Microorganisms_inline_holographic_image_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "94f333e1"
      },
      "outputs": [],
      "source": [
        "import os    \n",
        "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
      ],
      "id": "94f333e1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWpXhHtoqgy3",
        "outputId": "66e7cfa6-379e-4f68-8eb6-05ff95cedb5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "mWpXhHtoqgy3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "08eae425"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from skimage import io\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "import cv2\n",
        "import numpy as np\n",
        "import copy"
      ],
      "id": "08eae425"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "caa9094c"
      },
      "outputs": [],
      "source": [
        "in_channel=3\n",
        "num_classes=23\n",
        "learning_rate=.0001\n",
        "batch_size=50\n",
        "num_epochs=100"
      ],
      "id": "caa9094c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "095565ae"
      },
      "outputs": [],
      "source": [
        "\n",
        "classes=np.array(pd.read_csv(\"/content/drive/MyDrive/UNet_dataset (1)/label.csv\"))\n",
        "idx_to_class={}\n",
        "counter=0\n",
        "for i in classes:\n",
        "    idx_to_class[i[0]]=counter\n",
        "    counter+=1\n",
        "\n"
      ],
      "id": "095565ae"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "718e33c5"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array"
      ],
      "id": "718e33c5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "460791c9"
      },
      "outputs": [],
      "source": [
        "class microspecies(Dataset):\n",
        "    def __init__(self,csv_file,root_dir,transform=None):\n",
        "        self.annotation=pd.read_csv(csv_file)\n",
        "        self.root_dir=root_dir\n",
        "        self.transform=transform\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.annotation)\n",
        "    def __getitem__(self,index):\n",
        "        img_path=os.path.join(self.root_dir,self.annotation.iloc[index,3])\n",
        "        img=load_img(img_path)\n",
        "        image=img_to_array(img)\n",
        "        image=np.uint8(image)\n",
        "        y_label=self.annotation.iloc[index,0]\n",
        "        \n",
        "       \n",
        "        \n",
        "        if self.transform:\n",
        "            image=self.transform(image)\n",
        "        y_label=idx_to_class[y_label]\n",
        "        return (image,y_label)\n",
        "        "
      ],
      "id": "460791c9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "452291de"
      },
      "outputs": [],
      "source": [
        "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "transform=transforms.Compose([transforms.ToPILImage(),transforms.RandomHorizontalFlip(p=.5),transforms.RandomRotation(15),transforms.RandomVerticalFlip(p=.5),transforms.Resize([256,256]),transforms.ToTensor(),transforms.Normalize(.4921, .1554)])"
      ],
      "id": "452291de"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a60e12af"
      },
      "outputs": [],
      "source": [
        "train_set=microspecies(csv_file=\"/content/drive/MyDrive/UNet_dataset (1)/Triples_100Train_3600_splat_classification_resnet50.csv\",root_dir=\"/content/drive/MyDrive/UNet_dataset (1)/temp_120\",transform=transform)\n",
        "test_set=microspecies(csv_file=r\"/content/drive/MyDrive/UNet_dataset (1)/Triples_20Test_classification_resnet50.csv\",root_dir=\"/content/drive/MyDrive/UNet_dataset (1)/temp_120\",transform=transform)"
      ],
      "id": "a60e12af"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "adca96ed"
      },
      "outputs": [],
      "source": [
        "train_loader=DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True)\n",
        "test_loader=DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)"
      ],
      "id": "adca96ed"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9mXq5PBA3ek",
        "outputId": "5633177a-7945-4a9c-8f4d-2c677476bd0b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor(0.0806), tensor(1.0097))"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# normalizing images\n",
        "data = next(iter(train_loader))\n",
        "data[0].mean(), data[0].std()"
      ],
      "id": "C9mXq5PBA3ek"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1-BFM4JEc2hF"
      },
      "outputs": [],
      "source": [
        "dataloaders={\"train\":train_loader,\"val\":test_loader}"
      ],
