqKsKDBw8wbdo0AMA777yjlS82NhYajQb37t3DsmXLsHjxYpM2szREaGgot/rpRbZs2YKmTZvi0aNHFqkHAJYtW1apLmX6jBs3DlFRUXB0dMSpU6dw5coVpKamIiIiArm5uZDL5RAIBLh37x6Sk5Nx584d+Pr6oqioCKtWrbKYzgzjMdux0Wg0+OSTT9C5c2cEBwcDADIyMiCVSuHi4qKV1svLi5tJn5GRoWWYys6XnTOUJj8/H8XFxbC1ta2gz/z58zF37lxzm1PrGDNmjM6VCQxGbaAqe5TVJtthbXYDeL5KRyAQ6LxHT548wbx587i9n7Kzs1FQUIDS0lI8e/YMGo1Gy1FZtGgRRCIRpk2bxjmhQqEQ/v7++Pbbb9GtWzds374dN27cMGs/KYFAgLfffhuBgYGYOXMmt9T7RUQiEc6ePYvGjRubXL4+YmNjjUq3f/9+iMX/92fSy8sLa9euhZubm9aqrheh/7/kW6lU4tdff2V7R/GA2Y7NhAkTcPXq1VrTqzBz5kx8+umn3O/8/Hz4+PjwqFHVWLlyJd8qMBjVQm2yHdZmN/RBRNiwYQP+/e9/Iy0tzaS8CxYswMaNG3Hr1i1uCXYZ/fv3R//+/VFaWoqxY8fi2LFjuHfvnlHlenh4YOHChRgxYkSlu8U3atQIM2fOREpKCv7880+T9DeFY8eOwcHBAX5+fhCJRCgpKYFKpUJOTg4yMzPx448/Yvfu3QCAsWPHajk9ZQgEAly9ehWxsbHMqeEJsxybjz76CP/88w+OHz+u9VXg7e2N0tJS5OXlaX15ZWZmwtvbm0tz9uxZrfLKVj68mKb8aojMzEw4OTnp7K0Bns9yL//S1WVMCYDGYNQVapvtsDa7AQCjRo3C77//zv2Wy+UYPXo0/v77b7PLvH//Pl577TUcOHCA2wTyRaRSKdauXYvs7Gx89913+Pbbb6FUKvWW5+LiggsXLpjU8/f111+DiNC0aVMsWrTIok6DQqFAeno6IiIiIBKJ4OnpyTk2arUaz549Q2lpqVZv1ltvvYUJEyagffv2cHV11SpvyJAhZq2oYlgIUybkaDQamjBhAjVq1Ejn3hllEwC3bNnCHSvb4Kz8BMDMzEwuzcqVK8nJyYlKSkqI6PkEwODgYK2yo6Oj683k4fJtZzBqIzNmzDB6AmBdsR112W68KPPnz6clS5bQ/PnzLTrRNzQ0VGuCtz4OHz5ssBw/Pz+d+fLz82nu3Ln0119/0b59+/SWv379eoqMjCQ/Pz/y8vKiwMBATpydnSttx4uoVCoKCwujBg0amHw9RCIR+fn50SeffEIxMTH03Xff0Zw5c3i//9YiNbIqKjY2lpydneno0aOUnp7OSVFREZdm3Lhx5OvrS4cPH6Zz585Rx44dqWPHjloPUXBwMPXp04cuXrxIe/fuJQ8PD51LNqdNm0Y3btygpUuX1qvl3llZWabcFgaDN2bOnEmenp6VGqe6Yjvqst2oKREIBPTrr7/S6dOnKTExUefmluX/uEskEgoMDKQ33niDli5dSmfOnKmQR6PR0G+//cbVIRKJaPPmzXT06FGd90qlUlFubi49fvyYW6aen59PycnJ1KRJE4NteDEo4NixY3m/pkx0S404NvoqX7NmDZemuLiYxo8fT66urmRnZ0eDBg2i9PR0rXLu3btHUVFRZGtrS+7u7jRlyhRSKpVaaY4cOUKhoaEklUrJ399fqw5jqKsGatiwYSa1k8Hgmzt37lRqnOqK7airdoMPkUqlJJPJyM3NjSZPnkyfffYZffbZZ/TRRx9ppYuKiqJjx45Rfn6+3mi/165do4EDB5K9vb1WXoFAQGKxmM6dO0dFRUWUl5en98PvxWXtU6ZMMah7eHg4l5bv68hEv5jr2LBNMGsZM2fOxNdff823GgyGSRiaE8Y2way/iMVilJSUQCQSAUCFOVQXL17EunXr8Ntvv3EhAHRha2uLl156CYWFhSgpKcHOnTvRtm1brTT0/1cj7du3D2+88Ualy9DL/vTpe3YdHBzQu3dvbk6RQCCAWCzGiRMn8ODBA5SWllbafkbVYJtgWgkHDhxgjg2DwbAK9uzZwzk1wPPVnjNmzMC5c+eQnJyM0aNHo6ioqNJyiouLcfnyZe53165dsXHjRrzxxhucg1JcXIwDBw5g6NChRsXWOX/+PK5cuaL3/KlTpxAcHFzB8cnPz8eNGzewZ88e/Pnnn/jggw9w//59LFmypNI6GTUD67GpZYhEogp73zAYtR3WY8PQha4/L4sWLcLs2bOhUCiqtLJJKBSiV69eKC4uBvA8Rs+dO3eM3qPJzs4OxcXFOnVwdnausKeWLgoKCuDo6KhVHsNymGs7TNoEk1H9DB48mG8VGAwGwyLMnz9f6/cXX3yB6dOno6SkpMrLtTUaDQ4cOIATJ07gxIkTuHXrlkkbTxYVFVXQQSgUonv37khISDCqjDKnBgAbmqpFsKGoWsaL8ScYjLqAMUMJjPrJv//9b2RnZ2PYsGFYsWIFfvnlF75V0kuPHj0wadIkvPHGGzojIOvj4MGDSE5OttjWEoyqwxybWkRwcDBsbGz4VoPBMInIyEi+VWDUYr777jssXboUJSUlfKtikNGjR6Nt27ZaTo1SqdTr5CxfvhynT5/Ghg0bmFNTy2BzbGoReXl5dU5nRv2mtLS00si9bI4Noy4gFArh4uKCd955B1FRUdBoNPjuu+9w5MgRbusElUqFwsJCrFq1itsolFF9mGs7mGNTi7DSW8GwYtatW4eRI0caTMMcG0Zdo2zvKo1Gg969e6Nz587w9PTEpk2b8OjRI9y9e5dnDesHbLk3g8FgMBgW4MVJyAcOHMCBAwd41IZhKmxVVC2CjdMy6hrvvvsu3yowGAyGFsyxqUXs3r2bbxXqLSUlJcjIyEB+fn6laZ89e4Znz57h+vXrSExMRHZ2dg1oWDtZuHAh3yowGAyGFmwoqhbx7NkzvlWol2zevBlff/010tPT4ejoiJEjR2LYsGFQKBRITk7Go0eP4Ofnh3bt2mHmzJk4ceIEhEIh7t+/D5VKBU9PT4wbNw7jxo2Dm5sbgOdd2Wq1GgUFBcjJyUF+fj6uXbsGJycndOvWDa6urjy3uupcvHgRs2bN4lsNBoPB0IJNHq5FlJSUVLrChGFZjh49itdee63CxG0XFxeoVCoUFRVBo9FAJBKhcePGePDggd6y/Pz8MGbMGKjVaiQkJKCgoABpaWnIzc1FaWkpSkpKIBAI0LRpU8ycORMxMTHcUtIyp/bGjRtQKBQICgrinKTairOzs1E9XGzyMIPBMAe2Kqocdc1Affzxx/jxxx/5VqNeoVar4eDgwFt8jZ49e8Lf3x8KhQLnzp2DUCjE3bt3odFo0KhRI7z//vsYO3YsfHx8ADwPB6BWq5GdnY28vDw0atQITZo04UV3tVrNLYGtDObYMBgMc2COTTnqkoFq0qQJ0tLS+Faj3jFixAisX7+ebzUM4u7ujr59+0IsFuP48eNQKpXIy8uDQqGAs7Mzhg0bhilTpljcwSkbRgOAq1evQiwW49y5cygsLMTVq1fx6NEjHDlyxKiymGNTczg4OEChUECpVPKtCoNRZZhjU466ZKCEQiFbEcUDhjZurEt4eHjg77//RteuXc3K//TpU8THx+PZs2do3bo1SkpKMHv2bCQnJwMA0tLSIBAIzN4Lhzk2NUNERAR+/vlnPHnyBDdu3MDly5fx888/860Wg2E2zLEpR10zUFZ6G2o11uLYAIBUKsXJkycRFhZmUr6jR49ixIgRePjwIYgItra20Gg0UCgUFtONOTbVT5njWX548N1338Wff/5pUllhYWHc1i53795Fenq6UfleDGrHYFgCFqCPwTCBNWvW8K2CRSktLUW3bt1M2pBy79696N+/v9awRXFxcXWox6hm/P39dc55+v777412bPz8/DBy5Ej8+9//5pyUJ0+eoFGjRpXmtbe3x6ZNm7h5YgKBABMnTtSZ1s3NDZMmTdI6tn79egwfPhwCgQDFxcW4du0aAGDXrl2wtbVFo0aNQER49OiRRZ1uhpVCVopcLicAdUaWLVvG9yWrNyiVShIIBLzf8+qQM2fOGHUNiouLSSwW14hOcrm8mu+o5ahrdqNMRowYobdNxuT39PSknJwcs/N369aNS6/RaIiISCgU6kx7584dndf9Rfbs2UNqtZo6d+5MFy9epLy8PMrLy6PExERav349hYWF8X7NmVS/mGs7WI9NLWH8+PHIy8vDzJkz+VbF6lm+fLnVDv316tUL33//PUQiEbp164aAgACd6fr16weVSlXD2jGqi6oG9+zbt2+VYitduXKF+39GRgYaNmyoN62jo6PW7/j4+Arzw3bv3o2+ffviyJEjWrtrh4WFISwsDHfv3sW5c+dM1rNhw4YQCoUoLCyEi4sLOnbsCLFYjNzcXPzzzz8ml8eopZjlDtUB6uqX1++//873pbN63n33Xd7vc02Is7MzTZkyhbKyskitVpNarabMzExKT0+vUT1Yj03NiC5KS0uNynv48GEiIiopKaE1a9ZolWFMfgcHBy79jRs3iIjIxcVFZ9qmTZuSQqHg0s+dO5fr5TGGkydP0vz588nDw8Ok6/Pdd99RRkYGZWVlUUpKilYPlVqtpjFjxvB+D5loi7m2gzk2tUyEQiHfl87qOXToEO/3uSalSZMmFBUVRf3796dGjRqRt7d3jdbPHJuaEV1kZ2cblbfMsRg0aFCFsoytf8OGDVr51qxZozdt3759tdIOHTq0gu4FBQWUnJxMeXl53LHU1FSSSCTUpUsXk55jgUBQ6b1XKpVcepFIRM2bNyeZTMb7fa3PwoairAS2oqD66dmzJ98q1CgPHz7Ew4cP+VaDUY0EBQXpPG7s8NLSpUvRqFEjbNu2Tes4EUEikUAqlUIikXATlHNycirYqvKrV0aNGoWYmBid9ZXfLXvkyJFav48cOYKJEyfi4cOH8PLywq1btwA8t49KpRInTpwwql3A8+Gr2NjYStOJxWL89ttvKCwsxMsvv4ywsDDcvn0biYmJmDx5stnhDhg8YJY7VAewti8vhmXh+x7XJ2E9NtUvV65c0dsmY/I7OztrTagvQ6PR0MmTJ+natWuUmppKaWlplJaWRv7+/gSAmyAcFBRUod60tDSDdZYnIyOD5s+fTz/++CNJJBKdadPT0/UOcemSzp07U3FxsVH3XqVS0cGDB6moqKjCuf/85z+83+P6KKzHxkro27cv3yrUC6RSKfsCY1gNwcHBVcqfmJiIFi1aVDguEAgglUor9AitWrUKqampaNSoEa5du4YPP/ywQt6EhASTdBg3bhw6deoEHx8frRAEEokET548gYeHB7y9vZGQkICoqCiEh4dj06ZNessbNmwY1qxZozX5uIx169bh9u3bcHR0hEKhgFAoxOnTp7F3716EhYXh9OnTWulv3rxpUlsYPGOWO1QHqKtfXsZ+XTCqxv79+612yXdtE9ZjU/1S1fbMmjWLAFDLli21FjCUlJTQ3r17qaSkhEpKSoiIqLCwkLNTKpVKq74XJ+R+9dVXBussP2G4rMyEhAQuzYABAyg+Pp5KS0sr1JOammqwfKVSqZWnoKCA+79IJDLpeq5cuZL3e1wfpUYmDy9btoxeeeUVcnR0JEdHRwoPD6e4uDjufHFxMY0fP57c3NzI3t6e3nrrLcrIyNAq4/79+/T666+Tra0teXh40NSpUys8gEeOHKE2bdqQVCql5s2bV5ilbwx10UB9/fXXJreTYT5r1qxhzk0NiFwurzO2oy7aDUC3GU9NTaXOnTsblV8qlVKjRo1o4sSJFB8fz5XRvn176tChA7Vr147CwsIoJiaGQkJCqF27dhQTE0O9e/emX375hdatW0cHDx6kpk2bcnljYmIM1hkdHa1T79atW5NAICChUFjh/r5IbGysSdfk4sWLRES0ffv2Sq/Hjz/+WCE/3/e4PkqNODY7d+6k3bt30+3bt+nWrVv073//myQSCV29epWIiMaNG0c+Pj506NAhOnfuHIWHh1OnTp24/CqVioKDgykiIoKSkpIoLi6O3N3daebMmVyalJQUsrOzo08//ZSuX79OS5YsIZFIRHv37jWpYXXNQA0aNMik9jEsw19//UUtWrTg/f5bs8jl8jpjO+qa3SiT8mRkZNCECROMyhsVFUWnTp2izMzMCj0jxtYvEAjIzc2NFi9ezOVdu3atwTz6VoA+fvyYNm3aRIsWLaL8/PwK54uLi+njjz82WLZYLNa7hNyYjxk3N7cK+Xx8fHi/z/VNeFvu7erqSr/++ivl5eWRRCKhzZs3c+du3LhBACghIYGIiOLi4kgoFGp9iS1fvpycnJy4uAbTp0+nVq1aadUxdOhQioyMNEmvumag2BAUf8jlcvrhhx+ob9++FBUVRQ4ODrw/D9Yk+oxTbbQddc1u9OzZk2JiYnS2RaPRVJpfLBZXGE56EWN0iImJofDwcAoKCtLqYdEXebhMunTpYvR9KSoqooSEBIqJiaH27dtXqtP8+fO1rkMZOTk5RrVJLBZXsMlpaWnUsmVL3u95fZIad2xUKhVt3LiRpFIpXbt2jYsNkpubq5XO19eXvv/+eyIimjVrFoWEhGidT0lJIQB04cIFIiLq2rUrTZo0SSvNb7/9Rk5OTgb1KSkpIblczkllM/Jri4jFYlqyZIm5t4FhQcqC2F2/fp1GjBhB9vb2vD8f1iDljVNtsh111W4AoIYNG5JSqTQY3K6yMubOnWvwnTBGj40bN9LGjRupf//+WiuKyq9sKi+rVq0yWHdBQQFduHCBoqOjKTQ0lKRSqdHXZsmSJXT8+HHasmULDRs2jEaPHk0lJSUUEBBgdBljxozRqZOrqyvv976+SI05NpcvXyZ7e3sSiUTk7OxMu3fvJiKiDRs2kFQqrZD+1VdfpenTpxMR0ZgxY6hPnz5a5wsLCwkAN97+0ksvVZhrsnv3bgKgcxleGXPmzOH9JpgjZdePUfu4cuUK78+HNUiZcaqNtqOu2g0A3JwRQ1RWhqE5LC8GrDMkZQH01Gq1SXUbcsgSExOpVatWZGtra9a1sbW1pdDQUBo3bhytXr2aPvvsM+55MVY8PT116hYaGsr7va8vUmPLvQMDA3Hx4kXI5XJs2bIFI0eOxLFjx0wtxuLMnDkTn376Kfc7Pz8fPj4+PGpUOW3btsXrr7/OtxoMPVR1CS1Dm9poO+qi3QCACRMmICQkpMrl6NoRvIyUlBSjynB2dgYAbkdwAPj5558N5nFzc4NAINB7vlu3blXaaf7o0aMICgqCg4OD2WV06NBB5/GLFy+aXSajZjDZsZFKpdzGeu3atUNiYiJ+/PFHDB06FKWlpcjLy4OLiwuXPjMzE97e3gAAb29vnD17Vqu8zMxM7lzZv2XHXkzj5OQEW1tbvXrJZDLIZDJTm8Mb4eHhOHToEN9qMCrB3t4ehYWFfKthFdRG21HX7EYZjx8/xr59+9C+fXuzN68su276MLSRZWWo1WpIJBKteDQv0r17d715//3vf1fJqQGA9u3b6zzu6uqK3NzcSvMLhUKMGTMGKpXKoPPHqJ0IK09iGI1GA4VCgXbt2kEikWj9sb516xYePHiAjh07AgA6duyIK1euICsri0tz4MABODk5cQGgOnbsWOEP/oEDB7gy6hIvfpGIRCK4urqiYcOGiImJwZEjR2BnZ8ejdgxjsLGx4VsFq4XZDvPZtm0b3njjDbRp0wZz5szBkydPtHasJyJ8//33Bsvo0qWLVvryyOVys/WbNGkStm7dqvd8YGCgzuOHDx/G/Pnzza63MgYMGFBpmuDgYOzevRuzZs1C3759cffuXe7c7du3q003hgUxZdzqs88+o2PHjlFqaipdvnyZPvvsMxIIBLR//34ier5k09fXlw4fPkznzp2jjh07UseOHbn8ZUs2+/TpQxcvXqS9e/eSh4eHziWb06ZNoxs3btDSpUvrzHJvkUhETZo0ocaNG9OgQYNo06ZNtHTpUlq6dCnt37+fUlNTK8TmYNRuzB3jZ6I9Tl5XbEddWxVVJo0bN6aBAwfSZ599Rt9//z0NGTKk0mXNQqGQFAoFJScn0+DBgyk6OpoePXpERM/ny/j5+RlVtz4++OADvXl0rQI9cuRIpROOjRV9FBcXU6dOnfTmc3Nz4wL5BQYGEgBydXXljoWFhfF+r+uT1Mjk4X/961/UtGlTkkql5OHhQb169eIMU9lDM378eHJ1dSU7OzsaNGgQpaena5Vx7949ioqKIltbW3J3d6cpU6boDLJVNgve39+/TgToCw0NpQMHDtCTJ08oKyvL4MQ4Rt1g8eLFvL/Y1iByubzO2I666tiYK+Hh4eTm5kYNGjQgFxcXCggIoOjoaIqKijK6jPKUTSI2JQ8RcftVhYaG0ogRI6rULkMUFhZSjx49yNXVlRwdHcnNzY3Cw8Ppp59+opSUFC7d48eP6d133yVvb2/uWbWU48XEODHXsRH8/wfQ6sjPz+cmtdUEO3bsQP/+/WusPkb1IxQKdXbRM0xDLpdX2Pm5tlLTdoNPOnfujPDwcLRr1w5dunSBUqlEUVER4uPj4ejoiPfff9+ocsrekTNnziAoKAjJyckIDQ3Vmkz8InZ2djrnrUmlUqxevRpDhgyBra2twcnFlbFixQqd+1eVoVKp8PDhQyiVSshkMnh7e0MqlepMm5mZCS8vL5SWltbJ+Vh1GXNtB5sVZSEaN27MtwoMC1JSUsKcGobVEhkZiV27duncIDI4OBhnzpwxuUyZTAaBQIA2bdoYTLd8+XKdxxcsWMA5U1XdoHbcuHFo06aN3knEYrEYfn5+RpXl5eWF+/fvY/jw4VXSiVFzMMfGQrRt25ZvFRgWJCIigm8VGIxqY8iQITqdmrVr18LBwQEjR440uqzS0lLcunULoaGh3DF9q6EAYMSIETqPly27JyIMHDjQ6Pr10b17d0ycOBFt2rRBaWkpnJ2dYWdnh969e3O9Qc+ePYODgwNKS0vx8OFDNGrUCM+ePUNJSQnUajWePHmCxYsXY+/evcjOzq6yToyagTk2FmDkyJFV6jZl1C5UKhVOnjzJtxoMRrUxfvx4DBkyBA4ODlCr1RCJRCgqKkJMTAwEAoFJvZUff/wxvv32W6jVahQWFkIoFGLt2rU60/70008Gy9JoNIiJicGePXtMaY5OSkpKsGjRIq49AoEAQqEQ/fv3h5eXFxQKBc6ePYvOnTvj6tWruH79Ovz8/JCdnY3i4mJoNBoUFRWhpKSkyrowahY2x8YCqFQqiESiGqmLUf1Mnz4dixYt4lsNq4HNsamd+Pn5oVmzZigoKICdnR0yMjLMXs7cp08faDQa3Lt3D2KxGDdv3qyQZsaMGViwYIHeMp49e4bRo0fjr7/+MksHhvVhru1gjo0FsNJLWG+RyWRVHuNn/B/MsWEAz4P26ZtQnJKSgv79++PatWs1rBWjNmOu7ahygD4Gw9ro3Lkz3yowGFaHRqPRefzrr79GWFgYc2oYFoM5NgxGOf7++2++VWAwrI7XX38dycnJUKvVUKvVuH//PmJiYvCf//zHqG0OGAxjYUNRFsBKL2G9hk0GtxxsKIpRhouLC7e5ZGJiInJycnjWiFGbYXFseOTx48do1KgR32owGAxGrSYvLw/79u3jWw2GlcOGoiwAi2/AYDAYDEbtgDk2FqBsd2GGdcCGFhkMBqPuwoaiqki7du1YDBsrg82vYTD0Y2NjA5FIBIlEgry8PL7VYTAqwHpsqsjhw4f5VoFRDcTExPCtAoNR6xg2bBgSEhKQmJiICxcuYMqUKXyrxGBUgK2KqiJWevnqPUSETp064fTp03yrUudhq6KsB132bvz48Xo3tmQwqgKLPFwO5tgwqkpJSQmGDh2KvXv3skjEVYA5NhXx8/PDvXv3LF6uQCBAYGAg/Pz8oFAoADwfOjpx4gQ0Gg1sbGygUCjw7Nkzs8ovLS3VuXmmRCKBSqWqku4MRnnYcm8Gw8LY2Nhg27ZtOHv2LCIjI5Gfn8+3SgwrYcqUKZg4caJFyhIIBBg9ejS6du0KmUyGN954A3Z2dtBoNBAIBBAIBLh58yYEAgEcHBxQWFiIM2fOYMeOHdi2bZtJdUVFReHgwYMVjv/5558YPHiwRdrDYFQZslLkcjkBqHZh1A9u3LhRI8+TNYpcLuf79hlNTdgNLy8vi5YXHBxsVls1Gg398MMPJBAITKrvP//5j87yli1bxvuzxsS6xFzbwYaiqoBUKuW6exnWjVqthljMOjjNgQ1FaWNnZ4eioiKMGTMGx48fx61bt6pUXlhYGBITE83Ov379esTExECtVhudR9+GlitWrEBsbKzZuphLv379MGTIEDx69AiPHz9GSkoK5HI5Tp8+rXePKkbtx2zbYZY7VAeoiS+vuLg4vpvJqCE++OAD3r9e6qqwHhvdsmnTJkpJSalyOY6OjlVu965duyqtRyqVkq2tLdna2tLIkSP1lhUZGVmjz1eDBg1IrVZTQUEBp4NaraZdu3bR5s2bSSQS1YgeMpmMnJ2dKSAgoEbbb81iru1gy73NpGnTpoiKiuJbDUYNoNFo8Ouvv/KtBsPK6Ny5M5o1a4Zx48ZVqZyCgoIq69KvXz/MmDFD7/mIiAgcO3YMCQkJSEhIQPfu3fWm3bJlS5X1MYWOHTtCKBQiPDycOyYUCjF+/HjcuXMHixcvrnYdhEIhTp48ieXLl8PGxqba62NUglnuUB2gur+84uPj+W4io4b4/fffef9yqcvCemy0JTQ0lBYtWkQrVqygQ4cOUUlJSZXLLEOtVmu1R6lU0rx582jfvn106tQpg23XaDQ6y3ZxcSGlUmnSdQwJCamx58vDw4N++eUXEgqFWjqkp6fToEGDSKFQVHuvjVQqJSIiPz8/3t83axJzbQdzbMyUW7du8d1ERg0xYsQI3l/wuizMsdGWKVOmcH8AxWIxde7cuUrlDRgwgIiIZsyYQVFRUfTDDz9w7Zk2bRoBIJFIRFKplNavX2+w/brK79atm8nX8ejRozXybNnY2FDnzp2pQYMG9Pfff1fQo8zR279/f7XrMnbsWBIKhby/b9YkzLEpR3UbKI1Gw3cTGTXE9evXeX/B67Iwx6ZyMXVl0ouyfPlyIiJat24d9erVS2vVUvn5HmU9C/rQVb5EIjH5OqpUqhq5bidPnqTS0lK6d++eTj00Gg3l5eXRlStXLFpvly5dSCqV8v5uWbswx6Yc1Wmg3n33Xb6bx6hh+H7B67Iwx0a3NG7cmNq3b0+DBg2iv/76i+tdMVXOnj3L6a9SqbQ+usoPwXz11VcG26+vDnOoirNmjCxatKhSHYYPH07NmzcnOzs7i9Xr7OxMpaWlNGbMGN7fLWsXXhyb+fPnEwCaNGkSd6y4uJjGjx9Pbm5uZG9vT2+99RZlZGRo5bt//z69/vrrZGtrSx4eHjR16tQKY7hHjhyhNm3akFQqpebNm9OaNWtM0q06DZSp482Mug/fL3hdlvLGqb7ajRdl6NChlJWVRSUlJZwjotFoSCKRmFzW9evX9bYnICCABAIBSaVSaty4sUHbpVAodJY/fPhwk65hGRs2bKg258YYp4aI6O233yYfHx+ys7MjJycncnFxqXLdM2fOJCKixMRE3t8ta5cad2zOnj1Lfn5+1Lp1ay0DNW7cOPLx8aFDhw7RuXPnKDw8nDp16sSdV6lUFBwcTBEREZSUlERxcXHk7u7OPSxERCkpKWRnZ0effvopXb9+nZYsWUIikYj27t1rtH7VaaAY9Q++X/C6LC8ap/psN16Uc+fO6ax/165dJjsDZZOCHz16REeOHNEq7+nTp7R9+3ZKSEigJ0+e6G23QqGgfv36VSi7S5cueofd1Wo1bdiwgVQqld5y+/bta/FrN2PGDL31PX36lJKTk6mkpISInjuLT548oStXrtCdO3coJSWFJk+eXKX6b968ydXH97tl7VKjjk1BQQG99NJLdODAAerevTtnoPLy8kgikdDmzZu5tGURWxMSEoiIKC4ujoRCodbX2PLly8nJyYkUCgUREU2fPp1atWqlVefQoUMpMjLSaB2ZY8OwJHy/4HVZyoxTfbcbL4qHh4deHSZNmmRSWVlZWURElJOTQ8ePHze6rWUolUrq0aNHhXIdHByouLhYb77o6GgCQP3799eb5sGDBxa9bjKZTG9dGo2GfHx8yNnZmbp06UJ5eXnc8fLMnTvXrJVS3t7eWvXx/W5Zu9RoHJsJEybgjTfeQEREhNbx8+fPQ6lUah1v2bIlfH19kZCQAABISEjAK6+8Ai8vLy5N2T48165d49KULzsyMpIrQxcKhQL5+flawmBYgr///ptvFawCZjf+j19++UXvuaCgIJ0bTerDw8MDwPPr27VrV5N1ef3113H06NEKx3/66SeDMVmCgoLQunVrgxGLfXx84O7ubrJOupg1axbi4+P1nj979izS0tIgl8tx4sQJdOjQAQDw3nvv4ffff9dKO3v2bOzcudNkHV68b2SdQfutApMdm02bNuHChQuYP39+hXMZGRmQSqVwcXHROu7l5YWMjAwuzYvGqex82TlDafLz81FcXKxTr/nz58PZ2ZkTHx8fU5vGYOgkOjqabxXqPMxu/B+DBg3CwIED9Z4fM2YMXnvtNZPLNScPAJ3Owscff4yYmBiD+T7//HOcPn26UgfBkGNpDH5+fli8eDG+/PJLvPrqqxXOq1QqPHv2DK+//rrW8Vu3bmH27NnYtGmTzra8/vrrWkH9jKFz587c/81xjBg1g0mOTVpaGiZNmoQNGzbUuuiKM2fOhFwu5yQtLa3a6lIqldVWNqN2kZSUxO53FXn48CGzG/8fNzc3/Pnnn7hy5Qq2bt2KO3fuVEgjEAjwzjvvQCqVmlT2Bx98oPM4ESE7OxsPHjzA0aNH8cUXX2Ds2LEoLS0FABw/fhwBAQFcel9fX/z4449G1Wlra6tzz6gXCQgIMDtKe4sWLZCYmIhJkybpTdOnTx907NgROTk5Fc599dVXAIDWrVvrzHv48GEMGDDAaH1e7En7888/jc7HqFlM2tXv/PnzyMrKQtu2bbljarUax48fx88//4x9+/ahtLQUeXl5Wl9fmZmZ8Pb2BgB4e3vj7NmzWuVmZmZy58r+LTv2YhonJyfY2trq1E0mk0Emk5nSHLMpLi42qauYUXdZtGgR3yrUeS5evMjsxv8nNzcXr7/+Ok6dOoXi4mI4Oztj1qxZUKlUcHNzw7/+9S8IhUL06tUL+/btQ8+ePatU36effooHDx7g7NmzKCwsRH5+PlQqFQDg2rVrmDRpEl577TXExcWhRYsWAFDhOluCXbt2wc3NzaShvsmTJ2POnDkGNyXdsWMHjhw5UmlZ33//vc7jtra2+Pvvv+Hv748HDx4YrRvAPnBrNaZMyMnPz6crV65oSVhYGA0fPpyuXLnCTQLcsmULl+fmzZsEVJwEmJmZyaVZuXIlOTk5cTPZp0+fTsHBwVp1R0dH15pJgC8GwGJYNxs2bOB9Al1dl4cPHzK7YaQMHjxYq40ODg4G04tEIr1tUSqVldYnFAqpSZMm1LhxYwJALVu2NOo6FRcXU2ZmJhUVFVFRURFlZGTQ/fv36fLly3rznDx50ugJu6NGjTJKD2Mj/Xbq1MlgUNVDhw4ZVU5+fj6Xp3nz5ry/W9YuvAXoe3F1A9HzZZu+vr50+PBhOnfuHHXs2JE6duzInS9bttmnTx+6ePEi7d27lzw8PHQu25w2bRrduHGDli5dWquWbTZo0KCql41RR2ArH6rHONVHu2GMODs7a23Xcv36dfriiy/0pre1tdXblqysLJPqFggEFWIHlUehUNC8efMoNDSUGjZsSMHBwRQcHExeXl7k5uZGtra2tG/fPr35d+/eTWKx2KAeEydONPp+ffDBB5xTVplMmDDBYFlLly6ttIzc3Fwuvb29Pe/vlrVLrXFsygJtubq6kp2dHQ0aNIjS09O18ty7d4+ioqLI1taW3N3dacqUKToDbYWGhpJUKiV/f/9aF2iLUT8oLS3l/eWu62KMY1Nf7EZlEhYWplOvCRMm6ExvyLHRF3BPn7y4x5Qurl+/btS+VhKJRCvWS3n2799PTZs21dmWVatWGXWfXiQzM5O++eYb6tOnDzVs2FCnTmKxmD7//PNKy5oxY4bBtsXGxnJp2b5Q1S9sS4VyVLeBqkth4hnmUVhYSG+88QbvL3ddl7r0rvDt2DRu3Fivbj/88AP5+flpRScuH7fnRdRqtdH1hoaGGgy0t2LFikqHxV4UV1dXSk1N1Vve06dPtWLnyGQySkxMNOoe6UOlUlFqaqqWHr/99hutX7+e9u/fb1QZGo2GWrdubbBtZfD9XtUHYY5NOarbQBk7Fs2om6jVagoJCeH9xbYGYY6N8SIQCAzql5OTQ/Hx8Vx6Q9spPH361Kg6g4KCqKCgQG85O3bsMKstnp6elJOTo7fc0tJSmjp1KrVr147i4+MrvzmVoFarKTw8XEsHcygsLKQmTZrobdfdu3fp8uXLvL9X9UGYY1OOmjBQxn4FMOoe+rr+mdScceIDvh0bALRz504tnQoLCysMy5WlNcS5c+cqratbt25aE2LLk5mZaVaE3jIJCAigK1euGNSzbPJ3VVAqlbR8+fIK9VeW59q1a7Rv3z4qLS3VOpeRkaF3LpCLi4tFN9Vkol+YY1OOmjBQISEhfDeTUU3w/UJbkzDHxjQRCoW0evVqKioqouzsbHrnnXcoNTWVHjx4QI8ePaLff/+dAFDbtm0NtqVsywN98uabb3LbUehCo9FQYGBgldtjb29P06dPp19++YVmzZpl6VtGGo2GevToUWGPrXHjxhnMd/36dbK3tyexWEzz5s2jefPmac0zCg0N5f1ZqO/CHJty1ISB+uSTT/huJqMaOH36NO8vtDUJc2zMk9atW5Ofnx9169aN/vrrL3J3dycPDw/uD/ijR48MtsVQ2UKh0OCcmtzcXBoyZIhF22Nra0t2dnZm3ZeioiK9TtjMmTMJALm7u9P06dNpzZo19M033xhc3k303CFycXGhTZs2kUKhoI8//pg6dOjAnQ8ICOD9GajvYq7tEBBZ54YX+fn5BgM7WYKSkpIaDe7FqBkaNGigM4opwzzkcjmcnJz4VsMoasJumINAIKiwN5Eh07148WJMnjxZ73mhUKh3j6ecnBx06tQJt27dMk9ZPfWdOXMGEokEISEhlab/559/IJVKUVRUhJ07dyIpKQk2Njbo3bs3cnNzATwPdqjRaLBx40YAzyPjN2nSBADw+PFjbN68GQqFAkVFRSguLkbz5s1RXFyMV155hdt+IiUlBf7+/ly9paWlkEqlSE9PR6NGjSzWfoZ5mG07zHKH6gDV/eVV2SQ/Rt2lOp+b+iisx6Z6RNeEX41GQ19++aVR+c+ePUsFBQVUWFhIhYWFdPfuXZo9ezb5+flZXNetW7cafQ82bdpEQqGQRCJRheElQ/IiLVq00JtOLBZTXFyczrrT0tLot99+o0aNGvF+f5mwHpsK1MSXl5VeunqNQqGodfsZ1XVYj031EBwcjMGDB0MsFuPBgwdwcHDAvXv3sG3bNqPyy2QyNGvWDGLx8511Hj58iLy8PIvqGBAQgDFjxmD69OkAnttMgUCgM+3Bgwdx/PhxLFiwwKztCr755htMmzYNAoEAf/75Jz744AM8e/ZMZ1qRSIQrV67Az88PUqkU2dnZuHHjBt5++208efLE5LoZ1QPrsSlHTXx5MawPFrem9nx18UFd6rGp7dK+fXvKy8vTur7Dhg2jwsLCCtd93759VVp9VSbR0dG0a9cuSkhIqPRddnBwoJCQEOrVqxc1atSo0ojITGpeWI9NOViPDcMc9H1NMsyH9djUT3Jzc7U2NQWAmJgYLFmyBHZ2doiLi0NOTg4yMjIwd+5cFBUVWaReoVAIsVjM7V7OqLuYaztM2t2bwbBmjh49yrcKDIbVUN6pAYA1a9aAiPDOO+9g69at0Gg0Fq9Xo9Ewp6aeI+RbgbqMvvFbRt3j8OHDiIyM5FsNBsNqWLx4cYVebZVKhffeew9btmypFqeGwQAANhRVBY4cOYIePXpUax2MmsHGxgYKhYJvNawSNhRVf3n33Xfh6+uL6OhoyGQyTJkyBXv27OFbLUYdwVzbwRybKtC8eXMkJydXax2MmoHNrak+mGPDsLGxgUgkQmFhId+qMOoQbI4NDzRo0IBvFRgMBqPWU1JSwrcKjHoEm2NTBf7880++VWBYCBa7hsFgMKwD5tiYSfPmzeHn58e3GgwLsXz5cr5VYDAYDIYFYI6NGdjZ2eHUqVN8q8GwIKtXr+ZbBQaDwWBYAObYmMGkSZPg6enJtxoMC3Ly5Em+VWAwGAyGBWCOjRn8/vvvfKvAsCA5OTksijSDwWBYCWxVlBm0atWKbxUYFuT777/nWwUGg8PW1hY2NjYoLCyEUqmsFU63q6srgOfLb1/UxxjdykIp1IZ2mENAQAAaN24MADh27BjP2jCMgTk2ZjB79my+VWBYkH379vGtAoMBABCLxUhISICzszMyMjLw+PFjfPXVV7h+/XqVtglo3749bG1tce/ePRQVFXHLrwsKCgzmGzhwIF5++WWMHTsWAHD58mUUFhbC1tYWSqUSY8eOrXRH8HXr1sHBwQHZ2dk4c+YMjhw5ArlcbpFdtKVSKVxdXSESiVBcXIz8/HxoNBqLOVFvvPEGNmzYgJycHNy5c4c5NnUFs7bOrANU5y69f//9N9/NY1gQLy8v3nextWZhu3sbLy4uLhV0OnjwIMXHx1ep3JKSElIqlZSdnU1paWl0+/Ztun37Ns2ePVtvHgcHhwq7c5cnIiJCb36BQEATJkyokCcvL4/S0tJo2bJl5O7ubnabPvzwQzp16hSlp6dTZmYmpaam0r59++h///sfSSSSKt+LH3/8kdRqNc2dO5fc3d3Z7t88iLm2gzk2NXixGbUTSxhBJtbxvvDt2ADPnZAXGTBgAOXn55tdnq+vr8E2CwQCnfkcHBxo165dBvP26dNHb72rV6+u9HrfvXuXnJycjGqHt7c3NW7cmBwdHSk6Otpgudu2bSOBQEA2NjbUoUMHcnV1JaFQaFQ9gYGBNHbsWK4svp+H+izMsSlHdRmo8PBwvpvGsDD6DDsTfo0TH9QGx6Zbt26cPseOHaPi4mK6c+eO2T2LGRkZBtts6A/+8ePHDeaNiorSm7egoMCoa3758uVK29CrVy9KT0+nrKwsun37Nmk0mkrL3bx5M124cIEUCgXdu3ePtm/fTs2bNzdYz/DhwyvoHR4ezvszUV+FOTblqC4DlZmZyXfTGBaG75fX2oU5NqZLGWKxmAYMGEDp6el09+5dk8t5++23DbZXrVYbpYcuCgoKyNXVVW/eoKAgevz4sVFOSIMGDfSWI5PJSKlUGncDDaBUKumHH37QW8+CBQt05ispKSFfX1/en4n6KDXi2MyZM6dCxYGBgdz54uJiGj9+PLm5uZG9vT299dZbFb4W7t+/T6+//jrZ2tqSh4cHTZ06tcJDe+TIEWrTpg1JpVJq3rw5rVmzxuSGVZeBCgsLM1kXRu2G9dhUv3GqK7ajOh2bKVOm0Lx584xKS6TtdEyZMoWITHfCyw9rvYhGo6GBAwdWqoc+jOnJ8PLyoujoaIqLizNY1hdffKG3jO+//96ke1ie4uJievLkicHepcra2q9fP97fo/ooNebYtGrVitLT0zl58uQJd37cuHHk4+NDhw4donPnzlF4eDh16tSJO69SqSg4OJgiIiIoKSmJ4uLiyN3dnWbOnMmlSUlJITs7O/r000/p+vXrtGTJEhKJRLR3716TGladBophXTg4OPD+AluzlDk2dcF2VJfdkEgkXB2V/YF90cb4+PgQ8H8Oiqn1GiI2NrZK+U3RQygU0meffaa3LI1GozdvSkpKpfft/v37dPLkSVq2bJmWxMbGUtu2bbnrqE8++OADg+WLRCLe36P6KDXm2ISEhOg8l5eXRxKJhDZv3swdu3HjBgGghIQEIiKKi4sjoVCo9SW2fPlycnJyIoVCQURE06dPp1atWmmVPXToUIqMjDRFVebYMIymS5cuvL/A1ixljk1dsB3VZTdsbGyI6HkvjFgsJqlUapSNGTZsGIlEIu63KXU6ODgYbKsxPZX3798noue2vzzmXAd9lN1vXaJWq/XmS0tLo2+++YYaNGhQpUUAhobL4uLieH+H6quY69iYHHn4zp07aNSoEfz9/TFs2DA8ePAAAHD+/HkolUpERERwaVu2bAlfX18kJCQAABISEvDKK6/Ay8uLSxMZGYn8/Hxcu3aNS/NiGWVpysrQh0KhQH5+vpZUB++88061lMvgjylTpvCtQr2gNtqOmrIbCoUCAPDWW29BpVJBrVbjq6++gpOTk8F8K1asMDvOUpMmTQyeJyNivbRt2xbPnj3DkCFDtI6fPn3aLJ304e/vr/N4z549IRTq/zP16quvYsaMGcjOzoZSqdSbztbWFmKx/rBtZUEEdfHWW2/pPceonZjk2HTo0AFr167F3r17sXz5cqSmpqJr164oKChARkYGpFIpXFxctPJ4eXkhIyMDAJCRkaFlmMrOl50zlCY/Px/FxcV6dZs/fz6cnZ058fHxMaVpRrN+/fpqKZfBD+np6Xj33Xf5VsPqqa22o6bsho2NDQAgLi4OISEh+Pbbb/H555/jzJkzBvM5ODigV69eZtX57Nkzg+cN/aEvIzs7G+np6QgODtY6vmnTJrN0KiM+Ph63bt3ifkulUp3phg8frreMZ8+ecffeEIMHD0ZCQgL279+PGTNmVDhvZ2enN++9e/e4YIaMuoNJjk1UVBTefvtttG7dGpGRkYiLi0NeXh7++uuv6tLPaGbOnAm5XM5JWlqaxeuIjY3V+wIy6ibdunXjvqYZ1UdttR01YTcA4OuvvwYA7N27F6dOncInn3wC4HnP1Mcff1wtdT58+NDg+a1bt1ZaxuLFi/HSSy/pPF4VevfujT59+lRJx44dOxrMO3jwYCxatAh//fUXQkJC0LNnT4wZM6ZCur///ltvGXfv3jVYB6N2UqVNMF1cXNCiRQskJyfD29sbpaWlFcJrZ2ZmwtvbGwDg7e2NzMzMCufLzhlK4+TkBFtbW726yGQyODk5aYmlWbp0qcXLZPBLcnIy3yrUS2qL7agJuwEADRo0AADcunVLq4fgp59+0qlbUVGR1u+SkhIkJiZWSYeCggKt4ac333yz0jzjx4/Xe86YHh99bN68GQ8ePKi0DEPb11y9etVg3oEDByI4OBipqalQKpVQKpWYN29ehXR9+/bVW0aHDh0M1sGopZg1M+f/UxbH4Mcff+QmAG7ZsoU7f/PmTQIqTgB8MRbMypUrycnJiZv1P336dAoODtaqJzo6ulZMHmYxbKwPSz8jTIybAFhbbUd1TR728/OjFStW0NOnT7Xq++STT8jFxaVC+lOnTpFKpaIlS5bQhx9+SGFhYSSTyUyud9q0aVxdgYGB9Pbbb2tNwP78888N5lepVHqv1ebNm03SZciQIVzeslVQL6IrjyGMqVMoFJKzszN16dKFOnXqZHIdRET+/v68v0P1VWpkVdSUKVPo6NGjlJqaSidPnqSIiAhyd3enrKwsInq+ZNPX15cOHz5M586do44dO1LHjh25/GVLNvv06UMXL16kvXv3koeHh84lm9OmTaMbN27Q0qVLa81y76lTp5qkA6P2w/eLWx9ELpfXGdtR3QH6YmNjtepTq9V0+/ZtatiwIbm7u1NAQACJxWIaOHAgvfnmm9SuXTtq2bIl9ejRg8LCwsyqc+fOnURE9OGHH9Lp06e1VgAdPnyY3N3dSSgUkp2dHbVu3ZrCw8MpPDyc2rZty604s8T78+LqJo1GQx4eHrRq1SqDZVmqbmP1epGy61RZtGIm1Sc14tgMHTqUGjZsSFKplBo3bkxDhw6l5ORk7nxZkC1XV1eys7OjQYMGUXp6ulYZ9+7do6ioKLK1tSV3d3eaMmWKziBboaGhJJVKyd/fv9YE6Fu+fLnJejBqN3y/uPVB5HJ5nbEdNRF5eOzYsbRhwwY6dOgQ7dixg4iIMjIyKC0tjfLy8ujgwYM0evRo2rNnDxUXF1NBQQEplUr666+/zKrPzs7OYJsfPHhAu3btosuXL1NhYSGVlpaSQqGg4uJiLs2ff/6pM+/ixYuN0iEmJqZC3rS0NMrNzSUiop9++klnPkNY6n7oi2qcmppK169f5/39qc/CtlQoR3UYKEuE9WbULqqyuzCT6jVOfFCTWyqIRKJK93Eiej4Ml5ycTN26dTO7rr///rtK18VQLJmynjFde06JRCIaPHhwpdsq6Ns52xCWug+FhYV66/D29ub9/anPYq7tMH/2Vz1jyJAhVZosx6iduLu74+nTp3yrwaiHqNVqhIWF4f333+dWTeni1VdfhY2NDc6fP292XW+//Tb279+PDh06wMHBweh827Ztw6VLl9CiRQsMHDhQ59Lo8ePH46WXXoJCoUBaWhry8vLw+PFjqNVqvPXWW3jttdcMxolZvXo1VCqVWe2yBE+ePEHTpk11nvv+++/x3nvv1bBGjKoi+P+er9WRn58PZ2dni5WnVCqZY2OFHDp0qEJQN4Zlkcvl1bbayNJY2m4Yy2+//YaYmBiDaUQiETQajdl1SCQSNG/eHKtWrYKnpye8vLxgb28PsViMoqIiFBQU4O7duygqKsKTJ09w/vx5/PDDD9BoNBAIBGjTpg0WLlxodlwdXRQXF8Pe3l5vsEC1Wq03QJ8hZ8kUdu3ahX79+uk9b6l6GKZjru1gjo2RWOllqvcQkcHIpoyqwxybypHJZEhJSYFYLIZarcbRo0e5ZezFxcW4efOmxSJkSyQS2NjYwNPTE76+vggMDER8fDxycnLw9OlTaDQaqNVqnXnFYjH++9//okWLFujQoQMaNWpUJV369++PXbt26T0/atQozJkzB35+ftyx1NRUNGvWDEOHDsXFixdx7949lJaWcvo5OzujdevWSElJwf379yvVwdXVFTk5OXrPDxw4EDt27DC+UQyLYbbtMGsAqw5g6bFyhvViyeeEieXGyfmgJufYlBdPT09q2LAheXl5kUAgILFYTGKxWOfcFb5FIBCQp6cnXb582azrXFJSQgsWLDCqLnd3d0pNTeXy/vTTT0T0fNWSXC6nU6dOUWxsLK1fv54OHTpE9+/fJ6VSSdu2bTO6PYbmEKlUKnrzzTfZRpg8iLm2g/XYGImVXiYGgK5du+LEiRN8q2G1sB4b68XBwQHvv/8+Bg0ahNatW6NBgwY6h+yJCAqFArm5uZDL5YiJiTFpvyl3d3ds3boVXbt2NToPmdAbO3jwYPz1119606vVahw6dAipqan473//W2nEZIZlYENR5bC0gSobZ2ZYHzdv3sTLL7/MtxpWC3NsrB+RSARPT0+0bNmSG6aSyWQAgEuXLuHWrVt4+PAhHj58iMLCwkr3sdKFRCLBpEmT8M033xjlsOTk5HARn43h/fffx++//27QzhMRfvjhB7Zxbg3BHJtyWNpA3bp1Cy1atLBYeYzaQ5s2bXDx4kW+1bBamGPDsCQDBw7EgAEDMGjQIJSUlODJkyfYtm0bHBwcIJFIYGtrC4VCgQULFpi899ewYcPw008/wc3NTed5jUaDfv36Yc+ePZZoCqMSmGNTDksbqO3bt2PAgAEWK49Re2jSpAkePXrEtxpWC3NsGNVB8+bNud4fc3qA9OHv74+vv/4a/fv3h0wmq9A7pFKpIJFILFYfQz/MsSmHpQ2Ut7c30tPTLVYeo/Zw8OBB9O7dm281rBbm2BhPZGQkWrdujW3btrENWnlEIBAgJCQE3t7e8PT0RNeuXREUFASpVIoNGzZUeXdzhnEwx6Yc1WGgrPRS1XtKSkoM7hzPqBrMsTEOiUSCoqIiiMViyOVyzJgxAytXruRFF0ZFJBIJhEIhFAoF36rUG8y1HSyAB6PeUzbJkcHgk3Xr1nEripydnbFixQp88cUX/CrF4FAqlcypqSOwHhsTsNJLxQCLLlqdsB4b46Dne/dVeBZlMhkXgI7BqE+wHhsGg8GwQqqyjQKDUR9hjo0JWHLmPaP2sHHjRr5VYNRzylbelO+tefLkCa8bRDIYdRHm2JgAW6VgnYwYMYJvFRj1HH29Mvv27athTRiMug9zbEzg/fff51sFhoXZs2cP+yJm1Fo+/fRTvlVgMOoczLExAVOjWDJqP//973/5VoHBQFRUVIVjq1atwpMnT3jQhsGo21TcrYyhF7lczrcKDAvDemsYtYHt27dz/9+3bx927dqF5cuX86cQg1GHYY4No95SUFCAs2fP8q0GgwEiQmlpKVavXo2JEydCrVbzrRKDUWdhjg2j3sK2UWDUFoYMGYKsrCzmaDMYFoAF6DMRK71c9RIWlK9mYAH6GAyGObAAfQwGg8FgMOo9bCjKBJo2bcq3CgwGo44jk8nQqVMnyGQyHDt2DMXFxXyrxGBYFcyxMYHDhw/zrQKDwaiDyGQyEBEaNmwIiUSC/fv3QygU4sKFC3j11Vf5Vs+iiEQiaDQaCAQCth0EgxeYY2MkLVu2hL+/P99qMCwEW3XCqCmio6Px6aefQq1Wo0WLFgCehxmwsbFBWFgYz9rpRigUmuyUuLi4oHXr1vj888+RnZ0Ne3t7DBgwAERkVnkMhrmYPMfm0aNHGD58OBo0aABbW1u88sorOHfuHHeeiDB79mw0bNgQtra2iIiIwJ07d7TKyMnJwbBhw+Dk5AQXFxeMHj26wj5Mly9fRteuXWFjYwMfHx8sXLjQzCZahtatW/NaP8OyDBs2jG8V6h311Xa0a9cOJSUl6NChA1xdXeHq6oqEhARedTKEn58fduzYAU9PT53ny/a1epEvv/wSFy5cwMGDB9G7d2+8++67ePPNN/H6669j0aJF2LZtGwAgNDQULi4uJukjk8kwZMgQDBkyBC1btoSLiwskEolR+WQymUl1MawEMoGcnBxq2rQpjRo1is6cOUMpKSm0b98+Sk5O5tIsWLCAnJ2dafv27XTp0iXq378/NWvWjIqLi7k0ffv2pZCQEDp9+jTFx8dTQEAARUdHc+flcjl5eXnRsGHD6OrVq7Rx40aytbWllStXGq2rXC4nABaT1NRUUy4Vo5ZjyWeDiWGRy+V1xnZY2m6UiUQiobVr19KTJ0+4up4+fUpZWVm8358yadSoEf3222+Um5tLRETx8fFa+js6OtLChQtp586dtH79erK3t6eAgABq0aIFXblyRef1nD17Nh08eJB27NhBAKiwsJD27NlDIpGIhEIhtWzZkqtj5MiRNHnyZOrZsye9/fbb1KNHD2rfvj2dPXuWNBoNaTQaKigooJSUFDp+/DgBIHd3d51tiY2NpcTEREpMTKQlS5bQf/7zH5OuhUAgoIEDB5KLiwvv96U+i1wuN8Wsc5jk2MyYMYO6dOmi97xGoyFvb29atGgRdywvL49kMhlt3LiRiIiuX79OACgxMZFLs2fPHhIIBPTo0SMiIlq2bBm5urqSQqHQqjswMNBoXS1toJ4+fWp03YzazcGDB3l/YeuTyOXyOmM7qsuxKZMmTZrQihUraMmSJdS8eXNq3LixUfl8fX3Jx8eHfHx8qk239PR0unjxInctMzIyuHPHjx+nW7dukVqtJrVaTURE165do7y8PMrLyyOVSqXzen733Xd08uRJkkqlJBQKafr06eTn50cHDhygHTt2UEFBAa1cuZKCg4O5PEqlkjQaDSmVSq37WIZaraZRo0aRRCKhGzdu6GxLmY4vEh0dbdR1cHFxod9//500Gg3duXOHFi5cSBKJhPf3qD5KjTg2L7/8Mn3yySc0ZMgQ8vDwoNDQUPrll1+483fv3iUAlJSUpJWvW7du9PHHHxMR0erVq8nFxUXrvFKpJJFIRFu3biUiovfff58GDBiglebw4cMEgHJycnTqVlJSQnK5nJO0tDSLXuDNmzebcqkYtRhjDRwTyxmn2mo7qttuVFW++uorcnNzo6dPn3Ly5ZdfWrweqVRKI0eOJEdHR/rkk0+4a2Nvb08AaNWqVbRu3ToaMGAADRo0iOLj4+nKlSu0cuVKOnfuHMXGxtLUqVMpMTGR4uPjKSEhgW7cuEEFBQWkVqvp4MGDtG3bNtq2bRsdO3aswn0oLCw0+v2NjY0lABQfH0+TJk3S2R5dFBYWVnodfvjhB0pJSamQ99ChQ7w/C/VRasSxkclkJJPJaObMmXThwgVauXIl2djY0Nq1a4mI6OTJkwSAHj9+rJXv7bffpnfeeYeIiObNm0ctWrSoULaHhwctW7aMiIh69+5NY8eO1Tp/7do1AkDXr1/XqducOXOq9QLfu3fPlEvFqMXs27eP9xe2PolcLq+1tqO67UZVhYjo3r17VFhYSAUFBZzelqyjU6dOdOzYMVq/fj0dOnRIq7ejzCkEng/PlP1fIpGQnZ0dASCZTMYdl8lkJJFISCqVkoODA7Vq1YqioqLozTffpN69e9OZM2fMeWW1GDJkCAGg2bNn623TwoULdeat7Fp89NFHWr1ELz5LfD8L9VFqxLGRSCTUsWNHrWMTJ06k8PBwIuLXsanuLy+GdcH3C1ufRC6X11rbUdt7bJKTk2nXrl0UHBxMvXr1ooKCAtJoNJXmEwgEFBAQQH369Kk07YtDe+Vp2rSpRdvj5OREu3fvpgcPHlBeXp4Rbypxw19qtZpz8CqrRyqV6izLxsam0rw9evTg0vv6+nL/5/tZqI9irmNj0nLvhg0bIigoSOvYyy+/jL///hsA4O3tDQDIzMxEw4YNuTSZmZkIDQ3l0mRlZWmVoVKpkJOTw+X39vZGZmamVpqy32VpylOdM+BHjx5dLeUyGPWF2mo7avvKmXbt2qGgoAAajQZ+fn4IDw9Hy5YtK823bt069O/fH7a2tnB3d0d+fr7etMOGDcOyZcvQo0cPiEQi7vipU6dw//59i7SjjPz8fLz55pvc6rBPP/0UdnZ2GDlyJJcmIyMDK1euREpKChwdHZGSksKdS0tLM+p+lZaW6jweHBystRJPF0ePHsX48eOxbNkyJCUlGdkyRq3CFC8oOjq6wgTATz75hPsSK5sA+O2333Lny7qhy08APHfuHJdm3759OicAlpaWcmlmzpzJ2+RhXRPRGHUbSz0bTIz76qortqO6Jw8bEgcHB4PnnZ2dKSoqyqiyXuT999+vNL1IJKJevXrR/PnzadGiRfTdd9+Rq6trtbdZLBZTWFgYp+vatWupYcOGVS7X0dFR5/1t27at0WW8aPeTk5N5ey7qs9TIUNTZs2dJLBbTvHnz6M6dO7Rhwways7OjP/74g0uzYMECcnFxoR07dtDly5dpwIABOpdstmnThs6cOUMnTpygl156SWvJZl5eHnl5edH7779PV69epU2bNpGdnR1vy70vXrxoymVi1AH4fmHrk8jl8jpjO/hybJo0aULnz5+vNJ1QKKw0zbvvvqvVppycHN6fgRclMDCQwsLCaN26dXT48GGt+/viPJ6qiL45NmKx2OgysrOziYhozJgxbNk3T1Ijjg0RceO9MpmMWrZsqbWygej5l9esWbPIy8uLZDIZ9erVi27duqWVJjs7m6Kjo8nBwYGcnJwoJiZGa2IcEdGlS5eoS5cuJJPJqHHjxrRgwQKT9LSkgfrmm29MvUyMWo6HhwfvL219kTLjVBdsB1+OTZ8+fSgsLKzK5QgEggp/1BUKBe/PQPnnoaSkROf1t1QdaWlpVS7/wYMHRETUp08f8vLyIldXV3J1dSVvb2/er2F9EXMdG8H/v9lWR35+PpydnS1SVvv27XHmzBmLlMWoHbz00ktITk7mW416gVwuh5OTE99qGIUl7QYf/O9//8OQIUO4yLwpKSmYPXs2NmzYwLNm/4e+PzkPHz6Ej49PtdYhEAhMLkOtViMrKwsKhQLA872wfH19q64ko1LMtR3MsTESK71M9ZZNmzYhOjqabzXqBcyxMR1bW1u4urrCyckJ6enpKCwsBPB8srQhytspV1dX5OXlVZeaJuPj44MHDx7oPNesWTPcu3fPIvVY0rHRBfswqhnMtR0m7xXFYFgDQ4cO5VsFBkMvr776KpKSkpCYmIjz589j79692Ldvn970IpEIPXv2rHC8Njk1ALB+/Xq95/Q5PJai/Iq6qvD1119brCyG5WG7ezPqJaZ8uTEYlmL16tUAnj9/MpkMGo0G77//foV08fHx3CaUkydPxqNHj3QuVw8ICMCgQYPw2muvoVevXlrnym8OagxCoRBSqRROTk548uSJxXuqg4ODdR4vKSmx6O7fX3/9Nf79739rHRs8eLDR+WNiYvSee/jwIXJycszWjVEDmDUzpw5g6UmADOvDks8HE8tPAOSD6p48nJmZSfn5+ZSfn08//PADt4Rdl5w6dYqOHz9Ozs7OOs/b2NgYDHKn0WgqXUJeJlFRUTR06FDatWsXXbhwgR49ekRbt24lNzc3Lo1EIqnyqiV9xMXFWfQ6+/v7c8EXyzA2r1AoJI1GQ0RE48aNq6Dr3LlzqUmTJry/V/VBamxVVF3BkgaqZcuWfDeHUQ3w/dLWF2GOzf9Jo0aNKCAggAICAkggEBh0PKRSqcHNF11dXSttz/Xr18nf358CAwPJy8tLb1nvvPOOzo0sU1NTSSwW04QJEyg+Pp7bJfvjjz+mBQsWmNR2fdGAiciiu5zPnj2bcnNzSaVScQ5KaWmp0fl79erF6eXj46OlZ2JiokXi7DAxTphjUw5LGii2s7d1wvdLW1+EOTbVJ8YED83JyaH8/Hx6/PgxjRo1ikaOHEm2trYVyho4cCBdvny5Qv5Dhw4R0fOtDQIDAwkAzZgxg6ZPn260nkKhkCtHF/PmzbPI9WjatKnO8o0NbAiAdu/ezeV78uQJ9//4+HitfbGYVL8wx6YcljJQsbGxfDeFUU0Ys28ME/6MEx/UNcdm4MCBlJGRYXI7k5KSqEGDBhXKs7Ozo8mTJ2ulXbx4MS1cuJD69+9vtp4BAQEG9WnVqpVFrsf58+d1ln/jxg2j8kdFRenVUZczyKR6hTk25bCEgYqJieG7GYxqZPny5by/uPVBmGNTvdKkSRMaOXIkHT58WG/gO12kpKToLK9t27Za6SyxA/qgQYMM6mJMRGVjxBDG5M/Pz692HZkYL+baDrbcWw8SiYRbwcCwTnr06MG3CgxGlXn48CF+//13REZGokePHli8eDGuX7+OlJQUg3FvmjVrBjs7uwrHS0pKoFKpoNFoQESYOHFilXW8deuW3nNbt261yIqoyuKdGFrpBAB9+vSBo6NjheNExG1EyqgbsAB9OpBIJNi3b5/OuBAM6yEyMhL79+/nWw2rhwXoq3ns7e0hFovRpk0bvPzyy+jevTvc3d1hZ2eH8PBwLt3OnTsxcODACvm7dOkCe3t7uLq64unTpzh48GCVdTp69Ci6dOmitYP4iRMn0L17d4s4DefOnUO7du3w+PFjNGjQoMIu4EQEoVD/t3xpaSkXsbmMM2fOYP78+bhz5w6uX79eZR0ZpsEiD5ejKgZqz5496Nu3r4U1YtQmMjIy0LBhQ77VqBcwx4Z/BAIBhEIhRCIR3nrrLe74xYsXcfPmzRrRQSqVomfPnggKCoK/vz8AYMqUKSgtLa1y2a6ursjJycFnn32GtWvXYtOmTRV6ZHNzc+Hm5qYzf4MGDfD06VOd5da2IIf1CebYlMNcA9W0aVOLhfVm1E5ycnIQGhqKtLQ0vlWpFzDHhlHd3LlzBwEBAQgPD8eZM2egVqu1emdyc3Px6quv4u7duzrz9+vXD7t27apwnAXy5Be2pYKFCAoK4lsFRjXToUMH5tQwGFZEkyZNADwf7oqJidFyas6ePWvQqQGAUaNGVThmiZ4kBj+wLRXK8corr/CtAqMaOX/+PNu8jsGwMl5//XVs2bIFAoEAsbGx2LJlC0pKSnDq1CmsWbMGJSUlBvMHBgZq/Var1XjnnXeqU2VGNcKGospRVFQEW1vbatCIURtwcXGBXC7nW416BRuKYtQETZs2BfB8/lxpaalJ+1w5ODhg+vTp6NevH44fP45//vnHIhOmGVWDzbEphzkGqnv37jh69Gj1KMSoFbAx85qHOTaMuoKNjU2lvTuMmoPNsbEAcXFxfKvAYDAYDJ5gTo11wBybF9AVrIphXdjb2/OtAoPBYDCqEebYMOoVbCiKwWAwrBvm2DDqDRqNBkVFRXyrwWAwGIxqhC33fgGFQlEhDDfDevjggw/Yfi8Mq8DR0RG2trZQqVQoKipic0MYjBdgPTYv8PPPP/OtAqMaWbNmDd8qMBhVpmnTpjh37hySkpJw4cIFnDhxAr169eJbLQaj1sB6bF6AxTdhMBi1ncTERHh4eHC/mzZtij179kAqlfKoFYNRe2A9Ni8wdepUvlVgMBgMg7zo1JRRfldqBqM+Y5Jj4+fnB4FAUEEmTJgA4HkMgAkTJqBBgwZwcHDA4MGDkZmZqVXGgwcP8MYbb8DOzg6enp6YNm0aVCqVVpqjR4+ibdu2kMlkCAgIwNq1a6vWSiNo2bJlnQkixmDUNazZdtQk3bt351sFBqP2QyaQlZVF6enpnBw4cIAA0JEjR4iIaNy4ceTj40OHDh2ic+fOUXh4OHXq1InLr1KpKDg4mCIiIigpKYni4uLI3d2dZs6cyaVJSUkhOzs7+vTTT+n69eu0ZMkSEolEtHfvXlNUJblcTgCMlidPnphUPqPuYcrzwMRyIpfL64ztMNVulEmHDh2obdu21XodJRIJlZSUVMvzHRoaSm3btqXo6GiKjo6mPn36UGhoKHl7e/P+/DCpvyKXy0039ERkkmNTnkmTJlHz5s1Jo9FQXl4eSSQS2rx5M3f+xo0bBIASEhKIiCguLo6EQiFlZGRwaZYvX05OTk6kUCiIiGj69OnUqlUrrXqGDh1KkZGRJulmqoFiWD82Nja8v6j1UXQZp9pqO8x1bBQKBRUVFdGAAQOoSZMmFr+GTk5OFB8fr1fvv/76q0rl7927l4qLi4mIqKioiNavX09yuZzS09Ppp59+IgcHB96fIyb1T2rcsVEoFNSgQQOaN28eEREdOnSIAFBubq5WOl9fX/r++++JiGjWrFkUEhKidT4lJYUA0IULF4iIqGvXrjRp0iStNL/99hs5OTkZ1KekpITkcjknaWlpJl1AhvXj6urK+4taH6W8capNtqOqdqO8/dBoNPTkyRPy9fW16DXctm2bwWdbKBRWqXyJREKnT5+m0tJS6tChAwkEAhowYABX/pUrV6rcBrFYzPuzyKRuibmOjdmTh7dv3468vDyMGjUKwPMdVaVSKVxcXLTSeXl5ISMjg0vj5eVV4XzZOUNp8vPzUVxcrFef+fPnw9nZmRMfHx9zm8awUv7973/zrQIDtct2WMJuiMVi5ObmAnge2drd3R3ffvutyeUYwtfX1+B5R0fHKpWvVCrRvXt3vPbaazhz5gyICDY2NtwcpqCgIAwcONCssoVCIWbNmoVDhw7h999/x+TJk9GkSZMq6ctgGMJsx2b16tWIiopCo0aNLKmP2cycORNyuZyTtLQ0vlVi1DJGjx7NtwoM1C7bYQm7oVKp0LZtW61jb7/9tqVUBAA8fvzY4Pnjx49XuQ6FQoETJ04AANq1a4egoCBERERg0aJFEAgE2Lp1q0nlCYVCREZGYsWKFZgzZw7Cw8MxYsQIzJkzB2fPnjVZP5FIBLFYjMDAQDRt2tTk/Iz6g1lxbO7fv4+DBw9qPeje3t4oLS1FXl6e1pdXZmYmvL29uTTlH+iylQ8vpim/GiIzMxNOTk6wtbXVq5NMJqtS1OCHDx+yrwgrp6pftYyqU9tsR1XtBgBMmTIFLVq0qFIZlXH16lX069dP7/lWrVpZtL7Dhw9DKBRizpw5uHHjBiZPngyxWAyhUGhS9O4ffvgBL7/8MlauXIk//vgDkyZNwsyZM3Hnzh2T9AkKCsKgQYMQGRmJ0NBQKJVKNG7cuNKIy507d4aNjQ38/PyQk5ODbdu2mVQvo45izvjVnDlzyNvbm5RKJXesbALgli1buGM3b94koOIEwMzMTC7NypUrycnJiZvtP336dAoODtaqLzo6utonDycmJpp8HRh1D1OeCSaWHyev7bbDnMnDarW62p+17777Tq/Oa9eupYULF1q0Po1GQwqFggQCAe3evZsyMzPpxo0bZG9vb1I5zs7ORERUWFhIAEggEBAAysvLM7qMkSNHUkFBAZWWlmq1Ozw83GC+WbNmUWlpKalUKiIiUqvVvL8LTEyTGps8rFarydfXl2bMmFHh3Lhx48jX15cOHz5M586do44dO1LHjh2582VLNvv06UMXL16kvXv3koeHh84lm9OmTaMbN27Q0qVLa2S5d/mXhmF9qFQq3l/U+ihlxqku2A5zHBt9mHu9WrZsSYMGDaKvvvqKvv76a4qNjaWCggIiIsrOzqZLly5p1RMQEEACgYDs7OzIy8vLIvesqKiIiIiWLFlCgYGB1KhRI3J0dDSrrDlz5lBMTIzWsebNmxuVNzY2ljQajc7rK5FI9OZzd3fXmaddu3bk7e1NDRo04P29YFK51Jhjs2/fPgJAt27dqnCuuLiYxo8fT66urmRnZ0eDBg2i9PR0rTT37t2jqKgosrW1JXd3d5oyZYrW1xsR0ZEjRyg0NJSkUin5+/vTmjVrTFXTJAMVHh5ucvmMusewYcN4f1Hro5QZp7pgO/h2bFxdXamgoEDvH/OWLVuSvb29VtytvLw8WrJkCV25coUeP35MX3zxRZXvGRFRamoqHTlyhL777jsCQGvXriU7O7safXb09YadOnXKYL6UlBSd+YqKiig9PZ3u37/P+3vBpHLhJY5NbcYUA1VYWMi3uoxqpri4mPeXtL6KucaJD/h2bL766iu95SUlJXHp/Pz8DLZjwoQJVbpnRESenp7UoEEDEgqF3Mffrl27zCqvQYMGZvWSjBo1ilJTU0mpVHLO3rZt20gmk2mlc3R0JGdnZ/Ly8qL//Oc/ld7ne/fu8f5eMKlcmGNTDlMMFMP6GTx4MO8vaX2V+ujYFBQUmHWtyoacylNUVFRhfsvZs2cNtqUq90yj0VB8fDwlJyfTihUrtHqIhgwZQj4+PkbHzlmyZAk9ePCAHjx4QM7Ozibr4urqSj179qRVq1YR0fNeqxfPT5s2jW7dukV3796lx48fV3qPN2zYQA0bNuT9vWBSuTDHphzGGigbGxu+Va12lEolpaWl0aVLl+jatWtcpFZj0Wg0pNFoKDs7m3JycujChQsUHx9Pv/32G82ZM4fWrFlD+fn51aS9ZSibtMik7hgnPrCUY2Nqj4BIJKLBgwdXKEej0dBXX31FoaGhFfK4u7tTTk6O3rZMnz7d7HtmCI1GQ0+fPqWdO3dSly5dKi3r1q1bXG9LYmIid3zmzJkmBc1cv349ERH5+PgQ8Hwi8pdffmnsreVgtqDuCHNsymGsgdq4cSPfqlYr58+fpx49epC7uzvZ2tqSvb09hYeH086dO3WOX6vVaioqKqJdu3bR8uXLadasWTR48GAaMmQI+fr6kp+fH9nY2FSYuNeyZUtu35/aSFUjszKpeePEB+Y4NroWHpStAjJWfv/9d53vY3R0tMF8zZs31+vcVGWyvLEoFIpKy3JycqIBAwbQ+++/T5GRkdxxT09Pk3QaPXo0ERFNnjyZ/Pz86K+//jJazzJKS0t5fx+YGC/m2g4BERGskPz8fDg7OxtMY2NjYzCacV3n4cOHaNGihc42CoVC9O/fH6GhoQAAW1tbXLt2DTk5OXj48CEuX75sUrwKAJBIJFiwYAE+/fRTS6hvMY4ePYqePXvyrUa9RS6Xw8nJiW81jMIYu1GeixcvIiQkROtYcXEx7OzsTKr3xThLxcXFOH/+PLp27Vpp3ubNmyM5OVnnOYFAYLQOLzJp0iT069ePiyvk5OSEnJwcPHnyBFKpFO3bt+di2ri5uZlVh6mIRCKoVCoQEfLy8uDq6mp0XrVajdWrV+Pvv//G/v37q1FLhiUx23aY5Q7VAYz58jp27BjfalYbKpXKYks/TRV9Kzr4wtbWlvcvj/os1t5joysG1u3bt00q48X4PKdOnaLWrVuTVCo1On9WVlYFHZRKZZXum0gkIplMRjKZjBwdHUkqlZJQKCSRSER+fn7k7+9Pfn5+1fbcCIVCrVVYAoHA6PuoVCopIyOD0tPT6ciRI1o9RUzqjrChqHIYY6CsmdjYWN4exsuXL/PdfI6qGncmVRdrdmwCAwN1zllr0aJFpXlbt25NAwcOJFdXV26YZf78+WbtQu/r60sHDx4khULBDWl9//33vN/7qsiHH35Ily5d0jpWGUePHqVvvvmGIiMjydvbm7y9vdnmm3VYmGNTjvru2PA5Qa42OTa5ubm8v5z1XazZscnLy9NZzttvv20wX+fOnamwsJCys7OpX79+JJVKqxwZVyQSUZcuXahfv35E9Hy+XNu2bUkkEtXJCbPFxcVEpL26qzKMmczMpO5Ije/ubQ3cunWLbxWqDaGQv1sbHBzMW93lMXW+BINhCvqer8TERL15PD09cejQIdjZ2cHNzQ3Lly+HUqmEUChEQECAwfpEIhFEIpHOc2q1GidOnODefaFQiLi4OPzzzz91cmf7sr2xYmNjIRKJMGbMGFy/fh1Xr17Vm+fAgQOYMmVKTanIqKWYtQmmtaBUKvlWodpwcXFBdnZ2jdf73XffmT1hsTqoTbow6g/37t3TedzOzg6nTp3S2njT0dERRIRly5YhMTFR76TY5s2bY+XKlVCr1UhKSkJubi6ysrKwZs0arXTnzp2DRqOBXC7Hjz/+iPj4eGRlZVmsbTWBSCTCTz/9BAD4+eefMXjwYAQGBiIoKAgajQbjx4/HjBkz0KBBA618NjY2WLRoEVxcXPDLL7+YtVs7wwowq5+nDmBMl3Jtm+RqKdLS0njpNpw4cSLfTa+AqctumVherHkoSh+60i5ZsqTCPk9lrF27lg4cOEBEz5czt2zZkiIiIujjjz+mpk2bUmBgIGVnZ+vMO3To0Ap1RUVFVevE3uqWss0zX6Rnz55aaXx9fQ1GjX/69CmtXLmS97YwMV/YHJtyVGagrDkwn6+vb40/gL/88gvfzdZJp06deH8567swx+a5fP7550brkZuby+1K/eTJE8rNzdWb1phYMnVN9u3bx7WvtLSUNBqNznRt27at9FouW7aM9/YwMU9YHJtyVBaPYv/+/ejdu3cNalRz1PTwi0QiQWlpaY3WaQxyuZyLw8HgD2uOY/P06dMKwyGA/ncwOTkZPj4+kEqlBsuVy+VwdnYGEUGlUgF4/p7pQqPR6J13Uxdp2bIlbty4wf3u2rUrPDw8sG3bNp3px4wZg//+97/w9PTUW6aNjQ0UCoXFdWVUL+bajno7x8ZanRpTg+pZgr/++qvG6zSGAQMG8K0Cw8oJCwtDQkICNy8mNTUV69at05v+1VdfRatWrdCpUye4ubkhJycHAODm5gZbW1uEhoZyznheXh5WrFiBtLQ0EBEWLFgAOzs7rcB/T58+xaJFi6qvgTxw4sQJ7v9//fUXkpKSDDolq1atwu7duxEREYGwsDBMnDixQpqjR4+iY8eOesuQSqW18uOMYR71tsfGSpsNoGZ7bHr27InDhw/XWH2mIBQKrfo+1xWsuccGALy9vdG4cWMIBALcvHkTz549M7t+sVjM9cwolUqutwZ43mMjFAq1em4UCoXVLYJ48Z29f/8+CgoK8Ouvv+LHH380Kv+2bdvQt29fCAQCrUnaTZo0QX5+PgoKCrhjDg4OWL58OQICAtClSxeo1WrLNYRRZcy1HcyxsUJqyrERCoUoKSnR20XON4MGDcL27dv5VqPeY+2ODcOyjB49Gm5ubnBzc4NIJIKHhwdWrFiBM2fOGJVfKBQiNDQUQqEQZ8+e5ezhw4cPcfHiRbz55ptc2vDwcCQkJICIeA2RwdAN21KhHJVNArRmDLXbkrJixQq+m2qQo0eP8j75jYl1Tx5mUrtlxIgR3L3Nzc3VuVLs3r17NGrUKAKeT0bmW2cm/yfm2o56O8fGminbnK66+fDDD6u9jqpw5coVvlVgMBg88uKwU3h4uM74Qm3btuXmOl28eLGGNGNUJ/W2762uBawyBVN2va3LEBFKS0t1DiuqVCpMmjSJB60YDEZtwN3dHX/++Sf3e+bMmQgJCUFERASaNm3KHS9zagB+Fl8wLE+9nWMzYMAAq51/sXfvXkRFRVV7PYWFhVorNCwNEUGtViMvLw9SqRQymQwPHz7Ejh07kJeXh9u3b+P+/fto2LAhoqOj0adPHzg7O0OpVKJfv37Yv39/tenGMB42x4bBB6mpqfDz8+N+q9VqlJaWQiqVIi0tDc2bN2eOTC2HLfc2kczMTL5VqDZatWpVI/V06NAB3377LSIjI80uQ6FQQCgU4uHDhyAiXLlyBXl5ebh16xZSUlKQlZWF27dvw9bWFk5OTrh79y7kcnmFcrZv347mzZtj4cKFWL58OQ4cOFCVpjEYjDqOn58f0tLSkJCQgMePH+Off/5Bt27dUFhYiB07djCnxoqptz02V65cqVWbNVoSlUpVYyuVxGIxzp07h5CQkArnNBoNiAjp6ekAnncN5+XlYd26dbh37x5KS0tx8eJF2NjY4Pr169BoNCgoKKiSwamp+UUM42E9Ngw+mD59OpKSkpCWloabN2/yrQ7DDNhy73IYMlD9+vXDrl27alijmqUmY9nY2dlh06ZN8PT0xIMHD3D//n3k5OTg4sWLUKvVuHDhAoDncSQyMjKQkZFRY7ox+Ic5NgwGwxyYY1MOfQZKLBajpKTEqkKQ66Kmt1UQiUQQiURQKpVWHSOIYTrMsWEwGObA5tgYiUqlsnqnZvXq1TVep1qtZlE7GQwGg8E79Xa5t7Xy3//+Fx988AHfajAYDAaDwQsmOTZqtRqzZs1Cs2bNYGtri+bNm+Orr77SGnogIsyePRsNGzaEra0tIiIicOfOHa1ycnJyMGzYMDg5OcHFxQWjR4+usL/K5cuX0bVrV9jY2MDHxwcLFy6sQjPrB1u3bsWsWbP4VoPBqACzHQwGo8YwJUzxvHnzqEGDBvTPP/9Qamoqbd68mRwcHOjHH3/k0ixYsICcnZ1p+/btdOnSJerfvz81a9aMiouLuTR9+/alkJAQOn36NMXHx1NAQABFR0dz5+VyOXl5edGwYcPo6tWrtHHjRrK1taWVK1carauh0OhyuZySkpLo2rVrVFpaasolICIijUZD+fn5VFBQQCkpKXTt2jXKz88ntVpNe/fupXnz5tGPP/5Id+7cMblsc8nLyyOxWMx7CGwmTMqL/P+1d/cxTV5fHMAPFFtAKAUZLSAgDgQVNlEmqzL3B2RkIxluizrDCGPLFh0GiAuiMW7xD0YzsyyRqOS3F12ig2DiplsEQgB1LAiCvIgYxoYbhFhwYFuYMl76/f2x8LhnvITKW/v0fJKbyHNPH+49toeb0ss1Gm2mdvCRCty4WU970iMVLFrYJCYm4p133hFde/3115GcnAzgnx/4Go0Gx44dE/oNBgMUCgUKCwsBAG1tbSAi3LhxQ4gpKSmBg4MDenp6AAAnT56Ep6cn/v77byEmJycHYWFhsx7rTAUqLCwMzs7OWL58OWJjY1FaWgqz2Tyr+zY2NmLHjh1Ys2YN1q1bB09PTyxfvhxr1qxBUlISZDKZ8H08PDzw5ZdfCrkZGRmB0WjE4OAg2tvbcfPmTZSVleHzzz/HZ599hrS0NGRmZuLnn3+e9XgAYGxsDGFhYUv+JOTGbapmNBptpnbwwoYbN+tpi7Kwyc3NRVBQENrb2wEATU1N8PHxwdmzZwEAv/32G4gIjY2Nosdt27YNGRkZAICvvvoKKpVK1D86OgqZTIYLFy4AAFJSUpCUlCSKqaysBBFhYGBgyrENDw/DaDQKrbu7e9bJk8lk2LlzJ8bHx2ec/+HDh+Hi4mLxf05GRga2b9+OLVu2ICQkBOHh4XB3d4ezs7NoITTR5HI5du7cia6uLjx48AA9PT3o6OjA3bt3cenSJRQVFaG0tBQFBQUoKCjArl27lvwJyI3bdG3iHRtrrB1zqRvcuHFb2LYoh2AePHiQTCYThYeHk0wmo/HxccrNzaXk5GQiIuHvk6jVatHj1Gq10KfX68nHx0fU7+TkRF5eXqKY4ODgSfeY6JvqLKS8vDw6evSoJdMRjI+PU3FxMQGgo0eP0tq1a4mIaGRkhIaHh+mvv/4inU5Hx48ff6L7W/q4kZERKi4upqqqKnJ3d6ehoSFhi7rRaCQA5OjoyLuQmM2w1toxl7rBGLNOFi1siouL6dy5c/Ttt9/S+vXrqampibKyssjPz49SU1MXaoyzcujQIdq/f7/wtclkooCAAIvucf78eSopKaG0tDQaGhqitrY26u/vp8HBwSU5guH+/ft0//79Kft4UcNsibXWjvmoG4wx62LRwiY7O5sOHjxIb775JhERRUZG0h9//EF5eXmUmppKGo2GiP45h8nX11d4XG9vL23YsIGIiDQazaSTtcfGxmhgYEB4vEajmbSQmPh6Iua/FAoFKRQKS6YzpaGhIcrPz5/zfRhjj1lr7ZivusEYsx4Wbfd++PAhOTqKHyKTyYSzeYKDg0mj0VBFRYXQbzKZqLa2lrRaLRERabVaMhgM1NDQIMRUVlaS2WymmJgYIebatWs0OjoqxJSXl1NYWNiUv4ZijFk3rh2MsUVjyQdyUlNT4e/vL2zZvHDhAry9vXHgwAEhRqfTQaVS4eLFi2hpaUFSUtKUWzajoqJQW1uL6upqhIaGirZsGgwGqNVqpKSkoLW1FUVFRXB1dZ237d7cuHFbvGY0Gm2mdnDd4MbNetqi7IoymUzIzMxEYGAgnJ2dsXr1ahw+fFi0tdJsNuPIkSNQq9VQKBSIi4sTdkJM6O/vx+7du+Hm5galUom0tDQMDg6KYpqbmxEbGwuFQgF/f3/odDqLJmYwGJb8P4UbN24Eg8FgM7WD6wY3btbTDAaDRT/3J0j2EMzOzk56+umnl3oYjNm97u5uWrly5VIPY1a4bjBmPZ60dkj2EEwvLy8iIurq6uLTev9jYudHd3e3zZy6vFg4NzOzJD8AaHBwkPz8/BZpdHPHdWNm/PqYHudmepbmZq61Q7ILm4kPKnp4ePCTbBpKpZJzMw3Ozcxmmx9bWxxw3Zgdfn1Mj3MzPUtyM5fawad7M8YYY0wyeGHDGGOMMcmQ7MJGoVDQxx9/zH98awqcm+lxbmYm9fxIfX5zxfmZHudmeoudG8nuimKMMcaY/ZHsOzaMMcYYsz+8sGGMMcaYZPDChjHGGGOSwQsbxhhjjEmGJBc2J06coFWrVpGzszPFxMRQXV3dUg9p3uXl5dFzzz1H7u7u5OPjQ9u3b6f29nZRzPDwMKWnp9OKFSvIzc2N3njjDert7RXFdHV1UWJiIrm6upKPjw9lZ2fT2NiYKObKlSu0ceNGUigUFBISQmfOnFno6c0rnU5HDg4OlJWVJVyz59z09PTQW2+9RStWrCAXFxeKjIyk+vp6oR8AffTRR+Tr60suLi4UHx9PHR0donsMDAxQcnIyKZVKUqlU9O6779LQ0JAopqWlhV544QVydnamgIAA+vTTTxdlfnMh9drBdWP2uG5MZjO144lOmLJiRUVFkMvl+Prrr3H79m289957UKlU6O3tXeqhzauEhAScPn0ara2taGpqwiuvvILAwEAMDQ0JMXv27EFAQAAqKipQX1+P559/Hlu2bBH6x8bGEBERgfj4eDQ2NuLy5cvw9vbGoUOHhJjOzk64urpi//79aGtrQ35+PmQyGUpLSxd1vk+qrq4Oq1atwjPPPIPMzEzhur3mZmBgAEFBQXj77bdRW1uLzs5OlJWV4ddffxVidDodPDw88P3336O5uRmvvvrqlKdsP/vss7h+/Tp++uknhISEiE7ZNhqNUKvVSE5ORmtrKwoLC+Hi4jLrU7aXgj3UDq4bs8N1YzJbqh2SW9hs3rwZ6enpwtfj4+Pw8/NDXl7eEo5q4fX19YGIcPXqVQD/nFK8bNkynD9/Xoi5c+cOiAg1NTUAgMuXL8PR0RF6vV6IOXXqFJRKpXDq8oEDB7B+/XrR99q1axcSEhIWekpzNjg4iNDQUJSXl+PFF18UCpQ95yYnJwexsbHT9pvNZmg0Ghw7dky4ZjAYoFAoUFhYCABoa2sDEeHGjRtCTElJCRwcHNDT0wMAOHnyJDw9PUWnd+fk5CAsLGy+pzRv7LF2cN2YjOvG1GypdkjqV1EjIyPU0NBA8fHxwjVHR0eKj4+nmpqaJRzZwjMajUT0+BC/hoYGGh0dFeUiPDycAgMDhVzU1NRQZGQkqdVqISYhIYFMJhPdvn1biPn3PSZibCGf6enplJiYOGn89pybS5cuUXR0NO3YsYN8fHwoKiqKvvjiC6H/7t27pNfrRfPy8PCgmJgYUW5UKhVFR0cLMfHx8eTo6Ei1tbVCzLZt20gulwsxCQkJ1N7eTg8ePFjoaVrMXmsH143JuG5MzZZqh6QWNn/++SeNj4+LnlRERGq1mvR6/RKNauGZzWbKysqirVu3UkREBBER6fV6ksvlpFKpRLH/zoVer58yVxN9M8WYTCZ69OjRQkxnXhQVFdHNmzcpLy9vUp8956azs5NOnTpFoaGhVFZWRnv37qWMjAz65ptviOjx3GZ6Den1evLx8RH1Ozk5kZeXl0X5syb2WDu4bkzGdWN6tlQ7JHu6tz1JT0+n1tZWqq6uXuqhWIXu7m7KzMyk8vJycnZ2XurhWBWz2UzR0dH0ySefEBFRVFQUtba2UkFBAaWmpi7x6Nhi4rohxnVjZrZUOyT1jo23tzfJZLJJn1Lv7e0ljUazRKNaWPv27aMff/yRqqqqaOXKlcJ1jUZDIyMjZDAYRPH/zoVGo5kyVxN9M8UolUpycXGZ7+nMi4aGBurr66ONGzeSk5MTOTk50dWrV+n48ePk5OREarXabnPj6+tL69atE11bu3YtdXV1EdHjuc30GtJoNNTX1yfqHxsbo4GBAYvyZ03srXZw3ZiM68bMbKl2SGphI5fLadOmTVRRUSFcM5vNVFFRQVqtdglHNv8A0L59++i7776jyspKCg4OFvVv2rSJli1bJspFe3s7dXV1CbnQarV069Yt0ROtvLyclEql8ATWarWie0zEWHM+4+Li6NatW9TU1CS06OhoSk5OFv5tr7nZunXrpO29v/zyCwUFBRERUXBwMGk0GtG8TCYT1dbWinJjMBiooaFBiKmsrCSz2UwxMTFCzLVr12h0dFSIKS8vp7CwMPL09Fyw+T0pe6kdXDemx3VjZjZVO2b9MWMbUVRUBIVCgTNnzqCtrQ3vv/8+VCqV6FPqUrB37154eHjgypUruHfvntAePnwoxOzZsweBgYGorKxEfX09tFottFqt0D+xNfGll15CU1MTSktL8dRTT025NTE7Oxt37tzBiRMnbGJr4n/9e3cDYL+5qaurg5OTE3Jzc9HR0YFz587B1dUVZ8+eFWJ0Oh1UKhUuXryIlpYWJCUlTbllMyoqCrW1taiurkZoaKhoy6bBYIBarUZKSgpaW1tRVFQEV1dXq9/uLfXawXXDMlw3HrOl2iG5hQ0A5OfnIzAwEHK5HJs3b8b169eXekjzjoimbKdPnxZiHj16hA8++ACenp5wdXXFa6+9hnv37onu8/vvv+Pll1+Gi4sLvL298eGHH2J0dFQUU1VVhQ0bNkAul2P16tWi72Er/lug7Dk3P/zwAyIiIqBQKBAeHo7//e9/on6z2YwjR45ArVZDoVAgLi4O7e3topj+/n7s3r0bbm5uUCqVSEtLw+DgoCimubkZsbGxUCgU8Pf3h06nW/C5zZXUawfXDctw3RCzldrhAACzfi+KMcYYY8yKSeozNowxxhizb7ywYYwxxphk8MKGMcYYY5LBCxvGGGOMSQYvbBhjjDEmGbywYYwxxphk8MKGMcYYY5LBCxvGGGOMSQYvbBhjjDEmGbywYYwxxphk8MKGMcYYY5LBCxvGGGOMScb/AS/6kfAcvhOlAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["mask_test_a: (2727, 6330)\n","mask_test_b: (5454, 6330)\n","-\n","mask_train_1: (8181, 6330)\n","labels_train_1: (8181, 6330)\n","-\n","mask_train_2a: (5374, 9506)\n","labels_train_2a: (5374, 9506)\n","-\n","mask_train_2b: (9456, 9506)\n","labels_train_2b: (9456, 9506)\n","-\n","mask_train_3: (7606, 5249)\n","labels_train_3: (7606, 5249)\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 4 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["10it [00:00, 10.27it/s]\n"]},{"name":"stdout","output_type":"stream","text":["volume_train_1: (8181, 6330, 10), float32\n"]},{"name":"stderr","output_type":"stream","text":["10it [00:02,  4.76it/s]\n"]},{"name":"stdout","output_type":"stream","text":["volume_train_2a: (5374, 9506, 10), float32\n"]},{"name":"stderr","output_type":"stream","text":["10it [00:02,  4.39it/s]\n"]},{"name":"stdout","output_type":"stream","text":["volume_train_2b: (9456, 9506, 10), float32\n"]},{"name":"stderr","output_type":"stream","text":["10it [00:00, 13.75it/s]\n"]},{"name":"stdout","output_type":"stream","text":["volume_train_3: (7606, 5249, 10), float32\n"]}],"source":["pytorch_lightning.seed_everything(seed=42)\n","torch.set_float32_matmul_precision('high')\n","\n","\n","masks = load_mask(split=\"train\", index=1)\n","labels = load_labels(split=\"train\", index=1)\n","\n","fig, (ax1, ax2) = plt.subplots(1, 2)\n","ax1.set_title(\"mask.png\")\n","ax1.imshow(masks, cmap='gray')\n","ax2.set_title(\"inklabels.png\")\n","ax2.imshow(labels, cmap='gray')\n","plt.show()\n","\n","mask_test_a = load_mask(split=\"test\", index=\"a\")\n","mask_test_b = load_mask(split=\"test\", index=\"b\")\n","\n","mask_train_1 = load_mask(split=\"train\", index=\"1\")\n","labels_train_1 = load_labels(split=\"train\", index=\"1\")\n","\n","mask_train_2a = load_mask(split=\"train\", index=\"2a\")\n","labels_train_2a = load_labels(split=\"train\", index=\"2a\")\n","\n","mask_train_2b = load_mask(split=\"train\", index=\"2b\")\n","labels_train_2b = load_labels(split=\"train\", index=\"2b\")\n","\n","mask_train_3 = load_mask(split=\"train\", index=\"3\")\n","labels_train_3 = load_labels(split=\"train\", index=\"3\")\n","\n","print(f\"mask_test_a: {mask_test_a.shape}\")\n","print(f\"mask_test_b: {mask_test_b.shape}\")\n","print(\"-\")\n","print(f\"mask_train_1: {mask_train_1.shape}\")\n","print(f\"labels_train_1: {labels_train_1.shape}\")\n","print(\"-\")\n","print(f\"mask_train_2a: {mask_train_2a.shape}\")\n","print(f\"labels_train_2a: {labels_train_2a.shape}\")\n","print(\"-\")\n","print(f\"mask_train_2b: {mask_train_2b.shape}\")\n","print(f\"labels_train_2b: {labels_train_2b.shape}\")\n","print(\"-\")\n","print(f\"mask_train_3: {mask_train_3.shape}\")\n","print(f\"labels_train_3: {labels_train_3.shape}\")\n","\n","fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4)\n","\n","ax1.set_title(\"labels_train_1\")\n","ax1.imshow(labels_train_1, cmap='gray')\n","\n","ax2.set_title(\"labels_train_2a\")\n","ax2.imshow(labels_train_2a, cmap='gray')\n","\n","ax3.set_title(\"labels_train_2b\")\n","ax3.imshow(labels_train_2b, cmap='gray')\n","\n","ax4.set_title(\"labels_train_3\")\n","ax4.imshow(labels_train_3, cmap='gray')\n","plt.tight_layout()\n","plt.show()\n","\n","volume_train_1 = load_volume(split=\"train\", index=1)\n","print(f\"volume_train_1: {volume_train_1.shape}, {volume_train_1.dtype}\")\n","\n","volume_train_2a = load_volume(split=\"train\", index=\"2a\")\n","print(f\"volume_train_2a: {volume_train_2a.shape}, {volume_train_2a.dtype}\")\n","\n","volume_train_2b = load_volume(split=\"train\", index=\"2b\")\n","print(f\"volume_train_2b: {volume_train_2b.shape}, {volume_train_2b.dtype}\")\n","\n","volume_train_3 = load_volume(split=\"train\", index=3)\n","print(f\"volume_train_3: {volume_train_3.shape}, {volume_train_3.dtype}\")\n","\n","# volume = np.concatenate([volume_train_1, volume_train_2, volume_train_3], axis=1)\n","# volume = np.concatenate([volume_train_1, volume_train_2], axis=1)\n","# print(f\"total volume: {volume.shape}\")"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["10it [00:00, 10.32it/s]\n"]}],"source":["sample_data_name = \"1\"\n","sample_volume = load_volume(\"train\", sample_data_name)\n","sample_mask = load_mask(\"train\", sample_data_name)\n","sample_label = load_labels(\"train\", sample_data_name)\n","sample_locations = generate_locations_ds(sample_volume, sample_mask, sample_label, skip_zero=True)"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 800x600 with 2 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","text/plain":["<Figure 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StssbGxuvbaa1VaWuqc1rhxY0nSmTNnXNoOGzZMly9f1v/9v//XZfqSJUsUFhamoUOHSpLzypnf/e53Lu1eeuklt+usuFKn8pVNL7zwgtvrqI9hw4ZV+XzPP/+8JNV4h8GazJs3T6dOndKDDz6oS5cuXTV/06ZNWrt2rUfrrOq9MgxDv/3tb+tUoycSExM1YMAA/f73v9c333xz1fx//vOfHq+zqmU++eQTvfvuuxo8eLDCw+kqAwDgbVwxBQAAvKJLly4aMGCAevfurYSEBO3du1dvv/22pk2b5mzTu3dvSdIjjzyi1NRURUREaMyYMRo+fLjuuOMO/epXv9KxY8fUo0cPbdq0SWvWrNH06dPVoUMH5/KjR4/WCy+8oFOnTqlfv37avn27/v73v0ty7+txNptN/fv316JFi3Tx4kX94Ac/0KZNm3T06FEvvCtX69GjhyZMmKBXX31VZ86c0e23366PPvpIr732mkaOHKk77rijTuu95557lJ+fr2effVYff/yx7r33XiUnJ+vUqVPasGGDNm/erBUrVni0zs6dO6tDhw56/PHH9fXXX8tms+l//ud/qhwzyhuysrJ02223qVu3bpo8ebLat2+vwsJC5ebm6quvvtInn3zi0fruueceNWzYULfccosSExN18OBBvfrqq2rUqJEWLlzopVcBAACuRDAFAAC84pFHHtG7776rTZs2qbS0VMnJyXrmmWc0c+ZMZ5tRo0bp4Ycf1sqVK/X666/LMAyNGTNG4eHhevfddzV79my9+eabWrZsmdq2bavnnnvOebe6Cn/6059kt9v1xhtvaPXq1UpJSdGbb76pTp06KSYmxq1aV6xYoYcfflhZWVkyDEODBw/W+++/r5YtW5r6nlTnD3/4g9q3b6/s7GytXr1adrtdGRkZmjNnTr3W+8wzz2jgwIF68cUX9fLLL+v06dO65ppr1K9fP61Zs0Z33XWXR+tr0KCB3nvvPT3yyCPKzMxUTEyMfvzjH2vatGnq0aNHvWp1R5cuXbR3717NmzdP2dnZOnXqlBITE9WrVy+Xr2G6a+TIkVq+fLmef/55lZSUqHnz5ho1apTmzJlj2nhVAACgZmGGuyNyAgAABIj9+/erV69eev311zV27FirywEAAEA1+OI8AAAIaN9///1V01544QWFh4erf//+FlQEAAAAd/FVPgAAENAWLVqkvLw83XHHHYqMjNT777+v999/X1OmTFHr1q2tLg8AAAA14Kt8AAAgoOXk5GjevHk6ePCgzp07pzZt2mjcuHH61a9+pchIPoMDAADwZwRTAAAAAAAAsARjTAEAAAAAAMASBFMAAAAAAACwRNAOvFBeXq6TJ0+qSZMmCgsLs7ocAAAAAACAkGAYhs6ePauWLVsqPLzma6KCNpg6efIkd+IBAAAAAACwyIkTJ9SqVasa2wRtMNWkSRNJ/34TbDabxdUAAAAAAACEhpKSErVu3dqZzdQkaIOpiq/v2Ww2gikAAAAAAAAfc2doJQY/BwAAAAAAgCUIpgAAAAAAAGAJgikAAAAAAABYgmAKAAAAAAAAliCYAgAAAAAAgCUIpgAAAAAAAGAJrwdTCxcuVFhYmKZPn+6cduHCBaWnp6tp06aKjY3V6NGjVVhY6LLc8ePHlZaWpkaNGikxMVEzZ87UpUuXvF0uAAAAAAAAfMSrwdSePXv0+9//Xt27d3eZ/uijj+q9997TW2+9pe3bt+vkyZMaNWqUc/7ly5eVlpamsrIy7dy5U6+99pqys7M1e/Zsb5YLAAAAAAAAH/JaMHXu3DmNHTtW//3f/61rrrnGOb24uFhLly7V888/r4EDB6p3795atmyZdu7cqV27dkmSNm3apIMHD+r1119Xz549NXToUC1YsEBZWVkqKyvzVskAAAAAAADwIa8FU+np6UpLS1NKSorL9Ly8PF28eNFleufOndWmTRvl5uZKknJzc9WtWzclJSU526SmpqqkpEQHDhyo8vlKS0tVUlLi8gAAAAAAAID/ivTGSleuXKl9+/Zpz549V80rKChQVFSU4uPjXaYnJSWpoKDA2ebKUKpifsW8qmRmZmrevHkmVA8AAAAAAABfMP2KqRMnTugXv/iFli9frpiYGLNXX62MjAwVFxc7HydOnPDZcwMAAP/RdtY6q0sAAACAm0wPpvLy8lRUVKQbb7xRkZGRioyM1Pbt2/Xiiy8qMjJSSUlJKisr05kzZ1yWKywslN1ulyTZ7far7tJX8XtFm8qio6Nls9lcHgAAAAAAAPBfpgdTgwYNUn5+vvbv3+989OnTR2PHjnX+3KBBA23evNm5zKFDh3T8+HE5HA5JksPhUH5+voqKipxtcnJyZLPZ1KVLF7NLBgAAqBZXYAEAAHiP6WNMNWnSRF27dnWZ1rhxYzVt2tQ5fdKkSZoxY4YSEhJks9n08MMPy+FwqF+/fpKkwYMHq0uXLho3bpwWLVqkgoICPfXUU0pPT1d0dLTZJQMAAAAAAMACXrsrX02WLFmiH/3oRxo9erT69+8vu92uVatWOedHRERo7dq1ioiIkMPh0P3336/x48dr/vz5VpQLAEDQ8tXVQFc+jy+es6rnCPUrn0L99QMAAP/klbvyVbZt2zaX32NiYpSVlaWsrKxql0lOTtb69eu9XBkAAMGp7ax1OrYwLWDWG+p4XwEAQKiy5IopAAAQuELxyhszX3Movn8AAADVIZgCAAAIIHUJtgjDAACAvyKYAgAACHEEVwAAwCoEUwAAIKQRygAAAFiHYAoAAFSL0AYAAADeRDAFAADgJk+DOl8Ge4SIAAAgEBFMAQAASQQbZqrqveT9BQAAuBrBFAAA8HvciQ4AACA4EUwBAACfIzQCAACARDAFAEBAC/SAJ9Drr+Cvr8Nf6wIAAKhAMAUAQJCoKYS4cl7Fz4QW3sX7CwAAUDuCKQAAYKnKAQ6BTt2Z9d7xfwAAAHyFYAoAgADlT+GBP9XiS/UJ1bhyDQAAgGAKAICgR/BRO94jAAAAaxBMAQAAUxHyWI//AwAAECgIpgAACHHuhhi+CDtCLVAJhjGhQu3/DAAAmItgCgCAAEcwYA1/HSPK3+oBAACoCcEUAABBqr4BhRnLVzzgH8z6/+D/FAAAmIVgCgAAuCB0qFqovy+h/voBAIB3EEwBABCCvBEyWB1cWP38FfylDgAAgEBAMAUAQBDzNCTx9tf//Cm0qWst3nwNZg5E743/S3/6/wMAAMGBYAoAAAQkQhL3+DqcBAAA8ATBFAAACFhmhyhXro+A5t94HwAAgDcRTAEAgKBWl2AlkL6SCAAAEMhMD6Zefvllde/eXTabTTabTQ6HQ++//75z/oABAxQWFuby+PnPf+6yjuPHjystLU2NGjVSYmKiZs6cqUuXLpldKgAAXsVXqHyvPu+h1e+/1c8PAABgBdODqVatWmnhwoXKy8vT3r17NXDgQI0YMUIHDhxwtpk8ebK++eYb52PRokXOeZcvX1ZaWprKysq0c+dOvfbaa8rOztbs2bPNLhUAgIDSdta6el3J46078Xk7ULHy6iVvrdtfQyh/rQsAAAQv04Op4cOHa9iwYerYsaOuu+46Pfvss4qNjdWuXbucbRo1aiS73e582Gw257xNmzbp4MGDev3119WzZ08NHTpUCxYsUFZWlsrKyswuFwAABBjCE//kja9MAgCA4OfVMaYuX76slStX6vz583I4HM7py5cvV7NmzdS1a1dlZGTou+++c87Lzc