MobileNet_v3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Lcggou3dZaV_",
        "mu6lNvGgZiAe",
        "4LvVxZKleMqu",
        "gmXQEcWyibD3",
        "9xItkqWrjN0m",
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        "O2-k53yawVWb",
        "kFhnK6xE1nuJ",
        "a1N-mwDMKwkG",
        "Rp97eGEILADT"
      ],
      "authorship_tag": "ABX9TyO0GWk/EKYFg8yy+JK2zNiL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kim-Jeong-Ju/AI_Modeling_Computer_Vision/blob/main/MobileNet_v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Drive Mount and Packages/Modules Importation**"
      ],
      "metadata": {
        "id": "Lcggou3dZaV_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tZWwSqBLOE07",
        "outputId": "467a36f3-4dd9-42f2-c540-b4bf0c29e44b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import os\n",
        "import time\n",
        "from tqdm import tqdm\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from collections import OrderedDict\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "from torchsummary import summary\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **※ Dataset Preparation**"
      ],
      "metadata": {
        "id": "mu6lNvGgZiAe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**CIFAR-10 Dataset** -- *32 X 32 pixels RGB Color images* with 10 class labels  \n",
        "0(비행기), 1(자동차), 2(새), 3(고양이), 4(사슴), 5(개), 6(개구리), 7(말), 8(배), 9(트럭)"
      ],
      "metadata": {
        "id": "4LvVxZKleMqu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Define Dataset Root Path and Transforms\n",
        "root = os.path.join(os.getcwd(), \"drive\", \"MyDrive\", \"Colab Notebooks\", \"AI Algorithm Modeling\", \"data\")\n",
        "\n",
        "apply_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor()\n",
        "])\n",
        "\n",
        "\n",
        "## Get CIFAR-10 Image Dataset\n",
        "train_data = torchvision.datasets.CIFAR10(\n",
        "    root=root,\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=apply_transform\n",
        ")\n",
        "\n",
        "test_data = torchvision.datasets.CIFAR10(\n",
        "    root=root,\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=apply_transform\n",
        ")\n",
        "\n",
        "\n",
        "## Define Image Plot function\n",
        "def sample_img_show(data, figsize=(20, 10)):\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    for i in range(9):\n",
        "        image = data[i][0]\n",
        "        ax = fig.add_subplot(3, 3, i+1)\n",
        "        ax.imshow(image.permute(1, 2, 0))\n",
        "        ax.set_title(f\"Class Label : {data[i][1]}\")\n",
        "    fig.show()\n",
        "\n",
        "\n",
        "## Check CIFAR-10 Dataset\n",
        "print(f\"Train Data Length : {len(train_data.targets)}\")\n",
        "print(f\"# of Class Labels : {np.unique(train_data.targets)}\")\n",
        "sample_img_show(data=train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ii-4E2AsexnN",
        "outputId": "4a03f289-5085-428f-c4e9-b323754e8cff"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Train Data Length : 50000\n",
            "# of Class Labels : [0 1 2 3 4 5 6 7 8 9]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Training / Validation Dataset Split**"
      ],
      "metadata": {
        "id": "gmXQEcWyibD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Training / Validation Dataset Split with random_split\n",
        "random_seed = 0\n",
        "torch.manual_seed(random_seed)\n",
        "\n",
        "valid_size = int(len(train_data) * 0.2)\n",
        "train_size = len(train_data) - valid_size\n",
        "train_data, valid_data = random_split(train_data, [train_size, valid_size])\n",
        "\n",
        "print(f\"Training Dataset : {len(train_data)}\")\n",
        "print(f\"Validation Dataset : {len(valid_data)}\")\n",
        "print()\n",
        "\n",
        "print(train_data[0][0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g56WZDSoek_m",
        "outputId": "a0c7ed87-83c8-473f-cc23-6c01d381c642"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Dataset : 40000\n",
            "Validation Dataset : 10000\n",
            "\n",
            "torch.Size([3, 224, 224])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Definition of DataLoader Class**"
      ],
      "metadata": {
        "id": "9xItkqWrjN0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Definition of DataLoader Class\n",
        "train_loader = DataLoader(dataset=train_data, batch_size=50, shuffle=True, drop_last=True)\n",
        "valid_loader = DataLoader(dataset=valid_data, batch_size=50, shuffle=True, drop_last=True)\n",
        "test_loader = DataLoader(dataset=test_data, batch_size=50, shuffle=False, drop_last=False)\n",
        "\n",
        "loaders = {}\n",
        "loaders[\"train\"] = train_loader\n",
        "loaders[\"valid\"] = valid_loader\n",
        "loaders[\"test\"] = test_loader\n",
        "\n",
        "print(f\"Train Loader Length = {len(train_loader)},  Valid Loader Length = {len(valid_loader)},  Test Loader Length = {len(test_loader)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEJFhgPujbDI",
        "outputId": "3a5a21fa-40b3-490f-dfc5-2f937cd20b18"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loader Length = 800,  Valid Loader Length = 200,  Test Loader Length = 200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF0000\"> **※ MobileNet v3 Model and Actuator Definition** </font>"
      ],
      "metadata": {
        "id": "XnVaP2P6lPvN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#FF0000\"> **① Architecture of MobileNet v3 Model with Inverted Residual BottleNeck and SE Block and Efficient Last Stage** </font>"
      ],
      "metadata": {
        "id": "9ntxlDjZwOI8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## MobileNet v3 Model with Inverted Residual BottleNeck and SE Block and Efficient Last Stage\n",
        "class MobileNetv3(nn.Module):\n",
        "    def __init__(self, n_class=10):\n",
        "        super().__init__()                                      # Input Image Tensor = 3 X 224 X 224\n",
        "\n",
        "        self.input_block = nn.Sequential(                       # ① Input Conv Block = 16 X 112 X 112\n",
        "            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Hardswish()\n",
        "        )\n",
        "\n",
        "        self.a_block = nn.Sequential(                           # ② A Block of Inverted Residual BottleNeck = 24 X 56 X 56\n",
        "            invert_res_bottle(in_channels=16, out_channels=16, kernel_size=3, stride=1, exp_size=16, act=\"relu\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=16, out_channels=24, kernel_size=3, stride=2, exp_size=64, act=\"relu\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=24, out_channels=24, kernel_size=3, stride=1, exp_size=72, act=\"relu\", is_se_block=False)\n",
        "        )\n",
        "\n",
        "        self.b_block = nn.Sequential(                           # ③ B Block of Inverted Residual BottleNeck = 40 X 28 X 28\n",
        "            invert_res_bottle(in_channels=24, out_channels=40, kernel_size=5, stride=2, exp_size=72, act=\"relu\", is_se_block=True),\n",
        "            invert_res_bottle(in_channels=40, out_channels=40, kernel_size=5, stride=1, exp_size=120, act=\"relu\", is_se_block=True),\n",
        "            invert_res_bottle(in_channels=40, out_channels=40, kernel_size=5, stride=1, exp_size=120, act=\"relu\", is_se_block=True)\n",
        "        )\n",
        "\n",
        "        self.c_block = nn.Sequential(                           # ④ C Block of Inverted Residual BottleNeck = 112 X 14 X 14\n",
        "            invert_res_bottle(in_channels=40, out_channels=80, kernel_size=3, stride=2, exp_size=240, act=\"hswish\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=80, out_channels=80, kernel_size=3, stride=1, exp_size=200, act=\"hswish\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=80, out_channels=80, kernel_size=3, stride=1, exp_size=184, act=\"hswish\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=80, out_channels=80, kernel_size=3, stride=1, exp_size=184, act=\"hswish\", is_se_block=False),\n",
        "            invert_res_bottle(in_channels=80, out_channels=112, kernel_size=3, stride=1, exp_size=480, act=\"hswish\", is_se_block=True),\n",
        "            invert_res_bottle(in_channels=112, out_channels=112, kernel_size=3, stride=1, exp_size=672, act=\"hswish\", is_se_block=True)\n",
        "        )\n",
        "\n",
        "        self.d_block = nn.Sequential(                           # ⑤ D Block of Inverted Residual BottleNeck = 160 X 7 X 7\n",
        "            invert_res_bottle(in_channels=112, out_channels=160, kernel_size=5, stride=2, exp_size=672, act=\"hswish\", is_se_block=True),\n",
        "            invert_res_bottle(in_channels=160, out_channels=160, kernel_size=5, stride=1, exp_size=960, act=\"hswish\", is_se_block=True),\n",
        "            invert_res_bottle(in_channels=160, out_channels=160, kernel_size=5, stride=1, exp_size=960, act=\"hswish\", is_se_block=True)\n",
        "        )\n",
        "\n",
        "        self.output_block = nn.Sequential(                      # ⑥ Output Efficient Last Stage = 10 Vectorize\n",
        "            nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(960),\n",
        "            nn.Hardswish(),\n",
        "\n",
        "            nn.AdaptiveAvgPool2d(1),\n",
        "\n",
        "            nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1),\n",
        "            nn.Hardswish(),\n",
        "\n",
        "            nn.Conv2d(in_channels=1280, out_channels=10, kernel_size=1, stride=1),\n",
        "\n",
        "            nn.Flatten()\n",
        "        )\n",
        "        \n",
        "\n",
        "    def forward(self, input):\n",
        "        temp1 = self.input_block(input)\n",
        "        temp2 = self.a_block(temp1)\n",
        "        temp3 = self.b_block(temp2)\n",
        "        temp4 = self.c_block(temp3)\n",
        "        temp5 = self.d_block(temp4)\n",
        "        output = self.output_block(temp5)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Inverted Residual BottleNeck in MobileNet v3 Model\n",
        "class invert_res_bottle(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel_size, stride, exp_size, act, is_se_block):\n",
        "        super().__init__()\n",
        "\n",
        "        if act == \"relu\": self.act = nn.ReLU6()\n",
        "        else: self.act = nn.Hardswish()\n",
        "\n",
        "        self.padding = kernel_size // 2\n",
        "\n",
        "        self.expansion = nn.Sequential(                                                             # ① Expansion Convolution Architecture in Inverted Residual BottleNeck\n",
        "            nn.Conv2d(in_channels=in_channels, out_channels=exp_size, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(exp_size),\n",
        "            self.act\n",
        "        )\n",
        "\n",
        "        self.depthwise = nn.Sequential(                                                             # ② Depthwise Convolution Architecture in Inverted Residual BottleNeck\n",
        "            nn.Conv2d(in_channels=exp_size, out_channels=exp_size, kernel_size=kernel_size, stride=stride, padding=self.padding, groups=exp_size),\n",
        "            nn.BatchNorm2d(exp_size),\n",
        "            self.act\n",
        "        )\n",
        "\n",
        "        self.is_se_block = is_se_block                                                              # ③ Squeeze and Excitation Algorithm in Inverted Residual BottleNeck\n",
        "        if self.is_se_block: self.se_block = se_block(channels=exp_size)\n",
        "\n",
        "        self.projection = nn.Sequential(                                                            # ④ Projection Convolution Architecture in Inverted Residual BottleNeck\n",
