Inference Results.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_DwLAkmGPjO"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZnMFV3j89et"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKccFvNzGxJo"
      },
      "source": [
        "### Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MLel1gM1pgnh"
      },
      "outputs": [],
      "source": [
        "!pip install torchmetrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3eJJw8foCLSg"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/CV/Project/Code')\n",
        "\n",
        "import random\n",
        "import json\n",
        "import torch\n",
        "import pickle\n",
        "import os\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from models import GbcNet\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "\n",
        "import copy\n",
        "import time\n",
        "import math\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import torch.nn.functional as F\n",
        "from torchvision.transforms import Resize, ToTensor, Compose, GaussianBlur\n",
        "from torchvision.models.detection import retinanet_resnet50_fpn_v2\n",
        "from torchvision.models.detection import RetinaNet\n",
        "from torchvision.models.detection.backbone_utils import resnet_fpn_backbone\n",
        "import torch.nn.init as init\n",
        "from torch.optim import SGD, Adam, NAdam\n",
        "from torchmetrics.detection import MeanAveragePrecision\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "from torch.nn.functional import one_hot\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "from torchvision.utils import draw_bounding_boxes\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "torch.manual_seed(42)\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bMBwlyOGtn4"
      },
      "source": [
        "### File Paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "b0SNx-tXGd75"
      },
      "outputs": [],
      "source": [
        "!unzip \"/content/drive/MyDrive/CV/Project/data/images.zip\" -d \"/content\"\n",
        "imgs_folder_path = \"/content/images\"\n",
        "train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train.txt\" # Original train dataset\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/augmented_train.txt\" # Flipped malignant images for total balanced dataset\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train_double_malignant.txt\" # Rotated malignant images for total balanced dataset\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train_nr.txt\" # Only rotated normal images to be balanced with sum of malignant and benign\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train_nmr.txt\" # rotated malignanet to be equal to benign, rotated normal to be equal to benign+malignant, 3 and 6 degrees rotation\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train_nmr_2.txt\" # rotated malignanet to be equal to benign, rotated normal to be equal to benign+malignant, 3 and -3 degrees rotation\n",
        "val_txt_path = \"/content/drive/MyDrive/CV/Project/data/test.txt\"\n",
        "gold_file_path = \"/content/drive/MyDrive/CV/Project/data/gold_boxes.json\"\n",
        "gbcnet_weight_path = \"/content/drive/MyDrive/CV/Project/Code/Original Models/init_weights.pth\"\n",
        "\n",
        "#train_txt_path = \"/content/drive/MyDrive/CV/Project/data/train val test split/temp.txt\"\n",
        "#val_txt_path = \"/content/drive/MyDrive/CV/Project/data/train val test split/temp.txt\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8Or9FSSGrmI"
      },
      "source": [
        "### Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "wrvkF0tqGqj6"
      },
      "outputs": [],
      "source": [
        "def read_txt(txt_path):\n",
        "  ids = []\n",
        "  cls = []\n",
        "  with open(txt_path,\"r\") as f:\n",
        "    for line in f.readlines():\n",
        "      ids.append(line.strip().split(',')[0])\n",
        "      cls.append(int(line.strip().split(',')[1]))\n",
        "\n",
        "  return ids, cls\n",
        "\n",
        "def read_json(json_path):\n",
        "  with open(json_path, \"r\") as f:\n",
        "    json_file = json.load(f)\n",
        "\n",
        "  return json_file\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "def visualize(img, pred_bboxes=None, title=None):\n",
        "    print(\"visualize\")\n",
        "    edgecolors = [\"y\", \"r\", \"c\"]\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "\n",
        "    # Plot the original image with bounding box\n",
        "    if torch.is_tensor(img):\n",
        "      ax.imshow(img.permute(1, 2, 0).numpy())\n",
        "    else:\n",
        "      ax.imshow(img)\n",
        "    if pred_bboxes is not None:\n",
        "      for i in range(len(pred_bboxes)):\n",
        "        pred_bbox = pred_bboxes[i]\n",
        "        rect = patches.Rectangle((pred_bbox[0], pred_bbox[1]), pred_bbox[2]-pred_bbox[0], pred_bbox[3]-pred_bbox[1], linewidth=2, edgecolor=edgecolors[i], facecolor='none')\n",
        "        ax.add_patch(rect)\n",
        "    ax.set_title(title)\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def gold_box_proportion(x):\n",
        "  if x < 30:\n",
        "    return 1\n",
        "  elif 30 <= x < 35:\n",
        "    return 0.75\n",
        "  elif 35 <= x < 40:\n",
        "    return 0.5\n",
        "  elif 40 <= x < 45:\n",
        "    return 0.25\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "def print_status(txt, status_flag):\n",
        "  if status_flag==False:\n",
        "    print(txt)\n",
        "  return True\n",
        "\n",
        "def filter_background(predictions):\n",
        "  # Filter out the predictions with label 0\n",
        "  filtered_predictions = []\n",
        "  for prediction in predictions:\n",
        "      mask = prediction['labels'] != 0\n",
        "      filtered_prediction = {k: v[mask] for k, v in prediction.items()}\n",
        "      filtered_predictions.append(filtered_prediction)\n",
        "  return filtered_predictions\n",
        "\n",
        "def init_weights(m):\n",
        "    if isinstance(m, nn.Linear):\n",
        "        torch.nn.init.xavier_uniform_(m.weight)\n",
        "        m.bias.data.fill_(0.01)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGjYWc9fG3PC"
      },
      "source": [
        "### Transforms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "WCbCn5gAG45I"
      },
      "outputs": [],
      "source": [
        "class img_bbox_transform(object):\n",
        "    def __init__(self, target_width, target_height):\n",
        "      self.target_width = target_width\n",
        "      self.target_height = target_height\n",
        "      self.transforms = Compose([Resize((self.target_width, self.target_height)),\n",
        "                                ToTensor()])\n",
        "\n",
        "    def __call__(self, image=None, bbox = None, current_width=None, current_height=None):\n",
        "      transformed_image = None\n",
        "      if image is not None:\n",
        "        current_width, current_height = image.size\n",
        "        transformed_image = self.transforms(image)\n",
        "      transformed_bbox = None\n",
        "      if bbox is not None:\n",
        "        transformed_bbox = self.transform_bbox(bbox, current_width, current_height)\n",
        "      return transformed_image, transformed_bbox\n",
        "\n",
        "    def transform_bbox(self, bbox, current_width=None, current_height=None):\n",
        "      new_x1 = bbox[0]*self.target_width/current_width\n",
        "      new_y1 = bbox[1]*self.target_height/current_height\n",
        "      new_x2 = bbox[2]*self.target_width/current_width\n",
        "      new_y2 = bbox[3]*self.target_height/current_height\n",
        "      return [round(new_x1.item()), round(new_y1.item()), round(new_x2.item()), round(new_y2.item())]\n",
        "\n",
        "\n",
        "def crop_image(image, box, p):\n",
        "    x1, y1, x2, y2 = box\n",
        "    # Recieves images in format CHW\n",
        "    cropped_image = image[:, int((1-p)*y1):int((1+p)*y2), int((1-p)*x1):int((1+p)*x2)]\n",
        "    return cropped_image\n",
        "\n",
        "def get_train_transforms(width, height, p=None):\n",
        "    return A.Compose([\n",
        "        A.Rotate(limit=5, p=0.5, border_mode=cv2.BORDER_CONSTANT, value=0),\n",
        "        A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=0, p=0.6, border_mode=cv2.BORDER_CONSTANT, value=0),\n",
        "        A.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit= 0.1, p=0.5),\n",
        "        A.HueSaturationValue(hue_shift_limit=3, sat_shift_limit=5, val_shift_limit=0, p=0.5),\n",
        "        A.MotionBlur(blur_limit=(3, 5), p=0.5),\n",
        "        A.Perspective(scale=(0.01, 0.05), keep_size=True, pad_mode=0, pad_val=0, mask_pad_val=0, fit_output=False, interpolation=1, always_apply=False, p=0.7),\n",
        "        A.MultiplicativeNoise(multiplier=(0.9, 1.1), p=0.5),\n",
        "        A.Resize(height=height, width=width, p=1),\n",
        "        ToTensorV2(p=1.0)\n",
        "    ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['labels']))\n",
        "\n",
        "def get_eval_transforms(width, height):\n",
        "    return A.Compose([\n",
        "        A.Resize(height=height, width=width, p=1),\n",
        "        ToTensorV2(p=1.0)\n",
        "    ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['labels']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQmWuN8OHfIu"
      },
      "source": [
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "CT86v3INHh_3"
      },
      "outputs": [],
      "source": [
        "class InputDataset(Dataset):\n",
        "    def __init__(self, img_folder, id_txt, gold_json, p=0.15, width=224, height=224, img_transforms=None, do_crop=None, predicted_bboxes_json=None, to_blur=False, blur_kernel_size=(65,65), sigma=0):\n",
        "        self.img_folder = img_folder\n",
        "        self.id_txt = id_txt\n",
        "        self.gold_json = gold_json\n",
        "        self.to_blur = to_blur\n",
        "        self.blur_kernel_size = blur_kernel_size\n",
        "        self.sigma = sigma\n",
        "        self.do_crop=do_crop\n",
        "        self.p=p\n",
        "        self.width=width\n",
        "        self.height=height\n",
        "        self.transforms = img_transforms\n",
        "        self.ids, self.true_labels = read_txt(id_txt)\n",
        "        self.gold_dict = read_json(gold_json)\n",
        "        self.predicted_boxes = None\n",
        "        if predicted_bboxes_json is not None:\n",
        "          self.predicted_boxes=read_json(predicted_bboxes_json)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.ids)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_id = self.ids[idx]\n",
        "        img_label = self.true_labels[idx]\n",
        "        gold_bbox = self.gold_dict[img_id]['Gold']\n",
        "        img_path = f\"{self.img_folder}/{img_id}\"\n",
        "        img = cv2.imread(img_path)\n",
        "        img_height, img_width, _ = img.shape\n",
        "        if self.to_blur:\n",
        "          img = cv2.GaussianBlur(img, self.blur_kernel_size, self.sigma)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "        if (self.do_crop==None) or (self.do_crop==\"gold_boxes\"):\n",
        "          transformed_dict = self.transforms(image=img, bboxes=[gold_bbox], labels=[1])\n",
        "        elif self.do_crop==\"pred_boxes\":\n",
        "          pred_bbox = self.predicted_boxes[img_id]['Boxes']\n",
        "          transformed_dict = self.transforms(image=img, bboxes=[pred_bbox], labels=[1])\n",
        "\n",
        "        #visualize(img, pred_bboxes=[gold_bbox, pred_bbox], title=f\"raw image {img_id}\")\n",
        "        transformed_img = transformed_dict['image'].float().div(255)\n",
        "        transformed_bboxes = [round(x) for x in transformed_dict['bboxes'][0]]\n",
        "        #visualize(transformed_img, [[0,0,0,0], transformed_bboxes], title=f\"transformed img {img_id}\")\n",
        "\n",
        "        if self.do_crop is not None:\n",
        "          transformed_bboxes_area = (transformed_bboxes[2]-transformed_bboxes[0]) * (transformed_bboxes[3]-transformed_bboxes[1])\n",
        "          if transformed_bboxes_area > 0:\n",
        "            transformed_img = crop_image(transformed_img, transformed_bboxes, p=self.p)\n",
        "            transformed_img = F.interpolate(transformed_img.unsqueeze(0), size=(self.height, self.width), mode='bilinear', align_corners=False).squeeze(0)\n",
        "\n",
        "        #visualize(transformed_img, title=f\"cropped img {img_id}\")\n",
        "\n",
        "        target = {}\n",
        "        target['boxes'] = torch.tensor(transformed_bboxes).unsqueeze(0)\n",
        "        target['labels'] = torch.tensor(1).unsqueeze(0)\n",
        "        target['true_labels'] = torch.tensor(img_label, dtype=torch.int64)\n",
        "        target['image shape'] = {'height': img_height, 'width': img_width}\n",
        "        target['Gold'] = gold_bbox\n",
        "        return img_id, transformed_img, target\n",
        "\n",
        "def collate_fn(batch):\n",
        "  images_ids, images, targets = zip(*batch)\n",
        "  return list(images_ids), list(images), list(targets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xDhOcP6EI60D"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "otLHZqdGI8OS"
      },
      "outputs": [],
      "source": [
        "def compute_IoU(boxA, boxB):\n",
        "  x1_A, y1_A, x2_A, y2_A = boxA\n",
        "  x1_B, y1_B, x2_B, y2_B = boxB\n",
        "\n",
        "  x1 = max(x1_A, x1_B)\n",
        "  y1 = max(y1_A, y1_B)\n",
        "  x2 = min(x2_A, x2_B)\n",
        "  y2 = min(y2_A, y2_B)\n",
        "\n",
        "  if x2 <= x1 or y2 <= y1:\n",
        "    return torch.tensor(0).to(device)\n",
        "\n",
        "  intersection_area = (x2 - x1) * (y2 - y1)\n",
        "  boxA_area = (x2_A - x1_A) * (y2_A - y1_A)\n",
        "  boxB_area = (x2_B - x1_B) * (y2_B - y1_B)\n",
        "\n",
        "  iou = intersection_area / float(boxA_area + boxB_area - intersection_area)\n",
        "  return iou.float()\n",
        "\n",
        "def center_within_gold(box, gold):\n",
        "  x_center = (box[0] + box[2])/2\n",
        "  y_center = (box[1] + box[3])/2\n",
        "  x_min_gold, x_max_gold = gold[0], gold[2]\n",
        "  y_min_gold, y_max_gold = gold[1], gold[3]\n",
        "  if (x_min_gold<x_center<x_max_gold) and (y_min_gold<y_center<y_max_gold):\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "def compute_bbox_metrics(targets, predictions):\n",
        "  tp = 0\n",
        "  fp = 0\n",
        "  fn = 0\n",
        "  for i in range(len(predictions)):\n",
        "    target_box = targets[i]['boxes'][0]\n",
        "    pred_boxes = predictions[i]['boxes']\n",
        "    if len(pred_boxes) > 0:\n",
        "      for pred_box in pred_boxes:\n",
        "        if center_within_gold(pred_box, target_box):\n",
        "          tp += 1\n",
        "        else:\n",
        "          fp += 1\n",
        "    else:\n",
        "      fn += 1\n",
        "  return tp, fp, fn\n",
        "\n",
        "def evaluate_IoUs(targets, predictions):\n",
        "  IoUs = []\n",
        "  for i in range(len(predictions)):\n",
        "    target_box = targets[i]['boxes'][0]\n",
        "    pred_boxes = predictions[i]['boxes']\n",
        "    if len(pred_boxes) > 0:\n",
        "      for pred_box in pred_boxes:\n",
        "        IoUs.append(compute_IoU(target_box, pred_box.squeeze(0)))\n",
        "    else:\n",
        "      IoUs.append(torch.tensor(0.0).to(device))\n",
        "  return IoUs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjKuXgtC0RjV"
      },
      "source": [
        "# GBCRet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UW8gGFfs0RjW"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "hXoXPRCX0RjW"
      },
      "outputs": [],
      "source": [
        "def evaluate_model(model, dataloader, return_bboxes=False):\n",
        "  model.eval()\n",
        "  all_IoUs = []\n",
        "  mAP_metric = MeanAveragePrecision(box_format='xyxy', iou_type='bbox')\n",
        "  y_pred = []\n",
        "  y_true = []\n",
        "  fnames = []\n",
        "  TPs = 0\n",
        "  FNs = 0\n",
        "  FPs = 0\n",
        "  precision = 0\n",
        "  recall = 0\n",
        "  all_bboxes= {}\n",
        "  all_logits = [[], []]\n",
        "  with torch.inference_mode():\n",
        "    for i, data in enumerate(dataloader, 0):\n",
        "      images_ids, inputs, targets = data[0],\\\n",
        "                                    [image.to(device) for image in data[1]],\\\n",
        "                                    [{k: v.to(device) if torch.is_tensor(v) else v for k, v in t.items()} for t in data[2]]\n",
        "\n",
        "      pred_cls, pred_bboxes, logits = model(inputs, targets)\n",
        "\n",
        "      logits = [x.tolist() for x in logits]\n",
