with_weight_and_argumentation_classification.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1714379526058,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "wU67BWUzYvDo"
   },
   "outputs": [],
   "source": [
    "# !pip install lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19154,
     "status": "ok",
     "timestamp": 1714379552077,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Y8ODGCnVZq3e",
    "outputId": "d4cf0cc5-4f89-4311-aed3-c727cb2f0c03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 274,
     "status": "ok",
     "timestamp": 1714379594032,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "fsLXk0-KZ04w",
    "outputId": "eb9c7a0a-e506-4968-da08-5b3ab21f8a29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights\n"
     ]
    }
   ],
   "source": [
    "# %cd /content/gdrive/MyDrive/Colab Notebooks\n",
    "# %mkdir ClassPreTreined\n",
    "# %cd /content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined\n",
    "# %mkdir weights\n",
    "\n",
    "%cd /content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9767,
     "status": "ok",
     "timestamp": 1714379605914,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "h3B2QwwiaPMa",
    "outputId": "0ec5b25a-3804-4f61-c457-d9f4bcf52499"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "import torchmetrics\n",
    "from torchvision import datasets, models\n",
    "from torchvision.transforms import v2\n",
    "from torchsummary import summary\n",
    "\n",
    "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
    "\n",
    "import lightning.pytorch as pl\n",
    "from lightning.pytorch.loggers import TensorBoardLogger, CSVLogger\n",
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "from lightning.pytorch import Trainer, LightningModule\n",
    "\n",
    "RANDOM_SEED = 546\n",
    "\n",
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DYYWHys4aw2x"
   },
   "source": [
    "## DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1714379605915,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "kBHxyGQ6aVmn",
    "outputId": "3c814668-e777-4889-fc66-211cdd62819f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "image_transforms = {\n",
    "    'train': v2.Compose([\n",
    "    v2.Pad(4),\n",
    "    v2.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.5, 1.25), shear=5, interpolation=v2.InterpolationMode.BILINEAR),\n",
    "    v2.ColorJitter(brightness=(0.1, 0.5), contrast=(0.1, 0.5)),\n",
    "    v2.Lambda(lambda x: x.convert(\"RGB\")),\n",
    "    v2.Resize((132, 132)),\n",
    "    v2.RandomCrop(size=(128, 128)),\n",
    "    v2.ToImage(),\n",
    "    v2.ToDtype(torch.float32, scale=True), # transform.ToTensor()\n",
    "    v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "    ]),\n",
    "    'test': v2.Compose([\n",
    "    v2.Lambda(lambda x: x.convert(\"RGB\")),\n",
    "    v2.Resize((132, 132)),\n",
    "    v2.RandomCrop(size=(128, 128)),\n",
    "    v2.ToImage(),\n",
    "    v2.ToDtype(torch.float32, scale=True),\n",
    "    v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "    ])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2019,
     "status": "ok",
     "timestamp": 1714379607928,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "UnQ5ZAa-bKy_",
    "outputId": "09be2540-c910-45ce-d91f-4393c7257604"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "route = os.getcwd()\n",
    "print(route)\n",
    "\n",
    "train_dataset = datasets.MNIST(root=route,\n",
    "                                train=True,\n",
    "                                transform = image_transforms['train'],\n",
    "                                download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root=route,\n",
    "                              train=False,\n",
    "                              transform = image_transforms['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1714379607928,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "yLguYFq2dvfI",
    "outputId": "a5aafaa8-b1fe-46c2-babb-df732cea1919"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:  48000\n",
      "Valid size:  12000\n",
      "Test size:  1500\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "VALIDATION_FRACTION = 0.2\n",
    "TEST_SAMPLE = 1500\n",
    "\n",
    "n_sample = train_dataset.__len__()\n",
    "n_sample_test = test_dataset.__len__()\n",
    "\n",
    "if VALIDATION_FRACTION is not None:\n",
    "  num = int(VALIDATION_FRACTION * n_sample)\n",
    "\n",
    "  train_indices = torch.arange(0, n_sample  - num)\n",
    "  valid_indices = torch.arange(n_sample  - num, n_sample )\n",
    "  test_indices = torch.arange(0, TEST_SAMPLE)\n",
    "\n",
    "  indices = torch.arange(0, n_sample)\n",
    "  shuffled_tensor = indices[torch.randperm(indices.size(0))]\n",
    "\n",
    "  indices_test = torch.arange(0, n_sample_test)\n",
    "  shuffled_tensor_test = indices_test[torch.randperm(indices_test.size(0))]\n",
    "\n",
    "  train_sampler = SubsetRandomSampler(shuffled_tensor[train_indices])\n",
    "  valid_sampler = SubsetRandomSampler(shuffled_tensor[valid_indices])\n",
    "  test_sampler = SubsetRandomSampler(shuffled_tensor_test[test_indices])\n",
    "\n",
    "  print('Train size: ', len(train_sampler))\n",
    "  print('Valid size: ', len(valid_sampler))\n",
    "  print('Test size: ', len(test_sampler))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1101,
     "status": "ok",
     "timestamp": 1714379612965,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "iSwkLHsad9Q_",
    "outputId": "067e4986-b946-4869-b67b-ff5e838d3afb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of Train DataLoader: 1500\n",
      "Size of Valid DataLoader: 375\n",
      "Size of Test DataLoader: 1500\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "NUM_WORKERS = 8\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset,\n",
    "                          batch_size= BATCH_SIZE,\n",
    "                          num_workers=NUM_WORKERS,\n",
    "                          drop_last=True,\n",
    "                          sampler=train_sampler)\n",
    "\n",
    "valid_loader = DataLoader(dataset=train_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          num_workers=NUM_WORKERS,\n",
    "                          sampler=valid_sampler,\n",
    "                          shuffle=False)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset,\n",
    "                          batch_size=1,\n",
    "                          num_workers=NUM_WORKERS,\n",
    "                          sampler=test_sampler,\n",
    "                          shuffle=False)\n",
    "\n",
    "print(\"Size of Train DataLoader:\", len(train_loader))\n",
    "print(\"Size of Valid DataLoader:\", len(valid_loader))\n",
    "print(\"Size of Test DataLoader:\", len(test_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 243
    },
    "executionInfo": {
     "elapsed": 1441,
     "status": "ok",
     "timestamp": 1714379619984,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Y2CMzP-DeHfW",
    "outputId": "9f300745-bcda-4c7f-8e79-063a0f54cf68"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image batch dimensions: torch.Size([32, 3, 128, 128])\n",
      "Image label dimensions: torch.Size([32])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the dataset\n",
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "for batch in valid_loader:\n",
    "    images, labels = batch\n",
    "    # print(np.unique(images))\n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break\n",
    "\n",
    "fig, ax = plt.subplots(1, 4)\n",
    "\n",
    "for i in range(4):\n",
    "  img = v2.ToPILImage()(images[i])\n",
    "  ax[i].imshow(img)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "executionInfo": {
     "elapsed": 744,
     "status": "ok",
     "timestamp": 1714379623939,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "4ELlFTu3YPdM",
    "outputId": "3049ccbc-dc5c-4865-a81f-f63f7aa1d749"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image batch dimensions: torch.Size([1, 3, 128, 128])\n",
      "Image label dimensions: torch.Size([1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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LAECC6ONFh2D7Tf2+VtsPW1JS4uLS0tJQudGjR7v4kksucfGgQYNC5fr375/yenF9vHYlLNsfbacCSeHpO3YFKRv7rzdu3OjiuD7eVatWudhOGbLnS+E+Xvt9/H5mAK3DEy8AAAmi4QUAIEGkmtEh2FSzXTFKCqeGBw4c6OIBAwaEytlU87Bhw1z88Y9/PPJ6ZWVlLvanJ9nXNrYrUvmpZrv/rV1pyk4ZksLp4HXr1rl4xYoVoXJR6WWbgt6+fXvonLq6upSxvyoWgNbhiRcAgATR8AIAkCBSzegQ4kY19+vXz8V2RarLL788VG7EiBEuHjlypIvTHdXsr0gVtemCfd8fKWxHNe/Zs8fF/sjjN954w8U2bVxbWxsqt2TJEhfbEc+2XENDQ+gcm1KOWkkLQOvxxAsAQIJoeAEASBANLwAACaKPFznHTreJ2wzeHrPTdexKVVK4T/biiy928ahRo0LlbL+uLef38Z5//vku7t2794euq41bWlpC5aKmE/l9vFH9tf50IlvOTi2y/cJ+361fpw96H8CHwxMvAAAJouEFACBBpJqRE6LSxv6m9lEb2dt08vDhw0PnXHbZZS62m91fddVVoXKDBw9OeT2bTvbrEJdeToe/cpWdvmM3KPBXrnrzzTddbNPLfrn6+vqU17bflxQykCyeeAEASBANLwAACSLVjJxjU7n+CGWb9rXH/uEf/sHFdi9d/7VNO9tYCm+aYFek8uvgb4ZwUmvSzn6aNyrVbDdFkMIbI6xfv97FfqrZXsOuiuWnuOPqBCCzeOIFACBBNLwAACSIhhcAgATRx4ucEDWdyJ/KU1FR4eLy8nIX25Wm/L5bO4XIbmrvl7MrUhUVFaWMpdZNJ4oql+50In+z+t/85jcuXrt2rYv3798fKmevYa9t+3HT7dOl7xfIDJ54AQBIEA0vAAAJItWMnGBTsXHTiWx62a40ZacM2dSyFF6hypbzpx3Z72U3ru/atWuonD12uitXpTudyJ8mFDWdyJ7vv7abIbByFZA9PPECAJCgNm94H3nkEeXl5Wnq1KnuvcOHD2vy5Mnq1auXunfvrgkTJoTWlAUAoKNq01Tz8uXL9V//9V+npPTuvfde/eEPf9Bzzz2nwsJCTZkyRbfccov+8pe/tGV10E7Y1O5HPvKR0DGbDrZpZ7upgX3ff21jO4pZknr27JmyPummk/2UbVQ6175vV5OSpIaGBhfbEco27SxJu3fvdvGePXtSXluSjh49mjImvQxkT5s98R48eFATJ07Uz3/+89A/nk1NTfrFL36hH//4x/rkJz+pESNG6IknntBrr72mJUuWtFV1AADICW3W8E6ePFk33HCDqqqqQu/X1tbq2LFjofcHDx6s8vJy1dTUpLzWkSNH1NzcHPoCAKA9apNU87PPPquVK1dq+fLlpxyrq6tTly5dTlmUoLi4WHV1dSmvN3PmTD344INtUVXkoM6d/+9jaUcQS+ERzwUFBS4uLCx08RVXXBE6x6au7bVPd0Sy7/Dhw5Gv7Yhi+/6OHTtC59i/gaamJhf/8pe/DJWLShv7KWRSykDuyfgT786dO3XPPffo6aefPmUaRmtNnz5dTU1N7mvnzp0ZuS4AAEnLeMNbW1urffv26fLLL1fnzp3VuXNnLVq0SLNnz1bnzp1VXFyso0ePqrGxMXRefX19aCs2Kz8/XwUFBaEvAADao4ynmq+99lr99a9/Db13xx13aPDgwbrvvvvUt29fnX322aqurtaECRMkSRs2bNCOHTtUWVmZ6eoAAJBTMt7w9ujRI7RgvSSde+656tWrl3v/zjvv1LRp09SzZ08VFBToa1/7miorK0/pm8OZw/a32n5Yv7vCbqBgMx829jeqt9eIW3XqdPt8bb+rFJ4qZDM89v1du3aFzrHThuzUIn9Fqtb08dLfC+SGrCwZ+ZOf/ESdOnXShAkTdOTIEY0bN04/+9nPslEVAAASlUjD+8orr4Red+3aVXPmzNGcOXOS+PYAAOQMNklAzombTpROqtlOOZKSm04Ul2q2aWObTvZTzXY6UVyqOWpVLNLJQO5jkwQAABJEwwsAQIJINSPntGZU85gxY1KW8a+RZKrZpofthgd2U4O4latsqtmufOV/r7i9dUk9A7mHJ14AABJEwwsAQIJoeAEASBB9vMgov980qk/VL2enANkN6e3G91J48/p+/fq52G5w76/5bXfCsv2/fh2ipuWkuxqUv1n9tm3bXLxx40YXr1+/3sWrV68OnbN27VoX2/nv/rXtDkdMJwLaF554AQBIEA0vAAAJItWMjLKpZSm88pSN/WlCdmpQaWmpi20KWZIuuOACF3/qU59y8aBBgyLP6d27t4ttSjsu1Rw1Xcc/ZmM7TUgKp5RXrlzp4hUrVri4trY2dM6yZctcbNPTe/fuDZWz04vYJAFoX3jiBQAgQTS8AAAkiFQzMspPNduUsh1R7K8uZdPBdlSyHcUsSRUVFS4ePHiwi22q2R8JbV+nm2q26WV/1Sg7otjG/oYHNtVs08uLFi1y8UsvvRQ6Z8uWLS5+6623XGxXtPLrlG6qGUBu4IkXAIAE0fACAJAgGl4AABJEHy8yyu83tVOIbL+uXU1KCq9WZft446YT2X5dG9t+XCk8VSlu5Srbr2unCdl+XCm865CN/elEdjqQnTY0f/58F9v+Xim8I5GN7e5Gfp386U5R6O8FcgNPvAAAJIiGFwCABJFqRkbFTSeyKWCb/pWkj370oy62qebzzjsvVO7WW291cdR0Ir8O9nXUpg1S9MpVcanm5uZmF6c7nejll1928cKFC0Pn2LRxVOy/JoUMtC888QIAkCAaXgAAEkSqGRnlb35gRytH7aUrhUcrDxgwIOU5/vXs94pLIfuvo9iUcmNjo4v90cp2FSl7zI5ilqTdu3e7eN68eS62qWo/hZzunsAA2i+eeAEASBANLwAACaLhBQAgQfTxIqP8Pl6765Dt17XTfyRp6NChLh44cKCLq6qqQuVsH2/UKlT+dCJ7LK6/104hsqtG+X28mzdvdrHdTcjv492xY4eL7cpTdmehdPt4AXQcPPECAJAgGl4AABJEqhkZ5W9wHzWdyK46JUmXXHKJi+10orKyssjrne50Iv/9qOlE/opUNtX8xhtvuHjTpk2hcosXL3axTTXHTSey2OAe6Jh44gUAIEE0vAAAJIhUMzLKH9Xcq1cvF9u9df1RzcOGDXPx5Zdf7uK4latsWtsfyRwlE6Oa7ejlv/71ry62GyFI4VHNb7/9tovjRjVHIbUMdBw88QIAkCAaXgAAEkSqGRnlp5qLiopcbPfZ9TdJuPrqq11sU9L2HP96Xbp0cXG6GyHEsalmu8+u3RRBCqeQ7Z6727ZtC5Wz59lR0nb0NClk4MzDEy8AAAmi4QUAIEE0vAAAJIg+XmSU39dq+3y7d+/uYttXK4U3U7DH/JWwbL9uulOI0mWn9th+WDv9Rwr319oVqez7/nn2eulOIQLQMfHECwBAgmh4AQBIEKlmZJSf/k031WxXpCooKEh5jn+9TEwhsuzUHju1yG5qIElNTU0uXrBggYv9VLM9z16PKUTAmY0nXgAAEkTDCwBAgkg1I6Nam2puzajmTKeao0Y1+6nmdEc1HzhwIOX1GNUMnNl44gUAIEE0vAAAJIiGFwCABNHHi4yK6+O1/bV2+pAU3cebi9OJ0u3jZToRgFR44gUAIEE0vAAAJIhUMzLKT//a1LOdCmRjKZxCjtsIwV6/LVPNdsqPP/3Hpo3tNCH7ftw1SDV/OHG/Z3vMflbiPoeZ/txki/0cRX12/c9aulPZ+Iy2rY7xCQQAoJ1ok4Z39+7d+vznP69evXqpW7duGjZsmFasWOGOB0GgBx54QKWlperWrZuqqqq0adOmtqgKAAA5JeOp5r/97W8aO3asrrnmGv3pT3/SRz/6UW3atEkf+chHXJkf/ehHmj17tp566ilVVFRoxowZGjdunNatWxdKOQI4c0SlgONSw/bfi6hYCo+o7yj/xkR1eUTtA+2fE5Wq9pF2zryMN7w//OEP1bdvXz3xxBPuvYqKChcHQaBZs2bpO9/5jm6++WZJ0i9/+UsVFxdr3rx5uu222zJdJQAAckbGU82/+93vNHLkSH3uc59Tnz59NHz4cP385z93x7du3aq6ujpVVVW59woLCzVmzBjV1NSkvOaRI0fU3Nwc+gIAoD3KeMO7ZcsWPfrooxo4cKBeeOEFfeUrX9HXv/51PfXUU5Kkuro6SVJxcXHovOLiYnfMN3PmTBUWFrqvvn37ZrraAAAkIuOp5paWFo0cOVI/+MEPJEnDhw/XmjVr9Nhjj2nSpEmtuub06dM1bdo097q5uZnGF2jn0p0mFDeFzE49s6ucFRQUhMrZ1dD8Y+2VXRnNxjYj6GcHo6YdpYv+3szI+BNvaWmphg4dGnpvyJAh2rFjhySppKREklRfXx8qU19f74758vPzVVBQEPoCAKA9ynjDO3bsWG3YsCH03saNG9WvXz9JJwZalZSUqLq62h1vbm7W0qVLVVlZmenqAACQUzKear733nt15ZVX6gc/+IFuvfVWLVu2TI8//rgef/xxSVJeXp6mTp2q73//+xo4cKCbTlRWVqbx48dnujoA2ol00st+qtlODbKZMLvphv/6ox/96OlXNgf87W9/c3FDQ0PKMn462Z9edFLcCleklzMv4w3vqFGjNHfuXE2fPl0PPfSQKioqNGvWLE2cONGV+eY3v6l3331Xd999txobG3XVVVdpwYIFHWZ+HQAAUdpkrebPfvaz+uxnPxt5PC8vTw899JAeeuihtvj2AADkLDZJAJBzotLL/qYZUaOa/f2eS0tLXVxWVpaxemZT1N7UdnUqu4qVdOrmJCf5KemOspFEruLuAgCQIBpeAAASRKoZSCFuJK1Nd0bFUvT+w3H7EreG/b7+AEV7/bhNBKyoUaxxo1vjfqao/ZX9cunsrevfYztC+fzzz08Z+687SqrZrvS3a9cuF59cM8F/Xzqxc9xJjY2NLrYLcEjRi3D4o6LZaKF1eOIFACBBNLwAACSIhhcAgATRxwv8XbpTWKL6KeP6Ntuyj9du8m43A/BfR8W+qMXz4xbVt1N5CgsLI4/Z2O9nbk0fb58+fVxs+3HLy8tD5c4777yU5doz23+7fft2F2/ZsiVlLEnbtm1zse0j3rt3b6icPRbXjxu1wpVfjj7eMJ54AQBIEA0vAAAJItUMfAB/OlFU6tNPG0cdS3daT7ps+tbfHMButWlXb4raglOK3rM1Ln1ov6+/apR9bcv523uebqrZ7tHtp5NvuukmF/tp6PbKppGvvfZaF2/atMnFmzdvDp1jf+/2mP85tL9bO9XIn05kfzf2s2JXz/LLkXbmiRcAgETR8AIAkCBSzcDftddRzXGp5n79+rm4f//+KWOfTRlGxVI4ZWhXg/JXhrLpTTu62B9Z3ZpUs7329ddf72I/nZzuz96ebNy40cX2Z7Jp9rFjx4bOWbt2rYvtaHg//Ws3V9i3b5+L7WpXceI+K+CJFwCARNHwAgCQIBpeAAASRB8v2lTUajZ+H1BUX2KSK+DYvsSo6T9SuE/VTonxp1rY11E/XyY2HLf9nLZPV5IGDBjg4kGDBqWMfVF19aeI2N+F7VP162D7HK+++moX+9OOTreP19bB78eNO9Ze2fsaNZ3L70cfNWqUi2tra13sf3btFKK33347spztC7axz57HjkY88QIAkCgaXgAAEkSqGRnlpyNtyqqhocHFdhF2Kbx5txWX5o1btL817LVtGu/iiy8OlfvYxz7m4pqaGhfbn89/HRXbTcZby6YW/Wk09rVd2Slu9aaoVL//u7VsynfkyJGhY3Z1KVsubjpROrEUTrHa7+OvipWJFcJyjf2Z7GfAfnbjfmd2qpGdZiSFPx+DBw92sb/pQtRGC3v27AmVs1OSolbC8usad6y944kXAIAE0fACAJAgUs3IqLhU8/79+13sp5rt3qJ2RLFN/0rhlFqmU812JZ+4VLNNd95www0utj+rFE4j22P2/ffee+80anxCjx49XGzTrf7rT3/605HlrHRHottj9vfij1a2KWV7zN5vKT6lfJI/qtl+Puy1/VRzJlYIyzU21WxT7vZv0L+P9p7b+/WpT30qVM52/dh469atoXJ29az169enjCVpw4YNLrb/DkT9XUjpd3O0RzzxAgCQIBpeAAASRMMLAECC6ONFRqU7nchOPZCkT3ziEy6O223Hro6T1HQiv5/M9o3ZlaHiVq6Kiu+6667TqPEJX/ziF13s94nbvk4b++WsqBWD4lYRs9fz+27taxvHTfGJ6uP137fXiPo+H/S92quo6UT2Hvm/Z9u3HzVlSAr3w9p44sSJoXIrVqxw8fLly1PWTQr/u2D76e2/Cf7ny/976kh44gUAIEE0vAAAJIhUMzLKT/+2ZuUqm172N1XPRqrZnx5j08u2Pn6qLGoFqLhNIFrDpu786Tb2tZ1S45dLR1xd061D1OYHceLKRW2gEFeHjsKm0+3PZz/H/rQxm/KN6v6IO+avXGX/Pu2Kbi+++GKonE1XR/3d+nXIxKpuuYonXgAAEkTDCwBAgjpe/gVZFbevp0032ZWqJGn79u0utukxP9Vs009R+9360h0ha1OxNvYX80e8dNPncaOk092ztTV1ilvZyUpnJa1sikqt+yO6o8Td46hj/iyDqO6joUOHhspt27bNxVF78/r7+TY2Nro43fvfXvbwzb1PEwAAHRgNLwAACaLhBQAgQfTxIqP8laveeecdF9s+IH9agt3NxG7Qbaf1SOENtW1/r+0f8qeORE058eViP157EddfmM60Kv+1/RxFxVL604lsubhpVelMfeqIU5PS/ez7uzzZ1dBKSkpc3K9fv1C5RYsWuTjq83DgwIHQOXZMSNw9b+2YgmziXxoAABJEwwsAQII6Xs4EWeVPJ7JTAmyqOW4Rezu16JprrgmVs+kne22bavbTYVFpqo64OXqSotLLcSnkqHSyFP4dRsX+5ysqhez/bu1r+1nzF/OPukZH/KzYe+enYaNSz/59sFPtbKr5ggsuCJUbMmSIi1etWuXiqOmGkrRnz56U3zeuK8PKpdSyjydeAAASRMMLAECCSDUjo/z0oU0H+3vwRrFpKn+Fq3RGNfspJptO7IgjUnNBXKo5alRy3KL4NrbpSH/hfPv7jNqbN+5Y3IpNHX0kc7qi0vlS9Kjm/v37h8rZ39ugQYNcHLeanb22vf9xv7N03s8FPPECAJAgGl4AABJ05uZP0CbiRjXblJVfzqYn7QLrfnrapqai0pG+qJRhuiM58cFaszCGvyh+1N7N9jNkYyn8O7P70No0pRSfXo4SN5K2o8n0Ahp+qtl+BgYOHOjil19+2cWbN28OnWN/n/bv1v98RR3zf6Zc+h3yLw0AAAmi4QUAIEE0vAAAJIg+XmSU3/9i+3Lj+mFtP41d4crGknTjjTe62E4/sP1L/sb19nXU9BNkTtzKQnErV9nPiu3/tX35fh9vVJ99XJ9l3IpUUXXFCf59jfp7sv2zUrgv2Ma27z1uFbFMbJKQS3jiBQAgQTS8AAAkiFQzMipu1aK41aVsWslOGfJTzTa9vG3bNhf37t3bxTbt7LPpLH/KCVovLt0XNdUo7rMSlXb2uyui9syN2yjDfp+4qU9xewzjhKiUflxKOp1Yks4+++wP/D7p1ifXtI9aAgDQQWS84T1+/LhmzJihiooKdevWTRdeeKG+973vKQgCVyYIAj3wwAMqLS1Vt27dVFVVpU2bNmW6KgAA5JyMp5p/+MMf6tFHH9VTTz2liy66SCtWrNAdd9yhwsJCff3rX5ck/ehHP9Ls2bP11FNPqaKiQjNmzNC4ceO0bt06Rpq2c35Kzh+5elLc6jNxqebdu3e7+FOf+pSL+/TpE1mnqPQy6cMP1prUXdwi9nGpZptebk2qOW7Eun1tr+1/PqNSzUjNfj6i4rhjUV0Frb12Ou/ngow3vK+99ppuvvlm3XDDDZJOLB32q1/9SsuWLZN04ml31qxZ+s53vqObb75ZkvTLX/5SxcXFmjdvnm677bZMVwkAgJyR8f8SXHnllaqurtbGjRslSa+//rpeffVVfeYzn5Ekbd26VXV1daqqqnLnFBYWasyYMaqpqUl5zSNHjqi5uTn0BQBAe5TxJ977779fzc3NGjx4sM466ywdP35cDz/8sCZOnCjp/1KHxcXFofOKi4tPSSueNHPmTD344IOZrioAAInLeMP7m9/8Rk8//bSeeeYZXXTRRVq9erWmTp2qsrIyTZo0qVXXnD59uqZNm+ZeNzc3q2/fvpmqMjIobrUf22cW1fcrSW+//baL051OZPt443ZQseXow8ucuKk3Uf2m/megNdOJolahiuvjjZtOFNUfzWflg0X13fqvo/p10z3H/53F9f/mqow3vN/4xjd0//33u77aYcOGafv27Zo5c6YmTZrk5ljW19ertLTUnVdfX6/LLrss5TXz8/OVn5+f6aoCAJC4jP/34NChQ6f8r+Oss85y/2OsqKhQSUmJqqur3fHm5mYtXbpUlZWVma4OAAA5JeNPvDfeeKMefvhhlZeX66KLLtKqVav04x//WF/60pckSXl5eZo6daq+//3va+DAgW46UVlZmcaPH5/p6iBhcdOJ4tLLNn3UmpWr7GpV/opUNr0ct3oWMqO104kymWq2i+/714v7TJJe/nByeTpRLst4w/vTn/5UM2bM0Fe/+lXt27dPZWVl+td//Vc98MADrsw3v/lNvfvuu7r77rvV2Nioq666SgsWLGAOLwCgw8t4w9ujRw/NmjVLs2bNiiyTl5enhx56SA899FCmvz0AADmNTRIAnLa4DQpsJisq7SyF0762nE1H+ilk+71sF4O/J3PUMb+c3UfW1rs9pjM/jHTT6jbtL0l79uxx8fr16128evXqUDn7ura21sUnF1aSpM2bN4fOqa+vd3FrVhvL5a6Cjv1pAgAgx9DwAgCQIBpeAAASRB8vgNMWt6F5a2Yr2D5Ve77fJxvV/2v7auOO+X3G9rXtP/Z/po4grg80qq/U9rVK4el+69atc/GSJUtC5RYtWuTihQsXuvjkmv7SqX28+/btc3HcFLC4cQO5iideAAASRMMLAECCOl7+BEDGpDslI246UVS5uEXxo9LL/nSWqJR03JSmdDdTsOXOtOlEUanmdKcT+Vu8/u53v0tZbu/evSmvJYVTzTbFnW5dmU4EAAAk0fACAJAoUs0ATltcCtkes+k/P81rRxRHbaYQN2o1bsH9dPeATWcx/44iLi17uqlmf1TzCy+84GI7etlez7+2fR21qpmPVDMAADgFDS8AAAmi4QUAIEEdr+MCHUpc39Px48ddnO4G63Er4PivP6y4KSdxG3l3BB3958u0TPRTRu3KE/e3YMvF9ZtGXXvXrl2hcra/9s0333Txf//3f4fK2V2I/GucifgLAQAgQTS8AAAkiFQzco5Nc/npX7uCjZ1uMGLECBc3NjaGzrGv9+/f72K7Mo5/PSvd1Km/WlLUIvsdffF9nJBuOjmuyyOua6S5udnF9jNuY1vGf22vF9c9Y4/5Gxm89tprLrabJNjNE1Jd/0zHEy8AAAmi4QUAIEHkuZBz4lLNNh188OBBF0el3SSpoaHBxTbV7KfD7D6tcaN0o475e7valZnsMdLLsJ/xuBWb4lZ2sqtG2diOGvZHENvPfFwa23bp2Lpu27YtVO4vf/mLi1955RUX280PUl3/TMcTLwAACaLhBQAgQeS8kHNaM6rZpp3TTTXHjWpuzUL6BQUFoXL2ddR+tXF716J9SHcx/qhFLuxnWgp3m0TFUjjtazco2LhxY8r3JWnr1q0pv69fh6hjfvfMq6++6mI7qtn/22JUcxhPvAAAJIiGFwCABNHwAgCQIPp4kXPiVvWx/U2tmU5k+56KiopC5Wwfb9ym6lH9v35fX1S/bi5v0I3MidvgI66P136u7WfXxpK0ZcsWF69Zs8bFK1ascPGqVatC58ybN8/F9vPu1yFqGpP/t7V7924X26lLfl8w04nCeOIFACBBNLwAACSIVDNyjk17NTU1hY7ZVLFNbb311lsuLi8vD53Tr18/F0dtmCCFV66yKWR/OlHUsT59+oTK2de9e/dO+b6f7rarXUXFUvobLbTXvXGj0vFx+8bGTY+xqU7/2OlKt672te0a8dOy9rWN/Sk6b7zxhotXrlzpYptetmlnKZySjks1v/feeynL2TS4FP57sqlwvxzdK2Ht868SAIB2ioYXAIAEkWpGTrCpKJum8tNw5557bsrzbcprx44doWPV1dUutmne4uLiULmojQz81aWiUs1lZWWhciUlJSljW86+L4VT0lHpaf+YvzlDOnIxBW0/A+nEUnTXgd+NELV6WSZErcoUl2q29fY3Moja5MBuhCBJmzZtcrHdJ9emne0qVpK0c+dOF9v0ctyewHEpfPu3Gnc9Us1huffXBwBAB0bDCwBAgmh4AQBIEH28yAlRfbz+FArr3XffdbHtC/an6NjXdseguXPnhsrZvlw7fcefrhM1lcefxnTeeee52Pbr2nL+ORdccEHK2GenPtn6xPXdtqZfNxN9wa2ZbmP7TaPel8LTcmwfqF3VSQrv5ONv5n66WtPHa/uc/fps37495TG/3B//+EcX25/d/i3YlaX8Y7be/s8QtUNY3Epy9PGmjydeAAASRMMLAECCSDUjJ0Slmn12FR07LcROqfFXeYo65pfLz89PecyfThS1kX3//v1D5f70pz+52KaUbbkLL7wwdI5Nndp7YlPLUngakn8sHe1pOpFNW/op0ahpOf4G8HbFJrviUyakO/XJvn777bddbKcCSdL8+fNTHvPL/e1vf3Ox/dxExVL4byvdukal/f1jcb8nhOXeXx8AAB0YDS8AAAki1YycEJVqtqllKTzK2aZLWzOa13/fjlCOSzVHHRswYEConE0j2xHKttyf//zn0Dljx451sU0h+6ti2RGkNq3XnjdMiEpvRqUzpXAqNS7VbDcLqKmpOf3KGlGp2Lj07UsvveRiv6729YYNGyLL2c9Aa0aOpyupc84kuf2XCABAB0PDCwBAgmh4AQBIEH28yDlx/WRtyfaP2r5Ev980alUrfycl2/8bNT3G77NcunSpi4cPH+5iu9KRFF6pyO5c5Nc1qh88ri846vzWas2m9lFTU/z7ZafYrF692sV2hx5Jev3111OWy4S46TaWPWanwvkrUtnVpmw5f2oQU3baL554AQBIEA0vAAAJItUM/F1UittP6dl0py3npwLt1KeoVLM/XcpOEXnhhRdc7E9VGjhwoIt79uzpYj81HJVqTjeFnAubJMQt5m9T7m+99ZaL/Q3gly1b5mJ/BajTle4mCZZdcWvv3r2hY7Zbwf98oGPgiRcAgATR8AIAkKC8IAiCbFfiw2publZhYaEkKS8vL8u1QUeR7gjgqHJ2r1//dVRs08SSVFpa6mK7EYK/cpU9ZjeBSHcFr1xcxSqdFaD89K1Ny9pR5f4+zvaYn9rNpHRHNdsuBZt29l9HxR/0vZC8k01pU1PTKf8W+HLvrw8AgA7sQze8ixcv1o033qiysjLl5eVp3rx5oeNBEOiBBx5QaWmpunXrpqqqKm3atClUpqGhQRMnTlRBQYGKiop05513xm4FBwBAR/GhRzW/++67uvTSS/WlL31Jt9xyyynHf/SjH2n27Nl66qmnVFFRoRkzZmjcuHFat26dW3hg4sSJ2rt3r1588UUdO3ZMd9xxh+6++24988wzp/8TAa0UlbqLW6jApmz9Uc02nWiP2YU1/D2B7Yhbu0mCv+euTS/7mzikU9dclO7oZ8ve47jNNezrpP6TH1dv+5myP4MUHvXuH0PHcFp9vHl5eZo7d67Gjx8v6cTTbllZmf7t3/5N//7v/y7pRL67uLhYTz75pG677Ta9+eabGjp0qJYvX66RI0dKkhYsWKDrr79eu3btOqUvKxX6eNHWWrPzkd8ARjWwcQ2vbVBpeOPfl86shtdftYs+3tyStT7erVu3qq6uTlVVVe69wsJCjRkzxm3FVVNTo6KiItfoSlJVVZU6deoUWi7POnLkiJqbm0NfAAC0RxlteE+OHCwuLg69X1xc7I7V1dWF1paVTowa7dmz5ylr3Z40c+ZMFRYWuq++fftmstoAACSmXaxcNX36dE2bNs29bm5upvFFm2rNRg1+KhAAUsnoE+/JuYX19fWh9+vr692xkpKSU+bYvf/++2poaAjNTbTy8/NVUFAQ+gIAoD3KaMNbUVGhkpISVVdXu/eam