        "            nn.Conv2d(in_channels=exp_size, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "        )\n",
        "\n",
        "        self.adjust_size = nn.Sequential(                                                           # Adjustment Block for Half Size Division of Feature map\n",
        "            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=2),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.Hardswish()\n",
        "        )\n",
        "\n",
        "        self.adjust_channel = nn.Sequential(                                                        # Adjustment Block for Channel Difference of Feature map\n",
        "            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.Hardswish()\n",
        "        )\n",
        "    \n",
        "    def forward(self, input):\n",
        "        temp1 = self.expansion(input)\n",
        "        temp2 = self.depthwise(temp1)\n",
        "\n",
        "        if self.is_se_block:\n",
        "            temp2 = self.se_block(temp2)\n",
        "        \n",
        "        temp3 = self.projection(temp2)\n",
        "\n",
        "        if input.size(2) != temp3.size(2) and input.size(3) != temp3.size(3):           # Adjustment Process for Half Size Division of Feature map\n",
        "            input = self.adjust_size(input)\n",
        "        if input.size(1) != temp3.size(1):                                              # Adjustment Process for Channel Difference of Feature map\n",
        "            input = self.adjust_channel(input)\n",
        "\n",
        "        output = input + temp3\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Squeeze and Excitation Block in MobileNet v3 Model\n",
        "class se_block(nn.Module):\n",
        "    def __init__(self, channels, ratio=16):\n",
        "        super().__init__()\n",
        "\n",
        "        self.squeeze = nn.AdaptiveAvgPool2d(1)          # ① Squeeze Process of SE Algorithm => Adaptive Average Pooling\n",
        "\n",
        "        self.excitation = nn.Sequential(                # ② Excitation Process of SE Algorithm => ReLU & H-Swish for Actiavation function\n",
        "            nn.Flatten(),\n",
        "\n",
        "            nn.Linear(in_features=channels, out_features=int(channels/ratio)),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=int(channels/ratio), out_features=channels),\n",
        "            nn.Hardswish()\n",
        "        )\n",
        "    \n",
        "    def forward(self, input):\n",
        "        temp1 = self.squeeze(input)\n",
        "        temp2 = self.excitation(temp1)\n",
        "        contribute = temp2.view(temp2.size(0), temp2.size(1), 1, 1)\n",
        "        output = torch.mul(input, contribute)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "mobilenetv3_sample = MobileNetv3(n_class=10).to(device)           # MobileNet v3 Model for Sample Version\n",
        "summary(mobilenetv3_sample, (3, 224, 224))"
      ],
      "metadata": {
        "id": "AzFieg6VlVts",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2de9485-6b58-42ce-aff8-a6d66a0d87eb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 16, 112, 112]             448\n",
            "       BatchNorm2d-2         [-1, 16, 112, 112]              32\n",
            "         Hardswish-3         [-1, 16, 112, 112]               0\n",
            "            Conv2d-4         [-1, 16, 112, 112]             272\n",
            "       BatchNorm2d-5         [-1, 16, 112, 112]              32\n",
            "             ReLU6-6         [-1, 16, 112, 112]               0\n",
            "             ReLU6-7         [-1, 16, 112, 112]               0\n",
            "             ReLU6-8         [-1, 16, 112, 112]               0\n",
            "            Conv2d-9         [-1, 16, 112, 112]             160\n",
            "      BatchNorm2d-10         [-1, 16, 112, 112]              32\n",
            "            ReLU6-11         [-1, 16, 112, 112]               0\n",
            "            ReLU6-12         [-1, 16, 112, 112]               0\n",
            "            ReLU6-13         [-1, 16, 112, 112]               0\n",
            "           Conv2d-14         [-1, 16, 112, 112]             272\n",
            "      BatchNorm2d-15         [-1, 16, 112, 112]              32\n",
            "invert_res_bottle-16         [-1, 16, 112, 112]               0\n",
            "           Conv2d-17         [-1, 64, 112, 112]           1,088\n",
            "      BatchNorm2d-18         [-1, 64, 112, 112]             128\n",
            "            ReLU6-19         [-1, 64, 112, 112]               0\n",
            "            ReLU6-20         [-1, 64, 112, 112]               0\n",
            "            ReLU6-21         [-1, 64, 112, 112]               0\n",
            "           Conv2d-22           [-1, 64, 56, 56]             640\n",
            "      BatchNorm2d-23           [-1, 64, 56, 56]             128\n",
            "            ReLU6-24           [-1, 64, 56, 56]               0\n",
            "            ReLU6-25           [-1, 64, 56, 56]               0\n",
            "            ReLU6-26           [-1, 64, 56, 56]               0\n",
            "           Conv2d-27           [-1, 24, 56, 56]           1,560\n",
            "      BatchNorm2d-28           [-1, 24, 56, 56]              48\n",
            "           Conv2d-29           [-1, 24, 56, 56]             408\n",
            "      BatchNorm2d-30           [-1, 24, 56, 56]              48\n",
            "        Hardswish-31           [-1, 24, 56, 56]               0\n",
            "invert_res_bottle-32           [-1, 24, 56, 56]               0\n",
            "           Conv2d-33           [-1, 72, 56, 56]           1,800\n",
            "      BatchNorm2d-34           [-1, 72, 56, 56]             144\n",
            "            ReLU6-35           [-1, 72, 56, 56]               0\n",
            "            ReLU6-36           [-1, 72, 56, 56]               0\n",
            "            ReLU6-37           [-1, 72, 56, 56]               0\n",
            "           Conv2d-38           [-1, 72, 56, 56]             720\n",
            "      BatchNorm2d-39           [-1, 72, 56, 56]             144\n",
            "            ReLU6-40           [-1, 72, 56, 56]               0\n",
            "            ReLU6-41           [-1, 72, 56, 56]               0\n",
            "            ReLU6-42           [-1, 72, 56, 56]               0\n",
            "           Conv2d-43           [-1, 24, 56, 56]           1,752\n",
            "      BatchNorm2d-44           [-1, 24, 56, 56]              48\n",
            "invert_res_bottle-45           [-1, 24, 56, 56]               0\n",
            "           Conv2d-46           [-1, 72, 56, 56]           1,800\n",
            "      BatchNorm2d-47           [-1, 72, 56, 56]             144\n",
            "            ReLU6-48           [-1, 72, 56, 56]               0\n",
            "            ReLU6-49           [-1, 72, 56, 56]               0\n",
            "            ReLU6-50           [-1, 72, 56, 56]               0\n",
            "           Conv2d-51           [-1, 72, 28, 28]           1,872\n",
            "      BatchNorm2d-52           [-1, 72, 28, 28]             144\n",
            "            ReLU6-53           [-1, 72, 28, 28]               0\n",
            "            ReLU6-54           [-1, 72, 28, 28]               0\n",
            "            ReLU6-55           [-1, 72, 28, 28]               0\n",
            "AdaptiveAvgPool2d-56             [-1, 72, 1, 1]               0\n",
            "          Flatten-57                   [-1, 72]               0\n",
            "           Linear-58                    [-1, 4]             292\n",
            "             ReLU-59                    [-1, 4]               0\n",
            "           Linear-60                   [-1, 72]             360\n",
            "        Hardswish-61                   [-1, 72]               0\n",
            "         se_block-62           [-1, 72, 28, 28]               0\n",
            "           Conv2d-63           [-1, 40, 28, 28]           2,920\n",
            "      BatchNorm2d-64           [-1, 40, 28, 28]              80\n",
            "           Conv2d-65           [-1, 40, 28, 28]           1,000\n",
            "      BatchNorm2d-66           [-1, 40, 28, 28]              80\n",
            "        Hardswish-67           [-1, 40, 28, 28]               0\n",
            "invert_res_bottle-68           [-1, 40, 28, 28]               0\n",
            "           Conv2d-69          [-1, 120, 28, 28]           4,920\n",
            "      BatchNorm2d-70          [-1, 120, 28, 28]             240\n",
            "            ReLU6-71          [-1, 120, 28, 28]               0\n",
            "            ReLU6-72          [-1, 120, 28, 28]               0\n",
            "            ReLU6-73          [-1, 120, 28, 28]               0\n",
            "           Conv2d-74          [-1, 120, 28, 28]           3,120\n",
            "      BatchNorm2d-75          [-1, 120, 28, 28]             240\n",
            "            ReLU6-76          [-1, 120, 28, 28]               0\n",
            "            ReLU6-77          [-1, 120, 28, 28]               0\n",
            "            ReLU6-78          [-1, 120, 28, 28]               0\n",
            "AdaptiveAvgPool2d-79            [-1, 120, 1, 1]               0\n",
            "          Flatten-80                  [-1, 120]               0\n",
            "           Linear-81                    [-1, 7]             847\n",
            "             ReLU-82                    [-1, 7]               0\n",
            "           Linear-83                  [-1, 120]             960\n",
            "        Hardswish-84                  [-1, 120]               0\n",
            "         se_block-85          [-1, 120, 28, 28]               0\n",
            "           Conv2d-86           [-1, 40, 28, 28]           4,840\n",
            "      BatchNorm2d-87           [-1, 40, 28, 28]              80\n",
            "invert_res_bottle-88           [-1, 40, 28, 28]               0\n",
            "           Conv2d-89          [-1, 120, 28, 28]           4,920\n",
            "      BatchNorm2d-90          [-1, 120, 28, 28]             240\n",
            "            ReLU6-91          [-1, 120, 28, 28]               0\n",
            "            ReLU6-92          [-1, 120, 28, 28]               0\n",
            "            ReLU6-93          [-1, 120, 28, 28]               0\n",
            "           Conv2d-94          [-1, 120, 28, 28]           3,120\n",
            "      BatchNorm2d-95          [-1, 120, 28, 28]             240\n",
            "            ReLU6-96          [-1, 120, 28, 28]               0\n",
            "            ReLU6-97          [-1, 120, 28, 28]               0\n",
            "            ReLU6-98          [-1, 120, 28, 28]               0\n",
            "AdaptiveAvgPool2d-99            [-1, 120, 1, 1]               0\n",
            "         Flatten-100                  [-1, 120]               0\n",
            "          Linear-101                    [-1, 7]             847\n",
            "            ReLU-102                    [-1, 7]               0\n",
            "          Linear-103                  [-1, 120]             960\n",
            "       Hardswish-104                  [-1, 120]               0\n",
            "        se_block-105          [-1, 120, 28, 28]               0\n",
            "          Conv2d-106           [-1, 40, 28, 28]           4,840\n",
            "     BatchNorm2d-107           [-1, 40, 28, 28]              80\n",
            "invert_res_bottle-108           [-1, 40, 28, 28]               0\n",
            "          Conv2d-109          [-1, 240, 28, 28]           9,840\n",
            "     BatchNorm2d-110          [-1, 240, 28, 28]             480\n",