        "      all_logits = [a+b for a, b in zip(all_logits, logits)]\n",
        "      y_true += [x['true_labels'].item() for x in targets]\n",
        "      y_pred += pred_cls.tolist()\n",
        "      fnames += images_ids\n",
        "\n",
        "      IoUs = evaluate_IoUs(targets, pred_bboxes)\n",
        "      all_IoUs += IoUs\n",
        "\n",
        "      tp, fp, fn = compute_bbox_metrics(targets, pred_bboxes)\n",
        "      TPs += tp\n",
        "      FPs += fp\n",
        "      FNs += fn\n",
        "\n",
        "      mAP_metric.update(pred_bboxes, targets)\n",
        "\n",
        "      if return_bboxes:\n",
        "        for i in range(len(targets)):\n",
        "          fname = images_ids[i]\n",
        "          target_bbox = targets[i]['boxes']\n",
        "          pred_bbox = pred_bboxes[i]['boxes']\n",
        "          all_bboxes[fname] = {\"Boxes\": pred_bbox, \"Gold\": target_bbox}\n",
        "\n",
        "  mIoU = torch.stack(all_IoUs).mean()\n",
        "  medIoU = torch.stack(all_IoUs).median()\n",
        "  mAP_dict = mAP_metric.compute()\n",
        "  mAP = mAP_dict['map']\n",
        "  print(f\"mIoU = {mIoU}, median IoU: {medIoU}, mAP = {round(mAP_dict['map'].item(),3)}, mAP_50 = {round(mAP_dict['map_50'].item(),3)}, mAP_75 = {round(mAP_dict['map_75'].item(),3)}\")\n",
        "\n",
        "  if (TPs + FPs) != 0:\n",
        "    precision = TPs / (TPs + FPs)\n",
        "  if (TPs + FNs) != 0:\n",
        "    recall = TPs / (TPs + FNs)\n",
        "  print(f\"TP: {TPs}, FN: {FNs}, FP: {FPs}\")\n",
        "  print(\"Precision: {}\\nRecall: {}\".format(precision, recall))\n",
        "\n",
        "  acc = accuracy_score(y_true, y_pred)\n",
        "  cfm = confusion_matrix(y_true, y_pred)\n",
        "  spec = (cfm[0][0] + cfm[0][1] + cfm[1][0] + cfm[1][1])/(np.sum(cfm[0]) + np.sum(cfm[1]))\n",
        "  sens = cfm[2][2]/np.sum(cfm[2])\n",
        "  print('Acc: {:.4f} Spec: {:.4f} Sens: {:.4f}'.format(acc, spec, sens))\n",
        "\n",
        "  return acc, round(mAP_dict['map'].item(),3), cfm, y_true, y_pred, fnames, all_bboxes, all_logits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIOrBD1T1H2-"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FM0RTGvJt867"
      },
      "source": [
        "### Attention"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "02s9vndvt867"
      },
      "outputs": [],
      "source": [
        "class FeatureMapAttention(nn.Module):\n",
        "  def __init__(self, embed_dim, num_heads):\n",
        "    super().__init__()\n",
        "    self.multiheadattn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)\n",
        "\n",
        "  def forward(self, feature_map):\n",
        "    # feature map shape: [batch_size, 256, H, W]\n",
        "    # Reshape to: [batch_size, H*W, 256]\n",
        "    x = feature_map.reshape(feature_map.size(0), -1, 256)\n",
        "    # Apply Attention\n",
        "    attn_output, _ = self.multiheadattn(x,x,x)\n",
        "    # Reshape back to original shape: [batch_size, 256, H, W]\n",
        "    return attn_output.reshape(*feature_map.shape)\n",
        "\n",
        "class VectorAttention(nn.Module):\n",
        "  def __init__(self, embed_dim, num_heads):\n",
        "    super().__init__()\n",
        "    self.multiheadattn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)\n",
        "\n",
        "  def forward(self, x):\n",
        "    # x shape: [batch_size, 256 + 2048]\n",
        "    # Reshape to: [batch_size, 1, 256 + 2048]\n",
        "    x = x.unsqueeze(1)\n",
        "    # Apply Attention\n",
        "    attn_output, _ = self.multiheadattn(x, x, x)\n",
        "    # Remove the extra dimension: [batch_size, 256 + 2048]\n",
        "    return attn_output.squeeze(1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iq1yL_adAQ4g"
      },
      "source": [
        "### Main model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "NgCAhqb70ui7"
      },
      "outputs": [],
      "source": [
        "class GBCRet_combined(nn.Module):\n",
        "  def __init__(self, combined=\"None\", continue_training=False, criterion=None, gbcnet_weight_path=None, retinanet_weight_path=None,\n",
        "               do_hierarchical_cls=False, retinanet_backbone=\"resnet50-coco\", gold_curriculum=False, p=0.15, width=224, height=224,\n",
        "               attn_num_heads=4, no_attn=True, train_gold_bbox=True, min_size=800, max_size=1333, dropout=0.4, detections_per_img=1,\n",
        "               regression_loss=None, rn_trainable_layers=None, gbcnet_va_curriculum=False, load_pretrained_retinanet=False,\n",
        "               nnn_fc_layers=\"1\", bm_fc_layers=\"1\", return_logits=True,\n",
        "               loss_weights = {\"n_nn_loss_weight\": 1.0, \"b_m_loss_weight\": 1.0, \"classification_loss_weight\": 1.0, \"retinanet_loss_weight\": 1.0}):\n",
        "    super(GBCRet_combined, self).__init__()\n",
        "    # Settings\n",
        "    self.return_logits=return_logits\n",
        "    self.loss_weights=loss_weights\n",
        "    self.load_pretrained_retinanet=load_pretrained_retinanet\n",
        "    self.gbcnet_va_curriculum=gbcnet_va_curriculum\n",
        "    self.rn_trainable_layers=rn_trainable_layers\n",
        "    self.retinanet_backbone=retinanet_backbone\n",
        "    self.combined=combined\n",
        "    self.no_attn = no_attn\n",
        "    self.do_hierarchical_cls=do_hierarchical_cls\n",
        "    self.continue_training=continue_training\n",
        "    self.detections_per_img=detections_per_img\n",
        "    self.p = p\n",
        "    self.width = width\n",
        "    self.height = height\n",
        "    self.min_size = min_size\n",
        "    self.max_size = max_size\n",
        "    self.criterion = criterion\n",
        "    self.train_gold_bbox = train_gold_bbox\n",
        "    self.gold_curriculum = gold_curriculum\n",
        "    self.regression_loss = regression_loss\n",
        "    self.current_epoch = -1\n",
        "    if isinstance(dropout, dict):\n",
        "      n_nn_dropout=dropout['n_nn_dropout']\n",
        "      b_m_dropout=dropout['b_m_dropout']\n",
        "      dropout=0\n",
        "      print(f\"n_nn_dropout: {n_nn_dropout}, b_m_dropout: {b_m_dropout}\")\n",
        "    else:\n",
        "      n_nn_dropout=dropout\n",
        "      b_m_dropout=dropout\n",
        "    # Status Flags\n",
        "    self.gbcnet_va_curriculum_flag=False\n",
        "    self.combined_status_flag=False\n",
        "    self.no_attn_status_flag=False\n",
        "    self.train_gold_box_status_flag=False\n",
        "    self.gold_curriculum_status_flag=False\n",
        "    self.num_gold_boxes_status_flag=False\n",
        "    self.hierarchical_classification_status_flag=False\n",
        "    # Loading models\n",
        "    self.retinanet = self.load_retinanet(retinanet_weight_path)\n",
        "    self.gbcnet = self.load_gbcnet(gbcnet_weight_path)\n",
        "    # Hooks\n",
        "    self.retinanet_features = None\n",
        "    self.gbcnet_features = None\n",
        "    self.resnet_layer1_features = None\n",
        "    self.resnet_layer2_features = None\n",
        "    self.resnet_layer3_features = None\n",
        "    self.resnet_layer4_features = None\n",
        "    self.gbcnet.net.layer4.register_forward_hook(self._get_gbcnet_features_hook)\n",
        "    self.retinanet.backbone.register_forward_hook(self._get_retinanet_features_hook)\n",
        "    self.retinanet.backbone.body.layer1.register_forward_hook(self._get_resnet_layer1_features_hook)\n",
        "    self.retinanet.backbone.body.layer2.register_forward_hook(self._get_resnet_layer2_features_hook)\n",
        "    self.retinanet.backbone.body.layer3.register_forward_hook(self._get_resnet_layer3_features_hook)\n",
        "    self.retinanet.backbone.body.layer4.register_forward_hook(self._get_resnet_layer4_features_hook)\n",
        "    # Layers\n",
        "    self.avgpool = nn.AdaptiveAvgPool2d(1)\n",
        "    self.feature_map_attn = FeatureMapAttention(embed_dim=256, num_heads=attn_num_heads)\n",
        "    self.combined_attn_layer = VectorAttention(embed_dim= 2048 + 256, num_heads=attn_num_heads)\n",
        "\n",
        "    self.fc = nn.Sequential(\n",
        "                  nn.Linear((2048 + 256), 256),\n",
        "                  nn.ReLU(inplace=True),\n",
        "                  nn.Dropout(dropout),\n",
        "                  nn.Linear(256, 3)\n",
        "                )\n",
        "    self.FPN_linear = nn.Linear(5*256, 256)\n",
        "    self.Layer2_linear = nn.Linear(512, 256)\n",
        "    self.Layer3_linear = nn.Linear(1024, 256)\n",
        "    self.Layer4_linear = nn.Linear(2048, 256)\n",
        "    self.compress_combined_featuers_linear = nn.Linear((2048 + 256), 2048)\n",
        "\n",
        "    self.fc2 = nn.Sequential(\n",
        "                  nn.Linear((5*256), 256),\n",
        "                  nn.ReLU(inplace=True),\n",
        "                  nn.Dropout(dropout),\n",
        "                  nn.Linear(256, 3)\n",
        "                )\n",
        "    self.meta_learner = nn.Linear(6,3)\n",
        "\n",
        "    if nnn_fc_layers==\"1\":\n",
        "      self.normal_notnormal_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 256),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(n_nn_dropout),\n",
        "                                            nn.Linear(256,2)\n",
        "                                          )\n",
        "    elif nnn_fc_layers==\"3\":\n",
        "      self.normal_notnormal_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 512),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(n_nn_dropout),\n",
        "                                            nn.Linear(512,2)\n",
        "                                          )\n",
        "    if bm_fc_layers == \"1\":\n",
        "      self.benign_malignant_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 256),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(b_m_dropout),\n",
        "                                            nn.Linear(256,2)\n",
        "                                          )\n",
        "    elif bm_fc_layers == \"2\":\n",
        "      self.benign_malignant_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 512),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(dropout),\n",
        "                                            nn.Linear(512, 256),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(b_m_dropout),\n",
        "                                            nn.Linear(256, 2)\n",
        "                                          )\n",
        "    elif bm_fc_layers == \"3\":\n",
        "      self.benign_malignant_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 512),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(b_m_dropout),\n",
        "                                            nn.Linear(512,2)\n",
        "                                          )\n",
        "    elif bm_fc_layers == \"4\":\n",
        "      self.benign_malignant_fc = nn.Sequential(\n",
        "                                            nn.Linear(2048, 1024),\n",
        "                                            nn.ReLU(inplace=True),\n",
        "                                            nn.Dropout(b_m_dropout),\n",
        "                                            nn.Linear(1024,2)\n",
        "                                          )\n",
        "\n",
        "  def load_retinanet(self, retinanet_weight_path):\n",
        "    if self.retinanet_backbone==\"resnet50-gbusv\":\n",
        "      # No weights -> by default trainable_layers = 5 - https://github.com/pytorch/vision/blob/b1123cfd543d35d282de9bb28067e48ebec18afe/torchvision/models/detection/retinanet.py#L879\n",
        "      retinanet = retinanet_resnet50_fpn_v2(num_classes=2, detections_per_img = self.detections_per_img, score_thresh=0.05,\n",
        "                                            nms_thresh=0.5, min_size=self.min_size, max_size=self.max_size).to(device)\n",
        "      if not self.continue_training:\n",
        "        retinanet.load_state_dict(torch.load(retinanet_weight_path, map_location=device))\n",
        "        print(f\"Retinanet's GBUSV pre-trained weights are loaded from: {retinanet_weight_path}\")\n",
        "      return retinanet\n",
        "    elif self.retinanet_backbone==\"resnet50-coco\":\n",
        "      if self.rn_trainable_layers == None:\n",
        "        # Defined weights -> by default trainable layers = 3 - https://github.com/pytorch/vision/blob/b1123cfd543d35d282de9bb28067e48ebec18afe/torchvision/models/detection/retinanet.py#L879\n",
        "        retinanet = retinanet_resnet50_fpn_v2(weights=\"COCO_V1\", detections_per_img=self.detections_per_img, score_thresh=0.05,\n",
        "                                              nms_thresh=0.5, min_size=self.min_size, max_size=self.max_size).to(device)\n",
        "      else:\n",
        "        print(f\"Retinanet's Trainable Backbone Layers: {self.rn_trainable_layers}\")\n",
        "        retinanet = retinanet_resnet50_fpn_v2(weights=\"COCO_V1\", detections_per_img=self.detections_per_img, score_thresh=0.05,\n",
        "                                              nms_thresh=0.5, min_size=self.min_size, max_size=self.max_size,\n",
        "                                              trainable_backbone_layers=self.rn_trainable_layers).to(device)\n",
        "      print(\"Retinanet's COCO pre-trained weights are loaded.\")\n",
        "      num_classes=2\n",
        "      num_anchors = retinanet.head.classification_head.num_anchors\n",
        "      in_channels = retinanet.head.classification_head.cls_logits.in_channels\n",
        "      new_cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1).to(device)\n",
        "      torch.nn.init.normal_(new_cls_logits.weight, std=0.01)\n",
        "      torch.nn.init.constant_(new_cls_logits.bias, -math.log((1 - 0.01) / 0.01))\n",
        "      retinanet.head.classification_head.cls_logits = new_cls_logits\n",
        "      retinanet.head.classification_head.num_classes = num_classes\n",
        "      if self.load_pretrained_retinanet:\n",
        "        checkpoint = torch.load(retinanet_weight_path, map_location=device)\n",
        "        retinanet.load_state_dict(checkpoint['model_state_dict'])\n",
        "        print(f\"Retinanet's {checkpoint['epoch']} epoch Pretrained Weights from: {retinanet_weight_path}\")\n",
        "      return retinanet\n",
        "    elif self.retinanet_backbone==\"resnet34\" or self.retinanet_backbone==\"resnet18\":\n",
        "      # Create the backbone using ResNet34 with FPN\n",
        "      backbone = resnet_fpn_backbone(backbone_name=self.retinanet_backbone, trainable_layers=3, weights=\"IMAGENET1K_V1\").to(device)\n",
        "      # Create the RetinaNet model\n",
        "      retinanet = RetinaNet(backbone, num_classes=2, detections_per_img=self.detections_per_img, score_thresh=0.05, nms_thresh=0.5,\n",
        "                            min_size=self.min_size, max_size=self.max_size).to(device)\n",
        "      if self.regression_loss == 'giou':\n",
        "        retinanet.head.regression_head._loss_type = \"giou\"\n",
        "      return retinanet\n",
        "\n",
        "  def load_gbcnet(self, gbcnet_weight_path):\n",
        "    gbcnet = GbcNet(num_cls=3, pretrain=False, att_mode= \"1\")\n",
        "    if not self.continue_training:\n",
        "      gbcnet.load_state_dict(torch.load(gbcnet_weight_path, map_location=device))\n",
        "      print(f\"GBCNet's pretrained weights are loaded from: {gbcnet_weight_path}\")\n",
        "    gbcnet.net = gbcnet.net.float().to(device)\n",
        "    return gbcnet\n",
        "\n",
        "  def _get_retinanet_features_hook(self, module, input, output):\n",
        "    self.retinanet_features = output\n",
        "\n",
        "  def _get_gbcnet_features_hook(self, module, input, output):\n",
        "    self.gbcnet_features = output\n",
        "\n",
        "  def _get_resnet_layer1_features_hook(self, module, input, output):\n",
        "    self.resnet_layer1_features = output\n",
        "\n",
        "  def _get_resnet_layer2_features_hook(self, module, input, output):\n",
        "    self.resnet_layer2_features = output\n",
        "\n",
        "  def _get_resnet_layer3_features_hook(self, module, input, output):\n",
        "    self.resnet_layer3_features = output\n",
        "\n",
        "  def _get_resnet_layer4_features_hook(self, module, input, output):\n",
        "    self.resnet_layer4_features = output\n",
        "\n",
        "  def forward(self, inputs, targets, epoch=None):\n",
        "    if self.training:\n",
        "      retinanet_loss_dict = self.retinanet(inputs, targets)\n",
        "      retinanet_loss = sum(loss for loss in retinanet_loss_dict.values())\n",
        "      retinanet_features = self.retinanet_features\n",
        "      resnet_layer1_features = self.resnet_layer1_features\n",
        "      resnet_layer2_features = self.resnet_layer2_features\n",