5u1dOlSVVZWSpIqKyvV2Nio2tpaV2bhwoVqaWnRmDFjMlkdAAByzodONR88eDA05WHr1q1avXq1evbsqfLyck2dOlXf//73NXDgQDedqKyszI18HjJkiK677jrdddddeuyxx3Ts2DFNmTJFt912W1ojmgEAaM8+9HSiV155Rddcc80p70+aNElPPvmkgiDQd7/7XT3++ONqbGzUVVddpZ/97GcaNGiQK9vQ0KApU6bo97//vTp16qQJEyZo9uzZp0yZiMJ0IgBALvkw04lYqxkAgNPEWs0AAOQoGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAk6EM3vIsXL9aNN96osrIy5eXlad68ee7YsWPHdN9992nYsGE699xzVVZWpi984Qvas2dP6BoNDQ2aOHGiCgoKVFRUpDvvvFMHDx487R8GAIBc96Eb3nfffVeXXnqp5syZc8qxQ4cOaeXKlZoxY4ZWrlyp559/Xhs2bNBNN90UKjdx4kStXbtWL774oubPn6/Fixfr7rvvbv1PAQBAO5EXBEHQ6pPz8jR37lyNHz8+sszy5cs1evRobd++XeXl5XrzzTc1dOhQLV++XCNHjpQkLViwQNdff7127dqlsrKyD/y+zc3NKiwsdHUAACCbTjalTU1NKigoiC3b5n28TU1NysvLU1FRkSSppqZGRUVFrtGVpKqqKnXq1ElLly5NeY0jR46oubk59AUAQHvUpg3v4cOHdd999+n22293/wOoq6tTnz59QuU6d+6snj17qq6uLuV1Zs6cqcLCQvfVt2/ftqw2AABtps0a3mPHjunWW29VEAR69NFHT+ta06dPV1NTk/vauXNnhmoJAECyOrfFRU82utu3b9fChQtD+e6SkhLt27cvVP79999XQ0ODSkpKUl4vPz9f+fn5bVFVAAASlfEn3pON7qZNm/TSSy+pV69eoeOVlZVqbGxUbW2te2/hwoVqaWnRmDFjMl0dAAByyod+4j148KA2b97sXm/dulWrV69Wz549VVpaqn/6p3/SypUrNX/+fB0/ftz12/bs2VNdunTRkCFDdN111+muu+7SY489pmPHjmnKlCm67bbb0hrRDABAe/ahpxO98soruuaaa055f9KkSfp//+//qaKiIuV5L7/8sq6++mpJJxbQmDJlin7/+9+rU6dOmjBhgmbPnq3u3bunVQemEwEAcsmHmU50WvN4s4WGFwCQS3JqHi8AAPg/NLwAACSIhhcAgATR8AIAkCAaXgAAEkTDCwBAgmh4AQBIEA0vAAAJouEFACBBNLwAACSIhhcAgATR8AIAkCAaXgAAEkTDCwBAgmh4AQBIEA0vAAAJ6pztCrTGyQ2H/RgAgGxKp01ql0+8Bw4cyHYVAAA4RTrtU17QDh8ZW1patGfPHgVBoPLycu3cuVMFBQXZrlbWNDc3q2/fvtwH7oMk7sNJ3IcTuA8ntPV9CIJABw4cUFlZmTp1in+mbZep5k6dOun8889Xc3OzJKmgoOCM/kCdxH04gftwAvfhBO7DCdyHE9ryPhQWFqZVrl2mmgEAaK9oeAEASFC7bnjz8/P13e9+V/n5+dmuSlZxH07gPpzAfTiB+3AC9+GEXLoP7XJwFQAA7VW7fuIFAKC9oeEFACBBNLwAACSIhhcAgATR8AIAkKB22/DOmTNH/fv3V9euXTVmzBgtW7Ys21VqUzNnztSoUaPUo0cP9enTR+PHj9eGDRtCZQ4fPqzJkyerV69e6t69uyZMmKD6+vos1TgZjzzyiPLy8jR16lT33plyH3bv3q3Pf/7z6tWrl7p166Zhw4ZpxYoV7ngQBHrggQdUWlqqbt26qaqqSps2bcpijTPv+PHjmjFjhioqKtStWzddeOGF+t73vnfKRiod7T4sXrxYN954o8rKypSXl6d58+aFjqfzMzc0NGjixIkqKChQUVGR7rzzTh08eDDBn+L0xd2HY8eO6b777tOwYcN07rnnqqysTF/4whe0Z8+e0DWych+CdujZZ58NunTpEvzP//xPsHbt2uCuu+4KioqKgvr6+mxXrc2MGzcueOKJJ4I1a9YEq1evDq6//vqgvLw8OHjwoCvz5S9/Oejbt29QXV0drFixIrjiiiuCK6+8Mou1blvLli0L+vfvH1xyySXBPffc494/E+5DQ0ND0K9fv+CLX/xisHTp0mDLli3BCy+8EGzevNmVeeSRR4LCwsJg3rx5weuvvx7cdNNNQUVFRfDee+9lseaZ9fDDDwe9evUK5s+fH2zdujV47rnngu7duwf/+Z//6cp0xPvwxz/+Mfj2t78dPP/884GkYO7cuaHj6fzM1113XXDppZcGS5YsCf785z8HAwYMCG6//faEf5LTE3cfGhsbg6qqquDXv/51sH79+qCmpiYYPXp0MGLEiNA1snEf2mXDO3r06GDy5Mnu9fHjx4OysrJg5syZWaxVsvbt2xdIChYtWhQEwYkP2dlnnx0899xzrsybb74ZSApqamqyVc02c+DAgWDgwIHBiy++GHziE59wDe+Zch/uu+++4Kqrroo83tLSEpSUlAT/8R//4d5rbGwM8vPzg1/96ldJVDERN9xwQ/ClL30p9N4tt9wSTJw4MQiCM+M++A1OOj/zunXrAknB8uXLXZk//elPQV5eXrB79+7E6p5Jqf4D4lu2bFkgKdi+fXsQBNm7D+0u1Xz06FHV1taqqqrKvdepUydVVVWppqYmizVLVlNTkySpZ8+ekqTa2lodO3YsdF8GDx6s8vLyDnlfJk+erBtuuCH080pnzn343e9+p5EjR+pzn/uc+vTpo+HDh+vnP/+5O75161bV1dWF7kNhYaHGjBnToe7DlVdeqerqam3cuFGS9Prrr+vVV1/VZz7zGUlnzn2w0vmZa2pqVFRUpJEjR7oyVVVV6tSpk5YuXZp4nZPS1NSkvLw8FRUVScrefWh3uxPt379fx48fV3Fxcej94uJirV+/Pku1SlZLS4umTp2qsWPH6uKLL5Yk1dXVqUuXLu4DdVJxcbHq6uqyUMu28+yzz2rlypVavnz5KcfOlPuwZcsWPfroo5o2bZq+9a1vafny5fr617+uLl26aNKkSe5nTfV30pHuw/3336/m5mYNHjxYZ511lo4fP66HH35YEydOlKQz5j5Y6fzMdXV16tOnT+h4586d1bNnzw57Xw4fPqz77rtPt99+u9udKFv3od01vDjxtLdmzRq9+uqr2a5K4nbu3Kl77rlHL774orp27Zrt6mRNS0uLRo4cqR/84AeSpOHDh2vNmjV67LHHNGnSpCzXLjm/+c1v9PTTT+uZZ57RRRddpNWrV2vq1KkqKys7o+4D4h07dky33nqrgiDQo48+mu3qtL9Rzb1799ZZZ511yijV+vp6lZSUZKlWyZkyZYrmz5+vl19+Weeff757v6SkREePHlVjY2OofEe7L7W1tdq3b58uv/xyde7cWZ07d9aiRYs0e/Zsde7cWcXFxWfEfSgtLdXQoUND7w0ZMkQ7duyQJPezdvS/k2984xu6//77ddttt2nYsGH6l3/5F917772aOXOmpDPnPljp/MwlJSXat29f6Pj777+vhoaGDndfTja627dv14svvhjaizdb96HdNbxdunTRiBEjVF1d7d5raWlRdXW1Kisrs1izthUEgaZMmaK5c+dq4cKFqqioCB0fMWKEzj777NB92bBhg3bs2NGh7su1116rv/71r1q9erX7GjlypCZOnOjiM+E+jB079pTpZBs3blS/fv0kSRUVFSopKQndh+bmZi1durRD3YdDhw6pU6fwP2NnnXWWWlpaJJ0598FK52eurKxUY2OjamtrXZmFCxeqpaVFY8aMSbzObeVko7tp0ya99NJL6tWrV+h41u5Dmw3bakPPPvtskJ+fHzz55JPBunXrgrvvvjsoKioK6urqsl21NvOVr3wlKCwsDF555ZVg79697uvQoUOuzJe//OWgvLw8WLhwYbBixYqgsrIyqKyszGKtk2FHNQfBmXEfli1bFnTu3Dl4+OGHg02bNgVPP/10cM455wT/+7//68o88sgjQVFRUfDb3/42eOONN4Kbb7653U+j8U2aNCk477zz3HSi559/Pujdu3fwzW9+05XpiPfhwIEDwapVq4JVq1YFkoIf//jHwapVq9xo3XR+5uuuuy4YPnx4sHTp0uDVV18NBg4c2O6mE8Xdh6NHjwY33XRTcP755werV68O/bt55MgRd41s3Id22fAGQRD89Kc/DcrLy4MuXboEo0ePDpYsWZLtKrUpSSm/nnjiCVfmvffeC7761a8GH/nIR4Jzzjkn+Md//Mdg79692at0QvyG90y5D7///e+Diy++OMjPzw8GDx4cPP7446HjLS0twYwZM4Li4uIgPz8/uPbaa4MNGzZkqbZto7m5ObjnnnuC8vLyoGvXrsEFF1wQfPvb3w79w9oR78PLL7+c8t+DSZMmBUGQ3s/8zjvvBLfffnvQvXv3oKCgILjjjjuCAwcOZOGnab24+7B169bIfzdffvlld41s3Af24wUAIEHtro8XAID2jIYXAIAE0fACAJAgGl4AABJEwwsAQIJoeAEASBANLwAACaLhBQAgQTS8AAAkiIYXAIAE0fACAJCg/w8oFucistLyHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the dataset\n",
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "for batch in test_loader:\n",
    "    images, labels = batch\n",
    "    # print(np.unique(images))\n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break\n",
    "\n",
    "img = v2.ToPILImage()(images[0])\n",
    "plt.imshow(img)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uvUWgi5slE3u"
   },
   "source": [
    "## Deep learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1714379637663,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "wav9xZallEdH",
    "outputId": "26436388-5aad-4912-9163-518266ccf40a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 32, 64, 64]             864\n",
      "       BatchNorm2d-2           [-1, 32, 64, 64]              64\n",
      "              ReLU-3           [-1, 32, 64, 64]               0\n",
      "            Conv2d-4           [-1, 48, 32, 32]           1,536\n",
      "       BatchNorm2d-5           [-1, 48, 32, 32]              96\n",
      "            Conv2d-6           [-1, 48, 64, 64]           1,536\n",
      "       BatchNorm2d-7           [-1, 48, 64, 64]              96\n",
      "              ReLU-8           [-1, 48, 64, 64]               0\n",
      "            Conv2d-9           [-1, 48, 32, 32]           3,456\n",
      "      BatchNorm2d-10           [-1, 48, 32, 32]              96\n",
      "             ReLU-11           [-1, 48, 32, 32]               0\n",
      "AdaptiveAvgPool2d-12             [-1, 48, 1, 1]               0\n",
      "           Conv2d-13              [-1, 8, 1, 1]             392\n",
      "             ReLU-14              [-1, 8, 1, 1]               0\n",
      "           Conv2d-15             [-1, 48, 1, 1]             432\n",
      "          Sigmoid-16             [-1, 48, 1, 1]               0\n",
      "SqueezeExcitation-17           [-1, 48, 32, 32]               0\n",
      "           Conv2d-18           [-1, 48, 32, 32]           2,304\n",
      "      BatchNorm2d-19           [-1, 48, 32, 32]              96\n",
      "             ReLU-20           [-1, 48, 32, 32]               0\n",
      "ResBottleneckBlock-21           [-1, 48, 32, 32]               0\n",
      "           Conv2d-22          [-1, 104, 16, 16]           4,992\n",
      "      BatchNorm2d-23          [-1, 104, 16, 16]             208\n",
      "           Conv2d-24          [-1, 104, 32, 32]           4,992\n",
      "      BatchNorm2d-25          [-1, 104, 32, 32]             208\n",
      "             ReLU-26          [-1, 104, 32, 32]               0\n",
      "           Conv2d-27          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-28          [-1, 104, 16, 16]             208\n",
      "             ReLU-29          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-30            [-1, 104, 1, 1]               0\n",
      "           Conv2d-31             [-1, 12, 1, 1]           1,260\n",
      "             ReLU-32             [-1, 12, 1, 1]               0\n",
      "           Conv2d-33            [-1, 104, 1, 1]           1,352\n",
      "          Sigmoid-34            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-35          [-1, 104, 16, 16]               0\n",
      "           Conv2d-36          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-37          [-1, 104, 16, 16]             208\n",
      "             ReLU-38          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-39          [-1, 104, 16, 16]               0\n",
      "           Conv2d-40          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-41          [-1, 104, 16, 16]             208\n",
      "             ReLU-42          [-1, 104, 16, 16]               0\n",
      "           Conv2d-43          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-44          [-1, 104, 16, 16]             208\n",
      "             ReLU-45          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-46            [-1, 104, 1, 1]               0\n",
      "           Conv2d-47             [-1, 26, 1, 1]           2,730\n",
      "             ReLU-48             [-1, 26, 1, 1]               0\n",
      "           Conv2d-49            [-1, 104, 1, 1]           2,808\n",
      "          Sigmoid-50            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-51          [-1, 104, 16, 16]               0\n",
      "           Conv2d-52          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-53          [-1, 104, 16, 16]             208\n",
      "             ReLU-54          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-55          [-1, 104, 16, 16]               0\n",
      "           Conv2d-56          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-57          [-1, 104, 16, 16]             208\n",
      "             ReLU-58          [-1, 104, 16, 16]               0\n",
      "           Conv2d-59          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-60          [-1, 104, 16, 16]             208\n",
      "             ReLU-61          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-62            [-1, 104, 1, 1]               0\n",
      "           Conv2d-63             [-1, 26, 1, 1]           2,730\n",
      "             ReLU-64             [-1, 26, 1, 1]               0\n",
      "           Conv2d-65            [-1, 104, 1, 1]           2,808\n",
      "          Sigmoid-66            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-67          [-1, 104, 16, 16]               0\n",