      "id": "1-BFM4JEc2hF"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  f\"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, \"\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=GoogLeNet_Weights.IMAGENET1K_V1`. You can also use `weights=GoogLeNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/googlenet-1378be20.pth\" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\n"
          ]
        },
        {
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              "model_id": "fec4b7b8f8ed49d98a410e607f664ce4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/49.7M [00:00<?, ?B/s]"
            ]
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          "output_type": "display_data"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GoogLeNet(\n",
            "  (conv1): BasicConv2d(\n",
            "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
            "  (conv2): BasicConv2d(\n",
            "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (conv3): BasicConv2d(\n",
            "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
            "  (inception3a): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception3b): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
            "  (inception4a): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception4b): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception4c): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception4d): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception4e): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
            "  (inception5a): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (inception5b): Inception(\n",
            "    (branch1): BasicConv2d(\n",
            "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch2): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch3): Sequential(\n",
            "      (0): BasicConv2d(\n",
            "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (branch4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
            "      (1): BasicConv2d(\n",
            "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (aux1): None\n",
            "  (aux2): None\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (fc): Linear(in_features=1024, out_features=23, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "from torchvision import models\n",
        "def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):\n",
        "    # Initialize these variables which will be set in this if statement. Each of these\n",
        "    #   variables is model specific.\n",
        "    model_ft = None\n",
        "    input_size = 0\n",
        "\n",
        "    if model_name == \"resnet\":\n",
        "        \"\"\" Resnet18\n",
        "        \"\"\"\n",
        "        model_ft = models.resnet18(pretrained=use_pretrained)\n",
        "        set_parameter_requires_grad(model_ft, feature_extract)\n",
        "        num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
        "        input_size = 224\n",
        "\n",
        "    elif model_name == \"alexnet\":\n",
        "        \"\"\" Alexnet\n",
        "        \"\"\"\n",
        "        model_ft = models.alexnet(pretrained=use_pretrained)\n",
        "        set_parameter_requires_grad(model_ft, feature_extract)\n",
        "        num_ftrs = model_ft.classifier[6].in_features\n",
        "        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)\n",
        "        input_size = 224\n",
        "\n",
        "    elif model_name == \"vgg\":\n",
        "        \"\"\" VGG11_bn\n",
        "        \"\"\"\n",
        "        model_ft = models.vgg11_bn(pretrained=use_pretrained)\n",
        "        set_parameter_requires_grad(model_ft, feature_extract)\n",
        "        num_ftrs = model_ft.classifier[6].in_features\n",
        "        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)\n",
        "        input_size = 224\n",
        "\n",
        "    elif model_name == \"squeezenet\":\n",
        "        \"\"\" Squeezenet\n",
        "        \"\"\"\n",
        "        model_ft = models.squeezenet1_0(pretrained=use_pretrained)\n",
        "        set_parameter_requires_grad(model_ft, feature_extract)\n",
        "        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))\n",
        "        model_ft.num_classes = num_classes\n",
        "        input_size = 224\n",
        "\n",
        "    elif model_name == \"densenet\":\n",
        "        \"\"\" Densenet\n",
        "        \"\"\"\n",
        "        model_ft = models.densenet121(pretrained=use_pretrained)\n",
        "        set_parameter_requires_grad(model_ft, feature_extract)\n",
        "        num_ftrs = model_ft.classifier.in_features\n",
        "        model_ft.classifier = nn.Linear(num_ftrs, num_classes)\n",
        "        input_size = 224\n",
        "\n",
        "    elif model_name == \"googlenet\":\n",
