NASNet.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Lcggou3dZaV_",
        "mu6lNvGgZiAe",
        "4LvVxZKleMqu",
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        "kFhnK6xE1nuJ",
        "a1N-mwDMKwkG",
        "Rp97eGEILADT"
      ],
      "authorship_tag": "ABX9TyO8SDJXKgvXH0N/vtIXofaG",
      "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/NASNet.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": "9275eab3-804b-48a3-b0c3-7f13e6bd2b83"
      },
      "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": "aff99e51-c218-4229-cbd0-ac8515348143"
      },
      "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": "6de716ec-44fe-4b11-ca66-60534abba472"
      },
      "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": "9154e41e-4622-44d0-e6db-78afab764a1a"
      },
      "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\"> **※ NASNet Model and Actuator Definition** </font>"
      ],
      "metadata": {
        "id": "XnVaP2P6lPvN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#FF0000\"> **① Architecture of NASNet-A Model with Normal/Reduction Conv-Cell Generation** </font>"
      ],
      "metadata": {
        "id": "9ntxlDjZwOI8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## NASNet-A Model with Normal/Reduction Conv-Cell Generation for ImageNet dataset Architecture and N=1\n",
        "class NASNet(nn.Module):\n",
        "    def __init__(self, n_class=10):\n",
        "        super().__init__()                                                                                                      # Input Image Tensor = 3 X 224 X 224\n",
        "        \n",
        "        self.nasnet_conv = nn.Sequential(                                                                                       # ① 1st Vanilla Conv Block = 32 X 112 X 112\n",
        "            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(32),\n",
        "            swish()\n",
        "        )\n",
        "\n",
        "        self.reduction_cell_1 = reduction_cell(after_channels=32, before_channels=3, mid_channels=16, out_channels=16)          # ② 2nd Reduction Cell = 72 X 28 X 28\n",
        "        self.reduction_cell_2 = reduction_cell(after_channels=48, before_channels=32, mid_channels=24, out_channels=24)\n",
        "\n",
        "        self.normal_cell_1 = normal_cell(after_channels=72, before_channels=48, out_channels=36)                                # ③ 3rd Normal & Reduction Cell = 270 X 14 X 14\n",
        "        self.reduction_cell_3 = reduction_cell(after_channels=180, before_channels=72, mid_channels=90, out_channels=90)\n",
        "\n",
        "        self.normal_cell_2 = normal_cell(after_channels=270, before_channels=180, out_channels=90)                              # ④ 4th Normal & Reduction Cell = 900 X 7 X 7\n",
        "        self.reduction_cell_4 = reduction_cell(after_channels=450, before_channels=270, mid_channels=300, out_channels=300)\n",
        "\n",
        "        self.normal_cell_3 = normal_cell(after_channels=900, before_channels=450, out_channels=400)                             # ⑤ 5th Normal Cell = 2000 X 7 X 7\n",
        "\n",
        "        self.nasnet_fc = nn.Sequential(                                                                                         # ⑤ 6th Advanced Fully-Connected Block = 10 Vectorize\n",
        "            nn.AdaptiveAvgPool2d((1, 1)),\n",
        "            nn.Flatten(),\n",
        "\n",
        "            nn.Linear(in_features=2000, out_features=512),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.Dropout(p=0.25),\n",
        "            swish(),\n",
        "\n",
        "            nn.Linear(in_features=512, out_features=128),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.Dropout(p=0.25),\n",
        "            swish(),\n",
        "\n",
        "            nn.Linear(in_features=128, out_features=n_class)\n",
        "        )\n",
        "\n",
        "    def forward(self, input):\n",
        "        temp = self.nasnet_conv(input)\n",
        "\n",
        "        temp1 = self.reduction_cell_1(temp, input)\n",
        "        temp2 = self.reduction_cell_2(temp1, temp)\n",
        "\n",
        "        temp3 = self.normal_cell_1(temp2, temp1)\n",
        "        temp4 = self.reduction_cell_3(temp3, temp2)\n",
        "\n",
        "        temp5 = self.normal_cell_2(temp4, temp3)\n",
        "        temp6 = self.reduction_cell_4(temp5, temp4)\n",
        "\n",
        "        temp7 = self.normal_cell_3(temp6, temp5)\n",
        "\n",
        "        output = self.nasnet_fc(temp7)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Normal Convolutional Cell Generation in NASNet-A Model\n",
        "class normal_cell(nn.Module):\n",
        "    def __init__(self, after_channels, before_channels, out_channels):\n",
        "        super().__init__()\n",
        "\n",
        "        self.adjust_block = adjust_block(channels=before_channels)\n",
        "        self.convert1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=after_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            swish()\n",
        "        )\n",
        "        self.convert2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=before_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            swish()\n",
        "        )\n",
        "\n",
        "        self.after_ds_conv3 = ds_conv(in_channels=after_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)         # Hstate DS-Conv with size=3\n",
        "        self.before_ds_conv3 = ds_conv(in_channels=before_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)       # H-1state DS-Conv with size=3\n",
        "\n",
        "        self.after_ds_conv5 = ds_conv(in_channels=after_channels, out_channels=out_channels, kernel_size=5, stride=1, padding=2)         # Hstate DS-Conv with size=5\n",
        "        self.before_ds_conv5 = ds_conv(in_channels=before_channels, out_channels=out_channels, kernel_size=5, stride=1, padding=2)       # H-1state DS-Conv with size=5\n",
        "\n",
        "        self.avg_pool = nn.AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
        "\n",
        "    def forward(self, after_input, before_input):\n",
        "        if after_input.size()[2] != before_input.size()[2] and after_input.size()[3] != before_input.size()[3]:\n",
        "            before_input = self.adjust_block(before_input)\n",
        "\n",
        "        a_block = self.after_ds_conv3(after_input) + self.convert1(after_input)                                 # ① A Block in Normal Cell : After DS-Conv3 & After Identity Mapping\n",
        "        b_block = self.before_ds_conv3(before_input) + self.after_ds_conv5(after_input)                         # ② B Block in Normal Cell : Before DS-Conv3 & After DS-Conv5\n",
        "        c_block = self.avg_pool(self.convert1(after_input)) + self.convert2(before_input)                       # ③ C Block in Normal Cell : After AvgPool & Before Identity Mapping\n",
        "        d_block = self.avg_pool(self.convert2(before_input)) + self.avg_pool(self.convert2(before_input))       # ④ D Block in Normal Cell : Before AvgPool & Before AvgPool\n",
        "        e_block = self.before_ds_conv5(before_input) + self.before_ds_conv3(before_input)                       # ⑤ E Block in Normal Cell : Before DS-Conv5 & Before DS-Conv3\n",
        "\n",
        "        output = torch.cat([a_block, b_block, c_block, d_block, e_block], dim=1)                                # Concatenation with Channel Dimension\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Reduction Convolutional Cell Generation in NASNet-A Model\n",
        "class reduction_cell(nn.Module):\n",
        "    def __init__(self, after_channels, before_channels, mid_channels, out_channels):\n",
        "        super().__init__()\n",
        "\n",
        "        self.adjust_block = adjust_block(channels=before_channels)\n",
        "        self.convert1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=after_channels, out_channels=mid_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(mid_channels),\n",
        "            swish()\n",
        "        )\n",
        "        self.convert2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=after_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            swish()\n",
        "        )\n",
        "        self.convert3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            swish()\n",
        "        )\n",
        "\n",
        "        self.ds_conv7 = ds_conv(in_channels=before_channels, out_channels=mid_channels, kernel_size=7, stride=2, padding=3)             # H-1state DS-Conv with size=7\n",
        "        self.after_ds_conv5 = ds_conv(in_channels=after_channels, out_channels=mid_channels, kernel_size=5, stride=2, padding=2)        # Hstate DS-Conv with size=5\n",
        "        self.before_ds_conv5 = ds_conv(in_channels=before_channels, out_channels=out_channels, kernel_size=5, stride=2, padding=2)      # H-1state DS-Conv with size=5\n",
        "        \n",
        "        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.ds_conv3 = ds_conv(in_channels=mid_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)                # Process in Current Block DS-Conv with size=3\n",
        "\n",
        "        self.avg_pool_1 = nn.AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
        "        self.avg_pool_2 = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)\n",
        "    \n",
        "    def forward(self, after_input, before_input):\n",
        "        if after_input.size()[2] != before_input.size()[2] and after_input.size()[3] != before_input.size()[3]:\n",
        "            before_input = self.adjust_block(before_input)\n",
        "\n",
        "        a_block = self.ds_conv7(before_input) + self.after_ds_conv5(after_input)                        # ① A Block in Reduction Cell : Before DS-Conv7 & After DS-Conv5\n",
        "        b_block = self.max_pool(self.convert1(after_input)) + self.ds_conv7(before_input)               # ② B Block in Reduction Cell : After MaxPool & Before DS-Conv7\n",
        "        c_block = self.avg_pool_2(self.convert2(after_input)) + self.before_ds_conv5(before_input)      # ③ C Block in Reduction Cell : After AvgPool & Before DS-Conv5\n",
        "\n",
        "        d_block = self.max_pool(self.convert2(after_input)) + self.ds_conv3(a_block)                    # ④ D Block in Reduction Cell : After MaxPool & Identity Mapping with A Block\n",
        "        e_block = self.avg_pool_1(self.convert3(a_block)) + b_block                                     # ⑤ E Block in Reduction Cell : AvgPool with A Block & Identity Mapping with B Block\n",
        "\n",
        "        output = torch.cat([c_block, d_block, e_block], dim=1)                          # Concatenation with Channel Dimension\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Depth-wise Separable Convolution in NASNet-A Model\n",
        "class ds_conv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel_size, stride, padding):\n",
        "        super().__init__()\n",
        "\n",
        "        self.depthwise = nn.Sequential(                                                                     # ① Depth-wise Convolution in DS-Conv with Swish\n",
        "            nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=in_channels),\n",
        "            nn.BatchNorm2d(in_channels),\n",
        "            swish()\n",
        "        )\n",
        "\n",
        "        self.pointwise = nn.Sequential(                                                                     # ② Point-wise Convolution in DS-Conv with Swish\n",
        "            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            swish()\n",
        "        )\n",
        "    \n",
        "    def forward(self, input):\n",
        "        temp = self.depthwise(input)\n",
        "        output = self.pointwise(temp)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Adjustment of Feature Map Size by 2 Division for Addition Operation in NASNet-A Cells\n",
        "class adjust_block(nn.Module):\n",
        "    def __init__(self, channels):\n",
        "        super().__init__()\n",
        "\n",
