No_weight_and_argumentation_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1714380984388,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "wU67BWUzYvDo"
   },
   "outputs": [],
   "source": [
    "# !pip install lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 42480,
     "status": "ok",
     "timestamp": 1714380984388,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Y8ODGCnVZq3e",
    "outputId": "bb91d3e4-19ff-4c42-b238-ea84303a54ba"
   },
   "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": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1714381059211,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "fsLXk0-KZ04w",
    "outputId": "b14a08e1-341f-4865-c43f-abb1c002d3bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/no_weights\n"
     ]
    }
   ],
   "source": [
    "# %cd /content/gdrive/MyDrive/Colab Notebooks\n",
    "# %mkdir ClassPreTreined\n",
    "# %cd /content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined\n",
    "# %mkdir no_weights\n",
    "\n",
    "%cd /content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/no_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7054,
     "status": "ok",
     "timestamp": 1714381073582,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "h3B2QwwiaPMa",
    "outputId": "d28a30da-4ada-4ba7-e493-3387f7853052"
   },
   "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": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1714381073583,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "kBHxyGQ6aVmn",
    "outputId": "a7da27cf-138f-4457-87c8-6e09888845ed"
   },
   "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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 258,
     "status": "ok",
     "timestamp": 1714381093021,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "UnQ5ZAa-bKy_",
    "outputId": "24cc538d-5493-4a1b-c436-f68e0ad8bf8f"
   },
   "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/no_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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 252,
     "status": "ok",
     "timestamp": 1714381096337,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "yLguYFq2dvfI",
    "outputId": "3f99f7c7-e90f-4961-90ad-ede5163c58b9"
   },
   "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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 286,
     "status": "ok",
     "timestamp": 1714381098910,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "iSwkLHsad9Q_",
    "outputId": "2096f45d-7a82-4f5c-cd62-e853c7253088"
   },
   "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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 243
    },
    "executionInfo": {
     "elapsed": 1093,
     "status": "ok",
     "timestamp": 1714381106375,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Y2CMzP-DeHfW",
    "outputId": "2fe4cfbc-a40c-4852-878b-ab9c7653927d"
   },
   "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": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "executionInfo": {
     "elapsed": 528,
     "status": "ok",
     "timestamp": 1714381110263,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "4ELlFTu3YPdM",
    "outputId": "f1118eba-362f-4afa-ae4f-8b90e7c0fb09"
   },
   "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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\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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 630,
     "status": "ok",
     "timestamp": 1714381118010,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "wav9xZallEdH",
    "outputId": "23bac84c-a376-41b2-e345-932015a234ae"
   },
   "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": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1714381122297,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "Vvam7gYY6S_p",
    "outputId": "b8e8e2b8-4150-453b-839c-bf7b28d4f993"
   },
   "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: 3,907,554\n",
      "Non-trainable params: 0\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",
    "    param.requires_grad = True\n",
    "\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": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1714381153985,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "b6vioXkhp5Rt",
    "outputId": "e6172610-9f1e-4964-9456-6b56df558207"
   },
   "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": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1714381177626,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "PqlI7ZIKrs3j",
    "outputId": "be8db207-af56-489f-d63c-8c527789d2c9"
   },
   "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": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 293,
     "status": "ok",
     "timestamp": 1714383518842,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "tlrYVSXbsr6R",
    "outputId": "006e803d-d7fe-40ec-e4f9-b8501b0e3746"
   },
   "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: True (cuda), used: True\n",
      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\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": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
      "5736a920d93145cfa60d893606f8ec97",
      "ae35746fc2754799ac023914f6ecc36c",
      "40ece0ee14b0494b913085223b2f8815",
      "4af9db8cda61442b837bec1df8083a24",
      "6d59b4739dc04312a9817c1b3a2c61d0",
      "594be9af720c4b59bc30168fd9a27d44",
      "6e069e2cee6c4ef6afc3f2ed1d2bf8ef",
      "13dbccd9f1e642d596557d17acfc3e97",
      "0781efd1545e445ab3d4179b0532ebec",
      "c5db4491996d42158039080d16d03dc4",
      "fcea1a844eff49e7a39c1304a427e54e"
     ]
    },
    "executionInfo": {
     "elapsed": 24379,
     "status": "ok",
     "timestamp": 1714382838683,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "bTzKf6t4bYyz",
    "outputId": "932534c1-edd7-42a5-b5e8-cf3a1ada96d3"
   },
   "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": "5736a920d93145cfa60d893606f8ec97",
       "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': 1.4662026166915894, 'test_acc': 0.6566666960716248}]\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": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 286,
     "status": "ok",
     "timestamp": 1714383095148,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "W_GVNH_nnEFJ",