        "        \"\"\" googlenet\n",
        "        Be careful, expects (256,256) sized images and has auxiliary output\n",
        "        \"\"\"\n",
        "        model_ft =torchvision.models.googlenet(pretrained=True)\n",
        "        # set_parameter_requires_grad(model_ft, True)\n",
        "        # Handle the auxilary net\n",
        "        # num_ftrs = model_ft.AuxLogits.fc.in_features\n",
        "        # model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)\n",
        "        # Handle the primary net\n",
        "        # num_ftrs = model_ft.fc.in_features\n",
        "        model_ft.fc = nn.Linear(1024,num_classes)\n",
        "        input_size = 256\n",
        "\n",
        "    else:\n",
        "        print(\"Invalid model name, exiting...\")\n",
        "        exit()\n",
        "\n",
        "    return model_ft, input_size\n",
        "\n",
        "# Initialize the model for this run\n",
        "model, input_size = initialize_model(\"googlenet\", 23, feature_extract=True, use_pretrained=True)\n",
        "\n",
        "# Print the model we just instantiated\n",
        "print(model)"
      ],
      "id": "804dd04e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2618968",
        "outputId": "b1dcc7ce-71f3-4c87-fe4e-16e5c302011b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GoogLeNet(\n",
              "  (conv1): BasicConv2d(\n",
              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (conv2): BasicConv2d(\n",
              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (conv3): BasicConv2d(\n",
              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception3a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception3b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception4a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4c): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4d): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4e): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception5a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception5b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (aux1): None\n",
              "  (aux2): None\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              "  (fc): Linear(in_features=1024, out_features=23, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# model=torchvision.models.googlenet(pretrained=True)\n",
        "# model.\n",
        "model.to(device)"
      ],
      "id": "f2618968"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "58ca4cbd"
      },
      "outputs": [],
      "source": [
        "criterion=nn.CrossEntropyLoss()\n",
        "optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate)"
      ],
      "id": "58ca4cbd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "62-OwqI_UYUE"
      },
      "outputs": [],
      "source": [
        "import time"
      ],
      "id": "62-OwqI_UYUE"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DMmpPcuATZ5y"
      },
      "outputs": [],
      "source": [
        "def train_model(model, dataloaders, criterion, optimizer, num_epochs=100, is_inception=False):\n",
        "    since = time.time()\n",
        "\n",
        "    val_acc_history = []\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()   # Set model to evaluate mode\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                # zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # forward\n",
        "                # track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    # Get model outputs and calculate loss\n",
        "                    # Special case for inception because in training it has an auxiliary output. In train\n",
        "                    #   mode we calculate the loss by summing the final output and the auxiliary output\n",
        "                    #   but in testing we only consider the final output.\n",
        "                    if is_inception and phase == 'train':\n",
        "                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958\n",
        "                        outputs, aux_outputs = model(inputs)\n",
        "                        loss1 = criterion(outputs, labels)\n",
        "                        loss2 = criterion(aux_outputs, labels)\n",
        "                        loss = loss1 + 0.4*loss2\n",
        "                    else:\n",
        "                        outputs = model(inputs)\n",
        "                        loss = criterion(outputs, labels)\n",
        "\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "\n",
        "                    # backward + optimize only if in training phase\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # statistics\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