        "        self.adjust_block = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(channels),\n",
        "            swish()\n",
        "        )\n",
        "    \n",
        "    def forward(self, input):\n",
        "        output = self.adjust_block(input)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "## Automated Activation Function : Swish\n",
        "class swish(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "    \n",
        "    def forward(self, input):\n",
        "        output = input * self.sigmoid(input)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "nasnet_sample = NASNet(n_class=10).to(device)           # NASNet Model for Sample Version\n",
        "summary(nasnet_sample, (3, 224, 224))"
      ],
      "metadata": {
        "id": "AzFieg6VlVts",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f41dd9c1-5a4a-4bcd-fb46-5c372009c085"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 32, 112, 112]             896\n",
            "       BatchNorm2d-2         [-1, 32, 112, 112]              64\n",
            "           Sigmoid-3         [-1, 32, 112, 112]               0\n",
            "             swish-4         [-1, 32, 112, 112]               0\n",
            "            Conv2d-5          [-1, 3, 112, 112]              84\n",
            "       BatchNorm2d-6          [-1, 3, 112, 112]               6\n",
            "           Sigmoid-7          [-1, 3, 112, 112]               0\n",
            "             swish-8          [-1, 3, 112, 112]               0\n",
            "      adjust_block-9          [-1, 3, 112, 112]               0\n",
            "           Conv2d-10            [-1, 3, 56, 56]             150\n",
            "      BatchNorm2d-11            [-1, 3, 56, 56]               6\n",
            "          Sigmoid-12            [-1, 3, 56, 56]               0\n",
            "            swish-13            [-1, 3, 56, 56]               0\n",
            "           Conv2d-14           [-1, 16, 56, 56]              64\n",
            "      BatchNorm2d-15           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-16           [-1, 16, 56, 56]               0\n",
            "            swish-17           [-1, 16, 56, 56]               0\n",
            "          ds_conv-18           [-1, 16, 56, 56]               0\n",
            "           Conv2d-19           [-1, 32, 56, 56]             832\n",
            "      BatchNorm2d-20           [-1, 32, 56, 56]              64\n",
            "          Sigmoid-21           [-1, 32, 56, 56]               0\n",
            "            swish-22           [-1, 32, 56, 56]               0\n",
            "           Conv2d-23           [-1, 16, 56, 56]             528\n",
            "      BatchNorm2d-24           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-25           [-1, 16, 56, 56]               0\n",
            "            swish-26           [-1, 16, 56, 56]               0\n",
            "          ds_conv-27           [-1, 16, 56, 56]               0\n",
            "           Conv2d-28         [-1, 16, 112, 112]             528\n",
            "      BatchNorm2d-29         [-1, 16, 112, 112]              32\n",
            "          Sigmoid-30         [-1, 16, 112, 112]               0\n",
            "            swish-31         [-1, 16, 112, 112]               0\n",
            "        MaxPool2d-32           [-1, 16, 56, 56]               0\n",
            "           Conv2d-33            [-1, 3, 56, 56]             150\n",
            "      BatchNorm2d-34            [-1, 3, 56, 56]               6\n",
            "          Sigmoid-35            [-1, 3, 56, 56]               0\n",
            "            swish-36            [-1, 3, 56, 56]               0\n",
            "           Conv2d-37           [-1, 16, 56, 56]              64\n",
            "      BatchNorm2d-38           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-39           [-1, 16, 56, 56]               0\n",
            "            swish-40           [-1, 16, 56, 56]               0\n",
            "          ds_conv-41           [-1, 16, 56, 56]               0\n",
            "           Conv2d-42         [-1, 16, 112, 112]             528\n",
            "      BatchNorm2d-43         [-1, 16, 112, 112]              32\n",
            "          Sigmoid-44         [-1, 16, 112, 112]               0\n",
            "            swish-45         [-1, 16, 112, 112]               0\n",
            "        AvgPool2d-46           [-1, 16, 56, 56]               0\n",
            "           Conv2d-47            [-1, 3, 56, 56]              78\n",
            "      BatchNorm2d-48            [-1, 3, 56, 56]               6\n",
            "          Sigmoid-49            [-1, 3, 56, 56]               0\n",
            "            swish-50            [-1, 3, 56, 56]               0\n",
            "           Conv2d-51           [-1, 16, 56, 56]              64\n",
            "      BatchNorm2d-52           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-53           [-1, 16, 56, 56]               0\n",
            "            swish-54           [-1, 16, 56, 56]               0\n",
            "          ds_conv-55           [-1, 16, 56, 56]               0\n",
            "           Conv2d-56         [-1, 16, 112, 112]             528\n",
            "      BatchNorm2d-57         [-1, 16, 112, 112]              32\n",
            "          Sigmoid-58         [-1, 16, 112, 112]               0\n",
            "            swish-59         [-1, 16, 112, 112]               0\n",
            "        MaxPool2d-60           [-1, 16, 56, 56]               0\n",
            "           Conv2d-61           [-1, 16, 56, 56]             160\n",
            "      BatchNorm2d-62           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-63           [-1, 16, 56, 56]               0\n",
            "            swish-64           [-1, 16, 56, 56]               0\n",
            "           Conv2d-65           [-1, 16, 56, 56]             272\n",
            "      BatchNorm2d-66           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-67           [-1, 16, 56, 56]               0\n",
            "            swish-68           [-1, 16, 56, 56]               0\n",
            "          ds_conv-69           [-1, 16, 56, 56]               0\n",
            "           Conv2d-70           [-1, 16, 56, 56]             272\n",
            "      BatchNorm2d-71           [-1, 16, 56, 56]              32\n",
            "          Sigmoid-72           [-1, 16, 56, 56]               0\n",
            "            swish-73           [-1, 16, 56, 56]               0\n",
            "        AvgPool2d-74           [-1, 16, 56, 56]               0\n",
            "   reduction_cell-75           [-1, 48, 56, 56]               0\n",
            "           Conv2d-76           [-1, 32, 56, 56]           9,248\n",
            "      BatchNorm2d-77           [-1, 32, 56, 56]              64\n",
            "          Sigmoid-78           [-1, 32, 56, 56]               0\n",
            "            swish-79           [-1, 32, 56, 56]               0\n",
            "     adjust_block-80           [-1, 32, 56, 56]               0\n",
            "           Conv2d-81           [-1, 32, 28, 28]           1,600\n",
            "      BatchNorm2d-82           [-1, 32, 28, 28]              64\n",
            "          Sigmoid-83           [-1, 32, 28, 28]               0\n",
            "            swish-84           [-1, 32, 28, 28]               0\n",
            "           Conv2d-85           [-1, 24, 28, 28]             792\n",
            "      BatchNorm2d-86           [-1, 24, 28, 28]              48\n",
            "          Sigmoid-87           [-1, 24, 28, 28]               0\n",
            "            swish-88           [-1, 24, 28, 28]               0\n",
            "          ds_conv-89           [-1, 24, 28, 28]               0\n",
            "           Conv2d-90           [-1, 48, 28, 28]           1,248\n",
            "      BatchNorm2d-91           [-1, 48, 28, 28]              96\n",
            "          Sigmoid-92           [-1, 48, 28, 28]               0\n",
            "            swish-93           [-1, 48, 28, 28]               0\n",
            "           Conv2d-94           [-1, 24, 28, 28]           1,176\n",
            "      BatchNorm2d-95           [-1, 24, 28, 28]              48\n",
            "          Sigmoid-96           [-1, 24, 28, 28]               0\n",
            "            swish-97           [-1, 24, 28, 28]               0\n",
            "          ds_conv-98           [-1, 24, 28, 28]               0\n",
            "           Conv2d-99           [-1, 24, 56, 56]           1,176\n",
            "     BatchNorm2d-100           [-1, 24, 56, 56]              48\n",
            "         Sigmoid-101           [-1, 24, 56, 56]               0\n",
            "           swish-102           [-1, 24, 56, 56]               0\n",
            "       MaxPool2d-103           [-1, 24, 28, 28]               0\n",
            "          Conv2d-104           [-1, 32, 28, 28]           1,600\n",
            "     BatchNorm2d-105           [-1, 32, 28, 28]              64\n",
            "         Sigmoid-106           [-1, 32, 28, 28]               0\n",
            "           swish-107           [-1, 32, 28, 28]               0\n",
            "          Conv2d-108           [-1, 24, 28, 28]             792\n",
            "     BatchNorm2d-109           [-1, 24, 28, 28]              48\n",
            "         Sigmoid-110           [-1, 24, 28, 28]               0\n",
            "           swish-111           [-1, 24, 28, 28]               0\n",
            "         ds_conv-112           [-1, 24, 28, 28]               0\n",
            "          Conv2d-113           [-1, 24, 56, 56]           1,176\n",
            "     BatchNorm2d-114           [-1, 24, 56, 56]              48\n",
            "         Sigmoid-115           [-1, 24, 56, 56]               0\n",
            "           swish-116           [-1, 24, 56, 56]               0\n",
            "       AvgPool2d-117           [-1, 24, 28, 28]               0\n",
            "          Conv2d-118           [-1, 32, 28, 28]             832\n",
            "     BatchNorm2d-119           [-1, 32, 28, 28]              64\n",
            "         Sigmoid-120           [-1, 32, 28, 28]               0\n",
            "           swish-121           [-1, 32, 28, 28]               0\n",
            "          Conv2d-122           [-1, 24, 28, 28]             792\n",
            "     BatchNorm2d-123           [-1, 24, 28, 28]              48\n",
            "         Sigmoid-124           [-1, 24, 28, 28]               0\n",
            "           swish-125           [-1, 24, 28, 28]               0\n",
            "         ds_conv-126           [-1, 24, 28, 28]               0\n",
            "          Conv2d-127           [-1, 24, 56, 56]           1,176\n",
            "     BatchNorm2d-128           [-1, 24, 56, 56]              48\n",
            "         Sigmoid-129           [-1, 24, 56, 56]               0\n",
            "           swish-130           [-1, 24, 56, 56]               0\n",
            "       MaxPool2d-131           [-1, 24, 28, 28]               0\n",
            "          Conv2d-132           [-1, 24, 28, 28]             240\n",
            "     BatchNorm2d-133           [-1, 24, 28, 28]              48\n",
            "         Sigmoid-134           [-1, 24, 28, 28]               0\n",
            "           swish-135           [-1, 24, 28, 28]               0\n",
            "          Conv2d-136           [-1, 24, 28, 28]             600\n",
            "     BatchNorm2d-137           [-1, 24, 28, 28]              48\n",
            "         Sigmoid-138           [-1, 24, 28, 28]               0\n",
            "           swish-139           [-1, 24, 28, 28]               0\n",
            "         ds_conv-140           [-1, 24, 28, 28]               0\n",
            "          Conv2d-141           [-1, 24, 28, 28]             600\n",
            "     BatchNorm2d-142           [-1, 24, 28, 28]              48\n",
            "         Sigmoid-143           [-1, 24, 28, 28]               0\n",