        "      resnet_layer3_features = self.resnet_layer3_features\n",
        "      resnet_layer4_features = self.resnet_layer4_features\n",
        "\n",
        "      if self.gold_curriculum==False:\n",
        "        self.gold_curriculum_status_flag = print_status(\"Gold Curriculum Deactivated.\", self.gold_curriculum_status_flag)\n",
        "        if self.train_gold_bbox:\n",
        "          self.train_gold_box_status_flag = print_status(\"Training only with gold boxes.\", self.train_gold_box_status_flag)\n",
        "          gbcnet_inputs, true_labels = self.prepare_gbcnet_inputs(inputs, targets, targets, epoch)\n",
        "        else:\n",
        "          self.train_gold_box_status_flag = print_status(\"Training only with predicted boxes.\", self.train_gold_box_status_flag)\n",
        "          pred_bboxes = self.predict_bboxes(inputs)\n",
        "          gbcnet_inputs, true_labels = self.prepare_gbcnet_inputs(inputs, pred_bboxes, targets)\n",
        "\n",
        "      else:\n",
        "        self.gold_curriculum_status_flag = print_status(\"Gold Curriculum Activated.\", self.gold_curriculum_status_flag)\n",
        "        if self.current_epoch != epoch:\n",
        "          self.num_gold_boxes_status_flag=False\n",
        "        self.current_epoch=epoch\n",
        "        if gold_box_proportion(epoch) == 1:\n",
        "          self.num_gold_boxes_status_flag = print_status(\"All Gold Boxes are using.\", self.num_gold_boxes_status_flag)\n",
        "          gbcnet_inputs, true_labels = self.prepare_gbcnet_inputs(inputs, targets, targets)\n",
        "        else:\n",
        "          num_gold_boxes = int(gold_box_proportion(epoch)*len(targets))\n",
        "          self.num_gold_boxes_status_flag = print_status(f\"{num_gold_boxes} Gold Boxes, and {len(targets)-num_gold_boxes} Predicted Boxes are using.\", self.num_gold_boxes_status_flag)\n",
        "          pred_bboxes = self.predict_bboxes(inputs)\n",
        "          selected_bboxes = targets[:num_gold_boxes]\n",
        "          selected_bboxes += pred_bboxes[num_gold_boxes:]\n",
        "          gbcnet_inputs, true_labels = self.prepare_gbcnet_inputs(inputs, selected_bboxes, targets)\n",
        "\n",
        "      gbcnet_cls_logits = self.gbcnet(gbcnet_inputs)\n",
        "      gbcnet_features = self.gbcnet_features\n",
        "\n",
        "      if self.combined=='resnet-fpn':\n",
        "        self.combined_status_flag = print_status(\"Combining features of Retinanet and GBCNet backbones.\", self.combined_status_flag)\n",
        "        combined_features = self.concat_feature_maps(retinanet_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "        classification_loss = self.criterion(cls_output.to(device), true_labels.to(device))\n",
        "      elif self.combined==\"resnet_layer1\":\n",
        "        self.combined_status_flag = print_status(\"Combining ResNet layer1 and GBCNet backbones.\", self.combined_status_flag)\n",
        "        combined_features = self.concat_feature_maps(resnet_layer1_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "        classification_loss = self.criterion(cls_output.to(device), true_labels.to(device))\n",
        "      elif self.combined==\"resnet_layer2\":\n",
        "        self.combined_status_flag = print_status(\"Combining ResNet layer2 and GBCNet backbones.\", self.combined_status_flag)\n",
        "        combined_features = self.concat_feature_maps(resnet_layer2_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "        classification_loss = self.criterion(cls_output.to(device), true_labels.to(device))\n",
        "      elif self.combined==\"resnet_layer3\":\n",
        "        self.combined_status_flag = print_status(\"Combining ResNet layer3 and GBCNet backbones.\", self.combined_status_flag)\n",
        "        combined_features = self.concat_feature_maps(resnet_layer3_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "        classification_loss = self.criterion(cls_output.to(device), true_labels.to(device))\n",
        "      elif self.combined==\"resnet_layer4\":\n",
        "        self.combined_status_flag = print_status(\"Combining ResNet layer4 and GBCNet backbones.\", self.combined_status_flag)\n",
        "        combined_features = self.concat_feature_maps(resnet_layer4_features, gbcnet_features)\n",
        "        if not self.do_hierarchical_cls:\n",
        "          cls_output = self.fc(combined_features)\n",
        "          classification_loss = self.criterion(cls_output.to(device), true_labels.to(device))\n",
        "        else:\n",
        "          combined_features = self.compress_combined_featuers_linear(combined_features)\n",
        "          classification_loss = self.hierarchical_cls(combined_features, true_labels)\n",
        "      elif self.combined==\"classification\":\n",
        "        self.combined_status_flag = print_status(\"Combining classifications of Retinanet and GBCNet backbones.\", self.combined_status_flag)\n",
        "        retinanet_features = self.concat_feature_maps(retinanet_features)\n",
        "        retinanet_cls_logits = self.fc2(retinanet_features)\n",
        "        combined_cls_logits = self.meta_learner(torch.cat((gbcnet_cls_logits, retinanet_cls_logits), dim=1))\n",
        "        classification_loss = self.criterion(combined_cls_logits.to(device), true_labels.to(device))\n",
        "      elif self.combined=='None':\n",
        "        if not self.do_hierarchical_cls:\n",
        "          self.combined_status_flag = print_status(\"Classification only using GBCNet.\", self.combined_status_flag)\n",
        "          classification_loss = self.criterion(gbcnet_cls_logits.to(device), true_labels.to(device))\n",
        "        else:\n",
        "          self.hierarchical_classification_status_flag = print_status(\"Hierarchical Classification is Activated.\", self.hierarchical_classification_status_flag)\n",
        "          gbcnet_features = self.avgpool(gbcnet_features)\n",
        "          gbcnet_features = gbcnet_features.view(gbcnet_features.size(0), -1)\n",
        "          classification_loss = self.hierarchical_cls(gbcnet_features, true_labels)\n",
        "\n",
        "      total_loss = self.loss_weights[\"retinanet_loss_weight\"] * retinanet_loss + self.loss_weights[\"classification_loss_weight\"] * classification_loss\n",
        "      return total_loss\n",
        "\n",
        "    else:\n",
        "      pred_bboxes = self.predict_bboxes(inputs)\n",
        "      retinanet_features = self.retinanet_features\n",
        "      resnet_layer1_features = self.resnet_layer1_features\n",
        "      resnet_layer2_features = self.resnet_layer2_features\n",
        "      resnet_layer3_features = self.resnet_layer3_features\n",
        "      resnet_layer4_features = self.resnet_layer4_features\n",
        "      gbcnet_inputs, true_labels = self.prepare_gbcnet_inputs(inputs, pred_bboxes, targets)\n",
        "      gbcnet_cls_logits = self.gbcnet(gbcnet_inputs)\n",
        "      gbcnet_features = self.gbcnet_features\n",
        "\n",
        "      if self.combined=='resnet-fpn':\n",
        "        combined_features = self.concat_feature_maps(retinanet_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "      elif self.combined==\"resnet_layer1\":\n",
        "        combined_features = self.concat_feature_maps(resnet_layer1_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "      elif self.combined==\"resnet_layer2\":\n",
        "        combined_features = self.concat_feature_maps(resnet_layer2_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "      elif self.combined==\"resnet_layer3\":\n",
        "        combined_features = self.concat_feature_maps(resnet_layer3_features, gbcnet_features)\n",
        "        cls_output = self.fc(combined_features)\n",
        "      elif self.combined==\"resnet_layer4\":\n",
        "        combined_features = self.concat_feature_maps(resnet_layer4_features, gbcnet_features)\n",
        "        if not self.do_hierarchical_cls:\n",
        "          cls_output = self.fc(combined_features)\n",
        "        else:\n",
        "          combined_features = self.compress_combined_featuers_linear(combined_features)\n",
        "          final_h_preds, cls_output = self.hierarchical_cls(combined_features, true_labels)\n",
        "      elif self.combined=='classification':\n",
        "        retinanet_features = self.concat_feature_maps(retinanet_features)\n",
        "        retinanet_cls_logits = self.fc2(retinanet_features)\n",
        "        combined_cls_logits = self.meta_learner(torch.cat((gbcnet_cls_logits, retinanet_cls_logits), dim=1))\n",
        "        cls_output = combined_cls_logits\n",
        "      elif self.combined=='None':\n",
        "        if not self.do_hierarchical_cls:\n",
        "          cls_output = gbcnet_cls_logits\n",
        "        else:\n",
        "          gbcnet_features = self.avgpool(gbcnet_features)\n",
        "          gbcnet_features = gbcnet_features.view(gbcnet_features.size(0), -1)\n",
        "          final_h_preds, cls_output = self.hierarchical_cls(gbcnet_features, true_labels)\n",
        "\n",
        "      if not self.do_hierarchical_cls:\n",
        "        _, cls_pred = torch.max(cls_output, dim=1)\n",
        "      else:\n",
        "        cls_pred = final_h_preds\n",
        "\n",
        "      if self.return_logits:\n",
        "        return cls_pred, pred_bboxes, cls_output\n",
        "      else:\n",
        "        return cls_pred, pred_bboxes\n",
        "\n",
        "\n",
        "  def hierarchical_cls(self, features, true_labels):\n",
        "    if self.training:\n",
        "      self.hierarchical_classification_status_flag = print_status(\"Hierarchical Classification is Activated.\", self.hierarchical_classification_status_flag)\n",
        "\n",
        "      # Normal vs Non-Normal classification\n",
        "      n_nn_output = self.normal_notnormal_fc(features)\n",
        "      n_nn_true_labels = torch.tensor([0 if x == 0 else 1 for x in true_labels]) # Normal-> 0, Non-Normal -> 1\n",
        "\n",
        "      if isinstance(self.criterion, nn.BCEWithLogitsLoss):\n",
        "        n_nn_true_labels = one_hot(n_nn_true_labels, num_classes=2).float()\n",
        "      n_nn_loss = self.criterion(n_nn_output.to(device), n_nn_true_labels.to(device))\n",
        "\n",
        "      not_normal_indices = (true_labels != 0)\n",
        "      if not (not_normal_indices==False).all():\n",
        "        # Benign vs Malignant classification\n",
        "        b_m_output = self.benign_malignant_fc(features[not_normal_indices])\n",
        "        b_m_true_labels = true_labels[not_normal_indices]\n",
        "        b_m_true_labels = torch.tensor([0 if x==1 else 1 for x in b_m_true_labels]) # Benign -> 0, Malignant -> 1\n",
        "\n",
        "        if isinstance(self.criterion, nn.BCEWithLogitsLoss):\n",
        "          b_m_true_labels = one_hot(b_m_true_labels, num_classes=2).float()\n",
        "        b_m_loss = self.criterion(b_m_output.to(device), b_m_true_labels.to(device))\n",
        "      else:\n",
        "        b_m_loss = 0\n",
        "      loss = self.loss_weights[\"n_nn_loss_weight\"] * n_nn_loss + self.loss_weights[\"b_m_loss_weight\"] * b_m_loss\n",
        "      return loss\n",
        "    else:\n",
        "      n_nn_output = self.normal_notnormal_fc(features)\n",
        "      _, n_nn_cls_pred = torch.max(n_nn_output, dim=1)\n",
        "      final_h_preds = torch.zeros_like(n_nn_cls_pred)\n",
        "      not_normal_indices = (n_nn_cls_pred != 0)\n",
        "      if not (not_normal_indices==False).all():\n",
        "        b_m_output = self.benign_malignant_fc(features[not_normal_indices])\n",
        "        _, b_m_cls_pred = torch.max(b_m_output, dim=1)\n",
        "        b_m_cls_pred += 1\n",
        "        final_h_preds[not_normal_indices] = b_m_cls_pred\n",
        "      return final_h_preds, [n_nn_output, b_m_output]\n",
        "\n",
        "\n",
        "  def predict_bboxes(self, inputs):\n",
        "    self.retinanet.eval()\n",
        "    with torch.inference_mode():\n",
        "      predictions = self.retinanet(inputs)\n",
        "      foreground_preds = filter_background(predictions)\n",
        "    if self.training:\n",
        "      self.retinanet.train()\n",
        "    return foreground_preds\n",
        "\n",
        "  def va_blur(self, img, epoch):\n",
        "    self.gbcnet_va_curriculum_flag = print_status(\"GBCNet VA Curriculum Activated.\", self.gbcnet_va_curriculum_flag)\n",
        "    if epoch <= 10:\n",
        "      gaussian_blur = GaussianBlur((17,17), 8)\n",
        "      img = gaussian_blur(img)\n",
        "    elif epoch > 10 and epoch <= 15:\n",
        "      gaussian_blur = GaussianBlur((9,9), 4)\n",
        "      img = gaussian_blur(img)\n",
        "    elif epoch > 15 and epoch <= 20:\n",
        "      gaussian_blur = GaussianBlur((5,5), 2)\n",
        "      img = gaussian_blur(img)\n",
        "    elif epoch > 20 and epoch <= 25:\n",
        "      gaussian_blur = GaussianBlur((3,3), 1)\n",
        "      img = gaussian_blur(img)\n",
        "    elif epoch > 25 and epoch <= 30:\n",
        "      gaussian_blur = GaussianBlur((1,1), 0.5)\n",
        "      img = gaussian_blur(img)\n",
        "    else:\n",
        "      return img\n",
        "    return img\n",
        "\n",
        "  def prepare_gbcnet_inputs(self, inputs, predictions, targets, epoch=None):\n",
        "    true_labels = []\n",
        "    gbcnet_inputs = []\n",
        "    for i in range(len(inputs)):\n",
        "      img = inputs[i]\n",
        "      if (self.gbcnet_va_curriculum) and (epoch is not None):\n",
        "        img = self.va_blur(img, epoch)\n",
        "      true_labels.append(targets[i]['true_labels'])\n",
        "      pred_bboxes = predictions[i]['boxes']\n",
        "      #visualize(img)\n",
        "      if len(pred_bboxes) > 0:\n",
        "        for pred_bbox in pred_bboxes:\n",
        "          pred_bbox_area = (int(pred_bbox[2])-int(pred_bbox[0])) * (int(pred_bbox[3])-int(pred_bbox[1]))\n",
        "          if pred_bbox_area > 0:\n",
        "            img = crop_image(img, pred_bbox, self.p)\n",
        "            img = F.interpolate(img.unsqueeze(0), size=(self.height, self.width), mode='bilinear', align_corners=False).squeeze(0)\n",
        "      gbcnet_inputs.append(img)\n",
        "    return torch.stack(gbcnet_inputs), torch.stack(true_labels)\n",
        "\n",
        "  def concat_feature_maps(self, rn_fms, gb_fm=None):\n",
        "    if self.combined=='resnet-fpn':\n",
        "      rn_fms = list(rn_fms.values())\n",
        "      if not self.no_attn:\n",
        "        self.no_attn_status_flag = print_status(\"Self attention on Retinanet's feature maps are applied.\", self.no_attn_status_flag)\n",
        "        rn_fms = [self.feature_map_attn(x) for x in rn_fms]\n",
        "      rn_fms = [self.avgpool(x) for x in rn_fms]\n",
        "      rn_fms = [x.view(x.size(0), -1) for x in rn_fms]\n",
        "      rn_rms = torch.stack(rn_fms)\n",
        "      rn_fms = torch.cat(rn_fms, dim=-1)\n",
        "      rn_fms = self.FPN_linear(rn_fms)\n",
        "      gb_fm = self.avgpool(gb_fm)\n",
        "      gb_fm = gb_fm.view(gb_fm.size(0), -1)\n",
        "      concatenated_features = torch.cat([rn_fms, gb_fm], dim = 1)\n",
        "    elif self.combined in [\"resnet_layer1\", \"resnet_layer2\", \"resnet_layer3\", \"resnet_layer4\"]:\n",
        "      rn_fms = self.avgpool(rn_fms)\n",
        "      rn_fms = rn_fms.reshape(rn_fms.size(0), -1)\n",
        "      if self.combined==\"resnet_layer2\":\n",
        "        rn_fms = self.Layer2_linear(rn_fms)\n",
        "      elif self.combined==\"resnet_layer3\":\n",
        "        rn_fms = self.Layer3_linear(rn_fms)\n",
        "      elif self.combined==\"resnet_layer4\":\n",
        "        rn_fms = self.Layer4_linear(rn_fms)\n",
        "      gb_fm = self.avgpool(gb_fm)\n",
        "      gb_fm = gb_fm.reshape(gb_fm.size(0),-1)\n",
        "      concatenated_features = torch.cat([rn_fms, gb_fm], dim=1)\n",
        "      if not no_attn:\n",
        "        self.no_attn_status_flag = print_status(\"Self attention is applied on combined features.\", self.no_attn_status_flag)\n",
        "        concatenated_features = self.combined_attn_layer(concatenated_features)\n",
        "    elif self.combined=='classification':\n",
        "      rn_fms = list(rn_fms.values())\n",
        "      if not self.no_attn:\n",
        "        self.no_attn_status_flag = print_status(\"Self attention on Retinanet's feature maps are applied.\", self.no_attn_status_flag)\n",
        "        rn_fms = [self.feature_map_attn(x) for x in rn_fms]\n",
        "      rn_fms = [self.avgpool(x) for x in rn_fms]\n",
        "      rn_fms = [x.view(x.size(0), -1) for x in rn_fms]\n",
        "      concatenated_features = torch.cat(rn_fms, dim = 1)\n",
        "    return concatenated_features\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference"