1Vt27dlJSU5JyWmpqqkpISl6uuAAAIBf584m5mbf78Ot1lxWuw+quLVa2Dr7ACAABPRHpjpfn5+XI4HLpw4YJiY2O1evVqdenSRZJ03333KTk5WS1bttSnn36qJ598UocOHdKqVaskSQUFBS6hlCTn7wUFBdU+Z2lpqUpLS52/l5SUmP2yAABAgCMEAQAA8C9eCaY6deqk/fv3q7i4WG+//bYmTJig7du3q0uXLpoyZYqzXbdu3dSiRQsNGjRIR44cUYcOHer8nJmZmZo3b54Z5QMA4BVtZ63TsYVpXlu3P6/PLL6oy1tjcXna3lvbiq9UvOZAfx0AAMC7vPJVvqioKF177bXq3bu3MjMz1aNHD/32t7+tsm3fvn0lSYcPH5Yk2e12FRYWurSp+N1ut1f7nBkZGSouLnY+Tpw4YcZLAQDAcv4aEtXErJoD8bWbxVevveJ5AjX0AwAAgc2rY0xVKC8vd/ma3ZX2798vSWrRooUkyeFwKD8/X0VFRc42OTk5stlszq8DViU6Olo2m83lAQAAglMwBxzBPkZToNULAAC8y/RgKiMjQzt27NCxY8eUn5+vjIwMbdu2TWPHjtWRI0e0YMEC5eXl6dixY3r33Xc1fvx49e/fX927d5ckDR48WF26dNG4ceP0ySefaOPGjXrqqaeUnp6u6Ohos8sFAMDnrDwxD9XnDkb+/H56uzZ/fu0AAMAzpgdTRUVFGj9+vDp16qRBgwZpz5492rhxo+68805FRUXpgw8+0ODBg9W5c2c99thjGj16tN577z3n8hEREVq7dq0iIiLkcDh0//33a/z48Zo/f77ZpQIAAPg9fxjzytfrAwAAocP0wc+XLl1a7bzWrVtr+/btta4jOTlZ69evN7MsAABgokALIrhSrGru1nZlO39+PQAAIPD4ZIwpAABCWV1P5EM5APD1wN8AAACwBsEUAAAmIeQwV+X3k/c3sHny/8f/NQAAoYNgCgAA1BoEBMI4R8Gs7ax1Af1+1bf2QH7tAACgZgRTAAD4ACfWqAu2GwAAEOwIpgAAqAdPgwMzggbCCviKFdsa2zcAAKGFYAoAgDrw1slzoH9lC3AX2zkAAJAIpgAAAAAAAGARgikAQMgK1is2zH5dwfo+WYH38t94HwAAQAWCKQAA4BfqE1YQdAAAAAQmgikAQEghwEAgC+btt7rXFsyvGQAAEEwBAOBV3hwkHQAAAAh0BFMAAHjAqkCIIKpueN+8h/cWAACYgWAKAIBaePsEvO2sdZzkI6Sx/QMAELoIpgAAqCdOqoF/446QAADAUwRTAAB4QX1PqDkhBwAAQCggmAIAwA0ERQAAAID5CKYAALAQgRcAAABCGcEUAAAAAAAALEEwBQAAAAAAAEsQTAEAUEd8DQ/wLf7mAAAIPgRTAABcwVcnvpxgAwAAAARTAABYhnAKcB9/LwAABCeCKQAAAAAAAFiCYAoAEDS4ogIAAAAILARTAACYjIAM8D7+zgAACA6mB1Mvv/yyunfvLpvNJpvNJofDoffff985/8KFC0pPT1fTpk0VGxur0aNHq7Cw0GUdx48fV1pamho1aqTExETNnDlTly5dMrtUAABqxckvAAAA4D2mB1OtWrXSwoULlZeXp71792rgwIEaMWKEDhw4IEl69NFH9d577+mtt97S9u3bdfLkSY0aNcq5/OXLl5WWlqaysjLt3LlTr732mrKzszV79myzSwUAwHQEWQAAAID7TA+mhg8frmHDhqljx4667rrr9Oyzzyo2Nla7du1ScXGxli5dqueff14DBw5U7969tWzZMu3cuVO7du2SJG3atEkHDx7U66+/rp49e2ro0KFasGCBsrKyVFZWZna5AAAA8ENmhrwExgAA+C+vjjF1+fJlrVy5UufPn5fD4VBeXp4uXryolJQUZ5vOnTurTZs2ys3NlSTl5uaqW7duSkpKcrZJTU1VSUmJ86qrqpSWlqqkpMTlAQCAr3DiCwAAAHjOK8FUfn6+YmNjFR0drZ///OdavXq1unTpooKCAkVFRSk+Pt6lfVJSkgoKCiRJBQUFLqFUxfyKedXJzMxUXFyc89G6dWtzXxQAIKgQJAEAAADW80ow1alTJ+3fv1+7d+/W1KlTNWHCBB08eNAbT+WUkZGh4uJi5+PEiRNefT4AQOAilAIAAAD8g1eCqaioKF177bXq3bu3MjMz1aNHD/32t7+V3W5XWVmZzpw549K+sLBQdrtdkmS326+6S1/F7xVtqhIdHe28E2DFAwAATxBYAYGHv1sAAAKbV8eYqlBeXq7S0lL17t1bDRo00ObNm53zDh06pOPHj8vhcEiSHA6H8vPzVVRU5GyTk5Mjm82mLl26+KJcAAAAmITgCAAA1CTS7BVmZGRo6NChatOmjc6ePasVK1Zo27Zt2rhxo+Li4jRp0iTNmDFDCQkJstlsevjhh+VwONSvXz9J0uDBg9WlSxeNGzdOixYtUkFBgZ566imlp6crOjra7HIBACGEE2QAAADAv5h+xVRRUZHGjx+vTp06adCgQdqzZ482btyoO++8U5K0ZMkS/ehHP9Lo0aPVv39/2e12rVq1yrl8RESE1q5dq4iICDkcDt1///0aP3685s+fb3apAAAACGCVw+YrfyeIBgAgMJh+xdTSpUtrnB8TE6OsrCxlZWVV2yY5OVnr1683uzQAAACEAEIpAAACh0/GmAIAIJBwUgv4L/4+AQAILgRTAACIk10AAADACgRTAAAA8AsExAAAhB6CKQCAX6vpRNWbJ7GcIAOBj79jAAD8H8EUAABu4iQX8H/8nQIAEFgIpgAApjDzZJATSwAAACA0EEwBAPwSQRcAAAAQ/AimAAABr3Lw1HbWOsIoIMTwNw8AQGAimAIABDRORgEAAIDARTAFAPA6wiMAgYL9FQAAvkUwBQDwG/52Quhv9QAAAADBhmAKABASCJkAAAAA/0MwBQAIOIRMAAAAQHAgmAIAhDRCLiA08bcPAIB/IJgCAFiGE0MAAAAgtBFMAQD8Vl2CK1+EXQRqgH/x5G+Sv18AAPwLwRQAwKvcPQnkZBGAt7GfAQDA/xBMAQBCHierAAAAgDUIpgAAAAAAAGCJSKsLAADAW7gSCgAAAPBvXDEFADCdFYEQIRQAAAAQeAimAACWI1QC4G3sZwAA8E8EUwAAAAh5bWetc4ZXNYVYBFwAAJiLYAoAAAAAAACWMD2YyszM1E033aQmTZooMTFRI0eO1KFDh1zaDBgwQGFhYS6Pn//85y5tjh8/rrS0NDVq1EiJiYmaOXOmLl26ZHa5AIAAxVULAAAAQOAz/a5827dvV3p6um666SZdunRJv/zlLzV48GAdPHhQjRs3drabPHmy5s+f7/y9UaNGzp8vX76stLQ02e127dy5U998843Gjx+vBg0a6Ne//rXZJQMAAAAAAMACpgdTGzZscPk9OztbiYmJysvLU//+/Z3TGzVqJLvdXuU6Nm3apIMHD+qDDz5QUlKSevbsqQULFujJJ5/U3LlzFRUVZXbZAAA/VnF11LGFaRZXAgAAAMBMXh9jqri4WJKUkJDgMn358uVq1qyZunbtqoyMDH333XfOebm5uerWrZuSkpKc01JTU1VSUqIDBw54u2QAAAAECb72CwCAfzP9iqkrlZeXa/r06br11lvVtWtX5/T77rtPycnJatmypT799FM9+eSTOnTokFatWiVJKigocAmlJDl/LygoqPK5SktLVVpa6vy9pKTE7JcDAACAENR21jqu2AQAwEu8Gkylp6frs88+01//+leX6VOmTHH+3K1bN7Vo0UKDBg3SkSNH1KFDhzo9V2ZmpubNm1evegEA3lf5BI+rGQD4KwIpAAC8z2tf5Zs2bZrWrl2rrVu3qlWrVjW27du3ryTp8OHDkiS73a7CwkKXNhW/VzcuVUZGhoqLi52PEydO1PclAAAAAAAAwItMD6YMw9C0adO0evVqbdmyRe3atat1mf3790uSWrRoIUlyOBzKz89XUVGRs01OTo5sNpu6dOlS5Tqio6Nls9lcHgAA/8WVUgD8WeV9FPssAAC8w/RgKj09Xa+//rpWrFihJk2aqKCgQAUFBfr+++8lSUeOHNGCBQuUl5enY8eO6d1339X48ePVv39/de/eXZI0ePBgdenSRePGjdMnn3yijRs36qmnnlJ6erqio6PNLhkAYJK2s9a5nLzV5USOkz8AvlTffU7F8uy7AACoG9ODqZdfflnFxcUaMGCAWrRo4Xy8+eabkqSoqCh98MEHGjx4sDp37qzHHntMo0eP1nvvvedcR0REhNauXauIiAg5HA7df//9Gj9+vObPn292uQAAAAAAALCI6YOfG4ZR4/zWrVtr+/btta4nOTlZ69evN6ssAIAHGPAXAGrGfhIAAHN4bfBzAEBwqfw1PQAIBTXt92rbJ7LPBACgdgRTAABJnp1AcbIFIJCxDwMAwH8QTAEAfK4+J4WcUAIAAADBg2AKAAAAIYmgGwAA6xFMAQBqxckbAPxH5X0iX4UGAKDuCKYAAHXCyRUAAACA+iKYAgAAAAAAgCUIpgAA9earq6e4SgtAoGB/BQCAewimAAAu6jN2CgAAAAB4gmAKAFAtd0IpgisAgc6s/VhV62EfCQBAzQimAAAAAAAAYAmCKQAAAAAAAFgi0uoCAADBibGqAISiuuzr2s5ap2ML07xQDQAA/o8rpgAAAAAAAGAJgikA8HNcaQQAwYX9OgAA/0EwBQAhhJMhAAAAAP6EYAoAQhQhFQAAAACrEUwBQACq6+C6AAD/x/4aABBKCKYAIARwkgMAgY99OQAgGBFMAQAAAH6icvhEGAUACHaRVhcAAPAed09oqmrnzrKcMAGA+9hnAgBwNa6YAoAgUZ8QCgDgXex7AQCoGsEUAAQBX57wcHIFIFT5ev/H/hYAEAoIpgAAAAAAAGAJgikAAADAAlwRBQCAF4KpzMxM3XTTTWrSpIkSExM1cuRIHTp0yKXNhQsXlJ6erqZNmyo2NlajR49WYWGhS5vjx48rLS1NjRo1UmJiombOnKlLly6ZXS4A+Jw/n4j4c20AAAAAgo/pwdT27duVnp6uXbt2KScnRxcvXtTgwYN1/vx5Z5tHH31U7733nt566y1t375dJ0+e1KhRo5zzL1++rLS0NJWVlWnnzp167bXXlJ2drdmzZ5tdLgCYxpuhjtnrJoACAAAA4A9MD6Y2bNigiRMn6oYbblCPHj2UnZ2t48ePKy8vT5JUXFyspUuX6vnnn9fAgQPVu3dvLVu2TDt37tSuXbskSZs2bdLBgwf1+uuvq2fPnho6dKgWLFigrKwslZWVmV0yANRLfUIeAiIAgC9x3AEA+BuvjzFVXFwsSUpISJAk5eXl6eLFi0pJSXG26dy5s9q0aaPc3FxJUm5urrp166akpCRnm9TUVJWUlOjAgQNVPk9paalKSkpcHgAQajjhAABwLAAABBKvBlPl5eWaPn26br31VnXt2lWSVFBQoKioKMXHx7u0TUpKUkFBgbPNlaFUxfyKeVXJzMxUXFyc89G6dWuTXw0A+JeKE4/KJyDVTa9tPQAA67AvBgCEKq8GU+np6frss8+0cuVKbz6NJCkjI0PFxcXOx4kTJ7z+nAAAAEBdVBVEeWM8QQIvAIC/81owNW3aNK1du1Zbt25Vq1atnNPtdrvKysp05swZl/aFhYWy2+3ONpXv0lfxe0WbyqKjo2Wz2VweABCIOIkAAHiC4wYAIJCZHkwZhqFp06Zp9erV2rJli9q1a+cyv3fv3mrQoIE2b97snHbo0CEdP35cDodDkuRwOJSfn6+ioiJnm5ycHNlsNnXp0sXskgEAAAAAAGCBSLNXmJ6erhUrVmjNmjVq0qSJc0youLg4NWzYUHFxcZo0aZJmzJihhIQE2Ww2Pfzww3I4HOrXr58kafDgwerSpYvGjRunRYsWqaCgQE899ZTS09MVHR1tdskAAAAAAACwgOnB1MsvvyxJGjBggMv0ZcuWaeLEiZKkJUuWKDw8XKNHj1ZpaalSU1P1u9/9ztk2IiJCa9eu1dSpU+VwONS4cWNNmDBB8+fPN7tcAAAAAAAAWMT0YMowjFrbxMTEKCsrS1lZWdW2SU5O1vr1680sDQD8VttZ63RsYZpp7QAAAAAgEHj1rnwAgP/wx8Fp/bEmAIC5atvXcywAAFiJYAoAAAAIMIRJAIBgYfpX+QAA3sXJCACEnqr2/RwPAADBgCumACBAcAICAAAAINgQTAEAAAB+jA8mAADBjGAKACzQdtY6TjQAAKYdCyrWw7EFABBoCKYAAAAALzIzLCJ4AgAEG4IpADBBdScKnp5AVG5f1xMQTlwAAO4w67gDAEBdEUwBgMno1AMA/A3HJgCAvyKYAgAAAEIIIRUAwJ8QTAFAHZndsedEAQAAAECoIZgCAD/EXfsAILT5wzHAH2oAAAQ/gikAcIO/ds79tS4AQGDheAIAsArBFAB4CZ18AAAAAKgZwRQA1MKTgIkwCgDgrzhGAQD8EcEUANSATjwAIJRxHAQAeBvBFAAAABBACIsAAMGEYAoAPMQJAQDAH1l1fOK4CACoD4IpALAYHXoAgNm8cWzheAUA8AaCKQDwgsqddzrzAAAAAHA1gikAqMSbIRIBFQDA37Wdtc6t41VVbTjOAQA8RTAFAKIjDQAIPRz7AAD+gGAKQMgwswNOZx4AAI6HAID6I5gCgCtc2cGu69cYAAAAAADuMT2Y2rFjh4YPH66WLVsqLCxM77zzjsv8iRMnKiwszOUxZMgQlzanT5/W2LFjZbPZFB8fr0mTJuncuXNmlwoAAAAAAAALmR5MnT9/Xj169FBWVla1bYYMGaJvvvnG+XjjjTdc5o8dO1YHDhxQTk6O1q5dqx07dmjKlClmlwoAAADg/+PmHwAAK0SavcKhQ4dq6NChNbaJjo6W3W6vct7nn3+uDRs2aM+ePerTp48k6aWXXtKwYcO0ePFitWzZ0uySAQAAAAAAYAFLxpjatm2bEhMT1alTJ02dOlWnTp1yzsvNzVV8fLwzlJKklJQUhYeHa/fu3VaUCwAAAOD/4+onAICZTL9iqjZDhgzRqFGj1K5dOx05ckS//OUvNXToUOXm5ioiIkIFBQVKTEx0LTIyUgkJCSooKKh2vaWlpSotLXX+XlJS4rXXAACeoAMPAAAAAFXz+RVTY8aM0V133aVu3bpp5MiRWrt2rfbs2aNt27bVa72ZmZmKi4tzPlq3bm1OwQCCHsERACDUeHLsq+txkuMrAMAdlnyV70rt27dXs2bNdPjwYUmS3W5XUVGRS5tLly7p9OnT1Y5LJUkZGRkqLi52Pk6cOOHVugEAAAAAAFA/lgdTX331lU6dOqUWLVpIkhwOh86cOaO8vDxnmy1btqi8vFx9+/atdj3R0dGy2WwuDwCho+2sdW5/MssnuAAAAADgH0wfY+rcuXPOq58k6ejRo9q/f78SEhKUkJCgefPmafTo0bLb7Tpy5IieeOIJXXvttUpNTZUkXX/99RoyZIgmT56sV155RRcvXtS0adM0ZswY7sgHoN7qE14RaAEAQlXFMbDyv+4sd2xhmtfqAgAEPtOvmNq7d6969eqlXr16SZJmzJihXr16afbs2YqIiNCnn36qu+66S9ddd50mTZqk3r1763//938VHR3tXMfy5cvVuXNnDRo0SMOGDdNtt92mV1991exSAQAAAAAAYCHTr5gaMGCADMOodv7GjRtrXUdCQoJWrFhhZlkAAAAAvIgriwEAdWH5GFMA4G10lAEAAADAPxFMAQAAADAVHwoBANxFMAUAAAAAAABLEEwBCEme3lUIAABUz5O79F3ZluMwAIBgCkBQ86TDS+cYAADzmXl85VgNAMGHYAoAAACA1xEqAQCqQjAFAAAAAAAASxBMAQhI7owRVdsns3xyCwCAf6juuM6xGgCCX6TVBQAAAAAILQROAIAKXDEFICTQAQYAwD9xjAaA0EYwBQAAAAAAAEsQTAEIOHyyCgBA8ON4DwChgWAKQECgcwoAQOiiHwAAwYtgCgAAAIBpCJEAAJ4gmAIAAAAAAIAlCKYABCU+rQUAIDC5ewznWA8AwYFgCgAAAAAAAJYgmAIQdPgEFQCAwHPl8buqnzm+A0BwIpgC4Ldq64BWNZ9OKwAAAAAEDoIpAAAAAAGFD6IAIHgQTAEAAAAAAMASBFMAAAAAAACwBMEUgIDCpfsAAIQ2+gIAEFwIpgAAAAAAAGAJgikAAAAAAABYwvRgaseOHRo+fLhatmypsLAwvfPOOy7zDcPQ7Nmz1aJFCzVs2FApKSn64osvXNqcPn1aY8eOlc1mU3x8vCZNmqRz586ZXSoAAAAAAAAsZHowdf78efXo0UNZWVlVzl+0aJFefPFFvfLKK9q9e7caN26s1NRUXbhwwdlm7NixOnDggHJycrR27Vrt2LFDU6ZMMbtUAAHAnXEkGGsCAADUhL4CAPivSLNXOHToUA0dOrTKeYZh6IUXXtBTTz2lESNGSJL+9Kc/KSkpSe+8847GjBmjzz//XBs2bNCePXvUp08fSdJLL72kYcOGafHixWrZsqXZJQMAAAAAAMACPh1j6ujRoyooKFBKSopzWlxcnPr27avc3FxJUm5uruLj452hlCSlpKQoPDxcu3fvrnbdpaWlKikpcXkAAAAAAADAf/k0mCooKJAkJSUluUxPSkpyzisoKFBiYqLL/MjISCUkJDjbVCUzM1NxcXHOR+vWrU2uHoAvcck9AABwB30GAAhsQXNXvoyMDBUXFzsfJ06csLokACaj4wkAAKpCHwEAApdPgym73S5JKiwsdJleWFjonGe321VUVOQy/9KlSzp9+rSzTVWio6Nls9lcHgCCA51NAAAAAAhOPg2m2rVrJ7vdrs2bNzunlZSUaPfu3XI4HJIkh8OhM2fOKC8vz9lmy5YtKi8vV9++fX1ZLgAAAAA/VpcPr/jACwD8i+nB1Llz57R//37t379f0r8HPN+/f7+OHz+usLAwTZ8+Xc8884zeffdd5efna/z48WrZsqVGjhwpSbr++us1ZMgQTZ48WR999JE+/PBDTZs2TWPGjOGOfAAAAABq1HbWOmf4dGUIRSAFAP7J9GBq79696tWrl3r16iVJmjFjhnr16qXZs2dLkp544gk9/PDDmjJlim666SadO3dOGzZsUExMjHMdy5cvV+fOnTVo0CANGzZMt912m1599VWzSwVgITqHAADATJX7Fu70NQiuAMB6kWavcMCAATIMo9r5YWFhmj9/vubPn19tm4SEBK1YscLs0gAAAAAAAOBHguaufAAC25WX3QMAANQFfQkACDwEUwAAAAAAALAEwRQAy/HpJgAA8AX6HADgfwimAFiqqg4inUYAAAAACA0EUwAAAAAAALAEwRQAn+OKKAAA4CtcnQ0A/o1gCgAAAAAAAJYgmAJgGT6tBAAA/qByn4SrrADAdwimAAAAAAAAYAmCKQBexyeMAAAAAICqEEwB8Al3wylCLAAA4E/azlrn1lf9AAB1QzAFAAAAAG4ilAIAcxFMAQAAAAhZ1V0NRQAFAL5BMAUAAAAAHiK4AgBzEEwB8IrqOmt04gAAAAAAFQimALiFQAkAAAAAYDaCKQAAAAAAAFiCYAqAqbidMgAACATu9FHoxwCA9xFMAQAAAIAXEGwBQO0IpgB4xJMOFp0xAAAAV/SPAMAVwRSAeqFzBQAAAACoK4IpAAAAAKiDtrPWOR91XR4AQh3BFADTVHSu6GQBAIBA5O0+DH0kALgawRQAAAAA1FN1oRNhFADUzJJgau7cuQoLC3N5dO7c2Tn/woULSk9PV9OmTRUbG6vRo0ersLDQilIBAAAAwC2EUADguUirnviGG27QBx988J9CIv9TyqOPPqp169bprbfeUlxcnKZNm6ZRo0bpww8/tKJUAAAAAHAreCKcAgDPWBZMRUZGym63XzW9uLhYS5cu1YoVKzRw4EBJ0rJly3T99ddr165d6tevn69LBVALOmAAAADuaztrnY4tTKt1GgCEAsvGmPriiy/UsmVLtW/fXmPHjtXx48clSXl5ebp48aJSUlKcbTt37qw2bdooNzfXqnKBkEDABAAA4Hue9sHoswEIJpYEU3379lV2drY2bNigl19+WUePHtUPf/hDnT17VgUFBYqKilJ8fLzLMklJSSooKKh2naWlpSopKXF5AKifyp0eOkEAAADmurJ/1XbWOvpbAEKOJV/lGzp0qPPn7t27q2/fvkpOTtZf/vIXNWzYsE7rzMzM1Lx588wqEcAVqguoPL3cnI4WAAAIVfSDAKBqln2V70rx8fG67rrrdPjwYdntdpWVlenMmTMubQoLC6sck6pCRkaGiouLnY8TJ054uWogeNBRAgAAAABYwS+CqXPnzunIkSNq0aKFevfurQYNGmjz5s3O+YcOHdLx48flcDiqXUd0dLRsNpvLAwAAAAAAAP7LkmDq8ccf1/bt23Xs2DHt3LlTP/7xjxUREaF7771XcXFxmjRpkmbMmKGtW7cqLy9PDzzwgBwOB3fkA0xWeUwDAAAAmK8+/ayKZemrAQhWlowx9dVXX+nee+/VqVOn1Lx5c912223atWuXmjdvLklasmSJwsPDNXr0aJWWlio1NVW/+93vrCgVAAAAAAAAXmJJMLVy5coa58fExCgrK0tZWVk+qghAXfDJHQAAQN140o/y5Cr3ut6kBgCs4hdjTAEAAAAAACD0EEwBIaCmT9a46gkAAMD/tJ21jn4agJBgyVf5APgvOkAAAACBo+2sdXxtD0BA44opIAQRPgEAAPgvd/tq9OkABAOCKSCIVO6cVNVZoQMDAAAQ+Orbz/NkQHUA8CaCKSBE8UkcAABAcHD3Tn306wD4I4IpIETQEQEAAAAA+BuCKSCAuXMJNoEUAABAcPDkTst1vaufO0NDAICZCKaAIEQHAgAAAFeifwjAXxFMAQGuuk+16HwAAADAjJvh1PXqKwBwB8EUAAAAAAAALEEwBQQYPq0CAAAAAAQLginAT1Ue2Nydgc49WScAAABCW21f0aPvCMAXCKYAP8Q4UQAAAPAls/qdtfVj6d8CqIxgCvASbx90zbiCCgAAAKHF3X5jbcGSp4Oq018FUB2CKcDPcNAGAACAP6KfCsAbCKYAL6vv7XXpAAAAAMAXPB1Owp129GUB1IZgCqgnTw+2lQ/4nnwljwM7AAAArEafFICZCKYAk/HJEQAAAEIN/VsAdUUwBdSDp1c7VW7DARwAAACBzJ1B0KtrU/nhjVp8sSyA+iGYAryIAxwAAACCndl93vquz9M7BgKwFsEU4AfM+pQIAAAACCaVv6Fw5TittQ3WXlsfu679bzOv8gJAMAWYioMTAAAA8G81BUbVTfdkqAwg2ITqNk8wBbghVHcQAAAAgFn8ZQwob/TtOV8A6o5gKkCwo6sfM75n7kl7/r8AAACAuqtrX93d5Wr6SmB1/3qyXk/bcv6AUObXwVRWVpbatm2rmJgY9e3bVx999JHVJaEa/rYjrU/o5M7d9eryHAAAAAB8z9t9fW9+4O3NdQD+wm+DqTfffFMzZszQnDlztG/fPvXo0UOpqakqKiqyujS4yczbvtZ1QMOKNtUtU9fBFN25BS4AAACA+vPGAOZ1qaG284KqziXcWb6m9V450Lq7g73XdK5SW0hX32+aePP/w5f/11YI5XNKvw2mnn/+eU2ePFkPPPCAunTpoldeeUWNGjXSH//4R6tLs5SV6brZt21155MLdz/dcDeg8satZwEAAAD4jjt9cn/tt3v6obq76/RkoHlvvH+eDnRvJjPvkFjf81LUTaTVBVSlrKxMeXl5ysjIcE4LDw9XSkqKcnNzq1ymtLRUpaWlzt+Li4slSSUlJd4t1kfKS79TSUmJ8193dZ2zUZ/NS61yXdW1q2qZ2pYrL/1ObR59y2UdFdo8+pbLzxVtrlzflctf+fzlpd+5PF/l569qfsVzV6yvcpvKrqzPk3kAAAAAAkfF+VR9lvHk/KCibVXnJFWtp2Laled9VZ3vVEy78lyqqrqvnFf59yuf60oV52WVl6n4ueJc7crzvSvPByuvs7rzxKrON6tT3Tnnleur6rVcuWxV06o7V668rornqHzueuXrqO59cff1XSlYMoyK12EYRq1twwx3WvnYyZMn9YMf/EA7d+6Uw+FwTn/iiSe0fft27d69+6pl5s6dq3nz5vmyTAAAAAAAAFTjxIkTatWqVY1t/PKKqbrIyMjQjBkznL+Xl5fr9OnTatq0qcLCwuq9/pKSErVu3VonTpyQzWar9/qAytjG4E1sX/Amti94E9sXvI1tDN7E9gVv8uftyzAMnT17Vi1btqy1rV8GU82aNVNERIQKCwtdphcWFsput1e5THR0tKKjo12mxcfHm16bzWbzu/9wBBe2MXgT2xe8ie0L3sT2BW9jG4M3sX3Bm/x1+4qLi3OrnV8Ofh4VFaXevXtr8+bNzmnl5eXavHmzy1f7AAAAAAAAELj88oopSZoxY4YmTJigPn366Oabb9YLL7yg8+fP64EHHrC6NAAAAAAAAJjAb4Ope+65R//85z81e/ZsFRQUqGfPntqwYYOSkpIsqSc6Olpz5sy56uuCgFnYxuBNbF/wJrYveBPbF7yNbQzexPYFbwqW7csv78oHAAAAAACA4OeXY0wBAAAAAAAg+BFMAQAAAAAAwBIEUwAAAAAAALAEwRQAAAAAAAAsQTBVg2effVa33HKLGjVqpPj4eLeWmThxosLCwlweQ4YM8W6hCEh12b4Mw9Ds2bPVokULNWzYUCkpKfriiy+8WygC1unTpzV27FjZbDbFx8dr0qRJOnfuXI3LDBgw4Kp92M9//nMfVQx/lpWVpbZt2yomJkZ9+/bVRx99VGP7t956S507d1ZMTIy6deum9evX+6hSBCJPtq/s7Oyr9lMxMTE+rBaBZMeOHRo+fLhatmypsLAwvfPOO7Uus23bNt14442Kjo7Wtddeq+zsbK/XicDl6Ta2bdu2q/ZhYWFhKigo8E3BCBiZmZm66aab1KRJEyUmJmrkyJE6dOhQrcsFYh+MYKoGZWVl+slPfqKpU6d6tNyQIUP0zTffOB9vvPGGlypEIKvL9rVo0SK9+OKLeuWVV7R79241btxYqampunDhghcrRaAaO3asDhw4oJycHK1du1Y7duzQlClTal1u8uTJLvuwRYsW+aBa+LM333xTM2bM0Jw5c7Rv3z716NFDqampKioqqrL9zp07de+992rSpEn6+OOPNXLkSI0cOVKfffaZjytHIPB0+5Ikm83msp/68ssvfVgxAsn58+fVo0cPZWVludX+6NGjSktL0x133KH9+/dr+vTpevDBB7Vx40YvV4pA5ek2VuHQoUMu+7HExEQvVYhAtX37dqWnp2vXrl3KycnRxYsXNXjwYJ0/f77aZQK2D2agVsuWLTPi4uLcajthwgRjxIgRXq0HwcXd7au8vNyw2+3Gc88955x25swZIzo62njjjTe8WCEC0cGDBw1Jxp49e5zT3n//fSMsLMz4+uuvq13u9ttvN37xi1/4oEIEkptvvtlIT093/n758mWjZcuWRmZmZpXtf/rTnxppaWku0/r27Ws89NBDXq0TgcnT7cuTfhlwJUnG6tWra2zzxBNPGDfccIPLtHvuucdITU31YmUIFu5sY1u3bjUkGf/61798UhOCR1FRkSHJ2L59e7VtArUPxhVTXrBt2zYlJiaqU6dOmjp1qk6dOmV1SQgCR48eVUFBgVJSUpzT4uLi1LdvX+Xm5lpYGfxRbm6u4uPj1adPH+e0lJQUhYeHa/fu3TUuu3z5cjVr1kxdu3ZVRkaGvvvuO2+XCz9WVlamvLw8l31PeHi4UlJSqt335ObmurSXpNTUVPZVuEpdti9JOnfunJKTk9W6dWuNGDFCBw4c8EW5CAHsv+ArPXv2VIsWLXTnnXfqww8/tLocBIDi4mJJUkJCQrVtAnUfFml1AcFmyJAhGjVqlNq1a6cjR47ol7/8pYYOHarc3FxFRERYXR4CWMX3zpOSklymJyUl8Z10XKWgoOCqS8IjIyOVkJBQ4/Zy3333KTk5WS1bttSnn36qJ598UocOHdKqVau8XTL81LfffqvLly9Xue/529/+VuUyBQUF7KvglrpsX506ddIf//hHde/eXcXFxVq8eLFuueUWHThwQK1atfJF2Qhi1e2/SkpK9P3336thw4YWVYZg0aJFC73yyivq06ePSktL9Yc//EEDBgzQ7t27deONN1pdHvxUeXm5pk+frltvvVVdu3attl2g9sFCLpiaNWuWfvOb39TY5vPPP1fnzp3rtP4xY8Y4f+7WrZu6d++uDh06aNu2bRo0aFCd1onA4e3tC3B3G6urK8eg6tatm1q0aKFBgwbpyJEj6tChQ53XCwBmcTgccjgczt9vueUWXX/99fr973+vBQsWWFgZANSuU6dO6tSpk/P3W265RUeOHNGSJUv05z//2cLK4M/S09P12Wef6a9//avVpXhFyAVTjz32mCZOnFhjm/bt25v2fO3bt1ezZs10+PBhgqkQ4M3ty263S5IKCwvVokUL5/TCwkL17NmzTutE4HF3G7Pb7VcNHHzp0iWdPn3auS25o2/fvpKkw4cPE0yFqGbNmikiIkKFhYUu0wsLC6vdlux2u0ftEbrqsn1V1qBBA/Xq1UuHDx/2RokIMdXtv2w2G1dLwWtuvvnmoA0cUH/Tpk1z3siotiuDA7UPFnLBVPPmzdW8eXOfPd9XX32lU6dOuQQJCF7e3L7atWsnu92uzZs3O4OokpIS7d692+M7RyJwubuNORwOnTlzRnl5eerdu7ckacuWLSovL3eGTe7Yv3+/JLEPC2FRUVHq3bu3Nm/erJEjR0r69+Xkmzdv1rRp06pcxuFwaPPmzZo+fbpzWk5OjstVLoBUt+2rssuXLys/P1/Dhg3zYqUIFQ6H46pbq7P/grft37+fvhauYhiGHn74Ya1evVrbtm1Tu3btal0mYPtgVo++7s++/PJL4+OPPzbmzZtnxMbGGh9//LHx8ccfG2fPnnW26dSpk7Fq1SrDMAzj7NmzxuOPP27k5uYaR48eNT744APjxhtvNDp27GhcuHDBqpcBP+Xp9mUYhrFw4UIjPj7eWLNmjfHpp58aI0aMMNq1a2d8//33VrwE+LkhQ4YYvXr1Mnbv3m389a9/NTp27Gjce++9zvlfffWV0alTJ2P37t2GYRjG4cOHjfnz5xt79+41jh49aqxZs8Zo37690b9/f6teAvzEypUrjejoaCM7O9s4ePCgMWXKFCM+Pt4oKCgwDMMwxo0bZ8yaNcvZ/sMPPzQiIyONxYsXG59//rkxZ84co0GDBkZ+fr5VLwF+zNPta968ecbGjRuNI0eOGHl5ecaYMWOMmJgY48CBA1a9BPixs2fPOvtYkoznn3/e+Pjjj40vv/zSMAzDmDVrljFu3Dhn+3/84x9Go0aNjJkzZxqff/65kZWVZURERBgbNmyw6iXAz3m6jS1ZssR45513jC+++MLIz883fvGLXxjh4eHGBx98YNVLgJ+aOnWqERcXZ2zbts345ptvnI/vvvvO2SZY+mAEUzWYMGGCIemqx9atW51tJBnLli0zDMMwvvvuO2Pw4MFG8+bNjQYNGhjJycnG5MmTnR0r4Eqebl+GYRjl5eXG008/bSQlJRnR0dHGoEGDjEOHDvm+eASEU6dOGffee68RGxtr2Gw244EHHnAJPo8ePeqyzR0/ftzo37+/kZCQYERHRxvXXnutMXPmTKO4uNiiVwB/8tJLLxlt2rQxoqKijJtvvtnYtWuXc97tt99uTJgwwaX9X/7yF+O6664zoqKijBtuuMFYt26djytGIPFk+5o+fbqzbVJSkjFs2DBj3759FlSNQLB169Yq+1sV29SECROM22+//aplevbsaURFRRnt27d36YsBlXm6jf3mN78xOnToYMTExBgJCQnGgAEDjC1btlhTPPxaVdtV5fPDYOmDhRmGYfjgwiwAAAAAAADARbjVBQAAAAAAACA0EUwBAAAAAADAEgRTAAAAAAAAsATBFAAAAAAAACxBMAUAAAAAAABLEEwBAAAAAADAEgRTAAAAAAAAsATBFAAAAAAAACxBMAUAAAAAAABLEEwBAAAAAADAEgRTAAAAAAAAsATBFAAAAAAAACxBMAUAAAAAAABLEEwBAAAAAADAEgRTAAAAAAAAsATBFAAAAAAAACxBMAUAAAAAAABLEEwBAABTtG3bVhMnTrS6j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size 1200x3600 with 10 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sample_ds = SubvolumeDataset(\n","    sample_locations,\n","    sample_volume,\n","    sample_label,\n","    CFG.BUFFER,\n","    is_train=False,\n",")\n","\n","id = 400\n","sample_depth = CFG.Z_DIM // 2\n","\n","fig, ax = plt.subplots(1, 2, figsize=(8, 6))\n","\n","img = sample_ds[id][0][sample_depth, :, :]\n","label = sample_ds[id][1][0, :, :]\n","ax[0].imshow(img)\n","ax[1].imshow(label)\n","\n","fig, ax = plt.subplots(figsize=(8, 6))\n","\n","ax.imshow(sample_label)\n","\n","y, x = sample_locations[id]\n","patch = patches.Rectangle([x - CFG.BUFFER, y - CFG.BUFFER], 2 * CFG.BUFFER, 2 * CFG.BUFFER, linewidth=2, edgecolor='g', facecolor='none')\n","ax.add_patch(patch)\n","plt.show()    \n","\n","fig, ax = plt.subplots(CFG.Z_DIM, 1, figsize=(12, 36))\n","\n","for i in range(CFG.Z_DIM):\n","    img, _ = sample_ds[id]\n","    img = img[i, :, :]\n","    ax[i].hist(img.flatten(), bins=1000)  # Plot histogram of the flattened data\n","    ax[i].set_title(f\"Histogram of Channel {i}\")  # Add title to the plot    \n","fig.tight_layout()\n","fig.show()"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(10, 384, 384)\n"]},{"data":{"text/html":["<video src=\"fragment_crop.mp4\" controls  width=\"384\"  height=\"384\">\n","      Your browser does not support the <code>video</code> element.\n","    </video>"],"text/plain":["<IPython.core.display.Video object>"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["fragment_cropped = sample_ds[id][0]\n","fragment_cropped = ((fragment_cropped * CFG.std) + CFG.mean) * 255\n","fragment_cropped = fragment_cropped.astype(np.uint8)\n","print(fragment_cropped.shape)\n","imageio.mimwrite(\"fragment_crop.mp4\", fragment_cropped, \"ffmpeg\")\n","Video(\"fragment_crop.mp4\", height=CFG.CROP_SIZE, width=CFG.CROP_SIZE)"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2023-03-18T23:14:39.034124Z","iopub.status.busy":"2023-03-18T23:14:39.033748Z","iopub.status.idle":"2023-03-18T23:28:08.685364Z","shell.execute_reply":"2023-03-18T23:28:08.684324Z","shell.execute_reply.started":"2023-03-18T23:14:39.034086Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["FOLD 0\n","--------------------------------\n","train_data [1 2 3]\n","val_data [0]\n"]},{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mfummicc1\u001b[0m (\u001b[33mink-detecction\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"data":{"text/html":["wandb version 0.15.4 is available!  To upgrade, please run:\n"," $ pip install wandb --upgrade"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.15.3"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>./wandb/run-20230614_202141-ktna7wiv</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href='https://wandb.ai/ink-detecction/lightning_logs/runs/ktna7wiv' target=\"_blank\">2.5dimension-2023-06-1420:21:39.547313-fold-0-val-0</a></strong> to <a href='https://wandb.ai/ink-detecction/lightning_logs' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View project at <a href='https://wandb.ai/ink-detecction/lightning_logs' target=\"_blank\">https://wandb.ai/ink-detecction/lightning_logs</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View run at <a href='https://wandb.ai/ink-detecction/lightning_logs/runs/ktna7wiv' target=\"_blank\">https://wandb.ai/ink-detecction/lightning_logs/runs/ktna7wiv</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/lightning_fabric/plugins/environments/slurm.py:166: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/ ...