            "           swish-144           [-1, 24, 28, 28]               0\n",
            "       AvgPool2d-145           [-1, 24, 28, 28]               0\n",
            "  reduction_cell-146           [-1, 72, 28, 28]               0\n",
            "          Conv2d-147           [-1, 48, 28, 28]          20,784\n",
            "     BatchNorm2d-148           [-1, 48, 28, 28]              96\n",
            "         Sigmoid-149           [-1, 48, 28, 28]               0\n",
            "           swish-150           [-1, 48, 28, 28]               0\n",
            "    adjust_block-151           [-1, 48, 28, 28]               0\n",
            "          Conv2d-152           [-1, 72, 28, 28]             720\n",
            "     BatchNorm2d-153           [-1, 72, 28, 28]             144\n",
            "         Sigmoid-154           [-1, 72, 28, 28]               0\n",
            "           swish-155           [-1, 72, 28, 28]               0\n",
            "          Conv2d-156           [-1, 36, 28, 28]           2,628\n",
            "     BatchNorm2d-157           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-158           [-1, 36, 28, 28]               0\n",
            "           swish-159           [-1, 36, 28, 28]               0\n",
            "         ds_conv-160           [-1, 36, 28, 28]               0\n",
            "          Conv2d-161           [-1, 36, 28, 28]           2,628\n",
            "     BatchNorm2d-162           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-163           [-1, 36, 28, 28]               0\n",
            "           swish-164           [-1, 36, 28, 28]               0\n",
            "          Conv2d-165           [-1, 48, 28, 28]             480\n",
            "     BatchNorm2d-166           [-1, 48, 28, 28]              96\n",
            "         Sigmoid-167           [-1, 48, 28, 28]               0\n",
            "           swish-168           [-1, 48, 28, 28]               0\n",
            "          Conv2d-169           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-170           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-171           [-1, 36, 28, 28]               0\n",
            "           swish-172           [-1, 36, 28, 28]               0\n",
            "         ds_conv-173           [-1, 36, 28, 28]               0\n",
            "          Conv2d-174           [-1, 72, 28, 28]           1,872\n",
            "     BatchNorm2d-175           [-1, 72, 28, 28]             144\n",
            "         Sigmoid-176           [-1, 72, 28, 28]               0\n",
            "           swish-177           [-1, 72, 28, 28]               0\n",
            "          Conv2d-178           [-1, 36, 28, 28]           2,628\n",
            "     BatchNorm2d-179           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-180           [-1, 36, 28, 28]               0\n",
            "           swish-181           [-1, 36, 28, 28]               0\n",
            "         ds_conv-182           [-1, 36, 28, 28]               0\n",
            "          Conv2d-183           [-1, 36, 28, 28]           2,628\n",
            "     BatchNorm2d-184           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-185           [-1, 36, 28, 28]               0\n",
            "           swish-186           [-1, 36, 28, 28]               0\n",
            "       AvgPool2d-187           [-1, 36, 28, 28]               0\n",
            "          Conv2d-188           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-189           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-190           [-1, 36, 28, 28]               0\n",
            "           swish-191           [-1, 36, 28, 28]               0\n",
            "          Conv2d-192           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-193           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-194           [-1, 36, 28, 28]               0\n",
            "           swish-195           [-1, 36, 28, 28]               0\n",
            "       AvgPool2d-196           [-1, 36, 28, 28]               0\n",
            "          Conv2d-197           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-198           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-199           [-1, 36, 28, 28]               0\n",
            "           swish-200           [-1, 36, 28, 28]               0\n",
            "       AvgPool2d-201           [-1, 36, 28, 28]               0\n",
            "          Conv2d-202           [-1, 48, 28, 28]           1,248\n",
            "     BatchNorm2d-203           [-1, 48, 28, 28]              96\n",
            "         Sigmoid-204           [-1, 48, 28, 28]               0\n",
            "           swish-205           [-1, 48, 28, 28]               0\n",
            "          Conv2d-206           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-207           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-208           [-1, 36, 28, 28]               0\n",
            "           swish-209           [-1, 36, 28, 28]               0\n",
            "         ds_conv-210           [-1, 36, 28, 28]               0\n",
            "          Conv2d-211           [-1, 48, 28, 28]             480\n",
            "     BatchNorm2d-212           [-1, 48, 28, 28]              96\n",
            "         Sigmoid-213           [-1, 48, 28, 28]               0\n",
            "           swish-214           [-1, 48, 28, 28]               0\n",
            "          Conv2d-215           [-1, 36, 28, 28]           1,764\n",
            "     BatchNorm2d-216           [-1, 36, 28, 28]              72\n",
            "         Sigmoid-217           [-1, 36, 28, 28]               0\n",
            "           swish-218           [-1, 36, 28, 28]               0\n",
            "         ds_conv-219           [-1, 36, 28, 28]               0\n",
            "     normal_cell-220          [-1, 180, 28, 28]               0\n",
            "          Conv2d-221           [-1, 72, 14, 14]           3,600\n",
            "     BatchNorm2d-222           [-1, 72, 14, 14]             144\n",
            "         Sigmoid-223           [-1, 72, 14, 14]               0\n",
            "           swish-224           [-1, 72, 14, 14]               0\n",
            "          Conv2d-225           [-1, 90, 14, 14]           6,570\n",
            "     BatchNorm2d-226           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-227           [-1, 90, 14, 14]               0\n",
            "           swish-228           [-1, 90, 14, 14]               0\n",
            "         ds_conv-229           [-1, 90, 14, 14]               0\n",
            "          Conv2d-230          [-1, 180, 14, 14]           4,680\n",
            "     BatchNorm2d-231          [-1, 180, 14, 14]             360\n",
            "         Sigmoid-232          [-1, 180, 14, 14]               0\n",
            "           swish-233          [-1, 180, 14, 14]               0\n",
            "          Conv2d-234           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-235           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-236           [-1, 90, 14, 14]               0\n",
            "           swish-237           [-1, 90, 14, 14]               0\n",
            "         ds_conv-238           [-1, 90, 14, 14]               0\n",
            "          Conv2d-239           [-1, 90, 28, 28]          16,290\n",
            "     BatchNorm2d-240           [-1, 90, 28, 28]             180\n",
            "         Sigmoid-241           [-1, 90, 28, 28]               0\n",
            "           swish-242           [-1, 90, 28, 28]               0\n",
            "       MaxPool2d-243           [-1, 90, 14, 14]               0\n",
            "          Conv2d-244           [-1, 72, 14, 14]           3,600\n",
            "     BatchNorm2d-245           [-1, 72, 14, 14]             144\n",
            "         Sigmoid-246           [-1, 72, 14, 14]               0\n",
            "           swish-247           [-1, 72, 14, 14]               0\n",
            "          Conv2d-248           [-1, 90, 14, 14]           6,570\n",
            "     BatchNorm2d-249           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-250           [-1, 90, 14, 14]               0\n",
            "           swish-251           [-1, 90, 14, 14]               0\n",
            "         ds_conv-252           [-1, 90, 14, 14]               0\n",
            "          Conv2d-253           [-1, 90, 28, 28]          16,290\n",
            "     BatchNorm2d-254           [-1, 90, 28, 28]             180\n",
            "         Sigmoid-255           [-1, 90, 28, 28]               0\n",
            "           swish-256           [-1, 90, 28, 28]               0\n",
            "       AvgPool2d-257           [-1, 90, 14, 14]               0\n",
            "          Conv2d-258           [-1, 72, 14, 14]           1,872\n",
            "     BatchNorm2d-259           [-1, 72, 14, 14]             144\n",
            "         Sigmoid-260           [-1, 72, 14, 14]               0\n",
            "           swish-261           [-1, 72, 14, 14]               0\n",
            "          Conv2d-262           [-1, 90, 14, 14]           6,570\n",
            "     BatchNorm2d-263           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-264           [-1, 90, 14, 14]               0\n",
            "           swish-265           [-1, 90, 14, 14]               0\n",
            "         ds_conv-266           [-1, 90, 14, 14]               0\n",
            "          Conv2d-267           [-1, 90, 28, 28]          16,290\n",
            "     BatchNorm2d-268           [-1, 90, 28, 28]             180\n",
            "         Sigmoid-269           [-1, 90, 28, 28]               0\n",
            "           swish-270           [-1, 90, 28, 28]               0\n",
            "       MaxPool2d-271           [-1, 90, 14, 14]               0\n",
            "          Conv2d-272           [-1, 90, 14, 14]             900\n",
            "     BatchNorm2d-273           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-274           [-1, 90, 14, 14]               0\n",
            "           swish-275           [-1, 90, 14, 14]               0\n",
            "          Conv2d-276           [-1, 90, 14, 14]           8,190\n",
            "     BatchNorm2d-277           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-278           [-1, 90, 14, 14]               0\n",
            "           swish-279           [-1, 90, 14, 14]               0\n",
            "         ds_conv-280           [-1, 90, 14, 14]               0\n",
            "          Conv2d-281           [-1, 90, 14, 14]           8,190\n",
            "     BatchNorm2d-282           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-283           [-1, 90, 14, 14]               0\n",
            "           swish-284           [-1, 90, 14, 14]               0\n",
            "       AvgPool2d-285           [-1, 90, 14, 14]               0\n",
            "  reduction_cell-286          [-1, 270, 14, 14]               0\n",
            "          Conv2d-287          [-1, 180, 14, 14]         291,780\n",
            "     BatchNorm2d-288          [-1, 180, 14, 14]             360\n",
            "         Sigmoid-289          [-1, 180, 14, 14]               0\n",
            "           swish-290          [-1, 180, 14, 14]               0\n",
            "    adjust_block-291          [-1, 180, 14, 14]               0\n",
            "          Conv2d-292          [-1, 270, 14, 14]           2,700\n",
            "     BatchNorm2d-293          [-1, 270, 14, 14]             540\n",
            "         Sigmoid-294          [-1, 270, 14, 14]               0\n",
            "           swish-295          [-1, 270, 14, 14]               0\n",
            "          Conv2d-296           [-1, 90, 14, 14]          24,390\n",
            "     BatchNorm2d-297           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-298           [-1, 90, 14, 14]               0\n",
            "           swish-299           [-1, 90, 14, 14]               0\n",
            "         ds_conv-300           [-1, 90, 14, 14]               0\n",