      ],
      "metadata": {
        "id": "BMz28l7rj8om"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPbpwntzYJpL"
      },
      "source": [
        "### GBCRet, No Combination, gbcnet VA, 30 epoch pretrained RN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "560c6393-ccf7-44d3-aa14-617160117c89",
        "id": "qeglK_VpYJpM"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\" to /root/.cache/torch/hub/checkpoints/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\n",
            "100%|██████████| 146M/146M [00:01<00:00, 114MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6464312076568604, median IoU: 0.6825039982795715, mAP = 0.298, mAP_50 = 0.786, mAP_75 = 0.16\n",
            "TP: 117, FN: 0, FP: 5\n",
            "Precision: 0.9590163934426229\n",
            "Recall: 1.0\n",
            "Acc: 0.9016 Spec: 0.9750 Sens: 0.8095\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"None\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, no combination, gbcnet VA, 30 epoch Pretrained RN/checkpoints/best_model_epoch_43.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'])\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "9796356b-bb04-45d5-e5d0-bd761e89a4d4",
        "id": "fkEytmAkYJpN"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, No Combination, gbcnet VA, 30 epoch pretrained RN')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VC00HqekMR7x"
      },
      "source": [
        "### GBCRet, No Combination, gbcnet VA, 50 epoch pretrained RN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9efaa74b-478b-4e99-de6f-6509a27e870b",
        "id": "MrEfAfTNMR8S"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6580765247344971, median IoU: 0.695464551448822, mAP = 0.302, mAP_50 = 0.795, mAP_75 = 0.166\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.8934 Spec: 0.9625 Sens: 0.8095\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"None\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, no combination, gbcnet VA, 50 epoch Pretrained RN/checkpoints/best_model_epoch_43.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "76dbd248-f1bf-4823-bd8b-49b1b3f8d70e",
        "id": "Fk0BG-KlMR8T"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, No Combination, gbcnet VA, 50 epoch pretrained RN')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9cJiNq7Suwq"
      },
      "source": [
        "### GBCRet, No Combination, gbcnet VA, 100 epoch pretrained RN\n",
        "This is the result of the best model after training over 200 epochs. The results reported in the paper were obtained from the optimal epoch, which is epoch 67, considering a patience of 30 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0d4ec25-cf05-41ad-c964-cdbbdda63d4a",
        "id": "IfAt5H8ESuw1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.659550130367279, median IoU: 0.6832026243209839, mAP = 0.307, mAP_50 = 0.801, mAP_75 = 0.155\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.9016 Spec: 0.9750 Sens: 0.8333\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"None\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, no combination, gbcnet VA, 100 epoch Pretrained RN/checkpoints/best_model_epoch_110.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "9ae486bb-521b-4bd7-ca3b-14ffddecb545",
        "id": "WlG1R-XVSuw1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, No Combination, gbcnet VA, 100 epoch pretrained RN')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yetBcdEULuO"
      },
      "source": [
        "### GBCRet, Layer 1, gbcnet VA, RN 50\n",
        "This is the result of the best model after training over 200 epochs. The results reported in the paper were obtained from the optimal epoch, which is epoch 55, considering a patience of 30 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bb591fb-6d01-4ee6-a8e5-a63fe4028b75",
        "id": "Wphul43SULuW"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6561865210533142, median IoU: 0.6890654563903809, mAP = 0.307, mAP_50 = 0.779, mAP_75 = 0.171\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.8934 Spec: 0.9750 Sens: 0.8095\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer1\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Combination layer1, gbcnet VA, 50 epoch pretrained RN/checkpoints/best_model_epoch_141.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4ab6d340-d7e7-439f-8a0a-07c51ae05124",
        "id": "Jp3zcaI-ULuX"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, Layer1 cobmbination, gbcnet VA, RN 50')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buOgnlB2UgqE"
      },
      "source": [
        "### GBCRet, Layer 2, gbcnet VA, RN 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79d2d42d-ce6b-4614-a1cf-0816cdc14d11",
        "id": "AG7XL1DFUgqO"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6574364900588989, median IoU: 0.6878405809402466, mAP = 0.305, mAP_50 = 0.794, mAP_75 = 0.162\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.8852 Spec: 0.9625 Sens: 0.7857\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer2\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Combination layer2, gbcnet VA, 50 epoch pretrained RN/checkpoints/best_model_epoch_43.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a5bc617d-799c-4e4d-ff4a-1863eb0b5c8e",
        "id": "LJ9LlKRZUgqP"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, Layer2 cobmbination, gbcnet VA, RN 50')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-2y060tZ5TR"
      },
      "source": [
        "### GBCRet, Layer 3, gbcnet VA, RN 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "070d93aa-b1f2-4b48-ab3f-afe2f45602e8",
        "id": "tiao07KwZ5Tm"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6602466106414795, median IoU: 0.6965856552124023, mAP = 0.314, mAP_50 = 0.808, mAP_75 = 0.168\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.8525 Spec: 0.9625 Sens: 0.6905\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer3\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Combination layer3, gbcnet VA, 50 epoch pretrained RN/checkpoints/best_model_epoch_44.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "6b0d0338-3ead-4120-fa04-66683653def6",
        "id": "LPPMEx_dZ5Tn"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, Layer3 cobmbination, gbcnet VA, RN 50')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bi8E9p1lZ6uD"
      },
      "source": [
        "### GBCRet, Layer 4, gbcnet VA, RN 50\n",
        "This is the result of the best model after training over 200 epochs. The results reported in the paper were obtained from the optimal epoch, which is epoch 43, considering a patience of 30 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e143a78-2317-44cd-fe49-d9146cfce02b",
        "id": "aGmM1m3TZ6um"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6592575907707214, median IoU: 0.6941011548042297, mAP = 0.313, mAP_50 = 0.809, mAP_75 = 0.154\n",
            "TP: 117, FN: 0, FP: 5\n",
            "Precision: 0.9590163934426229\n",
            "Recall: 1.0\n",
            "Acc: 0.8770 Spec: 0.9875 Sens: 0.7143\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Combination layer4, gbcnet VA, 50 epoch pretrained RN/checkpoints/best_model_epoch_75.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "f36eed38-9c7d-454e-b945-5f11f4fc3e2a",
        "id": "sSUsdlVhZ6un"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, Layer4 cobmbination, gbcnet VA, RN 50')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cohPJVKweuXM"
      },
      "source": [
        "### GBCRet, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, binary entropy loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d30d53a-c334-41f1-d189-67b6fa268313",
        "id": "TfEo1NWVeuXW"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6405605673789978, median IoU: 0.6759586930274963, mAP = 0.282, mAP_50 = 0.746, mAP_75 = 0.145\n",
            "TP: 116, FN: 0, FP: 6\n",
            "Precision: 0.9508196721311475\n",
            "Recall: 1.0\n",
            "Acc: 0.8770 Spec: 0.9500 Sens: 0.7857\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"None\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN/checkpoints/best_model_epoch_37.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "2a4ba106-420c-41ad-a10d-4a75cf075b42",
        "id": "7sLAcQ3IeuXX"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, BCELoss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tIWKUY3htrq"
      },
      "source": [
        "### GBCRet, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, cross entropy loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0463ccbd-025b-4334-b00b-9c9e12eed5ce",
        "id": "VNal7R7whtrr"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6532207131385803, median IoU: 0.6906358003616333, mAP = 0.303, mAP_50 = 0.761, mAP_75 = 0.139\n",
            "TP: 119, FN: 0, FP: 3\n",
            "Precision: 0.9754098360655737\n",
            "Recall: 1.0\n",
            "Acc: 0.8852 Spec: 0.9125 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"None\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss/checkpoints/best_model_epoch_43.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "3d963e1f-2aaf-485a-9c35-0074a423f180",
        "id": "NZawFmA9htrs"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, CEntropyLoss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vfUxRCLrwdl"
      },
      "source": [
        "### GBCRet, layer4 combination, Hierarchical classifiation dropout=0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ka9bN5A-rwdm",
        "outputId": "35bed647-2ce5-457d-c65c-cf5e6d6d2151"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6521246433258057, median IoU: 0.6848047375679016, mAP = 0.29, mAP_50 = 0.765, mAP_75 = 0.156\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.9098 Spec: 0.9750 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss/checkpoints/best_model_epoch_44.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "nbQ_R9Jirwdn",
        "outputId": "5b9a4475-efef-43f1-f140-f81f75feb0f3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.4')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGjza3jN1IW2"
      },
      "source": [
        "### GBCRet, layer4 combination, Hierarchical classifiation dropout=0.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2Kti_zOcFj7",
        "outputId": "bd1323c2-30ae-44e1-a8c2-e61937bec543"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.649829626083374, median IoU: 0.6860803365707397, mAP = 0.292, mAP_50 = 0.768, mAP_75 = 0.149\n",
            "TP: 117, FN: 0, FP: 5\n",
            "Precision: 0.9590163934426229\n",
            "Recall: 1.0\n",
            "Acc: 0.9098 Spec: 0.9250 Sens: 0.9048\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss, dropout=0.5/checkpoints/best_model_epoch_35.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "YJe98hbDihAD",
        "outputId": "cdce5de5-6453-4263-db10-073c0b13425f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+vuu3yWQybNy4EU2aNMlz9UHuMVQ9j49Zt24dAgIC0Lx58/ceMy9vfzbp6el4+vQpvvnmGwBQiMPOzg4nTpzAgwcP8mwnt8dh586dWuvqfzvWV69e4enTp6hRowbS0tJw7do1ldtLTEzEuXPn0LFjRzg4OMjLy5cvj7p162Lbtm1K78nrenj27Jn836UvQYEMbeT+Ik9JSVHaN2/ePLx69QqPHj1S+CX3tpo1ayp0w7Vs2RIlSpRA3759ERMTAxsbGwBvLiRVVKlSBRMmTEBOTg4uXbqECRMm4MWLFzA1NZXXuXHjBpKTk+Hi4pJnG48fP1bpmOrYunUrJkyYgHPnziEjI0Ne/u4vgw4dOmDVqlU4fPgwatasiT179uDRo0f48ccf5XVUPR8fHx+lOrdu3cLUqVPx559/5ntd/dvi4+Ph5+f3wVntt2/fhoeHh1JyWLp0afn+txUtWlThde4vsXfn3OSWvzsfxMPDQ55s5SpZsiSAN+eb+4s0L2ZmZnB2dlYos7e3VzpGfr/H/Mr9DN4ebgLeJAjFihVT+oyKFCmidCx7e3ulZdeqevfnM9fHJrE9efIESUlJmD9/vsJqqrfl53qUyWSYOXMmZs+ejYSEBIXxcUdHR6X677aROy8nP/cSye93/bYnT57g5cuXH21f1fP4mPj4eISFhan8vufPn2Ps2LFYuXKl0uf/9tyGKVOmIDw8HJ6enggMDETDhg3RoUMHFCtWDMCbz3nQoEGYNm0ali9fjho1aqBp06Zo3759nsMa6rh8+TJGjBiBffv2Kf3D/Xas+fW+nyngze+enTt3IjU1VeF3xbu/e3KHxl+8eCH/t8nQFUgiYWtrC3d3d1y6dElpX+5fiKqMsVtZWaFKlSrYtGkTUlNTYWNjAw8Pjzzb/xAnJyfUqVMHABASEoJSpUqhcePGmDlzJgYNGgTgzQ+3i4sLli9fnmcb7/5S0bTDhw+jadOmqFmzJmbPng13d3eYmJhg8eLFWLFihULdkJAQuLq64u+//0bNmjXx999/w83NTX6O6pzPu3/9AW+WexYuXFhh4uLDhw8BvPmleevWLRQtWlTjM+E/5H2zud9XLt6ZsKmNY79Nle9RWwris1BF7hyK9u3bIzw8PM865cuXV3id1/U4ceJEjBw5Ep07d8b48ePh4OAAIyMjDBgwIM95Gnm1kV/aXDWj6nloS6tWrXD06FEMHToUFSpUkC+9r1+/vkIcrVq1Qo0aNbBhwwbs2rULU6dOxeTJk7F+/Xr5nJvffvsNHTt2xKZNm7Br1y7069cPUVFROH78OIoUKfJJcSYlJSE4OBg2NjYYN24cihcvDjMzM5w5cwbDhw8vsM9M336udKHAJls2atQICxcuxMmTJ1G5cuVPbi87OxvAm14OS0tLNG7cGPPnz8exY8cQFBSkdozBwcGYOHEievToAUtLSxQvXhx79uxBtWrVPvoLSN2/LD9k3bp1MDMzw86dOxWWSS1evFiprrGxMdq2bYvo6GhMnjwZGzduRLdu3RQudFXO533u3LmDuLg4+V8eb+vduzeAN9n4u93Eb8dw4sQJZGVlvXf9tpeXF/bs2YNXr14p9Erkdle+e6+RT/XgwQOlvzSuX78OABq506gq32N+r6PczyA2Nlbhu8jMzERCQoJCAqmPnJ2dYW1tjZycnE+Kde3atahduzYWLVqkUJ6UlPTeCYVvy53MeunSJa18Zs7OzrCxsfnoHzqfeh7vKl68uMp/XL148QJ79+7F2LFjFZZz37hxI8/67u7u6N27N3r37o3Hjx+jYsWK+OWXXxQm7/r7+8Pf3x8jRozA0aNHUa1aNcydOxcTJkxQ+ZzeduDAATx79gzr169HzZo15eUJCQlKddX5mXrXtWvX4OTkpNRzSQW4/HPYsGGwsLBA586d8ejRI6X9qmRvz58/x9GjR+Hm5ibvoh82bBgsLS3RtWvXPNuPj4/PcynXu4YPH45nz55hwYIFAN5k3Tk5ORg/frxS3ezsbIVldpaWlkrL7j6VsbExJBKJQjfnrVu33nvnwx9//BEvXrxAjx49kJKSojRcpMr5vM+ECROwYcMGhS23vWHDhmHDhg0f/GELCwvD06dP8ccffyjty70OGjZsiJycHKU606dPh0Qi+aRVBnnJzs5WWNqamZmJefPmwdnZGYGBgZ/cvirfY36vozp16sDU1BSzZs1S+PlZtGgRkpOT5StB9JWxsTHCwsKwbt26PP/By72fQH7aeff3x5o1a3D//v18vb9ixYrw8fHBjBkzlD53TfxVaWRkhNDQUGzZsgWnT59W2p97jE89j3eFhYXh/Pnz2LBhw3uP+a7cPzre3f/uXSFzcnKUhg5cXFzg4eEhH7Z7+fKl/A++XP7+/jAyMlIY2lNXXrFmZmZi9uzZSnUtLS3zNdTh7u6OChUqYMmSJQrXwqVLl7Br1y40bNjwk+M2RAXWI1GiRAmsWLECbdq0gZ+fn/zOlkIIJCQkYMWKFTAyMsqzu2vt2rWwsrKCEAIPHjzAokWL8OLFC8ydO1eeaRYvXhwrVqzADz/8gNKlSyvc2fLo0aNYs2ZNvu442aBBA5QrVw7Tpk1DREQEgoOD0aNHD0RFReHcuXOoV68eTExMcOPGDaxZswYzZ85Ey5YtAQCBgYGYM2cOJkyYAF9fX7i4uODbb78FAIV1y6po1KgRpk2bhvr166Nt27Z4/Pgx/vzzT/j6+uY5tv3VV1/Jb9ZVunRpVKxYUWG/KufzPtWrV1cqy+19+Prrr5XuyPiuDh06YOnSpRg0aBBOnjyJGjVqIDU1FXv27EHv3r3RrFkzNGnSBLVr18bPP/+MW7duISAgALt27cKmTZswYMCADy6JVIeHhwcmT56MW7duoWTJkli1ahXOnTuH+fPna+Sud6p8j4GBgdizZw+mTZsGDw8P+Pj45Lls2tnZGZGRkRg7dizq16+Ppk2bIjY2FrNnz8bXX3/93jlHH1OrVi0cPHiwQLpmJ02ahP3796NKlSro1q0bypQpg+fPn+PMmTPYs2fPe5eWvq1x48YYN24cOnXqhKpVq+LixYtYvnx5nj1meTEyMsKcOXPQpEkTVKhQAZ06dYK7uzuuXbuGy5cvY+fOnZ96mpg4cSJ27dqF4OBgdO/eHaVLl0ZiYiLWrFmD//77D3Z2dp98Hu8aOnQo1q5di++//x6dO3dGYGAgnj9/js2bN2Pu3Ll53nfGxsYGNWvWxJQpU5CVlYXChQtj165dSn/lv3r1CkWKFEHLli0REBAAKysr7NmzB6dOncJvv/0G4M29GPr06YPvv/8eJUuWRHZ2NpYtWyZPID9V1apVYW9vj/DwcPTr1w8SiQTLli3L87oNDAzEqlWrMGjQIHz99dewsrJCkyZN8mx36tSpaNCgAYKCgtClSxf58k9bW1u1n5+Su0x7//79Gru/kF4p0DUiQoi4uDjRq1cv4evrK8zMzIS5ubkoVaqU6Nmzpzh37pxC3byWl1laWoqgoCCxevXqPNu/fv266Natm/D29hampqbC2tpaVKtWTfz+++8Ky4G8vLyUlqblio6OVloSNH/+fBEYGCjMzc2FtbW18Pf3F8OGDRMPHjyQ13n48KFo1KiRsLa2FgAUloI6OTmJb7755qOfT17LPxctWiRKlCghpFKpKFWqlFi8eLH8s8nLlClTBAAxceLE9x4nP+fzoc/oXaos/xTizbKtn3/+Wfj4+AgTExPh5uYmWrZsKeLj4+V1Xr16JQYOHCg8PDyEiYmJKFGihJg6darCkjwhhNKSXSHev4QsrziDg4NF2bJlxenTp0VQUJAwMzMTXl5e4o8//sizzXeXf1paWiqdX17fT36/x2vXromaNWsKc3NzAUC+NC6v5W9CvFnuWapUKWFiYiJcXV1Fr169lJYx5p7ju/K63gIDA4Wbm5tS3fed45MnT/Lcn9f1g3eWRQohxKNHj0RERITw9PSUXwvfffedmD9/vrzOh66v9PR0MXjwYOHu7i7Mzc1FtWrVxLFjx0RwcLDCz+DHrtH//vtP1K1bV1hbWwtLS0tRvnx58fvvv8v3q/Jd53Wet2/fFh06dBDOzs5CKpWKYsWKiYiICJGRkaHSeeR3+acQQjx79kz06dNHFC5cWJiamooiRYqI8PBw+XLbvNq6d++eaN68ubCzsxO2trbi+++/Fw8ePFA4p4yMDDF06FAREBAg/7wCAgLE7Nmz5e3cvHlTdO7cWRQvXlyYmZkJBwcHUbt2bbFnzx6FGD9l+eeRI0fEN998I8zNzYWHh4cYNmyY2LlzpwAg9u/fL6+XkpIi2rZtK+zs7AQA+TX/vs9yz549olq1asLc3FzY2NiIJk2aiCtXrijUed/1n1ecgwcPFhKJRFy9evXdr8ggSIT4gmaE6MiVK1dQtmxZhRsPadPMmTMxcOBA+aRHovx69eoVHBwcMGPGDPnNvYjo01SuXBleXl5Ys2aNrkPRCj5VpgDs378fQUFBBZJECCGwaNEiBAcHM4kglR06dAiFCxfms2aINOTly5c4f/48lixZoutQtIY9EgYiNTUVmzdvxv79+7FgwQJs2rQJTZs21XVYRERk4JhIGIhbt27Bx8cHdnZ26N27N3755Rddh0RERF8AJhJERESktoK79SAREREZHCYSREREpDYmEkSf6MCBA5BIJDhw4ICuQyEiKnBMJIhIycSJE997G3Z1JSUloXv37nB2doalpSVq166d78djd+zYERKJRGkrVaqURmMkItXxPhJEpGTixIlo2bLlR293nl8ymQyNGjXC+fPnMXToUDg5OWH27NmoVasWYmJiUKJEiY+2IZVKsXDhQoUyTT2OmojUx0SCqICkpaXBwsJC12HoxNq1a+XPvMl9lkurVq1QsmRJjB49Ol+PUi9UqJDazw8hIu3h0AaRCu7du4fQ0FBYWlrCxcUFAwcOzPNJhrVq1UK5cuUQExODmjVrwsLCAv/73/8AAI8fP0aXLl3g6uoKMzMzBAQEKN317tatW5BIJPj1118xffp0eHl5wdzcHMHBwXk+KXPfvn2oUaMGLC0tYWdnh2bNmuHq1asKdTp27JjnI9HHjBmj8JhliUSC1NRULFmyRD6EkJ8H3n3I2rVr4erqihYtWsjLnJ2d0apVK2zatCnfT4PMycnBy5cvPykWItIs9kgQ5dPr16/x3Xff4c6dO+jXrx88PDywbNky7Nu3L8/6z549Q4MGDdC6dWu0b98erq6ueP36NWrVqoW4uDj06dMHPj4+8ifTJiUloX///gptLF26FK9evUJERATS09Mxc+ZMfPvtt7h48SJcXV0BAHv27EGDBg1QrFgxjBkzRv60wmrVquHMmTN5Jg8fsmzZMnTt2hWVK1dG9+7dAUD+tNWsrKx8PY4ZABwcHGBk9OZvlbNnz6JixYry17kqV66M+fPn4/r16/D39/9ge2lpabCxsUFaWhrs7e3Rpk0bTJ48GVZWViqdHxFpmM4eF0b0mZkxY4YAoPDk2dTUVOHr66v0tMHg4GABQMydOzfPNv7++295WWZmpggKChJWVlbi5cuXQoj/eyqhubm5uHfvnrzuiRMnBAAxcOBAeVmFChWEi4uLePbsmbzs/PnzwsjISHTo0EFelteTPoXI++mVlpaWCk9kzJX7BM38bG8//dDS0lJ07txZqb1///1XABA7duxQ2ve2n376SQwfPlysWrVK/PPPPyI8PFwAENWqVRNZWVkffC8RaRd7JIjyadu2bXB3d5eP8QOAhYUFunfvjmHDhinVl0ql6NSpk1Ibbm5uaNOmjbzMxMQE/fr1Q5s2bXDw4EE0btxYvi80NBSFCxeWv65cuTKqVKmCbdu2Ydq0aUhMTMS5c+cwbNgwODg4yOuVL18edevWxbZt2zRy7rkCAgKwe/fufNV1c3OT//fr168hlUqV6piZmcn3f0hUVJTC69atW6NkyZL4+eefsXbtWrRu3TpfMRGR5jGRIMqn27dvw9fXV2E+AQD4+fnlWb9w4cIwNTVVaqNEiRJKXfylS5eW739bXqsZSpYsidWrVyvUzyuG0qVLY+fOnUhNTYWlpeWHTi3f7O3tUadOHZXfZ25unuc8iPT0dPl+VQ0cOBAjR47Enj17mEgQ6RATCSItUecfR216NwHKlZOTk+82MjMz8fz583zVdXZ2hrGxMQDA3d0diYmJSnVyyzw8PPIdQy5zc3M4