\n","  rank_zero_warn(\n","GPU available: True (cuda), used: True\n","TPU available: False, using: 0 TPU cores\n","IPU available: False, using: 0 IPUs\n","HPU available: False, using: 0 HPUs\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/lightning_fabric/plugins/environments/slurm.py:166: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/ ...\n","  rank_zero_warn(\n","Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/4\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/lightning_fabric/plugins/environments/slurm.py:166: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/ ...\n","  rank_zero_warn(\n","Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/4\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/lightning_fabric/plugins/environments/slurm.py:166: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/ ...\n","  rank_zero_warn(\n","Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/4\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/lightning_fabric/plugins/environments/slurm.py:166: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/ ...\n","  rank_zero_warn(\n","Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/4\n","----------------------------------------------------------------------------------------------------\n","distributed_backend=nccl\n","All distributed processes registered. Starting with 4 processes\n","----------------------------------------------------------------------------------------------------\n","\n","LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n","LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n","LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n","LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n","\n","  | Name  | Type                  | Params\n","------------------------------------------------\n","0 | model | Net                   | 125 M \n","1 | loss1 | SoftBCEWithLogitsLoss | 0     \n","2 | loss2 | SoftBCEWithLogitsLoss | 0     \n","3 | loss3 | BCEWithLogitsLoss     | 0     \n","------------------------------------------------\n","125 M     Trainable params\n","0         Non-trainable params\n","125 M     Total params\n","501.701   Total estimated model params size (MB)\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b2f6b42ca5ef402ebb9877405935f618","version_major":2,"version_minor":0},"text/plain":["Sanity Checking: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:208: UserWarning: num_workers>0, persistent_workers=False, and strategy=ddp_spawn may result in data loading bottlenecks. Consider setting persistent_workers=True (this is a limitation of Python .spawn() and PyTorch)\n","  rank_zero_warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('valid_tp', ...)` in your `on_validation_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('valid_fp', ...)` in your `on_validation_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('valid_fn', ...)` in your `on_validation_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('valid_tn', ...)` in your `on_validation_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1609: PossibleUserWarning: The number of training batches (25) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n","  rank_zero_warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"06f484d9ee514f34a1b9e62773a33e65","version_major":2,"version_minor":0},"text/plain":["Training: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b836b57a8057455ea2a2860bec92b1bb","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('train_tp', ...)` in your `on_train_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('train_fp', ...)` in your `on_train_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('train_fn', ...)` in your `on_train_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n","/home/fummicc1/anaconda3/envs/ink-detection-3_8/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('train_tn', ...)` in your `on_train_epoch_end` but the value needs to be floating point. Converting it to torch.float32.\n","  warning_cache.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fd2101e0971046ddb25bfe8a8d65e57f","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3f0680d602bc4c13a46222e18dd2d303","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["\n","k_folds = 4\n","kfold = KFold(\n","    n_splits=k_folds,\n",")\n","\n","data_list = [\n","    [volume_train_1, labels_train_1, mask_train_1],\n","    [volume_train_3, labels_train_3, mask_train_3],\n","    [volume_train_2a, labels_train_2a, mask_train_2a], \n","    [volume_train_2b, labels_train_2b, mask_train_2b],    \n","]\n","\n","predictions_map = None\n","predictions_map_counts = None\n","\n","for fold, (train_data, val_data) in enumerate(kfold.split(data_list)):\n","    print(f'FOLD {fold}')\n","    print('--------------------------------')\n","    print(\"train_data\", train_data)\n","    print(\"val_data\", val_data)\n","    one = data_list[train_data[0]]\n","    two = data_list[train_data[1]]\n","    three = data_list[train_data[2]]\n","    train_height = max(one[0].shape[0], two[0].shape[0], three[0].shape[0])\n","    if one[0].shape[0] < train_height:\n","        diff = train_height - one[0].shape[0]\n","        one[0] = np.pad(one[0], ((0, diff), (0, 0), (0, 0)), constant_values=0)\n","        one[1] = np.pad(one[1], ((0, diff), (0, 0)), constant_values=0)\n","        one[2] = np.pad(one[2], ((0, diff), (0, 0)), constant_values=0)\n","    if two[0].shape[0] < train_height:\n","        diff = train_height - two[0].shape[0]\n","        two[0] = np.pad(two[0], ((0, diff), (0, 0), (0, 0)), constant_values=0)\n","        two[1] = np.pad(two[1], ((0, diff), (0, 0)), constant_values=0)\n","        two[2] = np.pad(two[2], ((0, diff), (0, 0)), constant_values=0)\n","    if three[0].shape[0] < train_height:\n","        diff = train_height - three[0].shape[0]\n","        three[0] = np.pad(three[0], ((0, diff), (0, 0), (0, 0)), constant_values=0)\n","        three[1] = np.pad(three[1], ((0, diff), (0, 0)), constant_values=0)\n","        three[2] = np.pad(three[2], ((0, diff), (0, 0)), constant_values=0)\n","    train_volume = np.concatenate([one[0], two[0], three[0]], axis=1)\n","    train_label = np.concatenate([one[1], two[1], three[1]], axis=1)\n","    train_mask = np.concatenate([one[2], two[2], three[2]], axis=1)\n","    val_volume, val_label, val_mask = data_list[val_data[0]]    \n","\n","    train_locations_ds = generate_locations_ds(train_volume, train_mask, train_label, skip_zero=True)\n","    val_location_ds = generate_locations_ds(val_volume, val_mask, skip_zero=False)\n","\n","    visualize_dataset_patches(train_locations_ds, train_label, \"train\", fold)\n","    visualize_dataset_patches(val_location_ds, val_label, \"val\", fold)\n","    \n","    # Init the neural network\n","    model = Model()\n","\n","    wandb.finish()\n","    # Initialize a trainer\n","    now = datetime.datetime.now()\n","    now = f\"{now}\".replace(\" \", \"\")\n","    \n","    checkpoint_callback = pytorch_lightning.callbacks.ModelCheckpoint(\n","        monitor='valid_score',\n","        dirpath=f'best-results-{now}/',\n","        mode=\"max\",\n","        filename='train-fold-' + str(fold) + '-{epoch:02d}-{valid_score:.3f}',\n","        save_last=True,\n","    )\n","    \n","    trainer = pl.Trainer(\n","        max_epochs=CFG.num_epochs,\n","        devices=\"0,1,2,3\",\n","        accelerator=\"gpu\",\n","        precision=16 if CFG.precision == \"float16\" else 32,\n","        # strategy=\"ddp_find_unused_parameters_false\",\n","        # strategy=\"ddp_fork\",\n","        logger=WandbLogger(\n","            name=f\"2.5dimension-{now}-fold-{fold}-val-{val_data[0]}\",\n","            notes=CFG.WANDB_NOTE,\n","            config=class2dict(CFG()),\n","            group=CFG.exp_name,\n","        ),\n","        callbacks=[checkpoint_callback],\n","        default_root_dir=os.path.join(CFG.DATA_DIR, f\"{now}-fold-{fold}-val-{val_data[0]}\"),\n","    )\n","    \n","    # Sample elements randomly from a given list of ids, no replacement.\n","    train_ds = SubvolumeDataset(\n","        train_locations_ds,\n","        train_volume,\n","        train_label,\n","        CFG.BUFFER,\n","        is_train=True\n","    )\n","    val_ds = SubvolumeDataset(\n","        val_location_ds,\n","        val_volume,\n","        val_label,\n","        CFG.BUFFER,\n","        is_train=False,\n","    )\n","\n","    # Define data loaders for training and testing data in this fold\n","    train_loader = torch.utils.data.DataLoader(\n","        train_ds,\n","        batch_size=CFG.BATCH_SIZE,\n","        num_workers=CFG.num_workers,\n","        shuffle=True,\n","        # persistent_workers=True,\n","        # pin_memory=True,\n","    )\n","    val_loader = torch.utils.data.DataLoader(\n","        val_ds, \n","        batch_size=CFG.BATCH_SIZE,\n","        num_workers=CFG.num_workers,\n","        shuffle=False,\n","        # pin_memory=True,\n","        # persistent_workers=True,\n","    )\n","\n","    # Train the model\n","    trainer.fit(model, train_loader, val_loader)   \n","\n","wandb.finish()\n"]}],"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":"3.8.16"}},"nbformat":4,"nbformat_minor":4}