            "          Conv2d-301           [-1, 90, 14, 14]          24,390\n",
            "     BatchNorm2d-302           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-303           [-1, 90, 14, 14]               0\n",
            "           swish-304           [-1, 90, 14, 14]               0\n",
            "          Conv2d-305          [-1, 180, 14, 14]           1,800\n",
            "     BatchNorm2d-306          [-1, 180, 14, 14]             360\n",
            "         Sigmoid-307          [-1, 180, 14, 14]               0\n",
            "           swish-308          [-1, 180, 14, 14]               0\n",
            "          Conv2d-309           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-310           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-311           [-1, 90, 14, 14]               0\n",
            "           swish-312           [-1, 90, 14, 14]               0\n",
            "         ds_conv-313           [-1, 90, 14, 14]               0\n",
            "          Conv2d-314          [-1, 270, 14, 14]           7,020\n",
            "     BatchNorm2d-315          [-1, 270, 14, 14]             540\n",
            "         Sigmoid-316          [-1, 270, 14, 14]               0\n",
            "           swish-317          [-1, 270, 14, 14]               0\n",
            "          Conv2d-318           [-1, 90, 14, 14]          24,390\n",
            "     BatchNorm2d-319           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-320           [-1, 90, 14, 14]               0\n",
            "           swish-321           [-1, 90, 14, 14]               0\n",
            "         ds_conv-322           [-1, 90, 14, 14]               0\n",
            "          Conv2d-323           [-1, 90, 14, 14]          24,390\n",
            "     BatchNorm2d-324           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-325           [-1, 90, 14, 14]               0\n",
            "           swish-326           [-1, 90, 14, 14]               0\n",
            "       AvgPool2d-327           [-1, 90, 14, 14]               0\n",
            "          Conv2d-328           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-329           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-330           [-1, 90, 14, 14]               0\n",
            "           swish-331           [-1, 90, 14, 14]               0\n",
            "          Conv2d-332           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-333           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-334           [-1, 90, 14, 14]               0\n",
            "           swish-335           [-1, 90, 14, 14]               0\n",
            "       AvgPool2d-336           [-1, 90, 14, 14]               0\n",
            "          Conv2d-337           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-338           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-339           [-1, 90, 14, 14]               0\n",
            "           swish-340           [-1, 90, 14, 14]               0\n",
            "       AvgPool2d-341           [-1, 90, 14, 14]               0\n",
            "          Conv2d-342          [-1, 180, 14, 14]           4,680\n",
            "     BatchNorm2d-343          [-1, 180, 14, 14]             360\n",
            "         Sigmoid-344          [-1, 180, 14, 14]               0\n",
            "           swish-345          [-1, 180, 14, 14]               0\n",
            "          Conv2d-346           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-347           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-348           [-1, 90, 14, 14]               0\n",
            "           swish-349           [-1, 90, 14, 14]               0\n",
            "         ds_conv-350           [-1, 90, 14, 14]               0\n",
            "          Conv2d-351          [-1, 180, 14, 14]           1,800\n",
            "     BatchNorm2d-352          [-1, 180, 14, 14]             360\n",
            "         Sigmoid-353          [-1, 180, 14, 14]               0\n",
            "           swish-354          [-1, 180, 14, 14]               0\n",
            "          Conv2d-355           [-1, 90, 14, 14]          16,290\n",
            "     BatchNorm2d-356           [-1, 90, 14, 14]             180\n",
            "         Sigmoid-357           [-1, 90, 14, 14]               0\n",
            "           swish-358           [-1, 90, 14, 14]               0\n",
            "         ds_conv-359           [-1, 90, 14, 14]               0\n",
            "     normal_cell-360          [-1, 450, 14, 14]               0\n",
            "          Conv2d-361            [-1, 270, 7, 7]          13,500\n",
            "     BatchNorm2d-362            [-1, 270, 7, 7]             540\n",
            "         Sigmoid-363            [-1, 270, 7, 7]               0\n",
            "           swish-364            [-1, 270, 7, 7]               0\n",
            "          Conv2d-365            [-1, 300, 7, 7]          81,300\n",
            "     BatchNorm2d-366            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-367            [-1, 300, 7, 7]               0\n",
            "           swish-368            [-1, 300, 7, 7]               0\n",
            "         ds_conv-369            [-1, 300, 7, 7]               0\n",
            "          Conv2d-370            [-1, 450, 7, 7]          11,700\n",
            "     BatchNorm2d-371            [-1, 450, 7, 7]             900\n",
            "         Sigmoid-372            [-1, 450, 7, 7]               0\n",
            "           swish-373            [-1, 450, 7, 7]               0\n",
            "          Conv2d-374            [-1, 300, 7, 7]         135,300\n",
            "     BatchNorm2d-375            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-376            [-1, 300, 7, 7]               0\n",
            "           swish-377            [-1, 300, 7, 7]               0\n",
            "         ds_conv-378            [-1, 300, 7, 7]               0\n",
            "          Conv2d-379          [-1, 300, 14, 14]         135,300\n",
            "     BatchNorm2d-380          [-1, 300, 14, 14]             600\n",
            "         Sigmoid-381          [-1, 300, 14, 14]               0\n",
            "           swish-382          [-1, 300, 14, 14]               0\n",
            "       MaxPool2d-383            [-1, 300, 7, 7]               0\n",
            "          Conv2d-384            [-1, 270, 7, 7]          13,500\n",
            "     BatchNorm2d-385            [-1, 270, 7, 7]             540\n",
            "         Sigmoid-386            [-1, 270, 7, 7]               0\n",
            "           swish-387            [-1, 270, 7, 7]               0\n",
            "          Conv2d-388            [-1, 300, 7, 7]          81,300\n",
            "     BatchNorm2d-389            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-390            [-1, 300, 7, 7]               0\n",
            "           swish-391            [-1, 300, 7, 7]               0\n",
            "         ds_conv-392            [-1, 300, 7, 7]               0\n",
            "          Conv2d-393          [-1, 300, 14, 14]         135,300\n",
            "     BatchNorm2d-394          [-1, 300, 14, 14]             600\n",
            "         Sigmoid-395          [-1, 300, 14, 14]               0\n",
            "           swish-396          [-1, 300, 14, 14]               0\n",
            "       AvgPool2d-397            [-1, 300, 7, 7]               0\n",
            "          Conv2d-398            [-1, 270, 7, 7]           7,020\n",
            "     BatchNorm2d-399            [-1, 270, 7, 7]             540\n",
            "         Sigmoid-400            [-1, 270, 7, 7]               0\n",
            "           swish-401            [-1, 270, 7, 7]               0\n",
            "          Conv2d-402            [-1, 300, 7, 7]          81,300\n",
            "     BatchNorm2d-403            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-404            [-1, 300, 7, 7]               0\n",
            "           swish-405            [-1, 300, 7, 7]               0\n",
            "         ds_conv-406            [-1, 300, 7, 7]               0\n",
            "          Conv2d-407          [-1, 300, 14, 14]         135,300\n",
            "     BatchNorm2d-408          [-1, 300, 14, 14]             600\n",
            "         Sigmoid-409          [-1, 300, 14, 14]               0\n",
            "           swish-410          [-1, 300, 14, 14]               0\n",
            "       MaxPool2d-411            [-1, 300, 7, 7]               0\n",
            "          Conv2d-412            [-1, 300, 7, 7]           3,000\n",
            "     BatchNorm2d-413            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-414            [-1, 300, 7, 7]               0\n",
            "           swish-415            [-1, 300, 7, 7]               0\n",
            "          Conv2d-416            [-1, 300, 7, 7]          90,300\n",
            "     BatchNorm2d-417            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-418            [-1, 300, 7, 7]               0\n",
            "           swish-419            [-1, 300, 7, 7]               0\n",
            "         ds_conv-420            [-1, 300, 7, 7]               0\n",
            "          Conv2d-421            [-1, 300, 7, 7]          90,300\n",
            "     BatchNorm2d-422            [-1, 300, 7, 7]             600\n",
            "         Sigmoid-423            [-1, 300, 7, 7]               0\n",
            "           swish-424            [-1, 300, 7, 7]               0\n",
            "       AvgPool2d-425            [-1, 300, 7, 7]               0\n",
            "  reduction_cell-426            [-1, 900, 7, 7]               0\n",
            "          Conv2d-427            [-1, 450, 7, 7]       1,822,950\n",
            "     BatchNorm2d-428            [-1, 450, 7, 7]             900\n",
            "         Sigmoid-429            [-1, 450, 7, 7]               0\n",
            "           swish-430            [-1, 450, 7, 7]               0\n",
            "    adjust_block-431            [-1, 450, 7, 7]               0\n",
            "          Conv2d-432            [-1, 900, 7, 7]           9,000\n",
            "     BatchNorm2d-433            [-1, 900, 7, 7]           1,800\n",
            "         Sigmoid-434            [-1, 900, 7, 7]               0\n",
            "           swish-435            [-1, 900, 7, 7]               0\n",
            "          Conv2d-436            [-1, 400, 7, 7]         360,400\n",
            "     BatchNorm2d-437            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-438            [-1, 400, 7, 7]               0\n",
            "           swish-439            [-1, 400, 7, 7]               0\n",
            "         ds_conv-440            [-1, 400, 7, 7]               0\n",
            "          Conv2d-441            [-1, 400, 7, 7]         360,400\n",
            "     BatchNorm2d-442            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-443            [-1, 400, 7, 7]               0\n",
            "           swish-444            [-1, 400, 7, 7]               0\n",
            "          Conv2d-445            [-1, 450, 7, 7]           4,500\n",
            "     BatchNorm2d-446            [-1, 450, 7, 7]             900\n",
            "         Sigmoid-447            [-1, 450, 7, 7]               0\n",
            "           swish-448            [-1, 450, 7, 7]               0\n",
            "          Conv2d-449            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-450            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-451            [-1, 400, 7, 7]               0\n",
            "           swish-452            [-1, 400, 7, 7]               0\n",
            "         ds_conv-453            [-1, 400, 7, 7]               0\n",
            "          Conv2d-454            [-1, 900, 7, 7]          23,400\n",
            "     BatchNorm2d-455            [-1, 900, 7, 7]           1,800\n",
            "         Sigmoid-456            [-1, 900, 7, 7]               0\n",