OjrmOx4i0g4mEkT55OXlhUuXLkEIofCPcmxsrEptXLhwATKZTKFX4tq1a/L9b7tx44ZSG9evX5dPoMytn1cM165dg5OTk7w3wt7eHklJSUr13u0FAd6fdBw9ehS1a9fOc9+7EhIS5HFWqFABhw8fVjrvEydOwMLCAiVLlsxXm2979eoVnj59CmdnZ5XfS0Saw+WfRPnUsGFDPHjwAGvXrpWXpaWlYf78+Sq18fDhQ6xatUpelp2djd9//x1WVlYIDg5WqL9x40bcv39f/vrkyZM4ceIEGjRoAODNX/oVKlTAkiVLFJKES5cuYdeuXWjYsKG8rHjx4khOTsaFCxfkZYmJidiwYYNSnJaWlnkmHblzJPKzvT1HomXLlnj06BHWr18vL3v69CnWrFmDJk2aKMyfiI+PR3x8vPx1eno6Xr16pRTL+PHjIYRA/fr1lfYRUcHhY8SJ8iktLQ0BAQG4d+8e+vfvD3d3dyxbtgxZWVm4cOEC9u/fj1q1agF4cx+Jp0+fKt3z4fXr1wgMDER8fDz69u0Lb29vrF27FgcPHsSMGTPkyz9v3boFHx8f+Pv749WrV+jVqxcyMjIwY8YMSCQSXLx4Ee7u7gD+b/mnr68vunTpIl/+mZ2djZiYGPj4+AB4sxzVy8sLrq6u6NevH9LS0jBnzhw4OzvjzJkzePtXQaNGjXDw4EGMGzcOHh4e8PHxQZUqVdT+7HJyclC9enVcunRJ4c6Wd+7cwalTpxTmeOT2Yty6dUv+/1999RXatGkjvyX2zp07sW3bNtSvXx///vuv0pwTIipAOl0zQvSZuX37tmjatKmwsLAQTk5Oon///mLHjh15Lv8sW7Zsnm08evRIdOrUSTg5OQlTU1Ph7+8vFi9erFAnd/nn1KlTxW+//SY8PT2FVCoVNWrUEOfPn1dqc8+ePaJatWrC3Nxc2NjYiCZNmogrV64o1du1a5coV66cMDU1FX5+fuLvv//Oc/nntWvXRM2aNYW5ubkAkOdSUFU9f/5cdOnSRTg6OgoLCwsRHBwsTp06pVTPy8tLYZnqixcvRPv27YWvr6+wsLAQUqlUlC1bVkycOFFkZmZ+clxE9GnYI0Gkh3J7JKZOnYohQ4boOhwiovdifyARERGpjYkEERERqY2JBBEREamNcySIiIhIbeyRICIiIrUxkSAiIiK1MZEgIiIitRnkszaSf/xO1yGQnnFbG6frEIhIT71+rfy8GU3LenpTI+2YOBXTSDuaxB4JIiIiUptB9kgQERHpFVmOriPQGiYSRERE2iZkuo5Aa5hIEBERaZvMcBMJzpEgIiIitbFHgoiISMsEhzaIiIhIbRzaICIiIlLGHgkiIiJt49AGERERqc2A7yPBoQ0iIiJSG3skiIiItI1DG0RERKQ2rtogIiIiUsYeCSIiIi3jDamIiIhIfQY8tMFEgoiISNsMuEeCcySIiIhIbeyRICIi0jYDviEVEwkiIiJt49AGERERkTL2SBAREWkbV20QERGR2ji0QURERKSMPRJERETaxqENIiIiUpcQhrv8k0MbREREpDb2SBAREWkbJ1sSERGR2mQyzWyfYNKkSZBIJBgwYIC8LD09HREREXB0dISVlRXCwsLw6NEjldplIkFERKRtQqaZTU2nTp3CvHnzUL58eYXygQMHYsuWLVizZg0OHjyIBw8eoEWLFiq1zUSCiIjIgKWkpKBdu3ZYsGAB7O3t5eXJyclYtGgRpk2bhm+//RaBgYFYvHgxjh49iuPHj+e7fSYSRERE2ibL0cymhoiICDRq1Ah16tRRKI+JiUFWVpZCealSpVC0aFEcO3Ys3+1zsiUREZG2aWiyZUZGBjIyMhTKpFIppFJpnvVXrlyJM2fO4NSpU0r7Hj58CFNTU9jZ2SmUu7q64uHDh/mOiT0SREREn4moqCjY2toqbFFRUXnWvXv3Lvr374/ly5fDzMxMazGxR4KIiEjbNHRny8jISAwaNEih7H29ETExMXj8+DEqVqwoL8vJycGhQ4fwxx9/YOfOncjMzERSUpJCr8SjR4/g5uaW75iYSBAREWmbhoY2PjSM8a7vvvsOFy9eVCjr1KkTSpUqheHDh8PT0xMmJibYu3cvwsLCAACxsbG4c+cOgoKC8h0TEwkiIiIDZG1tjXLlyimUWVpawtHRUV7epUsXDBo0CA4ODrCxsUHfvn0RFBSEb775Jt/HYSJBRESkbXr60K7p06fDyMgIYWFhyMjIQEhICGbPnq1SGxIhhNBSfDqT/ON3ug6B9Izb2jhdh0BEeur169taP0b64WUaacesxo8aaUeTdNYj8fLly3zXtbGx0WIkREREpC6dJRJ2dnaQSCQfrCOEgEQiQU6O4T5+lYiIDJ8hP0ZcZ4nE/v37dXVoIiKigqWncyQ0QWeJRHBwsK4OTUREVLAM+DHierVqIy0tDXfu3EFmZqZC+btPKyMiIiL9oBeJxJMnT9CpUyds3749z/2cI0FERJ81Ax7a0ItnbQwYMABJSUk4ceIEzM3NsWPHDixZsgQlSpTA5s2bdR0eERHRpxEyzWx6SC96JPbt24dNmzahUqVKMDIygpeXF+rWrQsbGxtERUWhUaNGug6RiIiI8qAXPRKpqalwcXEBANjb2+PJkycAAH9/f5w5c0aXoREREX06mUwzmx7Si0TCz88PsbGxAICAgADMmzcP9+/fx9y5c+Hu7q7j6IiIiD4Rhza0q3///khMTAQAjB49GvXr18fy5cthamqK6Oho3QZHRERE76UXiUT79u3l/x0YGIjbt2/j2rVrKFq0KJycnHQYGRERkQbo6bCEJuhFIvEuCwsLVKxYUddhEBERaQYTCe0SQmDt2rXYv38/Hj9+DNk7H/j69et1FBkRERF9iF4kEgMGDMC8efNQu3ZtuLq6fvRhXkRERJ8VPZ0oqQl6kUgsW7YM69evR8OGDXUdChERkeYZ8NCGXiz/tLW1RbFixXQdxmdJ2qQNLMf+CZv5W2D951pYDBgHI7ciCnWMXNxh0X8srP9cB5v5m2HeZyQkNvY6ipgKWrVqlbF27SLcvHkSr1/fRpMm9XQdEukQrwcdMeDln3qRSIwZMwZjx47F69evdR3KZ8e4VHlk7tmMlLF9kDp5GGBsDMvhUwCp2ZsKUjNYDJsCCIHUqCFIGdcfkkImsBg0AeAQ0hfB0tICFy9exYABI3UdCukBXg+kaXoxtNGqVSv8888/cHFxgbe3N0xMTBT28+6W75c2NVLh9ev5U2Azez2MvUsgJ/YiCpUoCyNnV6SM6AGkp715z7zJsJm7EcZlvkLOZX62hm7XrgPYteuArsMgPcHrQUcMeGhDLxKJ8PBwxMTEoH379pxs+Ykk5pYAAJH66k2BiSkgAGRn/V+lrExACBQqWY6JBBFRQdDTYQlN0ItE4t9//8XOnTtRvXp1XYfyeZNIYNY+AtmxFyG7dwsAkBN3Bch4DbMfuiF9zaI3dVp1hcTYGBI7R93GS0REnz29SCQ8PT1hY2Oj1nszMjKQkZGhWJYjg9RYL6Z/FCiz8H4wLuKNlPH95WXiVTLSfh8Hs44DYFOvOSAEso7tQ07CdYPOkImI9IoBD23oxb+2v/32G4YNG4Zbt26p/N6oqCjY2toqbNMuqd7O586sQ1+YVPgGKVGDIV48VdiXfSkGKUN+xKuIMLzs3Ryv502CxN4JsseJOoqWiOgLY8BP/9SLHon27dsjLS0NxYsXh4WFhdJky+fPn7/3vZGRkRg0aJBCWXrPZlqJU1+ZdegLk8DqSJ04COLJw/fWEykvAQDGZSpAYmOH7DNHCypEIiIyUHqRSMyYMUPt90qlUkilUoUy8QUNa5iF94Np0HdInTESIj0NEts394cQaalvJlUCMKkRAtmDOxCvkmDsWxZm7SOQuWMdZA/v6TJ0KiCWlhYoXtxb/trb2xPly5fBixdJuHv3ge4CI53g9aAjQug6Aq3ReSKRlZWFgwcPYuTIkfDx8dF1OJ8daZ03vS9WP09XKE+bPwVZh3cCAIzcPd9MsLSyhuzJI2RsXo7MHWsLPFbSjYoVy2PXrlXy11OmjAIALFu2Bt27D9FVWKQjvB50RE+HJTRBIoTu0yRbW1ucO3dOY4lE8o/faaQdMhxua+N0HQIR6anXr29r/xj/jNZIO+ZtxmqkHU3SizGA0NBQbNy4UddhEBERaQcnW2pXiRIlMG7cOBw5cgSBgYGwtLRU2N+vXz8dRUZERKQBBrzcXi8SiUWLFsHOzg4xMTGIiYlR2CeRSJhIEBHR501PexM0QS8SiYSEBF2HQERERGrQizkSbxNCQA/mfxIREWmOEJrZVDBnzhyUL18eNjY2sLGxQVBQELZv3y7fX6tWLUgkEoWtZ8+eKp+a3iQSS5cuhb+/P8zNzWFubo7y5ctj2bJlug6LiIjo0+lgsmWRIkUwadIkxMTE4PTp0/j222/RrFkzXL58WV6nW7duSExMlG9TpkxR+dT0Ymhj2rRpGDlyJPr06YNq1aoBAP777z/07NkTT58+xcCBA3UcIRER0eelSZMmCq9/+eUXzJkzB8ePH0fZsmUBABYWFnBzc/uk4+hFIvH7779jzpw56NChg7ysadOmKFu2LMaMGcNEgoiIPm86nmyZk5ODNWvWIDU1FUFBQfLy5cuX4++//4abmxuaNGmCkSNHwsLCQqW29SKRSExMRNWqVZXKq1atisREPliKiIg+cxpa/pnXE6/zelRErosXLyIoKAjp6emwsrLChg0bUKZMGQBA27Zt4eXlBQ8PD1y4cAHDhw9HbGws1q9fr1JMejFHwtfXF6tXr1YqX7VqFUqUKKGDiIiIiPRPXk+8joqKem99Pz8/nDt3DidOnECvXr0QHh6OK1euAAC6d++OkJAQ+Pv7o127dli6dCk2bNiA+Ph4lWLSi1tkr1u3Dj/88APq1KkjnyNx5MgR7N27F6tXr0bz5s1Vao+3yKZ38RbZRPQ+BXGL7LT5mhmiNw6fpFKPxLvq1KmD4sWLY968eUr7UlNTYWVlhR07diAkJCTfMenF0EZYWBhOnDiBadOmyW+VXbp0aZw8eRJfffWVboMjIiL6VBqaI6FK0pB3GDKlRCTXuXPnAADu7u4qtakXiQQABAYGYvny5boOg4iIyCBERkaiQYMGKFq0KF69eoUVK1bgwIED2LlzJ+Lj47FixQo0bNgQjo6OuHDhAgYOHIiaNWuifPnyKh1Hp4mEkZERJBLJB+tIJBJkZ2cXUERERERaoINnbTx+/BgdOnRAYmIibG1tUb58eezcuRN169bF3bt3sWfPHsyYMQOpqanw9PREWFgYRowYofJxdJpIbNiw4b37jh07hlmzZkFmwPcnJyKiL4Ss4KcjLlq06L37PD09cfDgQY0cR6eJRLNmzZTKYmNj8dNPP2HLli1o164dxo0bp4PIiIiINMiA/yjWi+WfAPDgwQN069YN/v7+yM7Oxrlz57BkyRJ4eXnpOjQiIiJ6D51PtkxOTsbEiRPx+++/o0KFCti7dy9q1Kih67CIiIg0x4B7JHSaSEyZMgWTJ0+Gm5sb/vnnnzyHOoiIiD57ur9lk9boNJH46aefYG5uDl9fXyxZsgRLlizJs56qt+skIiKigqHTRKJDhw4fXf5JRET02ePQhnZER0fr8vBEREQFQwfLPwuK3qzaICIios+PzldtEBERGTwd3NmyoDCRICIi0jYObRAREREpY48EERGRlgmu2iAiIiK1GfDQBhMJIiIibTPgyZacI0FERERqY48EERGRtnFog4iIiNRmwJMtObRBREREamOPBBERkbZxaIOIiIjUxlUbRERERMrYI0FERKRtHNogIiIidRnyLbI5tEFERERqY48EERGRtnFog4iIiNTGRIKIiIjUxuWfRERERMrYI0FERKRtHNogIiIidQkDTiQ4tEFERERqYyJBRESkbTKhmU0Fc+bMQfny5WFjYwMbGxsEBQVh+/bt8v3p6emIiIiAo6MjrKysEBYWhkePHql8akwkiIiItE0m08ymgiJFimDSpEmIiYnB6dOn8e2336JZs2a4fPkyAGDgwIHYsmUL1qxZg4MHD+LBgwdo0aKFyqcmEUIY3MBN8o/f6ToE0jNua+N0HQIR6anXr29r/Riv+jTUSDvWf2z7pPc7ODhg6tSpaNmyJZydnbFixQq0bNkSAHDt2jWULl0ax44dwzfffJPvNtkjQUREpG06GNp4W05ODlauXInU1FQEBQUhJiYGWVlZqFOnjrxOqVKlULRoURw7dkyltrlqg4iISNs0tGojIyMDGRkZCmVSqRRSqTTP+hcvXkRQUBDS09NhZWWFDRs2oEyZMjh37hxMTU1hZ2enUN/V1RUPHz5UKSb2SBAREX0moqKiYGtrq7BFRUW9t76fnx/OnTuHEydOoFevXggPD8eVK1c0GhN7JIiIiLRMU9MRIyMjMWjQIIWy9/VGAICpqSl8fX0BAIGBgTh16hRmzpyJH374AZmZmUhKSlLolXj06BHc3NxUiok9EkRERNqmoTkSUqlUvpwzd/tQIqEUhkyGjIwMBAYGwsTEBHv37pXvi42NxZ07dxAUFKTSqbFHgoiISNt0cGfLyMhINGjQAEWLFsWrV6+wYsUKHDhwADt37oStrS26dOmCQYMGwcHBATY2Nujbty+CgoJUWrEBMJEgIiIySI8fP0aHDh2QmJgIW1tblC9fHjt37kTdunUBANOnT4eRkRHCwsKQkZGBkJAQzJ49W+XjGOR9JAqZFtZ1CKRnXj84rOsQSI98VbatrkMgPXLp0XGtHyO5U52PV8oH28V7NNKOJrFHgoiISNv40C4iIiIiZeyRICIi0jbVHpPxWWEiQUREpGWCQxtEREREytgjQUREpG0G3CPBRIKIiEjbDHiOBIc2iIiISG3skSAiItIyQ55syUSCiIhI2wx4aIOJBBERkZYZco8E50gQERGR2tgjQUREpG0c2iAiIiJ1CQNOJDi0QURERGpjjwQREZG2GXCPBBMJIiIiLePQBhEREVEe2CNBRESkbQbcI8FEgoiISMsMeWiDiQQREZGWGXIiwTkSREREpDb2SBAREWmZIfdIMJEgIiLSNiHRdQRao5GhjaSkJE00Q0RERJ8ZlROJyZMnY9WqVfLXrVq1gqOjIwoXLozz589rNDgiIiJDIGSa2fSRyonE3Llz4enpCQDYvXs3du/eje3bt6NBgwYYOnSoxgMkIiL63AmZRCObPlJ5jsTDhw/licTWrVvRqlUr1KtXD97e3qhSpYrGAyQiIiL9pXKPhL29Pe7evQsA2LFjB+rUqQMAEEIgJydHs9EREREZAEMe2lC5R6JFixZo27YtSpQogWfPnqFBgwYAgLNnz8LX11fjARIREX3uhAGv2lA5kZg+fTq8vb1x9+5dTJkyBVZWVgCAxMRE9O7dW+MBEhERkf5SeWjDxMQEQ4YMwcyZM/HVV1/JywcOHIiuXbtqNDgiIiJDoIuhjaioKHz99dewtraGi4sLQkNDERsbq1CnVq1akEgkClvPnj1VOk6+eiQ2b96c7wabNm2qUgBERESGThcrLg4ePIiIiAh8/fXXyM7Oxv/+9z/Uq1cPV65cgaWlpbxet27dMG7cOPlrCwsLlY6Tr0QiNDQ0X41JJBJOuCQiInqHEAV/zB07dii8jo6OhouLC2JiYlCzZk15uYWFBdzc3NQ+Tr6GNmQyWb42JhFERET6KTk5GQDg4OCgUL58+XI4OTmhXLlyiIyMRFpamkrtftKzNtLT02FmZvYpTRARERk8TQ1tZGRkICMjQ6FMKpVCKpV+8H0ymQwDBgxAtWrVUK5cOXl527Zt4eXlBQ8PD1y4cAHDhw9HbGws1q9fn++YVJ5smZOTg/Hjx6Nw4cKwsrLCzZs3AQAjR47EokWLVG2OiIjI4GnqzpZRUVGwtbVV2KKioj56/IiICFy6dAkrV65UKO/evTtCQkLg7++Pdu3aYenSpdiwYQPi4+PzfW4qJxK//PILoqOjMWXKFJiamsrLy5Urh4ULF6raHBEREeVTZGQkkpOTFbbIyMgPvqdPnz7YunUr9u/fjyJFinywbu4dquPi4vIdk8qJxNKlSzF//ny0a9cOxsbG8vKAgABcu3ZN1eaIiIgMnhCa2aRSKWxsbBS29w1rCCHQp08fbNiwAfv27YOPj89H4zx37hwAwN3dPd/npvIcifv37+d5B0uZTIasrCxVmyMiIjJ4ulj+GRERgRUrVmDTpk2wtrbGw4cPAQC2trYwNzdHfHw8VqxYgYYNG8LR0REXLlzAwIEDUbNmTZQvXz7fx1E5kShTpgwOHz4MLy8vhfK1a9cq3KBKVTKZDHFxcXj8+DFkMsW7bry9TIWIiIg+bs6cOQDe3HTqbYsXL0bHjh1hamqKPXv2YMaMGUhNTYWnpyfCwsIwYsQIlY6jciIxatQohIeH4/79+5DJZFi/fj1iY2OxdOlSbN26VdXmAADHjx9H27Ztcfv2bYh3Ftvy3hRERPS508WzNt799/Rdnp6eOHjw4CcfR+U5Es2aNcOWLVuwZ88eWFpaYtSoUbh69Sq2bNmCunXrqhVEz549UalSJVy6dAnPnz/Hixcv5Nvz58/VapOIiEhf8Omf76hRowZ2796tsSBu3LiBtWvX8umhREREnxm1b0h1+vRpXL16FcCbeROBgYFqB1GlShXExcUxkSAiIoMk42PE/8+9e/fQpk0bHDlyBHZ2dgCApKQkVK1aFStXrvzoGtW89O3bF4MHD8bDhw/h7+8PExMThf2qzB4lIiLSN7qYI1FQVE4kunbtiqysLFy9ehV+fn4AgNjYWHTq1Aldu3ZVekhIfoSFhQEAOnfuLC+TSCQQQnCyJRERffZ0sfyzoKicSBw8eBBHjx6VJxEA4Ofnh99//x01atRQK4iEhAS13kdERES6pXIi4enpmeeNp3JycuDh4aFWEO/ek4KIiMiQ6OIx4gVF5URi6tSp6Nu3L/78809UqlQJwJuJl/3798evv/6qVhCbN2/Os1wikcDMzAy+vr75urUnERGRPjLkoQ2J+NgdKwDY29tDIvm/DyE1NRXZ2dkoVOhNHpL735aWlmrd98HIyEg+J0IhuLfmSVSvXh0bN26Evb39R9srZFpY5RjIsL1+cFjXIZAe+apsW12HQHrk0qPjWj/GleKNNNJOmfh/NdKOJuWrR2LGjBlaDWL37t34+eef8csvv6By5coAgJMnT2LkyJEYMWIEbG1t0aNHDwwZMoSPKicios/OF7/8Mzw8XKtB9O/fH/Pnz0fVqlXlZd999x3MzMzQvXt3XL58GTNmzFBY1UFERPS54PLP90hPT0dmZqZCmY2NjcrtxMfH5/k+Gxsb3Lx5EwBQokQJPH36VL1AiYiISCtUftZGamoq+vTpAxcXF1haWsLe3l5hU0dgYCCGDh2KJ0+eyMuePHmCYcOG4euvvwbw5jbanp6earVPRESkS0JoZtNHKicSw4YNw759+zBnzhxIpVIsXLgQY8eOhYeHB5YuXapWEIsWLUJCQgKKFCkCX19f+Pr6okiRIrh16xYWLlwIAEhJSVH50aZfsl49wxF3/ThSXsbj6H9b8HWlCroOiXRg4bLVKFetASbNmKu0TwiBnoNHoly1Bth76KgOoiNd6D2kKy49Oq6wbf5vpa7DMngyIdHIpo9UHtrYsmULli5dilq1aqFTp06oUaMGfH194eXlheXLl6Ndu3YqB+Hn54crV65g165duH79urysbt26MDJ6k+uEhoaq3O6X6vvvm+LXqaPRO+InnDx1Fv36dsW2f5ejTLmaePLkma7DowJy8Wos1mzahpK+eS+dXrZqI/Tz1xJp241r8ejasq/8Ne8eTJ9C5R6J58+fo1ixYgDezGHIXe5ZvXp1HDp0SP1AjIxQv3599OvXD/369UNISIg8iSDVDOzfDQsXrcCSpatx9eoN9I74CWlpr9GpY2tdh0YFJC3tNX4aOxVjhveHjbWV0v5r1+OxZOU6jP/fQB1ER7qWk52DZ0+ey7ek58m6DsngCSHRyKaPVO6RKFasGBISElC0aFGUKlUKq1evRuXKlbFlyxb5Q7zyY9asWejevTvMzMwwa9asD9bt16+fqmF+sUxMTFCxYnlMmvKHvEwIgb37/sM336j/hFb6vEz47U/UDPoaQV9/hXlL/lHY9zo9HcPGTsbPgyPg5OigowhJl4oW88S+81uQkZGJ86cvYcYvs/Hw/iNdh2XQ9HV+gyaonEh06tQJ58+fR3BwMH766Sc0adIEf/zxB7KysjBt2rR8tzN9+nS0a9cOZmZmmD59+nvrSSQSJhIqcHJyQKFChfD4keIKl8ePn6CUX3EdRUUFadueA7h6PR4rF87Mc/+UWfNRoVwZfFsjqIAjI31w4cxljOg3Hrfi78DJxRG9h3TB0k1zERrcDmmpaboOz2Dp6/wGTVA5kRg48P+6QuvUqYNr164hJiYGvr6+Kj3u++0HdX3KQ7syMjKQkZGhUJZ7N0yiL03ioyeYNGMeFsyYCKnUVGn//sPHcSLmPNYu/iOPd9OX4L99x+T/ff1KHC6euYxdMRtRv9l3WL9iiw4jo8/VJ91HAnjzwC1dPnQrKioKY8eOVSiTGFlBYqz6/SwMwdOnz5GdnQ0XVyeFchcXZzx89OQ97yJDcSX2Bp6/SEKrzn3kZTk5MsScu4R/1m/BD6GNcPd+IoLqt1R438Cff0HFgLKI/mNKQYdMOvbqZQpux99BUZ8iug7FoOnr/AZNyFci8bE5DG9TZxgiJycH0dHR2Lt3Lx4/fgyZTKawf9++fe99b2RkJAYNGqRQZu9YSuUYDEVWVhbOnLmAb2tXx+bNOwG8GR76tnZ1zJ6zWMfRkbZ9E1gBG5bNUSgb8cs0+Hh5okv772Fva4PvQxsq7G/+Yy8M69cdtapVKchQSU+YW5jD07swtqzdoetQDNoXP7TxoTkMb1N3PkP//v0RHR2NRo0aoVy5cioNS0ilUkilUqU4vmTTZy7A4kXTEXPmAk6dOot+fbvB0tIc0UtW6To00jJLSwuUKOatUGZubgY7G2t5eV4TLN1dnVHEw60AIiRdGzK6Lw7s+g8P7j2Ei6sTIoZ1Q06ODNs27NJ1aPSZylci8SlzGPJj5cqVWL16NRo2bPjxyvRRa9ZshrOTA8aMGgI3N2ecP38ZjRq3x+PHvMU40ZfO1cMFU+aOg529LZ4/S8LZk+fRrmFXvHiWpOvQDJoBL9rI32PEtc3DwwMHDhxAyZIlNdIeHyNO7+JjxOltfIw4va0gHiN+1D1MI+1UTVynkXY0SS/u+DR48GDMnDkTepDTEBERkQo+edWGJvz333/Yv38/tm/fjrJly8LExERh//r163UUGRER0af74ldtaJudnR2aN2+u6zCIiIi0QvbxKp8tvUgkFi/mskQiIqLPkVpzJA4fPoz27dsjKCgI9+/fBwAsW7YM//33n9qBZGdnY8+ePZg3bx5evXoFAHjw4AFSUlLUbpOIiEgfCEg0sukjlROJdevWISQkBObm5jh79qz89tTJycmYOHGiWkHcvn0b/v7+aNasGSIiIvDkyZs7ME6ePBlDhgxRq00iIiJ9IROa2fSRyonEhAkTMHfuXCxYsEBhUmS1atVw5swZtYLo378/KlWqhBcvXsDc3Fxe3rx5c+zdu1etNomIiPSFDBKNbPpI5UQiNjYWNWvWVCq3tbVFUlKSWkEcPnwYI0aMgKmp4kOGvL295UMnRERElH9RUVH4+uuvYW1tDRcXF4SGhiI2NlahTnp6OiIiIuDo6AgrKyuEhYXh0SPVHimvciLh5uaGuLg4pfL//vsPxYoVU7U5AIBMJkNOTo5S+b1792Btba1Wm0RERPpCF3MkDh48iIiICBw/fhy7d+9GVlYW6tWrh9TUVHmdgQMHYsuWLVizZg0OHjyIBw8eoEWLFiodR+VVG926dUP//v3x119/QSKR4MGDBzh27BiGDBmCkSNHqtocAKBevXqYMWMG5s+fD+DNszJSUlIwevRo3jabiIg+e7pY/rljh+KD2KKjo+Hi4oKYmBjUrFkTycnJWLRoEVasWIFvv/0WwJtVlKVLl8bx48fxzTff5Os4KicSP/30E2QyGb777jukpaWhZs2akEqlGDJkCPr27atqcwCA3377DSEhIShTpgzS09PRtm1b3LhxA46Ojvjnn3/UapOIiIj+T3JyMgDAweHNg/tiYmKQlZWFOnXqyOuUKlUKRYsWxbFjx7SXSEgkEvz8888YOnQo4uLikJKSgjJlysDKykrVpuSKFCmC8+fPY+XKlbhw4QJSUlLQpUsXtGvXTmHyJRER0edIU0s3MzIy5Kslc+X1FOx3yWQyDBgwANWqVUO5cuUAAA8fPoSpqSns7OwU6rq6uuLhw4f5jkntZ22YmpqiTJkyqFy58iclEQDw7NkzFCpUCO3bt0ffvn3h5OSE2NhYnD59+pPaJSIi0gcyDW1RUVGwtbVV2KKioj56/IiICFy6dAkrV67U+Lmp3CNRu3ZtSCTvz6z27duX77YuXryIJk2a4O7duyhRogRWrlyJ+vXrIzU1FUZGRpg+fTrWrl2L0NBQVcMkIiIyOJGRkRg0aJBC2cd6I/r06YOtW7fi0KFDKFKkiLzczc0NmZmZSEpKUuiVePToEdzc3PIdk8o9EhUqVEBAQIB8K1OmDDIzM3HmzBn4+/ur1NawYcPg7++PQ4cOoVatWmjcuDEaNWqE5ORkvHjxAj169MCkSZNUDZGIiEivaKpHQiqVwsbGRmF7XyIhhECfPn2wYcMG7Nu3Dz4+Pgr7AwMDYWJionC/ptjYWNy5cwdBQUH5PjeVeySmT5+eZ/mYMWNUvp31qVOnsG/fPpQvXx4BAQGYP38+evfuDSOjN/lN37598z3Zg4iISF/p4vbWERERWLFiBTZt2gRra2v5vAdbW1uYm5vD1tYWXbp0waBBg+Dg4AAbGxv07dsXQUFBKv3bq/YciXe1b98ef/31l0rvef78ubz7xMrKCpaWlrC3t5fvt7e3lz93g4iIiPJvzpw5SE5ORq1ateDu7i7fVq1aJa8zffp0NG7cGGFhYahZsybc3Nywfv16lY6jsad/Hjt2DGZmZiq/7935Fh+af0FERPQ5kungnzYhPv5wDjMzM/z555/4888/1T6OyonEu3e8EkIgMTERp0+fVuuGVB07dpSP76Snp6Nnz56wtLQEAKUlLkRERJ8jfX1OhiaonEjY2toqvDYyMoKfnx/GjRuHevXqqdRWeHi4wuv27dsr1enQoYOqIRIREekVPX1wp0aolEjk5OSgU6dO8Pf3V5jLoK7Fixd/chtERESkOypNtjQ2Nka9evXUfsonERHRl0hTyz/1kcqrNsqVK4ebN29qIxYiIiKDJJNINLLpI5UTiQkTJmDIkCHYunUrEhMT8fLlS4WNiIiIvhz5niMxbtw4DB48WP5Y76ZNmyos1RRCQCKRICcnR/NREhERfcY42RLA2LFj0bNnT+zfv1+b8RARERkcfZ3foAn5TiRyb2wRHBystWCIiIjo86LS8k/edZKIiEh1urizZUFRKZEoWbLkR5OJ58+ff1JAREREhoZ3tvz/xo4dq3RnSyIiIvpyqZRItG7dGi4uLtqKhYiIyCBx1QY4P4KIiEhdnCOB/D2OlIiIiJRx+ScAmcyQPwYiIiJSh8qPESciIiLVGHKfPhMJIiIiLTPkORIqP7SLiIiIKBd7JIiIiLTMkGcZMpEgIiLSMkNOJDi0QURERGpjjwQREZGWCQOebMlEgoiISMs4tEFERESUB/ZIEBERaZkh90gwkSAiItIy3tmSiIiI1MY7WxIRERHlgT0SREREWsY5EkRERKQ2Q04kOLRBRERkoA4dOoQmTZrAw8MDEokEGzduVNjfsWNHSCQSha1+/foqHYOJBBERkZYJDW2qSk1NRUBAAP7888/31qlfvz4SExPl2z///KPSMTi0QUREpGW6WrXRoEEDNGjQ4IN1pFIp3Nzc1D4GeySIiIi+YAcOHICLiwv8/PzQq1cvPHv2TKX3s0eCiIhIyzQ12TIjIwMZGRkKZVKpFFKpVK326tevjxYtWsDHxwfx8fH43//+hwYNGuDYsWMwNjbOVxvskSAiItIyTc2RiIqKgq2trcIWFRWldlytW7dG06ZN4e/vj9DQUGzduhWnTp3CgQMH8t0GEwkiIqLPRGRkJJKTkxW2yMhIjbVfrFgxODk5IS4uLt/v4dAGERGRlsk09LSNTxnGyI979+7h2bNncHd3z/d7mEjQF6FUqZa6DoH0yKXlnXUdAn1hdHVDqpSUFIXehYSEBJw7dw4ODg5wcHDA2LFjERYWBjc3N8THx2PYsGHw9fVFSEhIvo/BRIKIiEjLdPX0z9OnT6N27dry14MGDQIAhIeHY86cObhw4QKWLFmCpKQkeHh4oF69ehg/frxKvR5MJIiIiAxUrVq1IMT705idO3d+8jGYSBAREWmZIT9rg4kEERGRlunqzpYFgcs/iYiISG3skSAiItIyTS3/1EdMJIiIiLTMcNMIDm0QERHRJ2CPBBERkZZx1QYRERGpzZDnSHBog4iIiNTGHgkiIiItM9z+CCYSREREWsc5EkRERKQ2zpEgIiIiygN7JIiIiLTMcPsjmEgQERFpnSHPkeDQBhEREamNPRJERERaJgx4cIOJBBERkZZxaIOIiIgoD+yRICIi0jJDvo8EEwkiIiItM9w0gkMbRERE9AnYI0FERKRlHNogIiIitRnyqg0mEkRERFpmyPeR4BwJIiIiUht7JIiIiLTMkIc29KJHYty4cUhLS1Mqf/36NcaNG6eDiIiIiDRHaOh/+kgvEomxY8ciJSVFqTwtLQ1jx47VQURERESUH3oxtCGEgEQiUSo/f/48HBwcdBARERGR5hjy0IZOEwl7e3tIJBJIJBKULFlSIZnIyclBSkoKevbsqcMIiYiIPp1M6OewhCboNJGYMWMGhBDo3Lkzxo4dC1tbW/k+U1NTeHt7IygoSIcREhERfb4OHTqEqVOnIiYmBomJidiwYQNCQ0Pl+4UQGD16NBYsWICkpCRUq1YNc+bMQYkSJfJ9DJ0mEuHh4QAAHx8fVK1aFSYmJroMh4iISCt01R+RmpqKgIAAdO7cGS1atFDaP2XKFMyaNQtLliyBj48PRo4ciZCQEFy5cgVmZmb5OoZezJEIDg6GTCbD9evX8fjxY8hkiqNJNWvW1FFkREREn05Xt8hu0KABGjRokOc+IQRmzJiBESNGoFmzZgCApUuXwtXVFRs3bkTr1q3zdQy9SCSOHz+Otm3b4vbt2xDvjCNJJBLk5OToKDIiIiLDlJCQgIcPH6JOnTryMltbW1SpUgXHjh37vBKJnj17olKlSvj333/h7u6e5woOIiKiz5Wm7gGRkZGBjIwMhTKpVAqpVKpyWw8fPgQAuLq6KpS7urrK9+WHXtxH4saNG5g4cSJKly4NOzs72NraKmxERESfM5mGtqioKKV/I6Oiogr6dBToRSJRpUoVxMXF6ToMIiIirZBBaGSLjIxEcnKywhYZGalWTG5ubgCAR48eKZQ/evRIvi8/9GJoo2/fvhg8eDAePnwIf39/pdUb5cuX11FkRERE+kPdYYy8+Pj4wM3NDXv37kWFChUAAC9fvsSJEyfQq1evfLejF4lEWFgYAKBz587yMolEIr/jJSdbEhHR50xXz8lISUlR6PFPSEjAuXPn4ODggKJFi2LAgAGYMGECSpQoIV/+6eHhoXCviY/Ri0QiISFB1yEQERFpja5ukX369GnUrl1b/nrQoEEA3tzHKTo6GsOGDUNqaiq6d++OpKQkVK9eHTt27Mj3PSQAQCLeXW9pAAqZFtZ1CKRnvGxcP16JvhiXlnf+eCX6YpiH9NH6MVp4NdVIO+tvb9ZIO5qkFz0Sua5cuYI7d+4gMzNTobxpU818AURERLpggH+zy+lFInHz5k00b94cFy9elM+NACC/nwTnSBAR0edMV3e2LAh6sfyzf//+8PHxwePHj2FhYYHLly/j0KFDqFSpEg4cOKDr8IiIiOg99KJH4tixY9i3bx+cnJxgZGQEIyMjVK9eHVFRUejXrx/Onj2r6xCJiIjUpqvJlgVBL3okcnJyYG1tDQBwcnLCgwcPAABeXl6IjY3VZWhERESfTGjof/pIL3okypUrh/Pnz8PHxwdVqlTBlClTYGpqivnz56NYsWK6Do+IiIjeQy8SiREjRiA1NRUAMG7cODRu3Bg1atSAo6MjVq1apePoiIiIPo0hT7bUi0QiJCRE/t++vr64du0anj9/Dnt7ez4JlIiIPntc/qkDDg4Oug6BiIhIIwx5sqVeJBKpqamYNGkS9u7di8ePH0MmU/zIb968qaPIiIiI6EP0IpHo2rUrDh48iB9//BHu7u4cztCAXj3DMXhQL7i5OePChSvoP2AkTp0+p+uwSMd69OuIYaP6YfHcFZgw4lddh0NatvrwRaw5chEPnr0EABR3d0T3+l+jehlvAMDTl6mYvvEIjsfeRWpGJrxd7NG1XiXUqeCrw6gNk76uuNAEvUgktm/fjn///RfVqlXTdSgG4fvvm+LXqaPRO+InnDx1Fv36dsW2f5ejTLmaePLkma7DIx3x/6oM2oSH4eql67oOhQqIq50V+jWpiqLOdgAENp+8hgEL/sXKYa3h6+6IEct249XrDMzo3gj2lubYHnMdwxbvwIohP6CUp7OuwzcohjzZUi/uI2Fvb885ERo0sH83LFy0AkuWrsbVqzfQO+InpKW9RqeOrXUdGumIhaU5ps/9Bf8bOB7JyS91HQ4VkGB/H9Qo6w0vFzt4udijb+MgWEhNcPHWQwDA+YSHaFMzAP5ebijiZItuIV/D2lyKK3cf6zhy+pzoRSIxfvx4jBo1CmlpaboO5bNnYmKCihXLY+++w/IyIQT27vsP33wTqMPISJfGTv4J+3f/h6OHTuo6FNKRHJkMO2Ku43VGFsp7uwMAAnzcsPPsDSSnpkMmE9gRcx0Z2dmoVIJPUNY0IYRGNn2kF0Mbv/32G+Lj4+Hq6gpvb2+YmJgo7D9z5oyOIvv8ODk5oFChQnj86KlC+ePHT1DKr7iOoiJdaty8HsqWL4XQuj/qOhTSgRsPnqLDtLXIzM6GudQE07o2QnH3Nz3AUzo1wPDoHQiOXIBCRkYwMy2EaV0a/f+hENIkQx7a0ItEIjQ0VO33ZmRkICMjQ6FMCMEJm0QA3D1cMfKXoejQsjcyMzJ1HQ7pgLeLPVYNb42U15nYcy4Oo/7ejYX9wlDc3QGztx3Hq9cZmBcRCjsrM+y/cBPDordjcf8wlPBw0nXo9JnQi0Ri9OjRar83KioKY8eOVSiTGFlBYmzzqWF9lp4+fY7s7Gy4uCr+EnBxccbDR090FBXpSrmA0nByccTmfcvlZYUKFULloIr4sWsrlPb4Rmm5NRkWk0LG8h6GMkVdcPnOI6w4eA4dvwvEykMXsDayLXzdHQEAfoWdcTb+AVYdvogRP9TWYdSGx5BXbejFHIlPERkZieTkZIVNYmSt67B0JisrC2fOXMC3tavLyyQSCb6tXR3Hj8foMDLShaOHT6JB9e/RpFYb+Xbh7GVsWrsdTWq1YRLxBZIJIDM7B+lZWQAAo3d6b42MjCDT07H4z5lMCI1s+kgveiTedytsiUQCMzMz+Pr6omPHjujUqZNSHalUCqlUqvS+L9n0mQuweNF0xJy5gFOnzqJf326wtDRH9BI+t+RLk5qShuvX4hXK0tJeI+l5slI5GZ5Zm4+iWhkvuNlbIy0jE9tPX8fpuHuY3asZvF3t4elsiwmr9mNgaDXYWZhj/8V4HI+9g1ndm+g6dPqM6EUiMWrUKPzyyy9o0KABKleuDAA4efIkduzYgYiICCQkJKBXr17Izs5Gt27ddByt/luzZjOcnRwwZtQQuLk54/z5y2jUuD0eP3768TcTkcF4nvIaI/7ejafJqbAyl6KkhyNm92qGoFJFAQB/9GiKWVuOov/8rUjLyEJRJ1uMb1cXNcp66zZwA6SffQmaIRF6sJ4kLCwMdevWRc+ePRXK582bh127dmHdunX4/fffMX/+fFy8ePGj7RUy5dIlUuRl46rrEEiPXFreWdchkB4xD+mj9WNUK/ytRto5cn+fRtrRJL2YI7Fz507UqVNHqfy7777Dzp07AQANGzbkMzeIiOizJIPQyKaP9CKRcHBwwJYtW5TKt2zZIr/jZWpqKqytv9xJlERERPpIL+ZIjBw5Er169cL+/fvlcyROnTqFbdu2Ye7cuQCA3bt3Izg4WJdhEhERqUUPZhFojV4kEt26dUOZMmXwxx9/YP369QAAPz8/HDx4EFWrVgUADB48WJchEhERqU1fhyU0QS8SCQCoVq0an/5JRET0mdFZIvHy5UvY2NjI//tDcusRERF9jgz5zpY6SyTs7e2RmJgIFxcX2NnZ5XkTqdxnZuTk5OggQiIiIs3gHAkt2Ldvn3xFxv79+3UVBhEREX0CnSUSb6/A4GoMIiIyZJxsqQUXLlzId93y5ctrMRIiIiLt4tCGFlSoUAESieSjHy7nSBAREaluzJgxGDt2rEKZn58frl27ptHj6CyRSEhI0NWhiYiICpSuhjbKli2LPXv2yF8XKqT5f/Z1lkh4eXnp6tBEREQFSlfLPwsVKgQ3NzftHkOrravoypUruHPnDjIzMxXKmzZtqqOIiIiIPp1MR3Mkbty4AQ8PD5iZmSEoKAhRUVEoWrSoRo+hF4nEzZs30bx5c1y8eFFh3kTuvSU4R4KIiAjIyMhARkaGQplUKoVUKlWqW6VKFURHR8PPzw+JiYkYO3YsatSogUuXLmn0IZh68fTP/v37w8fHB48fP4aFhQUuX76MQ4cOoVKlSjhw4ICuwyMiIvokQkP/i4qKgq2trcIWFRWV5zEbNGiA77//HuXLl0dISAi2bduGpKQkrF69WqPnphc9EseOHcO+ffvg5OQEIyMjGBkZoXr16oiKikK/fv1w9uxZXYdIRESkNk0NbURGRmLQoEEKZXn1RuTFzs4OJUuWRFxcnEZiyaUXPRI5OTnybhYnJyc8ePAAwJsJmbGxsboMjYiISG9IpVLY2NgobPlNJFJSUhAfHw93d3eNxqQXPRLlypXD+fPn4ePjgypVqmDKlCkwNTXF/PnzUaxYMV2HR0RE9El0sWpjyJAhaNKkCby8vPDgwQOMHj0axsbGaNOmjUaPoxeJxIgRI5CamgoAGDt2LJo0aYIaNWrA0dERK1eu1HF0REREn0YXqzbu3buHNm3a4NmzZ3B2dkb16tVx/PhxODs7a/Q4epFIhISEyP+7RIkSuHbtGp4/fw57e/s8nwpKREREH1ZQf4jrNJHo3Llzvur99ddfWo6EiIhIe3R1Q6qCoNNEIjo6Gl5eXvjqq68M+oEmRET0ZdPVDakKgk4TiV69euGff/5BQkICOnXqhPbt28PBwUGXIREREZEKdLr8888//0RiYiKGDRuGLVu2wNPTE61atcLOnTvZQ0FERAZDUzek0kc6v4+EVCpFmzZtsHv3bly5cgVly5ZF79694e3tjZSUFF2HR0RE9MmEkGlk00d6sWojl5GRkfxZG3y+BhERGQpdPUa8IOi8RyIjIwP//PMP6tati5IlS+LixYv4448/cOfOHVhZWek6PCIiIvoAnfZI9O7dGytXroSnpyc6d+6Mf/75B05OTroMiYiISOMMed6fThOJuXPnomjRoihWrBgOHjyIgwcP5llv/fr1BRwZERGR5hjy0IZOE4kOHTrwzpVERESfMZ3fkIqIiMjQcWiDiIiI1GbId7bU+aoNIiIi+nyxR4KIiEjL9PWulJrARIKIiEjLDHmOBIc2iIiISG3skSAiItIy3keCiIiI1GbIQxtMJIiIiLSMyz+JiIiI8sAeCSIiIi3j0AYRERGpzZAnW3Jog4iIiNTGHgkiIiIt49AGERERqY2rNoiIiIjywB4JIiIiLeNDu4iIiEhtHNogIiIiygN7JIiIiLSMqzaIiIhIbYY8R4JDG0RERFomhNDIpo4///wT3t7eMDMzQ5UqVXDy5EmNnhsTCSIiIgO1atUqDBo0CKNHj8aZM2cQEBCAkJAQPH78WGPHYCJBRESkZbrqkZg2bRq6deuGTp06oUyZMpg7dy4sLCzw119/aezcmEgQERFpmdDQporMzEzExMSgTp068jIjIyPUqVMHx44d+6TzeRsnWxIREX0mMjIykJGRoVAmlUohlUqV6j59+hQ5OTlwdXVVKHd1dcW1a9c0FpNBJhLZmfd1HYLOZWRkICoqCpGRkXleYPTl4TVBb+P1ULA09e/SmDFjMHbsWIWy0aNHY8yYMRppXx0SYciLW79gL1++hK2tLZKTk2FjY6PrcEgP8Jqgt/F6+Dyp0iORmZkJCwsLrF27FqGhofLy8PBwJCUlYdOmTRqJiXMkiIiIPhNSqRQ2NjYK2/t6lExNTREYGIi9e/fKy2QyGfbu3YugoCCNxWSQQxtEREQEDBo0COHh4ahUqRIqV66MGTNmIDU1FZ06ddLYMZhIEBERGagffvgBT548wahRo/Dw4UNUqFABO3bsUJqA+SmYSBgoqVSK0aNHcxIVyfGaoLfxevhy9OnTB3369NFa+5xsSURERGrjZEsiIiJSGxMJIiIiUhsTCVLZgQMHIJFIkJSUpOtQ6BN4e3tjxowZug6DNODWrVuQSCQ4d+4cAP6MUsFiIqFjHTt2hEQiwaRJkxTKN27cCIlEoqOoSJdyr4nczdHREfXr18eFCxc0epxTp06he/fuGm2T8i/3e+7Zs6fSvoiICEgkEnTs2FGttqtWrYrExETY2tp+YpSaFx0dDTs7O12HQRrEREIPmJmZYfLkyXjx4oXG2szMzNRYW1Tw6tevj8TERCQmJmLv3r0oVKgQGjdurNFjODs7w8LCQqNtkmo8PT2xcuVKvH79Wl6Wnp6OFStWoGjRomq3a2pqCjc3N/4xQgWCiYQeqFOnDtzc3BAVFfXeOuvWrUPZsmUhlUrh7e2N3377TWG/t7c3xo8fjw4dOsDGxgbdu3eXZ/5bt26Fn58fLCws0LJlS6SlpWHJkiXw9vaGvb09+vXrh5ycHHlby5YtQ6VKlWBtbQ03Nze0bdtWo8+up4+TSqVwc3ODm5sbKlSogJ9++gl3797FkydPAAB3795Fq1atYGdnBwcHBzRr1gy3bt2Sv79jx44IDQ3Fr7/+Cnd3dzg6OiIiIgJZWVnyOu8ObVy7dg3Vq1eHmZkZypQpgz179kAikWDjxo0A/q/7fP369ahduzYsLCwQEBCg0acIfmkqVqwIT09PrF+/Xl62fv16FC1aFF999ZW8bMeOHahevTrs7Ozg6OiIxo0bIz4+/r3t5jW0sWDBAnh6esLCwgLNmzfHtGnTFHoGxowZgwoVKmDZsmXw9vaGra0tWrdujVevXuU7jo9dIwcOHECnTp2QnJws73HT5TMiSDOYSOgBY2NjTJw4Eb///jvu3buntD8mJgatWrVC69atcfHiRYwZMwYjR45EdHS0Qr1ff/0VAQEBOHv2LEaOHAkASEtLw6xZs7By5Urs2LEDBw4cQPPmzbFt2zZs27YNy5Ytw7x587B27Vp5O1lZWRg/fjzOnz+PjRs34tatW2p3sdKnS0lJwd9//w1fX184OjoiKysLISEhsLa2xuHDh3HkyBFYWVmhfv36Cj1R+/fvR3x8PPbv348lS5YgOjpa6ZrJlZOTg9DQUFhYWODEiROYP38+fv755zzr/vzzzxgyZAjOnTuHkiVLok2bNsjOztbGqX8ROnfujMWLF8tf//XXX0p3HUxNTcWgQYNw+vRp7N27F0ZGRmjevDlkMlm+jnHkyBH07NkT/fv3x7lz51C3bl388ssvSvXi4+OxceNGbN26FVu3bsXBgwcVhl3zG8f7rpGqVatixowZsLGxkfe4DRkyRJWPi/SRIJ0KDw8XzZo1E0II8c0334jOnTsLIYTYsGGDyP162rZtK+rWravwvqFDh4oyZcrIX3t5eYnQ0FCFOosXLxYARFxcnLysR48ewsLCQrx69UpeFhISInr06PHeGE+dOiUAyN+zf/9+AUC8ePFC9ROmjwoPDxfGxsbC0tJSWFpaCgDC3d1dxMTECCGEWLZsmfDz8xMymUz+noyMDGFubi527twpb8PLy0tkZ2fL63z//ffihx9+kL/28vIS06dPF0IIsX37dlGoUCGRmJgo3797924BQGzYsEEIIURCQoIAIBYuXCivc/nyZQFAXL16VeOfg6HL/dl//PixkEql4tatW+LWrVvCzMxMPHnyRDRr1kyEh4fn+d4nT54IAOLixYtCiP/7bs6ePSuEUP4Z/eGHH0SjRo0U2mjXrp2wtbWVvx49erSwsLAQL1++lJcNHTpUVKlS5b3n8L44PnSNLF68WOG49Pljj4QemTx5MpYsWYKrV68qlF+9ehXVqlVTKKtWrRpu3LihMCRRqVIlpTYtLCxQvHhx+WtXV1d4e3vDyspKoeztoYuYmBg0adIERYsWhbW1NYKDgwEAd+7c+bQTpHyrXbs2zp07h3PnzuHkyZMICQlBgwYNcPv2bZw/fx5xcXGwtraGlZUVrKys4ODggPT0dIVu5rJly8LY2Fj+2t3d/b1DVLGxsfD09ISbm5u8rHLlynnWLV++vEKbADj09QmcnZ3RqFEjREdHY/HixWjUqBGcnJwU6ty4cQNt2rRBsWLFYGNjA29vbwD5/5mMjY1V+j7z+n69vb1hbW0tf/3uNZPfOHiNfFl4i2w9UrNmTYSEhCAyMlKtoQRLS0ulMhMTE4XXEokkz7LcrsnU1FSEhIQgJCQEy5cvh7OzM+7cuYOQkBBO4CxAlpaW8PX1lb9euHAhbG1tsWDBAqSkpCAwMBDLly9Xep+zs7P8vz/0PX+Kt9vNncyniXa/ZJ07d5bfwvjPP/9U2t+kSRN4eXlhwYIF8PDwgEwmQ7ly5TT+M/mxaya/cfAa+bIwkdAzkyZNQoUKFeDn5ycvK126NI4cOaJQ78iRIyhZsqTCX5yacO3aNTx79gyTJk2Cp6cnAOD06dMaPQapTiKRwMjICK9fv0bFihWxatUquLi4wMbGRiPt+/n54e7du3j06JH8YT6nTp3SSNv0cbnzWyQSCUJCQhT2PXv2DLGxsViwYAFq1KgBAPjvv/9Uat/Pz0/p+1T1+9VEHMCbFSVv96TS549DG3rG398f7dq1w6xZs+RlgwcPxt69ezF+/Hhcv34dS5YswR9//KGVSUpFixaFqakpfv/9d9y8eRObN2/G+PHjNX4c+rCMjAw8fPgQDx8+xNWrV9G3b1+kpKSgSZMmaNeuHZycnNCsWTMcPnwYCQkJOHDgAPr165fnZN38qFu3LooXL47w8HBcuHABR44cwYgRIwCASwgLgLGxMa5evYorV64o/XFgb28PR0dHzJ8/H3Fxcdi3bx8GDRqkUvt9+/bFtm3bMG3aNNy4cQPz5s3D9u3bVfpuNREH8Gb4JCUlBXv37sXTp0+RlpamchukX5hI6KFx48YpdANWrFgRq1evxsqVK1GuXDmMGjUK48aN08pKCmdnZ0RHR2PNmjUoU6YMJk2ahF9//VXjx6EP27FjB9zd3eHu7o4qVarg1KlTWLNmDWrVqgULCwscOnQIRYsWRYsWLVC6dGl06dIF6enpavdQGBsbY+PGjUhJScHXX3+Nrl27yldtmJmZafLU6D1sbGzy/P6MjIywcuVKxMTEoFy5chg4cCCmTp2qUtvVqlXD3LlzMW3aNAQEBGDHjh0YOHCgSt+tJuIA3twsq2fPnvjhhx/g7OyMKVOmqNwG6Rc+/ZOI8nTkyBFUr14dcXFxChN2yTB069YN165dw+HDh3UdCn3mOEeCiAAAGzZsgJWVFUqUKIG4uDj0798f1apVYxJhIH799VfUrVsXlpaW2L59O5YsWYLZs2frOiwyAEwkiAgA8OrVKwwfPhx37tyBk5MT6tSpo3QHVfp8nTx5ElOmTMGrV69QrFgxzJo1C127dtV1WGQAOLRBREREauNkSyIiIlIbEwkiIiJSGxMJIiIiUhsTCSIiIlIbEwkiIiJSGxMJIh3q2LEjQkND5a9r1aqFAQMGFHgcBw4cgEQiQVJS0nvrSCQSbNy4Md9tjhkzBhUqVPikuG7dugWJRIJz5859UjtEpD1MJIje0bFjR0gkEkgkEpiamsLX1xfjxo1Ddna21o+9fv36fD/bJD//+BMRaRtvSEWUh/r162Px4sXIyMjAtm3bEBERARMTE0RGRirVzczMhKmpqUaO6+DgoJF2iIgKCnskiPIglUrh5uYGLy8v9OrVC3Xq1MHmzZsB/N9wxC+//AIPDw/5I9/v3r2LVq1awc7ODg4ODmjWrBlu3bolbzMnJweDBg2CnZ0dHB0dMWzYMLx7P7h3hzYyMjIwfPhweHp6QiqVwtfXF4sWLcKtW7dQu3ZtAG+eyiiRSOQPcZPJZIiKioKPjw/Mzc0REBCAtWvXKhxn27ZtKFmyJMzNzVG7dm2FOPNr+PDhKFmyJCwsLFCsWDGMHDkSWVlZSvXmzZsHT09PWFhYoFWrVkhOTlbYv3DhQpQuXRpmZmYoVarUB2/b/OLFC7Rr1w7Ozs4wNzdHiRIlsHjxYpVjJyLNYY8EUT6Ym5vj2bNn8td79+6FjY0Ndu/eDQDIyspCSEgIgoKCcPjwYRQqVAgTJkxA/fr1ceHCBZiamuK3335DdHQ0/vrrL5QuXRq//fYbNmzYgG+//fa9x+3QoQOOHTuGWbNmISAgAAkJCXj69Ck8PT2xbt06hIWFITY2FjY2NjA3NwcAREVF4e+//8bcuXNRokQJHDp0CO3bt4ezszOCg4Nx9+5dtGjRAhEREejevTtOnz6NwYMHq/yZWFtbIzo6Gh4eHrh48SK6desGa2trDBs2TF4nLi4Oq1evxpYtW/Dy5Ut06dIFvXv3xvLlywEAy5cvx6hRo/DHH3/gq6++wtmzZ9GtWzdYWloiPDxc6ZgjR47ElStXsH37djg5OSEuLg6vX79WOXYi0iBBRArCw8NFs2bNhBBCyGQysXv3biGVSsWQIUPk+11dXUVGRob8PcuWLRN+fn5CJpPJyzIyMoS5ubnYuXOnEEIId3d3MWXKFPn+rKwsUaRIEfmxhBAiODhY9O/fXwghRGxsrAAgdu/enWec+/fvFwDEixcv5GXp6enCwsJCHD16VKFuly5dRJs2bYQQQkRGRooyZcoo7B8+fLhSW+8CIDZs2PDe/VOnThWBgYHy16NHjxbGxsbi3r178rLt27cLIyMjkZiYKIQQonjx4mLFihUK7YwfP14EBQUJIYRISEgQAMTZs2eFEEI0adJEdOrU6b0xEFHBY48EUR62bt0KKysrZGVlQSaToW3bthgzZox8v7+/v8K8iPPnzyMuLg7W1tYK7aSnpyM+Ph7JyclITExElSpV5PsKFSqESpUqKQ1v5Dp37hyMjY0RHByc77jj4uKQlpaGunXrKpRnZmbiq6++AgBcvXpVIQ4ACAoKyvcxcq1atQqzZs1CfHw8UlJSkJ2dDRsbG4U6RYsWReHChRWOI5PJEBsbC2tra8THx6NLly7o1q2bvE52djZsbW3zPGavXr0QFhaGM2fOoF69eggNDUXVqlVVjp2INIeJBFEeateujTlz5sDU1BQeHh4oVEjxR8XS0lLhdUpKCgIDA+Vd9m9zdnZWK4bcoQpVpKSkAAD+/fdfhX/AgTfzPjTl2LFjaNeuHcaOHYuQkBDY2tpi5cqVKj0tNDfWBQsWKCU2xsbGeb6nQYMGuH37NrZt24bdu3fju+++Q0REBH799Vf1T4aIPgkTCaI8WFpawtfXN9/1K1asiFWrVsHFxUXpr/Jc7u7uOHHiBGrWrAngzV/eMTExqFixYp71/f39IZPJcPDgQdSpU0dpf26PSE5OjrysTJkykEqluHPnznt7MkqXLi2fOJrr+PHjHz/Jtxw9ehReXl74+eef5WW3b99Wqnfnzh08ePAAHh4e8uMYGRnBz88Prq6u8PDwwM2bN9GuXbt8H9vZ2Rnh4eEIDw9HjRo1MHToUCYSRDrEVRtEGtCuXTs4OTmhWbNmOHz4MBISEnDgwAH069cP9+7dAwD0798fkyZNwsaNG3Ht2jX07t37g/eA8Pb2Rnh4ODp37oyNGzfK21y9ejUAwMvLCxKJBFu3bsWTJ0+QkpICa2trDBkyBAMHDsSSJUsQHx+PM2fO4Pfff8eSJUsAAD179sSNGzcwdOhQxMbGYsWKFYiOjlbpfEuUKIE7d+5g5cqViI+Px6xZs7BhwwalemZmZggPD8f58+dx+PBh9OvXD61atYKbmxsAYOzYsYiKisKsWbNw/fp1XLx4EYsXL8a0adPyPO6oUaOwadMmxMXF4fLly9i6dStKly6tUuxEpFlMJIg0wMLCAocOHULRokXRokULlC5dGl26dEF6erq8h2Lw4MH48ccfER4ejqCgIFhbW6N58+YfbHfOnDlo2bIlevfujVKlSqFbt25ITU0FABQuXBhjx47FTz/9BFdXV/Tp0wcAMH78eIwcORJRUVEoXbo06tevj3///Rc+Pj4A3sxbWLduHTZu3IiAgADMnTsXEydOVOl8mzZtioEDB6JPnz6oUKECjh49ipEjRyrV8/X1RYsWLdCwYUPUq1cP5cuXV1je2bVrVyxcuBCLFy+Gv78/goODER0dLY/1XaampoiMjET58uVRs2ZNGBsbY+XKlSrFTkSaJRHvm+lFRERE9BHskSAiIiK1MZEgIiIitTGRICIiIrUxkSAiIiK1MZEgIiIitTGRICIiIrUxkSAiIiK1MZEgIiIitTGRICIiIrUxkSAiIiK1MZEgIiIitTGRICIiIrX9PyhLOVo93mw5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.5')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Z6MfLYUp8ML"
      },
      "source": [
        "### GBCRet, layer4 combination, Hierarchical classifiation dropout=0.6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zd-lCX3Sp8MM",
        "outputId": "4dc289be-0bc8-42c8-f5f4-d403c805e65b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.651375949382782, median IoU: 0.673351526260376, mAP = 0.288, mAP_50 = 0.764, mAP_75 = 0.153\n",
            "TP: 118, FN: 0, FP: 4\n",
            "Precision: 0.9672131147540983\n",
            "Recall: 1.0\n",
            "Acc: 0.9098 Spec: 0.9500 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss, dropout=0.6/checkpoints/best_model_epoch_35.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "YwJpXB2Sp8MN",
        "outputId": "4910ae96-1c08-4aa5-c28a-f664a6697f2e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.6')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X12Gw-s1hhnM"
      },
      "source": [
        "### GBCRet, layer4 combination, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, criterion = nn.CrossEntropyLoss(), dropout=0.5, with attention"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7mcz4uPhhnP",
        "outputId": "66626592-955f-4e9b-f085-fb0c8c12921c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\" to /root/.cache/torch/hub/checkpoints/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\n",
            "100%|██████████| 146M/146M [00:01<00:00, 116MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "Self attention is applied on combined features.\n",
            "mIoU = 0.6522068381309509, median IoU: 0.6847713589668274, mAP = 0.294, mAP_50 = 0.77, mAP_75 = 0.147\n",
            "TP: 117, FN: 0, FP: 5\n",
            "Precision: 0.9590163934426229\n",
            "Recall: 1.0\n",
            "Acc: 0.9180 Spec: 0.9500 Sens: 0.8810\n",
            " Normal vs Not Noraml Acc: 0.983607 Benign vs Malignant Acc: 0.912088\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = False # Apply attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "bm_fc_layers=\"1\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, bm_fc_layers=bm_fc_layers).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss, dropout=0.5, with attention/checkpoints/best_model_epoch_35.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "-THLU8RVhhnR",
        "outputId": "254e9dec-b0f4-476e-ad85-86d422af3c57"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.5, with attention')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTOmWDXrnFbU"
      },
      "source": [
        "### GBCRet, layer4 combination, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, criterion = nn.CrossEntropyLoss(), dropout=0.5, bm_fc_layers=\"2\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPs9sstinFbX",
        "outputId": "afdbfcbf-d7f4-49b8-a6af-088c7b261361"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6428935527801514, median IoU: 0.67383873462677, mAP = 0.293, mAP_50 = 0.764, mAP_75 = 0.169\n",
            "TP: 115, FN: 0, FP: 7\n",
            "Precision: 0.9426229508196722\n",
            "Recall: 1.0\n",
            "Acc: 0.9180 Spec: 0.9750 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "bm_fc_layers=\"2\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, bm_fc_layers=bm_fc_layers, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss, dropout=0.5, bm_fc_layers=2/checkpoints/best_model_epoch_70.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "gKxaLiRSnFbX",
        "outputId": "5cd22237-9a48-4103-87fe-45abb17c04cb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.5, bm_fc_layers=\"2\"')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkjWdUQDbx9D"
      },
      "source": [
        "### GBCRet, layer4 combination, gbcnet VA, 50 epochs pretrained RN, Hierarchical classifiation, criterion = nn.CrossEntropyLoss(), dropout=0.5, bm_fc_layers=\"3\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjUYn_9jbx9E",
        "outputId": "a3fee389-612b-4825-dfd5-e47d545c665b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\" to /root/.cache/torch/hub/checkpoints/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth\n",
            "100%|██████████| 146M/146M [00:00<00:00, 159MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6586982607841492, median IoU: 0.6912716031074524, mAP = 0.311, mAP_50 = 0.797, mAP_75 = 0.175\n",
            "TP: 116, FN: 0, FP: 6\n",
            "Precision: 0.9508196721311475\n",
            "Recall: 1.0\n",
            "Acc: 0.9262 Spec: 0.9750 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "bm_fc_layers=\"3\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, bm_fc_layers=bm_fc_layers, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/CV/Project/ResNet, GBCNet, GBCRet, 3/GBCRet, layer4 combination, Hierarchical cls, gbcnet VA, 50 epochs pretrained RN, CrossEntropyLoss, dropout=0.5, bm_fc_layers=3/checkpoints/best_model_epoch_51.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "_7jaLh8lbx9F",
        "outputId": "2c7efb81-5371-45f4-bcbf-1911c82bc84e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.5, bm_fc_layers=\"3\"')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CI2TBA6iywOZ"
      },
      "source": [
        "### best model's weights test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8f5c235-5ec1-466a-c196-ec078f0c176c",
        "id": "cpQPVSjHywOv"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retinanet's COCO pre-trained weights are loaded.\n",
            "mIoU = 0.6586982607841492, median IoU: 0.6912720203399658, mAP = 0.311, mAP_50 = 0.797, mAP_75 = 0.175\n",
            "TP: 116, FN: 0, FP: 6\n",
            "Precision: 0.9508196721311475\n",
            "Recall: 1.0\n",
            "Acc: 0.9262 Spec: 0.9750 Sens: 0.8571\n"
          ]
        }
      ],
      "source": [
        "width = 224\n",
        "height = 224\n",
        "min_size = 224\n",
        "max_size = 224\n",
        "gold_curriculum = False\n",
        "no_attn = True # No attention\n",
        "combined=\"resnet_layer4\"\n",
        "retinanet_backbone=\"resnet50-coco\"\n",
        "do_hierarchical_cls=True\n",
        "bm_fc_layers=\"3\"\n",
        "\n",
        "gbcret = GBCRet_combined(continue_training = True, combined=combined, gold_curriculum=gold_curriculum, retinanet_backbone=retinanet_backbone,\n",
        "                         width=width, height=height, min_size=min_size, max_size=max_size, no_attn=no_attn,\n",
        "                         do_hierarchical_cls=do_hierarchical_cls, bm_fc_layers=bm_fc_layers, return_logits=True).to(device)\n",
        "\n",
        "checkpoint = torch.load(\"/content/drive/MyDrive/best_gbcret.pth\", map_location=device)\n",
        "gbcret.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "gbcret.eval()\n",
        "\n",
        "eval_transform = get_eval_transforms(width, height)\n",
        "\n",
        "val_dataset = InputDataset(imgs_folder_path, val_txt_path, gold_file_path, to_blur=False, img_transforms=eval_transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size=70, shuffle=False, num_workers=5, collate_fn= collate_fn)\n",
        "\n",
        "acc, mAP, cfm, y_true, y_pred, fnames, all_bboxes, all_logits = evaluate_model(gbcret, val_loader, return_bboxes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "7142fe10-3592-465c-ad44-e9c9d3817bf4",