            "       Hardswish-111          [-1, 240, 28, 28]               0\n",
            "       Hardswish-112          [-1, 240, 28, 28]               0\n",
            "       Hardswish-113          [-1, 240, 28, 28]               0\n",
            "          Conv2d-114          [-1, 240, 14, 14]           2,400\n",
            "     BatchNorm2d-115          [-1, 240, 14, 14]             480\n",
            "       Hardswish-116          [-1, 240, 14, 14]               0\n",
            "       Hardswish-117          [-1, 240, 14, 14]               0\n",
            "       Hardswish-118          [-1, 240, 14, 14]               0\n",
            "          Conv2d-119           [-1, 80, 14, 14]          19,280\n",
            "     BatchNorm2d-120           [-1, 80, 14, 14]             160\n",
            "          Conv2d-121           [-1, 80, 14, 14]           3,280\n",
            "     BatchNorm2d-122           [-1, 80, 14, 14]             160\n",
            "       Hardswish-123           [-1, 80, 14, 14]               0\n",
            "invert_res_bottle-124           [-1, 80, 14, 14]               0\n",
            "          Conv2d-125          [-1, 200, 14, 14]          16,200\n",
            "     BatchNorm2d-126          [-1, 200, 14, 14]             400\n",
            "       Hardswish-127          [-1, 200, 14, 14]               0\n",
            "       Hardswish-128          [-1, 200, 14, 14]               0\n",
            "       Hardswish-129          [-1, 200, 14, 14]               0\n",
            "          Conv2d-130          [-1, 200, 14, 14]           2,000\n",
            "     BatchNorm2d-131          [-1, 200, 14, 14]             400\n",
            "       Hardswish-132          [-1, 200, 14, 14]               0\n",
            "       Hardswish-133          [-1, 200, 14, 14]               0\n",
            "       Hardswish-134          [-1, 200, 14, 14]               0\n",
            "          Conv2d-135           [-1, 80, 14, 14]          16,080\n",
            "     BatchNorm2d-136           [-1, 80, 14, 14]             160\n",
            "invert_res_bottle-137           [-1, 80, 14, 14]               0\n",
            "          Conv2d-138          [-1, 184, 14, 14]          14,904\n",
            "     BatchNorm2d-139          [-1, 184, 14, 14]             368\n",
            "       Hardswish-140          [-1, 184, 14, 14]               0\n",
            "       Hardswish-141          [-1, 184, 14, 14]               0\n",
            "       Hardswish-142          [-1, 184, 14, 14]               0\n",
            "          Conv2d-143          [-1, 184, 14, 14]           1,840\n",
            "     BatchNorm2d-144          [-1, 184, 14, 14]             368\n",
            "       Hardswish-145          [-1, 184, 14, 14]               0\n",
            "       Hardswish-146          [-1, 184, 14, 14]               0\n",
            "       Hardswish-147          [-1, 184, 14, 14]               0\n",
            "          Conv2d-148           [-1, 80, 14, 14]          14,800\n",
            "     BatchNorm2d-149           [-1, 80, 14, 14]             160\n",
            "invert_res_bottle-150           [-1, 80, 14, 14]               0\n",
            "          Conv2d-151          [-1, 184, 14, 14]          14,904\n",
            "     BatchNorm2d-152          [-1, 184, 14, 14]             368\n",
            "       Hardswish-153          [-1, 184, 14, 14]               0\n",
            "       Hardswish-154          [-1, 184, 14, 14]               0\n",
            "       Hardswish-155          [-1, 184, 14, 14]               0\n",
            "          Conv2d-156          [-1, 184, 14, 14]           1,840\n",
            "     BatchNorm2d-157          [-1, 184, 14, 14]             368\n",
            "       Hardswish-158          [-1, 184, 14, 14]               0\n",
            "       Hardswish-159          [-1, 184, 14, 14]               0\n",
            "       Hardswish-160          [-1, 184, 14, 14]               0\n",
            "          Conv2d-161           [-1, 80, 14, 14]          14,800\n",
            "     BatchNorm2d-162           [-1, 80, 14, 14]             160\n",
            "invert_res_bottle-163           [-1, 80, 14, 14]               0\n",
            "          Conv2d-164          [-1, 480, 14, 14]          38,880\n",
            "     BatchNorm2d-165          [-1, 480, 14, 14]             960\n",
            "       Hardswish-166          [-1, 480, 14, 14]               0\n",
            "       Hardswish-167          [-1, 480, 14, 14]               0\n",
            "       Hardswish-168          [-1, 480, 14, 14]               0\n",
            "          Conv2d-169          [-1, 480, 14, 14]           4,800\n",
            "     BatchNorm2d-170          [-1, 480, 14, 14]             960\n",
            "       Hardswish-171          [-1, 480, 14, 14]               0\n",
            "       Hardswish-172          [-1, 480, 14, 14]               0\n",
            "       Hardswish-173          [-1, 480, 14, 14]               0\n",
            "AdaptiveAvgPool2d-174            [-1, 480, 1, 1]               0\n",
            "         Flatten-175                  [-1, 480]               0\n",
            "          Linear-176                   [-1, 30]          14,430\n",
            "            ReLU-177                   [-1, 30]               0\n",
            "          Linear-178                  [-1, 480]          14,880\n",
            "       Hardswish-179                  [-1, 480]               0\n",
            "        se_block-180          [-1, 480, 14, 14]               0\n",
            "          Conv2d-181          [-1, 112, 14, 14]          53,872\n",
            "     BatchNorm2d-182          [-1, 112, 14, 14]             224\n",
            "          Conv2d-183          [-1, 112, 14, 14]           9,072\n",
            "     BatchNorm2d-184          [-1, 112, 14, 14]             224\n",
            "       Hardswish-185          [-1, 112, 14, 14]               0\n",
            "invert_res_bottle-186          [-1, 112, 14, 14]               0\n",
            "          Conv2d-187          [-1, 672, 14, 14]          75,936\n",
            "     BatchNorm2d-188          [-1, 672, 14, 14]           1,344\n",
            "       Hardswish-189          [-1, 672, 14, 14]               0\n",
            "       Hardswish-190          [-1, 672, 14, 14]               0\n",
            "       Hardswish-191          [-1, 672, 14, 14]               0\n",
            "          Conv2d-192          [-1, 672, 14, 14]           6,720\n",
            "     BatchNorm2d-193          [-1, 672, 14, 14]           1,344\n",
            "       Hardswish-194          [-1, 672, 14, 14]               0\n",
            "       Hardswish-195          [-1, 672, 14, 14]               0\n",
            "       Hardswish-196          [-1, 672, 14, 14]               0\n",
            "AdaptiveAvgPool2d-197            [-1, 672, 1, 1]               0\n",
            "         Flatten-198                  [-1, 672]               0\n",
            "          Linear-199                   [-1, 42]          28,266\n",
            "            ReLU-200                   [-1, 42]               0\n",
            "          Linear-201                  [-1, 672]          28,896\n",
            "       Hardswish-202                  [-1, 672]               0\n",
            "        se_block-203          [-1, 672, 14, 14]               0\n",
            "          Conv2d-204          [-1, 112, 14, 14]          75,376\n",
            "     BatchNorm2d-205          [-1, 112, 14, 14]             224\n",
            "invert_res_bottle-206          [-1, 112, 14, 14]               0\n",
            "          Conv2d-207          [-1, 672, 14, 14]          75,936\n",
            "     BatchNorm2d-208          [-1, 672, 14, 14]           1,344\n",
            "       Hardswish-209          [-1, 672, 14, 14]               0\n",
            "       Hardswish-210          [-1, 672, 14, 14]               0\n",
            "       Hardswish-211          [-1, 672, 14, 14]               0\n",
            "          Conv2d-212            [-1, 672, 7, 7]          17,472\n",
            "     BatchNorm2d-213            [-1, 672, 7, 7]           1,344\n",
            "       Hardswish-214            [-1, 672, 7, 7]               0\n",
            "       Hardswish-215            [-1, 672, 7, 7]               0\n",
            "       Hardswish-216            [-1, 672, 7, 7]               0\n",
            "AdaptiveAvgPool2d-217            [-1, 672, 1, 1]               0\n",
            "         Flatten-218                  [-1, 672]               0\n",
            "          Linear-219                   [-1, 42]          28,266\n",
            "            ReLU-220                   [-1, 42]               0\n",
            "          Linear-221                  [-1, 672]          28,896\n",
            "       Hardswish-222                  [-1, 672]               0\n",
            "        se_block-223            [-1, 672, 7, 7]               0\n",
            "          Conv2d-224            [-1, 160, 7, 7]         107,680\n",
            "     BatchNorm2d-225            [-1, 160, 7, 7]             320\n",
            "          Conv2d-226            [-1, 160, 7, 7]          18,080\n",
            "     BatchNorm2d-227            [-1, 160, 7, 7]             320\n",
            "       Hardswish-228            [-1, 160, 7, 7]               0\n",
            "invert_res_bottle-229            [-1, 160, 7, 7]               0\n",
            "          Conv2d-230            [-1, 960, 7, 7]         154,560\n",
            "     BatchNorm2d-231            [-1, 960, 7, 7]           1,920\n",
            "       Hardswish-232            [-1, 960, 7, 7]               0\n",
            "       Hardswish-233            [-1, 960, 7, 7]               0\n",
            "       Hardswish-234            [-1, 960, 7, 7]               0\n",
            "          Conv2d-235            [-1, 960, 7, 7]          24,960\n",
            "     BatchNorm2d-236            [-1, 960, 7, 7]           1,920\n",
            "       Hardswish-237            [-1, 960, 7, 7]               0\n",
            "       Hardswish-238            [-1, 960, 7, 7]               0\n",
            "       Hardswish-239            [-1, 960, 7, 7]               0\n",
            "AdaptiveAvgPool2d-240            [-1, 960, 1, 1]               0\n",
            "         Flatten-241                  [-1, 960]               0\n",
            "          Linear-242                   [-1, 60]          57,660\n",
            "            ReLU-243                   [-1, 60]               0\n",
            "          Linear-244                  [-1, 960]          58,560\n",
            "       Hardswish-245                  [-1, 960]               0\n",
            "        se_block-246            [-1, 960, 7, 7]               0\n",
            "          Conv2d-247            [-1, 160, 7, 7]         153,760\n",
            "     BatchNorm2d-248            [-1, 160, 7, 7]             320\n",
            "invert_res_bottle-249            [-1, 160, 7, 7]               0\n",
            "          Conv2d-250            [-1, 960, 7, 7]         154,560\n",
            "     BatchNorm2d-251            [-1, 960, 7, 7]           1,920\n",
            "       Hardswish-252            [-1, 960, 7, 7]               0\n",
            "       Hardswish-253            [-1, 960, 7, 7]               0\n",
            "       Hardswish-254            [-1, 960, 7, 7]               0\n",
            "          Conv2d-255            [-1, 960, 7, 7]          24,960\n",
            "     BatchNorm2d-256            [-1, 960, 7, 7]           1,920\n",
            "       Hardswish-257            [-1, 960, 7, 7]               0\n",
            "       Hardswish-258            [-1, 960, 7, 7]               0\n",
            "       Hardswish-259            [-1, 960, 7, 7]               0\n",