      "           Conv2d-68          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-69          [-1, 104, 16, 16]             208\n",
      "             ReLU-70          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-71          [-1, 104, 16, 16]               0\n",
      "           Conv2d-72            [-1, 208, 8, 8]          21,632\n",
      "      BatchNorm2d-73            [-1, 208, 8, 8]             416\n",
      "           Conv2d-74          [-1, 208, 16, 16]          21,632\n",
      "      BatchNorm2d-75          [-1, 208, 16, 16]             416\n",
      "             ReLU-76          [-1, 208, 16, 16]               0\n",
      "           Conv2d-77            [-1, 208, 8, 8]          14,976\n",
      "      BatchNorm2d-78            [-1, 208, 8, 8]             416\n",
      "             ReLU-79            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-80            [-1, 208, 1, 1]               0\n",
      "           Conv2d-81             [-1, 26, 1, 1]           5,434\n",
      "             ReLU-82             [-1, 26, 1, 1]               0\n",
      "           Conv2d-83            [-1, 208, 1, 1]           5,616\n",
      "          Sigmoid-84            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-85            [-1, 208, 8, 8]               0\n",
      "           Conv2d-86            [-1, 208, 8, 8]          43,264\n",
      "      BatchNorm2d-87            [-1, 208, 8, 8]             416\n",
      "             ReLU-88            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-89            [-1, 208, 8, 8]               0\n",
      "           Conv2d-90            [-1, 208, 8, 8]          43,264\n",
      "      BatchNorm2d-91            [-1, 208, 8, 8]             416\n",
      "             ReLU-92            [-1, 208, 8, 8]               0\n",
      "           Conv2d-93            [-1, 208, 8, 8]          14,976\n",
      "      BatchNorm2d-94            [-1, 208, 8, 8]             416\n",
      "             ReLU-95            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-96            [-1, 208, 1, 1]               0\n",
      "           Conv2d-97             [-1, 52, 1, 1]          10,868\n",
      "             ReLU-98             [-1, 52, 1, 1]               0\n",
      "           Conv2d-99            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-100            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-101            [-1, 208, 8, 8]               0\n",
      "          Conv2d-102            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-103            [-1, 208, 8, 8]             416\n",
      "            ReLU-104            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-105            [-1, 208, 8, 8]               0\n",
      "          Conv2d-106            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-107            [-1, 208, 8, 8]             416\n",
      "            ReLU-108            [-1, 208, 8, 8]               0\n",
      "          Conv2d-109            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-110            [-1, 208, 8, 8]             416\n",
      "            ReLU-111            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-112            [-1, 208, 1, 1]               0\n",
      "          Conv2d-113             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-114             [-1, 52, 1, 1]               0\n",
      "          Conv2d-115            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-116            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-117            [-1, 208, 8, 8]               0\n",
      "          Conv2d-118            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-119            [-1, 208, 8, 8]             416\n",
      "            ReLU-120            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-121            [-1, 208, 8, 8]               0\n",
      "          Conv2d-122            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-123            [-1, 208, 8, 8]             416\n",
      "            ReLU-124            [-1, 208, 8, 8]               0\n",
      "          Conv2d-125            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-126            [-1, 208, 8, 8]             416\n",
      "            ReLU-127            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-128            [-1, 208, 1, 1]               0\n",
      "          Conv2d-129             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-130             [-1, 52, 1, 1]               0\n",
      "          Conv2d-131            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-132            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-133            [-1, 208, 8, 8]               0\n",
      "          Conv2d-134            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-135            [-1, 208, 8, 8]             416\n",
      "            ReLU-136            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-137            [-1, 208, 8, 8]               0\n",
      "          Conv2d-138            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-139            [-1, 208, 8, 8]             416\n",
      "            ReLU-140            [-1, 208, 8, 8]               0\n",
      "          Conv2d-141            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-142            [-1, 208, 8, 8]             416\n",
      "            ReLU-143            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-144            [-1, 208, 1, 1]               0\n",
      "          Conv2d-145             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-146             [-1, 52, 1, 1]               0\n",
      "          Conv2d-147            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-148            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-149            [-1, 208, 8, 8]               0\n",
      "          Conv2d-150            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-151            [-1, 208, 8, 8]             416\n",
      "            ReLU-152            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-153            [-1, 208, 8, 8]               0\n",
      "          Conv2d-154            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-155            [-1, 208, 8, 8]             416\n",
      "            ReLU-156            [-1, 208, 8, 8]               0\n",
      "          Conv2d-157            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-158            [-1, 208, 8, 8]             416\n",
      "            ReLU-159            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-160            [-1, 208, 1, 1]               0\n",
      "          Conv2d-161             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-162             [-1, 52, 1, 1]               0\n",
      "          Conv2d-163            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-164            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-165            [-1, 208, 8, 8]               0\n",
      "          Conv2d-166            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-167            [-1, 208, 8, 8]             416\n",
      "            ReLU-168            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-169            [-1, 208, 8, 8]               0\n",
      "          Conv2d-170            [-1, 440, 4, 4]          91,520\n",
      "     BatchNorm2d-171            [-1, 440, 4, 4]             880\n",
      "          Conv2d-172            [-1, 440, 8, 8]          91,520\n",
      "     BatchNorm2d-173            [-1, 440, 8, 8]             880\n",
      "            ReLU-174            [-1, 440, 8, 8]               0\n",
      "          Conv2d-175            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-176            [-1, 440, 4, 4]             880\n",
      "            ReLU-177            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-178            [-1, 440, 1, 1]               0\n",
      "          Conv2d-179             [-1, 52, 1, 1]          22,932\n",
      "            ReLU-180             [-1, 52, 1, 1]               0\n",
      "          Conv2d-181            [-1, 440, 1, 1]          23,320\n",
      "         Sigmoid-182            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-183            [-1, 440, 4, 4]               0\n",
      "          Conv2d-184            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-185            [-1, 440, 4, 4]             880\n",
      "            ReLU-186            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-187            [-1, 440, 4, 4]               0\n",
      "          Conv2d-188            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-189            [-1, 440, 4, 4]             880\n",
      "            ReLU-190            [-1, 440, 4, 4]               0\n",
      "          Conv2d-191            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-192            [-1, 440, 4, 4]             880\n",
      "            ReLU-193            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-194            [-1, 440, 1, 1]               0\n",
      "          Conv2d-195            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-196            [-1, 110, 1, 1]               0\n",
      "          Conv2d-197            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-198            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-199            [-1, 440, 4, 4]               0\n",
      "          Conv2d-200            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-201            [-1, 440, 4, 4]             880\n",
      "            ReLU-202            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-203            [-1, 440, 4, 4]               0\n",
      "          Conv2d-204            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-205            [-1, 440, 4, 4]             880\n",
      "            ReLU-206            [-1, 440, 4, 4]               0\n",
      "          Conv2d-207            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-208            [-1, 440, 4, 4]             880\n",
      "            ReLU-209            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-210            [-1, 440, 1, 1]               0\n",