        "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            # deep copy the model\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == 'val':\n",
        "                val_acc_history.append(epoch_acc)\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model, val_acc_history"
      ],
      "id": "DMmpPcuATZ5y"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rNrnN23UGNd",
        "outputId": "92ce38b6-94a7-4872-86fb-8ce02aee9fa0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 0/99\n",
            "----------\n",
            "train Loss: 2.7890 Acc: 0.2414\n",
            "val Loss: 2.3263 Acc: 0.4009\n",
            "\n",
            "Epoch 1/99\n",
            "----------\n",
            "train Loss: 2.0046 Acc: 0.4876\n",
            "val Loss: 1.6201 Acc: 0.5861\n",
            "\n",
            "Epoch 2/99\n",
            "----------\n",
            "train Loss: 1.4398 Acc: 0.6342\n",
            "val Loss: 1.2262 Acc: 0.6710\n",
            "\n",
            "Epoch 3/99\n",
            "----------\n",
            "train Loss: 1.0950 Acc: 0.7081\n",
            "val Loss: 0.9866 Acc: 0.7386\n",
            "\n",
            "Epoch 4/99\n",
            "----------\n",
            "train Loss: 0.8714 Acc: 0.7651\n",
            "val Loss: 0.8508 Acc: 0.7364\n",
            "\n",
            "Epoch 5/99\n",
            "----------\n",
            "train Loss: 0.7141 Acc: 0.8086\n",
            "val Loss: 0.7587 Acc: 0.7691\n",
            "\n",
            "Epoch 6/99\n",
            "----------\n",
            "train Loss: 0.6200 Acc: 0.8173\n",
            "val Loss: 0.6957 Acc: 0.7974\n",
            "\n",
            "Epoch 7/99\n",
            "----------\n",
            "train Loss: 0.5048 Acc: 0.8556\n",
            "val Loss: 0.6238 Acc: 0.8061\n",
            "\n",
            "Epoch 8/99\n",
            "----------\n",
            "train Loss: 0.4410 Acc: 0.8795\n",
            "val Loss: 0.6091 Acc: 0.8279\n",
            "\n",
            "Epoch 9/99\n",
            "----------\n",
            "train Loss: 0.3750 Acc: 0.9078\n",
            "val Loss: 0.6015 Acc: 0.8105\n",
            "\n",
            "Epoch 10/99\n",
            "----------\n",
            "train Loss: 0.3364 Acc: 0.9091\n",
            "val Loss: 0.5726 Acc: 0.8322\n",
            "\n",
            "Epoch 11/99\n",
            "----------\n",
            "train Loss: 0.3149 Acc: 0.9195\n",
            "val Loss: 0.5245 Acc: 0.8344\n",
            "\n",
            "Epoch 12/99\n",
            "----------\n",
            "train Loss: 0.2724 Acc: 0.9247\n",
            "val Loss: 0.5610 Acc: 0.8301\n",
            "\n",
            "Epoch 13/99\n",
            "----------\n",
            "train Loss: 0.2427 Acc: 0.9378\n",
            "val Loss: 0.5454 Acc: 0.8322\n",
            "\n",
            "Epoch 14/99\n",
            "----------\n",
            "train Loss: 0.2185 Acc: 0.9426\n",
            "val Loss: 0.5484 Acc: 0.8235\n",
            "\n",
            "Epoch 15/99\n",
            "----------\n",
            "train Loss: 0.2009 Acc: 0.9500\n",
            "val Loss: 0.5612 Acc: 0.8235\n",
            "\n",
            "Epoch 16/99\n",
            "----------\n",
            "train Loss: 0.1747 Acc: 0.9530\n",
            "val Loss: 0.5562 Acc: 0.8344\n",
            "\n",
            "Epoch 17/99\n",
            "----------\n",
            "train Loss: 0.1637 Acc: 0.9556\n",
            "val Loss: 0.5150 Acc: 0.8301\n",
            "\n",
            "Epoch 18/99\n",
            "----------\n",
            "train Loss: 0.1443 Acc: 0.9665\n",
            "val Loss: 0.4911 Acc: 0.8519\n",
            "\n",
            "Epoch 19/99\n",
            "----------\n",
            "train Loss: 0.1426 Acc: 0.9648\n",
            "val Loss: 0.5028 Acc: 0.8410\n",
            "\n",
            "Epoch 20/99\n",
            "----------\n",
            "train Loss: 0.1217 Acc: 0.9696\n",
            "val Loss: 0.5245 Acc: 0.8366\n",
            "\n",
            "Epoch 21/99\n",
            "----------\n",
            "train Loss: 0.1146 Acc: 0.9730\n",
            "val Loss: 0.5300 Acc: 0.8540\n",
            "\n",
            "Epoch 22/99\n",
            "----------\n",
            "train Loss: 0.0996 Acc: 0.9761\n",
            "val Loss: 0.5457 Acc: 0.8453\n",
            "\n",
            "Epoch 23/99\n",
            "----------\n",
            "train Loss: 0.1106 Acc: 0.9748\n",
            "val Loss: 0.5816 Acc: 0.8322\n",
            "\n",
            "Epoch 24/99\n",
            "----------\n",
            "train Loss: 0.0791 Acc: 0.9830\n",
            "val Loss: 0.5557 Acc: 0.8170\n",
            "\n",
            "Epoch 25/99\n",
            "----------\n",