            "AdaptiveAvgPool2d-260            [-1, 960, 1, 1]               0\n",
            "         Flatten-261                  [-1, 960]               0\n",
            "          Linear-262                   [-1, 60]          57,660\n",
            "            ReLU-263                   [-1, 60]               0\n",
            "          Linear-264                  [-1, 960]          58,560\n",
            "       Hardswish-265                  [-1, 960]               0\n",
            "        se_block-266            [-1, 960, 7, 7]               0\n",
            "          Conv2d-267            [-1, 160, 7, 7]         153,760\n",
            "     BatchNorm2d-268            [-1, 160, 7, 7]             320\n",
            "invert_res_bottle-269            [-1, 160, 7, 7]               0\n",
            "          Conv2d-270            [-1, 960, 7, 7]         154,560\n",
            "     BatchNorm2d-271            [-1, 960, 7, 7]           1,920\n",
            "       Hardswish-272            [-1, 960, 7, 7]               0\n",
            "AdaptiveAvgPool2d-273            [-1, 960, 1, 1]               0\n",
            "          Conv2d-274           [-1, 1280, 1, 1]       1,230,080\n",
            "       Hardswish-275           [-1, 1280, 1, 1]               0\n",
            "          Conv2d-276             [-1, 10, 1, 1]          12,810\n",
            "         Flatten-277                   [-1, 10]               0\n",
            "================================================================\n",
            "Total params: 3,128,078\n",
            "Trainable params: 3,128,078\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.57\n",
            "Forward/backward pass size (MB): 171.13\n",
            "Params size (MB): 11.93\n",
            "Estimated Total Size (MB): 183.64\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#FF0000\"> **② Actuator(Trainer/Tester) Construction for MobileNet v3** </font>"
      ],
      "metadata": {
        "id": "O2-k53yawVWb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## MobileNet v3 Actuator Definition\n",
        "class MobileNetv3_Actuator(nn.Module):\n",
        "    def __init__(self, model, loaders, opt=\"sgd\", lr=0.01, has_scheduler=False, device=\"cuda\"):\n",
        "        super().__init__()\n",
        "\n",
        "        self.model = model\n",
        "        self.loss = nn.CrossEntropyLoss()\n",
        "\n",
        "        self.train_loader = loaders[\"train\"]\n",
        "        self.valid_loader = loaders[\"valid\"]\n",
        "        self.test_loader = loaders[\"test\"]\n",
        "\n",
        "        self._get_optimizer(opt=opt.lower(), lr=lr)\n",
        "        self.has_scheduler = has_scheduler\n",
        "        if self.has_scheduler:\n",
        "            self._get_scheduler()\n",
        "\n",
        "        self.device = device\n",
        "    \n",
        "    def _get_optimizer(self, opt=\"sgd\", lr=0.01):\n",
        "        if opt == \"sgd\":\n",
        "            self.optimizer = torch.optim.SGD(params=self.model.parameters(), lr=lr, momentum=0.9, weight_decay=0.0001)\n",
        "        elif opt == \"adagrad\":\n",
        "            self.optimizer = torch.optim.Adagrad(params=self.model.parameters(), lr=lr)\n",
        "        elif opt == \"adadelta\":\n",
        "            self.optimizer = torch.optim.Adadelta(params=self.model.parameters(), lr=lr)\n",
        "        elif opt == \"rmsprop\":\n",
        "            self.optimizer = torch.optim.RMSprop(params=self.model.parameters(), lr=lr)\n",
        "        elif opt == \"adam\":\n",
        "            self.optimizer = torch.optim.Adam(params=self.model.parameters(), lr=lr)\n",
        "        else:\n",
        "            raise ValueError(f\"Inputted Optimizer {opt} is not Supported\")\n",
        "    \n",
        "    def _get_scheduler(self):\n",
        "        self.scheduler = torch.optim.lr_scheduler.StepLR(optimizer=self.optimizer, step_size=10, gamma=0.3, verbose=True)    # 학습의 총 epoch에서 step_size마다 gamma의 비율로 learning rate를 순차적으로 줄임\n",
        "        # self.scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=self.optimizer, lr_lambda=lambda epoch: 0.95**epoch)   # 학습 도중 lr_lambda에 정의한 function에 의해 learning rate을 순차적으로 줄임\n",
        "    \n",
        "    def train_and_valid(self, max_epochs=10, disp_epochs=1):\n",
        "        print(\"========== Train and Valid Start ==========\")\n",
        "        start_time = time.time()\n",
        "\n",
        "        history = {\"Train_Loss\" : [], \"Train_Acc\" : [], \"Valid_Loss\" : [], \"Valid_Acc\" : []}\n",
        "\n",
        "        for epoch in tqdm(range(max_epochs), desc=\"Training Process...\"):\n",
        "            train_loss, train_acc = self._train_epoch()\n",
        "            valid_loss, valid_acc = self._valid_epoch()\n",
        "\n",
        "            history[\"Train_Loss\"].append(train_loss)\n",
        "            history[\"Train_Acc\"].append(train_acc)\n",
        "            history[\"Valid_Loss\"].append(valid_loss)\n",
        "            history[\"Valid_Acc\"].append(valid_acc)\n",
        "\n",
        "            if self.has_scheduler:\n",
        "                self.scheduler.step()\n",
        "            \n",
        "            if epoch % disp_epochs == 0:\n",
        "                print(f\"{epoch+1}/{max_epochs} Epoch ==> Train Loss : {train_loss:>6f}, Train Acc : {train_acc:>6f}, Valid Loss : {valid_loss:>6f}, Valid Acc : {valid_acc:>6f}, Time : {time.time() - start_time:>3f}\")\n",
        "                start_time = time.time()\n",
        "        \n",
        "        self.plot_history(history)\n",
        "    \n",
        "    def _train_epoch(self):\n",
        "        train_epoch_loss, train_epoch_acc = 0, 0\n",
        "        self.model.train()\n",
        "\n",
        "        for (a, b) in self.train_loader:\n",
        "            a = a.to(device=self.device)\n",
        "            b = b.to(device=self.device)\n",
        "\n",
        "            b_hat = self.model(a)\n",
        "            loss = self.loss(b_hat, b)\n",
        "\n",
        "            self.optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            self.optimizer.step()\n",
        "\n",
        "            train_epoch_loss += loss.to(\"cpu\").item()\n",
        "            train_epoch_acc += (b_hat.argmax(1) == b).type(torch.float).to(\"cpu\").mean().item()\n",
        "        \n",
        "        train_epoch_loss /= len(self.train_loader)\n",
        "        train_epoch_acc /= len(self.train_loader)\n",
        "\n",
        "        return train_epoch_loss, train_epoch_acc\n",
        "    \n",
        "    def _valid_epoch(self):\n",
        "        valid_epoch_loss, valid_epoch_acc = 0, 0\n",
        "        self.model.eval()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for (a, b) in self.valid_loader:\n",
        "                a = a.to(device=self.device)\n",
        "                b = b.to(device=self.device)\n",
        "\n",
        "                b_hat = self.model(a)\n",
        "                loss = self.loss(b_hat, b)\n",
        "\n",
        "                valid_epoch_loss += loss.to(\"cpu\").item()\n",
        "                valid_epoch_acc += (b_hat.argmax(1) == b).type(torch.float).to(\"cpu\").mean().item()\n",
        "        \n",
        "        valid_epoch_loss /= len(self.valid_loader)\n",
        "        valid_epoch_acc /= len(self.valid_loader)\n",
        "\n",
        "        return valid_epoch_loss, valid_epoch_acc\n",
        "    \n",
        "    def plot_history(self, history):\n",
        "        fig = plt.figure(figsize=(20, 10))\n",
        "\n",
        "        ax = fig.add_subplot(1, 2, 1)\n",
        "        ax.plot(history[\"Train_Loss\"], color=\"red\", label=\"Train Loss\")\n",
        "        ax.plot(history[\"Valid_Loss\"], color=\"orange\", label=\"Valid Loss\")\n",
        "        ax.set_title(\"Loss\")\n",
        "        ax.legend()\n",
        "\n",
        "        ax = fig.add_subplot(1, 2, 2)\n",
        "        ax.plot(history[\"Train_Acc\"], color=\"red\", label=\"Train Accuracy\")\n",
        "        ax.plot(history[\"Valid_Acc\"], color=\"orange\", label=\"Valid Accuracy\")\n",
        "        ax.set_title(\"Accuracy\")\n",
        "        ax.legend()\n",
        "\n",
        "        fig.show()\n",
        "    \n",
        "    def test(self):\n",
        "        print(\"========== Test Start ==========\")\n",
        "        start_time = time.time()\n",
        "\n",
        "        test_epoch_loss, test_epoch_acc = 0, 0\n",
        "        self.model.eval()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for (a, b) in self.test_loader:\n",
        "                a = a.to(device=self.device)\n",
        "                b = b.to(device=self.device)\n",
        "\n",
        "                b_hat = self.model(a)\n",
        "                loss = self.loss(b_hat, b)\n",
        "\n",
        "                test_epoch_loss += loss.to(\"cpu\").item()\n",
        "                test_epoch_acc += (b_hat.argmax(1) == b).type(torch.float).to(\"cpu\").mean().item()\n",
        "        \n",
        "        test_epoch_loss /= len(self.test_loader)\n",
        "        test_epoch_acc /= len(self.test_loader)\n",
        "\n",
        "        print(f\"Test loss: {test_epoch_loss:>6f}, Test acc: {test_epoch_acc:>3f}, Test time : {time.time() - start_time:>3f}\")"
      ],
      "metadata": {
        "id": "rqz9-g4Vwgc7"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FFBB00\"> **※ Training / Validating / Testing** </font>"
      ],
      "metadata": {
        "id": "kFhnK6xE1nuJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Training and Validating and Testing for CIFAR-10 by MobileNet v3\n",
        "device = \"cuda\"\n",
        "mobilenetv3_model = MobileNetv3(n_class=10).to(device)                          # MobileNet v3 Model for Training Version\n",
        "mobilenetv3_actuator = MobileNetv3_Actuator(model=mobilenetv3_model, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "\n",
        "start_time = time.time()\n",
        "mobilenetv3_actuator.train_and_valid(max_epochs=20, disp_epochs=1)\n",
        "print(f\"Total Training Time : {time.time() - start_time:>3f}\")\n",
        "\n",
        "mobilenetv3_actuator.test()                                                     # MobileNet v3 Model for Testing Version"
      ],
      "metadata": {