      "          Conv2d-211            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-212            [-1, 110, 1, 1]               0\n",
      "          Conv2d-213            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-214            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-215            [-1, 440, 4, 4]               0\n",
      "          Conv2d-216            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-217            [-1, 440, 4, 4]             880\n",
      "            ReLU-218            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-219            [-1, 440, 4, 4]               0\n",
      "          Conv2d-220            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-221            [-1, 440, 4, 4]             880\n",
      "            ReLU-222            [-1, 440, 4, 4]               0\n",
      "          Conv2d-223            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-224            [-1, 440, 4, 4]             880\n",
      "            ReLU-225            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-226            [-1, 440, 1, 1]               0\n",
      "          Conv2d-227            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-228            [-1, 110, 1, 1]               0\n",
      "          Conv2d-229            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-230            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-231            [-1, 440, 4, 4]               0\n",
      "          Conv2d-232            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-233            [-1, 440, 4, 4]             880\n",
      "            ReLU-234            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-235            [-1, 440, 4, 4]               0\n",
      "          Conv2d-236            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-237            [-1, 440, 4, 4]             880\n",
      "            ReLU-238            [-1, 440, 4, 4]               0\n",
      "          Conv2d-239            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-240            [-1, 440, 4, 4]             880\n",
      "            ReLU-241            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-242            [-1, 440, 1, 1]               0\n",
      "          Conv2d-243            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-244            [-1, 110, 1, 1]               0\n",
      "          Conv2d-245            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-246            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-247            [-1, 440, 4, 4]               0\n",
      "          Conv2d-248            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-249            [-1, 440, 4, 4]             880\n",
      "            ReLU-250            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-251            [-1, 440, 4, 4]               0\n",
      "          Conv2d-252            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-253            [-1, 440, 4, 4]             880\n",
      "            ReLU-254            [-1, 440, 4, 4]               0\n",
      "          Conv2d-255            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-256            [-1, 440, 4, 4]             880\n",
      "            ReLU-257            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-258            [-1, 440, 1, 1]               0\n",
      "          Conv2d-259            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-260            [-1, 110, 1, 1]               0\n",
      "          Conv2d-261            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-262            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-263            [-1, 440, 4, 4]               0\n",
      "          Conv2d-264            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-265            [-1, 440, 4, 4]             880\n",
      "            ReLU-266            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-267            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-268            [-1, 440, 1, 1]               0\n",
      "          Linear-269                 [-1, 1000]         441,000\n",
      "================================================================\n",
      "Total params: 4,344,144\n",
      "Trainable params: 4,344,144\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.19\n",
      "Forward/backward pass size (MB): 32.27\n",
      "Params size (MB): 16.57\n",
      "Estimated Total Size (MB): 49.03\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "weights = models.RegNet_Y_400MF_Weights.DEFAULT\n",
    "model = models.regnet_y_400mf(weights=weights)\n",
    "input_size = (3, 128, 128)\n",
    "summary(model, input_size, device=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 265,
     "status": "ok",
     "timestamp": 1714379661530,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Vvam7gYY6S_p",
    "outputId": "0f59111f-fbf5-4347-cd28-1f0007a704bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 32, 64, 64]             864\n",
      "       BatchNorm2d-2           [-1, 32, 64, 64]              64\n",
      "              ReLU-3           [-1, 32, 64, 64]               0\n",
      "            Conv2d-4           [-1, 48, 32, 32]           1,536\n",
      "       BatchNorm2d-5           [-1, 48, 32, 32]              96\n",
      "            Conv2d-6           [-1, 48, 64, 64]           1,536\n",
      "       BatchNorm2d-7           [-1, 48, 64, 64]              96\n",
      "              ReLU-8           [-1, 48, 64, 64]               0\n",
      "            Conv2d-9           [-1, 48, 32, 32]           3,456\n",
      "      BatchNorm2d-10           [-1, 48, 32, 32]              96\n",
      "             ReLU-11           [-1, 48, 32, 32]               0\n",
      "AdaptiveAvgPool2d-12             [-1, 48, 1, 1]               0\n",
      "           Conv2d-13              [-1, 8, 1, 1]             392\n",
      "             ReLU-14              [-1, 8, 1, 1]               0\n",
      "           Conv2d-15             [-1, 48, 1, 1]             432\n",
      "          Sigmoid-16             [-1, 48, 1, 1]               0\n",
      "SqueezeExcitation-17           [-1, 48, 32, 32]               0\n",
      "           Conv2d-18           [-1, 48, 32, 32]           2,304\n",
      "      BatchNorm2d-19           [-1, 48, 32, 32]              96\n",
      "             ReLU-20           [-1, 48, 32, 32]               0\n",
      "ResBottleneckBlock-21           [-1, 48, 32, 32]               0\n",
      "           Conv2d-22          [-1, 104, 16, 16]           4,992\n",
      "      BatchNorm2d-23          [-1, 104, 16, 16]             208\n",
      "           Conv2d-24          [-1, 104, 32, 32]           4,992\n",
      "      BatchNorm2d-25          [-1, 104, 32, 32]             208\n",
      "             ReLU-26          [-1, 104, 32, 32]               0\n",
      "           Conv2d-27          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-28          [-1, 104, 16, 16]             208\n",
      "             ReLU-29          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-30            [-1, 104, 1, 1]               0\n",
      "           Conv2d-31             [-1, 12, 1, 1]           1,260\n",
      "             ReLU-32             [-1, 12, 1, 1]               0\n",
      "           Conv2d-33            [-1, 104, 1, 1]           1,352\n",
      "          Sigmoid-34            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-35          [-1, 104, 16, 16]               0\n",
      "           Conv2d-36          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-37          [-1, 104, 16, 16]             208\n",
      "             ReLU-38          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-39          [-1, 104, 16, 16]               0\n",
      "           Conv2d-40          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-41          [-1, 104, 16, 16]             208\n",
      "             ReLU-42          [-1, 104, 16, 16]               0\n",
      "           Conv2d-43          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-44          [-1, 104, 16, 16]             208\n",
      "             ReLU-45          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-46            [-1, 104, 1, 1]               0\n",
      "           Conv2d-47             [-1, 26, 1, 1]           2,730\n",
      "             ReLU-48             [-1, 26, 1, 1]               0\n",
      "           Conv2d-49            [-1, 104, 1, 1]           2,808\n",
      "          Sigmoid-50            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-51          [-1, 104, 16, 16]               0\n",
      "           Conv2d-52          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-53          [-1, 104, 16, 16]             208\n",
      "             ReLU-54          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-55          [-1, 104, 16, 16]               0\n",
      "           Conv2d-56          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-57          [-1, 104, 16, 16]             208\n",
      "             ReLU-58          [-1, 104, 16, 16]               0\n",
      "           Conv2d-59          [-1, 104, 16, 16]           7,488\n",
      "      BatchNorm2d-60          [-1, 104, 16, 16]             208\n",
      "             ReLU-61          [-1, 104, 16, 16]               0\n",
      "AdaptiveAvgPool2d-62            [-1, 104, 1, 1]               0\n",
      "           Conv2d-63             [-1, 26, 1, 1]           2,730\n",
      "             ReLU-64             [-1, 26, 1, 1]               0\n",
      "           Conv2d-65            [-1, 104, 1, 1]           2,808\n",
      "          Sigmoid-66            [-1, 104, 1, 1]               0\n",
      "SqueezeExcitation-67          [-1, 104, 16, 16]               0\n",
      "           Conv2d-68          [-1, 104, 16, 16]          10,816\n",
      "      BatchNorm2d-69          [-1, 104, 16, 16]             208\n",
      "             ReLU-70          [-1, 104, 16, 16]               0\n",
      "ResBottleneckBlock-71          [-1, 104, 16, 16]               0\n",