            "train Loss: 0.0737 Acc: 0.9861\n",
            "val Loss: 0.5631 Acc: 0.8301\n",
            "\n",
            "Epoch 26/99\n",
            "----------\n",
            "train Loss: 0.0757 Acc: 0.9787\n",
            "val Loss: 0.4952 Acc: 0.8540\n",
            "\n",
            "Epoch 27/99\n",
            "----------\n",
            "train Loss: 0.0736 Acc: 0.9787\n",
            "val Loss: 0.5366 Acc: 0.8410\n",
            "\n",
            "Epoch 28/99\n",
            "----------\n",
            "train Loss: 0.0741 Acc: 0.9813\n",
            "val Loss: 0.5165 Acc: 0.8366\n",
            "\n",
            "Epoch 29/99\n",
            "----------\n",
            "train Loss: 0.0592 Acc: 0.9887\n",
            "val Loss: 0.5040 Acc: 0.8410\n",
            "\n",
            "Epoch 30/99\n",
            "----------\n",
            "train Loss: 0.0488 Acc: 0.9922\n",
            "val Loss: 0.5785 Acc: 0.8388\n",
            "\n",
            "Epoch 31/99\n",
            "----------\n",
            "train Loss: 0.0474 Acc: 0.9913\n",
            "val Loss: 0.5753 Acc: 0.8257\n",
            "\n",
            "Epoch 32/99\n",
            "----------\n",
            "train Loss: 0.0436 Acc: 0.9909\n",
            "val Loss: 0.5740 Acc: 0.8279\n",
            "\n",
            "Epoch 33/99\n",
            "----------\n",
            "train Loss: 0.0520 Acc: 0.9852\n",
            "val Loss: 0.4918 Acc: 0.8606\n",
            "\n",
            "Epoch 34/99\n",
            "----------\n",
            "train Loss: 0.0524 Acc: 0.9874\n",
            "val Loss: 0.5760 Acc: 0.8235\n",
            "\n",
            "Epoch 35/99\n",
            "----------\n",
            "train Loss: 0.0642 Acc: 0.9856\n",
            "val Loss: 0.5880 Acc: 0.8301\n",
            "\n",
            "Epoch 36/99\n",
            "----------\n",
            "train Loss: 0.0425 Acc: 0.9926\n",
            "val Loss: 0.4691 Acc: 0.8736\n",
            "\n",
            "Epoch 37/99\n",
            "----------\n",
            "train Loss: 0.0472 Acc: 0.9900\n",
            "val Loss: 0.6076 Acc: 0.8366\n",
            "\n",
            "Epoch 38/99\n",
            "----------\n",
            "train Loss: 0.0367 Acc: 0.9913\n",
            "val Loss: 0.5606 Acc: 0.8322\n",
            "\n",
            "Epoch 39/99\n",
            "----------\n",
            "train Loss: 0.0429 Acc: 0.9874\n",
            "val Loss: 0.5764 Acc: 0.8410\n",
            "\n",
            "Epoch 40/99\n",
            "----------\n",
            "train Loss: 0.0379 Acc: 0.9930\n",
            "val Loss: 0.5769 Acc: 0.8497\n",
            "\n",
            "Epoch 41/99\n",
            "----------\n",
            "train Loss: 0.0389 Acc: 0.9891\n",
            "val Loss: 0.5248 Acc: 0.8562\n",
            "\n",
            "Epoch 42/99\n",
            "----------\n",
            "train Loss: 0.0418 Acc: 0.9891\n",
            "val Loss: 0.5552 Acc: 0.8431\n",
            "\n",
            "Epoch 43/99\n",
            "----------\n",
            "train Loss: 0.0400 Acc: 0.9883\n",
            "val Loss: 0.5909 Acc: 0.8301\n",
            "\n",
            "Epoch 44/99\n",
            "----------\n",
            "train Loss: 0.0341 Acc: 0.9913\n",
            "val Loss: 0.6443 Acc: 0.8388\n",
            "\n",
            "Epoch 45/99\n",
            "----------\n",
            "train Loss: 0.0314 Acc: 0.9926\n",
            "val Loss: 0.5197 Acc: 0.8519\n",
            "\n",
            "Epoch 46/99\n",
            "----------\n",
            "train Loss: 0.0295 Acc: 0.9939\n",
            "val Loss: 0.5923 Acc: 0.8257\n",
            "\n",
            "Epoch 47/99\n",
            "----------\n",
            "train Loss: 0.0283 Acc: 0.9943\n",
            "val Loss: 0.5610 Acc: 0.8519\n",
            "\n",
            "Epoch 48/99\n",
            "----------\n",
            "train Loss: 0.0392 Acc: 0.9904\n",
            "val Loss: 0.6083 Acc: 0.8540\n",
            "\n",
            "Epoch 49/99\n",
            "----------\n",
            "train Loss: 0.0185 Acc: 0.9970\n",
            "val Loss: 0.5581 Acc: 0.8606\n",
            "\n",
            "Epoch 50/99\n",
            "----------\n",
            "train Loss: 0.0248 Acc: 0.9957\n",
            "val Loss: 0.5959 Acc: 0.8497\n",
            "\n",
            "Epoch 51/99\n",
            "----------\n",
            "train Loss: 0.0340 Acc: 0.9909\n",
            "val Loss: 0.4660 Acc: 0.8802\n",
            "\n",
            "Epoch 52/99\n",
            "----------\n",
            "train Loss: 0.0363 Acc: 0.9917\n",
            "val Loss: 0.5439 Acc: 0.8627\n",
            "\n",
            "Epoch 53/99\n",
            "----------\n",
            "train Loss: 0.0234 Acc: 0.9948\n",