        "id": "LoNZVXoM1sRS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b098f8c4-ab08-4999-d112-a0c3799a6ccc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========== Train and Valid Start ==========\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Process...:   5%|▌         | 1/20 [03:20<1:03:20, 200.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/20 Epoch ==> Train Loss : 1.550303, Train Acc : 0.415050, Valid Loss : 1.276156, Valid Acc : 0.541000, Time : 200.006597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  10%|█         | 2/20 [06:40<1:00:03, 200.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/20 Epoch ==> Train Loss : 1.046902, Train Acc : 0.626175, Valid Loss : 1.289583, Valid Acc : 0.650300, Time : 200.362207\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  15%|█▌        | 3/20 [10:00<56:44, 200.26s/it]  "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/20 Epoch ==> Train Loss : 0.837811, Train Acc : 0.704150, Valid Loss : 296.167264, Valid Acc : 0.717000, Time : 200.309777\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  20%|██        | 4/20 [13:21<53:29, 200.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/20 Epoch ==> Train Loss : 0.681542, Train Acc : 0.762600, Valid Loss : 1.064824, Valid Acc : 0.766600, Time : 201.124753\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  25%|██▌       | 5/20 [16:42<50:11, 200.74s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/20 Epoch ==> Train Loss : 0.571136, Train Acc : 0.803775, Valid Loss : 5.601489, Valid Acc : 0.778400, Time : 200.984177\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  30%|███       | 6/20 [20:03<46:49, 200.67s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/20 Epoch ==> Train Loss : 0.476173, Train Acc : 0.833225, Valid Loss : 1.996780, Valid Acc : 0.810200, Time : 200.541337\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  35%|███▌      | 7/20 [23:23<43:28, 200.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/20 Epoch ==> Train Loss : 0.412071, Train Acc : 0.855025, Valid Loss : 0.697386, Valid Acc : 0.809700, Time : 200.576461\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  40%|████      | 8/20 [26:44<40:06, 200.57s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/20 Epoch ==> Train Loss : 0.356852, Train Acc : 0.874775, Valid Loss : 1.400178, Valid Acc : 0.810900, Time : 200.425796\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  45%|████▌     | 9/20 [30:04<36:44, 200.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/20 Epoch ==> Train Loss : 0.310107, Train Acc : 0.890150, Valid Loss : 0.575702, Valid Acc : 0.810900, Time : 199.909272\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  50%|█████     | 10/20 [33:24<33:24, 200.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/20 Epoch ==> Train Loss : 0.265103, Train Acc : 0.906025, Valid Loss : 3.745420, Valid Acc : 0.814800, Time : 200.544025\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  55%|█████▌    | 11/20 [36:44<30:02, 200.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11/20 Epoch ==> Train Loss : 0.228575, Train Acc : 0.919250, Valid Loss : 0.558570, Valid Acc : 0.828700, Time : 200.088648\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  60%|██████    | 12/20 [40:05<26:42, 200.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/20 Epoch ==> Train Loss : 0.196142, Train Acc : 0.929850, Valid Loss : 78.297648, Valid Acc : 0.780600, Time : 200.166979\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  65%|██████▌   | 13/20 [43:25<23:22, 200.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/20 Epoch ==> Train Loss : 0.177648, Train Acc : 0.936900, Valid Loss : 1.730046, Valid Acc : 0.822900, Time : 200.379678\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  70%|███████   | 14/20 [46:46<20:02, 200.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/20 Epoch ==> Train Loss : 0.150618, Train Acc : 0.947250, Valid Loss : 0.606062, Valid Acc : 0.832600, Time : 200.878995\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  75%|███████▌  | 15/20 [50:07<16:42, 200.55s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/20 Epoch ==> Train Loss : 0.137353, Train Acc : 0.952000, Valid Loss : 0.633893, Valid Acc : 0.828600, Time : 200.703912\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  80%|████████  | 16/20 [53:27<13:22, 200.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/20 Epoch ==> Train Loss : 0.127229, Train Acc : 0.954975, Valid Loss : 0.653773, Valid Acc : 0.827500, Time : 200.739619\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  85%|████████▌ | 17/20 [56:48<10:02, 200.77s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/20 Epoch ==> Train Loss : 0.113617, Train Acc : 0.960875, Valid Loss : 0.681692, Valid Acc : 0.834900, Time : 201.148782\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  90%|█████████ | 18/20 [1:00:09<06:41, 200.68s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/20 Epoch ==> Train Loss : 0.110025, Train Acc : 0.960600, Valid Loss : 0.755004, Valid Acc : 0.827000, Time : 200.479759\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  95%|█████████▌| 19/20 [1:03:30<03:20, 200.80s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/20 Epoch ==> Train Loss : 0.099142, Train Acc : 0.965250, Valid Loss : 0.755945, Valid Acc : 0.827400, Time : 201.059648\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Process...: 100%|██████████| 20/20 [1:06:51<00:00, 200.56s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20/20 Epoch ==> Train Loss : 0.095516, Train Acc : 0.965425, Valid Loss : 0.763568, Valid Acc : 0.821600, Time : 200.713499\n",
            "Total Training Time : 4011.189941\n",
            "========== Test Start ==========\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.799388, Test acc: 0.828300, Test time : 20.553706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#2F9D27\"> **※ MobileNet v3 Model Save and Load** </font>"
      ],
      "metadata": {
        "id": "LjY-XmNZKq8d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#2F9D27\"> **① MobileNet v3 Model Save based on Train/Valid History** </font>"
      ],
      "metadata": {
        "id": "a1N-mwDMKwkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## MobileNet v3 Model Save\n",
        "for param_tensor in mobilenetv3_model.state_dict():\n",
        "    print(f\"{param_tensor} : \\t {mobilenetv3_model.state_dict()[param_tensor].size()}\")   # MobileNet v3 model의 state dictionary 출력 -> 각 layer에서의 parameter들\n",
        "\n",
        "model_dir = os.path.join(os.getcwd(), \"drive\", \"MyDrive\", \"Colab Notebooks\", \"AI Algorithm Modeling\", \"model\")\n",
        "if not os.path.exists(model_dir):\n",
        "    os.makedirs(model_dir)\n",
        "\n",
        "torch.save(mobilenetv3_model.state_dict(), os.path.join(model_dir, \"MobileNet_v3.pth\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSohCba-K_lN",
        "outputId": "99ea6c2e-2869-4f57-e43c-e9fa3bdae713"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_block.0.weight : \t torch.Size([16, 3, 3, 3])\n",
            "input_block.0.bias : \t torch.Size([16])\n",
            "input_block.1.weight : \t torch.Size([16])\n",
            "input_block.1.bias : \t torch.Size([16])\n",
            "input_block.1.running_mean : \t torch.Size([16])\n",
            "input_block.1.running_var : \t torch.Size([16])\n",
            "input_block.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.0.expansion.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "a_block.0.expansion.0.bias : \t torch.Size([16])\n",
            "a_block.0.expansion.1.weight : \t torch.Size([16])\n",
            "a_block.0.expansion.1.bias : \t torch.Size([16])\n",
            "a_block.0.expansion.1.running_mean : \t torch.Size([16])\n",
            "a_block.0.expansion.1.running_var : \t torch.Size([16])\n",
            "a_block.0.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.0.depthwise.0.weight : \t torch.Size([16, 1, 3, 3])\n",
            "a_block.0.depthwise.0.bias : \t torch.Size([16])\n",
            "a_block.0.depthwise.1.weight : \t torch.Size([16])\n",
            "a_block.0.depthwise.1.bias : \t torch.Size([16])\n",
            "a_block.0.depthwise.1.running_mean : \t torch.Size([16])\n",
            "a_block.0.depthwise.1.running_var : \t torch.Size([16])\n",
            "a_block.0.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.0.projection.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "a_block.0.projection.0.bias : \t torch.Size([16])\n",
            "a_block.0.projection.1.weight : \t torch.Size([16])\n",
            "a_block.0.projection.1.bias : \t torch.Size([16])\n",
            "a_block.0.projection.1.running_mean : \t torch.Size([16])\n",
            "a_block.0.projection.1.running_var : \t torch.Size([16])\n",
            "a_block.0.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.0.adjust_size.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "a_block.0.adjust_size.0.bias : \t torch.Size([16])\n",
            "a_block.0.adjust_size.1.weight : \t torch.Size([16])\n",
            "a_block.0.adjust_size.1.bias : \t torch.Size([16])\n",
            "a_block.0.adjust_size.1.running_mean : \t torch.Size([16])\n",
            "a_block.0.adjust_size.1.running_var : \t torch.Size([16])\n",
            "a_block.0.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.0.adjust_channel.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "a_block.0.adjust_channel.0.bias : \t torch.Size([16])\n",
            "a_block.0.adjust_channel.1.weight : \t torch.Size([16])\n",
            "a_block.0.adjust_channel.1.bias : \t torch.Size([16])\n",
            "a_block.0.adjust_channel.1.running_mean : \t torch.Size([16])\n",
            "a_block.0.adjust_channel.1.running_var : \t torch.Size([16])\n",
            "a_block.0.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.1.expansion.0.weight : \t torch.Size([64, 16, 1, 1])\n",
            "a_block.1.expansion.0.bias : \t torch.Size([64])\n",
            "a_block.1.expansion.1.weight : \t torch.Size([64])\n",
            "a_block.1.expansion.1.bias : \t torch.Size([64])\n",
            "a_block.1.expansion.1.running_mean : \t torch.Size([64])\n",
            "a_block.1.expansion.1.running_var : \t torch.Size([64])\n",
            "a_block.1.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.1.depthwise.0.weight : \t torch.Size([64, 1, 3, 3])\n",
            "a_block.1.depthwise.0.bias : \t torch.Size([64])\n",
            "a_block.1.depthwise.1.weight : \t torch.Size([64])\n",
            "a_block.1.depthwise.1.bias : \t torch.Size([64])\n",
            "a_block.1.depthwise.1.running_mean : \t torch.Size([64])\n",
            "a_block.1.depthwise.1.running_var : \t torch.Size([64])\n",
            "a_block.1.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.1.projection.0.weight : \t torch.Size([24, 64, 1, 1])\n",
            "a_block.1.projection.0.bias : \t torch.Size([24])\n",
            "a_block.1.projection.1.weight : \t torch.Size([24])\n",
            "a_block.1.projection.1.bias : \t torch.Size([24])\n",
            "a_block.1.projection.1.running_mean : \t torch.Size([24])\n",
            "a_block.1.projection.1.running_var : \t torch.Size([24])\n",
            "a_block.1.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.1.adjust_size.0.weight : \t torch.Size([24, 16, 1, 1])\n",
            "a_block.1.adjust_size.0.bias : \t torch.Size([24])\n",
            "a_block.1.adjust_size.1.weight : \t torch.Size([24])\n",
            "a_block.1.adjust_size.1.bias : \t torch.Size([24])\n",
            "a_block.1.adjust_size.1.running_mean : \t torch.Size([24])\n",
            "a_block.1.adjust_size.1.running_var : \t torch.Size([24])\n",
            "a_block.1.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.1.adjust_channel.0.weight : \t torch.Size([24, 16, 1, 1])\n",
            "a_block.1.adjust_channel.0.bias : \t torch.Size([24])\n",
            "a_block.1.adjust_channel.1.weight : \t torch.Size([24])\n",
            "a_block.1.adjust_channel.1.bias : \t torch.Size([24])\n",
            "a_block.1.adjust_channel.1.running_mean : \t torch.Size([24])\n",
            "a_block.1.adjust_channel.1.running_var : \t torch.Size([24])\n",
            "a_block.1.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.2.expansion.0.weight : \t torch.Size([72, 24, 1, 1])\n",
            "a_block.2.expansion.0.bias : \t torch.Size([72])\n",
            "a_block.2.expansion.1.weight : \t torch.Size([72])\n",
            "a_block.2.expansion.1.bias : \t torch.Size([72])\n",
            "a_block.2.expansion.1.running_mean : \t torch.Size([72])\n",
            "a_block.2.expansion.1.running_var : \t torch.Size([72])\n",
            "a_block.2.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.2.depthwise.0.weight : \t torch.Size([72, 1, 3, 3])\n",
            "a_block.2.depthwise.0.bias : \t torch.Size([72])\n",
            "a_block.2.depthwise.1.weight : \t torch.Size([72])\n",
            "a_block.2.depthwise.1.bias : \t torch.Size([72])\n",
            "a_block.2.depthwise.1.running_mean : \t torch.Size([72])\n",
            "a_block.2.depthwise.1.running_var : \t torch.Size([72])\n",
            "a_block.2.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.2.projection.0.weight : \t torch.Size([24, 72, 1, 1])\n",
            "a_block.2.projection.0.bias : \t torch.Size([24])\n",
            "a_block.2.projection.1.weight : \t torch.Size([24])\n",