      "           Conv2d-72            [-1, 208, 8, 8]          21,632\n",
      "      BatchNorm2d-73            [-1, 208, 8, 8]             416\n",
      "           Conv2d-74          [-1, 208, 16, 16]          21,632\n",
      "      BatchNorm2d-75          [-1, 208, 16, 16]             416\n",
      "             ReLU-76          [-1, 208, 16, 16]               0\n",
      "           Conv2d-77            [-1, 208, 8, 8]          14,976\n",
      "      BatchNorm2d-78            [-1, 208, 8, 8]             416\n",
      "             ReLU-79            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-80            [-1, 208, 1, 1]               0\n",
      "           Conv2d-81             [-1, 26, 1, 1]           5,434\n",
      "             ReLU-82             [-1, 26, 1, 1]               0\n",
      "           Conv2d-83            [-1, 208, 1, 1]           5,616\n",
      "          Sigmoid-84            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-85            [-1, 208, 8, 8]               0\n",
      "           Conv2d-86            [-1, 208, 8, 8]          43,264\n",
      "      BatchNorm2d-87            [-1, 208, 8, 8]             416\n",
      "             ReLU-88            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-89            [-1, 208, 8, 8]               0\n",
      "           Conv2d-90            [-1, 208, 8, 8]          43,264\n",
      "      BatchNorm2d-91            [-1, 208, 8, 8]             416\n",
      "             ReLU-92            [-1, 208, 8, 8]               0\n",
      "           Conv2d-93            [-1, 208, 8, 8]          14,976\n",
      "      BatchNorm2d-94            [-1, 208, 8, 8]             416\n",
      "             ReLU-95            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-96            [-1, 208, 1, 1]               0\n",
      "           Conv2d-97             [-1, 52, 1, 1]          10,868\n",
      "             ReLU-98             [-1, 52, 1, 1]               0\n",
      "           Conv2d-99            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-100            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-101            [-1, 208, 8, 8]               0\n",
      "          Conv2d-102            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-103            [-1, 208, 8, 8]             416\n",
      "            ReLU-104            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-105            [-1, 208, 8, 8]               0\n",
      "          Conv2d-106            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-107            [-1, 208, 8, 8]             416\n",
      "            ReLU-108            [-1, 208, 8, 8]               0\n",
      "          Conv2d-109            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-110            [-1, 208, 8, 8]             416\n",
      "            ReLU-111            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-112            [-1, 208, 1, 1]               0\n",
      "          Conv2d-113             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-114             [-1, 52, 1, 1]               0\n",
      "          Conv2d-115            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-116            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-117            [-1, 208, 8, 8]               0\n",
      "          Conv2d-118            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-119            [-1, 208, 8, 8]             416\n",
      "            ReLU-120            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-121            [-1, 208, 8, 8]               0\n",
      "          Conv2d-122            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-123            [-1, 208, 8, 8]             416\n",
      "            ReLU-124            [-1, 208, 8, 8]               0\n",
      "          Conv2d-125            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-126            [-1, 208, 8, 8]             416\n",
      "            ReLU-127            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-128            [-1, 208, 1, 1]               0\n",
      "          Conv2d-129             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-130             [-1, 52, 1, 1]               0\n",
      "          Conv2d-131            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-132            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-133            [-1, 208, 8, 8]               0\n",
      "          Conv2d-134            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-135            [-1, 208, 8, 8]             416\n",
      "            ReLU-136            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-137            [-1, 208, 8, 8]               0\n",
      "          Conv2d-138            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-139            [-1, 208, 8, 8]             416\n",
      "            ReLU-140            [-1, 208, 8, 8]               0\n",
      "          Conv2d-141            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-142            [-1, 208, 8, 8]             416\n",
      "            ReLU-143            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-144            [-1, 208, 1, 1]               0\n",
      "          Conv2d-145             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-146             [-1, 52, 1, 1]               0\n",
      "          Conv2d-147            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-148            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-149            [-1, 208, 8, 8]               0\n",
      "          Conv2d-150            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-151            [-1, 208, 8, 8]             416\n",
      "            ReLU-152            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-153            [-1, 208, 8, 8]               0\n",
      "          Conv2d-154            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-155            [-1, 208, 8, 8]             416\n",
      "            ReLU-156            [-1, 208, 8, 8]               0\n",
      "          Conv2d-157            [-1, 208, 8, 8]          14,976\n",
      "     BatchNorm2d-158            [-1, 208, 8, 8]             416\n",
      "            ReLU-159            [-1, 208, 8, 8]               0\n",
      "AdaptiveAvgPool2d-160            [-1, 208, 1, 1]               0\n",
      "          Conv2d-161             [-1, 52, 1, 1]          10,868\n",
      "            ReLU-162             [-1, 52, 1, 1]               0\n",
      "          Conv2d-163            [-1, 208, 1, 1]          11,024\n",
      "         Sigmoid-164            [-1, 208, 1, 1]               0\n",
      "SqueezeExcitation-165            [-1, 208, 8, 8]               0\n",
      "          Conv2d-166            [-1, 208, 8, 8]          43,264\n",
      "     BatchNorm2d-167            [-1, 208, 8, 8]             416\n",
      "            ReLU-168            [-1, 208, 8, 8]               0\n",
      "ResBottleneckBlock-169            [-1, 208, 8, 8]               0\n",
      "          Conv2d-170            [-1, 440, 4, 4]          91,520\n",
      "     BatchNorm2d-171            [-1, 440, 4, 4]             880\n",
      "          Conv2d-172            [-1, 440, 8, 8]          91,520\n",
      "     BatchNorm2d-173            [-1, 440, 8, 8]             880\n",
      "            ReLU-174            [-1, 440, 8, 8]               0\n",
      "          Conv2d-175            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-176            [-1, 440, 4, 4]             880\n",
      "            ReLU-177            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-178            [-1, 440, 1, 1]               0\n",
      "          Conv2d-179             [-1, 52, 1, 1]          22,932\n",
      "            ReLU-180             [-1, 52, 1, 1]               0\n",
      "          Conv2d-181            [-1, 440, 1, 1]          23,320\n",
      "         Sigmoid-182            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-183            [-1, 440, 4, 4]               0\n",
      "          Conv2d-184            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-185            [-1, 440, 4, 4]             880\n",
      "            ReLU-186            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-187            [-1, 440, 4, 4]               0\n",
      "          Conv2d-188            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-189            [-1, 440, 4, 4]             880\n",
      "            ReLU-190            [-1, 440, 4, 4]               0\n",
      "          Conv2d-191            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-192            [-1, 440, 4, 4]             880\n",
      "            ReLU-193            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-194            [-1, 440, 1, 1]               0\n",
      "          Conv2d-195            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-196            [-1, 110, 1, 1]               0\n",
      "          Conv2d-197            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-198            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-199            [-1, 440, 4, 4]               0\n",
      "          Conv2d-200            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-201            [-1, 440, 4, 4]             880\n",
      "            ReLU-202            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-203            [-1, 440, 4, 4]               0\n",
      "          Conv2d-204            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-205            [-1, 440, 4, 4]             880\n",
      "            ReLU-206            [-1, 440, 4, 4]               0\n",
      "          Conv2d-207            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-208            [-1, 440, 4, 4]             880\n",
      "            ReLU-209            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-210            [-1, 440, 1, 1]               0\n",
      "          Conv2d-211            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-212            [-1, 110, 1, 1]               0\n",