            "val Loss: 0.5673 Acc: 0.8322\n",
            "\n",
            "Epoch 54/99\n",
            "----------\n",
            "train Loss: 0.0241 Acc: 0.9952\n",
            "val Loss: 0.5618 Acc: 0.8453\n",
            "\n",
            "Epoch 55/99\n",
            "----------\n",
            "train Loss: 0.0296 Acc: 0.9913\n",
            "val Loss: 0.5941 Acc: 0.8497\n",
            "\n",
            "Epoch 56/99\n",
            "----------\n",
            "train Loss: 0.0252 Acc: 0.9948\n",
            "val Loss: 0.5911 Acc: 0.8606\n",
            "\n",
            "Epoch 57/99\n",
            "----------\n",
            "train Loss: 0.0308 Acc: 0.9913\n",
            "val Loss: 0.5983 Acc: 0.8453\n",
            "\n",
            "Epoch 58/99\n",
            "----------\n",
            "train Loss: 0.0282 Acc: 0.9939\n",
            "val Loss: 0.6532 Acc: 0.8192\n",
            "\n",
            "Epoch 59/99\n",
            "----------\n",
            "train Loss: 0.0278 Acc: 0.9930\n",
            "val Loss: 0.5764 Acc: 0.8366\n",
            "\n",
            "Epoch 60/99\n",
            "----------\n",
            "train Loss: 0.0407 Acc: 0.9865\n",
            "val Loss: 0.5969 Acc: 0.8301\n",
            "\n",
            "Epoch 61/99\n",
            "----------\n",
            "train Loss: 0.0293 Acc: 0.9917\n",
            "val Loss: 0.6260 Acc: 0.8279\n",
            "\n",
            "Epoch 62/99\n",
            "----------\n",
            "train Loss: 0.0206 Acc: 0.9974\n",
            "val Loss: 0.5672 Acc: 0.8497\n",
            "\n",
            "Epoch 63/99\n",
            "----------\n",
            "train Loss: 0.0317 Acc: 0.9891\n",
            "val Loss: 0.6487 Acc: 0.8235\n",
            "\n",
            "Epoch 64/99\n",
            "----------\n",
            "train Loss: 0.0273 Acc: 0.9930\n",
            "val Loss: 0.6097 Acc: 0.8431\n",
            "\n",
            "Epoch 65/99\n",
            "----------\n",
            "train Loss: 0.0349 Acc: 0.9891\n",
            "val Loss: 0.6159 Acc: 0.8649\n",
            "\n",
            "Epoch 66/99\n",
            "----------\n",
            "train Loss: 0.0266 Acc: 0.9926\n",
            "val Loss: 0.6422 Acc: 0.8431\n",
            "\n",
            "Epoch 67/99\n",
            "----------\n",
            "train Loss: 0.0252 Acc: 0.9935\n",
            "val Loss: 0.5902 Acc: 0.8475\n",
            "\n",
            "Epoch 68/99\n",
            "----------\n",
            "train Loss: 0.0338 Acc: 0.9878\n",
            "val Loss: 0.5866 Acc: 0.8257\n",
            "\n",
            "Epoch 69/99\n",
            "----------\n",
            "train Loss: 0.0258 Acc: 0.9926\n",
            "val Loss: 0.5403 Acc: 0.8606\n",
            "\n",
            "Epoch 70/99\n",
            "----------\n",
            "train Loss: 0.0236 Acc: 0.9952\n",
            "val Loss: 0.5620 Acc: 0.8475\n",
            "\n",
            "Epoch 71/99\n",
            "----------\n",
            "train Loss: 0.0160 Acc: 0.9965\n",
            "val Loss: 0.6207 Acc: 0.8562\n",
            "\n",
            "Epoch 72/99\n",
            "----------\n",
            "train Loss: 0.0183 Acc: 0.9957\n",
            "val Loss: 0.6337 Acc: 0.8475\n",
            "\n",
            "Epoch 73/99\n",
            "----------\n",
            "train Loss: 0.0250 Acc: 0.9943\n",
            "val Loss: 0.5359 Acc: 0.8519\n",
            "\n",
            "Epoch 74/99\n",
            "----------\n",
            "train Loss: 0.0157 Acc: 0.9974\n",
            "val Loss: 0.7021 Acc: 0.8344\n",
            "\n",
            "Epoch 75/99\n",
            "----------\n",
            "train Loss: 0.0154 Acc: 0.9970\n",
            "val Loss: 0.6986 Acc: 0.8366\n",
            "\n",
            "Epoch 76/99\n",
            "----------\n",
            "train Loss: 0.0266 Acc: 0.9935\n",
            "val Loss: 0.5933 Acc: 0.8344\n",
            "\n",
            "Epoch 77/99\n",
            "----------\n",
            "train Loss: 0.0229 Acc: 0.9935\n",
            "val Loss: 0.6945 Acc: 0.8105\n",
            "\n",
            "Epoch 78/99\n",
            "----------\n",
            "train Loss: 0.0208 Acc: 0.9948\n",
            "val Loss: 0.6418 Acc: 0.8519\n",
            "\n",
            "Epoch 79/99\n",
            "----------\n",
            "train Loss: 0.0151 Acc: 0.9965\n",
            "val Loss: 0.6530 Acc: 0.8453\n",
            "\n",
            "Epoch 80/99\n",
            "----------\n",
            "train Loss: 0.0242 Acc: 0.9943\n",
            "val Loss: 0.6484 Acc: 0.8279\n",
            "\n",
            "Epoch 81/99\n",
            "----------\n",
            "train Loss: 0.0289 Acc: 0.9913\n",
            "val Loss: 0.6978 Acc: 0.8148\n",
            "\n",
