chapter08/Class_wise_difficulty_balanced_loss.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/packtpublishing/Machine-Learning-for-Imbalanced-Data/blob/main/chapter08/Class_wise_difficulty_balanced_loss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6lM6_ByfkJBU"
   },
   "source": [
    "Reference: https://github.com/hitachi-rd-cv/CDB-loss/tree/143ea5c17c237c71cfbf0a7f2df66a4009394cdc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gnt0xjxJCfYR"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import copy\n",
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.metrics as sm\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torchvision import datasets, transforms\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eM5S-LpTrkqY",
    "outputId": "1a221b1c-ff51-441b-c1fa-904796621929"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9912422/9912422 [00:00<00:00, 25844853.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28881/28881 [00:00<00:00, 122980399.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1648877/1648877 [00:00<00:00, 28272209.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4542/4542 [00:00<00:00, 21261750.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n",
    "random.seed(seed)\n",
    "torch.backends.cudnn.benchmark = False\n",
    "torch.backends.cudnn.deterministic = True\n",
    "\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "kwargs = {\"num_workers\": 1, \"pin_memory\": True} if use_cuda else {}\n",
    "\n",
    "# load data\n",
    "test_seq = [\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.1307,), (0.3081,)),\n",
    "]\n",
    "train_seq = [\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.1307,), (0.3081,)),\n",
    "]\n",
    "test_transform = torchvision.transforms.Compose(test_seq)\n",
    "train_transform = torchvision.transforms.Compose(train_seq)\n",
    "train_dataset = torchvision.datasets.MNIST(\n",
    "    \".\", train=True, download=True, transform=train_transform\n",
    ")\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_dataset, batch_size=64, shuffle=True, **kwargs\n",
    ")\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    torchvision.datasets.MNIST(\".\", train=False, transform=test_transform),\n",
    "    batch_size=500,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 812
    },
    "id": "2cWkyejRqhVc",
    "outputId": "ed3b8680-d1cf-4f4b-991a-dbcc8b56f027"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9912422/9912422 [00:00<00:00, 87044213.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28881/28881 [00:00<00:00, 119228045.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1648877/1648877 [00:00<00:00, 48621283.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4542/4542 [00:00<00:00, 20032101.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of classes in Imbalanced dataset:\n",
      "Counter({0: 5923, 1: 3596, 2: 2156, 3: 1292, 4: 774, 5: 464, 6: 278, 7: 166, 8: 100, 9: 60})\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainset = torchvision.datasets.MNIST(\n",
    "    \"../data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=torchvision.transforms.Compose(\n",
    "        [\n",
    "            torchvision.transforms.ToTensor(),\n",
    "            torchvision.transforms.Normalize((0.1302,), (0.3069,)),\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "# generate imbalanced dataset\n",
    "num_classes = 10\n",
    "classe_labels = range(num_classes)\n",
    "torch.manual_seed(seed)\n",
    "sample_probs = torch.rand(num_classes)\n",
    "idx_to_remove = []\n",
    "\n",
    "\n",
    "def get_img_num_per_cls(data, num_classes=10, imb_type=\"exp\", imb_factor=0.01):\n",
    "    img_max = len(data) / num_classes\n",
    "    img_num_per_cls = []\n",
    "    if imb_type == \"exp\":\n",
    "        for cls_idx in range(num_classes):\n",
    "            num = img_max * (imb_factor ** (cls_idx / (num_classes - 1.0)))\n",
    "            img_num_per_cls.append(int(num))\n",
    "    return img_num_per_cls\n",
    "\n",
    "\n",
    "def gen_imbalanced_data(targets, img_num_per_cls):\n",
    "    targets_np = np.array(targets, dtype=np.int64)\n",
    "    classes = np.unique(targets_np)\n",
    "    num_per_cls_dict = dict()\n",
    "    idx_to_remove = []\n",
    "    for the_class, the_img_num in zip(classes, img_num_per_cls):\n",
    "        num_per_cls_dict[the_class] = the_img_num\n",
    "        idx = np.where(targets_np == the_class)[0]\n",
    "        np.random.shuffle(idx)\n",
    "        selec_idx = idx[:the_img_num]\n",
    "        idx_to_remove.extend(list(np.setdiff1d(idx, selec_idx)))\n",
    "    return idx_to_remove\n",
    "\n",
    "\n",
    "img_num_per_cls = get_img_num_per_cls(trainset)\n",
    "idx_to_remove = gen_imbalanced_data(trainset.targets, img_num_per_cls)\n",
    "\n",
    "# print('idx_to_remove: ', idx_to_remove)\n",
    "imbalanced_train_dataset = copy.deepcopy(trainset)\n",
    "imbalanced_train_dataset.targets = np.delete(trainset.targets, idx_to_remove, axis=0)\n",
    "imbalanced_train_dataset.data = np.delete(trainset.data, idx_to_remove, axis=0)\n",
    "imbalanced_train_loader = torch.utils.data.DataLoader(\n",
    "    imbalanced_train_dataset, batch_size=64\n",
    ")\n",
    "\n",
    "print(\"Distribution of classes in Imbalanced dataset:\")\n",
    "print(Counter(imbalanced_train_dataset.targets.numpy()))\n",
    "fig, ax = plt.subplots()\n",
    "_, counts = np.unique(imbalanced_train_dataset.targets, return_counts=True)\n",
    "ax.bar(range(10), counts)\n",
    "ax.set_xticks(range(10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "I8BPxNYZJK_I",
    "outputId": "e9914bfd-be82-4917-dc6b-6b7a727dffbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset: 60000 training samples & 10000 testing samples\n",
      "\n",
      "Distribution of classes in original dataset:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py:65: UserWarning: train_labels has been renamed targets\n",
      "  warnings.warn(\"train_labels has been renamed targets\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling probability for each class:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGgCAYAAAB45mdaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAexElEQVR4nO3df2xV9f3H8Vd7obeg/FLsTwsVVH5ToF2bgsZk3tG5ppGYuQ6ZdEVZ1DYDbkSpIFdGpGhmU6OVCoouzo46J8wJwmq1EENNodgFNoEhYhu0BaK2WGarvef7h9/Vb7/9ecuPd1uej+T80Q/n3Pu+hIRnz73nniDHcRwBAAAYCbYeAAAAXN6IEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgKuAY2bNnj9LS0hQVFaWgoCBt27at22PKyso0a9Ysud1uXX/99Xr55Zd7MSoAABiIBgV6QGNjo+Li4rRo0SLdcccd3e7/ySefKDU1Vffdd59effVVlZaW6t5771VkZKRSUlJ69Jx+v1+fffaZhg0bpqCgoEBHBgAABhzH0dmzZxUVFaXg4C7OfzjnQZKzdevWLvd56KGHnClTprRZS09Pd1JSUnr8PDU1NY4kNjY2NjY2tn641dTUdPn/fMBnRgJVXl4uj8fTZi0lJUVLly7t9JimpiY1NTW1/uz8742Fa2pqNHz48IsyJwAAuLAaGhoUExOjYcOGdbnfRY+R2tpahYeHt1kLDw9XQ0OD/vOf/2jIkCHtjsnNzdWaNWvarQ8fPpwYAQCgn+nuIxZ98mqanJwc1dfXt241NTXWIwEAgIvkop8ZiYiIUF1dXZu1uro6DR8+vMOzIpLkdrvldrsv9mgAAKAPuOhnRpKTk1VaWtpmraSkRMnJyRf7qQEAQD8QcIx8/fXXqqqqUlVVlaTvL92tqqpSdXW1pO/fYlm4cGHr/vfdd5+OHz+uhx56SIcPH9Zzzz2n1157TcuWLbswrwAAAPRrAcfI/v37NXPmTM2cOVOS5PV6NXPmTK1evVqS9Pnnn7eGiSRdd9112r59u0pKShQXF6ennnpKL7zwQo+/YwQAAAxsQc5/r5vtwxoaGjRixAjV19dzNQ0AAP1ET///7pNX0wAAgMsHMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAExd9BvlAQNB7Irt1iO0c2J9qvUIAHBBcGYEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKS7tBQD0S1xyP3BwZgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmOIbWPspvnkQADBQcGYEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmLrs703DPV4AALDFmREAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmehUjBQUFio2NVWhoqJKSklRRUdHl/vn5+ZowYYKGDBmimJgYLVu2TN98802vBgYAAANLwDFSXFwsr9crn8+nAwcOKC4uTikpKTp16lSH+xcVFWnFihXy+Xz66KOP9OKLL6q4uFiPPPLIeQ8PAAD6v4BjJC8vT4sXL1ZmZqYmT56swsJCDR06VJs3b+5w/71792rOnDm66667FBsbq7lz52r+/Pndnk0BAACXh4BipLm5WZWVlfJ4PD88QHCwPB6PysvLOzxm9uzZqqysbI2P48ePa8eOHfrZz352HmMDAICBYlAgO585c0YtLS0KDw9vsx4eHq7Dhw93eMxdd92lM2fO6KabbpLjOPruu+903333dfk2TVNTk5qamlp/bmhoCGRMAADQj1z0q2nKysq0bt06Pffcczpw4IDeeOMNbd++XWvXru30mNzcXI0YMaJ1i4mJudhjAgAAIwGdGRk9erRcLpfq6urarNfV1SkiIqLDYx599FHdfffduvfeeyVJ06ZNU2Njo37zm99o5cqVCg5u30M5OTnyer2tPzc0NBAkAAAMUAHFSEhIiOLj41VaWqp58+ZJkvx+v0pLS5Wdnd3hMefOnWsXHC6XS5LkOE6Hx7jdbrnd7kBGAwD0UuyK7dYjtHNifar1CLiEAooRSfJ6vcrIyFBCQoISExOVn5+vxsZGZWZmSpIWLlyo6Oho5ebmSpLS0tKUl5enmTNnKikpSceOHdOjjz6qtLS01igBAACXr4BjJD09XadPn9bq1atVW1urGTNmaOfOna0faq2urm5zJmTVqlUKCgrSqlWrdPLkSV1zzTVKS0vT448/fuFeBQAA6LcCjhFJys7O7vRtmbKysrZPMGiQfD6ffD5fb54KAAAMcNybBgAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACY6lWMFBQUKDY2VqGhoUpKSlJFRUWX+3/11VfKyspSZGSk3G63brzxRu3YsaNXAwMAgIFlUKAHFBcXy+v1qrCwUElJScrPz1dKSoqOHDmisLCwdvs3NzfrJz/5icLCwvT6668rOjpan376qUaOHHkh5gcAAP1cwDGSl5enxYsXKzMzU5JUWFio7du3a/PmzVqxYkW7/Tdv3qwvvvhCe/fu1eDBgyVJsbGx5zc1AAAYMAJ6m6a5uVmVlZXyeDw/PEBwsDwej8rLyzs85s0331RycrKysrIUHh6uqVOnat26dWppaen0eZqamtTQ0NBmAwAAA1NAZ0bOnDmjlpYWhYeHt1kPDw/X4cOHOzzm+PHjevfdd7VgwQLt2LFDx44d0wMPPKBvv/1WPp+vw2Nyc3O1Zs2aQEYDMIDErthuPUI7J9anWo8ADFgX/Woav9+vsLAwbdy4UfHx8UpPT9fKlStVWFjY6TE5OTmqr69v3Wpqai72mAAAwEhAZ0ZGjx4tl8ulurq6Nut1dXWKiIjo8JjIyEgNHjxYLperdW3SpEmqra1Vc3OzQkJC2h3jdrvldrsDGQ0AAPRTAZ0ZCQkJUXx8vEpLS1vX/H6/SktLlZyc3OExc+bM0bFjx+T3+1vXjh49qsjIyA5DBAAAXF4CfpvG6/Vq06ZN+sMf/qCPPvpI999/vxobG1uvrlm4cKFycnJa97///vv1xRdfaMmSJTp69Ki2b9+udevWKSsr68K9CgAA0G8FfGlvenq6Tp8+rdWrV6u2tlYzZszQzp07Wz/UWl1dreDgHxonJiZGu3bt0rJlyzR9+nRFR0dryZIlevjhhy/cqwAAAP1WwDEiSdnZ2crOzu7wz8rKytqtJScn64MPPujNUwEAgAGOe9MAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVqxgpKChQbGysQkNDlZSUpIqKih4dt2XLFgUFBWnevHm9eVoAADAABRwjxcXF8nq98vl8OnDggOLi4pSSkqJTp051edyJEyf04IMP6uabb+71sAAAYOAJOEby8vK0ePFiZWZmavLkySosLNTQoUO1efPmTo9paWnRggULtGbNGo0bN+68BgYAAANLQDHS3NysyspKeTyeHx4gOFgej0fl5eWdHve73/1OYWFhuueee3o/KQAAGJAGBbLzmTNn1NLSovDw8Dbr4eHhOnz4cIfHvP/++3rxxRdVVVXV4+dpampSU1NT688NDQ2BjAkAAPqRi3o1zdmzZ3X33Xdr06ZNGj16dI+Py83N1YgRI1q3mJiYizglAACwFNCZkdGjR8vlcqmurq7Nel1dnSIiItrt//HHH+vEiRNKS0trXfP7/d8/8aBBOnLkiMaPH9/uuJycHHm93tafGxoaCBIAAAaogGIkJCRE8fHxKi0tbb081+/3q7S0VNnZ2e32nzhxog4ePNhmbdWqVTp79qyefvrpTgPD7XbL7XYHMhoAAOinAooRSfJ6vcrIyFBCQoISExOVn5+vxsZGZWZmSpIWLlyo6Oho5ebmKjQ0VFOnTm1z/MiRIyWp3TqACy92xXbrEdo5sT7VegQAfUzAMZKenq7Tp09r9erVqq2t1YwZM7Rz587WD7VWV1crOJgvdgUAAD0TcIxIUnZ2dodvy0hSWVlZl8e+/PLLvXlKAAAwQPUqRoDe4m0DAMD/x/spAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMDUIOsBAGCgiF2x3XqEdk6sT7UeAegWZ0YAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmOpVjBQUFCg2NlahoaFKSkpSRUVFp/tu2rRJN998s0aNGqVRo0bJ4/F0uT8AALi8BBwjxcXF8nq98vl8OnDggOLi4pSSkqJTp051uH9ZWZnmz5+v9957T+Xl5YqJidHcuXN18uTJ8x4eAAD0fwHHSF5enhYvXqzMzExNnjxZhYWFGjp0qDZv3tzh/q+++qoeeOABzZgxQxMnTtQLL7wgv9+v0tLS8x4eAAD0fwHFSHNzsyorK+XxeH54gOBgeTwelZeX9+gxzp07p2+//VZXXXVVp/s0NTWpoaGhzQYAAAamgGLkzJkzamlpUXh4eJv18PBw1dbW9ugxHn74YUVFRbUJmv8vNzdXI0aMaN1iYmICGRMAAPQjl/RqmvXr12vLli3aunWrQkNDO90vJydH9fX1rVtNTc0lnBIAAFxKgwLZefTo0XK5XKqrq2uzXldXp4iIiC6P/f3vf6/169frnXfe0fTp07vc1+12y+12BzIaAADopwI6MxISEqL4+Pg2Hz7974dRk5OTOz3uySef1Nq1a7Vz504lJCT0floAADDgBHRmRJK8Xq8yMjKUkJCgxMRE5efnq7GxUZmZmZKkhQsXKjo6Wrm5uZKkJ554QqtXr1ZRUZFiY2NbP1ty5ZVX6sorr7yALwUAAPRHAcdIenq6Tp8+rdWrV6u2tlYzZszQzp07Wz/UWl1dreDgH064bNiwQc3Nzfr5z3/e5nF8Pp8ee+yx85seAAD0ewHHiCRlZ2crOzu7wz8rKytr8/OJEyd68xQAAOAywb1pAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgKlB1gMAAHA5iV2x3XqEdk6sTzV9fs6MAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAw1asYKSgoUGxsrEJDQ5WUlKSKioou9//zn/+siRMnKjQ0VNOmTdOOHTt6NSwAABh4Ao6R4uJieb1e+Xw+HThwQHFxcUpJSdGpU6c63H/v3r2aP3++7rnnHn344YeaN2+e5s2bp0OHDp338AAAoP8LOEby8vK0ePFiZWZmavLkySosLNTQoUO1efPmDvd/+umn9dOf/lTLly/XpEmTtHbtWs2aNUvPPvvseQ8PAAD6v0GB7Nzc3KzKykrl5OS0rgUHB8vj8ai8vLzDY8rLy+X1etuspaSkaNu2bZ0+T1NTk5qamlp/rq+vlyQ1NDQEMm6P+JvOXfDHPF89eZ3MfeEw96XF3JcWc19aA3nu83lcx3G63tEJwMmTJx1Jzt69e9usL1++3ElMTOzwmMGDBztFRUVt1goKCpywsLBOn8fn8zmS2NjY2NjY2AbAVlNT02VfBHRm5FLJyclpczbF7/friy++0NVXX62goCDDyTrX0NCgmJgY1dTUaPjw4dbj9BhzX1rMfWkx96XF3JdWf5jbcRydPXtWUVFRXe4XUIyMHj1aLpdLdXV1bdbr6uoUERHR4TEREREB7S9Jbrdbbre7zdrIkSMDGdXM8OHD++w/iq4w96XF3JcWc19azH1p9fW5R4wY0e0+AX2ANSQkRPHx8SotLW1d8/v9Ki0tVXJycofHJCcnt9lfkkpKSjrdHwAAXF4CfpvG6/UqIyNDCQkJSkxMVH5+vhobG5WZmSlJWrhwoaKjo5WbmytJWrJkiW655RY99dRTSk1N1ZYtW7R//35t3Ljxwr4SAADQLwUcI+np6Tp9+rRWr16t2tpazZgxQzt37lR4eLgkqbq6WsHBP5xwmT17toqKirRq1So98sgjuuGGG7Rt2zZNnTr1wr2KPsDtdsvn87V7e6mvY+5Li7kvLea+tJj70uqvc3ckyHG6u94GAADg4uHeNAAAwBQxAgAATBEjAADAFDECAABMESMXQEFBgWJjYxUaGqqkpCRVVFRYj9StPXv2KC0tTVFRUQoKCuryXkF9RW5urn70ox9p2LBhCgsL07x583TkyBHrsbq1YcMGTZ8+vfWLiZKTk/X2229bjxWw9evXKygoSEuXLrUepUuPPfaYgoKC2mwTJ060HqtHTp48qV/96le6+uqrNWTIEE2bNk379++3HqtbsbGx7f7Og4KClJWVZT1ap1paWvToo4/quuuu05AhQzR+/HitXbu2+3uo9AFnz57V0qVLNXbsWA0ZMkSzZ8/Wvn37rMc6L8TIeSouLpbX65XP59OBAwcUFxenlJQUnTp1ynq0LjU2NiouLk4FBQXWo/TY7t27lZWVpQ8++EAlJSX69ttvNXfuXDU2NlqP1qVrr71W69evV2Vlpfbv368f//jHuv322/XPf/7TerQe27dvn55//nlNnz7depQemTJlij7//PPW7f3337ceqVtffvml5syZo8GDB+vtt9/Wv/71Lz311FMaNWqU9Wjd2rdvX5u/75KSEknSnXfeaTxZ55544glt2LBBzz77rD766CM98cQTevLJJ/XMM89Yj9ate++9VyUlJXrllVd08OBBzZ07Vx6PRydPnrQerfd6cH88dCExMdHJyspq/bmlpcWJiopycnNzDacKjCRn69at1mME7NSpU44kZ/fu3dajBGzUqFHOCy+8YD1Gj5w9e9a54YYbnJKSEueWW25xlixZYj1Sl3w+nxMXF2c9RsAefvhh56abbrIe44JYsmSJM378eMfv91uP0qnU1FRn0aJFbdbuuOMOZ8GCBUYT9cy5c+ccl8vlvPXWW23WZ82a5axcudJoqvPHmZHz0NzcrMrKSnk8nta14OBgeTwelZeXG052eaivr5ckXXXVVcaT9FxLS4u2bNmixsbGfnNLhKysLKWmprb5d97X/fvf/1ZUVJTGjRunBQsWqLq62nqkbr355ptKSEjQnXfeqbCwMM2cOVObNm2yHitgzc3N+uMf/6hFixb12RubSt9/IWdpaamOHj0qSfrHP/6h999/X7fddpvxZF377rvv1NLSotDQ0DbrQ4YM6RdnADvTJ+/a21+cOXNGLS0trd8++1/h4eE6fPiw0VSXB7/fr6VLl2rOnDn94tt8Dx48qOTkZH3zzTe68sortXXrVk2ePNl6rG5t2bJFBw4c6FfvRyclJenll1/WhAkT9Pnnn2vNmjW6+eabdejQIQ0bNsx6vE4dP35cGzZskNfr1SOPPKJ9+/bpt7/9rUJCQpSRkWE9Xo9t27ZNX331lX79619bj9KlFStWqKGhQRMnTpTL5VJLS4sef/xxLViwwHq0Lg0bNkzJyclau3atJk2apPDwcP3pT39SeXm5rr/+euvxeo0YQb+UlZWlQ4cO9ZvfBCZMmKCqqirV19fr9ddfV0ZGhnbv3t2ng6SmpkZLlixRSUlJu9/C+rL/+5vt9OnTlZSUpLFjx+q1117TPffcYzhZ1/x+vxISErRu3TpJ0syZM3Xo0CEVFhb2qxh58cUXddttt3V7y3hrr732ml599VUVFRVpypQpqqqq0tKlSxUVFdXn/75feeUVLVq0SNHR0XK5XJo1a5bmz5+vyspK69F6jRg5D6NHj5bL5VJdXV2b9bq6OkVERBhNNfBlZ2frrbfe0p49e3Tttddaj9MjISEhrb+1xMfHa9++fXr66af1/PPPG0/WucrKSp06dUqzZs1qXWtpadGePXv07LPPqqmpSS6Xy3DCnhk5cqRuvPFGHTt2zHqULkVGRraL00mTJukvf/mL0USB+/TTT/XOO+/ojTfesB6lW8uXL9eKFSv0y1/+UpI0bdo0ffrpp8rNze3zMTJ+/Hjt3r1bjY2NamhoUGRkpNLT0zVu3Djr0XqNz4ych5CQEMXHx6u0tLR1ze/3q7S0tN98HqA/cRxH2dnZ2rp1q959911dd9111iP1mt/vV1NTk/UYXbr11lt18OBBVVVVtW4JCQlasGCBqqqq+kWISNLXX3+tjz/+WJGRkdajdGnOnDntLlU/evSoxo4dazRR4F566SWFhYUpNTXVepRunTt3rs1NXSXJ5XLJ7/cbTRS4K664QpGRkfryyy+1a9cu3X777dYj9RpnRs6T1+tVRkaGEhISlJiYqPz8fDU2NiozM9N6tC59/fXXbX5T/OSTT1RVVaWrrrpKY8aMMZysc1lZWSoqKtJf//pXDRs2TLW1tZKkESNGaMiQIcbTdS4nJ0e33XabxowZo7Nnz6qoqEhlZWXatWuX9WhdGjZsWLvP41xxxRW6+uqr+/TndB588EGlpaVp7Nix+uyzz+Tz+eRyuTR//nzr0bq0bNkyzZ49W+vWrdMvfvELVVRUaOPGjdq4caP1aD3i9/v10ksvKSMjQ4MG9f3/WtLS0vT4449rzJgxmjJlij788EPl5eVp0aJF1qN1a9euXXIcRxMmTNCxY8e0fPlyTZw4sc//v9Ml68t5BoJnnnnGGTNmjBMSEuIkJiY6H3zwgfVI3XrvvfccSe22jIwM69E61dG8kpyXXnrJerQuLVq0yBk7dqwTEhLiXHPNNc6tt97q/P3vf7ceq1f6w6W96enpTmRkpBMSEuJER0c76enpzrFjx6zH6pG//e1vztSpUx232+1MnDjR2bhxo/VIPbZr1y5HknPkyBHrUXqkoaHBWbJkiTNmzBgnNDTUGTdunLNy5UqnqanJerRuFRcXO+PGjXNCQkKciIgIJysry/nqq6+sxzovQY7TD75uDgAADFh8ZgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAICp/wHk2f5nxK/LVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imbalanced dataset: 14809 training samples & 10000 testing samples\n",
      "\n",
      "Distribution of classes in imbalanced dataset:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_mnist(arr, nrow=5, ncol=10, figsize=None):\n",
    "    if figsize is None:\n",
    "        figsize = (ncol, nrow)\n",
    "\n",
    "    f, a = plt.subplots(nrow, ncol, figsize=figsize)\n",
    "\n",
    "    def _do_show(the_figure, the_array):\n",
    "        the_figure.imshow(the_array)\n",
    "        the_figure.axis(\"off\")\n",
    "\n",
    "    for i in range(nrow):\n",
    "        for j in range(ncol):\n",
    "            _do_show(a[i][j], np.reshape(arr[i * ncol + j], (28, 28)))\n",
    "\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.1)\n",
    "    plt.draw()\n",
    "\n",
    "\n",
    "def vis(test_accs, confusion_mtxes, labels, figsize=(20, 8)):\n",
    "    cm = confusion_mtxes[np.argmax(test_accs)]\n",
    "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
    "    cm_perc = cm / cm_sum * 100\n",
    "    annot = np.empty_like(cm).astype(str)\n",
    "    nrows, ncols = cm.shape\n",
    "    for i in range(nrows):\n",
    "        for j in range(ncols):\n",
    "            c = cm[i, j]\n",
    "            p = cm_perc[i, j]\n",
    "            if c == 0:\n",
    "                annot[i, j] = \"\"\n",
    "            else:\n",
    "                annot[i, j] = \"%.1f%%\" % p\n",
    "    cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
    "    cm.index.name = \"Actual\"\n",
    "    cm.columns.name = \"Predicted\"\n",
    "\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(test_accs, \"g\")\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.heatmap(cm, annot=annot, fmt=\"\", cmap=\"Blues\", annot_kws={\"size\": 10})\n",
    "    plt.show()\n",
    "    return annot\n",
    "\n",
    "\n",
    "print(\n",
    "    \"Original dataset: %d training samples & %d testing samples\\n\"\n",
    "    % (len(train_loader.dataset), len(test_loader.dataset))\n",
    ")\n",
    "\n",
    "print(\"Distribution of classes in original dataset:\")\n",
    "fig, ax = plt.subplots()\n",
    "_, counts = np.unique(train_loader.dataset.train_labels, return_counts=True)\n",
    "ax.bar(classe_labels, counts)\n",
    "ax.set_xticks(classe_labels)\n",
    "plt.rc(\"xtick\", labelsize=10)\n",
    "plt.rc(\"ytick\", labelsize=10)\n",
    "plt.show()\n",
    "\n",
    "print(\"Sampling probability for each class:\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(classe_labels, sample_probs)\n",
    "ax.set_xticks(classe_labels)\n",
    "plt.rc(\"xtick\", labelsize=10)\n",
    "plt.rc(\"ytick\", labelsize=10)\n",
    "plt.show()\n",
    "\n",
    "print(\n",
    "    \"Imbalanced dataset: %d training samples & %d testing samples\\n\"\n",
    "    % (len(imbalanced_train_loader.dataset), len(test_loader.dataset))\n",
    ")\n",
    "\n",
    "print(\"Distribution of classes in imbalanced dataset:\")\n",
    "fig, ax = plt.subplots()\n",
    "_, counts = np.unique(imbalanced_train_loader.dataset.train_labels, return_counts=True)\n",
    "ax.bar(classe_labels, counts)\n",
    "ax.set_xticks(classe_labels)\n",
    "plt.rc(\"xtick\", labelsize=10)\n",
    "plt.rc(\"ytick\", labelsize=10)\n",
    "plt.show()\n",
    "\n",
    "for data, _ in train_loader:\n",
    "    show_mnist(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xwcy9KhNJtiq"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "\n",
    "# Preparing for validaion test\n",
    "indices = list(range(len(imbalanced_train_dataset)))\n",
    "np.random.seed(seed)\n",
    "np.random.shuffle(indices)\n",
    "# to get 20% of the train set\n",
    "split = int(np.floor(0.2 * len(imbalanced_train_dataset)))\n",
    "\n",
    "valid_samples = SubsetRandomSampler(indices[:split])\n",
    "train_samples = SubsetRandomSampler(indices[split:])\n",
    "\n",
    "# Data Loader\n",
    "imbalanced_train_loader = torch.utils.data.DataLoader(\n",
    "    imbalanced_train_dataset, sampler=train_samples, batch_size=64\n",
    ")\n",
    "imbalanced_val_loader = torch.utils.data.DataLoader(\n",
    "    imbalanced_train_dataset, sampler=valid_samples, batch_size=64\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R97FpDmfV_W5"
   },
   "outputs": [],
   "source": [
    "class ClassDifficultyBalancedLoss(torch.nn.Module):\n",
    "    def __init__(self, class_difficulty, tau):\n",
    "        super().__init__()\n",
    "        print(\"class_difficulty: \", class_difficulty)\n",
    "        self.class_difficulty = class_difficulty\n",
    "        self.weights = self.class_difficulty ** float(tau)\n",
    "        self.weights = self.weights / (self.weights.sum() * len(self.weights))\n",
    "        self.weights = torch.from_numpy(self.weights).float().to(device)\n",
    "        print(\"self.weights: \", self.weights)\n",
    "        self.loss = torch.nn.NLLLoss(\n",
    "            weight=self.weights\n",
    "        )  # need to use NLLLoss here instead\n",
    "\n",
    "    def forward(self, input: torch.Tensor, target: torch.Tensor):\n",
    "        return self.loss(input, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zdId9qj4CfYS"
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "\n",
    "class Net(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = torch.nn.Dropout2d()\n",
    "        self.fc1 = torch.nn.Linear(320, 50)\n",
    "        self.fc2 = torch.nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "\n",
    "def process(train_loader, valid_loader, test_loader, lossCriterion, weight=None):\n",
    "    model = Net().to(device)\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n",
    "\n",
    "    if lossCriterion == torch.nn.NLLLoss:\n",
    "        print(\"loss is torch.nn.NLLLoss\")\n",
    "        criterion = lossCriterion().to(device)\n",
    "    else:\n",
    "        print(\"entres\")\n",
    "        class_difficulty = np.ones(num_classes)\n",
    "        criterion = lossCriterion(class_difficulty, tau=1.5).to(device)\n",
    "\n",
    "    def train(train_loader):\n",
    "        model.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            loss = criterion(output, target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "    def validate(val_loader):\n",