      "          Conv2d-213            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-214            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-215            [-1, 440, 4, 4]               0\n",
      "          Conv2d-216            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-217            [-1, 440, 4, 4]             880\n",
      "            ReLU-218            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-219            [-1, 440, 4, 4]               0\n",
      "          Conv2d-220            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-221            [-1, 440, 4, 4]             880\n",
      "            ReLU-222            [-1, 440, 4, 4]               0\n",
      "          Conv2d-223            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-224            [-1, 440, 4, 4]             880\n",
      "            ReLU-225            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-226            [-1, 440, 1, 1]               0\n",
      "          Conv2d-227            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-228            [-1, 110, 1, 1]               0\n",
      "          Conv2d-229            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-230            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-231            [-1, 440, 4, 4]               0\n",
      "          Conv2d-232            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-233            [-1, 440, 4, 4]             880\n",
      "            ReLU-234            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-235            [-1, 440, 4, 4]               0\n",
      "          Conv2d-236            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-237            [-1, 440, 4, 4]             880\n",
      "            ReLU-238            [-1, 440, 4, 4]               0\n",
      "          Conv2d-239            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-240            [-1, 440, 4, 4]             880\n",
      "            ReLU-241            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-242            [-1, 440, 1, 1]               0\n",
      "          Conv2d-243            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-244            [-1, 110, 1, 1]               0\n",
      "          Conv2d-245            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-246            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-247            [-1, 440, 4, 4]               0\n",
      "          Conv2d-248            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-249            [-1, 440, 4, 4]             880\n",
      "            ReLU-250            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-251            [-1, 440, 4, 4]               0\n",
      "          Conv2d-252            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-253            [-1, 440, 4, 4]             880\n",
      "            ReLU-254            [-1, 440, 4, 4]               0\n",
      "          Conv2d-255            [-1, 440, 4, 4]          31,680\n",
      "     BatchNorm2d-256            [-1, 440, 4, 4]             880\n",
      "            ReLU-257            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-258            [-1, 440, 1, 1]               0\n",
      "          Conv2d-259            [-1, 110, 1, 1]          48,510\n",
      "            ReLU-260            [-1, 110, 1, 1]               0\n",
      "          Conv2d-261            [-1, 440, 1, 1]          48,840\n",
      "         Sigmoid-262            [-1, 440, 1, 1]               0\n",
      "SqueezeExcitation-263            [-1, 440, 4, 4]               0\n",
      "          Conv2d-264            [-1, 440, 4, 4]         193,600\n",
      "     BatchNorm2d-265            [-1, 440, 4, 4]             880\n",
      "            ReLU-266            [-1, 440, 4, 4]               0\n",
      "ResBottleneckBlock-267            [-1, 440, 4, 4]               0\n",
      "AdaptiveAvgPool2d-268            [-1, 440, 1, 1]               0\n",
      "          Linear-269                   [-1, 10]           4,410\n",
      "================================================================\n",
      "Total params: 3,907,554\n",
      "Trainable params: 672,434\n",
      "Non-trainable params: 3,235,120\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.19\n",
      "Forward/backward pass size (MB): 32.26\n",
      "Params size (MB): 14.91\n",
      "Estimated Total Size (MB): 47.35\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for index, (name, param) in enumerate(model.named_parameters()):\n",
    "  if \"fc\" in name:\n",
    "    param.requires_grad = True\n",
    "  else:\n",
    "    param.requires_grad = False\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "model.fc = torch.nn.Linear(model.fc.in_features, NUM_CLASSES)\n",
    "\n",
    "summary(model, input_size, device=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 267,
     "status": "ok",
     "timestamp": 1714379655173,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "b6vioXkhp5Rt",
    "outputId": "5c71a2c8-8ae7-4cb2-bbb5-759337ed291f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, optimizer_name, optimizer_hparams):\n",
    "        \"\"\"LightningModel.\n",
    "        Args:\n",
    "            model: Name of the model/CNN to run. Used for creating the model\n",
    "            optimizer_name: Name of the optimizer to use. Currently supported: Adam, SGD\n",
    "            optimizer_hparams: Hyperparameters for the optimizer, as dictionary. This includes learning rate, weight decay, etc.\n",
    "         \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.model = model\n",
    "        self.save_hyperparameters(ignore=['model'])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy(task='multiclass', num_classes=NUM_CLASSES)\n",
    "        self.valid_acc = torchmetrics.Accuracy(task='multiclass', num_classes=NUM_CLASSES)\n",
    "        self.test_acc = torchmetrics.Accuracy(task='multiclass', num_classes=NUM_CLASSES)\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc.update(predicted_labels, true_labels)\n",
    "        metrics = {'train_loss': loss, 'train_acc': self.train_acc}\n",
    "        self.log_dict(metrics, on_epoch=True, on_step=False, prog_bar=True)\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        metrics = {'valid_loss': loss, 'valid_acc': self.valid_acc}\n",
    "        self.log_dict(metrics, on_epoch=True, on_step=False, prog_bar=True)\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        metrics = {'test_loss': loss, 'test_acc': self.test_acc}\n",
    "        self.log_dict(metrics, on_epoch=True, on_step=False, prog_bar=True)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "      # We will support Adam or SGD as optimizers.\n",
    "      if self.hparams.optimizer_name == \"Adam\":\n",
    "          optimizer = torch.optim.AdamW(self.parameters(), **self.hparams.optimizer_hparams)\n",
    "      elif self.hparams.optimizer_name == \"SGD\":\n",
    "          optimizer = torch.optim.SGD(self.parameters(), **self.hparams.optimizer_hparams)\n",
    "      else:\n",
    "          assert False, f'Unknown optimizer: \"{self.hparams.optimizer_name}\"'\n",
    "\n",
    "      # scheduler = torch.optim.swa_utils.SWALR(optimizer, anneal_strategy=\"cos\", anneal_epochs=5, swa_lr=0.002)\n",
    "      scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=5)\n",
    "      return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler, \"monitor\": \"valid_loss\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1714379669211,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "PqlI7ZIKrs3j",
    "outputId": "2fd85126-eb4f-4d59-8727-03bdd7d8d358"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "optimizer_name = \"Adam\"\n",
    "optimizer_hparams = {\"lr\": 0.00003, \"betas\": (0.9, 0.999), \"eps\": 1e-08, \"weight_decay\": 0.01, \"amsgrad\": False}\n",
    "\n",
    "model_light = LightningModel(model, optimizer_name, optimizer_hparams)\n",
    "\n",
    "arch = 'regnet_y_400m'\n",
    "\n",
    "cbs = ModelCheckpoint(dirpath = f'./checkpoints_{arch}',\n",
    "                                   filename = arch,\n",
    "                                   verbose = True,\n",
    "                                   monitor = 'valid_loss',\n",
    "                                   mode = 'min')\n",
    "\n",
    "logger_csv = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 400,
     "status": "ok",
     "timestamp": 1714379671975,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "tlrYVSXbsr6R",
    "outputId": "d05f44d4-e3f7-459f-d17d-ded4ef8d07da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n",
      "INFO: GPU available: False, used: False\n",
      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO: `Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n",
      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "PERCENT_VALID_EXAMPLES = 1\n",
    "EPOCHS = 20\n",
    "LOG_EVERY_N_STEPS = 1\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    limit_val_batches = PERCENT_VALID_EXAMPLES,\n",
    "    max_epochs = EPOCHS,\n",
    "    logger=logger_csv,\n",
    "    callbacks = cbs,\n",
    "    log_every_n_steps=LOG_EVERY_N_STEPS,\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "\n",
    ")\n",
    "\n",
    "# trainer.fit(model_light, train_dataloaders = train_loader, val_dataloaders=valid_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
      "c5a981e2065449c6849df7497879dfb5",
      "a1e7ab435d3c467f85f8013788ff1567",
      "d0eb528677e44ae0a3319d7721ddf440",
      "7b0a78403d7e4b32aefc3073b8aefe44",
      "21aaa679798c42069523a9ef2c17b456",
      "7b997e35c75042b98687b8569c85ebde",
      "5ff54865f2f548219b85d772f94fde71",
      "785fa641acce4135ad97efef23f1536f",
      "8aa950e0294744eb92e1779d56a80e65",
      "0faa12988f5a40ffa826fe392d6bd7ce",
      "e46c6351ee68414e9b287ffe96aa77a2"
     ]
    },
    "executionInfo": {
     "elapsed": 24433,
     "status": "ok",
     "timestamp": 1714353791110,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "bTzKf6t4bYyz",