            "a_block.2.projection.1.bias : \t torch.Size([24])\n",
            "a_block.2.projection.1.running_mean : \t torch.Size([24])\n",
            "a_block.2.projection.1.running_var : \t torch.Size([24])\n",
            "a_block.2.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.2.adjust_size.0.weight : \t torch.Size([24, 24, 1, 1])\n",
            "a_block.2.adjust_size.0.bias : \t torch.Size([24])\n",
            "a_block.2.adjust_size.1.weight : \t torch.Size([24])\n",
            "a_block.2.adjust_size.1.bias : \t torch.Size([24])\n",
            "a_block.2.adjust_size.1.running_mean : \t torch.Size([24])\n",
            "a_block.2.adjust_size.1.running_var : \t torch.Size([24])\n",
            "a_block.2.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "a_block.2.adjust_channel.0.weight : \t torch.Size([24, 24, 1, 1])\n",
            "a_block.2.adjust_channel.0.bias : \t torch.Size([24])\n",
            "a_block.2.adjust_channel.1.weight : \t torch.Size([24])\n",
            "a_block.2.adjust_channel.1.bias : \t torch.Size([24])\n",
            "a_block.2.adjust_channel.1.running_mean : \t torch.Size([24])\n",
            "a_block.2.adjust_channel.1.running_var : \t torch.Size([24])\n",
            "a_block.2.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.0.expansion.0.weight : \t torch.Size([72, 24, 1, 1])\n",
            "b_block.0.expansion.0.bias : \t torch.Size([72])\n",
            "b_block.0.expansion.1.weight : \t torch.Size([72])\n",
            "b_block.0.expansion.1.bias : \t torch.Size([72])\n",
            "b_block.0.expansion.1.running_mean : \t torch.Size([72])\n",
            "b_block.0.expansion.1.running_var : \t torch.Size([72])\n",
            "b_block.0.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.0.depthwise.0.weight : \t torch.Size([72, 1, 5, 5])\n",
            "b_block.0.depthwise.0.bias : \t torch.Size([72])\n",
            "b_block.0.depthwise.1.weight : \t torch.Size([72])\n",
            "b_block.0.depthwise.1.bias : \t torch.Size([72])\n",
            "b_block.0.depthwise.1.running_mean : \t torch.Size([72])\n",
            "b_block.0.depthwise.1.running_var : \t torch.Size([72])\n",
            "b_block.0.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.0.se_block.excitation.1.weight : \t torch.Size([4, 72])\n",
            "b_block.0.se_block.excitation.1.bias : \t torch.Size([4])\n",
            "b_block.0.se_block.excitation.3.weight : \t torch.Size([72, 4])\n",
            "b_block.0.se_block.excitation.3.bias : \t torch.Size([72])\n",
            "b_block.0.projection.0.weight : \t torch.Size([40, 72, 1, 1])\n",
            "b_block.0.projection.0.bias : \t torch.Size([40])\n",
            "b_block.0.projection.1.weight : \t torch.Size([40])\n",
            "b_block.0.projection.1.bias : \t torch.Size([40])\n",
            "b_block.0.projection.1.running_mean : \t torch.Size([40])\n",
            "b_block.0.projection.1.running_var : \t torch.Size([40])\n",
            "b_block.0.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.0.adjust_size.0.weight : \t torch.Size([40, 24, 1, 1])\n",
            "b_block.0.adjust_size.0.bias : \t torch.Size([40])\n",
            "b_block.0.adjust_size.1.weight : \t torch.Size([40])\n",
            "b_block.0.adjust_size.1.bias : \t torch.Size([40])\n",
            "b_block.0.adjust_size.1.running_mean : \t torch.Size([40])\n",
            "b_block.0.adjust_size.1.running_var : \t torch.Size([40])\n",
            "b_block.0.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.0.adjust_channel.0.weight : \t torch.Size([40, 24, 1, 1])\n",
            "b_block.0.adjust_channel.0.bias : \t torch.Size([40])\n",
            "b_block.0.adjust_channel.1.weight : \t torch.Size([40])\n",
            "b_block.0.adjust_channel.1.bias : \t torch.Size([40])\n",
            "b_block.0.adjust_channel.1.running_mean : \t torch.Size([40])\n",
            "b_block.0.adjust_channel.1.running_var : \t torch.Size([40])\n",
            "b_block.0.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.1.expansion.0.weight : \t torch.Size([120, 40, 1, 1])\n",
            "b_block.1.expansion.0.bias : \t torch.Size([120])\n",
            "b_block.1.expansion.1.weight : \t torch.Size([120])\n",
            "b_block.1.expansion.1.bias : \t torch.Size([120])\n",
            "b_block.1.expansion.1.running_mean : \t torch.Size([120])\n",
            "b_block.1.expansion.1.running_var : \t torch.Size([120])\n",
            "b_block.1.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.1.depthwise.0.weight : \t torch.Size([120, 1, 5, 5])\n",
            "b_block.1.depthwise.0.bias : \t torch.Size([120])\n",
            "b_block.1.depthwise.1.weight : \t torch.Size([120])\n",
            "b_block.1.depthwise.1.bias : \t torch.Size([120])\n",
            "b_block.1.depthwise.1.running_mean : \t torch.Size([120])\n",
            "b_block.1.depthwise.1.running_var : \t torch.Size([120])\n",
            "b_block.1.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.1.se_block.excitation.1.weight : \t torch.Size([7, 120])\n",
            "b_block.1.se_block.excitation.1.bias : \t torch.Size([7])\n",
            "b_block.1.se_block.excitation.3.weight : \t torch.Size([120, 7])\n",
            "b_block.1.se_block.excitation.3.bias : \t torch.Size([120])\n",
            "b_block.1.projection.0.weight : \t torch.Size([40, 120, 1, 1])\n",
            "b_block.1.projection.0.bias : \t torch.Size([40])\n",
            "b_block.1.projection.1.weight : \t torch.Size([40])\n",
            "b_block.1.projection.1.bias : \t torch.Size([40])\n",
            "b_block.1.projection.1.running_mean : \t torch.Size([40])\n",
            "b_block.1.projection.1.running_var : \t torch.Size([40])\n",
            "b_block.1.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.1.adjust_size.0.weight : \t torch.Size([40, 40, 1, 1])\n",
            "b_block.1.adjust_size.0.bias : \t torch.Size([40])\n",
            "b_block.1.adjust_size.1.weight : \t torch.Size([40])\n",
            "b_block.1.adjust_size.1.bias : \t torch.Size([40])\n",
            "b_block.1.adjust_size.1.running_mean : \t torch.Size([40])\n",
            "b_block.1.adjust_size.1.running_var : \t torch.Size([40])\n",
            "b_block.1.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.1.adjust_channel.0.weight : \t torch.Size([40, 40, 1, 1])\n",
            "b_block.1.adjust_channel.0.bias : \t torch.Size([40])\n",
            "b_block.1.adjust_channel.1.weight : \t torch.Size([40])\n",
            "b_block.1.adjust_channel.1.bias : \t torch.Size([40])\n",
            "b_block.1.adjust_channel.1.running_mean : \t torch.Size([40])\n",
            "b_block.1.adjust_channel.1.running_var : \t torch.Size([40])\n",
            "b_block.1.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.2.expansion.0.weight : \t torch.Size([120, 40, 1, 1])\n",
            "b_block.2.expansion.0.bias : \t torch.Size([120])\n",
            "b_block.2.expansion.1.weight : \t torch.Size([120])\n",
            "b_block.2.expansion.1.bias : \t torch.Size([120])\n",
            "b_block.2.expansion.1.running_mean : \t torch.Size([120])\n",
            "b_block.2.expansion.1.running_var : \t torch.Size([120])\n",
            "b_block.2.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.2.depthwise.0.weight : \t torch.Size([120, 1, 5, 5])\n",
            "b_block.2.depthwise.0.bias : \t torch.Size([120])\n",
            "b_block.2.depthwise.1.weight : \t torch.Size([120])\n",
            "b_block.2.depthwise.1.bias : \t torch.Size([120])\n",
            "b_block.2.depthwise.1.running_mean : \t torch.Size([120])\n",
            "b_block.2.depthwise.1.running_var : \t torch.Size([120])\n",
            "b_block.2.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.2.se_block.excitation.1.weight : \t torch.Size([7, 120])\n",
            "b_block.2.se_block.excitation.1.bias : \t torch.Size([7])\n",
            "b_block.2.se_block.excitation.3.weight : \t torch.Size([120, 7])\n",
            "b_block.2.se_block.excitation.3.bias : \t torch.Size([120])\n",
            "b_block.2.projection.0.weight : \t torch.Size([40, 120, 1, 1])\n",
            "b_block.2.projection.0.bias : \t torch.Size([40])\n",
            "b_block.2.projection.1.weight : \t torch.Size([40])\n",
            "b_block.2.projection.1.bias : \t torch.Size([40])\n",
            "b_block.2.projection.1.running_mean : \t torch.Size([40])\n",
            "b_block.2.projection.1.running_var : \t torch.Size([40])\n",
            "b_block.2.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.2.adjust_size.0.weight : \t torch.Size([40, 40, 1, 1])\n",
            "b_block.2.adjust_size.0.bias : \t torch.Size([40])\n",
            "b_block.2.adjust_size.1.weight : \t torch.Size([40])\n",
            "b_block.2.adjust_size.1.bias : \t torch.Size([40])\n",
            "b_block.2.adjust_size.1.running_mean : \t torch.Size([40])\n",
            "b_block.2.adjust_size.1.running_var : \t torch.Size([40])\n",
            "b_block.2.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "b_block.2.adjust_channel.0.weight : \t torch.Size([40, 40, 1, 1])\n",
            "b_block.2.adjust_channel.0.bias : \t torch.Size([40])\n",
            "b_block.2.adjust_channel.1.weight : \t torch.Size([40])\n",
            "b_block.2.adjust_channel.1.bias : \t torch.Size([40])\n",
            "b_block.2.adjust_channel.1.running_mean : \t torch.Size([40])\n",
            "b_block.2.adjust_channel.1.running_var : \t torch.Size([40])\n",
            "b_block.2.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.0.expansion.0.weight : \t torch.Size([240, 40, 1, 1])\n",
            "c_block.0.expansion.0.bias : \t torch.Size([240])\n",
            "c_block.0.expansion.1.weight : \t torch.Size([240])\n",
            "c_block.0.expansion.1.bias : \t torch.Size([240])\n",
            "c_block.0.expansion.1.running_mean : \t torch.Size([240])\n",
            "c_block.0.expansion.1.running_var : \t torch.Size([240])\n",
            "c_block.0.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.0.depthwise.0.weight : \t torch.Size([240, 1, 3, 3])\n",
            "c_block.0.depthwise.0.bias : \t torch.Size([240])\n",
            "c_block.0.depthwise.1.weight : \t torch.Size([240])\n",
            "c_block.0.depthwise.1.bias : \t torch.Size([240])\n",
            "c_block.0.depthwise.1.running_mean : \t torch.Size([240])\n",
            "c_block.0.depthwise.1.running_var : \t torch.Size([240])\n",
            "c_block.0.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.0.projection.0.weight : \t torch.Size([80, 240, 1, 1])\n",
            "c_block.0.projection.0.bias : \t torch.Size([80])\n",
            "c_block.0.projection.1.weight : \t torch.Size([80])\n",
            "c_block.0.projection.1.bias : \t torch.Size([80])\n",
            "c_block.0.projection.1.running_mean : \t torch.Size([80])\n",
            "c_block.0.projection.1.running_var : \t torch.Size([80])\n",
            "c_block.0.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.0.adjust_size.0.weight : \t torch.Size([80, 40, 1, 1])\n",
            "c_block.0.adjust_size.0.bias : \t torch.Size([80])\n",
            "c_block.0.adjust_size.1.weight : \t torch.Size([80])\n",
            "c_block.0.adjust_size.1.bias : \t torch.Size([80])\n",
            "c_block.0.adjust_size.1.running_mean : \t torch.Size([80])\n",
            "c_block.0.adjust_size.1.running_var : \t torch.Size([80])\n",
            "c_block.0.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.0.adjust_channel.0.weight : \t torch.Size([80, 40, 1, 1])\n",
            "c_block.0.adjust_channel.0.bias : \t torch.Size([80])\n",
            "c_block.0.adjust_channel.1.weight : \t torch.Size([80])\n",
            "c_block.0.adjust_channel.1.bias : \t torch.Size([80])\n",
            "c_block.0.adjust_channel.1.running_mean : \t torch.Size([80])\n",
            "c_block.0.adjust_channel.1.running_var : \t torch.Size([80])\n",