            "           swish-457            [-1, 900, 7, 7]               0\n",
            "          Conv2d-458            [-1, 400, 7, 7]         360,400\n",
            "     BatchNorm2d-459            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-460            [-1, 400, 7, 7]               0\n",
            "           swish-461            [-1, 400, 7, 7]               0\n",
            "         ds_conv-462            [-1, 400, 7, 7]               0\n",
            "          Conv2d-463            [-1, 400, 7, 7]         360,400\n",
            "     BatchNorm2d-464            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-465            [-1, 400, 7, 7]               0\n",
            "           swish-466            [-1, 400, 7, 7]               0\n",
            "       AvgPool2d-467            [-1, 400, 7, 7]               0\n",
            "          Conv2d-468            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-469            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-470            [-1, 400, 7, 7]               0\n",
            "           swish-471            [-1, 400, 7, 7]               0\n",
            "          Conv2d-472            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-473            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-474            [-1, 400, 7, 7]               0\n",
            "           swish-475            [-1, 400, 7, 7]               0\n",
            "       AvgPool2d-476            [-1, 400, 7, 7]               0\n",
            "          Conv2d-477            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-478            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-479            [-1, 400, 7, 7]               0\n",
            "           swish-480            [-1, 400, 7, 7]               0\n",
            "       AvgPool2d-481            [-1, 400, 7, 7]               0\n",
            "          Conv2d-482            [-1, 450, 7, 7]          11,700\n",
            "     BatchNorm2d-483            [-1, 450, 7, 7]             900\n",
            "         Sigmoid-484            [-1, 450, 7, 7]               0\n",
            "           swish-485            [-1, 450, 7, 7]               0\n",
            "          Conv2d-486            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-487            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-488            [-1, 400, 7, 7]               0\n",
            "           swish-489            [-1, 400, 7, 7]               0\n",
            "         ds_conv-490            [-1, 400, 7, 7]               0\n",
            "          Conv2d-491            [-1, 450, 7, 7]           4,500\n",
            "     BatchNorm2d-492            [-1, 450, 7, 7]             900\n",
            "         Sigmoid-493            [-1, 450, 7, 7]               0\n",
            "           swish-494            [-1, 450, 7, 7]               0\n",
            "          Conv2d-495            [-1, 400, 7, 7]         180,400\n",
            "     BatchNorm2d-496            [-1, 400, 7, 7]             800\n",
            "         Sigmoid-497            [-1, 400, 7, 7]               0\n",
            "           swish-498            [-1, 400, 7, 7]               0\n",
            "         ds_conv-499            [-1, 400, 7, 7]               0\n",
            "     normal_cell-500           [-1, 2000, 7, 7]               0\n",
            "AdaptiveAvgPool2d-501           [-1, 2000, 1, 1]               0\n",
            "         Flatten-502                 [-1, 2000]               0\n",
            "          Linear-503                  [-1, 512]       1,024,512\n",
            "     BatchNorm1d-504                  [-1, 512]           1,024\n",
            "         Dropout-505                  [-1, 512]               0\n",
            "         Sigmoid-506                  [-1, 512]               0\n",
            "           swish-507                  [-1, 512]               0\n",
            "          Linear-508                  [-1, 128]          65,664\n",
            "     BatchNorm1d-509                  [-1, 128]             256\n",
            "         Dropout-510                  [-1, 128]               0\n",
            "         Sigmoid-511                  [-1, 128]               0\n",
            "           swish-512                  [-1, 128]               0\n",
            "          Linear-513                   [-1, 10]           1,290\n",
            "================================================================\n",
            "Total params: 7,236,452\n",
            "Trainable params: 7,236,452\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.57\n",
            "Forward/backward pass size (MB): 145.38\n",
            "Params size (MB): 27.60\n",
            "Estimated Total Size (MB): 173.56\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#FF0000\"> **② Actuator(Trainer/Tester) Construction for NASNet** </font>"
      ],
      "metadata": {
        "id": "O2-k53yawVWb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## NASNet-A Actuator Definition\n",
        "class NASNet_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": 6,
      "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 NASNet-A\n",
        "device = \"cuda\"\n",
        "nasnet_model = NASNet(n_class=10).to(device)                            # NASNet-A Model for Training Version\n",
        "nasnet_actuator = NASNet_Actuator(model=nasnet_model, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "\n",
        "start_time = time.time()\n",
        "nasnet_actuator.train_and_valid(max_epochs=10, disp_epochs=1)\n",
        "print(f\"Total Training Time : {time.time() - start_time:>3f}\")\n",
        "\n",
        "nasnet_actuator.test()                                                  # NASNet-A Model for Testing Version"
      ],
      "metadata": {
        "id": "LoNZVXoM1sRS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "197c33ea-7848-4276-9482-828530a77563"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========== Train and Valid Start ==========\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Process...:  10%|█         | 1/10 [05:10<46:34, 310.51s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/10 Epoch ==> Train Loss : 1.215648, Train Acc : 0.559875, Valid Loss : 0.835190, Valid Acc : 0.705300, Time : 310.508489\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  20%|██        | 2/10 [10:20<41:20, 310.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/10 Epoch ==> Train Loss : 0.674878, Train Acc : 0.766750, Valid Loss : 0.642860, Valid Acc : 0.772800, Time : 309.840839\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  30%|███       | 3/10 [15:30<36:11, 310.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/10 Epoch ==> Train Loss : 0.461375, Train Acc : 0.843075, Valid Loss : 0.532667, Valid Acc : 0.817700, Time : 310.185728\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  40%|████      | 4/10 [20:40<31:00, 310.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/10 Epoch ==> Train Loss : 0.319093, Train Acc : 0.892475, Valid Loss : 0.589694, Valid Acc : 0.804700, Time : 310.045453\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  50%|█████     | 5/10 [25:50<25:51, 310.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/10 Epoch ==> Train Loss : 0.219048, Train Acc : 0.926875, Valid Loss : 0.515427, Valid Acc : 0.831200, Time : 310.407413\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  60%|██████    | 6/10 [31:01<20:40, 310.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/10 Epoch ==> Train Loss : 0.153708, Train Acc : 0.948850, Valid Loss : 0.632807, Valid Acc : 0.816300, Time : 310.225870\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  70%|███████   | 7/10 [36:11<15:30, 310.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/10 Epoch ==> Train Loss : 0.120974, Train Acc : 0.958625, Valid Loss : 0.590345, Valid Acc : 0.826400, Time : 310.414647\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  80%|████████  | 8/10 [41:21<10:20, 310.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/10 Epoch ==> Train Loss : 0.089043, Train Acc : 0.970200, Valid Loss : 0.644340, Valid Acc : 0.822900, Time : 310.165290\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rTraining Process...:  90%|█████████ | 9/10 [46:32<05:10, 310.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/10 Epoch ==> Train Loss : 0.088985, Train Acc : 0.969175, Valid Loss : 0.630473, Valid Acc : 0.832500, Time : 310.473651\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Process...: 100%|██████████| 10/10 [51:42<00:00, 310.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 Epoch ==> Train Loss : 0.074079, Train Acc : 0.975250, Valid Loss : 0.630491, Valid Acc : 0.829400, Time : 310.159312\n",
            "Total Training Time : 3102.457103\n",
            "========== Test Start ==========\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.650284, Test acc: 0.832700, Test time : 23.454672\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\"> **※ NASNet Model Save and Load** </font>"
      ],
      "metadata": {
        "id": "LjY-XmNZKq8d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#2F9D27\"> **① NASNet Model Save based on Train/Valid History** </font>"
      ],
      "metadata": {
        "id": "a1N-mwDMKwkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## NASNet-A Model Save\n",
        "for param_tensor in nasnet_model.state_dict():\n",
        "    print(f\"{param_tensor} : \\t {nasnet_model.state_dict()[param_tensor].size()}\")   # NASNet-A 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(nasnet_model.state_dict(), os.path.join(model_dir, \"NASNet-A.pth\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSohCba-K_lN",
        "outputId": "ce6643f6-4328-4d0c-c2ef-ca65d47da4f9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nasnet_conv.0.weight : \t torch.Size([32, 3, 3, 3])\n",
            "nasnet_conv.0.bias : \t torch.Size([32])\n",
            "nasnet_conv.1.weight : \t torch.Size([32])\n",
            "nasnet_conv.1.bias : \t torch.Size([32])\n",
            "nasnet_conv.1.running_mean : \t torch.Size([32])\n",
            "nasnet_conv.1.running_var : \t torch.Size([32])\n",
            "nasnet_conv.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.adjust_block.adjust_block.0.weight : \t torch.Size([3, 3, 3, 3])\n",
            "reduction_cell_1.adjust_block.adjust_block.0.bias : \t torch.Size([3])\n",
            "reduction_cell_1.adjust_block.adjust_block.1.weight : \t torch.Size([3])\n",
            "reduction_cell_1.adjust_block.adjust_block.1.bias : \t torch.Size([3])\n",
            "reduction_cell_1.adjust_block.adjust_block.1.running_mean : \t torch.Size([3])\n",
            "reduction_cell_1.adjust_block.adjust_block.1.running_var : \t torch.Size([3])\n",
            "reduction_cell_1.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.convert1.0.weight : \t torch.Size([16, 32, 1, 1])\n",
            "reduction_cell_1.convert1.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert1.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.convert1.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert1.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.convert1.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.convert2.0.weight : \t torch.Size([16, 32, 1, 1])\n",
            "reduction_cell_1.convert2.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert2.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.convert2.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert2.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.convert2.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.convert3.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "reduction_cell_1.convert3.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert3.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.convert3.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.convert3.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.convert3.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.convert3.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.ds_conv7.depthwise.0.weight : \t torch.Size([3, 1, 7, 7])\n",
            "reduction_cell_1.ds_conv7.depthwise.0.bias : \t torch.Size([3])\n",