    "outputId": "b1ed8bb3-1ceb-4d01-8a7d-48a76754a305"
   },
   "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": 29,
     "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",
    "# /content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/no_weights\n",
    "checkpoint_file = '/content/gdrive/MyDrive/Colab Notebooks/ClassPreTreined/no_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": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 899
    },
    "executionInfo": {
     "elapsed": 1641,
     "status": "ok",
     "timestamp": 1714383157078,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "S5wz2SU7L2CU",
    "outputId": "57510571-7f2e-4d15-c617-5822507fb7d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Epoch', ylabel='ACC'>"
      ]
     },
     "execution_count": 30,
     "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/no_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": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29728,
     "status": "ok",
     "timestamp": 1714383198515,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "6I0_Ic66S3Bs",
    "outputId": "be6d9759-8657-4211-f1f5-05fe18867af8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 546\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 546\n",
      "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  self.pid = os.fork()\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": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 528,
     "status": "ok",
     "timestamp": 1714383287613,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "nIDJlxAHVglU",
    "outputId": "e0919908-25cd-4f7a-b619-4356b7365644"
   },
   "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/no_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": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 381
    },
    "executionInfo": {
     "elapsed": 1543,
     "status": "ok",
     "timestamp": 1714383292134,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "54Lj2Zb6WUsM",
    "outputId": "d10acaae-2d43-4bec-809f-5823a72225f5"
   },
   "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\": 4,\n        \"min\": 0,\n        \"max\": 15,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 0,\n        \"max\": 178,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          178,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 0,\n        \"max\": 152,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          20,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 50,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 0,\n        \"max\": 112,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          112,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 0,\n        \"max\": 129,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          34,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 52,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": 0,\n        \"max\": 162,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 73,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          73,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 0,\n        \"max\": 97,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-f3ba0df1-feab-4104-a8c4-63f597543c1d\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3ba0df1-feab-4104-a8c4-63f597543c1d')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-f3ba0df1-feab-4104-a8c4-63f597543c1d button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-f3ba0df1-feab-4104-a8c4-63f597543c1d');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-58061218-f35e-4c2d-9612-b1d420d3fe66\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58061218-f35e-4c2d-9612-b1d420d3fe66')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-58061218-f35e-4c2d-9612-b1d420d3fe66 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    0    1    2   3    4    5   6    7   8   9\n",
       "0  15    0   98   1    0   21   0    4   0   1\n",
       "1   0  178    3   0    0    0   1    4   0   0\n",
       "2   0    0  152   0    0    2   0    0   0   0\n",
       "3   0    0   30  50    0   78   0    2   0   0\n",
       "4   0    0   29   0  112    7   0    4   0   0\n",
       "5   0    0    0   0    0  129   0    0   0   0\n",
       "6   0    0   18   0    1   75  52    0   0   0\n",
       "7   0    0    9   0    0    1   0  162   0   0\n",
       "8   0    0   20   0    0   34   0    3  73   0\n",
       "9   0    0    5   0    2   14   0   13   0  97"
      ]
     },
     "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": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1714383296226,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "a3S91Qp4WelE",
    "outputId": "6cc5907e-9bb5-4095-ea8d-f1d17d5ed7cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.11      0.19       140\n",
      "           1       1.00      0.96      0.98       186\n",
      "           2       0.42      0.99      0.59       154\n",
      "           3       0.98      0.31      0.47       160\n",
      "           4       0.97      0.74      0.84       152\n",
      "           5       0.36      1.00      0.53       129\n",
      "           6       0.98      0.36      0.52       146\n",
      "           7       0.84      0.94      0.89       172\n",
      "           8       1.00      0.56      0.72       130\n",
      "           9       0.99      0.74      0.85       131\n",
      "\n",
      "    accuracy                           0.68      1500\n",
      "   macro avg       0.85      0.67      0.66      1500\n",
      "weighted avg       0.86      0.68      0.67      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": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 893
    },
    "executionInfo": {
     "elapsed": 911,
     "status": "ok",
     "timestamp": 1714383306986,
     "user": {
      "displayName": "Bruno Soares de Castro",
      "userId": "11507589105817192721"
     },
     "user_tz": -60
    },
    "id": "41r3RZ9sc8m1",
    "outputId": "048936bb-d27c-4ed7-d014-c8ec074f7d90"
   },
   "outputs": [
    {
     "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": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyMDuLWpTckEkKWbFpcJXKjs",
   "gpuType": "L4",
   "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
}