            "c_block.0.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.1.expansion.0.weight : \t torch.Size([200, 80, 1, 1])\n",
            "c_block.1.expansion.0.bias : \t torch.Size([200])\n",
            "c_block.1.expansion.1.weight : \t torch.Size([200])\n",
            "c_block.1.expansion.1.bias : \t torch.Size([200])\n",
            "c_block.1.expansion.1.running_mean : \t torch.Size([200])\n",
            "c_block.1.expansion.1.running_var : \t torch.Size([200])\n",
            "c_block.1.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.1.depthwise.0.weight : \t torch.Size([200, 1, 3, 3])\n",
            "c_block.1.depthwise.0.bias : \t torch.Size([200])\n",
            "c_block.1.depthwise.1.weight : \t torch.Size([200])\n",
            "c_block.1.depthwise.1.bias : \t torch.Size([200])\n",
            "c_block.1.depthwise.1.running_mean : \t torch.Size([200])\n",
            "c_block.1.depthwise.1.running_var : \t torch.Size([200])\n",
            "c_block.1.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.1.projection.0.weight : \t torch.Size([80, 200, 1, 1])\n",
            "c_block.1.projection.0.bias : \t torch.Size([80])\n",
            "c_block.1.projection.1.weight : \t torch.Size([80])\n",
            "c_block.1.projection.1.bias : \t torch.Size([80])\n",
            "c_block.1.projection.1.running_mean : \t torch.Size([80])\n",
            "c_block.1.projection.1.running_var : \t torch.Size([80])\n",
            "c_block.1.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.1.adjust_size.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.1.adjust_size.0.bias : \t torch.Size([80])\n",
            "c_block.1.adjust_size.1.weight : \t torch.Size([80])\n",
            "c_block.1.adjust_size.1.bias : \t torch.Size([80])\n",
            "c_block.1.adjust_size.1.running_mean : \t torch.Size([80])\n",
            "c_block.1.adjust_size.1.running_var : \t torch.Size([80])\n",
            "c_block.1.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.1.adjust_channel.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.1.adjust_channel.0.bias : \t torch.Size([80])\n",
            "c_block.1.adjust_channel.1.weight : \t torch.Size([80])\n",
            "c_block.1.adjust_channel.1.bias : \t torch.Size([80])\n",
            "c_block.1.adjust_channel.1.running_mean : \t torch.Size([80])\n",
            "c_block.1.adjust_channel.1.running_var : \t torch.Size([80])\n",
            "c_block.1.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.2.expansion.0.weight : \t torch.Size([184, 80, 1, 1])\n",
            "c_block.2.expansion.0.bias : \t torch.Size([184])\n",
            "c_block.2.expansion.1.weight : \t torch.Size([184])\n",
            "c_block.2.expansion.1.bias : \t torch.Size([184])\n",
            "c_block.2.expansion.1.running_mean : \t torch.Size([184])\n",
            "c_block.2.expansion.1.running_var : \t torch.Size([184])\n",
            "c_block.2.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.2.depthwise.0.weight : \t torch.Size([184, 1, 3, 3])\n",
            "c_block.2.depthwise.0.bias : \t torch.Size([184])\n",
            "c_block.2.depthwise.1.weight : \t torch.Size([184])\n",
            "c_block.2.depthwise.1.bias : \t torch.Size([184])\n",
            "c_block.2.depthwise.1.running_mean : \t torch.Size([184])\n",
            "c_block.2.depthwise.1.running_var : \t torch.Size([184])\n",
            "c_block.2.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.2.projection.0.weight : \t torch.Size([80, 184, 1, 1])\n",
            "c_block.2.projection.0.bias : \t torch.Size([80])\n",
            "c_block.2.projection.1.weight : \t torch.Size([80])\n",
            "c_block.2.projection.1.bias : \t torch.Size([80])\n",
            "c_block.2.projection.1.running_mean : \t torch.Size([80])\n",
            "c_block.2.projection.1.running_var : \t torch.Size([80])\n",
            "c_block.2.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.2.adjust_size.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.2.adjust_size.0.bias : \t torch.Size([80])\n",
            "c_block.2.adjust_size.1.weight : \t torch.Size([80])\n",
            "c_block.2.adjust_size.1.bias : \t torch.Size([80])\n",
            "c_block.2.adjust_size.1.running_mean : \t torch.Size([80])\n",
            "c_block.2.adjust_size.1.running_var : \t torch.Size([80])\n",
            "c_block.2.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.2.adjust_channel.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.2.adjust_channel.0.bias : \t torch.Size([80])\n",
            "c_block.2.adjust_channel.1.weight : \t torch.Size([80])\n",
            "c_block.2.adjust_channel.1.bias : \t torch.Size([80])\n",
            "c_block.2.adjust_channel.1.running_mean : \t torch.Size([80])\n",
            "c_block.2.adjust_channel.1.running_var : \t torch.Size([80])\n",
            "c_block.2.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.3.expansion.0.weight : \t torch.Size([184, 80, 1, 1])\n",
            "c_block.3.expansion.0.bias : \t torch.Size([184])\n",
            "c_block.3.expansion.1.weight : \t torch.Size([184])\n",
            "c_block.3.expansion.1.bias : \t torch.Size([184])\n",
            "c_block.3.expansion.1.running_mean : \t torch.Size([184])\n",
            "c_block.3.expansion.1.running_var : \t torch.Size([184])\n",
            "c_block.3.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.3.depthwise.0.weight : \t torch.Size([184, 1, 3, 3])\n",
            "c_block.3.depthwise.0.bias : \t torch.Size([184])\n",
            "c_block.3.depthwise.1.weight : \t torch.Size([184])\n",
            "c_block.3.depthwise.1.bias : \t torch.Size([184])\n",
            "c_block.3.depthwise.1.running_mean : \t torch.Size([184])\n",
            "c_block.3.depthwise.1.running_var : \t torch.Size([184])\n",
            "c_block.3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.3.projection.0.weight : \t torch.Size([80, 184, 1, 1])\n",
            "c_block.3.projection.0.bias : \t torch.Size([80])\n",
            "c_block.3.projection.1.weight : \t torch.Size([80])\n",
            "c_block.3.projection.1.bias : \t torch.Size([80])\n",
            "c_block.3.projection.1.running_mean : \t torch.Size([80])\n",
            "c_block.3.projection.1.running_var : \t torch.Size([80])\n",
            "c_block.3.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.3.adjust_size.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.3.adjust_size.0.bias : \t torch.Size([80])\n",
            "c_block.3.adjust_size.1.weight : \t torch.Size([80])\n",
            "c_block.3.adjust_size.1.bias : \t torch.Size([80])\n",
            "c_block.3.adjust_size.1.running_mean : \t torch.Size([80])\n",
            "c_block.3.adjust_size.1.running_var : \t torch.Size([80])\n",
            "c_block.3.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.3.adjust_channel.0.weight : \t torch.Size([80, 80, 1, 1])\n",
            "c_block.3.adjust_channel.0.bias : \t torch.Size([80])\n",
            "c_block.3.adjust_channel.1.weight : \t torch.Size([80])\n",
            "c_block.3.adjust_channel.1.bias : \t torch.Size([80])\n",
            "c_block.3.adjust_channel.1.running_mean : \t torch.Size([80])\n",
            "c_block.3.adjust_channel.1.running_var : \t torch.Size([80])\n",
            "c_block.3.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.4.expansion.0.weight : \t torch.Size([480, 80, 1, 1])\n",
            "c_block.4.expansion.0.bias : \t torch.Size([480])\n",
            "c_block.4.expansion.1.weight : \t torch.Size([480])\n",
            "c_block.4.expansion.1.bias : \t torch.Size([480])\n",
            "c_block.4.expansion.1.running_mean : \t torch.Size([480])\n",
            "c_block.4.expansion.1.running_var : \t torch.Size([480])\n",
            "c_block.4.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.4.depthwise.0.weight : \t torch.Size([480, 1, 3, 3])\n",
            "c_block.4.depthwise.0.bias : \t torch.Size([480])\n",
            "c_block.4.depthwise.1.weight : \t torch.Size([480])\n",
            "c_block.4.depthwise.1.bias : \t torch.Size([480])\n",
            "c_block.4.depthwise.1.running_mean : \t torch.Size([480])\n",
            "c_block.4.depthwise.1.running_var : \t torch.Size([480])\n",
            "c_block.4.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.4.se_block.excitation.1.weight : \t torch.Size([30, 480])\n",
            "c_block.4.se_block.excitation.1.bias : \t torch.Size([30])\n",
            "c_block.4.se_block.excitation.3.weight : \t torch.Size([480, 30])\n",
            "c_block.4.se_block.excitation.3.bias : \t torch.Size([480])\n",
            "c_block.4.projection.0.weight : \t torch.Size([112, 480, 1, 1])\n",
            "c_block.4.projection.0.bias : \t torch.Size([112])\n",
            "c_block.4.projection.1.weight : \t torch.Size([112])\n",
            "c_block.4.projection.1.bias : \t torch.Size([112])\n",
            "c_block.4.projection.1.running_mean : \t torch.Size([112])\n",
            "c_block.4.projection.1.running_var : \t torch.Size([112])\n",
            "c_block.4.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.4.adjust_size.0.weight : \t torch.Size([112, 80, 1, 1])\n",
            "c_block.4.adjust_size.0.bias : \t torch.Size([112])\n",
            "c_block.4.adjust_size.1.weight : \t torch.Size([112])\n",
            "c_block.4.adjust_size.1.bias : \t torch.Size([112])\n",
            "c_block.4.adjust_size.1.running_mean : \t torch.Size([112])\n",
            "c_block.4.adjust_size.1.running_var : \t torch.Size([112])\n",
            "c_block.4.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.4.adjust_channel.0.weight : \t torch.Size([112, 80, 1, 1])\n",
            "c_block.4.adjust_channel.0.bias : \t torch.Size([112])\n",
            "c_block.4.adjust_channel.1.weight : \t torch.Size([112])\n",
            "c_block.4.adjust_channel.1.bias : \t torch.Size([112])\n",
            "c_block.4.adjust_channel.1.running_mean : \t torch.Size([112])\n",
            "c_block.4.adjust_channel.1.running_var : \t torch.Size([112])\n",
            "c_block.4.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.5.expansion.0.weight : \t torch.Size([672, 112, 1, 1])\n",
            "c_block.5.expansion.0.bias : \t torch.Size([672])\n",
            "c_block.5.expansion.1.weight : \t torch.Size([672])\n",
            "c_block.5.expansion.1.bias : \t torch.Size([672])\n",
            "c_block.5.expansion.1.running_mean : \t torch.Size([672])\n",
            "c_block.5.expansion.1.running_var : \t torch.Size([672])\n",
            "c_block.5.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.5.depthwise.0.weight : \t torch.Size([672, 1, 3, 3])\n",
            "c_block.5.depthwise.0.bias : \t torch.Size([672])\n",
            "c_block.5.depthwise.1.weight : \t torch.Size([672])\n",
            "c_block.5.depthwise.1.bias : \t torch.Size([672])\n",
            "c_block.5.depthwise.1.running_mean : \t torch.Size([672])\n",
            "c_block.5.depthwise.1.running_var : \t torch.Size([672])\n",
            "c_block.5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.5.se_block.excitation.1.weight : \t torch.Size([42, 672])\n",
            "c_block.5.se_block.excitation.1.bias : \t torch.Size([42])\n",
            "c_block.5.se_block.excitation.3.weight : \t torch.Size([672, 42])\n",
            "c_block.5.se_block.excitation.3.bias : \t torch.Size([672])\n",
            "c_block.5.projection.0.weight : \t torch.Size([112, 672, 1, 1])\n",
            "c_block.5.projection.0.bias : \t torch.Size([112])\n",
            "c_block.5.projection.1.weight : \t torch.Size([112])\n",
            "c_block.5.projection.1.bias : \t torch.Size([112])\n",
            "c_block.5.projection.1.running_mean : \t torch.Size([112])\n",
            "c_block.5.projection.1.running_var : \t torch.Size([112])\n",
            "c_block.5.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.5.adjust_size.0.weight : \t torch.Size([112, 112, 1, 1])\n",
            "c_block.5.adjust_size.0.bias : \t torch.Size([112])\n",