            "reduction_cell_1.ds_conv7.depthwise.1.weight : \t torch.Size([3])\n",
            "reduction_cell_1.ds_conv7.depthwise.1.bias : \t torch.Size([3])\n",
            "reduction_cell_1.ds_conv7.depthwise.1.running_mean : \t torch.Size([3])\n",
            "reduction_cell_1.ds_conv7.depthwise.1.running_var : \t torch.Size([3])\n",
            "reduction_cell_1.ds_conv7.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.ds_conv7.pointwise.0.weight : \t torch.Size([16, 3, 1, 1])\n",
            "reduction_cell_1.ds_conv7.pointwise.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv7.pointwise.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv7.pointwise.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv7.pointwise.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv7.pointwise.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv7.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.0.weight : \t torch.Size([32, 1, 5, 5])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.0.bias : \t torch.Size([32])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.1.weight : \t torch.Size([32])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.1.bias : \t torch.Size([32])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([32])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.1.running_var : \t torch.Size([32])\n",
            "reduction_cell_1.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.0.weight : \t torch.Size([16, 32, 1, 1])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.0.weight : \t torch.Size([3, 1, 5, 5])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.0.bias : \t torch.Size([3])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.1.weight : \t torch.Size([3])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.1.bias : \t torch.Size([3])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([3])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.1.running_var : \t torch.Size([3])\n",
            "reduction_cell_1.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.0.weight : \t torch.Size([16, 3, 1, 1])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.ds_conv3.depthwise.0.weight : \t torch.Size([16, 1, 3, 3])\n",
            "reduction_cell_1.ds_conv3.depthwise.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.depthwise.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.depthwise.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.depthwise.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.depthwise.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_1.ds_conv3.pointwise.0.weight : \t torch.Size([16, 16, 1, 1])\n",
            "reduction_cell_1.ds_conv3.pointwise.0.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.pointwise.1.weight : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.pointwise.1.bias : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.pointwise.1.running_mean : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.pointwise.1.running_var : \t torch.Size([16])\n",
            "reduction_cell_1.ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.adjust_block.adjust_block.0.weight : \t torch.Size([32, 32, 3, 3])\n",
            "reduction_cell_2.adjust_block.adjust_block.0.bias : \t torch.Size([32])\n",
            "reduction_cell_2.adjust_block.adjust_block.1.weight : \t torch.Size([32])\n",
            "reduction_cell_2.adjust_block.adjust_block.1.bias : \t torch.Size([32])\n",
            "reduction_cell_2.adjust_block.adjust_block.1.running_mean : \t torch.Size([32])\n",
            "reduction_cell_2.adjust_block.adjust_block.1.running_var : \t torch.Size([32])\n",
            "reduction_cell_2.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.convert1.0.weight : \t torch.Size([24, 48, 1, 1])\n",
            "reduction_cell_2.convert1.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert1.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.convert1.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert1.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.convert1.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.convert2.0.weight : \t torch.Size([24, 48, 1, 1])\n",
            "reduction_cell_2.convert2.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert2.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.convert2.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert2.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.convert2.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.convert3.0.weight : \t torch.Size([24, 24, 1, 1])\n",
            "reduction_cell_2.convert3.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert3.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.convert3.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.convert3.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.convert3.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.convert3.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.ds_conv7.depthwise.0.weight : \t torch.Size([32, 1, 7, 7])\n",
            "reduction_cell_2.ds_conv7.depthwise.0.bias : \t torch.Size([32])\n",
            "reduction_cell_2.ds_conv7.depthwise.1.weight : \t torch.Size([32])\n",
            "reduction_cell_2.ds_conv7.depthwise.1.bias : \t torch.Size([32])\n",
            "reduction_cell_2.ds_conv7.depthwise.1.running_mean : \t torch.Size([32])\n",
            "reduction_cell_2.ds_conv7.depthwise.1.running_var : \t torch.Size([32])\n",
            "reduction_cell_2.ds_conv7.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.ds_conv7.pointwise.0.weight : \t torch.Size([24, 32, 1, 1])\n",
            "reduction_cell_2.ds_conv7.pointwise.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv7.pointwise.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv7.pointwise.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv7.pointwise.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv7.pointwise.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv7.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.0.weight : \t torch.Size([48, 1, 5, 5])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.0.bias : \t torch.Size([48])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.1.weight : \t torch.Size([48])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.1.bias : \t torch.Size([48])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([48])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.1.running_var : \t torch.Size([48])\n",
            "reduction_cell_2.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.0.weight : \t torch.Size([24, 48, 1, 1])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.0.weight : \t torch.Size([32, 1, 5, 5])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.0.bias : \t torch.Size([32])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.1.weight : \t torch.Size([32])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.1.bias : \t torch.Size([32])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([32])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.1.running_var : \t torch.Size([32])\n",
            "reduction_cell_2.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.0.weight : \t torch.Size([24, 32, 1, 1])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.ds_conv3.depthwise.0.weight : \t torch.Size([24, 1, 3, 3])\n",
            "reduction_cell_2.ds_conv3.depthwise.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.depthwise.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.depthwise.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.depthwise.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.depthwise.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_2.ds_conv3.pointwise.0.weight : \t torch.Size([24, 24, 1, 1])\n",
            "reduction_cell_2.ds_conv3.pointwise.0.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.pointwise.1.weight : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.pointwise.1.bias : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.pointwise.1.running_mean : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.pointwise.1.running_var : \t torch.Size([24])\n",
            "reduction_cell_2.ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.adjust_block.adjust_block.0.weight : \t torch.Size([48, 48, 3, 3])\n",
            "normal_cell_1.adjust_block.adjust_block.0.bias : \t torch.Size([48])\n",
            "normal_cell_1.adjust_block.adjust_block.1.weight : \t torch.Size([48])\n",
            "normal_cell_1.adjust_block.adjust_block.1.bias : \t torch.Size([48])\n",
            "normal_cell_1.adjust_block.adjust_block.1.running_mean : \t torch.Size([48])\n",
            "normal_cell_1.adjust_block.adjust_block.1.running_var : \t torch.Size([48])\n",
            "normal_cell_1.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.convert1.0.weight : \t torch.Size([36, 72, 1, 1])\n",
            "normal_cell_1.convert1.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.convert1.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.convert1.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.convert1.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.convert1.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.convert2.0.weight : \t torch.Size([36, 48, 1, 1])\n",
            "normal_cell_1.convert2.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.convert2.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.convert2.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.convert2.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.convert2.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.after_ds_conv3.depthwise.0.weight : \t torch.Size([72, 1, 3, 3])\n",
            "normal_cell_1.after_ds_conv3.depthwise.0.bias : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv3.depthwise.1.weight : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv3.depthwise.1.bias : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv3.depthwise.1.running_mean : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv3.depthwise.1.running_var : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.after_ds_conv3.pointwise.0.weight : \t torch.Size([36, 72, 1, 1])\n",
            "normal_cell_1.after_ds_conv3.pointwise.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv3.pointwise.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv3.pointwise.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv3.pointwise.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv3.pointwise.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.before_ds_conv3.depthwise.0.weight : \t torch.Size([48, 1, 3, 3])\n",
            "normal_cell_1.before_ds_conv3.depthwise.0.bias : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv3.depthwise.1.weight : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv3.depthwise.1.bias : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv3.depthwise.1.running_mean : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv3.depthwise.1.running_var : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.before_ds_conv3.pointwise.0.weight : \t torch.Size([36, 48, 1, 1])\n",
            "normal_cell_1.before_ds_conv3.pointwise.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv3.pointwise.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv3.pointwise.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv3.pointwise.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv3.pointwise.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.after_ds_conv5.depthwise.0.weight : \t torch.Size([72, 1, 5, 5])\n",
            "normal_cell_1.after_ds_conv5.depthwise.0.bias : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv5.depthwise.1.weight : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv5.depthwise.1.bias : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv5.depthwise.1.running_var : \t torch.Size([72])\n",
            "normal_cell_1.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.after_ds_conv5.pointwise.0.weight : \t torch.Size([36, 72, 1, 1])\n",
            "normal_cell_1.after_ds_conv5.pointwise.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv5.pointwise.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv5.pointwise.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv5.pointwise.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.before_ds_conv5.depthwise.0.weight : \t torch.Size([48, 1, 5, 5])\n",