    "        model.eval()\n",
    "        val_total = 0\n",
    "        val_loss = 0\n",
    "        class_wise_accuracy = np.zeros(10)  # number of classes = 10\n",
    "        val_samples_per_class = np.zeros(10)\n",
    "        for val_images, val_labels in val_loader:\n",
    "            val_images = val_images.to(device)\n",
    "            val_labels = val_labels.to(device)\n",
    "            with torch.no_grad():\n",
    "                out = model(val_images)\n",
    "            _, val_predicted = out.max(1)\n",
    "            # print('val_predicted: ', val_predicted)\n",
    "            val_total += val_labels.size(0)\n",
    "            for id in range(len(val_predicted)):\n",
    "                if val_predicted[id] == val_labels[id]:\n",
    "                    class_wise_accuracy[int(val_predicted[id])] += 1\n",
    "            for label in val_labels:\n",
    "                val_samples_per_class[label] += 1\n",
    "\n",
    "        print(\"class_wise_accuracy is: \", class_wise_accuracy)\n",
    "        class_wise_accuracy = class_wise_accuracy / val_samples_per_class\n",
    "        print(\"after normalization, class_wise_accuracy is: \", class_wise_accuracy)\n",
    "        return class_wise_accuracy\n",
    "\n",
    "    def test(test_loader):\n",
    "        model.eval()\n",
    "        correct = 0\n",
    "        targets, preds = [], []\n",
    "        with torch.no_grad():\n",
    "            for data, target in test_loader:\n",
    "                data, target = data.to(device), target.to(device)\n",
    "                output = model(data)\n",
    "                pred = output.max(1, keepdim=True)[\n",
    "                    1\n",
    "                ]  # get the index of the max log-probability\n",
    "                correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "                targets += list(target.cpu().numpy())\n",
    "                preds += list(pred.cpu().numpy())\n",
    "\n",
    "        test_acc = 100.0 * correct / len(test_loader.dataset)\n",
    "        confusion_mtx = sm.confusion_matrix(targets, preds)\n",
    "        return test_acc, confusion_mtx\n",
    "\n",
    "    test_accs, confusion_mtxes = [], []\n",
    "    for epoch in range(1, 20 + 1):\n",
    "        train(train_loader)\n",
    "\n",
    "        class_wise_accuracy = validate(valid_loader)\n",
    "\n",
    "        if lossCriterion == ClassDifficultyBalancedLoss:\n",
    "            print(\"enters\")\n",
    "            criterion = ClassDifficultyBalancedLoss(\n",
    "                class_difficulty=1 - class_wise_accuracy, tau=1.5\n",
    "            ).to(device)  # tau =  0.5, 1, 1.5, 2, 5,\n",
    "\n",
    "        val_accuracy = class_wise_accuracy.mean()\n",
    "        print(\"Validation: val accuracy = %.4f\" % (val_accuracy))\n",
    "\n",
    "        test_acc, confusion_mtx = test(test_loader)\n",
    "        test_accs.append(test_acc)\n",
    "        confusion_mtxes.append(confusion_mtx)\n",
    "        print(\"\\rBest test acc = %2.2f%%\" % max(test_accs))\n",
    "\n",
    "    # vis(test_accs, confusion_mtxes, classe_labels)\n",
    "    confusion_mtx = vis(test_accs, confusion_mtxes, classe_labels)\n",
    "    return test_accs[np.argmax(test_accs)], confusion_mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "chKLRJ8wg-NO",
    "outputId": "eadb478a-8611-41ee-b3af-a8f9460e47e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss is torch.nn.NLLLoss\n",
      "class_wise_accuracy is:  [1156.  720.  370.  185.  121.    0.    0.    0.    0.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98550725 0.99037139 0.87885986 0.74596774 0.76100629 0.\n",
      " 0.         0.         0.         0.        ]\n",
      "Validation: val accuracy = 0.4362\n",
      "Best test acc = 45.55%\n",
      "class_wise_accuracy is:  [1160.  720.  380.  214.  148.   58.   22.    7.    0.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98891731 0.99037139 0.90261283 0.86290323 0.93081761 0.54205607\n",
      " 0.37931034 0.18918919 0.         0.        ]\n",
      "Validation: val accuracy = 0.5786\n",
      "Best test acc = 60.11%\n",
      "class_wise_accuracy is:  [1152.  721.  396.  222.  144.   60.   42.   22.    0.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98209719 0.99174691 0.94061758 0.89516129 0.90566038 0.56074766\n",
      " 0.72413793 0.59459459 0.         0.        ]\n",
      "Validation: val accuracy = 0.6595\n",
      "Best test acc = 67.42%\n",
      "class_wise_accuracy is:  [1.16e+03 7.17e+02 3.97e+02 2.21e+02 1.44e+02 7.30e+01 4.10e+01 2.90e+01\n",
      " 1.00e+00 0.00e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.98891731 0.98624484 0.94299287 0.89112903 0.90566038 0.68224299\n",
      " 0.70689655 0.78378378 0.04761905 0.        ]\n",
      "Validation: val accuracy = 0.6935\n",
      "Best test acc = 71.08%\n",
      "class_wise_accuracy is:  [1158.  721.  391.  220.  145.   76.   53.   31.    3.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98721228 0.99174691 0.92874109 0.88709677 0.91194969 0.71028037\n",
      " 0.9137931  0.83783784 0.14285714 0.        ]\n",
      "Validation: val accuracy = 0.7312\n",
      "Best test acc = 74.16%\n",
      "class_wise_accuracy is:  [1156.  720.  401.  226.  145.   79.   51.   30.    3.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98550725 0.99037139 0.95249406 0.91129032 0.91194969 0.73831776\n",
      " 0.87931034 0.81081081 0.14285714 0.        ]\n",
      "Validation: val accuracy = 0.7323\n",
      "Best test acc = 74.49%\n",
      "class_wise_accuracy is:  [1162.  722.  399.  224.  144.   81.   53.   29.    7.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.99062234 0.99312242 0.94774347 0.90322581 0.90566038 0.75700935\n",
      " 0.9137931  0.78378378 0.33333333 0.        ]\n",
      "Validation: val accuracy = 0.7528\n",
      "Best test acc = 76.58%\n",
      "class_wise_accuracy is:  [1156.  719.  408.  227.  144.   85.   53.   30.    6.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.98550725 0.98899587 0.96912114 0.91532258 0.90566038 0.79439252\n",
      " 0.9137931  0.81081081 0.28571429 0.        ]\n",
      "Validation: val accuracy = 0.7569\n",
      "Best test acc = 78.07%\n",
      "class_wise_accuracy is:  [1166.  721.  404.  234.  149.   83.   53.   30.    5.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.99174691 0.95961995 0.94354839 0.93710692 0.77570093\n",
      " 0.9137931  0.81081081 0.23809524 0.        ]\n",
      "Validation: val accuracy = 0.7564\n",
      "Best test acc = 78.07%\n",
      "class_wise_accuracy is:  [1163.  722.  404.  232.  152.   88.   54.   33.    7.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.99147485 0.99312242 0.95961995 0.93548387 0.95597484 0.82242991\n",
      " 0.93103448 0.89189189 0.33333333 0.        ]\n",
      "Validation: val accuracy = 0.7814\n",
      "Best test acc = 78.07%\n",
      "class_wise_accuracy is:  [1166.  719.  408.  232.  150.   82.   52.   31.    8.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.98899587 0.96912114 0.93548387 0.94339623 0.76635514\n",
      " 0.89655172 0.83783784 0.38095238 0.        ]\n",
      "Validation: val accuracy = 0.7713\n",
      "Best test acc = 79.56%\n",
      "class_wise_accuracy is:  [1166.  718.  411.  236.  151.   84.   52.   31.    8.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.98762036 0.97624703 0.9516129  0.94968553 0.78504673\n",
      " 0.89655172 0.83783784 0.38095238 0.        ]\n",
      "Validation: val accuracy = 0.7760\n",
      "Best test acc = 80.01%\n",
      "class_wise_accuracy is:  [1.166e+03 7.210e+02 4.110e+02 2.330e+02 1.530e+02 8.800e+01 5.200e+01\n",
      " 3.100e+01 8.000e+00 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.99174691 0.97624703 0.93951613 0.96226415 0.82242991\n",
      " 0.89655172 0.83783784 0.38095238 0.1       ]\n",
      "Validation: val accuracy = 0.7902\n",
      "Best test acc = 82.55%\n",
      "class_wise_accuracy is:  [1.165e+03 7.210e+02 4.090e+02 2.330e+02 1.540e+02 9.300e+01 5.400e+01\n",
      " 3.200e+01 9.000e+00 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.99317988 0.99174691 0.97149644 0.93951613 0.96855346 0.86915888\n",
      " 0.93103448 0.86486486 0.42857143 0.1       ]\n",
      "Validation: val accuracy = 0.8058\n",
      "Best test acc = 82.58%\n",
      "class_wise_accuracy is:  [1.164e+03 7.210e+02 4.130e+02 2.320e+02 1.530e+02 9.600e+01 5.600e+01\n",
      " 3.100e+01 1.100e+01 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.99232737 0.99174691 0.98099762 0.93548387 0.96226415 0.89719626\n",
      " 0.96551724 0.83783784 0.52380952 0.1       ]\n",
      "Validation: val accuracy = 0.8187\n",
      "Best test acc = 82.58%\n",
      "class_wise_accuracy is:  [1.16e+03 7.22e+02 4.12e+02 2.36e+02 1.53e+02 9.40e+01 5.40e+01 3.10e+01\n",
      " 8.00e+00 1.00e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.98891731 0.99312242 0.97862233 0.9516129  0.96226415 0.87850467\n",
      " 0.93103448 0.83783784 0.38095238 0.1       ]\n",
      "Validation: val accuracy = 0.8003\n",
      "Best test acc = 83.80%\n",
      "class_wise_accuracy is:  [1.165e+03 7.210e+02 4.100e+02 2.300e+02 1.550e+02 9.900e+01 5.600e+01\n",
      " 3.200e+01 1.100e+01 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.99317988 0.99174691 0.97387173 0.92741935 0.97484277 0.92523364\n",
      " 0.96551724 0.86486486 0.52380952 0.1       ]\n",
      "Validation: val accuracy = 0.8240\n",
      "Best test acc = 84.67%\n",
      "class_wise_accuracy is:  [1.163e+03 7.210e+02 4.130e+02 2.330e+02 1.530e+02 9.500e+01 5.600e+01\n",
      " 3.300e+01 8.000e+00 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.99147485 0.99174691 0.98099762 0.93951613 0.96226415 0.88785047\n",
      " 0.96551724 0.89189189 0.38095238 0.1       ]\n",
      "Validation: val accuracy = 0.8092\n",
      "Best test acc = 84.67%\n",
      "class_wise_accuracy is:  [1.166e+03 7.220e+02 4.100e+02 2.330e+02 1.540e+02 9.700e+01 5.600e+01\n",
      " 3.200e+01 1.100e+01 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.99312242 0.97387173 0.93951613 0.96855346 0.90654206\n",
      " 0.96551724 0.86486486 0.52380952 0.1       ]\n",
      "Validation: val accuracy = 0.8230\n",
      "Best test acc = 86.33%\n",
      "class_wise_accuracy is:  [1164.  721.  413.  231.  152.   99.   56.   32.   12.    2.]\n",
      "after normalization, class_wise_accuracy is:  [0.99232737 0.99174691 0.98099762 0.93145161 0.95597484 0.92523364\n",
      " 0.96551724 0.86486486 0.57142857 0.2       ]\n",
      "Validation: val accuracy = 0.8380\n",
      "Best test acc = 86.54%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# baseline of torch.nn.CrossEntropyLoss\n",