        "id": "u52NQAnNywOw"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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mk2HDhg1o3LhxnrMPcs+h6nV8zNq1axEYGIjmzZu/95x5efu9SU9Px5MnT1C5cmUAUIjD3t4ex48fx7179/IsJ7fFYceOHVpr6n871pcvX+LJkyeoXr06Xr16hcuXL6tc3v3793H69Gl07NgRjo6O8vRy5cqhTp062Lp1q9Jr8rofnj59Kv+79CUokK6N3F/kqampSsfmzZuHly9f4uHDhwq/5N5Wo0YNhWa4Vq1aoXjx4ujduzfi4+Nha2sL4M2NpIpKlSphzJgxyMnJwfnz5zFmzBgkJyfDzMxMnufq1at4/vw5XFxc8izj0aNHKp1THZs3b8aYMWNw+vRpZGRkyNPf/WUQHh6OVatW4dChQ6hRowZ2796Nhw8f4ocffpDnUfV6fHx8lPLcuHEDEydOxKxZs/I9r/5t165dg7+//wdHtd+8eRMeHh5KlcNSpUrJj7+taNGiCs9zf4m9O+YmN/3d8SAeHh7yylauEiVKAHhzvbm/SPNibm4OZ2dnhTQHBwelc+T3c8yv3Pfg7e4m4E0FwdfXV+k9KlKkiNK5HBwclKZdq+rdn89cHxvE9vjxY6SkpGD+/PkKs6nelp/7USaT4ffff8fs2bORlJSk0D9eqFAhpfzvlpE7Lic/a4nk97N+2+PHj/HixYuPlq/qdXzMtWvX0LJlS5Vf9+zZM0RHR2PlypVK7//bYxsmTJiAiIgIeHp6Ijg4GA0aNEB4eDh8fX0BvHmfBwwYgClTpmDZsmWoXr06mjRpgg4dOuTZraGOCxcu4Ndff8XevXuV/nC/HWt+ve9nCnjzu2fHjh1IS0tT+F3x7u+e3K7x5ORk+d8mQ1cgFQk7Ozu4u7vj/PnzSsdyvyGq0sdubW2NSpUq4e+//0ZaWhpsbW3h4eGRZ/kf4uTkhNq1awMAwsLCULJkSTRq1Ai///47BgwYAODND7eLiwuWLVuWZxnv/lLRtEOHDqFJkyaoUaMGZs+eDXd3d5iammLx4sVYvny5Qt6wsDC4urrir7/+Qo0aNfDXX3/Bzc1Nfo3qXM+73/6AN9M9CxcurDBw8cGDBwDe/NK8ceMGihYtqvGR8B/yvtHc70sX7wzY1Ma536bK56gtBfFeqCJ3DEWHDh0QERGRZ55y5copPM/rfhw7diyGDRuGTp06YfTo0XB0dISRkRH69euX5ziNvMrIL23OmlH1OrSlTZs2OHr0KAYPHoygoCD51Pt69eopxNGmTRtUr14d69evx86dOzFx4kSMHz8e69atk4+5mTx5Mjp27Ii///4bO3fuRJ8+fRATE4N//vkHRYoU+aQ4U1JSEBoaCltbW4waNQrFihWDubk5Tp48iZ9++qnA3jN9+7nShQIbbNmwYUMsXLgQ//77LypWrPjJ5WVnZwN408phZWWFRo0aYf78+Th27BhCQkLUjjE0NBRjx45Ft27dYGVlhWLFimH37t2oWrXqR38BqfvN8kPWrl0Lc3Nz7NixQ2Ga1OLFi5XyGhsb4/vvv0dsbCzGjx+PDRs2oEuXLgo3uirX8z63bt1CYmKi/JvH23r27AngTW383Wbit2M4fvw4srKy3jt/28vLC7t378bLly8VWiVymyvfXWvkU927d0/pm8aVK1cAQCMrjaryOeb3Psp9DxISEhQ+i8zMTCQlJSlUIPWRs7MzbGxskJOT80mxxsXFoVatWli0aJFCekpKynsHFL4tdzDr+fPntfKeOTs7w9bW9qNfdD71Ot5VrFgxlb9cJScnY8+ePYiOjlaYzn316tU887u7u6Nnz57o2bMnHj16hPLly+O3335TGLwbEBCAgIAA/Prrrzh69CiqVq2KuXPnYsyYMSpf09v279+Pp0+fYt26dahRo4Y8PSkpSSmvOj9T77p8+TKcnJyUWi6pAKd/DhkyBJaWlujUqRMePnyodFyV2tuzZ89w9OhRuLm5yZvohwwZAisrK/z44495ln/t2rU8p3K966effsLTp0+xYMECAG9q3Tk5ORg9erRS3uzsbIVpdlZWVkrT7j6VsbExJBKJQjPnjRs33rvy4Q8//IDk5GR069YNqampSt1FqlzP+4wZMwbr169XeOSWN2TIEKxfv/6DP2wtW7bEkydPMHPmTKVjufdBgwYNkJOTo5Rn6tSpkEgknzTLIC/Z2dkKU1szMzMxb948ODs7Izg4+JPLV+VzzO99VLt2bZiZmWH69OkKPz+LFi3C8+fP5TNB9JWxsTFatmyJtWvX5vkHL3c9gfyU8+7vjzVr1uDu3bv5en358uXh4+ODadOmKb3vmvhWaWRkhGbNmmHTpk3477//lI7nnuNTr+NdLVu2xJkzZ7B+/fr3nvNduV863j3+7qqQOTk5Sl0HLi4u8PDwkHfbvXjxQv6FL1dAQACMjIwUuvbUlVesmZmZmD17tlJeKyurfHV1uLu7IygoCEuWLFG4F86fP4+dO3eiQYMGnxy3ISqwFonixYtj+fLlaNeuHfz9/eUrWwohkJSUhOXLl8PIyCjP5q64uDhYW1tDCIF79+5h0aJFSE5Oxty5c+U1zWLFimH58uX47rvvUKpUKYWVLY8ePYo1a9bka8XJ+vXro2zZspgyZQqioqIQGhqKbt26ISYmBqdPn0bdunVhamqKq1evYs2aNfj999/RqlUrAEBwcDDmzJmDMWPGwM/PDy4uLvjmm28AQGHesioaNmyIKVOmoF69evj+++/x6NEjzJo1C35+fnn2bX/11VfyxbpKlSqF8uXLKxxX5Xrep1q1akppua0PX3/9tdKKjO8KDw/Hn3/+iQEDBuDff/9F9erVkZaWht27d6Nnz55o2rQpGjdujFq1auGXX37BjRs3EBgYiJ07d+Lvv/9Gv379PjglUh0eHh4YP348bty4gRIlSmDVqlU4ffo05s+fr5FV71T5HIODg7F7925MmTIFHh4e8PHxyXPatLOzM4YOHYro6GjUq1cPTZo0QUJCAmbPno2vv/76vWOOPqZmzZo4cOBAgTTNjhs3Dvv27UOlSpXQpUsXlC5dGs+ePcPJkyexe/fu904tfVujRo0watQoREZGokqVKjh37hyWLVuWZ4tZXoyMjDBnzhw0btwYQUFBiIyMhLu7Oy5fvowLFy5gx44dn3qZGDt2LHbu3InQ0FB07doVpUqVwv3797FmzRocPnwY9vb2n3wd7xo8eDDi4uLQunVrdOrUCcHBwXj27Bk2btyIuXPn5rnujK2tLWrUqIEJEyYgKysLhQsXxs6dO5W+5b98+RJFihRBq1atEBgYCGtra+zevRsnTpzA5MmTAbxZi6FXr15o3bo1SpQogezsbCxdulRegfxUVapUgYODAyIiItCnTx9IJBIsXbo0z/s2ODgYq1atwoABA/D111/D2toajRs3zrPciRMnon79+ggJCUHnzp3l0z/t7OzU3j8ld5r2vn37NLa+kF4p0DkiQojExETRo0cP4efnJ8zNzYWFhYUoWbKk6N69uzh9+rRC3ryml1lZWYmQkBCxevXqPMu/cuWK6NKli/D29hZmZmbCxsZGVK1aVcyYMUNhOpCXl5fS1LRcsbGxSlOC5s+fL4KDg4WFhYWwsbERAQEBYsiQIeLevXvyPA8ePBANGzYUNjY2AoDCVFAnJydRuXLlj74/eU3/XLRokShevLiQSqWiZMmSYvHixfL3Ji8TJkwQAMTYsWPfe578XM+H3qN3qTL9U4g307Z++eUX4ePjI0xNTYWbm5to1aqVuHbtmjzPy5cvRf/+/YWHh4cwNTUVxYsXFxMnTlSYkieEUJqyK8T7p5DlFWdoaKgoU6aM+O+//0RISIgwNzcXXl5eYubMmXmW+e70TysrK6Xry+vzye/nePnyZVGjRg1hYWEhAMinxuU1/U2IN9M9S5YsKUxNTYWrq6vo0aOH0jTG3Gt8V173W3BwsHBzc1PK+75rfPz4cZ7H87p/8M60SCGEePjwoYiKihKenp7ye+Hbb78V8+fPl+f50P2Vnp4uBg4cKNzd3YWFhYWoWrWqOHbsmAgNDVX4GfzYPXr48GFRp04dYWNjI6ysrES5cuXEjBkz5MdV+azzus6bN2+K8PBw4ezsLKRSqfD19RVRUVEiIyNDpevI7/RPIYR4+vSp6NWrlyhcuLAwMzMTRYoUEREREfLptnmVdefOHdG8eXNhb28v7OzsROvWrcW9e/cUrikjI0MMHjxYBAYGyt+vwMBAMXv2bHk5169fF506dRLFihUT5ubmwtHRUdSqVUvs3r1bIcZPmf555MgRUblyZWFhYSE8PDzEkCFDxI4dOwQAsW/fPnm+1NRU8f333wt7e3sBQH7Pv++93L17t6hataqwsLAQtra2onHjxuLixYsKed53/+cV58CBA4VEIhGXLl169yMyCBIhvqARITpy8eJFlClTRmHhIW36/fff0b9/f/mgR6L8evnyJRwdHTFt2jT54l5E9GkqVqwILy8vrFmzRtehaAV3lSkA+/btQ0hISIFUIoQQWLRoEUJDQ1mJIJUdPHgQhQsX5l4zRBry4sULnDlzBkuWLNF1KFrDFgkDkZaWho0bN2Lfvn1YsGAB/v77bzRp0kTXYRERkYFjRcJA3LhxAz4+PrC3t0fPnj3x22+/6TokIiL6ArAiQURERGoruKUHiYiIyOCwIkFERERqY0WCsH//fkgkEuzfv1/XoZAKatasma+NpgrK9u3bERQUBHNzc0gkEo2t8nrjxg1IJBLExsZqpDwi0ixWJMggjR079r3LiKsrJSUFXbt2hbOzM6ysrFCrVq18b+/csWNHSCQSpUfJkiU1GqOuPH36FG3atIGFhQVmzZqFpUuXck8CDapZs6Z8Zd6OHTsqrY7Yv39/lC9fHo6OjrC0tESpUqUwcuRIpR2XY2Nj5asB536BUHW1XaJ3cR0JMkhjx45Fq1atPrpcd37JZDI0bNgQZ86cweDBg+Hk5ITZs2ejZs2aiI+PR/HixT9ahlQqxcKFCxXSNLWdsq6dOHECL1++xOjRo/V+szBDdOLECVSvXh2RkZEwNzfHqVOnMG7cOOzevRsHDx4s0J146cvDigTl26tXr2BpaanrMHQiLi5OvmdL7l4kbdq0QYkSJTBixIh8bQVuYmKi9v4X+u7Ro0cA8N4dXw3Zu7vG6sLhw4eV0ooVK4ZBgwbh33//ReXKlXUQFX0pWE39wty5cwfNmjWDlZUVXFxc0L9//zx34svtf4+Pj0eNGjVgaWmJ//u//wPw5o9G586d4erqCnNzcwQGBiqt2pbbrz1p0iRMnToVXl5esLCwQGhoaJ47Pe7duxfVq1eHlZUV7O3t0bRpU1y6dEkhT8eOHfPc0nvkyJEK2wRLJBKkpaVhyZIl8i6E/GzY9iFxcXFwdXVFixYt5GnOzs5o06YN/v7773zvZpiTk4MXL158Uizvio+PR5UqVWBhYQEfHx/MnTtX4XhuE/bq1asRHR2NwoULw8bGBq1atcLz58+RkZGBfv36wcXFBdbW1oiMjFRpd8aaNWsiIiICwJtN2959v48fP44GDRrAwcEBVlZWKFeuXL524v2Qs2fPomPHjvD19YW5uTnc3NzQqVMnPH36VJ5n3759kEgkee5+uXz5ckgkEhw7dkyedvnyZbRq1QqOjo4wNzdHhQoVsHHjRoXX5XYNHDhwAD179oSLi4t8o8GXL1+iX79+8Pb2hlQqhYuLC+rUqZPv7i9Ny/1Z0fSOxETvYovEF+T169f49ttvcevWLfTp0wceHh5YunQp9u7dm2f+p0+fon79+mjbti06dOgAV1dXvH79GjVr1kRiYiJ69eoFHx8f+c6qKSkp6Nu3r0IZf/75J16+fImoqCikp6fj999/xzfffINz587B1dUVALB7927Ur18fvr6+GDlypHy3vapVq+LkyZN5Vh4+ZOnSpfjxxx9RsWJFdO3aFQDku4VmZWXlazthAHB0dJQ3CZ86dQrly5dXaiKuWLEi5s+fjytXriAgIOCD5b169Qq2trZ49eoVHBwc0K5dO4wfPx7W1tYqXd/bkpOT0aBBA7Rp0wbt2rXD6tWr0aNHD5iZmaFTp04KeWNiYmBhYYGff/4ZiYmJmDFjBkxNTWFkZITk5GSMHDkS//zzD2JjY+Hj44Phw4fnK4ZffvkF/v7+mD9/PkaNGgUfHx/5+71r1y40atQI7u7u6Nu3L9zc3HDp0iVs3rxZ6V5Rxa5du3D9+nVERkbCzc0NFy5cwPz583HhwgX8888/kEgkqFmzJjw9PbFs2TI0b95c4fXLli1DsWLFEBISAgC4cOECqlatisKFC+Pnn3+GlZUVVq9ejWbNmmHt2rVKr+/ZsyecnZ0xfPhwpKWlAQC6d++OuLg49OrVC6VLl8bTp09x+PBhXLp0Sb4L76tXr/Dq1auPXp+xsTEcHBxUek+ys7ORkpKCzMxMnD9/Hr/++itsbGxQsWJFlcohUpnOtgujAjdt2jQBQGHn1LS0NOHn56e0W15oaKgAIObOnZtnGX/99Zc8LTMzU4SEhAhra2vx4sULIcT/dtWzsLAQd+7ckec9fvy4ACD69+8vTwsKChIuLi7i6dOn8rQzZ84IIyMjER4eLk/La6dKIfLefdHKykphR8FcuTtA5ufx9u59VlZWolOnTkrlbdmyRQAQ27dvVzr2tp9//ln89NNPYtWqVWLFihUiIiJCABBVq1YVWVlZH3zt++R+RpMnT5anZWRkyN/PzMxMhWsuW7asPE0IIdq1ayckEomoX7++QrkhISF5vs8fkrvj4YkTJ+Rp2dnZwsfHR3h5eSntRvruDq4fktcOja9evVLKt2LFCgFAHDx4UJ42dOhQIZVKRUpKijzt0aNHwsTERGF3zm+//VYEBAQo7BAsk8lElSpVRPHixZWus1q1aiI7O1vh/HZ2dkq70L4rrx2N83qo+v4LIcSxY8cUyvD391f4mSbSFrZIfEG2bt0Kd3d3eR8/AFhaWqJr164YMmSIUn6pVIrIyEilMtzc3NCuXTt5mqmpKfr06YN27drhwIEDaNSokfxYs2bNULhwYfnzihUrolKlSti6dSumTJmC+/fv4/Tp0xgyZAgcHR3l+cqVK4c6depg69atGrn2XIGBgdi1a1e+8rq5ucn///r1a0ilUqU85ubm8uMfEhMTo/C8bdu2KFGiBH755RfExcWhbdu2+YrpXSYmJujWrZv8uZmZGbp164YePXogPj5eoW88PDwcpqam8ueVKlXCihUrlFouKlWqhOnTpyM7OxsmJur/ijh16hSSkpIwdepUpbETb3dFqcPCwkL+//T0dKSmpsqv9eTJk6hevTqAN9ccExODuLg4dO7cGQCwatUqZGdny8erPHv2DHv37sWoUaPw8uVLvHz5Ul52WFgYRowYgbt37yrcx126dIGxsbFCTPb29jh+/Dju3bsHDw+PPOMODw9HtWrVVLq+/CpdujR27dqFtLQ0HD16FLt371aatUGkDaxIfEFu3rwJPz8/pV/i/v7+eeYvXLgwzMzMlMooXry4UhN/qVKl5MffltdshhIlSmD16tUK+fOKoVSpUtixY4dGB7M5ODioNavAwsIiz3ED6enp8uOq6t+/P4YNG4bdu3erXZHw8PBQem9KlCgB4M04lbcrEu/uBps7Y8TT01MpXSaT4fnz5yhUqJBacQHAtWvXAEAra108e/YM0dHRWLlypXygZ663u65KliyJr7/+GsuWLZNXJJYtW4bKlSvDz88PAJCYmAghBIYNG4Zhw4bleb5Hjx4pVCR8fHyU8kyYMAERERHw9PREcHAwGjRogPDwcPj6+srz+Pr6KjzXJFtbW/m93bRpUyxfvhxNmzbFyZMnERgYqJVzEgGsSNAHqPPHUZve9y02Jycn32VkZmbi2bNn+crr7Ows/9bp7u6O+/fvK+XJTXvfN9APsbCwQKFChfIdz6d69xv0x9KFHm/D06ZNGxw9ehSDBw9GUFAQrK2tIZPJUK9ePchkMoW84eHh6Nu3L+7cuYOMjAz8888/mDlzpvx4bv5BgwYhLCwsz/PlVjpy5fWz0aZNG1SvXh3r16/Hzp07MXHiRIwfPx7r1q1D/fr1AQCpqan5aiUwNjaGs7PzR/N9SIsWLfDDDz9g5cqVrEiQVrEi8QXx8vLC+fPnIYRQ+KOckJCgUhlnz56FTCZTaJW4fPmy/Pjbrl69qlTGlStX5AMoc/PnFcPly5fh5OQk/8bt4OCQ5wj0d1tBgPdXOo4ePYpatWrleexdSUlJ8jiDgoJw6NAhpes+fvw4LC0t5a0Aqnj58iWePHnySX8w7t27p9Ric+XKFQBQeZCqpuUOuDx//rxG15ZITk7Gnj17EB0drTAgNK97DXjTjTRgwACsWLECr1+/hqmpKb777jv58dwWAlNT00+O093dHT179kTPnj3x6NEjlC9fHr/99pu8IjFp0iRER0d/tBwvL69PXigqIyND3rJEpE2sSHxBGjRogJ07dyIuLg6tW7cG8GYU+fz581UuY9WqVfJxEtnZ2ZgxYwasra0RGhqqkH/Dhg0K/cv//vsvjh8/jn79+gF484s3KCgIS5YswdChQ+V96efPn8fOnTsV1l0oVqwYnj9/jrNnz6JcuXIA3rQI5DW9z8rKKs9Kh7pjJFq1aoW4uDisW7dOPsbkyZMnWLNmDRo3bqwwfiK3ST/3D2l6ejqysrJgY2OjUP7o0aMhhEC9evXyFU9esrOzMW/ePAwYMADAmxaXefPmwdnZGcHBwWqXqwnly5eHj48Ppk2bho4dOyqMk3i3MquK3BaUd1tMpk2blmd+Jycn1K9fH3/99RfS09NRr149ODk5yY+7uLigZs2amDdvHnr37g13d3eF1z9+/Pijlb2cnBykpqYqLDDm4uICDw8PhS4xbYyRSElJgZWVlcL4FwDyxc8qVKiQ77KI1MGKxBekS5cumDlzJsLDwxEfHw93d3csXbpUpUWmunbtinnz5qFjx46Ij4+Ht7c34uLicOTIEUybNk3pj6Wfnx+qVauGHj16ICMjA9OmTUOhQoUUBndOnDgR9evXR0hICDp37iyf/mlnZ4eRI0fK87Vt2xY//fQTmjdvjj59+uDVq1eYM2cOSpQooTRXPzg4GLt378aUKVPg4eEBHx8fVKpUSe0xEq1atULlypURGRmJixcvyle2zMnJUfqG+e233wKA/BvlgwcP8NVXX6Fdu3byJbF37NiBrVu3ol69emjatKnC63NbEvLzjdTDwwPjx4/HjRs3UKJECaxatQqnT5/G/Pnzlf6wFDQjIyPMmTMHjRs3RlBQECIjI+Hu7o7Lly/jwoUL2LFjh1rl2traokaNGpgwYQKysrJQuHBh7Ny5E0lJSe99TXh4uLwCOHr0aKXjs2bNQrVq1RAQEIAuXbrA19cXDx8+xLFjx3Dnzh2cOXPmgzG9fPkSRYoUQatWrRAYGAhra2vs3r0bJ06cwOTJk+X5tDFGYv/+/ejTpw9atWqF4sWLIzMzE4cOHcK6detQoUIFg10EjfSILqeMUMG7efOmaNKkibC0tBROTk6ib9++Yvv27XlO/yxTpkyeZTx8+FBERkYKJycnYWZmJgICAhSm5gnxvyl7EydOFJMnTxaenp5CKpWK6tWrizNnziiVuXv3blG1alVhYWEhbG1tRePGjcXFixeV8u3cuVOULVtWmJmZCX9/f/HXX3/lOf3z8uXLokaNGsLCwkIAyHMqqKqePXsmOnfuLAoVKiQsLS1FaGiownTHXF5eXgrT95KTk0WHDh2En5+fsLS0FFKpVJQpU0aMHTtWYTpmLicnJ1G5cuWPxpP7Gf33338iJCREmJubCy8vLzFz5kyFfLnTP9esWaOQnteUTSH+N0Xx8ePHH43hY2UJIcThw4dFnTp1hI2NjbCyshLlypUTM2bMyHfZeU3/vHPnjmjevLmwt7cXdnZ2onXr1uLevXsCgMK0zlwZGRnCwcFB2NnZidevX+d5nmvXronw8HDh5uYmTE1NReHChUWjRo1EXFzcR68zIyNDDB48WAQGBsqvMzAwUMyePTvf16muxMREER4eLnx9fYWFhYUwNzcXZcqUESNGjBCpqalaPz+RRAg9HlFFn60bN27Ax8cHEydOxKBBg3Qdzmfj4sWLKFOmDDZv3oyGDRvqOhyDkZ2dDQ8PDzRu3BiLFi3SdThEBoVLZBPpkX379iEkJISVCA3bsGEDHj9+jPDwcF2HQmRwOEaCSI9ERUUhKipK12HIPX/+/KOLbb09KFUV+ZmKa2dn90nTkI8fP46zZ89i9OjR+Oqrr5QGAxPRp2NFgojeq2/fvkobsr1L3d7R/EzFXbx48SdtuDZnzhz89ddfCAoKQmxsrNrlENH7cYwEEb3XxYsXce/evQ/mUXftheTkZMTHx38wT5kyZZSmYxKRfmFFgoiIiNTGwZZERESkNlYkiIiISG0GOdgyuWVNXYdAesZj6/tXPaQvT1ZOtq5DID2SnXlX6+fIenJdI+WYOmln99hPwRYJIiIiUptBtkgQERHpFVmOriPQGlYkiIiItE3IdB2B1rAiQUREpG0yw61IcIwEERERqY0tEkRERFom2LVBREREamPXBhEREZEytkgQERFpG7s2iIiISG0GvI4EuzaIiIhIbWyRICIi0jZ2bRAREZHaOGuDiIiISBlbJIiIiLSMC1IRERGR+gy4a4MVCSIiIm0z4BYJjpEgIiIitbFFgoiISNsMeEEqViSIiIi0jV0bRERERMrYIkFERKRtnLVBREREamPXBhEREZEytkgQERFpG7s2iIiISF1CGO70T3ZtEBERkdrYIkFERKRtBjzYkhUJIiIibeMYCSIiIlKbAbdIcIwEERERqY0tEkRERNrGTbuIiIhIbezaICIiIlLGFgkiIiJt46wNIiIiUhu7NoiIiIiUsUWCiIhI29i1QURERGpjRULzXrx4ke+8tra2WoyEiIiI1KWzioS9vT0kEskH8wghIJFIkJNjuAt5EBGR4TPkbcR1VpHYt2+frk5NRERUsNi1oXmhoaG6OjUREVHBMuDpn3o12PLVq1e4desWMjMzFdLLlSuno4iIiIjoQ/SiIvH48WNERkZi27ZteR7nGAkiIvqsGXDXhl4sSNWvXz+kpKTg+PHjsLCwwPbt27FkyRIUL14cGzdu1HV4REREn0bINPPQQ3rRIrF37178/fffqFChAoyMjODl5YU6derA1tYWMTExaNiwoa5DJCIiojzoRYtEWloaXFxcAAAODg54/PgxACAgIAAnT57UZWhERESfTibTzEMP6UVFwt/fHwkJCQCAwMBAzJs3D3fv3sXcuXPh7u6u4+iIiIg+Ebs2tKtv3764f/8+AGDEiBGoV68eli1bBjMzM8TGxuo2OCIiInovvWiR6NChAzp27AgACA4Oxs2bN3HixAncvn0b3333nW6DIyIi+lR60LUxbtw4SCQS9OvXT56Wnp6OqKgoFCpUCNbW1mjZsiUePnyoUrl6UZF4l6WlJcqXLw8nJyddh0JERPTpdFyROHHiBObNm6e0LlP//v2xadMmrFmzBgcOHMC9e/fQokULlcrWi64NIQTi4uKwb98+PHr0CLJ33qx169bpKDIiIqLPW2pqKtq3b48FCxZgzJgx8vTnz59j0aJFWL58Ob755hsAwOLFi1GqVCn8888/qFy5cr7K14sWiX79+uGHH35AUlISrK2tYWdnp/AgIiL6rGlosGVGRgZevHih8MjIyPjgqaOiotCwYUPUrl1bIT0+Ph5ZWVkK6SVLlkTRokVx7NixfF+aXrRILF26FOvWrUODBg10HQoREZHmaWjqZkxMDKKjoxXSRowYgZEjR+aZf+XKlTh58iROnDihdOzBgwcwMzODvb29QrqrqysePHiQ75j0okXCzs4Ovr6+ug7js2QW1gQ2UxbBfukW2C/dApuxs2DyVcX/ZTA1g8WPfWEX+zfs/9oGq8HRkNg56C5gKnBVq1ZEXNwiXL/+L16/vonGjevqOiTSsR7dI5B45R+kvriGo4c34esKQboOyfBpqEVi6NCheP78ucJj6NCheZ7y9u3b6Nu3L5YtWwZzc3OtXZpeVCRGjhyJ6OhovH79WtehfHbE08d4/dd8vBjSFS+GdEPW+ZOw/uk3GHl6AwAsI6NgVqEK0iaNxMvhfWHk4ATrIaN0GzQVKCsrS5w7dwn9+g3TdSikB1q3boJJE0dg9Jgp+LpSPZw5exFbtyyDs3MhXYdG+SCVSmFra6vwkEqleeaNj4/Ho0ePUL58eZiYmMDExAQHDhzA9OnTYWJiAldXV2RmZiIlJUXhdQ8fPoSbm1u+Y9KLro02bdpgxYoVcHFxgbe3N0xNTRWOc3XL98v6T7EfK335IkjrNoVJidLIfPoYZt80QNq0Mcg+fwoAkDZrPOym/wnj4qWRc/WiLkKmArZz537s3Llf12GQnujftwsWLlqOJX+uBgD0jPoZDep/i8iObTFh4iwdR2fAdLAq5bfffotz584ppEVGRqJkyZL46aef4OnpCVNTU+zZswctW7YEACQkJODWrVsICQnJ93n0oiIRERGB+Ph4dOjQAa6urpBIJLoO6fNkZATTkJqQmJsjO+ECTHxLQGJqiuyz8fIssru3kPP4AUz8WZEg+tKYmpqifPlyGDdhpjxNCIE9ew+jcuVgHUb2BdDBqpQ2NjYoW7asQpqVlRUKFSokT+/cuTMGDBgAR0dH2Nraonfv3ggJCcn3jA1ATyoSW7ZswY4dO1CtWjVdh/JZMirqA9uxswEzM4j010idMAyyOzdh7O0HkZUJ8SpVIb9ISYaRvaOOoiUiXXFycoSJiQkePXyikP7o0WOU9C+mo6hIl6ZOnQojIyO0bNkSGRkZCAsLw+zZs1UqQy8qEp6enrC1tVXrtRkZGUpTXzJyZJAa68XwjwIhu3cbLwb9CImlFUxDQmHVayheDu+r67CIiCiXnmy4tX//foXn5ubmmDVrFmbNUr9bSy/+2k6ePBlDhgzBjRs3VH5tTEyM0roTUxNuaT5IfZadDdmDu8i5fgXpyxYg5+Y1mDdsCZHyDBJTM0gsrRWyS+wdIEt5pqNgiUhXnjx5huzsbLi4Kq4a7OLijAcPH+soqi+EHiyRrS16UZHo0KED9u3bh2LFisHGxgaOjo4Kjw/JaypMf/+iBRS5npJIAFMzZF+/ApGVBZNy5eWHjDw8YezshuwEjo8g+tJkZWXh5Mmz+KbW/7qRJRIJvqlVDf/8E/+BVxK9n150bUybNk3t10qlUqWpLzlfULeGefsuyD51HLLHjwALC5hVrw2TMkFIHT0YeJWGzL1bYdGxJ0TqC4hXr2DZuQ+yL5/nQMsviJWVJYoV85Y/9/b2RLlypZGcnILbt+/pLjDSiam/L8DiRVMRf/IsTpw4hT69u8DKygKxS1bpOjTDJoSuI9AanVcksrKycODAAQwbNgw+Pj66DuezY2RnD8ve/wcjB0eIV2nIuXkdqaMHy2dqvFo8CxYyGawGjYLE1BRZp0/g1YJpug2aClT58uWwc+f//khMmDAcALB06Rp07TpIV2GRjqxZsxHOTo4YOXwQ3NyccebMBTRs1AGPHj35+ItJfXraLaEJEiF0X02ys7PD6dOnNVaRSG5ZUyPlkOHw2Jqk6xBIj2TlZOs6BNIj2Zl3tX6O1ytGaKQci3bRH89UwPSiD6BZs2bYsGGDrsMgIiLSDgMebKnzrg0AKF68OEaNGoUjR44gODgYVlZWCsf79Omjo8iIiIg0QAcLUhUUvahILFq0CPb29oiPj0d8vOLIYYlEwooEERF93vS0NUET9KIikZTE/msiIqLPkV5UJN6WO/aT+20QEZHB0P28Bq3Ri8GWAPDnn38iICAAFhYWsLCwQLly5bB06VJdh0VERPTpONhSu6ZMmYJhw4ahV69eqFq1KgDg8OHD6N69O548eYL+/fvrOEIiIiLKi15UJGbMmIE5c+YgPDxcntakSROUKVMGI0eOZEWCiIg+b3ramqAJelGRuH//PqpUqaKUXqVKFdy/f18HEREREWmQAU//1IsxEn5+fli9erVS+qpVq1C8eHEdRERERET5oRctEtHR0fjuu+9w8OBB+RiJI0eOYM+ePXlWMIiIiD4nQma4szb0oiLRsmVLHD9+HFOmTJEvlV2qVCn8+++/+Oqrr3QbHBER0afiGAntCw4OxrJly3QdBhEREalApxUJIyOjjy48JZFIkJ3NnfqIiOgzZsCDLXVakVi/fv17jx07dgzTp0+HzICbg4iI6AvBMRLa0bRpU6W0hIQE/Pzzz9i0aRPat2+PUaNG6SAyIiIiDTLgL8V6Mf0TAO7du4cuXbogICAA2dnZOH36NJYsWQIvLy9dh0ZERETvofPBls+fP8fYsWMxY8YMBAUFYc+ePahevbquwyIiItIcA26R0GlFYsKECRg/fjzc3NywYsWKPLs6iIiIPnsGvPunRAjdXZ2RkREsLCxQu3ZtGBsbvzffunXrVCo3uWXNT4yMDI3H1iRdh0B6JCuHM8Hof7Iz72r9HK+mddNIOZb95mmkHE3SaYtEeHj4R6d/EhERffbYtaEdsbGxujw9ERFRwTDg6Z96M2uDiIiIPj86n7VBRERk8LiyJREREamNXRtEREREytgiQUREpGWCszaIiIhIbQbctcGKBBERkbYZ8GBLjpEgIiIitbFFgoiISNvYtUFERERqM+DBluzaICIiIrWxRYKIiEjb2LVBREREauOsDSIiIiJlbJEgIiLSNnZtEBERkboMeYlsdm0QERGR2tgiQUREpG3s2iAiIiK1sSJBREREauP0TyIiIiJlbJEgIiLSNnZtEBERkbqEAVck2LVBREREamOLBBERkbYZcIsEKxJERETaxpUtiYiIiJSxRYKIiEjb2LVBREREajPgigS7NoiIiEhtbJEgIiLSMiEMt0WCFQkiIiJtM+CuDVYkiIiItM2AKxIcI0FERERqM8gWCedNV3UdAumZ1/cO6ToE0iMu3nV1HQJ9YQx5rw2DrEgQERHpFQOuSLBrg4iIiNTGFgkiIiJtM9ytNliRICIi0jZDHiPBrg0iIiJSGysSRERE2iYTmnmoYM6cOShXrhxsbW1ha2uLkJAQbNu2TX48PT0dUVFRKFSoEKytrdGyZUs8fPhQ5UtjRYKIiEjbZBp6qKBIkSIYN24c4uPj8d9//+Gbb75B06ZNceHCBQBA//79sWnTJqxZswYHDhzAvXv30KJFC5UvTSIMcAFwE7PCug6B9AzXkaC3cR0JeltyaqLWz5HyXS2NlGO/at8nvd7R0RETJ05Eq1at4OzsjOXLl6NVq1YAgMuXL6NUqVI4duwYKleunO8y2SJBRESkZUImNPJQV05ODlauXIm0tDSEhIQgPj4eWVlZqF27tjxPyZIlUbRoURw7dkylsjlrg4iISNs0NP0zIyMDGRkZCmlSqRRSqTTP/OfOnUNISAjS09NhbW2N9evXo3Tp0jh9+jTMzMxgb2+vkN/V1RUPHjxQKSa2SBAREWmZplokYmJiYGdnp/CIiYl573n9/f1x+vRpHD9+HD169EBERAQuXryo0WtjiwQREdFnYujQoRgwYIBC2vtaIwDAzMwMfn5+AIDg4GCcOHECv//+O7777jtkZmYiJSVFoVXi4cOHcHNzUykmtkgQERFpm4ZmbUilUvl0ztzHhyoSSmHIZMjIyEBwcDBMTU2xZ88e+bGEhATcunULISEhKl0aWySIiIi0TOhgieyhQ4eifv36KFq0KF6+fInly5dj//792LFjB+zs7NC5c2cMGDAAjo6OsLW1Re/evRESEqLSjA2AFQkiIiKD9OjRI4SHh+P+/fuws7NDuXLlsGPHDtSpUwcAMHXqVBgZGaFly5bIyMhAWFgYZs+erfJ5uI4EfRG4jgS9jetI0NsKYh2Jpw1DNVJOoS0HNFKOJrFFgoiISMt00bVRUDjYkoiIiNTGFgkiIiJtM+AWCVYkiIiItMyQuzZYkSAiItIyQ65IcIwEERERqY0tEkRERFpmyC0SrEgQERFpm5DoOgKt0UjXRkpKiiaKISIios+MyhWJ8ePHY9WqVfLnbdq0QaFChVC4cGGcOXNGo8EREREZAiHTzEMfqVyRmDt3Ljw9PQEAu3btwq5du7Bt2zbUr18fgwcP1niAREREnzshk2jkoY9UHiPx4MEDeUVi8+bNaNOmDerWrQtvb29UqlRJ4wESERGR/lK5RcLBwQG3b98GAGzfvh21a9cGAAghkJOTo9noiIiIDIAhd22o3CLRokULfP/99yhevDiePn2K+vXrAwBOnToFPz8/jQdIRET0uRMGPGtD5YrE1KlT4e3tjdu3b2PChAmwtrYGANy/fx89e/bUeIBERESkvyRCCKHrIDTNxKywrkMgPfP63iFdh0B6xMW7rq5DID2SnJqo9XPcqfSNRsopcnyvRsrRpHy1SGzcuDHfBTZp0kTtYIiIiAyRvs640IR8VSSaNWuWr8IkEgkHXBIREb3D8Nr+/ydfFQmZTE+HihIREZFOfdJeG+np6TA3N9dULERERAbJkLs2VF5HIicnB6NHj0bhwoVhbW2N69evAwCGDRuGRYsWaTxAIiKiz50hr2ypckXit99+Q2xsLCZMmAAzMzN5etmyZbFw4UKNBkdERET6TeWKxJ9//on58+ejffv2MDY2lqcHBgbi8uXLGg2OiIjIEAihmYc+UnmMxN27d/NcwVImkyErK0sjQRERERkSfe2W0ASVKxKlS5fGoUOH4OXlpZAeFxeHr776Su1AZDIZEhMT8ejRI6VZIjVq1FC7XCIiItIelSsSw4cPR0REBO7evQuZTIZ169YhISEBf/75JzZv3qxWEP/88w++//573Lx5E+8utMm1KYiI6HNnyHttqDxGomnTpti0aRN2794NKysrDB8+HJcuXcKmTZtQp04dtYLo3r07KlSogPPnz+PZs2dITk6WP549e6ZWmURERPqCu3++o3r16ti1a5fGgrh69Sri4uK4eygREdFnRu0Fqf777z9cunQJwJtxE8HBwWoHUalSJSQmJrIiQUREBklmwF0bKlck7ty5g3bt2uHIkSOwt7cHAKSkpKBKlSpYuXIlihQponIQvXv3xsCBA/HgwQMEBATA1NRU4Xi5cuVULpOIiEhfGPIYCZW3Ea9Xrx5SUlKwZMkS+Pv7AwASEhIQGRkJW1tbbN++XeUgjIyUh2pIJBIIIdQabMltxOld3Eac3sZtxOltBbGN+OUSDTRSTskrWzVSjiap3CJx4MABHD16VF6JAAB/f3/MmDED1atXVyuIpKQktV5HREREuqVyRcLT0zPPhadycnLg4eGhVhDvrklBRERkSPR1VUpNULkiMXHiRPTu3RuzZs1ChQoVALwZeNm3b19MmjRJrSA2btyYZ7pEIoG5uTn8/Pzg4+OjVtlERES6ZsgrW+ZrjISDgwMkkv+9CWlpacjOzoaJyZt6SO7/rays1Fr3wcjISD4mQiG4t8ZJVKtWDRs2bICDg8NHy+MYCXoXx0jQ2zhGgt5WEGMkLhZrqJFySl/bopFyNClfLRLTpk3TahC7du3CL7/8gt9++w0VK1YEAPz7778YNmwYfv31V9jZ2aFbt24YNGgQtyonIqLPzhc//TMiIkKrQfTt2xfz589HlSpV5GnffvstzM3N0bVrV1y4cAHTpk1Dp06dtBoHERGRNhjy9E+1F6QCgPT0dGRmZiqk2draqlzOtWvX8nydra0trl+/DgAoXrw4njx5ol6gREREpBUq77WRlpaGXr16wcXFBVZWVnBwcFB4qCM4OBiDBw/G48eP5WmPHz/GkCFD8PXXXwN4s4y2p6enWuUTERHpkhCaeegjlSsSQ4YMwd69ezFnzhxIpVIsXLgQ0dHR8PDwwJ9//qlWEIsWLUJSUhKKFCkCPz8/+Pn5oUiRIrhx4wYWLlwIAEhNTcWvv/6qVvlfoh7dI5B45R+kvriGo4c34esKQboOiXRg4dLVKFu1PsZNmwsAuHv/IcpWrZ/nY8deDkj9EvQf2B17DqzDrfuncSXpOP5aMQd+xTkrTttkQqKRhz5SuWtj06ZN+PPPP1GzZk1ERkaievXq8PPzg5eXF5YtW4b27durHIS/vz8uXryInTt34sqVK/K0OnXqyFe9bNasmcrlfqlat26CSRNHoGfUz/j3xCn06f0jtm5ZhtJla+Dx46e6Do8KyLlLCVjz91aU8PvfHwk3Fyfs37hMId+av7dh8fK1qF65QkGHSDpQpVpFLJz/F06dPAcTY2MMGzkQ6/6OReUK9fDq1Wtdh0efIZUrEs+ePYOvry+AN2MYcqd7VqtWDT169FA7ECMjI9SrVw/16tVTuwx6o3/fLli4aDmW/LkaANAz6mc0qP8tIju2xYSJs3QcHRWEV69e4+foiRj5U1/MW7JCnm5sbAynQo4KefccPIqwb6vD0tKioMMkHWjdXHHQes/uPyHxxr8I+qosjh45oaOoDB8HW77F19cXSUlJKFq0KEqWLInVq1ejYsWK2LRpk3wTr/yYPn06unbtCnNzc0yfPv2Defv06aNqmF8sU1NTlC9fDuMmzJSnCSGwZ+9hVK6s/g6t9HkZM3kWaoR8jZCvv1KoSLzrwuWruHz1On4ZGFWA0ZE+sbW1AQAkJ6foNhADp6/jGzRB5YpEZGQkzpw5g9DQUPz8889o3LgxZs6ciaysLEyZMiXf5UydOhXt27eHubk5pk6d+t58EomEFQkVODk5wsTEBI8eKs5wefToMUr6F9NRVFSQtu7ej0tXrmHlwt8/mnfd5h3w9fbEVwGlCyAy0jcSiQQx43/BP0f/w6WLV3UdjkHT1/ENmqByRaJ///7y/9euXRuXL19GfHw8/Pz8VNru++2Nuj5l066MjAxkZGQopOWuhkn0pbn/8DHGTZuHBdPGQio1+2De9IwMbN21H906tiug6EjfTJo6EqVKl0D9Om11HQp9xj5pHQngzYZbutx0KyYmBtHR0QppEiNrSIxVX8/CEDx58gzZ2dlwcXVSSHdxccaDh4/f8yoyFBcTruJZcgradOolT8vJkSH+9HmsWLcJJ/dthLGxMQBg577DeJ2egSb1vtVVuKRDEyaPQFi9b9AgrB3u3Xug63AM3hc/RuJjYxjepk43RE5ODmJjY7Fnzx48evQIMplM4fjevXvf+9qhQ4diwIABCmkOhUqqHIOhyMrKwsmTZ/FNrWrYuHEHgDfNl9/UqobZcxbrODrStsrBQVi/dI5C2q+/TYGPlyc6d2gtr0QAb7o1alWrBEcH+wKOknRtwuQRaNi4DhrXb49bN+/oOpwvwhfftfGhMQxvU3c8Q9++fREbG4uGDRuibNmyKnVLSKVSSKVSpTi+ZFN/X4DFi6Yi/uRZnDhxCn16d4GVlQVil6zSdWikZVZWliju662QZmFhDntbG4X0W3fuIf70ecyZNKpgAySdmzQ1Gq1aN8b3bbsj9WUaXFzetF6+ePES6ekZH3k1kbJ8VSQ+ZQxDfqxcuRKrV69GgwYNtHqeL8WaNRvh7OSIkcMHwc3NGWfOXEDDRh3w6BGXGKc31m3eCVcXJ1SpWF7XoVAB69zlzVo/W7YvV0jv2W0IVixbp4uQvggGPGkjf9uIa5uHhwf279+PEiVKaKQ8biNO7+I24vQ2biNObyuIbcSPurfUSDlV7q/VSDmapPIS2dowcOBA/P7779CDOg0RERGp4JNnbWjC4cOHsW/fPmzbtg1lypSBqampwvF169jcRkREn68vftaGttnb26N58+a6DoOIiEgrZB/P8tnSi4rE4sWclkhERPQ5UmuMxKFDh9ChQweEhITg7t27AIClS5fi8OHDageSnZ2N3bt3Y968eXj58iUA4N69e0hNTVW7TCIiIn0gINHIQx+pXJFYu3YtwsLCYGFhgVOnTsmXp37+/DnGjh2rVhA3b95EQEAAmjZtiqioKDx+/GYFxvHjx2PQoEFqlUlERKQvZEIzD32kckVizJgxmDt3LhYsWKAwKLJq1ao4efKkWkH07dsXFSpUQHJyMiws/reVcfPmzbFnzx61yiQiItIXMkg08tBHKo+RSEhIQI0aNZTS7ezskJKSolYQhw4dwtGjR2FmprjJkLe3t7zrhIiIiPSPyi0Sbm5uSExUXrzj8OHD8PX1VSsImUyGnJwcpfQ7d+7AxsZGrTKJiIj0BcdIvKVLly7o27cvjh8/DolEgnv37mHZsmUYNGgQevTooVYQdevWxbRp0+TPJRIJUlNTMWLECC6bTUREnz2Zhh76SOWujZ9//hkymQzffvstXr16hRo1akAqlWLQoEHo3bu3WkFMnjwZYWFhKF26NNLT0/H999/j6tWrKFSoEFasWKFWmURERKR9au+1kZmZicTERKSmpqJ06dKwtrb+pECys7OxcuVKnD17FqmpqShfvjzat2+vMPgyv7jXBr2Le23Q27jXBr2tIPba2OnaViPl1H24UiPlaJLaC1KZmZmhdOnSGgni6dOnKFSoEDp06IDbt29jwYIFSEhIwH///Yfq1atr5BxERES6oq/dEpqgckWiVq1akEjeP+Bj7969+S7r3LlzaNy4MW7fvo3ixYtj5cqVqFevHtLS0mBkZISpU6ciLi4OzZo1UzVMIiIiKgAqD7YMCgpCYGCg/FG6dGlkZmbi5MmTCAgIUKmsIUOGICAgAAcPHkTNmjXRqFEjNGzYEM+fP0dycjK6deuGcePGqRoiERGRXuFgy7dMnTo1z/SRI0eqvJz1iRMnsHfvXpQrVw6BgYGYP38+evbsCSOjN/Wb3r17o3LlyqqGSEREpFf0deqmJqi110ZeOnTogD/++EOl1zx79gxubm4AAGtra1hZWcHBwUF+3MHBQb7vBhEREekfje3+eezYMZibm6v8unfHW3xo/AUREdHnSGbAf9pUrki0aNFC4bkQAvfv38d///2HYcOGqRxAx44dIZVKAQDp6eno3r07rKysAEC+IRgREdHnTBf7ZMTExGDdunW4fPkyLCwsUKVKFYwfPx7+/v7yPOnp6Rg4cCBWrlyJjIwMhIWFYfbs2XB1dc33eVSuSNjZ2Sk8NzIygr+/P0aNGoW6dVWbmx0REaHwvEOHDkp5wsPDVQ2RiIhIr+hi484DBw4gKioKX3/9NbKzs/F///d/qFu3Li5evCj/wt6/f39s2bIFa9asgZ2dHXr16oUWLVrgyJEj+T6PSgtS5eTk4MiRIwgICFAYy6BvuCAVvYsLUtHbuCAVva0gFqTa4Pa9Rspp9mC52q99/PgxXFxccODAAdSoUQPPnz+Hs7Mzli9fjlatWgEALl++jFKlSuHYsWP5nuyg0mBLY2Nj1K1bV+1dPomIiL5Empr+mZGRgRcvXig88jsM4Pnz5wAAR0dHAEB8fDyysrJQu3ZteZ6SJUuiaNGiOHbsWL6vTeVZG2XLlsX169dVfRkREdEXSyaRaOQRExMDOzs7hUdMTMzHzy+ToV+/fqhatSrKli0LAHjw4AHMzMxgb2+vkNfV1RUPHjzI97WpPEZizJgxGDRoEEaPHo3g4GB5P0suW1tbVYskIiKifBg6dCgGDBigkJY7YeFDoqKicP78eRw+fFjjMeW7IjFq1CgMHDhQvq13kyZNFKZqCiEgkUiQk5Oj8SCJiIg+Z5oabCmVSvNVcXhbr169sHnzZhw8eBBFihSRp7u5uSEzMxMpKSkKrRIPHz6Ur/GUH/muSERHR6N79+7Yt29fvgsnIiIi3SxvLYRA7969sX79euzfvx8+Pj4Kx4ODg2Fqaoo9e/agZcuWAICEhATcunULISEh+T5PvisSuZM7QkND8104ERER6UZUVBSWL1+Ov//+GzY2NvJxD3Z2drCwsICdnR06d+6MAQMGwNHREba2tujduzdCQkJU2p5CpTESXHWSiIhIdbpY2XLOnDkAgJo1ayqkL168GB07dgTwZv8sIyMjtGzZUmFBKlWoVJEoUaLERysTz549UykAIiIiQ6eLlS3zs0yUubk5Zs2ahVmzZql9HpUqEtHR0UorWxIREdGXS6WKRNu2beHi4qKtWIiIiAySLpbILij5rkhwfAQREZF6uPsn8tfXQkRERMp0Mf2zoOS7IiGTGfLbQEREROpQeYlsIiIiUo0ht+mzIkFERKRlhjxGQuXdP4mIiIhysUWCiIhIywx5lCErEkRERFpmyBUJdm0QERGR2tgiQUREpGXCgAdbsiJBRESkZezaICIiIsoDWySIiIi0zJBbJFiRICIi0jKubElERERq48qWRERERHlgiwQREZGWcYwEERERqc2QKxLs2iAiIiK1sUWCiIhIyzhrg4iIiNTGWRtEREREeWCLBBERkZYZ8mBLViSIiIi0zJDHSLBrg4iIiNTGFgkiIiItkxlwm4RBViRMjQ3ysugTlCvdVtchkB65O+ZbXYdAXxiOkSAiIiK1GW57BMdIEBER0SdgiwQREZGWsWuDiIiI1MaVLYmIiIjywBYJIiIiLeP0TyIiIlKb4VYj2LVBREREn4AtEkRERFrGWRtERESkNkMeI8GuDSIiIlIbWySIiIi0zHDbI1iRICIi0jqOkSAiIiK1cYwEERERUR7YIkFERKRlhtsewYoEERGR1hnyGAl2bRAREZHa2CJBRESkZcKAOzdYkSAiItIydm0QERER5YEtEkRERFpmyOtIsCJBRESkZYZbjWDXBhEREX0CtkgQERFpGbs2iIiISG2GPGuDFQkiIiItM+R1JDhGgoiIiNTGFgkiIiItM+SuDb1okRg1ahRevXqllP769WuMGjVKBxERERFpjtDQP32kFxWJ6OhopKamKqW/evUK0dHROoiIiIiI8kMvujaEEJBIJErpZ86cgaOjow4iIiIi0hxD7trQaUXCwcEBEokEEokEJUqUUKhM5OTkIDU1Fd27d9dhhERERJ9OJvSzW0ITdFqRmDZtGoQQ6NSpE6Kjo2FnZyc/ZmZmBm9vb4SEhOgwQiIiIvoQnVYkIiIiAAA+Pj6oUqUKTE1NdRkOERGRVhhue4SejJEIDQ2FTCbDlStX8OjRI8hkir1JNWrU0FFkREREn45LZGvZP//8g++//x43b96EeKcfSSKRICcnR0eRERER0YfoRUWie/fuqFChArZs2QJ3d/c8Z3AQERF9rvR1DQhN0IuKxNWrVxEXFwc/Pz9dh0JERKRxhjz9Uy8WpKpUqRISExN1HQYREZFWyCA08lDVwYMH0bhxY3h4eEAikWDDhg0Kx4UQGD58ONzd3WFhYYHatWvj6tWrKp1DL1okevfujYEDB+LBgwcICAhQmr1Rrlw5HUVGRET0+UpLS0NgYCA6deqEFi1aKB2fMGECpk+fjiVLlsDHxwfDhg1DWFgYLl68CHNz83ydQy8qEi1btgQAdOrUSZ4mkUjkK15ysCUREX3OdDVGon79+qhfv36ex4QQmDZtGn799Vc0bdoUAPDnn3/C1dUVGzZsQNu2bfN1Dr2oSCQlJek6BCIiIq3R1BiJjIwMZGRkKKRJpVJIpVKVy0pKSsKDBw9Qu3ZteZqdnR0qVaqEY8eOfV4VCS8vL12HQEREpPdiYmKUNrMcMWIERo4cqXJZDx48AAC4uroqpLu6usqP5YdeVCRyXbx4Ebdu3UJmZqZCepMmTXQUERER0ad7d40kdQ0dOhQDBgxQSFOnNUKT9KIicf36dTRv3hznzp2Tj40AIF9PgmMkiIjoc6aplS3V7cbIi5ubGwDg4cOHcHd3l6c/fPgQQUFB+S5HL6Z/9u3bFz4+Pnj06BEsLS1x4cIFHDx4EBUqVMD+/ft1HR4REZHB8fHxgZubG/bs2SNPe/HiBY4fP67Shpl60SJx7Ngx7N27F05OTjAyMoKRkRGqVauGmJgY9OnTB6dOndJ1iERERGrT1YJUqampCus0JSUl4fTp03B0dETRokXRr18/jBkzBsWLF5dP//Tw8ECzZs3yfQ69qEjk5OTAxsYGAODk5IR79+7B398fXl5eSEhI0HF0REREn0ZX0z//++8/1KpVS/48d3xFREQEYmNjMWTIEKSlpaFr165ISUlBtWrVsH379nyvIQHoSUWibNmyOHPmDHx8fFCpUiVMmDABZmZmmD9/Pnx9fXUdHhER0WepZs2aHxzoKZFIMGrUKIwaNUrtc+hFReLXX39FWloaAGDUqFFo1KgRqlevjkKFCmHVqlU6jo6IiOjTcBtxLQsLC5P/38/PD5cvX8azZ8/g4ODAnUCJiOizp6npn/pILyoSeXF0dNR1CERERBphyLt/6kVFIi0tDePGjcOePXvw6NEjyGSKb/n169d1FBkRERF9iF5UJH788UccOHAAP/zwA9zd3dmd8YmqVq2I/v27oXz5ALi7u6JNmy7YtGmnrsMiHYga3AW9BndRSLt+9QYaVm2jo4ioIJmUqwGTgFBIbAsBAGTP7iPr+GbIblyQ5zFy94VplaYwcvMBZDLIHt9BxvrfgZwsXYVtkHQ1a6Mg6EVFYtu2bdiyZQuqVq2q61AMgpWVJc6du4Q//1yNVavm6zoc0rGrl66hU+te8ufZ2dk6jIYKkniZgswj6yFSHgEATEqHQNq4J9KXjYF4dh9G7r6QNuuDrBPbkLlvJSBkMHIqAhjwHz1d4WBLLXNwcOCYCA3auXM/du7cr+swSE9k5+TgyaOnug6DdCAn6azC86yjf8OkXCiM3H2R8+w+TGu0Rvbpvcj+b8f/XpP8sKDDpM+cXiyRPXr0aAwfPhyvXr3SdShEBsfLxxMHzm7BzhPrMWHOKLgXdv34i8jwSCQwLlEBMDGD7P51wMIGxu6+EK9eQtpmCCy6TIS01UAYeRTTdaQGSQihkYc+0osWicmTJ+PatWtwdXWFt7c3TE1NFY6fPHlSR5ERfd7Oxp/H//UZhaRrN+Hs6oSoQT/ir43z0bhGO7xKY8X9SyAp5AHz734CTEyBrAxkbJ77plvDzQcAYFq5ETIPrYXs8W2YlKoMaYv+SP9rlLw7hDSDXRtapsqa3u/KyMhARkaGQpoQggM2iQAc2ntM/v8rFxNxNv489pzciPpNa2Pt8o06jIwKikh+iPRlYwCpBUyKl4e0bkekx00G/v/vyOxzh5Bz8SgAIOvxbRh7loRJmSrIOrJBh1HT50QvKhIjRoxQ+7UxMTGIjo5WSDM2toWpqf0nRkVkeF6+SMWNa7dQ1KeIrkOhgiLLgXj+GACQ9egWjFy9YfLVN8g+sf3N4Wf3FbMnP4DEhmPWNM2QZ23oxRiJTzF06FA8f/5c4WFiYqfrsIj0kqWVBTy9C+Pxwye6DoV0RSKBxNgE4sVTyFKTYeSgOGbGyN4F4sUzHQVnuGRCaOShj/SiReJ9S2FLJBKYm5vDz88PHTt2RGRkpFIeqVQKqVSq9LovmZWVJYoV85Y/9/b2RLlypZGcnILbt+/pLjAqcINH9sH+HYdw984DuLg5ofeQrpDlyLBlPdcV+RKYVm2GnBsXIF4+A0ylMClZEUZFSiBj/XQAQHb8LphWbgzZ4ztvxkiUDoHE0Q3ZW+bpOHL6nOhFRWL48OH47bffUL9+fVSsWBEA8O+//2L79u2IiopCUlISevTogezsbHTp0uUjpVH58uWwc+f/NjubMGE4AGDp0jXo2nWQrsIiHXBzd8GkeWNg72CHZ0+TcfL4GbRt0AnJT1N0HRoVAImFDczCOkJiaQdkvobsyV1krJ8O2a1LAIDsU3sAYxOYhraGxNzqzWJU66ZBPGeLlabpZ1uCZkiEHswnadmyJerUqYPu3bsrpM+bNw87d+7E2rVrMWPGDMyfPx/nzp37aHkWFl7aCpU+U942nPJI/xP/f1/pOgTSI5b9tN8CU7XwNxop58jdvRopR5P0YozEjh07ULt2baX0b7/9Fjt2vFkopUGDBtxzg4iIPksyCI089JFeVCQcHR2xadMmpfRNmzbJV7xMS0uDjY1NQYdGREREH6AXYySGDRuGHj16YN++ffIxEidOnMDWrVsxd+5cAMCuXbsQGhqqyzCJiIjUogejCLRGLyoSXbp0QenSpTFz5kysW7cOAODv748DBw6gSpUqAICBAwfqMkQiIiK16Wu3hCboRUUCAKpWrcrdP4mIiD4zOqtIvHjxAra2tvL/f0huPiIios+RIa9sqbOKhIODA+7fvw8XFxfY29vnuYhU7p4ZOTk5OoiQiIhIMzhGQgv27t0rn5Gxb98+XYVBREREn0BnFYm3Z2BwNgYRERkyDrbUgrNnz+Y7b7ly5bQYCRERkXaxa0MLgoKCIJFIPvrmcowEERGR/tJZRSIpKUlXpyYiIipQ7NrQAi8vbqxFRERfBk7/LCAXL17ErVu3kJmZqZDepEkTHUVERET06WQcI6Fd169fR/PmzXHu3DmFcRO5a0twjAQREZF+0ovdP/v27QsfHx88evQIlpaWuHDhAg4ePIgKFSpg//79ug6PiIjokwgN/dNHetEicezYMezduxdOTk4wMjKCkZERqlWrhpiYGPTp0wenTp3SdYhERERqM+SuDb1okcjJyYGNjQ0AwMnJCffu3QPwZkBmQkKCLkMjIiKiD9CLFomyZcvizJkz8PHxQaVKlTBhwgSYmZlh/vz58PX11XV4REREn0RfuyU0QS8qEr/++ivS0tIAANHR0WjcuDGqV6+OQoUKYeXKlTqOjoiI6NMYcteGXlQkwsLC5P8vXrw4Ll++jGfPnsHBwSHPXUGJiIhIP+i0ItGpU6d85fvjjz+0HAkREZH2sGtDS2JjY+Hl5YWvvvrKoDc0ISKiLxu7NrSkR48eWLFiBZKSkhAZGYkOHTrA0dFRlyERERGRCnQ6/XPWrFm4f/8+hgwZgk2bNsHT0xNt2rTBjh072EJBREQGw5AXpNL5OhJSqRTt2rXDrl27cPHiRZQpUwY9e/aEt7c3UlNTdR0eERHRJxNCppGHPtKLWRu5jIyM5HttcH8NIiIyFIa8jbjOWyQyMjKwYsUK1KlTByVKlMC5c+cwc+ZM3Lp1C9bW1roOj4iIiD5Apy0SPXv2xMqVK+Hp6YlOnTphxYoVcHJy0mVIREREGmfI4/50WpGYO3cuihYtCl9fXxw4cAAHDhzIM9+6desKODIiIiLNMeSuDZ1WJMLDw7lyJRER0WdM5wtSERERGTp2bRAREZHaDHllS53P2iAiIqLPF1skiIiItExfV6XUBFYkiIiItMyQx0iwa4OIiIjUxhYJIiIiLeM6EkRERKQ2Q+7aYEWCiIhIyzj9k4iIiCgPbJEgIiLSMnZtEBERkdoMebAluzaIiIhIbWyRICIi0jJ2bRAREZHaOGuDiIiIKA9skSAiItIybtpFREREamPXBhEREVEe2CJBRESkZZy1QURERGoz5DES7NogIiLSMiGERh7qmDVrFry9vWFubo5KlSrh33//1ei1sSJBRERkoFatWoUBAwZgxIgROHnyJAIDAxEWFoZHjx5p7BysSBAREWmZrlokpkyZgi5duiAyMhKlS5fG3LlzYWlpiT/++ENj18aKBBERkZYJDT1UkZmZifj4eNSuXVueZmRkhNq1a+PYsWOfdD1v42BLIiKiz0RGRgYyMjIU0qRSKaRSqVLeJ0+eICcnB66urgrprq6uuHz5ssZiMsiKxOvXN3Udgs5lZGQgJiYGQ4cOzfMGoy8P7wl6G++HgpWdeVcj5YwcORLR0dEKaSNGjMDIkSM1Ur46JMKQJ7d+wV68eAE7Ozs8f/4ctra2ug6H9ADvCXob74fPkyotEpmZmbC0tERcXByaNWsmT4+IiEBKSgr+/vtvjcTEMRJERESfCalUCltbW4XH+1qUzMzMEBwcjD179sjTZDIZ9uzZg5CQEI3FZJBdG0RERAQMGDAAERERqFChAipWrIhp06YhLS0NkZGRGjsHKxJEREQG6rvvvsPjx48xfPhwPHjwAEFBQdi+fbvSAMxPwYqEgZJKpRgxYgQHUZEc7wl6G++HL0evXr3Qq1cvrZXPwZZERESkNg62JCIiIrWxIkFERERqY0WCVLZ//35IJBKkpKToOhT6BN7e3pg2bZquwyANuHHjBiQSCU6fPg2AP6NUsFiR0LGOHTtCIpFg3LhxCukbNmyARCLRUVSkS7n3RO6jUKFCqFevHs6ePavR85w4cQJdu3bVaJmUf7mfc/fu3ZWORUVFQSKRoGPHjmqVXaVKFdy/fx92dnafGKXmxcbGwt7eXtdhkAaxIqEHzM3NMX78eCQnJ2uszMzMTI2VRQWvXr16uH//Pu7fv489e/bAxMQEjRo10ug5nJ2dYWlpqdEySTWenp5YuXIlXr9+LU9LT0/H8uXLUbRoUbXLNTMzg5ubG7+MUIFgRUIP1K5dG25uboiJiXlvnrVr16JMmTKQSqXw9vbG5MmTFY57e3tj9OjRCA8Ph62tLbp27Sqv+W/evBn+/v6wtLREq1at8OrVKyxZsgTe3t5wcHBAnz59kJOTIy9r6dKlqFChAmxsbODm5obvv/9eo3vX08dJpVK4ubnBzc0NQUFB+Pnnn3H79m08fvwYAHD79m20adMG9vb2cHR0RNOmTXHjxg356zt27IhmzZph0qRJcHd3R6FChRAVFYWsrCx5nne7Ni5fvoxq1arB3NwcpUuXxu7duyGRSLBhwwYA/2s+X7duHWrVqgVLS0sEBgZqdBfBL0358uXh6emJdevWydPWrVuHokWL4quvvpKnbd++HdWqVYO9vT0KFSqERo0a4dq1a+8tN6+ujQULFsDT0xOWlpZo3rw5pkyZotAyMHLkSAQFBWHp0qXw9vaGnZ0d2rZti5cvX+Y7jo/dI/v370dkZCSeP38ub3HT5R4RpBmsSOgBY2NjjB07FjNmzMCdO3eUjsfHx6NNmzZo27Ytzp07h5EjR2LYsGGIjY1VyDdp0iQEBgbi1KlTGDZsGADg1atXmD59OlauXInt27dj//79aN68ObZu3YqtW7di6dKlmDdvHuLi4uTlZGVlYfTo0Thz5gw2bNiAGzduqN3ESp8uNTUVf/31F/z8/FCoUCFkZWUhLCwMNjY2OHToEI4cOQJra2vUq1dPoSVq3759uHbtGvbt24clS5YgNjZW6Z7JlZOTg2bNmsHS0hLHjx/H/Pnz8csvv+SZ95dffsGgQYNw+vRplChRAu3atUN2drY2Lv2L0KlTJyxevFj+/I8//lBadTAtLQ0DBgzAf//9hz179sDIyAjNmzeHTCbL1zmOHDmC7t27o2/fvjh9+jTq1KmD3377TSnftWvXsGHDBmzevBmbN2/GgQMHFLpd8xvH++6RKlWqYNq0abC1tZW3uA0aNEiVt4v0kSCdioiIEE2bNhVCCFG5cmXRqVMnIYQQ69evF7kfz/fffy/q1Kmj8LrBgweL0qVLy597eXmJZs2aKeRZvHixACASExPlad26dROWlpbi5cuX8rSwsDDRrVu398Z44sQJAUD+mn379gkAIjk5WfULpo+KiIgQxsbGwsrKSlhZWQkAwt3dXcTHxwshhFi6dKnw9/cXMplM/pqMjAxhYWEhduzYIS/Dy8tLZGdny/O0bt1afPfdd/LnXl5eYurUqUIIIbZt2yZMTEzE/fv35cd37dolAIj169cLIYRISkoSAMTChQvleS5cuCAAiEuXLmn8fTB0uT/7jx49ElKpVNy4cUPcuHFDmJubi8ePH4umTZuKiIiIPF/7+PFjAUCcO3dOCPG/z+bUqVNCCOWf0e+++040bNhQoYz27dsLOzs7+fMRI0YIS0tL8eLFC3na4MGDRaVKld57De+L40P3yOLFixXOS58/tkjokfHjx2PJkiW4dOmSQvqlS5dQtWpVhbSqVavi6tWrCl0SFSpUUCrT0tISxYoVkz93dXWFt7c3rK2tFdLe7rqIj49H48aNUbRoUdjY2CA0NBQAcOvWrU+7QMq3WrVq4fTp0zh9+jT+/fdfhIWFoX79+rh58ybOnDmDxMRE2NjYwNraGtbW1nB0dER6erpCM3OZMmVgbGwsf+7u7v7eLqqEhAR4enrCzc1NnlaxYsU885YrV06hTADs+voEzs7OaNiwIWJjY7F48WI0bNgQTk5OCnmuXr2Kdu3awdfXF7a2tvD29gaQ/5/JhIQEpc8zr8/X29sbNjY28ufv3jP5jYP3yJeFS2TrkRo1aiAsLAxDhw5VqyvByspKKc3U1FThuUQiyTMtt2kyLS0NYWFhCAsLw7Jly+Ds7Ixbt24hLCyMAzgLkJWVFfz8/OTPFy5cCDs7OyxYsACpqakIDg7GsmXLlF7n7Ows//+HPudP8Xa5uYP5NFHul6xTp07yJYxnzZqldLxx48bw8vLCggUL4OHhAZlMhrJly2r8Z/Jj90x+4+A98mVhRULPjBs3DkFBQfD395enlSpVCkeOHFHId+TIEZQoUULhG6cmXL58GU+fPsW4cePg6ekJAPjvv/80eg5SnUQigZGREV6/fo3y5ctj1apVcHFxga2trUbK9/f3x+3bt/Hw4UP5Zj4nTpzQSNn0cbnjWyQSCcLCwhSOPX36FAkJCViwYAGqV68OADh8+LBK5fv7+yt9nqp+vpqIA3gzo+TtllT6/LFrQ88EBASgffv2mD59ujxt4MCB2LNnD0aPHo0rV65gyZIlmDlzplYGKRUtWhRmZmaYMWMGrl+/jo0bN2L06NEaPw99WEZGBh48eIAHDx7g0qVL6N27N1JTU9G4cWO0b98eTk5OaNq0KQ4dOoSkpCTs378fffr0yXOwbn7UqVMHxYoVQ0REBM6ePYsjR47g119/BQBOISwAxsbGuHTpEi5evKj05cDBwQGFChXC/PnzkZiYiL1792LAgAEqld+7d29s3boVU6ZMwdWrVzFv3jxs27ZNpc9WE3EAb7pPUlNTsWfPHjx58gSvXr1SuQzSL6xI6KFRo0YpNAOWL18eq1evxsqVK1G2bFkMHz4co0aN0spMCmdnZ8TGxmLNmjUoXbo0xo0bh0mTJmn8PPRh27dvh7u7O9zd3VGpUiWcOHECa9asQc2aNWFpaYmDBw+iaNGiaNGiBUqVKoXOnTsjPT1d7RYKY2NjbNiwAampqfj666/x448/ymdtmJuba/LS6D1sbW3z/PyMjIywcuVKxMfHo2zZsujfvz8mTpyoUtlVq1bF3LlzMWXKFAQGBmL79u3o37+/Sp+tJuIA3iyW1b17d3z33XdwdnbGhAkTVC6D9At3/ySiPB05cgTVqlVDYmKiwoBdMgxdunTB5cuXcejQIV2HQp85jpEgIgDA+vXrYW1tjeLFiyMxMRF9+/ZF1apVWYkwEJMmTUKdOnVgZWWFbdu2YcmSJZg9e7auwyIDwIoEEQEAXr58iZ9++gm3bt2Ck5MTateurbSCKn2+/v33X0yYMAEvX76Er68vpk+fjh9//FHXYZEBYNcGERERqY2DLYmIiEhtrEgQERGR2liRICIiIrWxIkFERERqY0WCiIiI1MaKBJEOdezYEc2aNZM/r1mzJvr161fgcezfvx8SiQQpKSnvzSORSLBhw4Z8lzly5EgEBQV9Ulw3btyARCLB6dOnP6kcItIeViSI3tGxY0dIJBJIJBKYmZnBz88Po0aNQnZ2ttbPvW7dunzvbZKfP/5ERNrGBamI8lCvXj0sXrwYGRkZ2Lp1K6KiomBqaoqhQ4cq5c3MzISZmZlGzuvo6KiRcoiICgpbJIjyIJVK4ebmBi8vL/To0QO1a9fGxo0bAfyvO+K3336Dh4eHfMv327dvo02bNrC3t4ejoyOaNm2KGzduyMvMycnBgAEDYG9vj0KFCmHIkCF4dz24d7s2MjIy8NNPP8HT0xNSqRR+fn5YtGgRbty4gVq1agF4syujRCKRb+Imk8kQExMDHx8fWFhYIDAwEHFxcQrn2bp1K0qUKAELCwvUqlVLIc78+umnn1CiRAlYWlrC19cXw4YNQ1ZWllK+efPmwdPTE5aWlmjTpg2eP3+ucHzhwoUoVaoUzM3NUbJkyQ8u25ycnIz27dvD2dkZFhYWKF68OBYvXqxy7ESkOWyRIMoHCwsLPH36VP58z549sLW1xa5duwAAWVlZCAsLQ0hICA4dOgQTExOMGTMG9erVw9mzZ2FmZobJkycjNjYWf/zxB0qVKoXJkydj/fr1+Oabb9573vDwcBw7dgzTp09HYGAgkpKS8OTJE3h6emLt2rVo2bIlEhISYGtrCwsLCwBATEwM/vrrL8ydOxfFixfHwYMH0aFDBzg7OyM0NBS3b99GixYtEBUVha5du+K///7DwIEDVX5PbGxsEBsbCw8PD5w7dw5dunSBjY0NhgwZIs+TmJiI1atXY9OmTXjx4gU6d+6Mnj17YtmyZQCAZcuWYfjw4Zg5cya++uornDp1Cl26dIGVlRUiIiKUzjls2DBcvHgR27Ztg5OTExITE/H69WuVYyciDRJEpCAiIkI0bdpUCCGETCYTu3btElKpVAwaNEh+3NXVVWRkZMhfs3TpUuHv7y9kMpk8LSMjQ1hYWIgdO3YIIYRwd3cXEyZMkB/PysoSRYoUkZ9LCCFCQ0NF3759hRBCJCQkCABi165deca5b98+AUAkJyfL09LT04WlpaU4evSoQt7OnTuLdu3aCSGEGDp0qChdurTC8Z9++kmprHcBEOvXr3/v8YkTJ4rg4GD58xEjRghjY2Nx584dedq2bduEkZGRuH//vhBCiGLFionly5crlDN69GgREhIihBAiKSlJABCnTp0SQgjRuHFjERkZ+d4YiKjgsUWCKA+bN2+GtbU1srKyIJPJ8P3332PkyJHy4wEBAQrjIs6cOYPExETY2NgolJOeno5r167h+fPnuH//PipVqiQ/ZmJiggoVKih1b+Q6ffo0jI2NERoamu+4ExMT8erVK9SpU0chPTMzE1999RUA4NKlSwpxAEBISEi+z5Fr1apVmD59Oq5du4bU1FRkZ2fD1tZWIU/RokVRuHBhhfPIZDIkJCTAxsYG165dQ+fOndGlSxd5nuzsbNjZ2eV5zh49eqBly5Y4efIk6tati2bNmqFKlSoqx05EmsOKBFEeatWqhTlz5sDMzAweHh4wMVH8UbGyslJ4npqaiuDgYHmT/ducnZ3ViiG3q0IVqampAIAtW7Yo/AEH3oz70JRjx46hffv2iI6ORlhYGOzs7LBy5UqVdgvNjXXBggVKFRtjY+M8X1O/fn3cvHkTW7duxa5du/Dtt98iKioKkyZNUv9iiOiTsCJBlAcrKyv4+fnlO3/58uWxatUquLi4KH0rz+Xu7o7jx4+jRo0aAN58846Pj0f58uXzzB8QEACZTIYDBw6gdu3aSsdzW0RycnLkaaVLl4ZUKsWtW7fe25JRqlQp+cDRXP/888/HL/ItR48ehZeXF3755Rd52s2bN5Xy3bp1C/fu3YOHh4f8PEZGRvD394erqys8PDxw/fp1tG/fPt/ndnZ2RkREBCIiIlC9enUMHjyYFQkiHeKsDSINaN++PZycnNC0aVMcOnQISUlJ2L9/P/r06YM7d+4AAPr27Ytx48Zhw4YNuHz5Mnr27PnBNSC8vb0RERGBTp06YcOGDfIyV69eDQDw8vKCRCLB5s2b8fjxY6SmpsLGxgaDBg1C//79sWTJEly7dg0nT57EjBkzsGTJEgBA9+7dcfXqVQwePBgJCQlYvnw5YmNjVbre4sWL49atW1i5ciWuXbuG6dOnY/369Ur5zM3NERERgTNnzuDQoUPo06cP2rRpAzc3NwBAdHQ0YmJiMH36dFy5cgXnzp3D4sWLMWXKlDzPO3z4cPz9999ITEzEhQsXsHnzZpQqVUql2IlIs1iRINIAS0tLHDx4EEWLFkWLFi1QqlQpdO7cGenp6fIWioEDB+KHH35AREQEQkJCYGNjg+bNm3+w3Dlz5qBVq1bo2bMnSpYsiS5duiAtLQ0AULhwYURHR+Pnn3+Gq6srevXqBQAYPXo0hg0bhpiYGJQqVQr16tXDli1b4OPjA+DNuIW1a9diw4YNCAwMxNy5czF27FiVrrdJkybo378/evXqhaCgIBw9ehTDhg1Tyufn54cWLVqgQYMGqFu3LsqVK6cwvfPHH3/EwoULsXjxYgQEBCA0NBSxsbHyWN9lZmaGoUOHoly5cqhRowaMjY2xcuVKlWInIs2SiPeN9CIiIiL6CLZIEBERkdpYkSAiIiK1sSJBREREamNFgoiIiNTGigQRERGpjRUJIiIiUhsrEkRERKQ2ViSIiIhIbaxIEBERkdpYkSAiIiK1sSJBREREamNFgoiIiNT2/wCe8jvyBmhDXwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Define the labels\n",
        "labels = ['Normal', 'Benign', 'Malignant']\n",
        "\n",
        "# Plot the confusion matrix with custom labels\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cfm, annot=True, fmt='g', ax=ax, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('GBCRet, layer4 combination, Hierarchical classifiation,\\n dropout=0.5, bm_fc_layers=\"3\"')\n",
        "plt.show()"
      ]
    }
  ]
}