            "normal_cell_1.before_ds_conv5.depthwise.0.bias : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv5.depthwise.1.weight : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv5.depthwise.1.bias : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv5.depthwise.1.running_var : \t torch.Size([48])\n",
            "normal_cell_1.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_1.before_ds_conv5.pointwise.0.weight : \t torch.Size([36, 48, 1, 1])\n",
            "normal_cell_1.before_ds_conv5.pointwise.0.bias : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv5.pointwise.1.weight : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv5.pointwise.1.bias : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv5.pointwise.1.running_var : \t torch.Size([36])\n",
            "normal_cell_1.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.adjust_block.adjust_block.0.weight : \t torch.Size([72, 72, 3, 3])\n",
            "reduction_cell_3.adjust_block.adjust_block.0.bias : \t torch.Size([72])\n",
            "reduction_cell_3.adjust_block.adjust_block.1.weight : \t torch.Size([72])\n",
            "reduction_cell_3.adjust_block.adjust_block.1.bias : \t torch.Size([72])\n",
            "reduction_cell_3.adjust_block.adjust_block.1.running_mean : \t torch.Size([72])\n",
            "reduction_cell_3.adjust_block.adjust_block.1.running_var : \t torch.Size([72])\n",
            "reduction_cell_3.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.convert1.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "reduction_cell_3.convert1.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert1.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.convert1.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert1.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.convert1.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.convert2.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "reduction_cell_3.convert2.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert2.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.convert2.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert2.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.convert2.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.convert3.0.weight : \t torch.Size([90, 90, 1, 1])\n",
            "reduction_cell_3.convert3.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert3.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.convert3.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.convert3.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.convert3.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.convert3.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.ds_conv7.depthwise.0.weight : \t torch.Size([72, 1, 7, 7])\n",
            "reduction_cell_3.ds_conv7.depthwise.0.bias : \t torch.Size([72])\n",
            "reduction_cell_3.ds_conv7.depthwise.1.weight : \t torch.Size([72])\n",
            "reduction_cell_3.ds_conv7.depthwise.1.bias : \t torch.Size([72])\n",
            "reduction_cell_3.ds_conv7.depthwise.1.running_mean : \t torch.Size([72])\n",
            "reduction_cell_3.ds_conv7.depthwise.1.running_var : \t torch.Size([72])\n",
            "reduction_cell_3.ds_conv7.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.ds_conv7.pointwise.0.weight : \t torch.Size([90, 72, 1, 1])\n",
            "reduction_cell_3.ds_conv7.pointwise.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv7.pointwise.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv7.pointwise.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv7.pointwise.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv7.pointwise.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv7.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.0.weight : \t torch.Size([180, 1, 5, 5])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.0.bias : \t torch.Size([180])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.1.weight : \t torch.Size([180])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.1.bias : \t torch.Size([180])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([180])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.1.running_var : \t torch.Size([180])\n",
            "reduction_cell_3.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.0.weight : \t torch.Size([72, 1, 5, 5])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.0.bias : \t torch.Size([72])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.1.weight : \t torch.Size([72])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.1.bias : \t torch.Size([72])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([72])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.1.running_var : \t torch.Size([72])\n",
            "reduction_cell_3.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.0.weight : \t torch.Size([90, 72, 1, 1])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.ds_conv3.depthwise.0.weight : \t torch.Size([90, 1, 3, 3])\n",
            "reduction_cell_3.ds_conv3.depthwise.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.depthwise.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.depthwise.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.depthwise.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.depthwise.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_3.ds_conv3.pointwise.0.weight : \t torch.Size([90, 90, 1, 1])\n",
            "reduction_cell_3.ds_conv3.pointwise.0.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.pointwise.1.weight : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.pointwise.1.bias : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.pointwise.1.running_mean : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.pointwise.1.running_var : \t torch.Size([90])\n",
            "reduction_cell_3.ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.adjust_block.adjust_block.0.weight : \t torch.Size([180, 180, 3, 3])\n",
            "normal_cell_2.adjust_block.adjust_block.0.bias : \t torch.Size([180])\n",
            "normal_cell_2.adjust_block.adjust_block.1.weight : \t torch.Size([180])\n",
            "normal_cell_2.adjust_block.adjust_block.1.bias : \t torch.Size([180])\n",
            "normal_cell_2.adjust_block.adjust_block.1.running_mean : \t torch.Size([180])\n",
            "normal_cell_2.adjust_block.adjust_block.1.running_var : \t torch.Size([180])\n",
            "normal_cell_2.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.convert1.0.weight : \t torch.Size([90, 270, 1, 1])\n",
            "normal_cell_2.convert1.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.convert1.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.convert1.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.convert1.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.convert1.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.convert2.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "normal_cell_2.convert2.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.convert2.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.convert2.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.convert2.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.convert2.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.after_ds_conv3.depthwise.0.weight : \t torch.Size([270, 1, 3, 3])\n",
            "normal_cell_2.after_ds_conv3.depthwise.0.bias : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv3.depthwise.1.weight : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv3.depthwise.1.bias : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv3.depthwise.1.running_mean : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv3.depthwise.1.running_var : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.after_ds_conv3.pointwise.0.weight : \t torch.Size([90, 270, 1, 1])\n",
            "normal_cell_2.after_ds_conv3.pointwise.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv3.pointwise.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv3.pointwise.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv3.pointwise.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv3.pointwise.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.before_ds_conv3.depthwise.0.weight : \t torch.Size([180, 1, 3, 3])\n",
            "normal_cell_2.before_ds_conv3.depthwise.0.bias : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv3.depthwise.1.weight : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv3.depthwise.1.bias : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv3.depthwise.1.running_mean : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv3.depthwise.1.running_var : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.before_ds_conv3.pointwise.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "normal_cell_2.before_ds_conv3.pointwise.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv3.pointwise.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv3.pointwise.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv3.pointwise.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv3.pointwise.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.after_ds_conv5.depthwise.0.weight : \t torch.Size([270, 1, 5, 5])\n",
            "normal_cell_2.after_ds_conv5.depthwise.0.bias : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv5.depthwise.1.weight : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv5.depthwise.1.bias : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv5.depthwise.1.running_var : \t torch.Size([270])\n",
            "normal_cell_2.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.after_ds_conv5.pointwise.0.weight : \t torch.Size([90, 270, 1, 1])\n",
            "normal_cell_2.after_ds_conv5.pointwise.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv5.pointwise.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv5.pointwise.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv5.pointwise.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.before_ds_conv5.depthwise.0.weight : \t torch.Size([180, 1, 5, 5])\n",
            "normal_cell_2.before_ds_conv5.depthwise.0.bias : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv5.depthwise.1.weight : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv5.depthwise.1.bias : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv5.depthwise.1.running_var : \t torch.Size([180])\n",
            "normal_cell_2.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_2.before_ds_conv5.pointwise.0.weight : \t torch.Size([90, 180, 1, 1])\n",
            "normal_cell_2.before_ds_conv5.pointwise.0.bias : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv5.pointwise.1.weight : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv5.pointwise.1.bias : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv5.pointwise.1.running_var : \t torch.Size([90])\n",
            "normal_cell_2.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.adjust_block.adjust_block.0.weight : \t torch.Size([270, 270, 3, 3])\n",
            "reduction_cell_4.adjust_block.adjust_block.0.bias : \t torch.Size([270])\n",
            "reduction_cell_4.adjust_block.adjust_block.1.weight : \t torch.Size([270])\n",
            "reduction_cell_4.adjust_block.adjust_block.1.bias : \t torch.Size([270])\n",
            "reduction_cell_4.adjust_block.adjust_block.1.running_mean : \t torch.Size([270])\n",
            "reduction_cell_4.adjust_block.adjust_block.1.running_var : \t torch.Size([270])\n",
            "reduction_cell_4.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.convert1.0.weight : \t torch.Size([300, 450, 1, 1])\n",
            "reduction_cell_4.convert1.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert1.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.convert1.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert1.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.convert1.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.convert2.0.weight : \t torch.Size([300, 450, 1, 1])\n",
            "reduction_cell_4.convert2.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert2.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.convert2.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert2.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.convert2.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.convert3.0.weight : \t torch.Size([300, 300, 1, 1])\n",