            "Epoch 82/99\n",
            "----------\n",
            "train Loss: 0.0328 Acc: 0.9887\n",
            "val Loss: 0.7853 Acc: 0.7974\n",
            "\n",
            "Epoch 83/99\n",
            "----------\n",
            "train Loss: 0.0224 Acc: 0.9930\n",
            "val Loss: 0.6995 Acc: 0.8214\n",
            "\n",
            "Epoch 84/99\n",
            "----------\n",
            "train Loss: 0.0301 Acc: 0.9900\n",
            "val Loss: 0.7723 Acc: 0.8126\n",
            "\n",
            "Epoch 85/99\n",
            "----------\n",
            "train Loss: 0.0252 Acc: 0.9939\n",
            "val Loss: 0.5851 Acc: 0.8453\n",
            "\n",
            "Epoch 86/99\n",
            "----------\n",
            "train Loss: 0.0226 Acc: 0.9939\n",
            "val Loss: 0.6251 Acc: 0.8497\n",
            "\n",
            "Epoch 87/99\n",
            "----------\n",
            "train Loss: 0.0285 Acc: 0.9909\n",
            "val Loss: 0.5945 Acc: 0.8584\n",
            "\n",
            "Epoch 88/99\n",
            "----------\n",
            "train Loss: 0.0203 Acc: 0.9939\n",
            "val Loss: 0.6951 Acc: 0.8344\n",
            "\n",
            "Epoch 89/99\n",
            "----------\n",
            "train Loss: 0.0234 Acc: 0.9930\n",
            "val Loss: 0.6273 Acc: 0.8257\n",
            "\n",
            "Epoch 90/99\n",
            "----------\n",
            "train Loss: 0.0141 Acc: 0.9965\n",
            "val Loss: 0.6495 Acc: 0.8431\n",
            "\n",
            "Epoch 91/99\n",
            "----------\n",
            "train Loss: 0.0223 Acc: 0.9943\n",
            "val Loss: 0.6580 Acc: 0.8257\n",
            "\n",
            "Epoch 92/99\n",
            "----------\n",
            "train Loss: 0.0178 Acc: 0.9952\n",
            "val Loss: 0.6591 Acc: 0.8453\n",
            "\n",
            "Epoch 93/99\n",
            "----------\n",
            "train Loss: 0.0146 Acc: 0.9970\n",
            "val Loss: 0.6030 Acc: 0.8388\n",
            "\n",
            "Epoch 94/99\n",
            "----------\n",
            "train Loss: 0.0131 Acc: 0.9970\n",
            "val Loss: 0.6914 Acc: 0.8410\n",
            "\n",
            "Epoch 95/99\n",
            "----------\n",
            "train Loss: 0.0107 Acc: 0.9978\n",
            "val Loss: 0.9584 Acc: 0.8017\n",
            "\n",
            "Epoch 96/99\n",
            "----------\n",
            "train Loss: 0.0156 Acc: 0.9965\n",
            "val Loss: 0.6691 Acc: 0.8431\n",
            "\n",
            "Epoch 97/99\n",
            "----------\n",
            "train Loss: 0.0264 Acc: 0.9922\n",
            "val Loss: 0.8258 Acc: 0.7974\n",
            "\n",
            "Epoch 98/99\n",
            "----------\n",
            "train Loss: 0.0231 Acc: 0.9939\n",
            "val Loss: 0.6883 Acc: 0.8366\n",
            "\n",
            "Epoch 99/99\n",
            "----------\n",
            "train Loss: 0.0295 Acc: 0.9913\n",
            "val Loss: 0.5915 Acc: 0.8475\n",
            "\n",
            "Training complete in 53m 31s\n",
            "Best val Acc: 0.880174\n"
          ]
        }
      ],
      "source": [
        "x=train_model(model, dataloaders, criterion, optimizer, num_epochs=100, is_inception=False)"
      ],
      "id": "0rNrnN23UGNd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "06Ce3Xx2fzar"
      },
      "outputs": [],
      "source": [
        "file=\"/content/drive/MyDrive/UNet_dataset (1)/model_googlenet_100epoch.pth\"\n",
        "# torch.save(model.state_dict(),file)"
      ],
      "id": "06Ce3Xx2fzar"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q04FOShbaL2F",
        "outputId": "683b2efd-d489-4467-bd27-2ce00bb0015d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  f\"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, \"\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=GoogLeNet_Weights.IMAGENET1K_V1`. You can also use `weights=GoogLeNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "GoogLeNet(\n",
              "  (conv1): BasicConv2d(\n",
              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (conv2): BasicConv2d(\n",
              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (conv3): BasicConv2d(\n",
              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception3a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception3b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception4a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4c): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4d): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4e): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception5a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception5b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (aux1): None\n",