    "test_acc_crossentropy, confusion_mtx_crossentropy = process(\n",
    "    imbalanced_train_loader, imbalanced_val_loader, test_loader, torch.nn.NLLLoss\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eqz8QoLP0-0Z",
    "outputId": "e320ca06-9570-45c2-d4f4-b34e98c45dfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entres\n",
      "class_difficulty:  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "self.weights:  tensor([0.0100, 0.0100, 0.0100, 0.0100, 0.0100, 0.0100, 0.0100, 0.0100, 0.0100,\n",
      "        0.0100], device='cuda:0')\n",
      "class_wise_accuracy is:  [1.16e+03 7.22e+02 3.75e+02 1.81e+02 1.13e+02 2.00e+00 1.00e+00 0.00e+00\n",
      " 0.00e+00 0.00e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.98891731 0.99312242 0.89073634 0.72983871 0.71069182 0.01869159\n",
      " 0.01724138 0.         0.         0.        ]\n",
      "enters\n",
      "class_difficulty:  [0.01108269 0.00687758 0.10926366 0.27016129 0.28930818 0.98130841\n",
      " 0.98275862 1.         1.         1.        ]\n",
      "self.weights:  tensor([2.2096e-05, 1.0802e-05, 6.8401e-04, 2.6594e-03, 2.9471e-03, 1.8410e-02,\n",
      "        1.8451e-02, 1.8939e-02, 1.8939e-02, 1.8939e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.4349\n",
      "Best test acc = 45.88%\n",
      "class_wise_accuracy is:  [172. 319. 386. 219. 131. 103.  53.  31.   1.   2.]\n",
      "after normalization, class_wise_accuracy is:  [0.14663257 0.43878955 0.91686461 0.88306452 0.82389937 0.96261682\n",
      " 0.9137931  0.83783784 0.04761905 0.2       ]\n",
      "enters\n",
      "class_difficulty:  [0.85336743 0.56121045 0.08313539 0.11693548 0.17610063 0.03738318\n",
      " 0.0862069  0.16216216 0.95238095 0.8       ]\n",
      "self.weights:  tensor([0.0255, 0.0136, 0.0008, 0.0013, 0.0024, 0.0002, 0.0008, 0.0021, 0.0301,\n",
      "        0.0232], device='cuda:0')\n",
      "Validation: val accuracy = 0.6171\n",
      "Best test acc = 62.63%\n",
      "class_wise_accuracy is:  [1170.  723.  292.  199.  118.   33.   15.   16.   11.    3.]\n",
      "after normalization, class_wise_accuracy is:  [0.99744246 0.99449794 0.6935867  0.80241935 0.74213836 0.30841121\n",
      " 0.25862069 0.43243243 0.52380952 0.3       ]\n",
      "enters\n",
      "class_difficulty:  [0.00255754 0.00550206 0.3064133  0.19758065 0.25786164 0.69158879\n",
      " 0.74137931 0.56756757 0.47619048 0.7       ]\n",
      "self.weights:  tensor([4.3930e-06, 1.3862e-05, 5.7608e-03, 2.9829e-03, 4.4474e-03, 1.9534e-02,\n",
      "        2.1681e-02, 1.4523e-02, 1.1161e-02, 1.9892e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.6053\n",
      "Best test acc = 65.12%\n",
      "class_wise_accuracy is:  [238. 429. 411. 220. 146. 100.  58.  29.   3.   2.]\n",
      "after normalization, class_wise_accuracy is:  [0.20289855 0.59009629 0.97624703 0.88709677 0.91823899 0.93457944\n",
      " 1.         0.78378378 0.14285714 0.2       ]\n",
      "enters\n",
      "class_difficulty:  [0.79710145 0.40990371 0.02375297 0.11290323 0.08176101 0.06542056\n",
      " 0.         0.21621622 0.85714286 0.8       ]\n",
      "self.weights:  tensor([0.0267, 0.0098, 0.0001, 0.0014, 0.0009, 0.0006, 0.0000, 0.0038, 0.0298,\n",
      "        0.0268], device='cuda:0')\n",
      "Validation: val accuracy = 0.6636\n",
      "Best test acc = 68.92%\n",
      "class_wise_accuracy is:  [1171.  722.  296.  211.   90.   54.   24.   26.   18.    6.]\n",
      "after normalization, class_wise_accuracy is:  [0.99829497 0.99312242 0.70308789 0.85080645 0.56603774 0.5046729\n",
      " 0.4137931  0.7027027  0.85714286 0.6       ]\n",
      "enters\n",
      "class_difficulty:  [0.00170503 0.00687758 0.29691211 0.14919355 0.43396226 0.4953271\n",
      " 0.5862069  0.2972973  0.14285714 0.4       ]\n",
      "self.weights:  tensor([3.9722e-06, 3.2180e-05, 9.1279e-03, 3.2513e-03, 1.6129e-02, 1.9668e-02,\n",
      "        2.5322e-02, 9.1456e-03, 3.0464e-03, 1.4273e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.7190\n",
      "Best test acc = 74.14%\n",
      "class_wise_accuracy is:  [732. 479. 412. 226. 154. 102.  55.  28.   0.   2.]\n",
      "after normalization, class_wise_accuracy is:  [0.62404092 0.65887208 0.97862233 0.91129032 0.96855346 0.95327103\n",
      " 0.94827586 0.75675676 0.         0.2       ]\n",
      "enters\n",
      "class_difficulty:  [0.37595908 0.34112792 0.02137767 0.08870968 0.03144654 0.04672897\n",
      " 0.05172414 0.24324324 1.         0.8       ]\n",
      "self.weights:  tensor([0.0099, 0.0086, 0.0001, 0.0011, 0.0002, 0.0004, 0.0005, 0.0052, 0.0431,\n",
      "        0.0308], device='cuda:0')\n",
      "Validation: val accuracy = 0.7000\n",
      "Best test acc = 74.14%\n",
      "class_wise_accuracy is:  [1169.  722.  324.  221.   46.   25.   26.   25.   16.    9.]\n",
      "after normalization, class_wise_accuracy is:  [0.99658994 0.99312242 0.7695962  0.89112903 0.28930818 0.23364486\n",
      " 0.44827586 0.67567568 0.76190476 0.9       ]\n",
      "enters\n",
      "class_difficulty:  [0.00341006 0.00687758 0.2304038  0.10887097 0.71069182 0.76635514\n",
      " 0.55172414 0.32432432 0.23809524 0.1       ]\n",
      "self.weights:  tensor([9.2208e-06, 2.6411e-05, 5.1210e-03, 1.6634e-03, 2.7743e-02, 3.1065e-02,\n",
      "        1.8976e-02, 8.5525e-03, 5.3796e-03, 1.4643e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.6959\n",
      "Best test acc = 74.14%\n",
      "class_wise_accuracy is:  [761. 650. 408. 215. 157. 103.  57.  31.  10.   0.]\n",
      "after normalization, class_wise_accuracy is:  [0.64876385 0.89408528 0.96912114 0.86693548 0.98742138 0.96261682\n",
      " 0.98275862 0.83783784 0.47619048 0.        ]\n",
      "enters\n",
      "class_difficulty:  [0.35123615 0.10591472 0.03087886 0.13306452 0.01257862 0.03738318\n",
      " 0.01724138 0.16216216 0.52380952 1.        ]\n",
      "self.weights:  tensor([1.1882e-02, 1.9675e-03, 3.0973e-04, 2.7707e-03, 8.0526e-05, 4.1258e-04,\n",
      "        1.2923e-04, 3.7275e-03, 2.1640e-02, 5.7081e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.7626\n",
      "Best test acc = 76.06%\n",
      "class_wise_accuracy is:  [1172.  719.  369.  229.   63.   72.   33.   26.   19.    8.]\n",
      "after normalization, class_wise_accuracy is:  [0.99914749 0.98899587 0.87648456 0.9233871  0.39622642 0.6728972\n",
      " 0.56896552 0.7027027  0.9047619  0.8       ]\n",
      "enters\n",
      "class_difficulty:  [0.00085251 0.01100413 0.12351544 0.0766129  0.60377358 0.3271028\n",
      " 0.43103448 0.2972973  0.0952381  0.2       ]\n",
      "self.weights:  tensor([1.9357e-06, 8.9766e-05, 3.3757e-03, 1.6490e-03, 3.6483e-02, 1.4548e-02,\n",
      "        2.2006e-02, 1.2606e-02, 2.2856e-03, 6.9554e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.7834\n",
      "Best test acc = 79.99%\n",
      "class_wise_accuracy is:  [1000.  642.  412.  225.  158.   99.   57.   30.    7.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.85251492 0.88308116 0.97862233 0.90725806 0.99371069 0.92523364\n",
      " 0.98275862 0.81081081 0.33333333 0.        ]\n",
      "enters\n",
      "class_difficulty:  [0.14748508 0.11691884 0.02137767 0.09274194 0.00628931 0.07476636\n",
      " 0.01724138 0.18918919 0.66666667 1.        ]\n",
      "self.weights:  tensor([3.1859e-03, 2.2487e-03, 1.7581e-04, 1.5886e-03, 2.8055e-05, 1.1499e-03,\n",
      "        1.2734e-04, 4.6287e-03, 3.0618e-02, 5.6249e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.7667\n",
      "Best test acc = 79.99%\n",
      "class_wise_accuracy is:  [1167.  722.  352.  230.   40.   73.   34.   29.   18.    9.]\n",
      "after normalization, class_wise_accuracy is:  [0.99488491 0.99312242 0.83610451 0.92741935 0.25157233 0.68224299\n",
      " 0.5862069  0.78378378 0.85714286 0.9       ]\n",
      "enters\n",
      "class_difficulty:  [0.00511509 0.00687758 0.16389549 0.07258065 0.74842767 0.31775701\n",
      " 0.4137931  0.21621622 0.14285714 0.1       ]\n",
      "self.weights:  tensor([2.6786e-05, 4.1761e-05, 4.8582e-03, 1.4317e-03, 4.7407e-02, 1.3115e-02,\n",
      "        1.9489e-02, 7.3613e-03, 3.9534e-03, 2.3154e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.7812\n",
      "Best test acc = 79.99%\n",
      "class_wise_accuracy is:  [1037.  656.  410.  221.  157.  103.   58.   34.    8.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.88405797 0.90233838 0.97387173 0.89112903 0.98742138 0.96261682\n",
      " 1.         0.91891892 0.38095238 0.        ]\n",
      "enters\n",
      "class_difficulty:  [0.11594203 0.09766162 0.02612827 0.10887097 0.01257862 0.03738318\n",
      " 0.         0.08108108 0.61904762 1.        ]\n",
      "self.weights:  tensor([2.4236e-03, 1.8736e-03, 2.5928e-04, 2.2053e-03, 8.6606e-05, 4.4372e-04,\n",
      "        0.0000e+00, 1.4173e-03, 2.9901e-02, 6.1390e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.7901\n",
      "Best test acc = 79.99%\n",
      "class_wise_accuracy is:  [1170.  724.  376.  234.   70.   83.   39.   25.   18.    9.]\n",
      "after normalization, class_wise_accuracy is:  [0.99744246 0.99587345 0.89311164 0.94354839 0.44025157 0.77570093\n",
      " 0.67241379 0.67567568 0.85714286 0.9       ]\n",
      "enters\n",
      "class_difficulty:  [0.00255754 0.00412655 0.10688836 0.05645161 0.55974843 0.22429907\n",
      " 0.32758621 0.32432432 0.14285714 0.1       ]\n",
      "self.weights:  tensor([1.2538e-05, 2.5697e-05, 3.3876e-03, 1.3002e-03, 4.0596e-02, 1.0298e-02,\n",
      "        1.8176e-02, 1.7905e-02, 5.2342e-03, 3.0655e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.8151\n",
      "Best test acc = 82.22%\n",
      "class_wise_accuracy is:  [1006.  687.  412.  222.  157.  102.   58.   35.   11.    0.]\n",
      "after normalization, class_wise_accuracy is:  [0.85763001 0.94497937 0.97862233 0.89516129 0.98742138 0.95327103\n",
      " 1.         0.94594595 0.52380952 0.        ]\n",
      "enters\n",
      "class_difficulty:  [0.14236999 0.05502063 0.02137767 0.10483871 0.01257862 0.04672897\n",
      " 0.         0.05405405 0.47619048 1.        ]\n",
      "self.weights:  tensor([3.6885e-03, 8.8616e-04, 2.1462e-04, 2.3308e-03, 9.6867e-05, 6.9359e-04,\n",
      "        0.0000e+00, 8.6292e-04, 2.2563e-02, 6.8663e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.8087\n",
      "Best test acc = 82.49%\n",
      "class_wise_accuracy is:  [1168.  721.  380.  234.   83.   83.   46.   26.   18.    9.]\n",
      "after normalization, class_wise_accuracy is:  [0.99573743 0.99174691 0.90261283 0.94354839 0.52201258 0.77570093\n",
      " 0.79310345 0.7027027  0.85714286 0.9       ]\n",
      "enters\n",