            "reduction_cell_4.convert3.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert3.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.convert3.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.convert3.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.convert3.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.convert3.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.ds_conv7.depthwise.0.weight : \t torch.Size([270, 1, 7, 7])\n",
            "reduction_cell_4.ds_conv7.depthwise.0.bias : \t torch.Size([270])\n",
            "reduction_cell_4.ds_conv7.depthwise.1.weight : \t torch.Size([270])\n",
            "reduction_cell_4.ds_conv7.depthwise.1.bias : \t torch.Size([270])\n",
            "reduction_cell_4.ds_conv7.depthwise.1.running_mean : \t torch.Size([270])\n",
            "reduction_cell_4.ds_conv7.depthwise.1.running_var : \t torch.Size([270])\n",
            "reduction_cell_4.ds_conv7.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.ds_conv7.pointwise.0.weight : \t torch.Size([300, 270, 1, 1])\n",
            "reduction_cell_4.ds_conv7.pointwise.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv7.pointwise.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv7.pointwise.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv7.pointwise.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv7.pointwise.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv7.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.0.weight : \t torch.Size([450, 1, 5, 5])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.0.bias : \t torch.Size([450])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.1.weight : \t torch.Size([450])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.1.bias : \t torch.Size([450])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([450])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.1.running_var : \t torch.Size([450])\n",
            "reduction_cell_4.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.0.weight : \t torch.Size([300, 450, 1, 1])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.0.weight : \t torch.Size([270, 1, 5, 5])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.0.bias : \t torch.Size([270])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.1.weight : \t torch.Size([270])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.1.bias : \t torch.Size([270])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([270])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.1.running_var : \t torch.Size([270])\n",
            "reduction_cell_4.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.0.weight : \t torch.Size([300, 270, 1, 1])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.ds_conv3.depthwise.0.weight : \t torch.Size([300, 1, 3, 3])\n",
            "reduction_cell_4.ds_conv3.depthwise.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.depthwise.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.depthwise.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.depthwise.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.depthwise.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "reduction_cell_4.ds_conv3.pointwise.0.weight : \t torch.Size([300, 300, 1, 1])\n",
            "reduction_cell_4.ds_conv3.pointwise.0.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.pointwise.1.weight : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.pointwise.1.bias : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.pointwise.1.running_mean : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.pointwise.1.running_var : \t torch.Size([300])\n",
            "reduction_cell_4.ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.adjust_block.adjust_block.0.weight : \t torch.Size([450, 450, 3, 3])\n",
            "normal_cell_3.adjust_block.adjust_block.0.bias : \t torch.Size([450])\n",
            "normal_cell_3.adjust_block.adjust_block.1.weight : \t torch.Size([450])\n",
            "normal_cell_3.adjust_block.adjust_block.1.bias : \t torch.Size([450])\n",
            "normal_cell_3.adjust_block.adjust_block.1.running_mean : \t torch.Size([450])\n",
            "normal_cell_3.adjust_block.adjust_block.1.running_var : \t torch.Size([450])\n",
            "normal_cell_3.adjust_block.adjust_block.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.convert1.0.weight : \t torch.Size([400, 900, 1, 1])\n",
            "normal_cell_3.convert1.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.convert1.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.convert1.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.convert1.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.convert1.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.convert1.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.convert2.0.weight : \t torch.Size([400, 450, 1, 1])\n",
            "normal_cell_3.convert2.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.convert2.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.convert2.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.convert2.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.convert2.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.convert2.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.after_ds_conv3.depthwise.0.weight : \t torch.Size([900, 1, 3, 3])\n",
            "normal_cell_3.after_ds_conv3.depthwise.0.bias : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv3.depthwise.1.weight : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv3.depthwise.1.bias : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv3.depthwise.1.running_mean : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv3.depthwise.1.running_var : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.after_ds_conv3.pointwise.0.weight : \t torch.Size([400, 900, 1, 1])\n",
            "normal_cell_3.after_ds_conv3.pointwise.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv3.pointwise.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv3.pointwise.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv3.pointwise.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv3.pointwise.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.before_ds_conv3.depthwise.0.weight : \t torch.Size([450, 1, 3, 3])\n",
            "normal_cell_3.before_ds_conv3.depthwise.0.bias : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv3.depthwise.1.weight : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv3.depthwise.1.bias : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv3.depthwise.1.running_mean : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv3.depthwise.1.running_var : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv3.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.before_ds_conv3.pointwise.0.weight : \t torch.Size([400, 450, 1, 1])\n",
            "normal_cell_3.before_ds_conv3.pointwise.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv3.pointwise.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv3.pointwise.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv3.pointwise.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv3.pointwise.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv3.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.after_ds_conv5.depthwise.0.weight : \t torch.Size([900, 1, 5, 5])\n",
            "normal_cell_3.after_ds_conv5.depthwise.0.bias : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv5.depthwise.1.weight : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv5.depthwise.1.bias : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv5.depthwise.1.running_mean : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv5.depthwise.1.running_var : \t torch.Size([900])\n",
            "normal_cell_3.after_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.after_ds_conv5.pointwise.0.weight : \t torch.Size([400, 900, 1, 1])\n",
            "normal_cell_3.after_ds_conv5.pointwise.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv5.pointwise.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv5.pointwise.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv5.pointwise.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv5.pointwise.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.after_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.before_ds_conv5.depthwise.0.weight : \t torch.Size([450, 1, 5, 5])\n",
            "normal_cell_3.before_ds_conv5.depthwise.0.bias : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv5.depthwise.1.weight : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv5.depthwise.1.bias : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv5.depthwise.1.running_mean : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv5.depthwise.1.running_var : \t torch.Size([450])\n",
            "normal_cell_3.before_ds_conv5.depthwise.1.num_batches_tracked : \t torch.Size([])\n",
            "normal_cell_3.before_ds_conv5.pointwise.0.weight : \t torch.Size([400, 450, 1, 1])\n",
            "normal_cell_3.before_ds_conv5.pointwise.0.bias : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv5.pointwise.1.weight : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv5.pointwise.1.bias : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv5.pointwise.1.running_mean : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv5.pointwise.1.running_var : \t torch.Size([400])\n",
            "normal_cell_3.before_ds_conv5.pointwise.1.num_batches_tracked : \t torch.Size([])\n",
            "nasnet_fc.2.weight : \t torch.Size([512, 2000])\n",
            "nasnet_fc.2.bias : \t torch.Size([512])\n",
            "nasnet_fc.3.weight : \t torch.Size([512])\n",
            "nasnet_fc.3.bias : \t torch.Size([512])\n",
            "nasnet_fc.3.running_mean : \t torch.Size([512])\n",
            "nasnet_fc.3.running_var : \t torch.Size([512])\n",
            "nasnet_fc.3.num_batches_tracked : \t torch.Size([])\n",
            "nasnet_fc.6.weight : \t torch.Size([128, 512])\n",
            "nasnet_fc.6.bias : \t torch.Size([128])\n",
            "nasnet_fc.7.weight : \t torch.Size([128])\n",
            "nasnet_fc.7.bias : \t torch.Size([128])\n",
            "nasnet_fc.7.running_mean : \t torch.Size([128])\n",
            "nasnet_fc.7.running_var : \t torch.Size([128])\n",
            "nasnet_fc.7.num_batches_tracked : \t torch.Size([])\n",
            "nasnet_fc.10.weight : \t torch.Size([10, 128])\n",
            "nasnet_fc.10.bias : \t torch.Size([10])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color=\"#2F9D27\"> **② NASNet Model Load and Performance Estimation** </font>"
      ],
      "metadata": {
        "id": "Rp97eGEILADT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## NASNet-A Model Load\n",
        "device = \"cuda\"\n",
        "\n",
        "print(\"========== NASNet-A Test without Load ==========\")    # ① 먼저 학습하지 않은 상태로 Actuator를 통해 Test dataset 성능 측정\n",
        "nasnet = NASNet(n_class=10).to(device)\n",
        "nasnet_actuator = NASNet_Actuator(model=nasnet, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "nasnet_actuator.test()\n",
        "\n",
        "model_path = os.path.join(model_dir, \"NASNet-A.pth\")\n",
        "print(model_path)\n",
        "print()\n",
        "\n",
        "print(\"========== NASNet-A Test with Load ==========\")       # ② 이전에 학습된 Model의 parameter들을 Load한 후, Test dataset 성능 측정\n",
        "nasnet.load_state_dict(torch.load(model_path))\n",
        "nasnet.eval()\n",
        "\n",
        "nasnet_loaded_actuator = NASNet_Actuator(model=nasnet, loaders=loaders, opt=\"adam\", lr=0.001, has_scheduler=False, device=device).to(device)\n",
        "nasnet_loaded_actuator.test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4O96LgBLGZW",
        "outputId": "ea693a29-c250-4653-9061-73610ff5e5cb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========== NASNet-A Test without Load ==========\n",
            "========== Test Start ==========\n",
            "Test loss: 2.303753, Test acc: 0.100000, Test time : 24.411066\n",
            "/content/drive/MyDrive/Colab Notebooks/AI Algorithm Modeling/model/NASNet-A.pth\n",
            "\n",
            "========== NASNet-A Test with Load ==========\n",
            "========== Test Start ==========\n",
            "Test loss: 0.650284, Test acc: 0.832700, Test time : 23.866916\n"
          ]
        }
      ]
    }
  ]
}