            "c_block.5.adjust_size.1.weight : \t torch.Size([112])\n",
            "c_block.5.adjust_size.1.bias : \t torch.Size([112])\n",
            "c_block.5.adjust_size.1.running_mean : \t torch.Size([112])\n",
            "c_block.5.adjust_size.1.running_var : \t torch.Size([112])\n",
            "c_block.5.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "c_block.5.adjust_channel.0.weight : \t torch.Size([112, 112, 1, 1])\n",
            "c_block.5.adjust_channel.0.bias : \t torch.Size([112])\n",
            "c_block.5.adjust_channel.1.weight : \t torch.Size([112])\n",
            "c_block.5.adjust_channel.1.bias : \t torch.Size([112])\n",
            "c_block.5.adjust_channel.1.running_mean : \t torch.Size([112])\n",
            "c_block.5.adjust_channel.1.running_var : \t torch.Size([112])\n",
            "c_block.5.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.0.expansion.0.weight : \t torch.Size([672, 112, 1, 1])\n",
            "d_block.0.expansion.0.bias : \t torch.Size([672])\n",
            "d_block.0.expansion.1.weight : \t torch.Size([672])\n",
            "d_block.0.expansion.1.bias : \t torch.Size([672])\n",
            "d_block.0.expansion.1.running_mean : \t torch.Size([672])\n",
            "d_block.0.expansion.1.running_var : \t torch.Size([672])\n",
            "d_block.0.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.0.depthwise.0.weight : \t torch.Size([672, 1, 5, 5])\n",
            "d_block.0.depthwise.0.bias : \t torch.Size([672])\n",
            "d_block.0.depthwise.1.weight : \t torch.Size([672])\n",
            "d_block.0.depthwise.1.bias : \t torch.Size([672])\n",
            "d_block.0.depthwise.1.running_mean : \t torch.Size([672])\n",
            "d_block.0.depthwise.1.running_var : \t torch.Size([672])\n",
            "d_block.0.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.0.se_block.excitation.1.weight : \t torch.Size([42, 672])\n",
            "d_block.0.se_block.excitation.1.bias : \t torch.Size([42])\n",
            "d_block.0.se_block.excitation.3.weight : \t torch.Size([672, 42])\n",
            "d_block.0.se_block.excitation.3.bias : \t torch.Size([672])\n",
            "d_block.0.projection.0.weight : \t torch.Size([160, 672, 1, 1])\n",
            "d_block.0.projection.0.bias : \t torch.Size([160])\n",
            "d_block.0.projection.1.weight : \t torch.Size([160])\n",
            "d_block.0.projection.1.bias : \t torch.Size([160])\n",
            "d_block.0.projection.1.running_mean : \t torch.Size([160])\n",
            "d_block.0.projection.1.running_var : \t torch.Size([160])\n",
            "d_block.0.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.0.adjust_size.0.weight : \t torch.Size([160, 112, 1, 1])\n",
            "d_block.0.adjust_size.0.bias : \t torch.Size([160])\n",
            "d_block.0.adjust_size.1.weight : \t torch.Size([160])\n",
            "d_block.0.adjust_size.1.bias : \t torch.Size([160])\n",
            "d_block.0.adjust_size.1.running_mean : \t torch.Size([160])\n",
            "d_block.0.adjust_size.1.running_var : \t torch.Size([160])\n",
            "d_block.0.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.0.adjust_channel.0.weight : \t torch.Size([160, 112, 1, 1])\n",
            "d_block.0.adjust_channel.0.bias : \t torch.Size([160])\n",
            "d_block.0.adjust_channel.1.weight : \t torch.Size([160])\n",
            "d_block.0.adjust_channel.1.bias : \t torch.Size([160])\n",
            "d_block.0.adjust_channel.1.running_mean : \t torch.Size([160])\n",
            "d_block.0.adjust_channel.1.running_var : \t torch.Size([160])\n",
            "d_block.0.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.1.expansion.0.weight : \t torch.Size([960, 160, 1, 1])\n",
            "d_block.1.expansion.0.bias : \t torch.Size([960])\n",
            "d_block.1.expansion.1.weight : \t torch.Size([960])\n",
            "d_block.1.expansion.1.bias : \t torch.Size([960])\n",
            "d_block.1.expansion.1.running_mean : \t torch.Size([960])\n",
            "d_block.1.expansion.1.running_var : \t torch.Size([960])\n",
            "d_block.1.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.1.depthwise.0.weight : \t torch.Size([960, 1, 5, 5])\n",
            "d_block.1.depthwise.0.bias : \t torch.Size([960])\n",
            "d_block.1.depthwise.1.weight : \t torch.Size([960])\n",
            "d_block.1.depthwise.1.bias : \t torch.Size([960])\n",
            "d_block.1.depthwise.1.running_mean : \t torch.Size([960])\n",
            "d_block.1.depthwise.1.running_var : \t torch.Size([960])\n",
            "d_block.1.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.1.se_block.excitation.1.weight : \t torch.Size([60, 960])\n",
            "d_block.1.se_block.excitation.1.bias : \t torch.Size([60])\n",
            "d_block.1.se_block.excitation.3.weight : \t torch.Size([960, 60])\n",
            "d_block.1.se_block.excitation.3.bias : \t torch.Size([960])\n",
            "d_block.1.projection.0.weight : \t torch.Size([160, 960, 1, 1])\n",
            "d_block.1.projection.0.bias : \t torch.Size([160])\n",
            "d_block.1.projection.1.weight : \t torch.Size([160])\n",
            "d_block.1.projection.1.bias : \t torch.Size([160])\n",
            "d_block.1.projection.1.running_mean : \t torch.Size([160])\n",
            "d_block.1.projection.1.running_var : \t torch.Size([160])\n",
            "d_block.1.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.1.adjust_size.0.weight : \t torch.Size([160, 160, 1, 1])\n",
            "d_block.1.adjust_size.0.bias : \t torch.Size([160])\n",
            "d_block.1.adjust_size.1.weight : \t torch.Size([160])\n",
            "d_block.1.adjust_size.1.bias : \t torch.Size([160])\n",
            "d_block.1.adjust_size.1.running_mean : \t torch.Size([160])\n",
            "d_block.1.adjust_size.1.running_var : \t torch.Size([160])\n",
            "d_block.1.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.1.adjust_channel.0.weight : \t torch.Size([160, 160, 1, 1])\n",
            "d_block.1.adjust_channel.0.bias : \t torch.Size([160])\n",
            "d_block.1.adjust_channel.1.weight : \t torch.Size([160])\n",
            "d_block.1.adjust_channel.1.bias : \t torch.Size([160])\n",
            "d_block.1.adjust_channel.1.running_mean : \t torch.Size([160])\n",
            "d_block.1.adjust_channel.1.running_var : \t torch.Size([160])\n",
            "d_block.1.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.2.expansion.0.weight : \t torch.Size([960, 160, 1, 1])\n",
            "d_block.2.expansion.0.bias : \t torch.Size([960])\n",
            "d_block.2.expansion.1.weight : \t torch.Size([960])\n",
            "d_block.2.expansion.1.bias : \t torch.Size([960])\n",
            "d_block.2.expansion.1.running_mean : \t torch.Size([960])\n",
            "d_block.2.expansion.1.running_var : \t torch.Size([960])\n",
            "d_block.2.expansion.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.2.depthwise.0.weight : \t torch.Size([960, 1, 5, 5])\n",
            "d_block.2.depthwise.0.bias : \t torch.Size([960])\n",
            "d_block.2.depthwise.1.weight : \t torch.Size([960])\n",
            "d_block.2.depthwise.1.bias : \t torch.Size([960])\n",
            "d_block.2.depthwise.1.running_mean : \t torch.Size([960])\n",
            "d_block.2.depthwise.1.running_var : \t torch.Size([960])\n",
            "d_block.2.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.2.se_block.excitation.1.weight : \t torch.Size([60, 960])\n",
            "d_block.2.se_block.excitation.1.bias : \t torch.Size([60])\n",
            "d_block.2.se_block.excitation.3.weight : \t torch.Size([960, 60])\n",
            "d_block.2.se_block.excitation.3.bias : \t torch.Size([960])\n",
            "d_block.2.projection.0.weight : \t torch.Size([160, 960, 1, 1])\n",
            "d_block.2.projection.0.bias : \t torch.Size([160])\n",
            "d_block.2.projection.1.weight : \t torch.Size([160])\n",
            "d_block.2.projection.1.bias : \t torch.Size([160])\n",
            "d_block.2.projection.1.running_mean : \t torch.Size([160])\n",
            "d_block.2.projection.1.running_var : \t torch.Size([160])\n",
            "d_block.2.projection.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.2.adjust_size.0.weight : \t torch.Size([160, 160, 1, 1])\n",
            "d_block.2.adjust_size.0.bias : \t torch.Size([160])\n",
            "d_block.2.adjust_size.1.weight : \t torch.Size([160])\n",
            "d_block.2.adjust_size.1.bias : \t torch.Size([160])\n",
            "d_block.2.adjust_size.1.running_mean : \t torch.Size([160])\n",
            "d_block.2.adjust_size.1.running_var : \t torch.Size([160])\n",
            "d_block.2.adjust_size.1.num_batches_tracked : \t torch.Size([])\n",
            "d_block.2.adjust_channel.0.weight : \t torch.Size([160, 160, 1, 1])\n",
            "d_block.2.adjust_channel.0.bias : \t torch.Size([160])\n",
            "d_block.2.adjust_channel.1.weight : \t torch.Size([160])\n",
            "d_block.2.adjust_channel.1.bias : \t torch.Size([160])\n",
            "d_block.2.adjust_channel.1.running_mean : \t torch.Size([160])\n",
            "d_block.2.adjust_channel.1.running_var : \t torch.Size([160])\n",
            "d_block.2.adjust_channel.1.num_batches_tracked : \t torch.Size([])\n",
            "output_block.0.weight : \t torch.Size([960, 160, 1, 1])\n",
            "output_block.0.bias : \t torch.Size([960])\n",
            "output_block.1.weight : \t torch.Size([960])\n",
            "output_block.1.bias : \t torch.Size([960])\n",
            "output_block.1.running_mean : \t torch.Size([960])\n",
            "output_block.1.running_var : \t torch.Size([960])\n",
            "output_block.1.num_batches_tracked : \t torch.Size([])\n",
            "output_block.4.weight : \t torch.Size([1280, 960, 1, 1])\n",
            "output_block.4.bias : \t torch.Size([1280])\n",
            "output_block.6.weight : \t torch.Size([10, 1280, 1, 1])\n",
            "output_block.6.bias : \t torch.Size([10])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#2F9D27\"> **② MobileNet v3 Model Load and Performance Estimation** </font>"
      ],
      "metadata": {
        "id": "Rp97eGEILADT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## MobileNet v3 Model Load\n",
        "device = \"cuda\"\n",
        "\n",
        "print(\"========== MobileNet v3 Test without Load ==========\")    # ① 먼저 학습하지 않은 상태로 Actuator를 통해 Test dataset 성능 측정\n",
        "mobilenetv3 = MobileNetv3(n_class=10).to(device)\n",
        "mobilenetv3_actuator = MobileNetv3_Actuator(model=mobilenetv3, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "mobilenetv3_actuator.test()\n",
        "\n",
        "model_path = os.path.join(model_dir, \"MobileNet_v3.pth\")\n",
        "print(model_path)\n",
        "print()\n",
        "\n",
        "print(\"========== MobileNet v3 Test with Load ==========\")       # ② 이전에 학습된 Model의 parameter들을 Load한 후, Test dataset 성능 측정\n",
        "mobilenetv3.load_state_dict(torch.load(model_path))\n",
        "mobilenetv3.eval()\n",
        "\n",
        "mobilenetv3_loaded_actuator = MobileNetv3_Actuator(model=mobilenetv3, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "mobilenetv3_loaded_actuator.test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4O96LgBLGZW",
        "outputId": "4a9a47d0-3792-49f1-8676-d3980590bbab"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========== MobileNet v3 Test without Load ==========\n",
            "========== Test Start ==========\n",
            "Test loss: 2.302721, Test acc: 0.100000, Test time : 20.386503\n",
            "/content/drive/MyDrive/Colab Notebooks/AI Algorithm Modeling/model/MobileNet_v3.pth\n",
            "\n",
            "========== MobileNet v3 Test with Load ==========\n",
            "========== Test Start ==========\n",
            "Test loss: 0.799388, Test acc: 0.828300, Test time : 19.902439\n"
          ]
        }
      ]
    }
  ]
}