              "  (aux2): None\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              "  (fc): Linear(in_features=1024, out_features=23, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model, input_size = initialize_model(\"googlenet\", 23, feature_extract=True, use_pretrained=True)\n",
        "model.load_state_dict(torch.load(file))\n",
        "model.eval()"
      ],
      "id": "Q04FOShbaL2F"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dL8O8gnhbotq",
        "outputId": "98038abe-b2f3-49e5-b966-1e6176d7ce88"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GoogLeNet(\n",
              "  (conv1): BasicConv2d(\n",
              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (conv2): BasicConv2d(\n",
              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (conv3): BasicConv2d(\n",
              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception3a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception3b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception4a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4c): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4d): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4e): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception5a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception5b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (aux1): None\n",
              "  (aux2): None\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              "  (fc): Linear(in_features=1024, out_features=23, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.to(device)"
      ],
      "id": "dL8O8gnhbotq"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcFBqF8sUTI7"
      },
      "source": [
        ""
      ],
      "id": "fcFBqF8sUTI7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "8d353911",
        "outputId": "b881b353-791c-4a1d-ee0e-85b11d240421"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[14  1  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0  0  0  0  0  1  0]\n",
            " [ 0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3]\n",
            " [ 2  0 16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1]\n",
            " [ 0  0  0 18  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 15  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  1 18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
            " [ 0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  1  0  0  0  0  2  0]\n",
            " [ 0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 1  2  1  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  1  1]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  1  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  2  0  0  0  0 18  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  1  0  0  2  0  0  0  0  0  0 15  0  0  0  0  2  0]\n",
            " [ 0  0  0  0  0  0  0  1  0  0  0  2  0  0  0  0  0 13  1  0  0  3  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  1  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  1  0 18  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0]\n",
            " [ 1  3  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 14]]\n",
            "[ 73.68421053  85.          80.          90.          75.\n",
            "  90.         100.          85.         100.         100.\n",
            " 100.          70.         100.         100.          95.\n",
            "  90.          75.          65.          95.         100.\n",
            "  90.         100.          70.        ]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "nb_classes = 23\n",
        "\n",
        "# Initialize the prediction and label lists(tensors)\n",
        "predlist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
        "lbllist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
        "\n",
        "with torch.no_grad():\n",
        "    for i, (inputs, classes) in enumerate(dataloaders['val']):\n",
        "        inputs = inputs.to(device)\n",
        "        classes = classes.to(device)\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "\n",
        "        # Append batch prediction results\n",
        "        predlist=torch.cat([predlist,preds.view(-1).cpu()])\n",
        "        lbllist=torch.cat([lbllist,classes.view(-1).cpu()])\n",
        "\n",
        "# Confusion matrix\n",
        "conf_mat=confusion_matrix(lbllist.numpy(), predlist.numpy())\n",
        "print(conf_mat)\n",
        "\n",
        "# Per-class accuracy\n",
        "class_accuracy=100*conf_mat.diagonal()/conf_mat.sum(1)\n",
        "print(class_accuracy)"
      ],
      "id": "8d353911"
    }
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