    "outputId": "3d3c8a8c-6ba0-4539-9a5a-258d96b93462"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5a981e2065449c6849df7497879dfb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'test_loss': 3.5914242267608643, 'test_acc': 0.24933333694934845}]\n"
     ]
    }
   ],
   "source": [
    "# run test dataset\n",
    "test_metrics = trainer.test(model_light, dataloaders=test_loader, verbose=False)\n",
    "print(test_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RHvCItUZS6Lg"
   },
   "source": [
    "## Load the model from the checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1032,
     "status": "ok",
     "timestamp": 1714379684333,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "W_GVNH_nnEFJ",
    "outputId": "1c5a1b4c-ded9-4393-959d-411880454286"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you are working in GPU, please, you do not run: device = torch.device('cpu')\n",
    "device = torch.device('cpu')\n",
    "checkpoint_file = '/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights/checkpoints_regnet_y_400m/regnet_y_400m.ckpt'\n",
    "\n",
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "optimizer_name = \"Adam\"\n",
    "optimizer_hparams = {\"lr\": 0.00003, \"betas\": (0.9, 0.999), \"eps\": 1e-08, \"weight_decay\": 0.01, \"amsgrad\": False}\n",
    "\n",
    "model_light = LightningModel(model, optimizer_name, optimizer_hparams)\n",
    "\n",
    "checkpoint = torch.load(checkpoint_file, map_location=device)\n",
    "model_light.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 899
    },
    "executionInfo": {
     "elapsed": 1806,
     "status": "ok",
     "timestamp": 1714379690432,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "S5wz2SU7L2CU",
    "outputId": "c74be155-878d-4e49-d35a-fbcb2af35900"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Epoch', ylabel='ACC'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def printdf(df):\n",
    "  return display(pd.DataFrame(df))\n",
    "\n",
    "path = '/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights/logs/my-model/version_0'\n",
    "metrics = pd.read_csv(f\"{path}/metrics.csv\")\n",
    "metrics\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "\n",
    "df_metrics[[\"train_loss\", \"valid_loss\"]].plot(grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\n",
    "df_metrics[[\"train_acc\", \"valid_acc\"]].plot(grid=True, legend=True, xlabel='Epoch', ylabel='ACC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RIGc90uwTDE3"
   },
   "source": [
    "## This cell must be run if it is the first time.\n",
    "\n",
    "### The results can called from the predict folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29433,
     "status": "ok",
     "timestamp": 1714354074292,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "6I0_Ic66S3Bs",
    "outputId": "84598c08-c4d6-4091-d403-18e464fbf1aa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n"
     ]
    }
   ],
   "source": [
    "pl.seed_everything(RANDOM_SEED)\n",
    "\n",
    "target_list = []\n",
    "predict_list = []\n",
    "prob_predict_list = []\n",
    "\n",
    "for idx, batch in enumerate(test_loader):\n",
    "    images, targets = batch\n",
    "    with torch.no_grad():\n",
    "      model.eval()\n",
    "      logits = model(images)\n",
    "\n",
    "    prob_predict = logits.sigmoid()\n",
    "\n",
    "    pr_masks = torch.argmax(logits, 1).unsqueeze(1)\n",
    "\n",
    "    target = targets[0].cpu().numpy().astype(int)\n",
    "    target_predict = pr_masks[0][0].cpu().numpy().astype(int)\n",
    "\n",
    "    target_list.append(target)\n",
    "    predict_list.append(target_predict)\n",
    "    prob_predict_list.append(prob_predict)\n",
    "    # if idx == 3:\n",
    "    #   break\n",
    "\n",
    "target_ = np.vstack(target_list)\n",
    "predict_ = np.vstack(predict_list)\n",
    "prob_predict_ = np.vstack(prob_predict_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vc0YivJ5V5Bf"
   },
   "source": [
    "## It will call the results saved from the previous cell in folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 263,
     "status": "ok",
     "timestamp": 1714379708256,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "nIDJlxAHVglU",
    "outputId": "3fb11534-1ce7-4336-8475-46cdbd968687"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 1)\n",
      "(1500, 1)\n",
      "(1500, 10)\n"
     ]
    }
   ],
   "source": [
    "# %mkdir predict\n",
    "\n",
    "path = '/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/weights/predict'\n",
    "\n",
    "# np.save(f'{path}/target.npy', target_)\n",
    "# np.save(f'{path}/predict.npy', predict_)\n",
    "# np.save(f'{path}/prob_predict.npy', prob_predict_)\n",
    "\n",
    "target = np.load(f'{path}/target.npy')\n",
    "predict = np.load(f'{path}/predict.npy')\n",
    "prob_predict = np.load(f'{path}/prob_predict.npy')\n",
    "\n",
    "print(target.shape)\n",
    "print(predict.shape)\n",
    "print(prob_predict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 381
    },
    "executionInfo": {
     "elapsed": 1058,
     "status": "ok",
     "timestamp": 1714379711786,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "54Lj2Zb6WUsM",
    "outputId": "a2dd7a7d-4368-4d56-aa36-ba6bf6ceb6d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"printdf(conf_matrix)\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 24,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          10,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 21,\n        \"max\": 162,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          21,\n          162,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 2,\n        \"max\": 20,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          19,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1,\n        \"max\": 22,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          12,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 9,\n        \"max\": 55,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          15,\n          9,\n          55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1,\n        \"max\": 46,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          6,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          3,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
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       "  }\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "</div>\n",
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      ],
      "text/plain": [
       "    0    1   2   3  4   5  6   7   8  9\n",
       "0  24   31   8  22  0  17  4   4  29  1\n",
       "1   0  162   2   1  0   9  1   8   3  0\n",
       "2  20   31  20  14  1  33  0   9  26  0\n",
       "3  13   26  10  21  1  38  1  10  40  0\n",
       "4   6   64  12  11  6  18  5  17  11  2\n",
       "5   1   30   3  12  1  55  2   1  24  0\n",
       "6  12   44  16  21  1  25  3   4  20  0\n",
       "7  10   80   3   8  2  15  0  46   8  0\n",
       "8   6   21  19  22  0  30  0   6  24  2\n",
       "9   9   40  11  13  0  18  1  23  14  2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Assuming target_flatten and target_pred_flatten are your actual and predicted labels respectively\n",
    "conf_matrix = confusion_matrix(target.flatten(), predict.flatten())\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "printdf(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1714379715471,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "a3S91Qp4WelE",
    "outputId": "67ab29fe-66a7-4fa0-d046-03a7abeb607a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.24      0.17      0.20       140\n",
      "           1       0.31      0.87      0.45       186\n",
      "           2       0.19      0.13      0.16       154\n",
      "           3       0.14      0.13      0.14       160\n",
      "           4       0.50      0.04      0.07       152\n",
      "           5       0.21      0.43      0.28       129\n",
      "           6       0.18      0.02      0.04       146\n",
      "           7       0.36      0.27      0.31       172\n",
      "           8       0.12      0.18      0.15       130\n",
      "           9       0.29      0.02      0.03       131\n",
      "\n",
      "    accuracy                           0.24      1500\n",
      "   macro avg       0.25      0.23      0.18      1500\n",
      "weighted avg       0.26      0.24      0.19      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "print(classification_report(target, predict, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 930
    },
    "executionInfo": {
     "elapsed": 814,
     "status": "ok",
     "timestamp": 1714379718626,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "41r3RZ9sc8m1",
    "outputId": "046a5a60-e2b1-44c7-bdfa-032f0b6f02cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 10)\n",
      "(1500, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "from sklearn.metrics import auc, roc_curve\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "label_binarizer = LabelBinarizer().fit(target)\n",
    "target_onehot = label_binarizer.transform(target)\n",
    "predict_onehot = label_binarizer.transform(predict)\n",
    "\n",
    "target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\", \"red\", \"green\", \"blue\", \"yellow\", \"purple\", \"pink\", \"orange\"])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "n_classes = 10\n",
    "for class_id, color in zip(range(n_classes), colors):\n",
    "  RocCurveDisplay.from_predictions(\n",
    "        target_onehot[:, class_id],\n",
    "        prob_predict[:, class_id],\n",
    "        name=f\"ROC curve for {target_names[class_id]}\",\n",
    "        color=color,\n",
    "        ax=ax\n",
    "    )\n",
    "\n",
    "_ = ax.set(\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    "    title=\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nh3hsW7B_viB"
   },
   "source": [
    "### It is possible to see that despite having achieved good metrics during the optimization process on the validation data, there is still an overfit on the test data."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyP6WHa+GOMB5kmIDy3ynPZM",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}