      "class_difficulty:  [0.00426257 0.00825309 0.09738717 0.05645161 0.47798742 0.22429907\n",
      " 0.20689655 0.2972973  0.14285714 0.1       ]\n",
      "self.weights:  tensor([3.3800e-05, 9.1062e-05, 3.6912e-03, 1.6290e-03, 4.0136e-02, 1.2902e-02,\n",
      "        1.1430e-02, 1.9688e-02, 6.5579e-03, 3.8407e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.8384\n",
      "Best test acc = 84.13%\n",
      "class_wise_accuracy is:  [1.073e+03 6.910e+02 4.070e+02 2.200e+02 1.570e+02 1.060e+02 5.800e+01\n",
      " 3.400e+01 1.400e+01 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.91474851 0.95048143 0.96674584 0.88709677 0.98742138 0.99065421\n",
      " 1.         0.91891892 0.66666667 0.1       ]\n",
      "enters\n",
      "class_difficulty:  [0.08525149 0.04951857 0.03325416 0.11290323 0.01257862 0.00934579\n",
      " 0.         0.08108108 0.33333333 0.9       ]\n",
      "self.weights:  tensor([2.1615e-03, 9.5688e-04, 5.2659e-04, 3.2943e-03, 1.2251e-04, 7.8457e-05,\n",
      "        0.0000e+00, 2.0049e-03, 1.6712e-02, 7.4143e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.8383\n",
      "Best test acc = 85.72%\n",
      "class_wise_accuracy is:  [1166.  721.  395.  232.   85.   70.   46.   28.   19.    9.]\n",
      "after normalization, class_wise_accuracy is:  [0.9940324  0.99174691 0.93824228 0.93548387 0.53459119 0.65420561\n",
      " 0.79310345 0.75675676 0.9047619  0.9       ]\n",
      "enters\n",
      "class_difficulty:  [0.0059676  0.00825309 0.06175772 0.06451613 0.46540881 0.34579439\n",
      " 0.20689655 0.24324324 0.0952381  0.1       ]\n",
      "self.weights:  tensor([5.5617e-05, 9.0455e-05, 1.8516e-03, 1.9770e-03, 3.8305e-02, 2.4532e-02,\n",
      "        1.1354e-02, 1.4473e-02, 3.5459e-03, 3.8151e-03], device='cuda:0')\n",
      "Validation: val accuracy = 0.8403\n",
      "Best test acc = 85.72%\n",
      "class_wise_accuracy is:  [1.128e+03 7.020e+02 4.040e+02 2.240e+02 1.580e+02 1.030e+02 5.700e+01\n",
      " 3.400e+01 1.200e+01 1.000e+00]\n",
      "after normalization, class_wise_accuracy is:  [0.96163683 0.9656121  0.95961995 0.90322581 0.99371069 0.96261682\n",
      " 0.98275862 0.91891892 0.57142857 0.1       ]\n",
      "enters\n",
      "class_difficulty:  [0.03836317 0.0343879  0.04038005 0.09677419 0.00628931 0.03738318\n",
      " 0.01724138 0.08108108 0.42857143 0.9       ]\n",
      "self.weights:  tensor([6.1612e-04, 5.2288e-04, 6.6534e-04, 2.4685e-03, 4.0898e-05, 5.9266e-04,\n",
      "        1.8563e-04, 1.8931e-03, 2.3005e-02, 7.0010e-02], device='cuda:0')\n",
      "Validation: val accuracy = 0.8320\n",
      "Best test acc = 85.72%\n",
      "class_wise_accuracy is:  [1163.  719.  403.  233.  120.   70.   50.   26.   21.    8.]\n",
      "after normalization, class_wise_accuracy is:  [0.99147485 0.98899587 0.95724466 0.93951613 0.75471698 0.65420561\n",
      " 0.86206897 0.7027027  1.         0.8       ]\n",
      "enters\n",
      "class_difficulty:  [0.00852515 0.01100413 0.04275534 0.06048387 0.24528302 0.34579439\n",
      " 0.13793103 0.2972973  0.         0.2       ]\n",
      "self.weights:  tensor([0.0001, 0.0002, 0.0014, 0.0023, 0.0186, 0.0311, 0.0078, 0.0248, 0.0000,\n",
      "        0.0137], device='cuda:0')\n",
      "Validation: val accuracy = 0.8651\n",
      "Best test acc = 87.83%\n",
      "class_wise_accuracy is:  [1124.  712.  400.  223.  156.  107.   58.   33.   13.    5.]\n",
      "after normalization, class_wise_accuracy is:  [0.95822677 0.97936726 0.95011876 0.89919355 0.98113208 1.\n",
      " 1.         0.89189189 0.61904762 0.5       ]\n",
      "enters\n",
      "class_difficulty:  [0.04177323 0.02063274 0.04988124 0.10080645 0.01886792 0.\n",
      " 0.         0.10810811 0.38095238 0.5       ]\n",
      "self.weights:  tensor([0.0013, 0.0004, 0.0016, 0.0047, 0.0004, 0.0000, 0.0000, 0.0052, 0.0345,\n",
      "        0.0519], device='cuda:0')\n",
      "Validation: val accuracy = 0.8779\n",
      "Best test acc = 89.06%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc_CDB, confusion_mtx_CDB = process(\n",
    "    imbalanced_train_loader,\n",
    "    imbalanced_val_loader,\n",
    "    test_loader,\n",
    "    ClassDifficultyBalancedLoss,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "J3AGKQgbpXKu",
    "outputId": "a973ac6e-28f9-473a-acab-788e60d89555"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B6a8NfX5chJ+IiL4Z79+/F+rUqSMGFT09PWHRokXChw8f5NolJSUJGzduFMzMzMS2JUqUEB48eJBt3z///HOWIGRgYCDUrVtXcHd3F0qXLi23b8KECXLbK1asUOnc3rx5I9ffjz/+qPSxn9YSExOTpY2hoaFcG3t7+1yfWx8VFSX3CNj69evn9bQUmjRpktjngQMHxPcfPnwoBlNNTU1h8uTJwunTp4Xt27cLtWrVEo8ZOXJkgdRBhYPhlIiIvhlDhgyRGy319/fPsX1wcLBgYmIiHuPq6ppj+1WrVgkGBgY5ThDS19cX/vzzT+HKlSty72/ZskWlc0tKSpLrb8SIEUofO3z4cLljU1JSsrSxtraWa/PHH38o1Xfm0VNNTc0svwjk1a1bt8RR73bt2snt69Gjh/hZq1evltv37t07oUyZMgKQ/nSrkJAQleqgwvPZJkQNGDAAgYGBqnZDRESUL5GRkfD29ha3Bw0ahPr16+d4TKVKlTB58mRxOyAgAGfPns22/dChQxEaGorff/8dTZs2haWlJbS1tWFsbAwHBwdMnToVd+/exYABA8QlqjJkfmJUfmhqaspNbIqMjFT62MxtjY2Noa6unqVNiRIl5LY/XdIpO7Vr1xZfJycnIywsTOm6PiUIAoYOHYrk5GTo6+tj6dKl4r74+Hjs3r0bAGBhYZHlIQEGBgYYPXq02M/mzZvzXQdJg8rhdMOGDXB1dUXt2rXx559/4uPHjwVRFxERkVJOnTolNyGnffv2Sh3Xrl07ue3Tp0/n2L548eL48ccfceLECURERCAhIQExMTG4du0afv31V3Fh/Nu3b4vHaGpqwtHRUckzyV7mmfJPnz5V+rjMbatUqaKwzaeTwkxNTZXq28zMTG77zZs3Stf1qXXr1uH8+fMAgJkzZ8o9ZODatWviQxUaNWokrsOaWYsWLcTXmddtpS9Tgc3Wv379OoYMGQIrKyuMHDkSt27dKqiuiYiIsvXpiJ0yT08C0p+glFleRiRzkjkcOTg4FMiz4KtVqya+vnPnTq5PwALSn4KVefH+7FYmsLe3z3KcMj59TGxeVhHILCoqCpMmTQKQ/vUaM2aM3P6IiAjxtbW1tcI+Mj8JK3N7+jKpHE6rVasGIf3eVQDp67mtWrUKDg4OqF+/PjZv3qzUPyIiIqL80NbWlttWtJanIp9e6dPT01O5lri4OBw+fFjc7tWrl8p9AoC7u7v4OiEhAQEBAbkeExAQIBc0GzdurLCdh4eH3HbmZZ5y8ujRI7nt/D7Bafz48YiJiYGamhrWrFmT5dYDZcNyhk9DM315VA6nt27dgr+/P77//nvxfxAZYfXSpUvo06cPrKysMGHCBISEhKhcMBERUWZWVlZy2xmPvMzNp+0yj77l18KFC8VwpKenh969e6vcJwC0adNG7nL2X3/9lesxmdtoaWmhdevWCtu5uLigdOnS4nbGAvy5OXbsmPi6bNmy+QqnJ0+eFB9uMHToULi4uGRpk/mhA+Hh4Qr7yfx+Xh5SQBJVkLOw3rx5IyxcuFCoUqWKIJPJBJlMJqipqYn/VVNTEzw8PIRdu3YpnDGYX5ytT0T07YqMjBRkMpk4m7t69epCUlJSjsekpaUJTZs2lZulHhQUpFIdAQEBcssrzZkzR6X+PtW9e3exb11d3RxnpT948EDQ1dUV2/fo0SPHvmfPni221dHRyXFpLUEQhB07dmRZPiuv4uPjhQoVKggABAsLC+Ht27cK2z169Ej8nJIlS2ZZY1YQ5B8126dPnzzXQp9HkS8l5evrK3Tr1k3Q1tYWg2rmkGphYSH8/PPPQmhoqMqfxXBKRPRta9u2rVxY6ty5c7ZLGyUnJwsjRoyQa+/i4pJt35s2bRLi4uJy/Pz9+/cLxsbGYn9OTk65DsI8efJErobcQtXDhw/lHjDg6OgoREVFZWn34sULwcHBQW5prUePHuXY98ePHwUbGxvxmKpVqwphYWEK254/f17uXIsVKya8ePEix/4VyfyAgO3bt+fYtmLFimLblStXyu3LvJQUAGHr1q15roU+D2XzmkwQCvd5X69fv8aGDRuwbt06PHz4UG6fTCaDmpoaWrRogaFDh6JVq1biI87y4t27dzAyMkJsbCwMDQ0LqnQiIvpCPHz4EC4uLoiJiRHfs7a2Rr9+/eDq6gpjY2PExcUhKCgIPj4+cj+PdHV14efnB2dnZ4V9W1hYIC4uDi1atICbmxvs7OxgaGiImJgY3L9/H/v27cPFixfF9nZ2djh9+jQsLS1zrDk0NBRly5YVt/v06QMfH58cj1m8eDHGjRsnbltaWmL48OFwdnaGIAi4cuUKVq5ciRcvXsgd8+kkI0V8fX3h6ekp3uNpYGCAwYMHo1GjRjA2NkZkZCQOHTqErVu3IjU1VTxux44d6NKlS679Z3b//n3UrFkTSUlJaNGiBY4ePZpj+1WrVmH48OEA0ldAmDBhApo3b46oqCjMmzcPQUFBAIDSpUvjwYMH0NLSylM99Hkondc+R1LOcPLkSaFTp06ClpaWwtFUW1tb4bfffhMiIyPz1C9HTomIKCAgIMuC8rn9KV68uHDkyJEc+y1ZsqTS/bm7uyv9MyyvI6cZpkyZIncbQ3Z/ZDKZ8MsvvyjVZ4a9e/cK+vr6Sp2rtra2sH79+jz1n6FRo0bi7Qm5jeoKQvojU1u0aJFjPTo6OoKfn1++6qHPo8gv6+fkxYsXwqxZswQ9PT3xntTMQVVLS0vo0aOHcOvWLaX6YzglIiJBEISYmBhh0qRJuQZKY2NjYfTo0UoFyf79+wtGRkY59le7dm1h8+bNeao1v+FUENIHe5ycnLKtx8nJSTh16lSe6skQGhoqdOrUSdDW1lbYt7q6utCxY0fh9u3b+ep//fr1Yl+zZ89W+rj4+Hhh6NChgpqaWpaaKlSoIJw9ezZf9dDnI5nL+p969eoVvL29sW7dOoSGhorvf1qGTCaDTCbDgAEDsGDBAujr62fbJy/rExFRZmlpabh79y6uX7+O169f4/3799DT04OpqSlq1KgBe3t7aGho5Lm/kJAQREVFITo6GoaGhrC0tISzs3OWNVM/l5CQEFy5ckVcozWjnooVK6rc97t37+Dn54fw8HDExMTAyMgIpUuXRoMGDeSeWJVXy5cvx+vXr6GpqYmJEycqXFQ/JxERETh58iQiIiKgq6uLmjVrokGDBgqffkXSomxe+2zh9PTp01izZg0OHDiA5ORk8X1BECCTyeDh4YFatWphy5Ytcgshy2QyODo64ty5c9kuZMxwSkRERCRtyua1AntClCLR0dFYsGABKleujGbNmmH37t1ISkoS10E1NDTEmDFjcO/ePZw4cQK///47nj17hl27donP7BUEAUFBQZg/f35hlkpEREREElAoI6fnzp3D6tWrsXfvXvHpUJk/platWhg2bBh69uyZ4+POpk6ditmzZ0Mmk6F69eq4ceOGwnYcOSUiIiKSts9+WT82NhY+Pj5Yu3Yt7t+/D0A+kGpra6Nz584YPnw46tSpo1SfaWlpKFOmDMLDw6Gjo5PlUXMZGE6JiIiIpE3ZvKb83eDZuHjxItasWYNdu3aJj2zLHErLlCmDIUOGYMCAATAzM8tT32pqaqhWrRrCw8Pz/GxdIiIiIvryqBxO69evD5lMJhdIMxbWHz58OLy8vPK1sH6G7CZBEREREdHXR+VwmpmpqSn69euHYcOGyT31QhUeHh4wNjYukL6IiL4oM/K/XA8RUa5mxBZ1BQoVSDh1dnbG8OHD0bVrV2hraxdEl6JRo0YVaH9EREREJF0qh9PAwEA4OjoWRC1ERERE9I1TeZ1TBlMiIiIiKiiFugg/EREREVFeFMg9p1evXsWHDx8AAC4uLnmaYX///n1ERUUBAOzt7WFiYlIQJRERERHRF0jlkdOoqCjUq1cPjRs3xuDBg/O89NPVq1fRuHFjNG7cGHPnzlW1HJK4hIQEbN26FT169ICdnR2MjY2hoaEBY2Nj2NnZoXv37ti8eTPi4+Pz/Rnnz5/HDz/8ACcnJ1hbW0NLSwv6+vqoWLEi2rVrh4ULF+Lq1atIS0srsPOaMWMGZDJZnv9MmjRJ5c9++fIlTE1N5fp1d3dX+viHDx9i8ODBKFeuHHR0dGBqaorGjRvDx8cnz1+jdu3aQSaTQVtbG8HBwXk8EyIiogIYOd27dy+Sk5Mhk8kwfPjwPB/frVs3jBs3Dq9fv8a2bdswb948VUsiifrnn38wdOhQhIeHZ9kXGxuL2NhYBAcHY/v27fjpp5+watUqtG3bVun+7969ixEjRsDX1zfLvuTkZDx8+BAPHz7EgQMHAAAhISGoUKFCvs9HKoYNG4Y3b97k69i9e/eiR48ecg+5SExMhK+vL3x9fbFt2zbs3bsXxYoVy7WvAwcOiF/bSZMmoXLlyvmqiYiIvm0qh9MzZ86Irzt27Jjn49XV1dG2bVusX78ez58/x4MHD1CpUiVVyyKJ2bx5M/r06SM3Eqerq4tq1arByMgIb9++xZ07d8SnjEVERKBdu3bw9vZGv379cu3/77//RufOneVClq6uLipUqIASJUogISEBz549UxiMC5qJiQlcXFyUaqtqgNu+fTv27duXr2Nv3bolF0xNTU1hb2+PyMhIcdTz+PHjGDZsGP76668c+3r//r247FvFihUxZcqUfNVERESkcji9efMmAMDKygrW1tb56qNu3bpYv3692B/D6dfl2bNnGDJkiBhMdXV1MXfuXAwaNAi6urpiu48fP2LNmjWYMmUKEhISIAgCRowYgUaNGqFcuXLZ9n/48GF07NgRycnJAIAqVapg1qxZ8PLyynKbSVRUFI4ePYqNGzeq9OSynNSoUQNHjx4tlL4ze/XqlRgIS5YsiZIlS4r/HpUxZcoUMZj2798fK1euFNcp3rt3L7p164bk5GRs2rQJ48ePh4ODQ7Z9TZs2DWFhYQAg1w8REVFeqXzPaUREBGQyGUqXLp3vPjIf+/z5c1VLIolZs2YNPn78KG5v2rQJo0ePlgumAKCnp4dx48Zh48aN4nvx8fFYs2ZNtn2/evUKffv2FYNpu3btcP36dXTo0EHh/c/m5ubo3bs3Tp06hfLly6t6akVqxIgReP36NQBg+fLleZpMGBsbi8OHDwMALCwssGLFCrlA2aFDBwwaNEjc3rJlS7Z9Xbt2DUuXLgUA9OzZE02bNs3TeRAREWWmcjjNmLiS14lQmWU+NmPWP309/Pz8xNdVq1bN9faPLl26oEqVKuK2v79/tm3HjRuHV69eAQCqVauGnTt3QktLS8WKpW/Pnj3YtWsXgPRA3qlTpzwdn3lCWNeuXRX++x0wYID4+sqVKwr7SUtLw5AhQ5CamgoTExMsXLgwT3UQERF9SuVwmjFakzGCkx+ZjzUwMFC1JJKYjKXCgPRL3srI3C67v1vh4eHYvn27uL1w4UJoamrms8ovR3R0tDj50MjICCtWrMhzH5m/J2XLllXYJvPI8suXLxW2WblypRhc586dC3Nz8zzXQkRElJnK4bRkyZIQBAH37t1DXFxcvvq4dOmSXH/0dcn8C0fGhKfcZG6X3eXqP//8E6mpqQAAGxsbNGvWTIUqvxwjR44Uw+Uff/wBKyurPPehzP22mSevKWofERGBn3/+GUD6feOZbwMgIiLKL5XDab169QAAKSkpuc7oVSQxMRFbt24Vt11dXVUtiSSmbt264usLFy4gKSkpx/aJiYm4cOGCuN2wYUOF7TJPOmratGmhTXCSkv3794ujxY0bN853IMw8wvnkyROFbTK/r2hEdMyYMXj37h00NDSwZs2ab+LrT0REhU/lcNqyZUvx9YwZM/Do0aM8HT9p0iSEh4dDJpPBzs5OpYlVJE3Dhw+Hhkb6whBRUVHiaFt2Jk+eLN5Hqq+vj5EjR2Zpk5iYiGvXronbNWvWBAC8ffsWS5YsgZubGywsLKCtrQ1LS0u4ublhxowZ4ozywvT48WO0b98epUuXhq6uLvT19WFra4vmzZvjt99+yzYM5iYmJgbDhg0DkL7iwdq1a/Ndo6OjI9TU0v/579y5U24Jrgw+Pj7iaycnJ7l9hw8fxu7duwEA48ePh729fb5rISIiykzlcNq2bVvY2dkBSL8XzsPDA+fPn8/1uMTERIwaNUqc5QsAP/30k6rlkARVrVoVixYtErfnz5+PVq1a4dixY3jz5g1SU1MRHR2NI0eOwNPTU2xrYGCAXbt2wdbWNkuf9+7dkxuBtbKywrFjx1CtWjWMHTsW58+fx8uXL5GUlIQXL17g/PnzmDlzJipWrIhp06YV6NOhPhUWFob9+/fj2bNnSEhIwIcPHxAWFoYTJ05g2rRpqFixIvr06YPY2Ng89Tt69Gi8ePECADBz5kyVHiBgZGQk/mIZGRmJ0aNHy309Dx06JBd+e/ToIb7++PEjRowYASB9pY3p06fnuw4iIqJPqbzOqUwmw5IlS+Dl5YW0tDSEhYWhUaNGaNq0Kbp27QpnZ2eYm5tDW1tbfALQmTNn4OPjg5cvX0IQBMhkMtSvXx+9evUqiHMiCRo5ciRKlSqFcePGITQ0FIcPHxaXMvqUuro6vLy8MGfOHFSrVk1hm08nSV2+fBlLliwRl5QyMzND5cqVIZPJEBwcLI7EJiYm4rfffkNISAi2bdtWgGf4HzU1NZQtWxaWlpbQ0tJCdHS0XJhOTU3FX3/9hbNnz8LX11epqwWHDh3C5s2bAQC1a9fG+PHjVa5z9uzZOHHiBJKSkrB27Vrs3bsX9vb2ePHiBe7duye269atGxwdHcXtmTNnIjQ0FACwYsUK6OnpqVwLERFRBpVHTgGgWbNmWLBggRg009LScOLECQwcOBA1a9aEpaUlihcvjrJly8LT0xPz5s0TR4CA9CfK7Nu3j/esfeXatWuHM2fOoFWrVjm2a9KkCYYNG4aqVatm2+bt27dy2/Pnz0dycjIsLCywZ88evHz5Ev7+/jh37hxevHiBAwcOyD0kYvv27ViwYIFK55OZjo4OevbsiQMHDiA2NhYPHz7EuXPncOrUKVy/fh2xsbHYvHmz3Az40NBQeHp64v379zn2/fbtWwwZMgQAoKGhAW9vb6irq6tcc82aNbF582Zx6a3Xr1/jzJkzcsHU3d1dbgT11q1b4nJRHTt2lPteRkdHY9KkSahatSr09PRgZGSEOnXqYNmyZeIvDURERLmRCYIgFFRn+/btQ//+/cXLlTKZDIq6z/x+u3btsGHDBhgZGeX7c9+9ewcjIyPExsbC0NAw3/1Q4fnw4QN++uknrF27VgwqWlpaqFatGkxMTBAXF4c7d+7ILdbv7OyMzZs3K3xi2ObNm7OMtBsaGiIwMBAVK1ZUWENoaCgcHR0RExMDADA2NsbTp08/69+ZuLg4tG3bFr6+vuJ706dPx4wZM7I9pl+/fuL9n1OmTMHs2bMVtnN3dxfXlG3UqJHcZ+TkwYMH+P3333Hy5ElERkZCT08P9vb26N27N/r16ycGYUEQUL9+fVy8eBEGBga4d++eGPivXbsGT09PuSWqMnN1dcWRI0fy9KAAAjAj//9fJCLK1Yy83V6mKmXzWoGMnGZo3749QkND8euvv8LOzk5hMAXSJ7m0b98eZ8+exd69e1UKpiR9Hz58QNOmTbFixQokJydDT08PixYtQkxMDIKCgnDq1ClcvnwZb9++xcaNG2FmZgYgfeF3Nzc3hISEZOmzWLFiWd7LuJ8zO2XKlMHMmTPF7bdv32Lnzp0FcIbKMzAwwN69e2Fqaiq+t2TJEnFJrE8dPXpUDKaVK1fGtGnTCrymSpUq4c8//0RoaCgSExMRExODs2fPYuDAgXIjtGvXrsXFixcBALNmzRKDaUxMDFq1aoWoqCjIZDIMHToUJ06cwP79++Hh4QEACAgIQJ8+fQq8diIi+voU6Mjpp6KionDv3j28efMGCQkJMDExgaWlJezt7cWZwgWBI6fSNnToUPERpFpaWjh9+jTq16+fbfsHDx6gTp064ginq6ur3Fq4AHDy5Mks65pGRUWhRIkSOdYSGxuL4sWLixOievbsKd7L+TlNmzYNv/32m7h97tw5uLm5ybV59+4dqlevjrCwMMhkMvj5+aFBgwbZ9pnfkVNlvHz5EnZ2dnj79i1q166NgIAAMbj+/PPP+N///gcgffWNOXPmiMelpKTAzc0NAQEBAIBTp06JgZWUwJFTIipM38LI6afMzc3RqFEjtG/fHt27d4enpydq1qxZoMGUpC0yMhLe3t7i9qBBg3IMpkD6SN7kyZPF7YCAAJw9e1auzachtHTp0rkGUyB9lnrmWe55XfqsoHwa0O7fv5+lzf/+9z9x6auhQ4fmGEwL27hx4/D27VuoqalhzZo1ciOqmzZtAgBoa2vjhx9+kDtOQ0NDbhWO/KyFTERE3xamRCpUp06dQkpKirjdvn17pY5r166d3Pbp06fltitXriwXkDJfJs9Nxm0DAPDmzRuljytIlpaWctuKHtGaedLgqlWrIJPJcvyTMWoKAH5+fnL7xo4dm+9ajx8/Lq5sMGLECNSuXVvcFxERIQZoBwcHua9thhYtWoivPx0BJyIi+hTDKRWqTxe9t7GxUeq4T9c2jYyMlNvW0dGRGwFVtIh8djI/GlVXV1fp4wpS5olfRVlHbhISEjB8+HAA6WvJzpo1S25/RESE+DrzagiZ6enpiROhMrcnIiJSROV1Tolyoq2tLbcdHx+v1HGfhjdFa2l6eHggODgYAPD06VOkpqbmusSSIAhyT2iysLBQqp6Cdvv2bbltRXUYGBjkaUQ4NjZWHKXW0NCQm2ior6+frzpnzZol3vqwZMmSLPcI5eWXAkD+FwMiIiJFCiWcCoKAu3fvIjw8HG/fvs3TD7DevXsXRklURKysrOS2L1++LD5qNCeXL1+W2y5VqlSWNp07d8aqVasAAO/fv4e/vz8aNWqUY78BAQHiRCsAqFevXq61FIZPJ2F9OhkKAJYtW4Zly5Yp3WfmCVH169dXeULUvXv38McffwAAvLy80KlTpyxtjI2Nxdfh4eEK+/n48aPc8l1EREQ5KdBwev36dSxYsAAHDx7MdWHx7DCcfl3c3d3l1rVdunQp+vbtC01NzWyPEQQB8+fPl3tP0Qzvhg0bws7OTpxMNGvWLDRs2DDHhzlkniEvk8mUvge2IG3fvh3Hjx8Xt+vWrZvtJfGiIggChgwZgqSkJOjp6WHFihUK25UvXx6amppITk7GjRs38OrVqywT044dOya+zunBCkREREAB3nM6b948uLi4YOvWrYiLi4MgCAr/AMhxH31dLCws0KZNG3H79u3b6NmzZ5bL9hlSUlIwatQonDx5UnzPxcUFtWrVytJWXV1dHNkD0peXGj9+vLhMVGaCIGDSpElyj0zt0KED7O3ts60984Qid3f3bNvNmDEDK1asQFxcXLZtMmpYtWoV+vbtK/d+5qWXpGL9+vU4d+4cgPRlr8qUKaOwnY6OjjhanZiYmOWXipSUFMybN0/czjw5ioiISJECWed09erV4qSJDKampvjw4QMSEhIgk8lga2uLuLg4vH37VgwPGSNcNjY24uvM9wMqi+ucStvDhw/h4uIidznd2toa/fr1g6urK4yNjREXF4egoCD4+Pjg4cOHYjtdXV34+fnB2dk52/6HDBki94jNGjVqYMCAAahWrRpkMhnu3r2LDRs2ICgoSGxTunRpXLlyJcflpzKPwOa0bmjfvn2xceNG6OjooGnTpnB1dUWVKlVgYmICTU1NREdHIygoCDt37hTvkc3w66+/YurUqdnWkBcFtc7pq1evYGdnhzdv3qB69eoICgrKcaT7yJEj8PLyApD+NRsyZAg6deqEDx8+YMmSJeJKC0ZGRggJCVFqyS/6F9c5JaLCJNF1TlW+rP/mzRv8+OOPANJ/MHl5eWHRokWoUKECWrZsKV7SywidHz58wIULF/Dnn39i9+7dAAB7e3ts2bKFwfIrVaFCBRw9ehQdOnTA8+fPAQDPnz/PMvP7U8WLF8eWLVtyDKYAsHLlSiQkJIhraN68eRNjxozJtn3VqlVx4MCBAg9JCQkJOHToEA4dOpRrWx0dHcydOzfHOovKhAkT8ObNG8hkMqxevTrHYAoALVu2xKBBg7Bu3ToIgoDVq1dj9erVcm3U1NSwevVqBlMiIsqVypf1169fjw8fPkAmk6Fhw4Y4cOCA3BI/nypWrBiaNWuGHTt24Pjx4zA0NMThw4fRtGlTufUw6evi4uKC27dvY9KkSShZsmSObY2NjTF69GjcuXMHnp6eufatrq6OjRs3Ys+ePahRo0a27czMzPDrr78iICAgx7+jedWyZUs0atRIqeWgjI2NMXLkSNy6dUuSwfTMmTNiyB84cGCuD0zIsHr1akydOjXL6gxA+q0du3fvRrdu3Qq0ViIi+jqpfFm/VatWOHLkCGQyGS5cuABXV1dxX8bIqUwmy/bZ4b6+vmjSpAkAYPLkybmOpinCy/pflrS0NNy9exfXr1/H69ev8f79e+jp6cHU1BQ1atSAvb09NDTyP6gfHByMoKAgREZGIjk5GWZmZrC3t4eTk1OhPp0sNTUV9+/fR0hICCIiIvDu3TukpKTA0NAQpqamqFmzJqpWrSrpJ6Rt3rwZDx8+hEwmw+jRo8X1SZUVHR2NEydO4NmzZ9DQ0ECVKlXg4eGhMLSSEnhZn4gKk0Qv66scTkuXLo2wsDCYmZkhKipKbl/mcJqSkpLtLOpOnTph7969KF68OF6+fJnrWpWfYjgloq8SwykRFSaJhlOVh3Ay7k2rVKlSln2ZQ2ZOi69/9913AICYmBi5RzASERER0bdF5XCalJQEIP1e0k8ZGBiIr1+9epVtH6VLlxZfh4aGqloSEREREX2hVA6nGY9IVLTofvHixcXXjx8/zraPjIAL5BxiiYiIiOjrpnI4LVu2LARBQERERJZ9mZ8Gc/bs2Wz7CAgIEF8rM+OZiIiIiL5OKofTjCfsPHv2LMsTcjIvQ+Pt7a3wCTpv3rzBypUrxW1F964SERER0bdB5UX4GzZsiPXr1wNIf3xk5meVOzg4oEqVKrh//z6eP3+O5s2bY968eeKi6mfPnsWECRMQGRkJADA0NETDhg1VLemzKDPpn6IugYi+cqE6RV0BEdHnp/LIaYsWLcR1GzOe+JTZnDlzkLFa1eXLl9G4cWPo6+tDX18fXl5euHv3LoD0p0tNmDABenp6qpZERERERF8olcNpyZIl0bt3bzg6OuLp06f4+PGj3P62bdtiypQpYkAVBEH8k7ENAB07dsSUKVNULYeIiIiIvmAF8qia9evX48qVK/D391c48jlr1izs27cPjo6Ocu8LgoDy5ctj5cqV2LlzZ7aL9BMRERHRt0Hle06V9d133+G7777D69ev8ezZMyQnJ8PKygo2NjafqwQiIiIikrjPFk4zmJmZwczM7HN/LBERERF9AVQOp+PHjxdfjx07Fra2tqp2SURERETfKJXD6eLFiyGTyWBqaor58+cXRE1ERERE9I1SeUJUxgSoChUqiEtKERERERHlh8pp0sLCAgCgpaWlcjFERERE9G1TOZxWrVoVgiDgyZMnBVEPEREREX3DVA6nnTt3BgCEh4cjKChI5YKIiIiI6NtVIOHUzs4OQPps/eTkZJWLIiIiIqJvk8rhVEdHB9u2bYOJiQnOnz+PVq1a4fnz5wVRGxERERF9Y1ReSurs2bMAgPnz52Ps2LE4deoUypcvj5YtW6Jhw4YoX748DA0NlZ7J37BhQ1VLIiIiIqIvlMrh1N3dHTKZTNwWBAFJSUk4ePAgDh48mKe+ZDIZUlJSVC2JiIiIiL5QBf740sxBNS8EQSjgSoiIiIjoS1Mg4ZTBkoiIiIgKgsrhNC0trSDqICIiIiJSfbY+EREREVFBYTglIiIiIslgOCUiIiIiyWA4JSIiIiLJYDglIiIiIslQebb+r7/+WhB1iKZNm1ag/RERERHRl0PlcDpjxox8L7yvCMMpERER0berSBbhzwiznx5XkCGXiIiIiL48KofThg0bKh0qU1NTERMTg5CQECQlJQFID6S1a9dGsWLFVC2FiIiIiL5wKodTX1/fPB+TlJSEv//+GzNmzMCdO3eQlJSE3bt3w9bWVtVyiIiIiOgLViSz9bW0tNCxY0cEBgaidevWuHnzJpo0aYK4uLiiKIeIiIiIJKJIl5LS1tbGtm3bYGFhgcePH2P8+PFFWQ4RERERFbEiX+e0WLFiGDhwIARBwJYtWxAbG1vUJRERERFRESnycAoAderUAQAkJibizJkzRVwNERERERUVSYRTQ0ND8fWzZ8+KsBIiIiIiKkqSCKfh4eHi68TExCKshIiIiIiKkiTC6datW8XXFhYWRVgJERERERWlIg+ns2fPxqFDh8Tthg0bFmE1RERERFSUVF6E/+zZs3lqn5ycjDdv3uDmzZvYvXs3Hjx4ACD9SVHNmjVD6dKlVS2JiIiIiL5QKodTd3d3pR9fmpMSJUpgxYoVKvdDRERERF+uz35ZXxCELH+cnZ3h6+uL8uXLf+5yiIiIiEhCVB45BdIDp7I0NTVhaGiI0qVLw8nJCZ06dUKTJk0KZPSViIiIiL5sKofTtLS0gqiDiIiIiKjoZ+sTEREREWVgOCUiIiIiyWA4JSIiIiLJYDglIiIiIskokNn648ePx9u3b6GmpoalS5dCT09P6WO3bduGEydOAAAGDhyIevXqFURJRERERPQFUjmcXrlyBYsXL4ZMJsN3332Xp2AKANWqVUPPnj0hk8nw9u1b7N27V9WSiIiIiOgLpfJl/b///lt83bdv3zwfX6NGDTg4OEAQBBw7dgxJSUmqlkREREREXyiVw+mFCxcAAOrq6mjRokW++vDy8gIAJCQkIDAwUNWSiIiIiOgLpXI4vX//PmQyGSpXrgxtbe189VGrVi3xdXBwsKolEREREdEXSuVw+ubNGwCAubl5vvsoUaKE+Do6OlrVkoiIiIjoC1VgS0klJyfn+9iUlBSFr4mIiIjo26JyODUzM4MgCHj27Fm++wgLCxNfm5qaqloSEREREX2hVA6nZcuWBZAeMB88eJCvPo4dOya+trW1VbUkIiIiIvpCqRxOPTw8xNf/+9//8nz8w4cPsXv3bgCApqYmGjRooGpJRERERPSFUjmcdunSBTKZDACwadMmrFmzRulj3759iw4dOiAlJQUymQxeXl55XsSfiIiIiL4eKofTKlWqoHPnzhAEAYIgYPjw4Rg8eDDCw8NzPG7//v1wcHDAnTt3AAAymQwzZ85UtRwiIiIi+oKp/PhSAFi+fDkuXbqEsLAwCIIAb29v+Pj4oG7dunB2doa5uTm0tbURGxuL4OBgnD17FhERERAEATKZDDKZDLNmzYK9vX1BlENEREREX6gCCadmZmY4fPgwWrdujdDQUADpS0L5+/vD398/S3tBEOReT5w4EZMmTSqIUoiIiIjoC1Zg65xWrVoVQUFB6N27N9TV1QFAvNSf4dPtihUrYv/+/Zg7d25BlUFEREREX7ACGTnNYGxsDB8fH8ycORM7duyAn58f7t69izdv3iAhIQEmJiawtLREvXr14OnpidatW4uTqYiIiIiICjScZihdujQmTpyIiRMnFkb3RERERPSVKrDL+kREREREqmI4JSIiIiLJYDglIiIiIskokHDaoUMHeHh4wNPTEx8/fszTsWvXroWHhwc8PDxw+PDhgiiHiIiIiL5QKk+I8vX1xf79+yGTydCrV688P360adOmGDZsGABAU1MTXl5eqpZERERERF8olUdOM4929urVK8/HlytXDvXq1YMgCPD19cWHDx9ULYmIiIiIvlAqh9NLly4BALS1teHu7p6vPpo3bw4g/alSgYGBqpZERERERF8olcPpgwcPIJPJULlyZfHJUHlVo0YNuf6IiIiI6Nukcjh9+/YtAMDU1DTffWQ+NiYmRtWSiIiIiOgLpXI41dTUBADEx8fnu4+EhARVyyAiIiKir4DK4dTMzAyCIODJkyf57uPx48dy/RERERHRt0nlcFqpUiUAwMuXL3Ht2rV89XHo0CHxdbly5VQtiYiIiIi+UCqH0yZNmoivp02blufjAwMD8c8//wAAdHV1Ua9ePVVLIiIiIqIvlMrhtFu3buJ9p4cPH8aUKVOUPvbJkyfo0KEDBEGATCZD586doaWlpWpJRERERPSFUjmc2traYsCAARAEAQAwb948tGjRAgEBAdkeExcXh8WLF8PR0RHPnz8HAGhpaWH69OmqlkNEREREXzCVH18KAPPnz8eFCxdw8+ZNyGQynDx5EidPnkSpUqXg7OwMc3NzaGtrIzY2FsHBwbh27RoSExPFQCuTybB27VqUKVOmIMohIiIioi9UgYRTPT09HDlyBO3atcOVK1cgk8kgCALCwsIQHh6epX3GZXwgfSmqxYsX5+vRp0RERET0dVH5sn4GS0tL+Pv7Y9q0aTA2NhbfFwQhy5+M9z08PHD+/HkMGzasoMogIiIioi9YgYycZtDU1MSMGTMwYcIEHD58GH5+frh79y7evHmDhIQEmJiYwNLSEvXq1YOnpyfs7e0L8uOJiIiI6AtXoOE0g76+Prp06YIuXbrk6bjAwEA4OTkVRklERERE9AUosMv6+RUTE4OlS5eiZs2aqFOnTlGXQ0RERERFqFBGTpVx/PhxeHt74+DBg0hKSpKbJEVERERE36bPGk6fPn2KDRs2wMfHB2FhYQDAUEpEREREokIPp0lJSdi7dy+8vb1x5swZuRn7GQRBgIODA7p27VrY5RARERGRhBVaOL1+/Tq8vb2xdetWvH37FgCyhNIqVaqgW7du6Nq1KypVqlRYpRARERHRF6JAw2lsbCy2bNkCb29vXL9+HUDWQCqTyTBlyhR07doV1atXL8iPJyIiIqIvXIGE09OnT8Pb2xv79+9HQkICAPlQqqenh8TERKSmpgIAfvvtt4L4WCIiIiL6yuQ7nIaHh8PHxwcbNmxAaGgogKyjpPXr10ffvn3RpUsXlCtXDtHR0SoVS0RERERftzyF05SUFOzfvx/e3t44efIk0tLSAMiHUhsbG/Tu3Rt9+/ZF+fLlC7ZaIiIiIvqqKR1Ox40bhy1btoijn59etm/fvj369u0LDw8PLg1FRERERPmidDhdsmQJZDKZXCjNfNnewMCgUAokIiIiom9Hnu85lclkaNmyJZYuXYpy5coVRk1ERERE9I1Sy89BR44cQbt27bBw4UK8fPmyoGsiIiIiom+U0uG0YsWKck93unPnDn788UfY2NigdevW2LNnD5KTkwutUCIiIiL6+ikdToODg3H27Fn07t0burq6YlBNSUnBkSNH0KVLF1hYWGDkyJG4fPlyYdZMRERERF+pPF3Wd3Nzg4+PDyIjI7F69Wq4uLgAgBhUY2JisGrVKtStWxdVq1bF77//jsjIyEIpnIiIiIi+Pvm659TAwACDBw/GpUuXcOvWLYwZMwZmZmYA/guqwcHBmDx5MmxtbeHp6YmPHz8WaOFERERE9PXJVzjNrFq1ali0aBGeP3+OHTt2oEWLFlBTUxNDampqKk6cOCE+1hQAzpw5k+VpUkREREREKofTDJqamujcuTOOHDmC0NBQzJgxA2XKlAHw34L9GYvzN23aFFZWVhg1ahTOnz9fUCUQERER0ReuwMJpZqVKlcK0adPw+PFjHD9+HF26dIGWlpY4mioIAl6+fImVK1eiYcOGsLW1xYQJE3DlypXCKIeIiIiIvhCFEk4za9q0KbZv346IiAgsXrwYNWrUkNsvCALCw8OxaNEi1K1bt7DLISIiIiIJK/RwmsHExASjR4/G9evXceXKFQwZMgSGhobi/sxrqBIRERHRt+mzhdPMateujVWrViEyMhIbN25Eo0aNxPtRiYiIiOjbVSThNIOOjg569eqFM2fOIDg4GJMmTSrKcoiIiIioiBVpOM2sQoUKmD17dlGXQURERERFSDLhlIiIiIiI4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlIiIiIgkQ6OoCyCSChmA4rpq0FaXQSYr6mqIgARtm6IugYi+ZgkJ+TpMXV0dmpqaBVzMfxhO6ZtXXFcNrSrqwb6kLox01KHGZEoS8US2oKhLIKKv2ZMn+T5UW1sbZmZmMDQ0LMCC0jGc0jettJEGRrgYw9ZUHzp6BoCGFmRqvNuFpKEs/yoSUWEyL5vnQwRBQHJyMmJjY/H8+XMAKPCAynBK37SWFfRQpoQRtI3NGUpJcnTUOIpPRIVIRydfh+nq6sLAwADh4eF4/fp1gYdT/jSmb5a+lgw1LHShVcyAwZSIiCgPZDIZjIyMkJiYiOTk5ALtmz+R6ZtloKUGTXU1yNQL76ZuIiKir1XGpKjU1NQC7ZfhlL5ZvGJKRESUf7JCmkDMcEpEREREksFwSkRERESSwXBKRERERJLBpaSIctF2+fmiLiHPDo6sX2h9Xzx7Bof378b1wABEv4pCUlIijIxNUKFyFbg1boZWHbqguKlZoX3+5zCgc2sEXsr9+z503E8YNn7SZ6jo6+B7IRCNOw8GAAjPg4q4GiKSKoZTIlJKzJtoTBo5EJfO+QIArGxs4VzXDbp6enj9KgrXAy/j0jlfrFwwB2u27UONWk5FWW6BqFy1OipXtc9hf/b78mLquOE4uHsbfl2wAt916VEgfRIRfakYTokoV3HvYtG3Q0uEPgpB2QqVMG3uIji61pNrk5SYiIO7t2HVwrl4/fJlEVVasBq3aMWRUSKiz4zhlIhyNXfaTwh9FAIrG1ts3HsURiYmWdpoaWujU8++aNzcC3HvYougSiIi+hpwQhQR5Sj8aSiO7N8NAJgwdbbCYJqZaQlzlClfEQCwauFc1LQxwaqFcxH5PAzTJ4xCc5dqqF22BKaOGy4eEx//Ed4rFqFry0aoa2cD14pWaN+kLpb/Pgvv3r5V+Dl3b17Hj8P6o5lzNdQuZ456VWzhVd8B4wf3xpljh+XapqWlYfcWH/Rp3wJu1UqjdtkScHeoiM7N3TBn6kQ8D3umwlfoPy3r1kBNGxM8D3uGyxfOYUiPDnCrXgYuFSzRtWUj/L17u1z752HPUNPGBAd3bwMATPthBGramIh/ZixYLbaVWTtCZu0IANiw4wDqtukDI7uGkFk7IjQsQmx3+dptdBnyE6wcm0OrjAvMazRBmz5jcOLsJYU19x07HTJrR/jsOIgbdx6gw8AfUMLeA7rl66JG0y5Y8ufWLAts9xkzDTJrR8xZtj7br8XOg8chs3aES6teefsiKnDM9wJa9x4N8xpNoFXGBVaOzdF16E8IvHFXYfvYd3H4Zd4K2DfpgmIV6kG7rCusHJuj/nf9MO2PVVmeZnP15l10HfoTStX2hFYZFxhWboByddug46AJOHDMV+X6iShvOHJKRDnyO3UMqampMDAygnvzlvnq49mTR+jq2QiaWlpwcHKFIAgwLm4KAIiNicGg7t8h+M4t6BsYwKVeA2hoauLqpfNYt2wBDh/YjXXbD8LaxlbsL8DfD8N7d0ZKcjIqV62OmrWdkZqaiqgXkTh3+jjSUlPRuIWX2H7Gj6NxYOcWaGvroJZLHZgUN0Xs27cIfxaK7T7r4Fq/kVz/qtq/YzPWLZ2PKtVron6jJogID8PNoCv4ZdwwxL6NwfcDhwEA9IoVQ9tO3XHtyiWEPX0CB2dX2JYuJ/bjUK1ylr5H/TIPKzfuQj2nGmjVxA2Pn4YjYx3sdVv2Yuik/yEtLQ21qtvBva4TnoZH4tDJczh08hxm/DAE08cPUVjz5et3MGzKHFiUMEUTNxfExL6D78WrGDt9PvwvX8fONfPEBbfHDOyOv3YfwupNuzFxeB+oq6tn6W/Fxp0AgJF9u6r0tZz6+0rMWvInZDIZ6jnVhK21Be6FPMHOv09gz+HTWPv7z+jfrZ3Y/mN8PNzaD8Dt+w9RwtQETdxcUExPFy+ionH/USguLF6H8YO/h7FR+pNtTp0LQMteo5CcnIKaVSuhbu0aSE1LxfMXr/DPKX+kpqbhuxbuKp0DEeUNwykR5ejuzWsAgCrVayoMIco4vH83WnXoghm/L4WWtrbcvtk//4DgO7dgX8sJyzfugLFJcQDAxw/v8eOwfvA/cxJTRg/Cxn3HxGPWLVuAlORk/G/pGrRq30Wuv7h3sXjy8IG4Hfk8DAd2bkFJSytsPXQaZuYl5do/DgmGrp5evs4rO+tXLsaS9VvRqKmn+N6BnVsx7YcRWL1oLjr17AsdXV2YFDfFb4tWYuq44Qh7+gQduvWWmxBVQ+1Jlr7/2v0Pzu9fjzq1a8i9f+teCIZPmQtBEPDXkl/Rq1Nrcd+R0+fRbsB4zFiwBvWcaqJZwzpZ+l311y4M79MZS379ERoa6T8a7gQ/QuPOg7H7n5NYu3kPhvTqBABwtK+C+s4OOH/lOg4e90P7lh5yfd2+/xBnLwWhhKkJurZtno+vYLqjZ85j1pI/oaOjjYMbFsnV7b1tPwZO+BVDJ/0PrrXsUa1yeQDA7kOncPv+Q7T0qI8D6xeKj1cE0kfQzwVcg56ujvje7KXeSE5OweZls9Czw3+/0ADpI7D3QrJ+D4iocPGyPhHlKCb6NQCotDyUkbEJJv/2e5ZgGvk8DCf+OQCZTIZp8xaJwRQA9IrpY9q8JdDW1sH1wMu4Hhgg7ot+/QoA0KBx1uBjYGiEGo7O/7V9ld62SvWaWYIpAJSrWBmW1jYK6169aJ7cZfZP/9y/c0vhcd37DpYLpgDwXZceKFuhEuLevRMDf35MGNIrSzAFgCXe25CSkoL2LRvLBVMAaOlRH4N7dgAA/LHqL4X9WpY0w4Jp48VgCgDVKpfHtHGDAAAL1myWaz9mQHcAwAqfnVn6Wr5hBwBgYPd20NbWUvbUspi/ehMAYHjvzlkC9YDu7dC6aQMkJ6dgifdW8f2Xr6MBAM0auMoFUwBQU1NDo7q1oaWlman9GwCAl4dbls83MjRQ+LUmosLFcEpEhc7VzR0GhkZZ3r8acAFpaWmwq14DlapUz7K/pKUV6jVKH5W7cuGc+H71mun3Xk4ePQhBly8iJSUl288uW6EiiukbwP/MCaxbOh/hz54qXXflqtXRtlP3bP8YGRsrPK5RM0+F75etUAkA8PJFpNI1fKpT6yYK3/e9GAgA6Nu5jcL9A/699H3u8rUs95ACQJfWzaCjo53l/T7/9hfy5BkiXrwS32/fsjFsrCxwyv8y7j/8b3Qx9l0cNu89DHV1dQzr3Vm5k1IgJSUF5wNvpJ9Tl2zOqXv6OZ05Hyi+51yzGgDg91V/4a9dh/AmJufJeS4O6e17jvoZ/pev5fh3iYg+D17WJ6Icmfw7Yvrm3xHU/LCyUTwyGfVvSLO2KZ3tsaVKl5VrCwBjJk1DyP078D9zEv5nTkJHRxd29jXgXMcNXu07o1zF/+7VLKZvgF8XLMe0H0Zi+R+zsfyP2ShhbgF7RyfUd28Cr3adoFdMX+Fn53cpKUurUgrf19c3AJC+7FZ+lSllpfD95/8Gx7K21gr3ly+TXlNCQiKiY2JhblZcbn92xxnoF4OpiTGiY94iPPIlrCxKAAA0NDQwvE9nTJ6zDMs37MDy2elfp427DuHDx/j08GptkfcT/Fd0TCwSEhJzPqfS6ef0PFNodq/nhJ9G9MUfq/5Cn7HTIJPJULGsLeo718R3LdzRpllDqKn9Ny4zZ/Io3LwXgiOnz+PI6fPQ1dGBo70d3OvWRs8OLVGlYrksn0tEhYsjp0SUo6r2DgCAe7dvKBxxU4aOjm4BVgSYmZfEtn/O4M+df2PQqB9QvVZt3L91E+uWLUCHJnWxfuViufZNvdriWMAtzFq8Ch179IGBsTFOHz2E3yaNQ+sGtRFy706B1idTK7z/tepmul/ycxMEQW57UI/20NXRwV+7/0Hc+w8QBAErN+4CoPpEKFXMnTIajy4cxNLfJqJz66b48DEeG3YcRLv+41GnTR98+BgvtrUwN0PgkS04s2stfh49AK61qiPo1n3MXuqNao07Y94KnyI7D6JvFcMpEeWoYZMWUFNTQ1xsLHyPHynQvs0tLAEA4c9Cs22TsS+jbQaZTAbnum4YOfEXeO/8G2dvPcbP/1sAmUyGZfN+Q1io/EQWA0MjtOnYDdPmLca+UxdxLOAWGjf3QvSrKMyZOrFAz6soWP87ovn42XOF+x8/TX9fR0cbxY0Ns+x/ks1xce8/IDrmLQCglKX8PbumxY3Rs0NLxL3/gL92H8LJcwEIfhSKqpXKwcPNJb+nkt63iZF4v+rjp+EK22Sca8a5Z1bGxgqj+nfDjtXzEH71KC7/swmVypXGlet38PtKH7m2MpkM7vWcMOunETizey3e3DmDVXOmQCaTYcrc5XgUGqbSuRBR3jCcElGObMqUhed3HQEAC2b9gtiYmBzbR79+hdBHIUr1Xdu1HtTU1BB85xaC72adXPTq5Qtc8D0FAHCu1yDHvrR1dNClV39UrFINaWlpeJDLaKiFVSkM+yH9UrSiz/6cNLXSQ1hqav7vd3Svm/64WJ+dBxXuX7/jAACggUstuUlPGXb9cxKJiUlZ3t+0+x8AQIUyNrC2NM+yf3T/bgDSJ0ZlTIQa0adLlnZ5paGhATdnBwCAz86/FbZZvz39nBrXz/1Ruc4O1TC8T/o9sNfvPMixrY6ONob27oQaVSoiLS0NN+8p9/eZiAoGwykR5Wryr7/Dtkw5PH/2FH07tkTQ5YtZ2iQnJWHf9s3o6tkIj0Ny/uGfwdLaBs1afQdBEPDbpPF4G/NG3Pfx4wf8OmksEhMT4ODkAgcnV3HfxtXLEPk862jWk4cP8OzJYwCAVan0+1zv3b6Jowf3IiE+Pkt7vxPHxDqKkrll+n2kj4Lv57uPMQO6Q0NDA/uP+mLznn/k9h33u4g1m/cAACYMVbwofsSLV5jw2yK5WzfuhTzGr4vXAQDGDeqp8Dj7KhXhUd8Z90Ke4OBxPxga6KN359YK2+bVD0PSa121aTdOnQuQ2+ez4yAOHveDpqYGxgz4b/mtfUdO4+ylq0hLS5Nrn5ycjKO+FwAApUv9Nwo/f/VfePY86wS1+w+fIOTJsyztiajwcUIUEeXK0NgYPvuOYuLw/gi86I9+Hb1gbVsaleyqQUdXF9GvX+H29SB8/PAe+gYGKFFS+YkwU2bPR+ijENy6FojWbrXgVLcBNDQ0EHjpPGKiX8PatjT+t3Sd3DHrls3HwtnTULZCJZStUAk6OjqIevkC169cQkpKCtp06oYq9jUBAJHhYfhpxABx0pSFpTVSU1MRcv8uQh+FQFNLC+OmzFRY25lj/yAih6dH2dnXRM/+ihe1zwuPFl5Ys2getm5Yg4cP7qGkpTXU1NTQt4Uj2jZvpFQf9lUqYsXsnzBs8hz0Gj0Vi9ZtgV2FsngaHokLgTcgCAJm/DAEzRvVVXj80F6d8Oe2/fjnlD9ca1VHTOw7nLkQiKSkZLRv2RjD+mQ/8370gO44ff4KAKBP59bQL5b7urF1WvfOdp9lyRLY570ALT3q45cxAzFryZ9o1n046jvXhK21Je4/fIKgW/ehrq6O1XOniGucAoDfxatY4r0NZsWNUau6HczNTBD3/iMuBd1C1Os3sLYwx8RhfcT2s5b8iR9/Wwy7CmVQpWJZ6OroIOLFK/hfuY6UlBT07tQajvZVcj0fIio4DKdEpBRTsxLw3vk3zp85iSMH9+BG4GUEnD+LpKREGJsURw1HZzRs0hytO3TN9RGnmRmbFMfG/cewdf0aHPt7Hy6ePQMhLQ3WtqXRsXtv9BkyCoafLNk0edYfCPA/izs3gnD10nnEx3+EWQlz1Gngjo49+so9HaqGoxPGTJ6OoIALePzwAe7fvgUNDXWYW1qja5+B6NFvsPi41U8F372N4Lu3s6097l1sgYTTSlWqY8Gav7Bx7TLcunYVAf5+EAQBNax0lQ6nADD4+46oWbUS5q/eBP8r13Hz3kMYGejDy6M+xgzsoXDx/Qyutapj8PcdMH3+apw4G4D3Hz+iYllbDOj2HUb17yY+HUqRJm4uUFdXR1pamtKX9AOuZf91zTxS+dvE9FC6bP0OBFy7jUtBt2FW3BidWzfDhKG94FJLfgmyvl3aQldXB/6Xr+Hug8fwuxQDIwN92FpbYOzAHhjcswNMixuL7VfMnoRT/pdx5cZd+F0Mwof4eFiUMEWzhq4Y3LMDnw5FVARkwqfTL79A7969g5GREWJjY2FomPVG/8JQZtI/uTciSbM2UMeMxuYwtyoFmUb+FwonKiyKnhBV0PqOnY6Nu/7GhoUz0Ldr23z18efWfRj0429o3qgOjm1dWcAVElGhsaql0uEJCQl48uQJypYtCx2d3FcSUTav8Z5TIiLKtw8f4zFn+QYA/90jSkSkCl7WJyKiPPtj1Ubcvv8I/leu4/HTcHg2rpft/axERHnBcEpERHn2zyl/+F28CrPixujbpQ0WTv+hqEsioq8EwykR0TfMZ/FM+CxWvFpBTnx3r8u9ERFRPvCeUyIiIiKSDIZTIiIiIpIMhlMiIiIikgyGUyIiIiKSDIZTIiIiIpIMhlMiIiIikgyGUyIiIiKSDIZTIiIiIpIMhlMiIiIikgyGUyIiIiKSDD6+lCgXNf4sXdQl5NnNgU8Lre+LZ8/g8P7duB4YgOhXUUhKSoSRsQkqVK4Ct8bN0KpDFxQ3NRPbH9i5FdN+GIG2nbrjt0UrC62uL03LujUQER6GwxduwNrGtqjLUZrvhUA07jwYjerWlnuEaWhYBMrWaY3SpSwRGvBPluP+Pu6H31dtxI27IYh7/wEAcGbXWrjXcwIAbNhxACt8duJeyBN8jE8AADy5dAgAcuz3c5ixYDVmLlyL6eMHY8YPQ4ukhszcOw2C38Wrcl+/wlTGtRWehkfiyaVDKGNjVeifJ3Uya0cAgPA8SOljMr5nUvk7JHUMp0SklJg30Zg0ciAunfMFAFjZ2MK5rht09fTw+lUUrgdexqVzvli5YA7WbNuHGrUK/4cmfRmu3w5Gx8E/Ii1NgEd9Z1iam0Emk8HC3BQA8M/Jc+g/fiZ0dLTR1M0FpibGAAD9Yrp4/yG+CCvPWXZBnYhUw3BKRLmKexeLvh1aIvRRCMpWqIRpcxfB0bWeXJukxEQc3L0NqxbOxeuXL4uo0i/H2m0HkJKSDHMLy6IupUBYW5TAPb890NTI+mNl/7EzSE5OwZRR/TF70sgs+3cdOgkAWPrrjxjUs4PcPiMD/Wz7JaKvE/+1E1Gu5k77CaGPQmBlY4uNe4/CyMQkSxstbW106tkXjZt7Ie5dbBFU+WWxKVO2qEsoUJqamrCroPicnj1/AQCoWFbx7QvPnkdmuz+nfono68QJUUSUo/CnoTiyfzcAYMLU2QqDaWamJcxRpnxFpfo+eeRvzPhxNDo0qQu36mXgXMECLevVxLQfRiL0UYjCY5ISE+Gzeim6ebmjrp0Napczh4djZfRo5YFFs6chNiZGrv3TJ48w7YeRaFmvJpzKl0SdyqXgWcceI/t0wf4dW5SqEwAe3LuNmjYm6NSsfpZ9G1YtQU0bE9S0McGjB/fl9kU+D0NNGxO0cK0u937LujVQ08YEz8Oeyb0f9y4Wy3+fhY5N66FYhXrQLusKK8fmqP9dP0z7YxWSk5OzfH7M23eYPn8VHJp1g0ElN+iVrwf7Jl0wa/Gf+Bifv8vif+06BGev76FXvh6KV3OHZ88ROBeQ/T12oWERkFk7ooxrK/G9GQtWQ2btiA07DgIA+o2fAZm1I2TWjnDvNAh9x06HzNoRZy4EAgAadx4s7u87dnq2/Wb2MT4ei9dtgVu7/jCp2gjaZV1R2sULbfqMwdZ9R+TalnFtBZm1I0LDIhT2lVGPz7/15sS90yA07jwYAOB38apYd+ZaG3UcCJm1I7btP5ptP7+v9IHM2hFdhvyU62cq4nfxKpp3H47i1dyhV74eXFr1wqbdhxS2fRUdg6Xe2+DVaxTK1mkN3fJ1YVi5AZxa9sS8FT5ISEjM02c/DY/AvBU+8Og8GLbOXtAu6wrjKg3h1q4/1mzajbS0tCzHZP5+CoKAtZv3oLZnDxSrUA9Gdg3RvPtwXAy8ke1n5uX7nWH3oZPw7DkCJew9oFXGBda1W+D7UT/j7oPH2X7OxcAbaPn9SBhXaQj9ivXh1LIn1m/fn6evj6rexMRiypxlqNa4E/TK14NBJTfU9uyB31f6IP7f+7I/dfJsANr0GYOSNZtCs7QLTKo2QsX63+H7UT/j7KWrcm0TE5Pwxx9/oHbt2jAwMICWlhYsLCzg7OyMiRMn4s2bN5/jNBXiyCkR5cjv1DGkpqbCwMgI7s1bFmjfE4f1g6aWNspXrAyXeg2RmpKCh8H3cGDnFhw/tB+rt+yBg5Or2D4tLQ0j+3ZFgL8f9A0M4OhSBwaGRoh5E41nTx7BZ/UytGzXWQzQIffvom8HT7yPi0OZ8hXRsEkLqKur42Xkc1wNuIioF5Fo17WnUrVWtKuG4mYl8DD4HqJfv4KpWQlx36Vzfv+99vdD+Up2Wfa5urnn+hnx8R/Rt0NLPAy+BxNTMzRxc0ExPV28iIrG/UehuLB4HcYP/h7GRpriMXcfPIZnz5EIi3gBy5JmcHNxgKaGBi5fv4Opf6zEnsOn4Lt7LYwMDZQ6TwAYM+0PLPXeBjU1Nbi5OMCqZAncvBcC906DMapfV6X7cahWGX06t4H/lWt4FBqO+s4OqFDGBgBgV6EMzIobAwCO+l7Ay1fRaOFeFxYl0ifTubk45Np/2PMX8Px+JO4+eAw9XR3Ud3aAqYkRnr+IwrnL13Dr/kP0aF+wf2czeLrXg462Fo75XkTJEqbwdP/vNpeM8xozoDvOXgrC8g070L2dZ5Y+0tLSsOqv9F/8Rvbrkuca9h09g+UbdsCuQhm0aFQXES9fwf/ydfQeMw3X7zzAgunj5dof872AMdP+gLWFOSqUtUEdR3u8io5BwLXbmPS/pThwzBdndq2FtraWUp+/afdhTP1jJcraWqNSOVvUd66JyKjXuHj1Js5fuY7jZy9h99o/IJPJFB7fb9wMbN1/BA1caqF10wa4fucBTpy9hLMBQfDbvQ6ujvZy7fP6/U5JSUHPkT9j598noK2thdr2VWBtYY4Hj59iy94j2Hv4DPb++Qc8G8v/wrnr7xPoPmIKUlNTUd2uAuztKiAs4gUGTvgNd4KzD7QF6fHTcHh0GYKn4ZEoYWoCL4/6SE5JwZkLgfhp9lLsOHgcJ7evhomxoXjMxp1/o9/4GQAAF4dqaFzPCfEJiQiPfIntB47DzMQYDevUBpD+d69V79E45X8ZhoaGaNCgAYyNjfHq1SuEhITgjz/+QI8ePVC8ePHPcr6fYjglohzdvXkNAFClek2oq6sXaN//W7oWDZu2gJ5eMfE9QRCw8y9v/O+XH/HrT2Ox5+QF8YfbtcsXEeDvB7vqNbB+1yEU05cPXHduXIOFlbW4vWndSryPi8PIH3/GoNET5NomxMfjzg3lZ9vKZDK41m+IIwf2IOCcL7zadwaQPpJ7/collK9khycPHyDgnC969h8iHhfg7wsAqOPWKNfPOPnPQTwMvge3xk2x2HsramuHi/vS0tJwLuAa9HR1xPfi4xPQtt9YhEW8wC9jBmLq2EHQ0koPrh/j4zFwwm/Ytv8oxs1YgPULZyh1nv+cPIel3ttQTE8XRzYvQwNXR3HfnGXrMWXucqX6AYB2no3RzrMx+o6djkeh4RjYvR36dm0r12Zgj/Zw7zQIL19FY9KIfkrPPk9LS0OHQRNw98FjNG9UB5uXzUYJ0/9G9RMSEnH6/BWla82rSSP7oY6jPY75XoRdhTLwWTwzS5vvWrijdClLXAi8gWu376NWdTu5/YdP+SM0LAI1qlQUQ0NeLPXehv9NGonJo/qL7/ldvIqW34/CwrWb0bxRHbTIFJpr16iCiwd9UKd2Dbl+Yt6+Q7fhk3Dc7xKWrt+GH4f1UerzW7jXRTtPd1S3qyD3fsSLV/DqNQp7D5/G7kMn0blNsyzHPg2PhO/FQNw+tQuVyqeviJKamorBE2dh/fYDmDZ/FY5t/W91j/x8v6fPX42df5+Aa63q2LZyDsra/vf/ht2HTqLb8MnoMfJnPL7wN4yN0v9f8iLqNQZM+BWpqalYOH08xg3+Xjzm1LkAtO47Vqmvjap6jJiCp+GRaNu8Ebau+B+K6ekCSB/99uw5AkG37mPkL/OwZfls8ZiZi9ZCEASc2+cNN5dacv1FvX6D5y+ixG3/y9dwyv8yatWqBT8/PxgYyP+/NDAwEDY2NoV4hjnjZX0iylFM9GsAkFseqqB4tu0gF0yB9BDYtc9A1KztjEcP7uNxSLC4L/r1KwCAo0vdLMEUAKrVrAVjk/9+03/zOv1/xm4ezbO01dHVRe06WS/R5yRj9PPiOV/xvWuBAUhIiEezVt+hir0DAi+dR0pKCoD0oH35wrn0YKtEOI3+t946DRpDU1NTbp+amhoa1a0thk8A2LjrEB6FhqN10wb4beJwuX16urpY+/svMDcrjk17DiPm7TulznHxn1sBACP7dZULpgAweVR/OFSrrFQ/he3vE2cReOMuLEuaYc+6+XJBBQB0dLTh1cStiKpLp66ujhF900dEV/jszLJ/uc8OABDb5FWt6nZywRQAGtWtjeF90n9xWrBms9y+KhXLZQmmAGBibIhlv6XfVpAxOU0Zzg7VsgRTALCyKIHffxmTa3/LfpsoBlMg/es1+6cRAAC/S0Fyt7Dk9fv9JiYWi9ZthY6ONvasmy8XTAGgU+umGPJ9B8S8fYfNe/9bosx7237Evf+AOo72csEUAJo0cMWQTybsFQb/y9cQcO029HR1sPb3X8RgCgAlTE2w9vdfAADbDxxDeMR/k09fvoqGkaF+lmAKAOZmxeV+OXr5Kv2SfYMGDbIEUwBwcnKCqalpgZ1TXnHklIiK1LMnj3He7yTCQp/gw/v3SEtNBQBEv0oPoqGPHoqXyTNGb/fv2ILSZcujScs2KFHSItu+qzvUxrnTJzB7yg8YNn4SnOrUh7aOTrbtc1OnQXrAvHz+v8v4Aed8/93njuSkJNy5EYRb1wJRy7kOQu7fQfSrKFS0qwrTEua59l+tZnoY9Fm9FMYmxVGqmR2Kmxhl2/6fU+cAAF3bZg3fAKBfTA9ONari8Gl/XLlxB80b1c3x81NSUuB/5ToA4PsOXgrb9O7UCtfvBCvc9zkdPXMBANCjXUvoF9Mr4mqyN7B7e8xYsAZb9x3FH7+MFS/DPnzyDMf9LsHYyADfd1T8tc5N706K78Pt07k1FqzZBP/L15Gamip3xSM1NRW+FwJxIfAmIqNeIz4hAYKQ/osUAAQ/ytsayYmJSTjudxFXbtxF1Os3SExKgiAAcR8+/NtfqMLjNDQ04Nm4Xpb3LczNYGJsiJi37xAdEwsL8/RfivP6/T5zIRDxCQlo4uYCa0vF//bc6zph5cZduBB4EyP7dQMA+F5Mvy+zZwfFt4P06dIGS7y35fr5qvC9kF6DZ+N6KFkia0CsXaMqalathBt3H8Dv0lX0/PffqotDdfheDETv0VMxZmB31KpuBzU1xWOQjvZ2UFdXx/r161GpUiV06NABlpbSWTmE4ZSIcmTy74jpm39HUAtKamoq5vzyI3Zv8RF/MCry4X2c+NqmTFlMmP4/LJo9DXOmTsScqRNhVcoGNRyd0bBpCzRv1Q6aWv/dL9dn6Chcu3IJl875YnivTtDQ1ETlKtXh6FoPnm07oLrDfyODTx4+wPoVi7N8fi2XOujQvTcAwNLaBrZly+PZk0cIfRSCMuUr4tK/97/a13JCUlISvFcsQoC/H2o518nT/aYA4FzXDf2Gj8HG1cvwy7hhmCqToWLZ9Hv5vmvhjjbNGsr9sHn87DkAoNfoqeg1emqOfb+KjslxPwBEx8SKk2I+HWnKkN37n9vTf2f421UoU7SF5MLE2BC9OrbCms174L19PyYMTf+7tHLjLgiCgH5d2kJP97+RsbnLN+D+w9As/cyfNhZmxeVHC3P7HsUnJCA6JhbmZulXE0IeP0P7gT/gTvCjbOt9F/de6XO7dPUmug6bJK7GoLC/fx+48ClLc7MsVwcyGOoXQ8zbd0hITBLfy+v3+/Gz9FtiTvlfFhfNz07mfxvhkekjkWVtsvnaZvN+Qcq4/J7TZ5UvUwo37j6Qu1S/cs4ktO4zFpv2/INNe/6BgX4xONesCo/6zujVqRVsrS0zHW+DRTN+wI+zlmDkyJEYOXIkSpcujbp166J169bo3LkztLSUu/e4MDCcElGOqto74NCeHbh3+0aWURhVbPFejV2bN8DMvCQmTJ2Fmk4uMDUzF0c2J40ciCMH9mQJrj36DUaL1u3ge+IIrl25hGuXL+Howb04enAvVi2Yiw17Doujqbq6elizdR9uXw/Ced9TuHE1ADeuXsadm9ewad0KdO09AFNmzwcAvH4VhYO7FY+IZIRTIP3e0WdPHuHiuTMobmaOe7euo2GTFtDQ0EAtJ1fo6Oji4rkzGDrup//uN22Q+yX9DGMnz0Dn7/vD7+RRPLlyEuev3MCGHQexYcdBODtUw5lda8XLfBmzoT0b10NJs5wvwZUuJZ1REalSNLu8IIwe0B1rNu/Bqr92Y/zg75GQmIgNOw9CJpNluaR/1PcC/C5ezdLHjB+GZAmnysj876fT4B9xJ/gRWjdtgInD+6BqxXIwNCgGTU1NJCUlQ7usaw49yfsYH492A37Ay1fR6Ne1LYb17owKZWxgaFAM6urqePDoKSo3bJ/tL55qaoonSRWUtLT0z61Qxgb1nR1ybCv1X3CUVaViOQSf3Yvjfpdw+vwVXAi8gXOXr+H0+Sv4dfE6eM+fhu87/jfaPqp/N3QZNB4HDx6Ev78//P39sX37dmzfvh3Tp0/HuXPnimw0leGUiHLUsEkLzP/1Z8TFxsL3+BE0adm6QPo9fmg/AGDqnIVwb571suazJ9nPijUtYY6OPfqgY4/0iRtPHj7A9AkjcePqFSyZOxOzFq2Sa1/dwVEcJU1JScGZY//gl7HDsOMvbzRt9R1c6jWAc1033AjLfXTRtYE7dm5aj4BzfihR0hJpaWlwbeAOIH2tVwfnOgi8eA5vY97gasBFaGhq5vneVmsbW/ToNxg1BqRPJLly/Q6+H/ULrly/g99X+mDmhGEAABsrC9x/GIoB3dqhU+umefoMRUxNjKCtrYXExCSEhkWgWuXyWdpktwzT52Zrlf4LiKJRxuxo/TtSF5fNaF7G6FxBq1qpHJo2cMXJcwE4cvo8Il6+wtvYOLT0qI/yZeQnneTlSVNP/h05/1TG90hHRxum/94Wcv/hE9y8FwJzs+LY570AGp881CDkybMs/eTk7KUgvHwVDUd7O4WT7fLaX27y+v22sSoJAKhcvrTCyWrZsbYwx/2HoQgNV/z3/HP8/be2SL8NIWP0V5HHT5/Ltc2goaEBryZu4v237+LeY+HazZi5cC2G/DQb7Vt6yN3DWrJkSQwaNAiDBg0CANy/fx/9+/fHxYsXMWnSJGzcuLFAz01ZnBBFRDmyKVMWnt91BAAsmPVLlnVEPxX9+lW2a5RmFvs2vR/LUllnhD4Mvofgu7eUrrFshUroN2wsACD4Ts7HaWhooFmr71C3kYdS7T/lUi/90vqVi+dwwe8UAKDuv+EUSB8lTUlJwfoVixD/8QNq1HLKMukrr5wdqomTXK7feSC+3/Lfe/Z2/n1Cpf4zaGhooL5TTQDAlr2HFbbZtKdonm//qYz7FbcdOIoPH5VbyzXjB/m9kCdZ9r2Ieo2gW/ezvJ+TjLCbkpKaa9sxA7oDSJ8ElTE5amRf5ZflUmRzNt+jv/5d59TN2UEMoW9i0ifEWZUskSWY5tRXdt78O8Eu86ViVfrLTV6/303cXKClpQnfi1cR9Vr59Tob/btqwpa9itdM/SubNWQLknu99BqOnrmIl6+is+y/dvs+rt8JhpqaGhq65nzLgqGBPmb8MBTGRgb4GJ+AB49zvqfYzs4OP/2UPjnu+vXr+TuBAsBwSkS5mvzr77AtUw7Pnz1F344tEXT5YpY2yUlJ2Ld9M7p6NsLjkAcKepFXrkIlAMCOjd5yl1NfvXyBX8YNE2e8ZxZw/izOnT6eZSF6QRBw9tQxAOn3hWbYsfFPhUH5ddRLcYksReE4J4ZGRqhi74D3cXE4tHsHSlpaoey/5wIAdf69v3S7z58AII6qKuPUkUO4eul8lsvLycnJOOqbPiEk8+X5wd93ROlSlth16AR+mr1E4Yjgi6jXWLdlr9I1jB3YAwCwbMMOXLgivxj67yt98hzgCkvb5o1Qq7odIl68QuchExH95q3c/oSERBw5fV7uvaYNXAAA81b64G3sf/cyv4qOQe8x0/D+w8c81VDKKj3shjx5pvDhCJl5NXFDhTI2OHrmAm7cfYDyZUqhpUfeRtQ/dfXmPfy+0kfuPf/L18TwO27Qf2v4VipvC3V1ddy6/xC+/z70IMPfx/2waJ3yD6QAgCoV05/adcr/cpbF7Ndu3oMdB4/nqb/c5PX7XbKEKUb164YPH+PRpu9Y3LqX9f8DiYlJOHjcD/cf/vfLyoDu7aBfTA8Xr97E0k8mPvleCMTqTXsK9LwUcXOpBdda1RGfkIAhP82We5DG6zcxGPJT+vJR3b5rARvr9BHlj/HxWLhms8J7y88FBOFtbBzU1dVRyjJ9RPm0/2UcPuWv8P+lhw6lB/DSpUtn6etz4WV9IsqVobExfPYdxcTh/RF40R/9OnrB2rY0KtlVg46uLqJfv8Lt60H4+OE99A0McpxBn2HAqPE473cKe7ZuxJUL52BXvSY+vH+Hq5cuwNq2NDw8W+P0UflRipB7d/DHzCnQNzCAXfWaMC9pgYSEBNy/fQMR4WEwMDTE8AlTxPZ7tm7E/375Eda2pVGhUhUUMzBATHQ0rl2+iISEeLjUbwj3ZnlfpL2OWyPcuRGExMQE1GnQWG6fXfUaMDYpjrcxb8S2yroacB5bvFfDpLgpKlergfIldBD3/iMuBd1C1Os3sLYwx8RMa1AW09PFP38tRes+Y/D7yo1Yu2UvalSpiFKWJcVRknshT2BuVjzLM+uz06Z5I4zo2wUrfHaiQYcBaOjqCMuSZrh5LwT3Qp5gzIDuhT5bWRlqamrY5z0fLXqMxJHT52Hr4gU3FweYmhjj+Yso3Lj7AMaGBggN+G+kd0Tfrli3dR+Cbt1H5YbtUbd2DXz4GI8rN+7A1toC7TwbY//RM0rXYGttCaeaVRF44y7sm3SFU80q0NHWhllxY8ydMjpLvSP7dcXY6en3OA/v3SXbxemVNXpAd0yesxx/7f4HNapURMTLVzgXcA1paWkYM6C73NJKZsVNMLJvFyzx3oYmXYeigWstWJUsgeBHoQi6dR+/jBmIWUv+VPqza1W3w3ct3HHgmC9qtegO97q1UdzYCNfvBCP40VNMGdUfs5d6q3R+meXn+z13yihERr3G1n1H4NC8O2pWrYRyttbQ0FBHeGQUrt8JxoeP8TiyeZn4iFwrixJY98dUfD/qF4yZ9gf+3LYP1StXSF/oP+Aaxg7skecgn9mfW/eLKw8oMnXsILRq2gBbV/wPHl2G4MAxX5St0wYNXR3FRfjfxb2Ho70dls/676liSUkp+OHXhfhx1mLY21VAxbK20NTUQGhYBC4FpV8d+nl0f3EJrpv3QjBuxgIYjvwFjo6OsLKyQnx8PIKCgvD06VMYGRnh119/zfd5qorhlIiUYmpWAt47/8b5Mydx5OAe3Ai8jIDzZ5GUlAhjk+LpM+abNEfrDl1zfcQpANSo5YRth05j+R+zcefGNfidOIKSVtbo1m8QBo+egLlTsz7OsVFTT7yPe4egyxfx7Mkj3AoKhLaODiysrNF/xFh06zMQJS3/m+E6cuIvOHfqOG5eC8TNa4F4H/cOxU3NUL1WbbTr0hOe33VUeIkzN64N3OG9YhGArOFTJpPBpX5DHD+0H/oGBqjuoPzi6m0794C2jg6uXb6ExyHBCAp4BSMDfdhaW2DswB4Y3LMDTP99+lCGapXL4+bJHVi9aTf2HTmDm/dCcPHqTZgVN0Ypy5KYMLQX2ns2VvyB2Vg+exJq16iCFT47cenaLWhracK5ZjXxh6EUwikAlC5lhcAjm7Fy407s/ucULl69haTkZFiUMEWjOrXR45OnMhkbGeD8/g2YMnc5jvpewJEz52FtYY7BPTtg2rjBGPnzvDzXsGfdH5g8ZznOXLiCHQdPICUlBaVLWWYJp0D6ovUAoKerg/7dvsvfSWfS3rMxvmveCP9bth6HT/sjKSkFjvZ2GNm3K/p0aZOl/aKZE1CjakWs3LgLV2/ew3X1YNjbVcD2lXPQ9bsWeQqnALBrzTws8d6Gv3Yfgv/l69DR1oZTzSpY+ttEVCxrW6DhFMj791tDQwNbls/G9x288Oe2fQgIuo3bwQ9RTE8XluZmaNOsIdo2b4SGdeQvjXf7rgVKWZpj1pI/cfHqLTwKDUfl8mWweu4UDP6+o0rh9PmLKLkZ9p/KGPksV7oUgo5uxfzVf2H/MV8cOnUOajIZKpcvg65tm2N0/27QzfRADv1iulg9dwr8LgXh2u37OHHuEpKSUmBlYYYOLT0wvE9neLi5iO3bNGuI2Lj3OHf9IUJCQnDp0iXo6urCxsYGkyZNwogRI1CqVKl8n6eqZEJOa7h8Id69ewcjIyPExsbC0NAw9wMKQJlJ0rjvivLP2kAdMxqbw9yqFGQaRbdkBlF2aqhlvTeSvly/zFuB2Uu9MbhnB6z5dyF1oiJllXXB/rxISEjAkydPULZsWegosYa0snmN95wSEREVssiXr7Bi406oqalh7KAeRV0OkaTxsj4REVEhmfS/pXgeGYWT/pfxNjYOQ3t1QpWK5Yq6LCJJYzglIiIqJNsPHMOz5y9gYW6KsQN7KLwXlYjkMZwSEREVksyzx4lIObznlIiIiIgkg+GUiIiIiCSD4ZSIiIiIJIPhlL5ZX/4Kv0REREWnsJbKZzilb1Z8ioC0NAFCWmpRl0JERPTFSU1N//mpplawcZLhlL5ZMQlpePE+GUJyQlGXQkRE9MWJj4+Huro6NDU1C7RfhlP6pl0Oj8f7d+8gpCQVdSlERERfjNTUVMTGxkJPTw8ymaxA++Y6p/RNO/kkHuWLx8EZgL6hIWSaOpCpqRd1WUQAgAQ13hhNRIUoIe9XDgVBQGJiIt68eYO0tDSYm5sXeFkMp/RNS0wVsObqOzx6kwyXUvGw0NeEmlrB/gZIlF9asldFXQIRfc0+PMn3ocWKFYOFhQW0tLQKsKB0DKf0zUtMFfDPw4/45+FHmOioQUdDBuZTkoJT2hOKugQi+pqNDMzXYRoaGtDQKLwIyXBKlElMQlpRl0Ak0kkOK+oSiOhrpqNT1BUoxAlRRERERCQZDKdEREREJBkMp0REREQkGQynRERERCQZDKdEREREJBkMp0REREQkGQynRERERCQZX8U6p4KQ/oi/d+/efbbPTEv8+Nk+i4i+Te9kfHwpERWiz5ib0j8u/fMyclt2ZEJuLb4A4eHhsLGxKeoyiIiIiCgXYWFhKFWqVLb7v4pwmpaWhoiICBgYGEAm43MniYiIiKRGEATExcXBysoKamrZ31n6VYRTIiIiIvo6cEIUEREREUkGwykRERERSQbDKRERERFJBsMpEREREUkGwykRERERSQbDKRERERFJBsMpEREREUnG/wEfCpGH6FmekQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "br1 = np.arange(1)\n",
    "br2 = [x + 0.1 for x in br1]\n",
    "\n",
    "p1 = plt.bar(\n",
    "    br1,\n",
    "    np.array(test_acc_crossentropy).astype(float),\n",
    "    width=0.1,\n",
    "    label=\"CrossEntropyLoss\",\n",
    ")\n",
    "p2 = plt.bar(\n",
    "    br2,\n",
    "    np.array(test_acc_CDB).astype(float),\n",
    "    width=0.1,\n",
    "    label=\"Class-wise difficulty-balanced Loss\",\n",
    ")\n",
    "\n",
    "for rect1 in p1:\n",
    "    height = rect1.get_height()\n",
    "    plt.annotate(\n",
    "        \"{:.2f}%\".format(height),\n",
    "        (rect1.get_x() + rect1.get_width() / 2, height + 0.05),\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=26,\n",
    "    )\n",
    "for rect1 in p2:\n",
    "    height = rect1.get_height()\n",
    "    plt.annotate(\n",
    "        \"{:.2f}%\".format(height),\n",
    "        (rect1.get_x() + rect1.get_width() / 2, height + 0.05),\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=26,\n",
    "    )\n",
    "\n",
    "plt.ylim(70, 100)\n",
    "plt.tick_params(\n",
    "    left=False, right=False, labelleft=False, labelbottom=False, bottom=False\n",
    ")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.ylabel(\"Accuracy\", fontsize=26)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "SN267GI_2cV1",
    "outputId": "d8aec9cb-0857-4c03-da50-f473e22e4b07"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "br1 = np.arange(10)\n",
    "br2 = [x + 0.4 for x in br1]\n",
    "class_percentages_crossentropy = [\n",
    "    confusion_mtx_crossentropy[i][i][:-1] for i in range(10)\n",
    "]\n",
    "class_percentages_CDB = [confusion_mtx_CDB[i][i][:-1] for i in range(10)]\n",
    "\n",
    "p1 = plt.barh(\n",
    "    br1,\n",
    "    np.array(class_percentages_crossentropy).astype(float),\n",
    "    height=0.4,\n",
    "    label=\"CrossEntropyLoss\",\n",
    ")\n",
    "p2 = plt.barh(\n",
    "    br2,\n",
    "    np.array(class_percentages_CDB).astype(float),\n",
    "    height=0.4,\n",
    "    label=\"Class-wise difficulty-balanced Loss\",\n",
    ")\n",
    "\n",
    "plt.yticks(\n",
    "    [r + 0.2 for r in range(10)],\n",
    "    [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"],\n",
    "    fontsize=26,\n",
    ")\n",
    "plt.xlim(0, 110)\n",
    "plt.xticks(fontsize=26)\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.ylabel(\"Class\", fontsize=26)\n",
    "plt.xlabel(\"Accuracy %\", fontsize=26)\n",
    "plt.legend(loc=\"lower left\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "EqTQrfZi2cJu",
    "outputId": "30acedcc-609d-4ede-d0e2-c920d4dc9a32"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-743b692f-7429-436c-835e-939fb7c1a82a\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrossEntropyLoss</th>\n",
       "      <th>CDBLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.7</td>\n",
       "      <td>97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.5</td>\n",
       "      <td>98.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.3</td>\n",
       "      <td>95.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.7</td>\n",
       "      <td>92.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.5</td>\n",
       "      <td>99.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>94.5</td>\n",
       "      <td>99.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>93.1</td>\n",
       "      <td>95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84.8</td>\n",
       "      <td>93.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61.5</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40.0</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-743b692f-7429-436c-835e-939fb7c1a82a')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "    60% {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "\n",
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       "\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
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       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_5654e648-3477-4918-b3b1-1ce86371aa62 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('df');\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "  CrossEntropyLoss CDBLoss\n",
       "0             99.7    97.6\n",
       "1             99.5    98.2\n",
       "2             98.3    95.1\n",
       "3             95.7    92.1\n",
       "4             97.5    99.2\n",
       "5             94.5    99.2\n",
       "6             93.1    95.7\n",
       "7             84.8    93.8\n",
       "8             61.5    55.7\n",
       "9             40.0    63.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"CrossEntropyLoss\": class_percentages_crossentropy,\n",
    "        \"CDBLoss\": class_percentages_CDB,\n",
    "    },\n",
    "    index=range(10),\n",
    ")\n",
    "df"
   ]
  }
 ],
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